PREVALENCE OF CARDIOVASCULAR DISEASE RISK FACTORS AND MEETING NUTRITION RECOMMENDATIONS IN KUWAITI SCHOOLCHILDREN BY GENDER AND WEIGHT STATUS By Abdulaziz Kh. Al - Farhan A DISSERTATION Submitted to Michigan State University in partial fulfilment of the requirements for the degree of Human Nutrition Doctor of Philosophy 2019 ABSTRACT PREVALENCE OF CARDIOVASCULAR DISEASE RISK FACTORS AND MEETING NUTRITION RECOMMENDATIONS IN KUWAITI SCHOOLCHILDREN BY GENDER AND WEIGHT STATUS By Abdulaziz Kh. Al - Farhan Cardiovascular disease (CVD) is the leading cause of death globally, responsible for 17.5 million deaths (31%) annually. In Kuwait, CVD accounts for 41% of all deaths. In the 1980s Kuwait had economic growth that influenced food supply and led to a higher intake of caloric dense foods in children, which contributed to increases in childhood OB rates (<1% in 198 5 and 31% in 2012 ). The most recent nutrition study on Kuwaiti children was conducted in 2008 2009 and indicated that ~80% of children did not meet nutrition recommendations , and ~50% exceeded their kcal needs . H owever , limited studies reported on the prevalence of multip le CVD risks or gender differences . Thus, the primary study objectives were to evaluate a sample of Kuwaiti schoolchildren to determine: 1) the prevalence of CVD risk factors, and if boys will have greater risks than girls; 2) the proportion meeting the US Dietary Guidelines for Americans (DGA) and DRIs , and the WHO joint Food and Agriculture Organization (FAO) nutrition recommendations, and if boys meet fewer recommendations than girls ; 3 ) in a sample of OB, OW, NW and UW children determine if there are differenc es between weight categories in 1) prevalence of CVD risks 2) meeting US and WHO/FAO nutrition recommendations. Design : A cross - sectional analysis of 367 fifth - graders ( age 10.4 ± 0.4 years; 53% girls) in Kuwait. Measures: Anthropometric and biome tric assessments were conducted to obtain CVD risk factor variables, and a n Arabic/English food frequency questionnaire (FFQ) w as used to obtain nutrition variables. The CVD risk factor variables were: OW , OB, at risk levels of TC, TG, HDL , TC: HDL, non - HDL, LDL , systolic blood pressure (SBP) and diastolic BP ( DBP ) . Nutrition variables: food groups, macronutrients, and selected micronutrients. Statistical analysis included g eneral linear models and logistic regression controlling for physical activity (PA ), screen time (ST), and Kcals with P : Objective 1: The overall prevalence of OB 39% , at risk BP 23% , and dyslipidemia ranged from 13.2 % to 45.5%. The OB prevalence was not significantly different between gender , though girls had a sign ificantly higher prevalence of OW, and at risk TG, HDL and BP, than boys. Objective 2: The proportion of children meeting the nutrition recommendations was <50% for most variables and ~70% exceeded Kcal needs. There were few significant gender differences . M ore boys met fruit recommendations while more girls met vegetable and sodium recommendations. Objective 3: OB children had a greater prevalence of at risk for TG, HDL and TC:HDL and BP, while NW children had a greater prevalence of TC and LDL than OB ch ildren. The only significant differences for meeting nutrition recommendations by weight status group; more OW met sodium, more OB met protein, and more UW met carbohydrate recommendations. Conclusion: Based on the findings of the three objectives in Kuwa iti children 1) The prevalence of OB has increased, and the prevalence of dyslipidemia and elevated BP are high , particularly among girls, and was contrary to our hypothesis. 2) Few children met nutrition recommendation s with few gender differences. 3) As expected , OB children had the highest prevalence of CVD risk s; however, NW also had concerning levels of dyslipidemia. These findings indicate a need for CVD risk assessment, and intervention programs for Kuwaiti children regardless of weight status, to i mprove dietary and other lifestyle behaviors to prevent or reduce CVD risks iv This dissertation is gratefully dedicated to the children of Kuwait, to my family and friends, and to every person in Kuwait and in the United States who contributed to the success of this work. v ACKNOWLEDGEMENTS A big thank you to Shoghig Sahakyan, advisor at the Cultural Division of the Embassy of the State of Kuwait in Washington, D.C., for her continued support during the five years I pursued my Ph.D. in Human Nutrition at Michigan State University (MSU). Much gratitude to the MSU professors who dedicated themselves to furthering my education and training and deepened my research knowledge and capabilities. Special thanks to my research advisor, Dr. Joseph Carlson, and to my research committee members, including Dr. Lorraine Weatherspoon, Dr. Karin Pfeiffer, and Dr. Wei Li. I am incredibly thankful to my colleague and former classmate Dr. Tyler Becker, who supported me during training in the (S)partners for Heart Health program and with my research. Special thank s to the (S)partners team leaders, especially Tyler Kemnic, D.O., Connie Benedict, and Maighlin Kolesar, as well as the faculty and staff of the Radiology Building, MSU, for their kindness and support. In Kuwait, I would like to express my sincerest gratit ude towards the faculty and staff of the College of Nursing, especially former dean Nabil Badawy, M.D., for facilitating the conduction of my research. Many thanks to the research team including nursing trainers Mardia Alhossaini, Alia Alsaibie, Zohour Als hamari, Suha Alazmi, Fatema Alzamam, Fatma Alabdullah, and Ismael Alkandari, and the nursing students. Additional thanks to the nutrition students from the College of Health Sciences who supported the research team encouraged by Dr. Nayef Bumarium and diet itian Khaleda Al - Hamar. A big thank you to the schools and the children and parents who agreed to participate in this research. Lastly, my sincerest gratitude towards my wife and children, my mother and family, and my friends in Kuwait and Michigan, especi ally the Itsell family, who were always behind me during this unforgettable journey. vi TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ ........................ x LIST OF FIGURES ................................ ................................ ................................ ..................... xi KEY TO ABBREVIATIONS ................................ ................................ ................................ .... xii CHAPTER 1: I NTRODUCTION ................................ ................................ ................................ 1 Background/Overview ................................ ................................ ................................ ................ 1 Gap in Literature ................................ ................................ ................................ ......................... 9 Specific Aims an d Hypotheses ................................ ................................ ................................ .. 10 CHAPTER 2: LITERATURE REVIEW ................................ ................................ ................. 13 Kuwait ................................ ................................ ................................ ................................ ....... 13 Cardiovascular Diseases (CVD) ................................ ................................ ................................ 14 Card iovascular Disease Risk Factors ................................ ................................ ........................ 15 Gender - specific Predictors of Cardiovascular Disease in Children ................................ .......... 15 Pediatric Cardiovascular Risk Cut - Points ................................ ................................ ................. 16 Obesity ................................ ................................ ................................ ................................ ... 16 Blood Pressure ................................ ................................ ................................ ...................... 16 Blood Lipids ................................ ................................ ................................ ........................... 16 Cardiometabolic Risk (metabolic syndrome [MetS]) ................................ ............................ 17 The Prevalence of Cardiovascular Diseases and Risk Factors in Kuwait ................................ . 17 Prevalence of Overweight and Obesity ................................ ................................ ..................... 18 Childhood Obesity ................................ ................................ ................................ ................. 19 Childhood Obesity and Cardiovascular Disease (CVD) ................................ ....................... 20 Overweight and Obesity among Kuwaiti Children ................................ ................................ ... 20 Population ................................ ................................ ................................ ................................ .. 21 International Definition of Childhood Overweight and Obesity ................................ ............... 2 2 Nutritio nal Status - Growth Charts ................................ ................................ ............................ 23 NCHS/WHO Growth Charts ................................ ................................ ................................ . 24 CDC 2000 Growth Charts ................................ ................................ ................................ ..... 25 IOTF Refer ence ................................ ................................ ................................ ..................... 25 WHO 2007 Reference ................................ ................................ ................................ ............ 25 Contrasting of WHO 2007, CDC 2000, and IOTF Cut - points ................................ .............. 26 Anthropometrics - Assessing Obesity ................................ ................................ ...................... 27 Body Mass Index (BMI) ................................ ................................ ................................ ......... 27 BMI Z - scores for defining weight status in children ................................ ............................. 28 Reference Centiles Curves/LMS Method (Smoothed parameters): ................................ ....... 29 Generating BMI - for - age Standard Deviatio n Scores (Z - scores) ................................ .......... 29 BMI in Kuwait Children ................................ ................................ ................................ ........ 30 Waist Circumference (WC) ................................ ................................ ................................ .... 31 WC and Waist - to - Height Ratio (WHtR) ................................ ................................ ................ 31 Waist Circumference (WC) of Kuwaiti Children ................................ ................................ ... 32 vii Waist - to - Height Ratio (WHtR) in Kuwait ................................ ................................ .............. 33 Percentage Body Fat (%BF) ................................ ................................ ................................ . 33 Percentage Body Fat in Kuwait ................................ ................................ ........................... 33 Dyslipidemia ................................ ................................ ................................ ............................. 33 Prevalence of Dyslipidemia among Kuwait Children ................................ ........................... 34 Hypertension ................................ ................................ ................................ ............................. 34 Hypertension in Children ................................ ................................ ................................ ...... 35 Prevalence of Hypertension in Kuwait Children ................................ ................................ ... 35 Role of Nutrition in Pediatric/Childhood Health and CVD Risk Factor Status ........................ 36 Nutritional Recommendations - Dietary Guidelines for Americans (DGA), WHO/FAO ........ 37 The Dietar y Reference Intakes (DRIs) ................................ ................................ .................. 37 FAO/WHO International Nutritional Intake Ranges ................................ ............................ 38 Food groups and food proportions (US DGA and FAO/WHO) ................................ ............ 38 Macronutrients ................................ ................................ ................................ ...................... 39 Micronutrients ................................ ................................ ................................ ....................... 39 Nutritional Intake and Behavior among Kuwait Children ................................ ........................ 40 Nutritional Assessment Instruments ................................ ................................ .......................... 43 Food Frequency Questionnaire (FFQ) ................................ ................................ ................. 43 The Block Kids 2004 Food Frequency Questionnaire (FFQ) ................................ ............... 45 Dietary Indices for Measuring Diet Quality ................................ ................................ .............. 46 Healthy Eating Index (HEI) ................................ ................................ ................................ ... 47 The Fi ber Index (FI) ................................ ................................ ................................ .............. 49 Benefits of Physical Activity (PA) in Children and Recommendations ................................ ... 50 Current Physical Activity Behaviors in Kuwait ................................ ................................ ..... 50 Barriers among Kuwaiti Population ................................ ................................ ..................... 51 Physical Activity related Assessment Instruments ................................ ................................ .... 52 Self - reported methods ................................ ................................ ................................ ............... 52 Nutritional & Physical Activity Educational Program ................................ .............................. 54 The (S)Partners for Heart Health ................................ ................................ .......................... 54 Summary of Literature Review ................................ ................................ ................................ . 55 CHAPTER 3: PREVALENCE OF CARDIOVASULAR DISEASE RISK FACTORS IN KUWAITI SCHOOLCHILDREN COMPARED BY GENDER ................................ ........... 56 Abstract ................................ ................................ ................................ ................................ ..... 56 Introduction ................................ ................................ ................................ ............................... 57 Methods ................................ ................................ ................................ ................................ ..... 59 S tudy D esign and Participants ................................ ................................ .............................. 59 Meas urement ................................ ................................ ................................ ......................... 59 Anthropometric A ssessments ................................ ................................ ................................ . 60 CVD risk factors ( dependent variables) ................................ ................................ .................... 61 Obesity ................................ ................................ ................................ ................................ ... 61 Dyslipidemia ................................ ................................ ................................ .......................... 61 Resting B lood P ressure ................................ ................................ ................................ ......... 62 Covariate A ssessment ................................ ................................ ................................ ............... 63 Physical A ctivity (PA) ................................ ................................ ................................ ............ 63 Screen T ime (ST) ................................ ................................ ................................ ................... 64 viii Statistical A nalysis ................................ ................................ ................................ .................... 64 Results ................................ ................................ ................................ ................................ ....... 65 Discussion ................................ ................................ ................................ ................................ . 71 Summary and Conclusion ................................ ................................ ................................ ......... 78 CHAPTER 4: PROPORTION OF KUWAIT SCHOOLCHILDREN MEETING US AND WHO/FAO DIETARY RECOMMENDATIONS FOR FOOD GROUPS AND NUTRIENT INTAKE, COMPARED BY GENDER ................................ ................................ ..................... 79 Abstract ................................ ................................ ................................ ................................ ..... 79 In troduction ................................ ................................ ................................ ............................... 80 Methods ................................ ................................ ................................ ................................ ..... 84 Study D esign and P articipants ................................ ................................ .............................. 84 Meas urements ................................ ................................ ................................ ........................ 84 Nutritional B ehaviors ................................ ................................ ................................ ................ 85 Block Kids 2004 Food Frequency Questionnaire ................................ ................................ . 85 Modified Arabic/English version of Block Kids 2004 FFQ ................................ .................. 85 Administering the M odified and T ranslated version of the Block Kids 2004 FFQ ............... 87 Nutrition variables ................................ ................................ ................................ ................. 87 Food G roups ................................ ................................ ................................ ...................... 88 Macronutrients ................................ ................................ ................................ ................... 88 Micronutrients ................................ ................................ ................................ .................... 88 Dietary I ndices ................................ ................................ ................................ ...................... 88 Healthy Eating Index 2010 ................................ ................................ ................................ 88 Calculating total Healthy Eating Index (HEI - 20 10 and 2015) ................................ .......... 89 Fiber Index ................................ ................................ ................................ ......................... 90 Covariate A ssessment ................................ ................................ ................................ ............... 91 Physical A ct ivity (PA) ................................ ................................ ................................ ............ 91 Screen T ime (ST) ................................ ................................ ................................ ................... 91 Statistical A nalysis ................................ ................................ ................................ .................... 92 Identifying O utliers : Verifying and R e - testing the M odified T ranslated version of the Block Kids 2004 FFQ ................................ ................................ ................................ ...................... 92 Results ................................ ................................ ................................ ................................ ....... 93 Discussion ................................ ................................ ................................ ............................... 104 Summary and Conclusion ................................ ................................ ................................ ....... 107 CHAPTER 5: PREVALENCE OF CARDIOVASULAR DISEASE RISK FACTORS AND MEETING US AND WHO/FAO DIETARY RECOMMENDATIONS IN KUWAITI SCHOOLCHILDREN BY WEIGHT STATUS ................................ ................................ ..... 109 Abstract ................................ ................................ ................................ ................................ ... 109 Introduction ................................ ................................ ................................ ............................. 110 Methods ................................ ................................ ................................ ................................ ... 113 Study D esign and Participants ................................ ................................ ............................ 113 Me asurements ................................ ................................ ................................ .......................... 113 Anthropometric A ssessments ................................ ................................ ............................... 114 Independent V ariables ................................ ................................ ................................ ......... 115 Dependent V ariables ................................ ................................ ................................ ........... 115 ix CVD R isk F ac tors ................................ ................................ ................................ ............ 115 Dyslipidemia ................................ ................................ ................................ ................. 115 Resting B lood B ressure ................................ ................................ ................................ 116 Nutritional B ehaviors ................................ ................................ ................................ .......... 117 Block Kids 2004 Food Frequency Questionnaire ................................ ............................ 117 Modified Arabic/English version of Block Kids 2004 FFQ ................................ ............ 117 Administering the modified and translated version of the Block Kids 2004 FFQ .......... 119 Nutrition variables ................................ ................................ ................................ ............... 119 Food G roups ................................ ................................ ................................ .................... 120 Macronutrients ................................ ................................ ................................ ................. 120 Micronutrients ................................ ................................ ................................ .................. 120 Covariate A ssessment ................................ ................................ ................................ .......... 120 Physical A ctivity (PA) ................................ ................................ ................................ ..... 120 Screen T im e (ST) ................................ ................................ ................................ ............. 121 Statistical A nalysis ................................ ................................ ................................ ............... 121 Identifying O utliers : Verifying and R e - testing the M odified T ranslated version of the Block Kids 2004 FFQ ................................ ................................ ................................ ...... 121 Results ................................ ................................ ................................ ................................ ..... 122 Discussion ................................ ................................ ................................ ............................... 135 Summary and Conclusion ................................ ................................ ................................ ....... 139 CHAPTER 6: SUMMARY AND CONCLUSIONS ................................ .............................. 140 Aim 1: Prevalence of Cardiovascular Disease Risk Factors in Kuwaiti Schoolchildren, Compared by Gender ................................ ................................ ................................ .............. 141 Aim 2: Proportion of Kuwait schoolchildren meeting US and WHO/FAO Dietary Recommendations for Food Groups and Nutrient Intakes, compared by Gender .................. 142 Aim 3: Prevalence of Cardiovascular Disease Risk Factors and meeting US and FAO/WHO Dietary Recommendations in Kuwaiti Schoolchildren by Weight Status .............................. 144 Strengths and Weaknesses ................................ ................................ ................................ ...... 146 Conclusion ................................ ................................ ................................ ............................... 147 Study Significance and Future Directions ................................ ................................ ............... 147 APPENDICES ................................ ................................ ................................ ........................... 149 APPENDIX A: Research Study Approvals ................................ ................................ ............ 150 APPENDIX B: Parent c oncent and child assent ................................ ................................ ..... 159 APPENDIX C: Measurement battery ................................ ................................ ..................... 166 APPENDIX D: Translated modified Bl ock 2004 Kids food frequency questionnaire (FFQ) ................................ ................................ ................................ ................................ ................. 170 APPENDIX E: Addendum A to Cost Proposal for Modified Arabic/English Version of Block Kids 2004 FFQ List of Changes to Food Questions ................................ ............................... 177 APPENDIX F: FFQ Kcals Outliers ................................ ................................ ........................ 186 APPENDIX G: Reliability Test ................................ ................................ .............................. 188 BIBLIOGRAPHY ................................ ................................ ................................ ..................... 191 x LIST OF TABLES Table 1. Demographic characteristics and mean values of anthropometrics, biometrics, and covariates (moderate - to - vigorous physical activity [MVPA] and screen time [ST]) of boy and girl fifth graders in Kuwait 1 ................................ ................................ ................................ ................................ ...................... 66 Table 2. Prevalence (at risk) of CVD risk factors between fifth grade Kuwaiti boys § and girls 1 .......... 69 Table 3 . Reliability test of the translated (Arabic/English) and modified (cultural food) Block Kids 2004 FFQ completed by Kuwaiti fifth grade boys ( n =22) and girls ( n =29) 1 ................................ ...................... 94 Table 4. Demographic characteristics and anthropometrics, and covariates (moderate - to - vigorous physical activity [MVPA] and screen time [ST]) of Kuwait fifth grade boys versus girls 1 ....................... 95 Table 5. Mean food group, macronutrient, and micronutrient intakes, and fiber index (FI) of fifth gra de Kuwaiti boys versus girls 1 ................................ ................................ ................................ .......................... 96 Table 6. Proportion of Kuwaiti schoolchildren meeting the Dietary Guidelines & Dietary Reference Intakes (DRIs) and WHO/FAO (Range/RNI) Recommendations 1 , compared by gender .......................... 99 Table 7. Healthy Eating Index - 2010 (HEI - 2010) 12 - Components (0 - 100 points) of Kuwait 5th grade boys (n = 148) vs. girls (n = 165) 1 ................................ ................................ ................................ ........... 102 Table 8. Healthy Eating Index - 2015 (HEI - 2015) 13 - Components (0 - 100 points) of Kuwait fifth grade boys (n = 148) vs. girls (n = 165) 1 ................................ ................................ ................................ .......... 103 Table 9. Mean anthropometrics, blood lipids, BP, covariates (moderate - to - vigorous physical activity [MVPA] and screen time [ST]) 1 of Kuwait fifth graders by BMI - for - age categories 2 ............................. 124 Table 10. Prevalence at risk for dyslipidemia and BP, and covariates (moderate - to - vigorous physical activity [MVPA] and screen time [ST]) 1 in Kuwait fifth graders by BMI - for - age categories 2 ................ 127 Table 11. Mean food group, macronutrient, and micronutrient intakes 1 in Kuwaiti fifth graders by BMI - for - age categories 2 ................................ ................................ ................................ ............................ 129 Table 12. Proportion of Kuwaiti fifth graders meeting the US Dietary Guidelines (DGA), Dietary Reference Intake (DRI), and WHO/ FAO (Range/RNI) recommendations by BMI - for - age categories 1 .. 132 Table 13. Study participants excluded from analysis based on predicti ve equation of Schofield - HW and .......................... 186 Table 14. Relia bility (test - retest) of the translated (Arabic/English) and modified (cultural food) Block Kids 2004 FFQ performed on 5th grade boys (n=26) and girls (n=32) in Kuwait ................................ ... 188 xi LIST OF FIGURES Figure 1. Prevalence of Overweight (OW) and Obesity (OB) in Kuwaiti fifth Grade Boy and Girl [47] 5th to <97th centile and OB [54] [124] ......... 67 xii KEY TO ABBREVIATIONS %BF: percent body fat AI: adequate intake AMDR: acceptable macronutrient distribution Range AHA: American Heart Association BMI: body mass index BP: blood pressure CVD: cardiovascular disease DBP: diastolic blood pressure DGA: dietary guidelines for Americans DRI: dietary reference intake FAO: Food and Agriculture Organization of the United Nations FI: dietary fiber index g/day: grams per day HDL:TC: high - density lipoprotein - total cholesterol ratio HDL - C: high - density lipoprotein cholesterol HEI: healthy eating index IOTF: International Obesity Task Force kcals: kilocalories kg: kilogram KNSS: Kuwait National Nutrition Surveillance System LDL - C: low - density lipoprotein cholesterol mcg/day: micrograms per day xiii mg/day: milligrams per day MVPA: moderate and vigorous physical activity NCEP: National Cholesterol Education Program NCHS: National Center for Health Statistics NHANES: National Health and Nutrition Examination Survey non - HDL: non - high - density lipoprotein OB: obese OW: overweight PA: physical activity RDA: recommended dietary allowance RNI: recommended nutrient intake SBP: systolic blood pressure ST: screen time TG: triglycerides TC: total cholesterol UL: tolerable upper intake level WC: waist circumference WHtR: waist - to - height ratio WHO: World Health Organization 1 CHAPTER 1 : INTRODUCTION Background/Overview A This chapter will first provide a global overview of concerns related to cardiovascular disease and an overview of concerns on children in Kuwait. This is follow ed by a summary of CVD risk factors, including the role of diet behaviors and inactivityThis includes literature on the prevalence of childhood OB, dyslipidemia, and elevated blood pressure BP is reviewed. The emphasis of literature review will be on Kuwa iti children since they are the focus of this research. This will be followed by the gap in the literature and the study rationale for the study objectives which are listed. A ccording to the World Health Organization (WHO), cardiovascular disease (CVD) is the leading cause of non - communicable disease (NCD) deaths worldwide [6] and responsible for approximately 31% of total global deaths annually [7 - 9] . It is predicted that by the year 2020, coronary artery disease (CAD) and stroke will be the first and second leading cause of death and of loss of disability - adjusted life years [9] . A concern in many countries is the increase in obesity (OB) and other CVD risk factors in children, which has been linked to changes in dietary behaviors including increases in refined and added sugar and saturated fat intakes and decreases in physical activity (PA). The most recent OB data from Kuwait indicates 31% of children aged 6 18 (34% of boys and 28% o f girls) are OB [1 2] . Cardiovascular disease (CVD) is a broad term, which includes diseases of the heart and systemic circulatory sys tem, including coronary heart disease (CHD), myocardial infarction, stroke and transient ischemia, congestive heart failure, valvular heart disease, rheumatic heart disease, peripheral arterial disease and congenital heart failure [1] . The Framingham Heart Study 2 indicates that hypertension, dyslipidemia, smoking, diabetes and OB are primary risk factors of CVD in adults [3 - 8] . The Bogalusa Heart Study indicates that the initial signs of atherosclerosis ors), which have been shown to accumulate in the thoracic and abdominal aorta and the right coronary artery as early as age two . [9] Cardiovascular risk factors tend to increase with age and vary according to gender, ethnicity, activity level, diet quality, alcohol consumption, smoking, stress and depression [10 - 12] . Selected studies have indicated that CVD risk factors vary between boys and girls. Possible reasons for this include physiological factors such as variance in rates of growth and maturation, environmental factors (such as parental OB), chronic diseases, nutrition and physical inactivity [13 ] . Poor dietary intake (high - energy and nutrient - dense diet ) is an independent risk factor for atherosclerosis in children . [14] [9] [10] According to the Global Burden of Disease database, OB, hypertension, dyslipidemia, poor dietary quality, and physical inactivity are significant contributors to chronic diseases including CVD. [20 - 22] It is established that nutrition influences many CVD risk factors, [15] including, dyslipidemia, type 2 dia betes, and hypertension with or without OB. [16 17] Based on this and other evidence, there are many organizations that have guidelines to combat this burden. This includes the US Department of Health and Human Services (HHS), US Dietary Guidelines for Americans, the WHO, the American Heart Association (AHA), and the American Academy of Pediatrics, which all have pediatric specific guidelines that emphasize the importance of heal thy nutrition and PA behaviors to prevent CVD and other chronic diseases. [18 - 22] and other health organizations have created nutrition and PA recommendations. [23 - 28] The HHS, AHA, and WHO recommend at least 60 minutes of moderate - to - vigorous PA per day for children and adolescents. [59, 176] 3 Physical inactivity is the fourth leading cause of death worldwide [23] . The PURE study (2003 2010) examined the association between the amount of PA and mortality from CVD in 130,000 people from 17 high, middle and low - income countries and showed that low PA was associated with mort ality (6%), myocardial infarction (3%), stroke (2%) and heart failure (0.42%) [24] . The European Youth Heart Study assessed associations between PA duration and clustering CVD risk factors in 1,732 randomly selected 9 15 year - old schoolchildren and indicated decreased odds of metabolic d isorders with PA bouts above 2000 cpm (equivalent to walking about 4 km/h) for 116 min in 9 - year - olds and 88 min in 15 - year - olds [25] . In essence, CVD morbidity and mortality increase with physical inactivity, which can lead to other potential CVD risks such as OB. Obesity is an independent risk factor, hence a potential determinant of CVD through increased risk for hypertension, dyslipidemia and el evated blood glucose, in addition to inflammatory markers and thrombogenic factors . [ 5, 30 - 32] Between 1990 and 2010, the percentage of child hood OB worldwide increased from 4.2% to 6.7%; it is expected to reach 9.1% (60 million children) by 2020 [21] . In creased intake of unhealthy dietary calories (high fat, processed food and sugar) has been linked to OB, dyslipidemia and elevated blood glucose in children . [18] Obese children tend to have more CVD risk factors than non - OB children. Nutrição et al. (2012) assessed factors associated with dyslipidemia (elevated TG and TC) in 937 children aged 7 14. The study reported higher prevalence and likelihood of dyslipidemia in OW and OB categories (26.4% and OR 3.10 [1.949 4.931]) th an in normal weight (NW) categories (20.2% and OR 0.83 [0.471 1.369]) [26] . Among 1,482 children aged 8 17, the National Health and Nutrition Examination Survey (NHANES) (2011 2012) reported higher prevalence of 4 dyslipidemia (high TC, low HDL or high non - HDL) in OB (39.3%) than in OW (18.2%) and NW (14.6%) children . [27] Moreover, NHANES (1999 2014) data on 15,647 children and adolescents aged 5 18 demonstrates prevalence t h percentile, 19.5%) children . [28] Despite this, some non - OB children may have one or more CVD risk factors, though the prevalence of CVD risk factors in non - OB Kuwaiti children is unknown. In Kuwait, CVDs contributed to the highest portion (41%) of total d eaths from non - communicable diseases in 2012 . [29] One main reason is poor public knowledge about CVD and its causes and risk factors, leading to lack of prevention . [30] Previous studies and reports in Kuwait have presented evidence that poor dietary intake and physi cal inactivity are prevalent among the majority of the Kuwaiti population . [1 31 - 38] There is a lack of assessment and tracking of CVD risk factors in non - OB childre n especially [45] , and the risk of CVD exists with and without OB . The majority of previous published studies focus on the increasing OB rate in Kuwait . [46 - 62] The Kuwaiti Nutrition Surveillance System (KNSS, 2012) reported rates of 21.7 % for OW and 3 6% for OB adults [39] , which has been considered among the highest rates in the Arab Peninsula [40] and globally. Moreover, childhood OB is notably of concern among preschoolers and school - aged boys and girls . [41 - 45] A study during 200 8 2009 on Kuwaiti children aged 9 13 estimated prevalence of OB at 36.5% of (a sample of 111) boys and at 24% of (a sample of 94) girls . [63] A study from 2012 2013 on Kuwaiti youth aged 6 18 reported a prevalence of OW (17.7% and 21.6% based on CDC and WH O criteria, respectively) and an alarming rate of OB (33.9% and 30.5% based on CDC and WHO criteria, respectively) . [2] 5 Data describing rates of OW and OB in Kuwaiti children vary between studies. This is in part due to variations in sample sizes and statistical methods and the use of different categorical standards and cut points. For example, NHANES - I has cut points of 85 th and 95th percentiles (NCHS/WHO); the age - and sex - specific BMI cut points are based on the International Obesity Task Force (IOTF) and weight or height standard scores (z - scores) are based on the National Center for Health Statistics (NCHS) reference (u tilized by childhood OB studies in Kuwait) [2 46] . In contrast to this, a 2012 2013 evaluation shows variations in estimating the prevalence of OW and OB among youths aged 6 18, based on CDC (2000), WHO (2007) and IOTF references. The IOTF and WHO values demonstrate a higher rate of OW at 23.3% and 21.6% respectively, compared to the CDC value of 1 7.7%. However, the IOTF and WHO (2007) references show a lower rate of OB at 28.2% and 30.5% respectively, compared to the CDC value of 33.9% [2] . Clearly, the findings vary depending on the reference used. This may indicate that the CDC OB is not applicable for international use. The IOTF is constructed based on internationa l longitudinal data (US, UK, Hong Kong, the Netherlands, Singapore and Brazil) [47 48] . The WHO standards (2007) merge international cross - sectional data (0 5 years old) with NHANES (NCHS/WHO, 1977 ), whereas the CDC (2000) includes only NHANES II and III data [49 50] . In esse nce, any other equivalent measure for a specific population subgroup, the use of national reference curves, or a reference developed for international comparisons, such as the IOTF [51 52] , would be more beneficial for tracking the rate of OB in Kuwaiti children. BMI z - score is a measure of relative weight adjusted for child age and sex. It is commonly used in cross - sectional and cohort studies for evaluating weight status and changes in OB in schoolchildren. A pros pective study in children aged 6 13 indicates that BMI z - score 6 reveals a linear increase in cardiometabolic risk factors across the entire range of BMI values regardless of thresholds [53] . BMI z - sco re was found to be significantly ( P <.01) but negatively associated with energy intake and daily food intake in 2 9 year - old children [54] . In another cross - sectional study, of 148 scho olchildren in grades 4 8, BMI z - score was found to be significantly associated ( P <.001) with low HDL - C and high triglycerides (TG) [55] . However, BMI z - score has shown less validity for following data over time. Waist c ircumference (WC) and derived waist - to - height ratio (WHtR), in addition to percentage body fat (%BF, or skin folds) are surrogate anthropometric indices for estimating abdominal OB and body fat (visceral and subcutaneous), considered as predictors of risks for CVD in children [56] . For US child ren and adolescents, percentile curves for WC and cardiovascular risk factors (lipid profile) were established based on NHANES surveys (1988 1994 and 1999 2006) combined with the Bogalusa, Fels, Muscatine and Princet datasets. For example, abnormal cut - off 85th and 90th percentiles, respectively. At or after age 11, serum TG peaks in girls, while HDL declines in boys [57] . Agredo - Zúñiga et al. (2015) evalu ated the association of WC and WHtR with metabolic syndrome (MetS), adjusted for BMI and skin folds, in 1,672 adolescents aged 10 th percentile ) was ys ( OR = 2.57 [ 95% CI : 1.91 3.44 ) and girls ( OR = 1.92 [ 95% CI : 1.49 2.47 ] ) and with high BP, particularly in girls (OR = 3.07 [ 95% CI : 1.58 5.98 ] ), whereas high WHtR (>0.50) was associated with high TG in girls ( OR = 1.99 [ 95% CI : 1.55 2.56 ] ) only [58] . Percentage body fat (%BF) is an important indicator of body fatness, or adiposity, which is difficult to estimate via BMI, or WC and WHtR (which measure abdominal fatness). Bibiloni et al. (2013) interpr eted the WHO measures of BMI, fat mass index (FMI) and 7 WHtR across weight categories in 1,231 adolescents aged 12 17. The results show that some boys and girls with normal %BF (1.3% and 3%) and normal WHtR (9.5% and 4%) were classified as OB . [59] Therefore, %BF, and WC and WHtR, are useful surrogate anthropometric indices for distinguishing body fat from body mass (BMI), and for identifying differences in CVD risk factors in children of different sexes, respectively. Studies described t he prevalence at risk of CVD, particularly dyslipidemia, in Kuwaiti children or have compared by gender are not available. A paired - matched case - control study from 1995 1996 reported adverse effects of OB versus a non - OB control group on serum lipids and BP in 460 OB Kuwaiti schoolchildren aged 6 13 . [60] The data shows that 38 (8.3%) OB and 3 (0.7%) non - OB children had elevated systoli c blood pressure (SBP) >130 mmHg and only 3 OB children had elevated diastolic blood pressure (DBP) >90 mmHg. SBP and DBP indicated a significant correlation with BMI among OB ( r =.333, P < 0 .001) boys and girls , with significant weak correlation among non - OB (r =.156, P < 0 .01) children. The study also indicated that BMI was positively correlated with mean TG ( r =.198, P < 0.001), which was higher among OB boys than girls, and inversely correlated with mean high - density lipoprotein cholesterol (HDL - C) ( r = - .204, P < 0 .001) , which was relatively lower among OB girls than boys . The data of the study has also indicated other mean serum blood lipids which were higher in OB boys than girls include total cholesterol (TC), low - density lipoprotein (LDL ) , and TC:HDL . Whereas among non - OB, the mean levels of TG, LDL, and TC:HDL, except for TC, were similar between boys and girls. In summary, this study has indicated that boys, especially with a higher prevalence of OB than girls discussed earlier, would be at risk for CVD than more than girls. With respect to nutrition in Kuwait, there is a lack of data describing dietary intake such as macronutrients (including saturated fat and trans fat and added sugar) and food groups and 8 evaluating dietary quality. The KNSS was established in 1995 and is based on WHO/Eastern Mediterranean Regional Office (EMRO) guidelines [61 62] . The KNSS does not report data on dietary behavior and intake of children above five years of age [1] . Despite WHO jointed with the Food and Agriculture O rga nization ( FAO ) - established international nutrition guidelines for nations that lack national nutrition guidelines (including Kuwait) [21 22 63 - 65] , there are no available data indicating the use of WHO/FAO nutrition recommendations for Kuwaiti ch ildren. A study of 1704 members of the Kuwaiti population aged 3 86, conducted during the period 2008 2009 by Zaghloul et al. (2012), describes nutrient intakes based on US Department of Agriculture (USDA) 5 - step multiple - pass single 24 - hour recalls. Its f indings show that ~ 50% of children and ~ 33% of adults exceeded the recommended energy intake. In addition, 78% 100% of the study population exceeded the estimated average requirement (EAR) of protein and carbohydrates. The dietary recommendation for sodi um intake was exceeded by two - thirds of males above four years of age, while 80% of the overall sample did not meet the US Dietary Reference Intake (DRI) recommendations for vitamin D, vitamin E, calcium and essential fatty acids (FAs) n - 3 and n - 6 . In addi tion, more than 80% of children did not meet the recommended level for fiber [36] . Furthermore, the study described the nutrition status of 9 to 13 - year - olds, in which 43.5% of (111) boys and 63.3% of (94) girls exceeded the recommended calorie intake, at 2236 kcal for boys (recommended intake 1800 2000 kcal) and 1992 kcal for girls (recommended intake 1600 1800 kcal). Moreover, ~40% of boys and girls exceeded the acceptable macronutrient distribution range (AMDR) for fat , and the DRI for sodium intake (boys: 3508 mg; girls: 2975 and girls, mean intakes for calcium (717.6 mg and 543.8 mg), magnesium (216.6 mg and 182.1 mg), vitamin D (75.3 IU and 49.5 IU) and dietary fiber (18.1 g and 16 g) were below the US DRI recommendations [66] . 9 With respect to PA engagement, Kuwaiti children and adolescents, especially females, were reported to be less likely to engage in vigorous or moderate exercise. Based on self - reported data on PA performance in a cross - sectional study on 635 OB and 1,765 non - OB schoolchildren aged 6 13, Moussa et al. (1999) found that vigorous (36.7% and 33.5%), moderate (41.6 % and 45.5%) and light (21.7% and 20.9%) PA was low [67] . Based on a modified Harvard step test, El - Bayoumy (2009) showed that 97% of 5402 adolescents aged 10 14 had physical fitness scores below the medi um range (65 79) [37] . The absence of health education programs promoting PA and healthy lifestyles in schools and communities, dusty and hot weather, the lack of cities designed for walking and biking, social engagements and social pressure and security are all considered major barriers to PA in Kuwait. Gap in Literature Overall, given that poor dietary behaviors and inactivity are prevale nt in Kuwaiti schoolchildren, there is limited literature providing insights into CVD risk factors other than increasing rates of OB amongst Kuwaiti schoolchildren: 36.5% of boys and 24% of girls aged 9 to 13 [36] and 34% of boys and 28% of girls aged 10 14 are OB . [1 68] Additionally, there are no research updates on the prevalence of elevated BP ( 5%) in Kuwaiti schoolchildren beyond the year 2000 [60 69] and whethe r the prevalence has incre ased. Importantly, no available data estimates the prevalence of dyslipidemia in Kuwaiti schoolchildren; one study indicates higher mean levels of blood lipids in OB boys than in OB girls; however, among non - OB, those levels were equavilant by gender. [60] No data assesses potential gender differences in the prevalence of multiple CVD risk factors in Kuwaiti schoolchildren, although the re is literature showing that girls may have higher levels of dyslipidemia and lower SBP and DBP levels than boys, at or beyond the age of ten . [70] 10 intake and quality are also limited. One study from 2008 2009 describes nutrient intakes and how far DRI r ecommendations are met in a small sample of 205 schoolchildren aged 9 13; however, there are no available data on the proportion of children meeting recommendations of food groups, saturated fat, trans - fat and added sugar, which are important in the preven tion of OB and risks of CVD [15 19 71 72] . There are also no data on dietary indices such as the Health y Eating Index (HEI) or Fiber Index (FI) in Kuwaiti schoolchildren. Therefore, the overall objective of this study is to determine the prevalence of CVD risk factors and to determine the proportion of Kuwaiti schoolchildren meeting nutrition recommendation scores and levels. Additionally, the study will determine whether there are differences in CVD risk factors and in the levels to which recommendations are met according to gender and weight status (BMI - for - age categories). Specific A ims and Hypotheses Specific Aim 1: In a sample of Kuwaiti fifth graders, determine the prevalence at risks for CVD (BMI, WC, WHtR, BF%, SBP, DBP, TG , TC, LDL, non - HDL, TC:HDL and HDL ) and determine if there are differences by gender; 2) determine if there are differences in mean levels of CVD risk factor by gender. . Hypotheses for Specific Aim 1: Hypothesis 1a: The prevalence of at - risk BMI, W C, WHtR, SBP, DBP, TG, TC, LDL, non - HDL and TC:HDL will be significantly higher in boys than in girls. Hypothesis 1 b: Mean BMI, W C, WHtR, SBP, DBP, TG, TC, LDL, non - HDL and TC :HDL will be significantly higher in boys than in girls. 11 Specific Aim 2: In a sample of Kuwaiti fifth graders, 1) determine the proportion of children that are meeting recommendations for food g roups (fruit, vegetables, dairy and whole grains), macronutrients (total calories, total fat, saturated fat, trans - fat, n - 6 and n - 3 FAs, protein, carbohydrates, fiber and added sugar), selected micronutrients (calcium [ Ca+], potassium [K+], magnesium [Mg+] , sodium [Na+] and vitamin D ) and two dietary indices (HEI and FI); 2) determine if there are gender differences in meeting the recommendations. Nutrition variables used are based on US Dietary Guidelines for food groups and DRI for macronutrients and micronutrients, in addition to WHO/FAO ranges and recommended nutrient intakes (RNI) for food groups, macronutrients and micronutrients. Hypotheses for Specific Aim 2: Hypothesis 2a: The proportion of boys meeting recommended intakes of fruit, vegetables, dairy and whole grains will be significantly lower than that of girls. 2b: The proportion of boys meeting recommended intakes total calories, total fat, saturated fat, trans - fat, FAs n - 6 and n - 3, protein, carbohydrates, fiber and added sugar will be signif icantly higher than that of girls. Hypothesis 2c: The proportion of boys meeting recommended intakes of Ca+, K+, Mg+ and vitamin D, but not Na +, will be significantly lower than that of girls. Hypothesis 2d: The overall diet quality (HEI ) and FI will be si gnificantly lower for boys than for girls. Specific Aim 3: 12 In a sample of OB, OW, NW and UW Kuwaiti fifth graders, determine if there are differences between weight categories in: 1) prevalence of CVD risks (SBP, DBP, TG, TC, LDL - C, non - HDL - C, TC/HDL - C and HDL - C); 2) meeting US and WHO/FAO nutrition recommendations for food groups (fruit, vegetables, dairy and whole grains), macronutrients (calories, total fat, saturated fat, trans - fat, protein, carbohydrates, fiber and added sugars), selected micronutri ents (Ca+, K+, Mg+, Na+ and vitamin D). Nutrition variables used are based on US Dietary Guidelines for food groups and DRI for macronutrients and micronutrients, in addition to WHO/FAO ranges and RNI for food groups, macronutrients and micronutrients. Hyp otheses for Specific Aim 3: Hypothesis 3a: The prevalence of at - risk SBP, DBP, TG, TC, LDL - C, non - HDL - C, TC/HDL - C and low HDL - C will be significantly higher for obese than for non - obese Kuwaiti children. Hypothesis 3b: Obese Kuwaiti children will be signif icantly less likely to meet recommendations for fruit, vegetables, dairy, whole grains, calories, total fat, saturated fat, trans - fat, protein, carbohydrates, added sugars, fiber, Ca+, K+, Mg+, Na+ and vitamin D than non - obese children. 13 CHAPTER 2 : LITERATURE REVIEW This chapter contains a brief overall background of the Kuwaiti children population related to the current research objectives and hypotheses followed by detailed sections discussing the main study variables and associated factors a s they relate to the population studied. Kuwait Kuwait is a small, affluent, oil - rich Arab country located in the Arab Gulf Region of the Middle East with an area of 17,818 km 2 (about 6,880 mi 2 ) [73] , populated with 4,239,006; 31% of which are citizens [74] . Kuwait is bordered on the north and west by Iraq, on the south by Saudi Arabia, and on the east by the Arabian Gulf [75] . The country is 92% urban, and divided into six governorates (Capital, Hawalli, Farwania, Ahmadi, Jahra, and Mu barak Al - Kabeer). The citizens of Kuwait are constitutionally privileged with a high standard of living which includes free education, medical care, and housing as major benefits [75] . In the past five decades, Kuwait has experienced a rapid economic growth and modernization. Following disc overy of oil in the late 1940s, the resulting increased revenue may have potentially impacted the nutritional and lifestyle pattern and diseases state of the Kuwaiti population [73] . An increase in energy intake from calorie - dense foods, especially, fat, s ugar, and protein are reflected by an increase in imported foods (approximately 90% of food consumed) and reduced food prices due to government subsidies [37] . As a result, rates of obesity have increased throughout most of the Kuwaiti governorates and acr oss all population age groups [1 67] . Kuwait now ranks among countries with the highest obesity rates worldwide [1 76] . Additionally, mortality rates from coronary heart disease (CHD) , hemiplegia (cerebrovascular), and type 2 diabetes mellitus have increased [77 - 80] . Moreover, these increases warrent when about a future with exacerbated health and disease 14 burdens. The increased magnitude of chronic diseases in Kuwait is most likely attributed to insufficient education strategies for preparing the society for modern lifestyle and socia l changes [81] . Cardiovascular Diseases (CVD) Cardiovascular diseases (CVD) a re diseases that affect the heart and circulatory system including coronary artery disease (CAD), stroke, congestive heart failure, valvular heart disease, rheumatic heart disease, peripheral arterial disease, and congenital heart failure [82] . According to the World Health Organization (WHO), CVD is considered a major contributor to morbidity and mortality from non - communicable diseases (NCDs) worldwide [29] , being responsible for 56% of total death from NCDs in 2014 [6] . Globally, by 2020 cardiovascular diseases (CVDs) are predicted to be responsible for 25 million deaths (36%) each year, surpassing deaths caused by infectious diseases. It is also predicted that by the year 2020, coronary heart disease (CHD) and stroke will be the leading causes of death and loss of disability - adjusted life years [9] worldwide. In the Middle East, coronary heart diseases (CHD) is the leading chronic disease associated with mortality from NCDs ( 60 % of NCD deaths) . According to Amuna et al., the rates of type 2 diabetes mellitus ( T2 DM) and CVD are estimated between 25% and 35% among adult population in the Arab Gulf region and are considered indicators of the emergence of metabolic syndrome (MetS) among children and adolescents [6, 7] . In addition , h igh blood pressure, high blood cholesterol, increased abdominal obesity associated with poor nutrition behavior and physical i nactivity, and tobacco use were also considered major contributors to the emergence of MetS in children and adolescents [83 84] . 15 Cardiovascular Disease Risk Factors There are several risk factors for CVD that are non - modifiable including , age, gender, ethnicity, and genetics (family history). In addition, there are modifiable risk factors which include unhealthy behaviors, (e.g., smoking, physical inactivity, and poor diet ) , and clinically measurable risk factors (e.g., dyslipidemia, hypertension, diabetes, and obesity ) [82] . Gender - specific Predictors of Cardiovascular Disease in Children Studies have shown tha t cardiovascular disease risk predictors are exclusively gender specific , which can be influenced by biological or environmental factors [85] . Boys or girls can have predominantly higher mean levels of certain cardiovascular and or cardiometabolic risk comp onents related to abdominal obesity, blood pressure and blood lipids, and insulin resistance . Stavnsbo et al . (2018) published an international reference value of for cardiometabolic risk variables based on cohorts of children (observations of 11,234 girls and 11,245 boys 6 - 11 years) from Europe and the United States (51.3%). The mean values of blood lipids such as total cholesterol, low - density lipoprotein, high - density lipoprotein, and triglycerides become higher in girls than boys bey o nd age 10 , while b oys maintain relatively higher mean systolic and diastolic blood pressure [70] . Some studies indicated higher means of certain risk factors in girls than boys during late childhood or early pre pub erty periods , related to growth and maturation, also related to parenteral factors . Peterson et al. (2012) assessed 2 , 800 6th graders cardiometabolic risk factors . G irls had higher mean triglycerides than boys, in addition to increased metabolic syndrome score (MetS) related to parenteral history of CVD [13] . Also, Morandi et al. (2014) evaluated MetS risk components in a cohort sub - sample of 425 obese children and reported marginally ( p =0.09) higher mean triglycerides in girls (11 years and older) than boys (12 years and older) [86] . 16 Pediatric Cardiovascular Risk Cut - P oints According to US standards from CDC 2000 Growth Charts for the US; Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents using cutpoints for children ages 1 to <13 years ; and the Expert Panel on Integrated Guidelines for Cardiovascular H ealth and R isk R eduction in C hil dren and A dolescents . Obesity The cut - points for children considered at - risk for CVD is defined for OW as BMI for sex and and < 95th percentile, and for OB as BMI for sex and age 95th percentile [87] . Blood Pressure The cutpoint for determining the prevalence of children at risk corresponds with the cutpoint is based on children population data ( derived from a comprehensive revi ew of almost 15,000 published articles between January 2004 and July 2016 p age 1) [88] which corresponds to 90 th to < 95 th percentile by sex, age, and height. Levels > 95 th percentile are defined as hypertension which includes stage I and II. Blood Lipids At risk cut point values: total cholesterol (TC) at or above 170 mg/dL, LDL at or above 110 mg/dL, HDL at or lower than 45 mg/dL, TG at or above 90 mg/dL, non - HDL at or above 120 mg/dL, and total cholesterol: HDL ratio at or above 3.5. High risk cut point values: total cholesterol (TC) at or above 200 mg/dL, LDL at or above 130 mg/dL, HDL at or lower than 40 mg/dL, TG at or above 130 mg/dL, non - HDL at or above 145 mg/dL [89] . 17 Cardiometabolic Risk ( metabolic syndrome [ MetS ] ) The WHO cut - points were set at >95th or 97 th percentile for WC or BMI; 95th per centile for TG (mg/dL); < 5th per centile for HDL - C (mg/dL); 95th per centile for BP (mmHg); 110 mg/dL for blood glucose [85] . The Prevalence of Cardiovascular Diseases and Risk Factors in Kuwait According to the World Health Organization (WHO) country profile for Kuwait, CVD contributed to 41% of total death from NCDs in 2012 [29] . Coronary heart disease (CHD), particularly, acute myocardial infarction with a rate of 13.1/10,000 population/year, (the rate in males is 3.7 times the rate for females) [78] , and ischemic heart disease (IHD) are considered major components of CVD in Kuw ait [76] . Secular data showed that within a period of 14 years (1980 - 1981 to 1993 - 1994), the prevalence of 10% in men (15% to 25%) and women (29% to 39.3%), respectively [90] . It continued increasing in both men 11.3% and women 14.6% during the periods 1998 - 1999 and 2008 - 2009 [76] . The most recen t data (2011 2012) indicated nearly steady obesity rates at 30.7% and 38.3% among men and women (>20 to <50 of age), respectively [1] . Dyslipidemia included, hypercholesterolemia was estimate d 7.6% and 6.5% in men and women, respectively [91] . Additionally, hypertriglyceridemia 13.2%, low HDL (41%), and elevated LDL (11.2%) [80] . El - Rashed et al. (1997) reported that hypertension was responsible for 93 5 hospital admissions as well as contributing to incidents of ischemic heart disease, cerebrovascular accidents (stroke), and congestive heart failure [92] . The prevalent rate of hyperte nsion among 2836 Kuwaiti adults was 26.3%, higher among males (28.3%) than in females (22.9%); 86% of subjects had mild to moderate diastolic BP (>109 mmHg) [92] . Increased age (>35 ye ars), presence of diabetes, and obesity were strongly associated with hypertension. Based on a national cross - sectional survey 18 (2006) of a representative sample of the Kuwaiti population (n=2280), the overall prevalence of hypertension was shown to be 24.3 %, and was higher among obese individuals (35.7%) as compared to non - obese individuals (13.9%) [93] . Several studies indicate that Kuwait is ranked among the top ten countries with elevated rates of T2DM, worldwide [94 - 96] . An overall prevalence of T2DM among adults was estimated 18.1% [93] , or ~17% in women and 19.4% in men [80] . In addition, there is a fluctuating trend in reduction of prevalence of impaired fasting glucose (7.4% and 6.8%) and T2DM (9.8% and 8.9%) among men and women, respectively [95] . Prevalence of Overweight and Obesity The p revalence of o verweight and o besity r efer s to a state of positive energy balance that ceeds their energy expenditure, as reflected by weight gain [97] . Obesity refer to an excess in fat mass great enough to increase the risk of morbidity, altered physical, psyc hological, or social well - being and/or mortality , defined by the World Health Organization (WHO) [98] . E 2 for adults , and 95 th or 97 th percentile for children based on CDC or WHO, respectively ) rates and related chronic diseases has been associated with dramatic chang es in living environments. Nutritional transitions associated with changes in diet and lifestyles driven by global income growth and rapid urbanization has affected low, middle, and high income populations and countries [97] . A global epidemic of overweight (OW) and obesity (OB) was recognized in 1980 as the most significant contributor to il l health worldwide ; adverse health attributed to increased accumulation of body fat. Obesity is a major, independent modifiable cardiovascular risk factor associated with high morbidity rates [14] , insulin resistance (IR) , and metabolic syndrome (MetS) or metabolic cardiovascular syndrome [MCS] [14] . Obesity is also 19 associated with hyperinsulinemia, hypertension, hyperlipidemia, T2DM , and an increased risk of atherosclerosis in adults and children [15 - 17] . Between 1980 and 2013, the number of OW and OB in dividuals increased from 857 million to 2.1 billion , worldwide [24] . The rates of OW and OB in children of developing countries increased from 12.9% to 23.8% among boys , and from 13.4% to 22.6% among girls [24] . An i ncreased availability of high - caloric foods along with a sedentary lifestyle, and genetic susceptibility are known to be specific ally correlated factors for obesity. Risk for adverse health consequences i n relation ship to econom ic transition s and industrialization or urbanization are associated with rapid shifting from a dietary deficit to excess caloric intake. During 2010, 3.4 million deaths were estimated to be caused by OW and OB , worldwide . Obesity has been attributed to up to 7% of total health costs in develo ped countries, representing a significant expenditure of national healthcare budgets [99] . Childhood Obesity Childhood Obesity is considered a group of diseases or conditions involving several health complications and/or clusters of CVD risk factors including hypertension, dyslipidemia, hype r insulinemia, increased blood clotting , and chronic inflammation [100] . Rates of c hildhood obesit y are rapidly increasing in countries that are experienc ing rapid economic growth or high - income created nutrition transition and sedentary lifestyle s, including Kuwait [97] . Between 1990 and 2010 the global rate of childhood OW and OB rose from 4.2% to 6.7% and is expected to reach 9.1% (60 million children ) by 2020 [97 101] . 20 Childhood Obesity and Cardiovascular Disease (CVD) Children have less disease related to obesity than adults, particularly, childhood obesity health risks extend into adulthood [48] . Childhood obesity is associated with increased risk parameters for CVD including , elevated blood pressure, and dyslipidemia , particularly abnormal TG , TC, LDL - C, and HDL - C levels [102] . The appearance of obesity during childhood increases the risk of i t s persistence throughout adolescence and adulthood [21, 104] , which increases the risk for chronic diseases and disability later in life [97] . An increase in BMI after age ten is considered a strong predictor for premature death associated with heart attack in adulthood. Several prospective studies have indicated that obesity tracks from childhood to adulthood and become s more associated with CVD risk factors and the incidence of CVD [103 104] . In essence, hypertension and dyslipidemia associated with childhood obesity are considered an early sign of CVD mo rbidity and mortality in adulthood [9 14 102 105] . Overweight and Obesity among Kuwaiti Children T he Kuwaiti population is considered uniquely heav y as compared to other modern affluent countries , the prevalence of OW and OB has been shown to affect every age group [2 76 106] . Generally, data indicate that the overall trend toward increased weight in Kuwait is remarkably eleva ted among women of childbearing years , schoolchildren , and adolescents [40 107] . Childhood obesity is rapidly increasing in Kuwait [40] , and the rate of OW and OB is considered among the highest in the Middle East and globally. The prevalence of OW and OB among Kuwaiti adolescents ha s exceeded some regional and global rates incl uding , Dubai, Saudi Arabia, Malta, US , Spain, Canada, England, Italy, Greece, France, and Germany [108] . According to a recent study of 6 , 574 Kuwaiti students (6 - 18 years) , the prevalence of OW and OB was 17.7% and 33.9% , respectively, based on CDC data ; 21.6% and 30.5% , respec tively, based on 21 WHO data ; and 23.3% and 28.2% , respectively, based on IOTF reference standards . [2] . Moreover, the Kuwait Nutrition Surveillance System ( KN S S 2011 - 2012) have shown that the prevalence of OW and OB by age in male and female children was 6.6% OW and 2.5% OB in preschoolers (<60 months ; genders combined ); 14.9% OW and 17.3% OB for male , and 18.9% OW and 17.3% OB for female primary school children (5 < 10 years); 19.1% OW and 34% OB for male , and 24.4% OW and 28.1% OB for female intermediate school children (10 - < 15 years) ; and 19.1% OW and 34.1% OB for male and 22.2% OW and 22.0% OB for female secondary school children (15 18 years) based on WHO/CDC reference standards [63] . Zaghloul et al. ( 2012 ) , indicated that 37% of boys and 24% of girls of Kuwaiti school children (9 - 13 years) were obese based on WHO standards [107] . Whereas, the prevalen ce of OW and OB among adolescents (10 - 14 years) ranged from 13.9% to 14.6% for prevalence of OW and from 30.7% to 30.9% for OB according to Al - Isa et al. [109] and El - Bayoumy et al. [37] . Males younger than 14 years old were predominantly heavier than females who tend to become obese at 14 years and older [41 109 110] . Changes in the Nutritional Status of Kuwait children based on NCHS/CDC Reference Population Recent research shows that modern Kuwaiti children (6 - 13 year s ) a re ta ller and heavier than their counterparts from thirty years earlier , with an enormous shift in BMI status [111] . A national cross - sectional survey of Kuwaiti children in ( 1983 - 1984 ) involved 2 , 554 children (1 , 319 males and 1 , 235 females) including , preschoolers (0 - 5 years) and school children (6 9 years) comparing stature and weight status in relation to their nutritional status , and in comparison to the US population reference standards (NCHS/CDC) [112] . In both age group s the stature was described as shorter (47.7% preschoolers and 48.3% 22 schoolchildren falling < 30 th centile) as compared to the US children . However, in regard s to weight, preschool children (0 5 years) were found to be 51.8% ( > 50 th per centile) [112] , whereas school children (6 9 years) were relatively heavier at 61.1 % ( > 50 th per centile) compared to the NCHS /CDC reference population [113] . Al - Isa et al. compared height and weight data from a national cross - sectional study between 1985 and 1995 of primary school children (6 - 10 years) , support ing the data that the height - for - age and weight - for - height per centiles of elementary school children ha s changed or significantly increased from 1985 to 1995 , especially in obesity among boys (15.7%) as compared to girls (1 3.8%). The data also demonstrated that Kuwaiti children (6 - 10 years) were heavier and shorter than the NCHS/CDC reference population [45] . International D efinition of C hildhood O ver weight and O besity The International Obesity Task Force (IOTF) proposed that adult cut off points for overweight and obesity (BMI 25 kg/m 2 , 30 kg/m 2 ) be linked (regressed) to BMI centiles for children to provide child cut off points [114] . Cole et al. (2000) developed an internationally acceptable definition of child overweight and obesity (2 18 years old) based on averaging the centiles of six countries (Brazil, Great Britain, Hong Kong, the Netherlands, Singapore, and the United States) [48] . Each of six datasets ( longitudinal surveys), had over 10,000 subjects with ages ranged from 6 to 18 years, were used to estimate the centiles that pass through the adult values of BMI 25 and 30 at age 18 years. The data were then averaged to provide the published cut - points [115] . The curves indicated a wide range of values span ning several units of BMI in both sexes ; in addition , national differences and variability in fatness, particularly centiles for overweight. Chinn et al (2001) compared international cut - points for overweight and obesity in children (n=6,000 white children aged 4 - 11 years) with alternative cut - points based on the UK 1990 reference data [16] . The r esults indicated that international cut - points exaggerate the 23 difference in prevalence of overweight and obesity between British boys and girls in comparison to comparable measures based on UK data by up to 7% and are not compatible with the UK reference c harts for BMI. A limitation of the international definitions associated with averaging data from different countries that vary in sa mple size and time, age range, and the choice of reference age (age 18 years) [8] . Furthermore, Cole et al. (200 7 ) reformula ted the international BMI cut - offs for each dataset by sex using the LMS age specific curves method , averaged the LMS curves for the six countries, allowing BMI to be expressed as centile or z - score. The new IOTF cut - offs enabled direct comparison with oth er BMI centiles and z - scores such as the The M and S curves correspond to the median and coefficient of variation of BMI at each age, whereas the L curve allows for the substantial age dependent skewness in the distribution of BMI [47] . L.M. Ke et al. (2015) compared the IOTF (reformulated), WHO 2007, and French references BMI cut - offs for age - sex of 1382 male and female schoolchildren (4 to 12 years). The study tested the agreements between the three references by using kappa coefficient, and concluded that agreement between the three references ra nged from moderate to perfect (0.43 k 1.00; P < 0.0001) . The results of the study also indicated overestimation in overweight and/or obesity by the WHO references compared to the French references and the IOTF . T he French references and IOTF yielded cl oser agreement in defining overweight [116] . Nut ritional Status - Growth Charts Growth charts are important diagnostic and tracking tools used in the prediction and prevention of diseases and health related nutrition disorders , including malnutrition , during infancy, childhood, and adolescence. They are used to assess the nutritional and health status of infants and older children and to monitor individual growth in pediatric medical practice s . [117] . 24 Besides evaluating individual health status, growth assessments can also provide an indi rect m easurement of quality of life of an entire population [62] . Several growth references , growth patterns of defined population s, have been developed to assess childhood obesity including, those developed by the National Center for Health Statistics and WHO ( NCHS/WHO ), Centers for Disease Con trol and Prevention (CDC 2000) , WHO 2007, and International Obesity Task Force (IOTF) [118] . In the US , growth charts developed in 1977 by the NCHS based on longitudinal data from the Fels study. In 1978, a normalized version of the NCHS percentile curves was produced by the CDC. T he CDC curves were later adapted by WHO as the international references , NCHS /WHO growth charts [119 120] . WHO then utilized the BMI cutoffs to define thinness in childr en (underweight, wasted, or stunted) as an international reference at - 2 z scores [121 122] ; these NCHS/WHO indicators were used to measure nutritional imbalance s resulting in under - nutrition and overweight . NCHS/WHO G rowth C harts The NCHS/WHO gr owth references for children and adolescents were based on the 1977 NCHS gr owth charts. T he reference s were developed using cross - sectional data collected from four separate surveys of US children and adoles cents between 1963 and 1974 . The a ge and sex specific BMI pe rcentiles endor sed by WHO for global use were based on the 1971 1974 NHANES I data [118] . Becaus e these references were constructed based on purely US data, they have limitations in geographical coverage and function when de scribing optimal growth patterns for children internationally [62] . For instance, the NCHS/WHO gr owth reference provides a p ositive skew in body we ight when com pared with other growth references such as , the CDC 2000 growth charts and the IOTF cutoffs [118] . 25 CDC 2000 Growth Charts The CDC 2000 growth charts (birth to 20 y ears ) , released in May of 2000, were based on a series of five national surveys collected between 1963 and 1994 . The surveys, also, provided data from two national surveys ( NHANES II [ 1976 80 ] and NHANES III [ 1988 1994 ]) to repla ce the limited Fels Longitudinal Study infant data used to construc t the 1977 NCHS infant reference data [50] . Kain et al. (2002), studying 6 year old Chilean children from 1987 to 2000 , demonstrated that the revised CDC growth charts reflect lower increased rates of OW ( 13.2 to 19.2% for boys and 12 to 18.5% for girls ) as compared to NCHS 1977 data ( 15% to 20% for boys and 17.2 to 21.8% for girls ) [123] . IOTF Reference The IOTF cut - points for children and adolescents ( 2 18 years ) was developed using a database of consisting of 97,876 males and 94,851 females (from birth to 25 ye ars of age) from six countries ( Brazil, Great Britain, Hong Kong, the Netherlands, Sin gapore and the US ) . Perc entile curves were co nstructed using the LMS method, and BMI values of 25 and 30 for 18 year old males and females, respectively [124] . According to a Fu et al. study (2003) of 623 Chinese children ( 6 11 y ears), the p revalence of obesity was lower using IOTF - BMI cutoffs (6.9%) than when using percentage - weight - for - height cutoffs (16.4%) . The IOTF - BMI cutoff values had a lower sensitivity ( 75.0 %) and specificity (91.6%) with sensitivity differing between boys (83.3%) and girls (66.6%) ( P <0.35). The percentage - weight - for - height cutoffs valu es had higher s ensitivity (91.6%) but lower specificity (86.6%), with no specific gender differences [125] . WHO 2007 Reference T he WHO 2007 reference for children and adolescents (5 to 19 years) developed by merging the 1977 NCHS/WHO growth reference with the WHO Child Growth Standards 26 ( Multicentre Growth Referen ce Study ). This allowed WHO to establish reference curves assessing children starting from age 5 years compared with previous reference curves (NCHS/WHO), which start from age nine years. It also provided transition from standard curves for the under - five s to the reference curves for older children [49 126 127] . In April 2006, WHO released new standards fo r assessing the growth and development of children from birth to five y ears of age known as the Child Growt h Standards (WHO standards ) . The standards are based on pri mary standardized data collected through the WHO Multicentre Growth Referen ce Study, a population - based study conducted b etween 1997 and 2003 in Brazil, Ghana, India, Norway, Oman, and the US [50] . The analysis combined a longitudinal follow - up study of 888 children (birth to 24 months) and a cross - sectional study of 6697 children ( 18 71 months) consisting of healthy breastfed infants and yo ung children raised in environ ments that do not constrain growth [127 128] . The goal of the analysis was to establish new parameters allowing for international comparisons of nutritional data of an under - five year old population [62] . Contrasting of WHO 2007, CD C 2000, and IOTF C ut - points T he NCHS/CDC and WHO references , in addition to IOTF cutoffs were utilized for studies in Kuwait [2 126 129] to estimat e nutrition status and rate of obesity [130] . Several studies indicated substantial ly skewed curves related to BMI, height - for - weight, and weight - for height of all three reference standards, resulting in substantial underestimat ing of OB in children and adolescents [50 118 128] . Similar results were seen with the use of references designed and smoothed for specific population, particularly the NCHS/CDC , and cutoffs b ased on a specific select samples [49 121] . For example, Elkum et al. ( 201 6 ) has i ndicated a varied prevalence of OW (CDC=17.7%, IOTF=23.3%, WHO=21.6%) and OB (CDC=33.9%, IOTF=28.2%, WHO=30.5%) among 6 , 574 Kuwaiti schoolchildren (6 to 18 years) [2] . T he variation s in 27 estimates by the WHO and IOTF references were closer than compared to the CDC cutoffs . However, Shields et al. (2010) found similar results in a combined data analysis (1978 - 1979 and 2004) of 10,501 Canadian children and adolescents ( 2 - 17 - year s). WHO cut - points indicated higher prevalence (35%) of combined OW and OB as compared to the IOTF (26%) or CDC (28%) cut - points. Whereas WHO and CDC cut - points provided similar e stimates of the prevalence of obesity (13%) compared with lower prevalence (8%) for t he IOTF cut - point estimate [131] . This time, the variations in estimate s by the WHO and CDC reference s were closer than compared to the IOTF cutoffs. As illustrated earlier, each of these references have their own unique characteristics and limitations. The IOTF was constructed based on international longitudinal data (US, UK, Hong Kong, the Netherlands, Singapore and Brazil), the WHO 200 7 merged international cross - sectional data with NHANES (NCHS/WHO), whereas the CDC 2000 included only NHANES data [43 - 45] . In essence, any measure means the same thing for a specific population subgroup. Anthropometrics - Asses s ing Obesity Body Mass Ind ex (BMI) Body mass index ( BMI ) d efines obesity and is the measurement most frequently used in epidemiological studies. BMI values provide valuable insight into overall body fatness using graded classifications for OW and OB. It helps comparing weight status within and between populations, in addition to identifying individuals and groups at risk of morbidity and mortality. However, BMI does not take into account the detrimental effect s of intra - abdominal fat on morbid ity and mortality associated with degrees of excess in body fatness including overweight. Moreover, BMI may vary in degree of fatness across different populations 28 [99] . Therefore, it is an imperfect measurement method of degree of adiposity because it does not differentiate between fat mass and lean mass in the bo dy [121] . Based on the Prospective Studies Collaboration on more than 66 , 000 deaths, a BMI of 22.5 - 25 kg/m 2 is considered an optimal survival range in the BMI distribution. Whereas moderate to high BMI (30 - 35 kg/m 2 ) and extreme BMI levels (40 50 kg/m 2 ) reflect a reduction in life expectancy of 3 to 10 years [56] . However, this might not always be the case considering there is evidence that CVD risk factors , such as hypertension , can exist below a BMI of 25 kg/m2 [132] . A ccording to the literature , e levated BMI is potentially attributed to developing CVD during var ious life stages beyond childhood [10 - 12] , and the risk of CVD increases with increase d BMI [22] . L ife expectancy may be reduced in individuals with moderate (BMI 30 35kg/m2) and extreme obesity (BMI 40 50kg/m2) [23] . BMI Z - scores for defining weight status in children A BMI standard deviation ( S . D .) scores are measures of relative weight adjusted for child age and sex [52] . A z - score, or standard deviation, is a measure ( of the dispersion of data. Waterlow et al. (1977) [133] recommended the use of z - scores for the definitions of underweight, wasting (weight for height), and stunting (height for age) proposed by Seoane and Latham (1971) [134] , with the cut offs defined in terms of standard deviations (SDs) below the median rather than as percentages of the median reference. Weight - height relation depends on age during infancy and adolescence [121] , especially, during adolescence, weight growth continues while height growth becomes stagnant. BMI for children can be calculated based on weight - to - height indexes including the ponderal index (weight:height 3 ) , or the Benn Index p . The Benn Index is adjusted for age and sex. The height power (p) restricts height, making the 29 index uncorrelated with height among children at each age [114] . This allowed many countries to have their own national reference centile for BMI - for - age such as the United States (US) [135 136] . Moreover, encouraged establishing of International BMI cut offs for child overweight and obesity, based on data from six countries [48] . BMI z - scores can be converted into their equivalent BMI - for - age percentil es. Both can be used to determine cut points and classify weight status of children and adolescents. The CDC 2000 (2 - 20 years) [137] and the WHO 2007 (5 - 19 years) [138 139] provide normal distribution tables for comparison of BMI z - scores. However, when assessing change in weight status longitudinally, BMI z - scores and BMI for - age percentiles w ill not be equivalent [52] . BMI z - score is appropriate in tracking changes in adiposity [53 140] , association with energy and food intake [54] , and cardiometabolic risks [55] in children. Reference Centiles Curves/LMS Method (Smoothed parameters): Reference centiles curves presents a distribution of a measure as it changes by age (covariate). The LMS method involve three curves describing changes in the distribution of the measure. Accordingly, LMS (lambda - mu - sigma) stands for, L ( median ) , M (coefficient of variation) , and S ( skewness ) . The LMS curves can be constructed as cubic splines by non - linear regression to be expressed a s smoothing parameters and or equivalent degrees of freedom [141] . Generating BMI - for - age S tandard D eviation Scores ( Z - score s) Body weight and height are measured to calculate BMI, which become transformed ( power in the Box - Cox ) into z - scores based on z= ([BMI/M] L 1)/ (L x S) [141] . The IOTF BMI - for - age z - score s (2 - 18 years) derived by Cole et al (2007) [47 121] include, thinness - 2SD [2 nd to <15 th centiles] , normal weight - 1SD to +1SD [ 15 th to <90 th centiles] , 30 overweight >+1SD [ 90 th to <9 8.7 th centile ] , and obese >+2SD [ 9 8.7 th centile ] [47 121] . The IOTF BMI z - scores can be generated via LMSg rowth http://www.healthforallchildren.com/shop - base/shop/software/lmsgrowth/ ) [116] . The WHO 2007 BMI - for - age - sex - specific z - scores range (5 to 19 years) are the following: sever thinness - 3SD [5 th ] , thinness - 2SD to < - 1SD [ 10 th to <25 th ] , normal weight - 1SD to + <+ 1SD [ 25 th to <85 th ] , overwei ght +1SD [ 85 th to <97 th centile ] , obese +2SD [ 97 th to <99 th centile ] , and +3SD morbid obese >9 9 th centile available at http://www.who.int/growthref/who2007_bmi_for_age/en/ . The WHO 2007 provide d L MS tables that can be obtained from http://www.who.int/growthref/who2007_bmi_for_age/en/ [49] . The CDC 2000 BMI - for - age - sex - specific z - scores range ( 2 to 20 years) are the following: - 2SD to < - 5 - 5th 5 th ]. The CDC LMS/centiles tables are available at https://www.cdc.gov/growthcharts/percen tile_data_files.htm [142] . BMI in Kuwait Children Research data indicates fluctuating, but also a contin uous increas e in mean BMI over the past two decades . In a pair - matched case study conducted in 1996 , Moussa et al. showed that mean BMI for obese and non - obese boys and girls (6 - 13 years) was 25.7 kg/m 2 and 26.5 kg/m 2 for boys, respectively, and 17.4 kg/m 2 and 18.1 kg/m 2 for girls, respectively [60] . Al - Isa et al. (2010) reported mean BMI among OW and OB male elementary school children (n = 662 , 6 10 years ) were 20.2 kg/m 2 and 16.8 kg/m 2 , respectively [143] , and Zaghloul et al. ( 2012 ) reported mean BMI among 9 to 13 years old children were 20.6 kg/m 2 for boys and 19.7 kg/m 2 for girl s 31 [107] . A more recent study (2012 - 2 013) reports that mean BMI of children and adolescents ( n = 6574, 6 - 18 years) are 23.5 kg/m 2 for boys and 22.8 kg/m 2 for girls [2] . Waist Circumference (WC) It i s known to be the best anthropometric obesity index including visceral (metabolically active) an d subcutaneous (under the skin) abdominal fat in relation to cadiometabolic risk factors in adults and in children [109 144 145] . Therefore, considered a reliable method for predicting CHD risk as it correlates with hypertension and blood lipid level [99 146] . Internationally, abdominal obesity is defined as WC >75th to 95th based International Diabetes Federation (IDF), or >95 th to 97 th centile based on World Health Organization (WHO). Also defined as 85 th to 97 th centile reatment Panel (ATP) [85] gender - and age - specific 90th percentile according to data from the National Health and Nutrition Examination Survey (NHANES) [147] . WC is known to be highly sensitive and specific marker to upper body fat accumulation providing effective measure of truncal adiposity in children and adolescents compared with waist - to - hip ratio (WHR) [148 149] . WC an d Waist - to - Height Ratio (WHtR) According to literature, WC and WHtR are considered effective and simple anthropometric surrogate measures of abdominal obesity (excessive accumulation of both central subcutaneous and visceral fat) [150] associated with multiple CVD risk factors including hypertension, T2DM, and dyslipidemia compared to BMI in adults, and in children and adolescents [56 129 151] . Because WHtR take s height into account, it is better to use as an index for visceral fat in children. It encompasses the adjustm ent in several different statures and in different population [152] . In addition , WHtR can identify metabolic risks in individuals within moderate BMI range [153] . For instance, a longitudinal research has indicated that WHtR is more 32 closely linked to childhood morbidi ty than BMI that neither provide indication of body fat distribution nor distinguish between fat mass and free - fat mass [148] . WHtR is calculated as the ratio of waist (cm) and Height (cm) to determine central obesity in children and adolescents of both gender, based on cutoff 0.5 according to CDC standard methods used by NHANES in (6 to 19 years) including different ethnic groups [150] . Savva et al. validated the prediction of CVD by BMI (75 th percentile), WC, and WHtR in children (1037 boys and 950 girls), he concluded that WC and WHtR were better pr edictors of plasma lipid and lipoproteins except for TG and HDL - C respectively compared with BMI that was better in predicting TG only. The three parameters were significant in predicting BP [146] . Freedman et al. examined the relation between BMI - for - age z score and WHtR in predicting BP, dyslipidemia , and fasting insulin in 2498 (5 to 17 years) in the Bogalusa (Louisiana) Heart Study. He indicated that WHtR was slightly better in predicting concentrations of total - to - HDL - C ratio as well as LDL - C, whereas BMI - for - age z score was better predictor for BP and fasting insulin [151] . Waist Circumference (WC ) of Kuwaiti Children Based on data from 2012 - 2013 , the overall mean WC in youth 6 to 18 years was 77.9 cm for boys and 74.3 cm for girls [88] . A study conducted on 9 , 593 children and adolescents (5 - 19 years old) during 2008 and 2009 revealed a dramatic trend of increasing WC with age starting from age 7 years, especially among boys. The prevalence of children who are at risk of CVD th percentile) become inclined around age of 10 [129] . In adolesce nts, data between 2009 and 2010 on 906 10 th to 12 th graders, the mean WC was somehow similar among males (83.1 ± 16.4) and females (84.9 ± 14.6) respectively [108] . 33 Waist - to - Height Ratio ( WHtR ) in Kuwait Available data on measuring abdominal obesity in the Kuwaiti youth popula tion were based on using waist - to - hip ratio (WHR) [60 109] and WC [109 129] indices, but not WHtR. However, one available study conducted on 906 adolescents during 2009 and 2010 and has shown a significant ( p <0.001) difference in WHtR between females ( 53.9 ± 9.0) and males (49.1 ± 9.3) respectively [108] . Percent age Body Fat (%BF) A percentile for body fat the can be derived either by bioelectrical impedance analysis (BIA) or by skinfold thickness values which can be converted into %BF via Slaughter equation [154 155] . According to NHANES ( 1999 2004 ) data %BF peaks for boys and girls at age s 10 and 11 with a median of 17.0% and 27.8% respectively [155] . Furthermore, %BF thresholds of 22.3% and 35.1% in boys and 31.4% and 38.6% in girls (at age 18 years) also help identifying MetS including predicting SBP and TC abnormality [156] . Some research has shown that percent body fat (%BF) is an effective indicator of CVD risk factors in children [151 157] and adults [158] . Percentage Body Fat in Kuwait According to data from a multistage cluster sample (n=1,830) collected during 2008 - 2009, mean %BF in adult males and females was 23.3 and 37.7 re spectively [159] . There is no available research data estimated %BF in Kuwaiti children. Dyslipidemia Dyslipidemia, most commonly hyperlipidemi a, involve s a disruption in the quantity of blood lipids (mg/dL) , including elevation of total cholesterol (TC) , low density lipoprotein cholesterol ( LDL - C ) , TC/HDL ratio , and triglycerides ( TGs ) , accompanied 34 elevated atherogenic LDL - C levels related to quantitative changes in small dens ity particles, in addition to declined high - density lipoprotein cholesterol ( HDL - C ) <40 levels. Dyslipidemia is most commonly associated with an improper diet and sedentary lifestyle, w hich can result in increased body weight [82 160] Overall, elevated blood lipids contribute to an increase incidence of coronary events and death s at various ages and stages of life depending on type and severity [161] . Prevalence of Dyslipidemia among Kuwait Children No data available could describe the prevalence of dyslipidemia in children of Kuwait. However, Moussa et al. 1999 conducted a pair - matched control study in schoolchildren (n = 920, 6 - 13 years ) . The study showed that o bese boys had higher mean TC (171.7 mg/dL ), LDL - C (108.6 mg/dL ), TG (76.2 mg/dL ), and TC:HDL ratio (3.64), and lower mean HDL - C (49.8 mg /dL ) than girls TC (165.5 mg/dL), LDL - C (103.6 mg/dL), TG (66.4 mg/dL), and TC:HDL ratio (3.50) . In addition, obese boys and girls had higher mean dyslipidemia when compared to non - obese boys and girls [60] . Hypertension Hypertension r efer s to an [162] . Hypertension is considered a potent risk factor for atherosclerosis as it track s with age from childhood into adulthood [163] , therefore , elevated BP is considered a potential risk factor for CVD [163] . Hypertension is associate d with increased risk for myocardial infarction, stroke, and cardiovascular mortality [164] . The e xpert p anel on i ntegrated g uidelines for cardiovascular health and risk reduction in children and adoles cents r ecommend that early detection of high BP , especially during childhood , is vital for improving long - term cardiovascular health outcomes [27 89] . According to Chobanian et al. (2003), hypertension affect s approximately one billion individuals , and is attribute d to 7.1 million deaths 35 annually , worldwide [165] . Moreover, subo ptimal BP (>115 mmHg SBP) is associated with 62% of cerebrovascular diseases and 49% of incidence of ischemic heart disease [165 166] . Hypertension in Children Ac cording to the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure , hypertension in children is defined as elevated BP at the 95th percentile or greater for age, height, and gender (Tabl e 18 , appendix ) on three or more separate occasions [167] . According to The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents [164] , prehypertension in children is defined as average BP between the 90th and 95th percentile . Hypertension is considered a potent risk factor for atherosclerosis [124] ; which can begin during childhood [87] , developing as early as nine years of age [122] , a nd the clinical end - point of CVD almost always occurs later in life [125] . P ersist ent hypertension in children is associated with increased risk of adverse outcomes including left ventricle hypertrophy (LVH), hypertensive encephalopathy, seizures, cerebrov ascular accidents, and congestive heart failure [164 166] . Adverse life style behaviors (physical inactivity, poor dietary habits including high intake of fat and salt, and increased body weight) are factors associated with elevated BP i n children [116 124] . Therefore, lifestyle modification , weight management through increased physical activity and intake of nutrient dense food (e.g., fruits, vegetables, and whole grains), while reducing dietary sodium is an effective method for preventi ng hypertension [151] . Prevalence of Hypertension in Kuwait Children Based on a study of 1312 primary school children (6 - 10 years), the overall prevalence of hypertension (>95 th percentile for age and sex , ) was 5.1%, and shown to be associated with obesity and family history of hypertension [69] . A cross - 36 sectional study by Moussa et al. ( 199 9 ) , reported t hat elevated blood pressure (SBP>120 mmHg, DBP >80 mmHg) was higher in obese (4.9%) compared to non - obese (0.4%) male and female schoolchildren (n= 2400 , 6 - 13 years) [67] . In a pair - matched study in scho olchildren 6 13 years, Moussa et al. (1999) showed that the mean SBP/DBP was 114/69 mmHg and 105/64 mmHg in obese and none - obese boys, respectively. Among obese and non - obese girls, mean SBP/DBP was 115/71 mmHg and 106/65 mmHg, respectively. The results of the study have indicated that 8% of obese males and females (n =460) had SBP ( >130 mmHg ), and 0.7% had DBP ( >90 mm Hg ). Also f ewer none - obese children (0.7%) had SBP > 130 mmHg [60] . . Overall , the study has indicated nearly a two - fold chance (1.7, [1.34 - 2.16] 95% CI) for hypertension among Kuwaiti schoolchildren [48] . Role of N utrition in P ediatric / C hildhood H ealth and CVD R isk F actor S tatus Proper nutrition intake is known to counteract OB , WC, hypertension, dyslipidemia, and blood glucose [15] , and t herefore , both help delay and prevent the onset of CVD [20] . For example , alteration s in intake of dietary fat have been shown to reduce cardiovascular events and mortality [168] . Consumption of whole grains is known to improve blood cholest erol levels, while reducing added sugar intake (<10% of energy intake) prevent s hypertriglyceridemia and IR [1 5] . According to the nutrition recommendations and guidelines, a sufficient or increased intake of fruits and vegetables (50% to 75% of each meal servings of fruit and 3 servings of vegetables per day) can help reduce the risk for many of the leadi ng causes of death including CVD ; i n addition to their important role in weight management [169] . Furthermore , poor nutrition and a sedentary life style are associated with an increase in CVD risk factors [170] . 37 Nutritional Recommendations - Dietary Guidelines for Americans (DGA) , WHO/FAO The Dietary Reference Intakes (DRI s ) The current dietary reference s for the US and Canada have expanded from the r ecommended d ietary a llowances (RDA) established in 1941 for Americans , and the r ecommended n utrient i ntakes (RNI) established for Canadians in 1938. The DRI s were established in 1994 and include four nutrient - based reference values ( the e stimated a verage r equirement [ EAR ] , the RDA, the a dequate i ntake [ AI ] , and the t olerable u pper i ntake l evel [ UL] The DRI s are used to assess and plan diets for healthy people based on scientifically founded relationships between nutrient intake and indicators of adequacy, as well as to the assist with chronic disease prevention [106] . They are intended to help individuals optimize their health, prevent disease, a nd avoid excesses of any nutrient ; and used by health practitioners, educators, and researchers. In some cases, (e.g., for children who are small for their age ) reference heights and weights for different life stage s and gender groups are useful in determi ning specific nutrient requirements in relation to body size. Most i mportantly , DRIs apply to healthy people and not to individuals who are sick or malnourished or whose special circumstances may alter their nutrient needs ; therefore, they are intended to provide a reasonable estimate of nutrient level s required to ensure adequa te dietary intake and to prevent adverse effects due to excess intake [106] . The DRI values and ranges for total energy, and macronutrients , as well as recommendations for PA are pro vided via a classified term ; whereas, the ac ceptable m acronutrient d istribution r ange (AMDR) sets ranges for intake of fat, carbohydrate, protein, and n - 6 ( Linoleic acid [ LA ]) and n - 3 ( Alpha - linolenic acid [ ALA ] ) polyunsaturated fatty acids (PUFAs) associated with reduced risk of chronic disease. The e stimated e nergy r equirement (EER) introduces dietary energy which is defined as the average dietary energy intake necessary to maintain a healthy 38 individuals energy balance or need as defined by age, g ender, height, weight, and level of physical activity (Table 14) [106] . FAO / WHO International Nutritional Intake Ranges T he Joint WHO/FAO Expert Consultation (2002) has reviewed and updated current international recommendations on diet, nutrition , and the prevention of chronic diseases established by the 1989 WHO Study Group [84] . T he Joint WHO/FAO Expert Consultation has considered some important changes in the value of several dietary components adjacent to the most recent scientific evidence reg arding adverse effect s of excess intake of some macronutrient such as , sugar [98 - 100] , sodium, and fat on cardiovascular [101 - 103] and metabolic health throughout life from infancy to old age [84, 104] . The 1989 WHO Study Group and the 2002 Joint WHO/FAO E xpert Consultation recommendations provided ranges of nutrient intake goals (% of total energy) for promoting balanced diet s and CVD prevention among nations lacking national nutrition and health guidelines , including Kuwait [21 22] . The ranges (WHO guidelines) were developed based on the FAO f ood b al ance sheet dataset , records of major food sources in each country, and their how they are consumed. Thus, providing estimates of per capita calorie, fat , and protein intake for each food category [63] . Food g roups and food p roportions ( US DGA and FAO/ WHO) The current study will assess the levels of macronutrients , micronutrients, and select vitamins and minerals i s according to the DRI for hear t health and disease prevention and the 2002 Joint FAO/WHO Expert Consultation . The WHO/FAO 2002 recommendations for food groups per day include, fruits (400 g [~1.7 cup]) and vegetables (400g [~1.7 cup]) , an d NSP ( whole grain s) from food [22 63] . 39 T he current food groups for children ( 9 - 13 years ) recommends a daily intake of 1.5 cups of fruit , 2 cups of vegetables for females , and 2.5 cups for males; 3 cups of dairy or dairy products for both male and female; 5 oz. of grains for females and 6 oz. for males , and 3 oz. equivalents of whole grain are recommended for both sexes [66] . Macronutrients Macronutrients are a type of nutrient (chemical compound) required in large amount s in the human diet includ ing, protein, carbohydrates, and fat . The Joint WHO/FAO Expert Consultation 2002 recommend ed : total fat (15% to 30% of daily kcals), saturated fat (<10% of daily kcals), trans - fat (<1%) , polyunsaturated fatty acids (PUFA 6% to 10%), n - 6 PUFA (5% to 8% of daily kcals ) , n - 3 PUFA (1% to 2% of daily kcals ) , total protein (10% to 15% of daily kcals), total carbohydrate s (55% to 75% of daily kcals), complex carbohydrate s (50 70% of daily kcals), free sugars (<10% of daily kcal) , dietary fi ber ( from food ), and NSP wholegrain ( from food ) for both males and females [21 22] . The acceptable macronutrient distribution range (AMDR) for children (9 to 13 year s) are as follows: total fat (25% to 35% of daily kcals), saturated fat (less than 10% of daily kcals), total carbohydrate (45% to 65% of daily kcals), dietary fiber (31 g for males and 26 g for females ), and total protein (10% to 30% of daily kcals) [262] . Micronutrients Micronutrients a re comprised of vitamins and minerals, which are required in small quantities to ensure normal metabolism, growth , and physical well - being . The 2002 Joint FAO/WHO Expert Consultation on h uman v itamin and m ineral r equirements provided recommendations for nutrient intake ( RNI ) defined as the amount 40 necessary to meet the needs of most (97.5%) of the p o pulation, set as the EAR plus 2 standard deviations. The RNI is equivalent to the recommended daily (or dietary) allowance ( RDA ) used by the Food and Nutri tion Board of the US National Academy of Science [65] , and is targeted for non - industrial ized nations or less develop ed countries. Therefore , the levels for some of the micronutrients such as , vitamin D for children (200 IU) are lower as compared to levels set for industrialized nations or developed countries (600 IU) [64] . The FAO/WHO ( RNI ) for children (7 to 9 and 10 to 18 years) for micronutrients ( select ed) are as follows: vitamin D (200 IU), Na ( < 2 000 m g), K (4500 mg), Ca (1300 mg), and Mg (230 mg) [65] . The DRIs (AI, UL, and RDA) fo r children (9 - 13 years ) were as follows : vitamin D (600 IU), Na (< 2300 mg), K (4500 mg), Ca (1300 mg), and Mg (240 mg) [262] . Selected m inerals in this study, potassium, magnesium, and calcium, in addition to vitamin D , are known to protect from the risks for CVD ; whereas, dietary sodium can a ffect CVD risk by increasing BP [14] . Nutritional Intake and Behavior among Kuwait Children It is important to note that in Kuwait , there is no national nutrition guidelines. Instead , Kuwait has retriev ed and or utiliz ed nutritional guidelines in accordance to WHO, the FAO, and the National Research Council (1989) RDA [31 39 171 172] . Sever al previous research studies of local and regional Kuwaiti diets described the characteristics of both traditional , domestically prepared, and modern , imported foods and cuisines, diets as atherogenic , which are known to contain high amounts of cholesterol and saturated fatty acid, in addition to excess trans - fat , sodium, and sugar [173 - 176] . Sawaya et al. 41 (1998) , indicated that most traditional Kuwaiti dishes are rich in cholesterol (up to 371 mg/100g) and trans - fat , where palmitic and ste a ric acid s are the predominant saturated fatty acids, in addition to high polyunsaturated fatty acid - to - monounsaturated fatty acids (P:S ratios) [177] . Most of the Kuwaiti traditional diet are of a plant origin such as , wheat and rice (75%) ; in addition to fish and animal products , and fat and oils, which con tribute 24% and 15% of the food energy - supply , respectively [31] . Neverthel ess, increased food availability and food import s have shifted the Kuwaiti diet into a mix of traditional and foreign cuisines , and westernized diets with varied eating styles. As a result, more dishes have become available with a wide range of micronutrie nt s per food proportion (100g) including Na (13 1567 mg), K (181 1033 mg), Ca ( 9.97 677 mg), Mg (10.4 133 mg), iron (14 5.12 mg), zinc (13 4.16 mg), and iodine (7.4 µ g 61.2 µ g) [178] . According to Al - Hooti et al. (2002) , the nutrient intake of the Kuwaiti population exceeded the RDA for energy (1.19 fold ), protein (2.1 fold ), vitamin A (2.59 fold ), thiamine (1.37 fold ), riboflavin (1.39 fold ), niacin (1.41 fold ), vitamin C (2.52 fold ), iron (1.56 fold ), and Ca ( 1. 10 fold) [31] . The Kuwait Nutrition Surveillance System ( KNSS 2012 ) repor t shows that the majority (81.5%) of young children (2 to <5 years) consumed less than five servings per day of fruit s and , based on the WHO/EMRO specialized survey . However, the KNSS did not report any data regarding nutrition intake and behavior of children above 5 years of age or adolescents [1] . Zaghloul et al. (2012) also described the nutritional in takes of Kuwaiti boys (n=111) and girls (n=94) (9 - 13 years) based on the USDA 5 - step multiple - pass single 24 - h dietary recall method [193 194] . The study results indicated higher intakes of energy and macronutrients, particularly among boys, and lower int ake of micronutrients and dietary fiber, particularly among 42 females. C alori c intake of both males and females (2236 ± 106 kcal , and 1992 ± 92 kcal, respectively ) exceeded the recommendations for children 9 to 13 years (1800 kcals, and 1600 kcals , respectiv ely ). The study showed higher percentage energy intake of fat (36%, 38%), in addition to excess sodium (3508 mg, 2975 mg) in males and females respectively. Moreover, males had a higher mean intake of cholesterol (272.7 mg) compared to females (220.2 mg). Intakes for calcium (717.6 mg, 543.8 mg) , magnesium (216.6 mg, 182.1mg), vitamin E (4.8 mg, 4.2 mg) , vitamin D (75.3 IU , 49.5 IU ), and dietary fiber (18.1g, 16g) were below the US DRI recommendations for males and females, respectively . The study did not report intakes from food groups such as fruits and vegetables, whole grains, and dairy, or provided insights on [36] . Research has sh own that high energy - density diets are associated with poor diet quality . Patterson et al. (2010) reported lower intake of fruits an d vegetables, and dietary fiber among 9 years old Swedish children (n=551) , which was significantly ( p < 0.001) associated with high - energy - density diets [195] . There is no perfectly accurate dietary tool to assess individual dietary intakes and behaviors in children. A concern with the use of 24 - hr recalls is it does not estimate an children considering their limits in cognition, quantification, and memory [46 47] . Montgomery et al. (2005) and Baxter et al. (2006) implemented the USDA 5 - step multiple - pass 24 - hr recall in children. It was reported that preschool and primary schoolchildren, particularly females, reported higher energy intake using this method [10] . In addition, over - reported items were observed among overweight as compared to normal weight children, and among boys as compared to girls on three occasions [11] . There are alternative dietary assessment tools such as food frequency questionnaires ( 43 are commonly used in epidemiological studies to associate health risk factors with dietary behaviors [196 - 198] . Nutritional Assessment Instruments Dietary assessment tools are used to assess and quantify individual dietary intake to help identify the nutritional status and/or needs relative to that particular individual dietary behavior. Food frequency questionnaire (FFQ), 24 - hour recall, f ood record, and diet history approach es are widely used subjective (self - reporting) dietary assessment methods for assessing individual(s) dietary intake in research including intervention studies. The current study will be e. Food Frequency Questionnaire (FFQ) The FFQ is a n assessment tool characterized by reporting the frequency of each food consumed from a list of foods for a specific time period. It is commonly used in cross - sectional/surveillance, case - control (retrospective), cohort (prospective) , and intervention studies. FFQ has been widely used in epidemiological studies regarding CVD since it is helpful in studying the relationships between particular dietary variables and the incidence of diseases [253 - 256 ] . Essentially , the FFQ collects dietary intake information regarding frequency (how often), portion size (how much), in addition to details involving characteristics of food as eaten ; such as methods of cooking or food preparation as single or combined m eals. The importance of incorporating a portion size question or specifying portion sizes as part of each question help s in estimating relative or absolute nutrient intake. FFQ can be developed or tailored for different populations and different purposes. The Health Habits and History Questionnaire (HHHQ) or 44 Block questionnaires, the Harvard University Food Frequency Questionnaires or Willett questionnaires, and the National Cancer Institute Diet History Questionnaire are among those evaluated and commonly used for US adults [179] . Also, there are other types of FFQs designed to capture the diverse diets of ethnic groups such as , Latinos and Native Americans [180] . There are also abbreviated FFQs which assesses total diet and are composed of shorter lists of 40 to 60 line items from the original 100 or more items [181] . There are s teps to process an FFQ involve collecting quantitative dietary intake information from a target population to define the typical nutrient dens ity of a particular food group category. For example, the macaroni and cheese food group food codes reported from all individuals in a population survey are collected, then the mean or median nutrient composition (by portion size , if necessary) is estimate d, and then, these values can be tabulated by age and gender. The last step is to comput e nutrient intakes for individual respondents using specific dietary analyses software [182] . The benefits of using FFQs are the lower costs of administration and processing as compared to diet records or recalls, estimating typical food intake over extende d period s of time, which can also help avoid recent changes in diet, and as a useful tool in collecting pre - data or baseline data, and retrospective diet reports. However, FFQs are not limited by issues involving missing food details associated with the appropriateness of the food list. Variability within individual diet s is a crucial factor in designing a typical food list. Also , there is a possibility for inaccuracy when obtaining reports for fo ods eaten both as single items and in mixtures [183] . In addition to issues involved with FFQ design including length, closed versus open - ended response categories and their definition, portion size, seaso nality, and period have to be defined . Moreover, l isting too many individual line items and additional portion size questions will increase the and affect FFQ validity. Other critical limitations include errors 45 involving measurement [184] , estimation of serving sizes [185] , self - reported energy intake and protein [184] , and inaccuracy in quantification of intake related to the incompl ete listing of all possible foods. The Block Kids 2004 Food Frequency Questionnaire (FFQ) The Block kids FFQ is a valid and reliable dietary method in dietary research studies. Marshall et al. investigated the relative validit y of the Iowa Fluoride Stud y using a targeted nutrient semi - FFQ in assessing beverage, calcium, and vitamin D intakes using 3 - day diaries for a reference. The findings showed that the method reported equal and high correlation regarding milk intakes (r=0.571), 100% juice intakes (r=0.550), diaries for calcium (r=0.515) , and vitamin D (r=0.512) when compared to the Iowa Fluoride Study targeted nutrient semi - quantitative questionnaire for relative validities [255] . Cullen et al. tested the reliability and validity of the Block Kids Questionnaire to assess diet of 10 - to 17 - year old children and adolescents during the past 7 days, who had completed two 24 - hour recalls. The Block kids FFQ showed a moderate reliability intraclass correlation (> 0 .30) when compared with 24 - hour dietary recall, except for percent energy from protein , and fruit and vegetable servings [256] . Some other studies implemented the Block Kids 2004 FFQ. Lauren E. Au et al. (2012) investigated association between saturated fat, carbohydrates, MUFA and PUFA, and car d iometabolic risk in 148 fourth - through eighth - grade students . However, no associations found [55] . In Michigan , A pre - post study examined 181 boys and girls (5 th grade) nutritional intake indicated increased intake of saturated fat intake 6% ( p =0.038) . Also significant positive changes in intakes from whole grains ( 40%; p =0.03), vegetables ( 33%; p =0.01) , and vitamin A ( 13%; p =0.047) [186] . 46 The current study has implemented a modified, translated (Arabic version) of the 2004 Block Kids (FFQ) (Block Dietary Data Systems, Berkeley, CA; ht tp://www.nutritionquest.com ) to assess the nutritional intake including food groups and nutrient intakes of Kuwait school children [249 218] . Dietary Ind ices for Measuring Diet Quality Changes in eating patterns are related to an increase in CVD risk factors including obesity in children. For example, intake of carbohydrate s, protein, and sugar in addition to snacking ha s increased as compared to intake of grains, fruit s, and vegetables [187] . Additionally, measuring nutrient density chronic diseases. There are several predefined indexes of overall diet quality developed to assess nutrition adequacy and associated health outcome. In addition, dietary quality can also be assessed by individual dietary factors , such as fiber , to predict morbidity and mortality . The h ealthy e ating i ndex (HEI ) is among one of the original die t quality scores including the Diet Quality Index , the Healthy Diet Indicator , and the Mediterrane an Diet Score . They are comprise d of both food groups and nutrients , and are reliable and extensively validated [188] . A meta - a nalysis of c ohort s tudies indicated that diets that score high on diet quality indices including h ealthy e ating i ndex (HEI), the a lternative h ealthy e ating i ndex (AHEI), and the d ietary a pproaches to s top h ypertension (DASH) score are associated with a significant reduction in the risk of all - cause mortality (22%), CVD (22%), cancer (15%), and T2DM (22%) [189] . 47 Healthy Eating Index (HEI) Healthy Eating Index (HEI) i s a scoring system (0 to 100) developed based on the USDA Food Patterns and editions of the D ietary G uidelines for Americans (DGA) , includ ing Dietary Approches to Stop Hypertension ( DASH ) eati ng plan , to measure diet quality in US population and subpopulations . The index examine s the overall diet components from food groups and single nutrients in adherence to DGA for chronic disease prevention in adults and in children related to obesity . The HEI 2010 (retained several features of the 2005 version) consisted of nine adequacy and three moderation (12) component s [190 ] . The index was updated with changes includ ing replacing d ark g reen and o range v egetables and l egumes with a g reens and b eans component, and replacing saturated fat with monounsaturated and PUFAs including the ratio of polyunsaturated and monounsaturated to saturated fatty acids. Moreover, adding s eafood and p lant p roteins , and a moderation component, r efined g rains , replaced the adequacy compo nent; and t otal g rains to assess overconsumption [190] . The HEI 2010 components consist of total f ruit , w hole f ruit , t otal v egetables , t otal g rains , and s odium (forward ed from the HEI 2005), dairy and total p rotein f oods , and empty calories (component c alories from s olid f ats , a lcoholic b everages , and a dded s ugars ) [190] . The HEI 2010 uses a density approach to set standards (per 1,000 calories or as a percentage of calories) and employs le ss restrictive standards (vary by energy level, sex, and/or age) for determining maximum score. Maximum points are all ocated when intakes at the level of the standard or higher for adequacy components, also when intakes at the level of the standard or lower for moderation components. For example, the maximum score for Na was assign ed to diets that have less than 1,100 mg of Na per 1,000 calories. T here are four steps for deriving HEI - 2010 scores : 1) identify the set of foods under consideration; 2) determine the 48 amount of each relevant food group, subgroup, and nutrient in the set of foods; 3) derive the pertinent ratios; and 4) score each component using the appropriate standard [190] . A study compared the quality of the diet (HEI) of 2,703 children ages 2 to 17 years who participated in NHANES ( 2003 - 2004, 2005 - 2006, and 2007 - 2008 ) dietary intake records . Thei r average scores, averaged 47 to 50%, were below the standards for all HEI - 2010 components across the three periods. Their intakes from dark green vegetables, beans, and whole grains were the lowest (14 - 18 % and 16 - 18 % , respectively) . Nonetheless, during the period of 2007 - 2008, intakes from an adequacy component such as fruit, including 100% fruit juice and whole fruit, were higher, in addition to lower intakes from empty calories (moderation component) compared to periods of 2003 - 2004 and 2005 - 2006 [191] . Drenowatz et al. (2012) reported that the median HEI score of 210 MI 5th graders was 62 , only 5 participants (2.5%) had scores (HEI> 80) , and 18 participants (8.6%) scores reflected as [192] . Feskanich and Rockett et al. (2016) developed the youth healthy eating index (YHEI), a modified and simplified HEI to stud y 551 children and adolescents (9 - 14 years) [193] . The YHEI focuses on foods high in trans - fatty acids, added sugars , and foods low in fiber , which contribute to obesity and CVD [56, 166, 167] . The study compared data from the Growing Up Today Study cohort with original HEI scores in relation to BMI and time spen t on daily activates. The results showed that the original HEI was strongly correlated ( r = 0.67) with total energy intake, in addition to several other correlated components including total and saturated fat ( r = 0.78) than did the YHEI ( r = 0.12) [193] . Some studies looked for associations or correlations between HEI and MetS and/or CVD risk factors. For example, Yang Pa n et al. (2008) found significant association ( P <0.001) between high overall HEI and fruit scores and low MetS in adolescents (12 - 19 years) [194] . On the other side, Drenowatz et al. (2013) did not find significant correlations between CVD risk 49 factors and HEI in 5 th graders [192] . The HEI 2010 is useful in investigating th e relationship of diet quality between parents and children. Robson and colleagues found a significant relationship between a parent 0 .39; P <0.001) [195] . Diet quality can also be measured using specific nutrient s or food factors such as the dietary fiber index (FI ) , which will be used in this study . The Fiber Index ( FI ) The Fiber Index (FI) is a surrogate marker for nutrient density (or the amount of nutrients/1,000 kcal) based o n a high - fiber diet consisting of plant - based foods , which includes t otal dietary fiber (soluble and insoluble) . The intake of dietary fiber - containing foods ( e.g. , frui ts, vegetables, whole grains, legumes, nut s, and seeds), are directly re lated to dietary fiber leve l as well as nutrients and phy tochemicals associated with cardiovascular health [196 - 198] . Carlson and colleagues (2015) compared the relationship of metabolic syndrome ( MetS ) in youth (12 - 19 years) with a fiber index ( FI ) , saturated fat index (gs/ 1000 Kcals) , an d the cholesterol index (mgs/1000 kcals) . The findings indicated a significant inverse relationship ( P <0.001) between increases in dietary fiber intake and lower risks of MetS . There were no significant relationships between saturated fat index ( P <0.87) o r the cholesterol index ( P <0.22) and MetS. [199] Ventura et al (2008) examined relationship between diet and MetS in 109 overweight Latino children in age 10 to 17 years, and found a lower mean dietary fiber (7.5 ± 2.8 gs/ 1000 kcal) in children (n=24) with MetS, while children without MetS (n=85) had a higher mean fiber intake (8.4 ± 3.1/ 1000 kcals) . [200] 50 Benefits of Physical Activity (PA) in Children and Recommendations According to WHO, physical inactivity is the fourth leading risk factor for early mo rtality worldwide [11] , and among major risk factors for CVD. According to the American College of Sports Medicine, PA involves any bodily movement produced by the contraction of skeletal muscles, which maintains and improves cardiorespiratory fitness alon g with reducing the risk of obesity and related comorbidities [ [201] ] . In children and adolescents, PA influence energy expenditure , improves cardiorespiratory fitness as well as metabolic performance [193] . PA has beneficial effects on CVD risk factors including adiposity, BP, plasma lipids and lipoproteins levels, also inflammatory markers, endothelial function, and heart rate variability. In addition to reducing CVD risk factors, PA has been shown to also improve mental health (including depression) and bone health [193] . Studies have indicated that the more PA, the greater the benefit, even with modest amounts (average of 60 min/day) of moderate intensity, this is especially true in high - risk youngsters. Therefore, for the past two decades AHA and WHO have recommended at least 60 minutes of moderate - to - vigorous PA per day for children and adolescents [11 119] . Current Physical Activity Behaviors in Kuwait In Kuwait, physical inactivity is affecting a c lear majority of the population at all ages and both genders , which is considered a major concern. Available local research has described the overall PA status as weak and needs to be given more attention [38] . Several studies have shown that increased screen time and physical inactivity were found to be associated with MetS, abnormal glucose, and fat metaboli sm in adults [186 - 188] , and elevated BP and OB in children and adolescents [189 - 191] . A sedentary lifestyle is known to be associated with an increase in CVD risk factors [193] . 51 Based on a paired - matched control study (n=920, 6 - 13 years), Moussa et al. (19 96) reported that the overall PA level in children was low, with participation in PA levels of light (20%), moderate (43%), and vigorous (30%) [48] . El - Bayoumy et al. (2006), reported that physical inactivity was prevalent among male (n=2657) and female (n =2745) adolescents (10 14 years). A modified Harvard step test showed that the majority (97%) had a low physical fitness score (65 to 79). In addition, they spent an average of 2.8 hours per day of screen time, 1.8 to 2.1 hours per day of outdoor activit y, and 4.8 to 5.2 hours of sporting activities per week [45] . Al - H a i fi et al. (2013) examined the relative contribution of selected lifestyle factors on OW and OB in 463 boys and 443 girls (14 19 years) via a self - reported PA questionnaire (Arab Teens Lifestyle Study). Lack of moderate and vigorous PA were strongly associated ( P <0.0001) with OW and OB among boys (50.5%) and girls (46.5%) [112] . Barriers among Kuwaiti Population Poor eating habits [137 - 141, 152] , lack of PA, and lack of nutrition knowle dge are major lifestyle factors associated with prevailing health problems including CVD [194] . The absence of national dietary guidelines and lack of food regulations and policies are major contributors to obesity and comorbidities [76 - 79] . The food envir onment in Kuwait involves a huge growth in the availability of fast - food restaurants, supermarkets, and hypermarkets that are affordable and easy to access, encouraging unhealthy eating pattern. After 1991, the number of fast - food restaurants increased dra matically and became attractive destinations for youth since they often offer playgrounds and electronic games to patronize. As a result, increased leisure time activities including screen time and associated poor dietary behaviors became rooted in the Kuw aiti society [114] - cultural norms, and 52 increased indoor shopping malls are strongly related to the general lack of physical activity among the vast majority of the Kuwaiti population [34 195] . Physical Activity related Assessment Instruments There are several categories of techniques used to assess PA in children, including self - report, electronic or mechanical monitoring (accelerometers, pedometers, heart rate monitors), direct observation, indirect calorimetry, doubly labeled water, and direct calorimetry [202] . Self - report instruments such as questionnaires, interviews, and activity diaries (logs) are commonly used method s for the assessment of PA in epidemiological research , which known as subjective methods [203] . Studies using self - report measures usually find more PA than tho se using objective measures [204] , but are limited by biased reporting and low validity [205] . Studies that were published between 1971 and 1997 indicated that test - retest reliability (the reliability of a scor e measured two or more times), interinstrument reliability (between two or more instruments), and validity (the degree to which an instrument measures what it is intended to measure) were variable and less likely to be consistent in young children [202 206] . Overall, there is a variation in studies worldwide regarding the estimation of PA in youth due to the lack of unified standards, criterions, and cut points, as well as the use of different study designs [207] . Self - reported methods Self - reporte d methods involve collecting i nformation retrospectively, which are influenced by the ability to recall details of PA by children or guardian s . The methods are relatively inexpensive, quickly administered, unobtrusive, and obtain several sources of PA info rmation ; therefore , they are frequently used in large - scale studies and compared using electronic monitors [202] . Nevertheless, self - report methods are influenced by the opinions and perceptions of the participants, prox y (parent/guardian) reporters, or investigators [203] , which 53 may result in misuse, lack of accuracy, and faulty interpretation s . Self - report methods are not typical estimators of energy expenditures in children as compared to a more precise laboratory instrument, such as calorimetry and doubly labeled water. Additionally, self - report uses energy costs standard of specific activities in adults. Also, children are less likely to recall their physical activity compared with ad olescents and adults, which may result in overestimations of PA [208] . In the current study used a self - reported retrieved from the You th Risk Behavior Surveillance (YRBS) [209] , and used by (S)Partners for Heart Health program [210] to assess moderate - to - vigorous physical activity (MVPA) on seven day s per week (>60 min) and ST [186 192 211] . The question stated ou physically active for a total of at least 60 minutes per day (add up all of the time you spend in any type of activity that increases your heart rate and makes you breath hard some of the - [211] . The study participants were provided with the question in both Arabic and English languages (A ppendix ). The s creen time question involved estimating t he weekly amount of time viewing television, playing video games, and online computer use , which was translated into Arabic Language (Appendix). Children indicate d the number of hours (watch/play) on weekdays and weekends for each of the three - screen media. Average hours of screen time per week was determined using the formula: (Hours of TV time on weekdays* 5 days) + (Hours of TV time on weekends * 2 days))/7 days + ((Hours of Video game time on weekdays * 5 days) + (Hours of Video game time on weekends * 2 days))/7 days + ((Hours of Computer time on weekdays * 5 days) + (Hours of Computer time on weekends * 2 days))/7 days [212] . Screen time equal or more than 2 h per day will be considered high ST. Less than 2 h/day of ST will be considered low ST [213] . 54 Nutritional & Physical Activity Educational Program The (S)Partners for Heart Health The results from the current study compared a sample of 5 th graders in Kuwait with the latest available data of the (S)partners for Heart Health on CVD risk factors and nutrition and lifestyle behaviors in Michigan 5th graders. The (S)partners for Heart Health is a multi - level intervention program intended to promote nutrition and P A behaviors related to cardiovascular health and CVD risk reduction. The program was designed by a multi - disciplinary team of Michigan State University (MSU) faculty of nutrition, exercise physiology, physical education, child growth and maturation, psycho logy, and public health, along with MSU health clinicians, medical students, allied health profession students, and MSU Extension staff. Moreover, The (S)partners for Heart Health program incorporates components of Bandura's Social Cognitive Theory involve d in promoting self - sufficiency toward making positive nutrition and PA choices to contributed to heart health in students. The (S)Partner s program develop s and implement s a cost - effective sustainable intervention program for CVD risk factor prevention amo ng 5th grade students; thereby, augmenting the existing 5th grade physical education, nutrition and health curriculum to sustain or improve heart - healthy behaviors and health status. The primary aims of the program are to increase the percentage of student s achieving national recommendations for PA and nutrition behaviors; to improve the public - school students' knowledge, attitudes, and self - efficacy about heart healthy nutrition and PA behaviors as recommended by national guidelines; and to improve or main tain the number of students with a desirable CVD risk factor status. In addition , as secondary aims of the program, to promote school staff and parental support for heart healthy activities to help children achieve their heart health goals, and to provide hands - on learning and training for MSU health profession students [210] . The program 55 involves intervention and measurement assessment performed by undergraduate dietetic and kinesiology students and second year medical students by partnering ([S]partnering) and mentoring 5th grade students. Summary of Literature Review Cardiovascular disease (CVD) is the leading cause of death globally, responsible for around 17.5 million deaths (31%) annually. In Kuwait , CVD accounts for 41% of all deaths. In children, C VD risk factors such as obesity, hypertension, dyslipidemia, and diabetes tend to track into adulthood and lead to premature morbidity and mortality. CVD risk factors vary between gender related to biological and environmental factors. P oor nutrition and p hysical inactivity contribute to numerous CVD risks, regardless of weight status. A concern in Kuwait is the increasing rates of childhood obesity from 0.3% in 1985 to 31% in 2012. Also, hypertension and diabetes were estimated at 5.1% and ~35% in schoolch ildren, respectively. Literature has shown that most of the food consumed in Kuwait (90%) is imported and subsidized, which result in exceeding the RDA for macronutrients and micronutrients. Moreover, a study during 2008 - 2009 has indicated that half of Kuw aiti schoolchildren were exceeding kcal intake, while 80% were not meet ing nutrients recommendations. Regardless, there is a lack of data on multiple CVD risk determine the prevalence of multiple CVD risk factors and the extent to which are met nutrition recommendations by gender and by weight status . 56 CHAPTER 3: PREVALENCE OF CARDIOVASULAR DISEASE RISK FACTORS IN KUWAITI SCHOOLCHILDREN COMPARED BY GENDER Abdulaziz kh. Al - Farhan 1,4 , Lorraine J. Weatherspoon 1 , Karin A. Pfeiffer 2 , Wei Li 1 , Joseph J. Carlson 1,3 1 Department of Food Science and Human Nutrition, and 2 Department of Kinesiology, 3 Department of Radiology , Michigan State University. 4 The Public Authority for Applied Education and Training, the College of Nursing, Kuwait Abstract Background : Cardiovascular disease (CVD) is the leading cause of death from non - communicable diseases worldwide ( 31 %), and in Kuwait, it accounts for 41% of deaths. Concerns in Kuwaiti children include their 31% OB rate in 2012, and boys had higher rates than girls. Also, data is limited on ot her CVD risk factors, and potential gender differences . Objective : To determine the prevalence of CVD risk factors in Kuwaiti schoolchildren, and if boys are at greater risk than girls. Methods : A cross - sectional evaluation of fifth grade schoolchildren (N=367, 53% girls, age 10.4 ± 0.4 years ) in Kuwait. Outcome variables ( at risk ) included: BMI ( overweight (OW) >+1SD, OB (>+ 2SD ), - to - th centiles, TC LDL HDL 45 mg/dL , non - HDL , TG , systolic blood pressure (SBP ) mmHg , and diastolic BP (DBP ) controlled for physical activity (PA) and screen time (ST ), with P Results . The overall % at risk for CVD include d : OW 21.6%, OB 42%, high WC 10.2%, WHtR 43.3%, % BF 10.1%, TC 26.4%, LDL 13.2%, HDL 36.9%, non - HDL 22.5%, TC:HDL 57 25.9%, TG 45.5%, BP 23.3%. Gender differensce included m ore girls were OW than boys (27. 1 vs . 15.5 %, P = 0 .007), and more boys (41.4%) were OB vs. girls (38%), but was not significant . Girls versus boys had a greater % at risk for TG ( 52.3 % vs 37.7 %, P = 0 .032), elevated SBP (21% vs 10.7 %, P = 0 .008 ), and DBP (21% vs 8.8 %, P = 0 .001 ), respectively. Conclusion s : The prevalence of CVD risk factors include OB and BP among Kuwaiti children is alarming. Contrary to our hypothesis, g irls had greater risks for OW, elevated TG, SBP and DBP compared to boys. Intervention studies on Kuwaiti children are warranted to reduce CVD risk factors. Introduction Cardiovascular disease (CVD) is the leading cause of death worldwide, and is responsible for 17.5 million deaths (31%) annually, including 41% in Kuwait. CVD r isk factors in children tend to track into adulthood and lead to premature morbidity and mortality [214] . A concern worldwide is that between 1990 and 2010, rates of childhood overweight (OW) and obesity (OB) increased from 4.2% to 6.7%, and are expected to reach 9.1% (60 million children) by 2020 w orldwide [97 215] . In Kuwait , there is significant concern about increases in the prevalence of childhood OB during the past several decades. For example , in 1985, Bayoumi et al. reported the prevalence of OB (BMI >2SD) in children aged six - to - nine was only 0.3% among boys and 0.2% for girls. [216] However, a study on the same age group from 1995 to 1996 indicates the prevalence of OB as 12.2% among boys and 12.3% among girls; additionally, among 10 - to - 13 - year - olds , OB prevalence was 16.3% for boys and 1 7.4% for girls [67] . Data from 2012 to 2013 indicate the overall OB rate in Kuwaiti children and adolescents ( six to 18 years) was 34% according to Center for Disease Control and Prevention ( CDC ), 31% according to WHO , and 28% according to International Obesity Task Force ( IOTF ) [2] . These data indicate that between 1985 [216] and 58 2013 [2 39] , the prevalence of childhood OB in Kuwait increased by more than 30%. In 2014, data from the Kuwait Nutrition Surveillance System report (KNSS , 2014) denoted OB rates by gender for boys and girls . In ages five to <10 yea rs OB rates were nearly identical in boys ( 20.1%) and girls (20.3%) though among ages of 10 to <15 years, the OB prevalence was higher in boys ( 34.1%) than in girls (27.1%). [68] The increasing levels of childhood OB in Kuwait is concerning given OB is linked with attenuating other CVD risk factors including blood pressure [67] and adverse levels of blood lipids . [60] There are several potential factors for the increases in childhood OB in Kuwait since 1985. These include Kuwait transitioning into rapid economic growth, increases in urbanization, and social and lifestyle changes including dietary changes from increased food imports , food subsidies, and a decrease in physical activity (PA). [34 177 217] With the exception of data on the prevalence of OB, OW and elevated BP, there are limited data on the prevalence of other CVD risk factors among Kuwaiti children. Moussa et al . (1999) reported the prevalence of hypertension as 5% in OB boys and girls aged six to 13 years . [67] Saleh et al. (2000) also reported hypertension as 5% among Kuwaiti schoolchildren ( six to 10 years) [69] . No data available on the prevalence of dyslipidemia in Kuwaiti children, however, a study conducted during 1995 and 1996 and indicated mean blood lipid levels among normal weight children were equivocal, but were higher in OB boys than in OB girls [60] Therefore, the study purpo se was to determine the prevalence of CVD risk f actors in Kuwaiti fifth graders , and if there are gender differences. It was hyp othesized that boys would have a greater prevalence of CVD risk factors than boys. Secondarly, to determine if the mean lev els of CVD risk factors are greater in boys versus girls. 59 Methods S tudy D esign and Participants A cross - sectional evaluation was conducted with 367 fifth graders ( 10.4 ± 0.4 years of age; 53% girls) in Kuwait. For student recruitment, informed parent/guardian consent and child assent forms were distributed to 39 primary schools (19 boys schools, 20 girls schools) within 6 Kuwaiti cities . Data was collected from 16 schools which were supportive of participating during the timeline available in S pring of 2019. The reasons for schools who did not participate included lack of interest or support from school administrators, and or the inability to participate during the limited timeline . Of the 493 consents and assents forms (boys [258] and girls [23 5]) that were collected, 35 parents chose not to have their child participate in the study, and 65 boys and 42 girls were absent on measurement day, resulting in a sample of 367 for this study . The study protocol was approved by the Michigan State Universi ty (MSU) Institutional Review Board (IRB) and the Ministry of Health Research Ethics Committee, and by the Department of Educational Research in Kuwait. Measurement Data collection occurred in Kuwait between February 2018 and March 2018 , and included ant hropometric, biometric, and lifestyle behavior assessments. Following training, data collection was performed by nursing trainers , as well as nursing and nutrition students from the College of Nursing and the College of Health Sciences of the Public Author ity for Applied Education and Training (PAAET ), Kuwait. The training protocol followed procedures used by [210] , to ensure the reliability, validity, and safety of measurement techniques. The protocol included following pediatric CVD risk factor assessment procedures from the American Academy of Pediatrics guidelines [20] , an expert panel 60 on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Chil dren and Adolescents [89] , and Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. [218] Anthropometric A ssessments Several anthropometric measures were used i n this study to derive outcome measures . Measures included height and weight , which was used to calculate body mass index (BMI), body fat percentage (% BF), waist circumference (WC ), and derived waist - to - height ratio (WHtR). Each of the anthropometric measurements are summarized below . Standing height was measured using a ShorrBoard (Shorr Production, Olney, MD) or wall mounted, or calibrated stadiometer (210 Holtain Limited, Dyfed, UK ), to the nearest 0.1cm , without shoes. Body weight (to the nearest 0.1 kilogram) and BF percentage were measured using a calibrated electronic scale ( Tanita BC - 534 ), which employs foot - to - foot bioelectrical impedance (BIA) ( Tanita Corporation, Tokyo, Japan). Height and weight were used to calculate BMI , which was converted to Z - scores based on methods devised by Cole et al. (1992 , Z= ). [219] Waist circumference (WC) was measured to the nearest 0.1 centimeter using a Gulick m easuring tape (Gulick Co., Tokyo, Japan). The Gulick tape was positioned in a horizontal plane around the abdomen at a level 1cm above the superior border of the iliac crest [144 220] . Waist - to - height ratio (WHtR) was derived from , according to Ashwell et al. (2005) . [153] 61 CVD risk factors ( dependent variables) Obesity Overweight and OB were assessed via BMI - for - age z - scores , according to the International Obesity Task Force (IOTF) cutpoints for OW >1+SD (90 th centile), and OB >2+SD ( 98. 7 th centile) [47] ; the WHO ( 2007 ) 85 th to <97 th centile ), OB th centile), [54] ; C DC ( 2000 ) th to <95 th centile), and th centile) . [124] The cutpoint for abdominal OB included WC at or above the 90 th percentile , and was determined according to the International Diabetes Federation (IDF) [221] , and growth curves for cardio - metabolic risk factors in children and adolescents , devised by Cook et al. ( 2009 ) (NHANES III) . [57] In addition, WHtR was calculated for children at or above 0.5 , as per Ashwell et al. (2005) [153] The cutpoint for high - risk BF percentage at or above the 90 th perc entile was indicated according to NHANES (1999 to 2004) smoothed curves . [156] Dyslipidemia Assessment of blood lipids involved the following cutpoints from the Expert Pan el on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents . [89] At risk cut point values : total cholesterol (TC) at or above 170 mg/dL, LDL at or above 110 mg/dL, HDL at or lower than 4 5 mg/dL, TG at or above 90 mg/dL, non - HDL at or above 1 20 mg/dL, and total cholesterol: HDL ratio at or above 3.5. High risk cutpoint values : total cho lesterol (TC) at or above 200 mg/dL , LDL at or above 130 mg/dL, HDL at or lower than 40 mg/dL, TG at or above 1 3 0 mg /dL, non - HDL at or above 145 mg/dL. 62 A blood sample was collected from participants in a non - fasted state by finger prick (40 µL) , using heparinized capillary tubes. The blood sample was analyzed using CardioCheck Plus (version 1.09; Maria Stein, OH). CardioCheck is a portable analyzer that was calibrated prior to testing at each school site. Per protocol , each blood sample was place d on a multi - lipid panel cassette to obtain analysis of total cholesterol (TC), HDL cholesterol, and triglycerides wi thin 90 seconds. Levels of LDL were calculated based on the Friedewald formula (LDL = TC - (HDL + TG/5) [222] . Additionally , the TC:HDL ratio, and non - HDL cholesterol were calculated. Regarding CardioCheck Plus accuracy, Whitehead et al. (2013) evaluated CardioCheck and Cholestech LDX accuracy using laboratory methods. CardioCheck exhibited higher intra and inter - batch imprecision and external quality assessment (EQA) scheme between - analyzer variation for the me asurement of TC, HDL , compared to the Cholestech LDX cholesterol analyzer in Li Hep whole blood and plasma . [223] Steiner et al. indicated that differences between non - fasted and fasted me asures of pediatric lipids LDL and TG were minor and clinically acceptable . [224] Moreover, it has been determin ed that non - fasting TC and HDL measurements are appropriate and in strong agreement ( intra - class correlation .92) with fasting values . [225] Non - HDL was calculated by subtracting HDL from TC and deemed acceptable for using non - fasting samples . [232] Non - HD L is used clinically as a marker for atherogenic apolipoprotein B containing lipoproteins [226 227] , and considered an effective predictor for dyslipidemia and subclinical atherosclerosis in adulthood . [227] Resting B lood P ressure Manual resting systolic and diastolic blood pressur e (BP) were assessed following standardized procedures [164] , us ing a stethoscope and a standard BP aneroid , with an , using a Professional Aneroid 63 Sphygmomanometer (AllHeart, Louisiana, MO). Once a participant had been seated for five minutes, two measures were taken at one minute intervals to determine an average. If the first two measures differed beyond parameters (4 mmHg), a third measure was taken. The blood pressure values were classified using the 2017 Clinical Practice Guideline for Screening and Man agement of High Blood Pressure in Children and Adolescents using cutpoints for children ages 1 to <13 years . [218] The cutpoint for determining the prevalence of children at risk ( der ived from a comprehensive review of almost 15,000 published articles between January 2004 and July 2016 p age 1) [88] which corresponds to 90 th to < 95 th percentile by sex, age, and height. Levels > 95 th percentile are defined as hypertension which includes stage I and II . The prevalanece of on same cutpoints (SBP >120 mmHg and DBP > 80 mmHg . Covariate assessment Physical A ctivity (PA) A self - reported question taken from the Youth Risk Behavior Survey (YRBSS) [209] was administered in both Arabic and English (Appendix B). The question was designed to assess the number of days over the preceding week participants engaged in > 60 minutes of moderate - to - vigorous physical activity (MVPA) . [209] The question states: days were you physically active for a total of at least 60 minutes per day (add up all of the time you spend in any type of activity that increases your heart r ate and makes you breath hard some of the time)? The scale range measured zero to seven days . [211] 64 Scree n T ime (ST) Screen time was assessed using the ST questions from the YRBSS [209] and was administered in both Arabic and English (see Appendix B). Participants of the study self - reported th e weekly amount of time spent viewing television, playing video games, and using an online computer, and indicated the number of hours (watch/play) on weekdays and weekends for each of the three screen media types . The average hours of screen time per week was determined using the following formula: (hours of TV time on weekdays * 5 days) + (hours of TV time on weekends * 2 days )/ 7 days + (hours of video game time on weekdays * 5 days) + (hours of video game time on weekends * 2 days )/ 7 days + (hours of computer time on weekdays * 5 days) + (hours of computer time on weekends * 2 days )/ 7 days . [212] Screen time equal to or above two hours per day is considered high ST. Less than two hours a day is considered low ST . [213] Statistical A nalysis P - test , used to test for differences between two independent means , indicated a minimum of 128 participants was required. Z - tests , used for determining differences between two independent groups (boys and girls ), indicated a minimum of 300 parti cipants was required ( results by G*Power software). All variables were tested for normality of distribution using a Q - Q box plot ; statistical skewness and kurtosis values >1 or less than - 1. TG was found to be positively skewed , and was logarithmically tra nsformed. Descriptive statistics and independent t - test were used for continuous variables, and chi - square tests were used for categorical variables (percentage at risk). A general linear model and binary logistic regression ( odds ratios) were used to comp are boys to girls in terms of mean differences , and their risk prevalence for CVD (BMI, WC, WHtR, %BF, TG, TC, TC/HDL, non - HDL - C, LDL - C, HDL - C SBP, DBP ), and controlled for covariates MV PA and ST. Data analysis was conducted 65 using SPSS version 24 (SPSS Inc ., 2016 , Chicago , IL). Results are presented as mean ± SD or S.E , at significance level p Results The final sample was composed of 36 7 Kuwait 5th graders ( 53% girls) . For selected measures there was a lower sample size due to participants opting out of the measure or technical issues when obtaining blood sample . Missing data included body weight ( so unable to calculate BMI or assess % BF ) 0.3 %, three WC ( 0.8 %), six responden ts (~2%) SBP and DBP , eight PA (2%), and two respondents ST (0.5%) . The re were 94 participants that did not have blood lipid data due to 64 participants who opt ed out from the test and 30 participants whom either did not have an adequate volume of blood for analysis, , and or technic al or assay issues . The missing blood lipid data included TC ( n=106 , 28% ), HDL - C ( n=96 , 26% ), LDL - C (n=133 , 36% ), TC:HDL - C ratio (n=108 , 29% ), non - HDL - C (n=109 , 29% ), and TG (n=123 , 33% ). Table 1 summ a rizes d emographic characterisitics and overall mean anthropometrics, biometrics and covarietes, and the comparison between boys and girls bas e d on general linear model. There were n o gender differences for mean BMI Z - score s and other anthropometric variables, except for greater ( P = 0 . 029) mean %BF in girls than boys. Mean TG, SBP , DBP , and MAP were significantly ( P < 0 .001) greater in girls than boys. Whereas, mean MVPA was significantly ( P < 0 .001) greater in boys, however, mean ST did not differ between boys and girls. 66 T able 1 . Demographic characteristics and mean values of anthropometrics, biometrics, and covariates ( moderate - to - vigorous physical activity [ MVPA ] and screen time [ ST ] ) of boy and girl fifth g raders in Kuwait 1 Participants Ov e r all ( N = 367 ) Boys ( n =174) Girls ( n = 19 3 ) P - value City Capital 44 (12%) 15 (8.6%) 29 (15%) 0 .060 Hawalli 137 (37.3%) 66 (37.9%) 71 (36.8%) 0 .822 Farwaniya 23 (6.3%) 12 (6.9%) 11 (5.7%) 0 .638 Mubarak Alkabir 83 (22.6%) 43 (24.7%) 40 (20.7%) 0 .363 Ahmadi 38 (10.4%) 5 (2.9%) 33 (17.1% 0 .001 Jahra 42 (11.4%) 33 (19%) 9 (4.7%) 0 .001 Age (years) 10.43 ± 0.40 10.42 ± 0.37 10.43 ± 0.41 0 .825 Anthropometry Height (cm) 141.5 ± 6.70 140.8 ± 7.17 142.1 ± 6.21 0 .067 Weight (kg) 43.52 ± 13.20 42.91 ± 14.38 44.06 ± 12.04 0 .403 BMI (kg/m 2 ) 21.39 ± 5.07 21.22 ± 5.46 21.55 ± 4.70 0 .541 BMI z - score IOTF 2 1.27 ± .066 1.22 ± .099 1.32 ± .089 0 .447 WHO 3 1.30 ± .074 1.32 ± .11 1.29 ± .098 0 .449 CDC 4 0.90 ± .061 0.88 ± .089 0.93 ± .084 0 .820 BMI Centiles IOTF 76.91 ± 1.48 74.07 ± 2.21 79.49 ± 1.98 0 .069 WHO 75.77 ± 1.59 73.49 ± 2.38 77.84 ± 2.13 0 .174 CDC 72.79 ± 1.59 70.73 ± 2.37 74.63 ± 2.13 0 .226 BF% 27.8 ± 10.3 26.6 ± 11.79 29.0 ± 8.72 0 .029 WC (cm) 69.16 ± 14.0 68.46 ± 15.18 69.79 ± 12.85 0 .365 WHtR 0.482 ± .085 0.478 ± 0.09 0.485 ± 0.07 0 .442 Blood Lipids 5 T C (mg) 152.4 ± 31.9 149.1 ± 28.1 155.5 ± 35.0 0 .106 0 .399 adj LDL (mg) 81.7 ± 25.3 82.2 ± 25.8 81.3 ± 25.0 0 .795 0 .538 adj HDL (mg) 52.2 ± 14.7 53.01 ± 13.5 51.4 ± 15.8 0 .390 0 .227 adj Non - HDL (mg ) 100.1 ± 26.2 98.2 ± 25.7 101.9 ± 26.7 0 .263 0 .459 adj TC:HDL 3.03 ± .825 2.94 ± 0.75 3.10 ± 0.88 0 .127 0 .176 adj T G (mg) 100.3 ± 52.5 91.03 ± 42.7 108.5 ± 58.6 0 .009 0 .014 adj Blood Pressure 6 SBP (mmHg) 105.5 ± 12.12 102.4 ± 11.5 107.7 ± 12.09 0 .001 0 .001 adj DBP (mmHg) 67.7 ± 9.58 64.8 ± 8.72 70.2 ± 9.64 0 .001 0 .001 adj MAP 150.6 ± 17.1 146.2 ± 15.8 154.58 ± 17.3 0 .001 0 .001 adj 67 Table 1 Covarietes 7 MV PA (d ays /wk) 2.95 ± 2.41 3.50 ± 2.44 2.47 ± 2.28 0 .001 % met MVPA 20.9% 26.9% 15.6% 0.008 ST (hrs/d) 4.84 ± 2.73 5.02 ± 2.60 4.68 ± 2.84 0.232 hrs/day 16.2% 9.9% 21.8% 0.002 1 Descriptive statistics. General linear model ( P adj controlling for MVPA and ST). Data shown as mean S.D (or S.E for BMI Z - Score), p 2 IOTF (International Obesity Task Force) [47 121] ; 3 WHO , 2007 (World Health Organization) [49 228] ; 4 CDC (Center for Health and Diseases Prevention) [135 137 142 229] ; BMI ( body mass index ); %BF ( percentage body fat); WC ( waist circumference); WHtR ( waist - for - height ratio ). 5 Blood lipids: total cholesterol (TC), LDL - C ( low density - lipoprotein cholesterol), HDL - C ( high density - lipoprotein cholesterol), non - HDL - C ( total cholestero l - HDL - C), TC: HDL - C ratio (TC/HDL - C), TG ( triglycerides ). 6 Blood pressure: SBP ( systolic blood pressure ), DBP ( diastolic blood pressure ), MAP ( mean arterial pressure ([SBP - DBP / 3) + DBP]). Blood lipids and BP cutpoints for children according to an expe rt panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents [89 170] . Clinical Practice Guideline for S creening and Management of High Blood Pressure in Children and Adolescents. [218] 7 Covariates: MVPA ( Moderate - to - vigorous physical activity [ number of days over past week (7 days) meeting > of 60 min]); ST (screen time). F igure 1 . Prevalence of Overweight (OW) and Obesity (OB) in Kuwaiti fifth Grade Boy and Girl (N= 367 ) according to the International Obesity Task Force (IOTF) cutpoints for OW 90th to < 98.7th centile , and OB [47] ; the WHO (2007) cutpoints for to <97th centile and [54] . [124] Figure 1 represents the prevalence of OW and OB , based on centiles. Table 2 illustrates the prevalence of CVD risk factors and the analysis of gender differences. The overall 68 prevalence of OW (BMI >1 SD) was 24.6% (IOTF), 21.6% (WHO), and 39.6% (CDC), and was greater among girls than boys. The overall prevalence of OB (BMI >2 SD) was 36.1% (IOTF), 39.6% (WHO), and 16.9% (CDC) , respectively, and was not statistically different between boys and girls. With respect to measures of abdominal OB and body fat mass, the overall prevalence of high WC ( 90 th percentile) was 10.1% and WHtR ( 0.5) was 43.3%, and neither were statistically different between boys and girls. The overall prevalence of a high % th percentile) was 10.2%, and was also not statistically different between genders. The prevalence for being at risk of dyslipidemia in t he overall sample (Table 2), based on logistic regression while controlling for MVPA and ST, indicated elevated TG ( 45.5 %), TC (26.4%), LDL ( 13.2 %), non - HDL ( 22.5 %), TC:HDL (25.9%), and low HDL ( 36.9% ). The re were significant gender difference s that girls had a higher likel i hood of at risk for elevated TG (OR = 1.809 [ 95% CI: 1.053 3.107] ; P = 0 .032), in addition to a greater likelihood of high risk for low HDL (OR = 2.196 [ 95% CI: 1.072 - 4.499] ; P = 0 .032) , compared to boys. Regarding BP, Table 2 illustrates the overall prevalence of children classified as being at risk of elevated SBP (16.1%) and DBP (15.2%), and elevated BP ( 23.3% ). Girls were more likely to be at risk of elevated SBP (OR = 2.446 [ 95% CI: 1.259 to 4.735 ] ; P = 0 .008), DBP ( OR = 3.448 [ 95% CI: 1.682 to 7.068 ] ; P < 0 .001), and elevated BP ( OR = 3. 232 [ 95% CI: 1.813 5.761 ]; P = 0 .0 01 ) than boys. 69 Table 2 . Prevalence (at risk) of CVD risk factors between fifth grade Kuwaiti boy s § and girl s 1 Crude Adjusted for MVPA and ST At Risk Cut Points Overall ( N =36 7 ) Boys ( n =174) Girls ( n =19 3 ) OR (95% CI) P OR (95% CI) P Overweight ( BMI >1SD) IOTF 2 24.6% 20.1% 28.6% 1.542 ( .944 - 2.519 ) 0 .0 58 1.496 ( .903 - 2.479 ) 0 .118 WHO 3 21.6% 15.5% 27.1% 1.889 ( 1.121 - 3.184 ) 0 .0 07 1.813 ( 1.061 - 3.097 ) 0 .029 CDC 4 39.6% 34.4% 43.8% 1.444 ( .941 - 2.216 ) 0 .0 70 1.349 ( .869 - 2.095 ) 0 .182 Overweight (BMI Centile) th ) 33.3% 29.9% 36.5% 1.286 ( .827 - 2.002 ) 0 .264 1.218 ( .773 1.918 ) 0 .396 th ) 18.6% 11.5% 25.0% 2.399 ( 1.355 - 4.248 ) 0 .003 2.262 ( 1.259 4.063 ) 0 .006 th ) 18.0% 12.6% 22.9% 2.020 ( 1.143 - 3.571 ) 0 .016 1.923 ( 1.073 3.447 ) 0 .028 Obesity (BMI >2SD) IOTF 36.1% 35.6% 36.6% 1.059 ( .687 - 1.632 ) 0 .795 .931 ( .594 - 1.459 ) 0 .755 WHO 39.6% 41.4% 38% .901 ( .589 - 1.377 ) 0 .629 .796 ( .512 - 1.238 ) 0 .311 CDC 16.9% 17.8% 16.1% .922 ( .529 - 1.606 ) 0 .774 .786 ( .440 - 1.390 ) 0 .402 Obesity (BMI Centile) th ) 22.7% 21.3% 24% 1.241 ( .751 - 2.050 ) 0 .399 1.083 ( .645 - 1.818 ) 0 .764 th ) 42.3% 44.8% 40.1% .849 ( .557 - 1.293 ) 0 .445 .757 ( .489 1.172 ) 0 .212 th ) 48.6% 49.4% 47.9% .963 ( .636 - 1 .459 ) 0 .860 .846 ( .548 1.305 ) 0 .449 Abdominal obesity 5 th centile 10.2% 10.4% 9.9% .974 (.488 - 1.942) 0 .940 .818 (.401 - 1.671) 0 .582 WHtR 43.3% 42.8% 43.7% 1.051 (.690 - 1.602) 0 .817 .926 (.598 - 1.433) 0 .729 Body fat mass 6 %BF th centile) 10.1% 9.8% 10.4% 1.185 (.586 - 2.397) 0 .636 .956 (.461 - 1.981) 0 .904 70 Table 2 ( c Dyslipidemia 7 At risk 45.5% (43/114) 37.7% (68/130) 52.3% 1.811 (1.086 - 3.021) 0 .022 1.809 (1.053 3.107) 0 .032 3 0 mg/dL 17 . 6 % ( 14 /114) 12.3 % ( 29/130 ) 22.3 % 2.051 ( 1.023 4.110 ) 0 . 040 2.041 (.9 79 4.258 ) 0 .0 57 At risk 26.4% (30/128) 23.4% (39/133) 29.3% 1.328 (.762 - 2.314) 0 .317 1.152 (.644 - 2.059) 0 .634 5% (7/128) 5.5% (6/133) 4.5% .817 (.267 - 2.499) 0 .722 1.686 (.513 - 5.537) 0 .389 At risk LDL 13.2% (15/110) 13.6% (16/124) 12.9% .938 (.440 - 1.999) 0 .869 1.167 (.522 - 2.609) 0 .707 High LDL 3.8% (5/110) 4.5% (4/124) 3.2% .686 (.179 - 2.622) 0 .581 .469 (.113 - 1.949) 0 .298 At risk HDL 45 mg/dL 36.9% (44/131) 33.6% (56/140) 40% 1.318 (.803 - 2.164) 0 .274 .718 (.426 - 1.209) 0 .213 Low HDL <40 mg/dL 16.6% (16/131) 12.2% (29/140) 20.7% 2.127 (1.077 - 4.278) 0 .030 2.196 (1.072 - 4.499) 0 .032 At risk n on - mg/dL 22.5% (24/126) 19% (34/132) 25.8% 1.474 (.816 - 2.664) 0 .197 .728 (.419 - 1.460) 0 .440 High non - mg/dL 6.6% (8/128) 6.3% (9/132) 6.8% 1.222 (.441 - 3.390) 0 .699 .936 (.319 2.742) 0 .904 25.9% (28/127) 22.0% (39/132) 29.5% 1.598 (.902 - 2.831) 0 .108 1.437 (.794 2.602) 0 .232 Blood Pressure 8 Elevated BP 23.3% (23/170) 13.5% (61/191) 31.9% 2.999 (1.757 5.118) 0.001 3.232 (1.813 5.761) 0.001 SBP 16.1% (18/170) 10.6% (40/191) 20.9% 2.237 (1.227 4.077) 0.007 2.446 (1.259 4.735) 0.008 DBP 15.2% (15/170) 8.8% (40/191) 20.9% 2.737 (1.452 - 5.162) 0.001 3.448 (1.682 - 7.068) 0.001 1 Chi - square test for proportional differences between boys ( § = reference) and girls. Binary logistic regression with controlling for MVPA and ST , P differences between genders . 2 IOTF (International Obesity Task Force) [47 121] ; 3 WHO ( 2007 ) (World Health Organization) [49] ; 4 CDC (Center for Health and Diseases Prevention) [87] ; BMI ( body mass index ). 5 Abdominal obesity: WC ( waist - circumference), WHtR (waist - to - height ratio). 6 Body fat mass: %BF (percentage body fat). 7 Dyslipidemia: TG ( triglycerides ), LDL - C ( low density - lipoprotein cholesterol), HDL - C ( high density - lipoprotein cholesterol), no - HDL - C ( total cholesterol HDL - C), TC: HDL - C ratio (TC/HDL - C). Blood lipids cut points according to an expert pan el on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report. [89] 8 and/or DBP (diastolic blood pressure). Bl oo d pressure cut points according to Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. [218] 71 Discussion Few studies have assessed multiple CVD risk factors other than OB and BP in Kuwaiti children . This is the first study , to the knowledge, to conduct a comprehensive CVD risk factor assessment on fifth grade children in Kuwait. The primary objectives were to determine the prevalence of CVD risk factors in Kuwaiti fifth graders, and as hypothesized we determined if boys had higher risks than girls. Our primary findings indicated a high prevalence of CV D risk factors in Kuwaiti children that is concerning. There is little previous data from Kuwait to compare prevalence rates except for OB and elevated BP and both have increased as compared to previous studies in Kuwait. For most risk factors the prevale nce rates of at risk was higher or equal to similar aged populations globally that have been identified to have a high prevalence of CVD risks. With respect to gender, contrary to our hypothesis the prevalence of several CVD risks were higher in girls th an in boys, while the prevalence of OB was not significantly different between boys and girls, which was not expected. Ea ch of the primary outcome variables are discussed below as well as mean levels of risk factors. Our findings of an overall OB prevalen ce of 39.6% is higher than previously reported in the literature on Kuwaiti children (Table 2). A study conducted during 2008 to 2009 reported childhood OB as being 36.5 %, and 24% in 111 boys and 94 girls aged nine to 13 years [36] , respectively. Additionally, the prevalence of OB was found to be higher than estimated by the KNSS (2011 to 2012 ), i.e. 34.6% and 28.1% in 2,300 boys and 2,261girls (10 to <15 years of age), respectively . [39] Additionally, data collected in 2012 to 2013 by Elkum et al. (2 016) reported slightly lower prevalence of childhood OB , 37.4% in boys and 25.3% in girls (n= 6,574, 60.4% girls, 12.0 ± 3.4 years), respectively . [2] 72 Regarding measures of abdominal OB , the overall prevalence of WC and WHtR (table 2) was not significantly different between boys and girls, also was lower than previously estimated in Kuwaiti primary school children. On the other hand , the mean WC levels , which was also not stat i st i cally differe nt between boys and girls of our study, was higher than reported in Kuwaiti primary schoolchildren. Jackson et al. (2012) evaluated WC in Kuwaiti children and adolescents aged five to 19 years ( N=9,593 ), including primary schoolchildren . [129] The prevalence of WC in boys and girls was estimated 13% and 12%, while the mean WC was approximated 58.4 cm and 58.1 cm, respectively. Among US children aged six to 11 years, NHANES ( 2011 to 2012 ) reported overall higher prevalence of WC (21.8%) [147] , but lower overall prevalence of WHtR ( 30.7 %), compared to our findings. The higher prevalence of WHtR in Kuwaiti vs US children may be related to variations in stature. Previously , Al - Isa et al. (2000) assessed Kuwaiti aged six to 10 years ) using a NCHS/CDC reference pop ulation, where they were found to be shorter than US children . [45] Nevertheless, a study conducted in 2007 to 2008 on Spanish children aged six to 14 years ( N =2,323; 50% girls), repo rted boys and girls being of relatively similar height (140 cm and 140.5 cm), but with a lower mean WC ( 66.3 cm and 62 cm ) and WHtR ( 0.45 and 0.46 ) , respectively, compared to our sample . [230] This may also have been due to Spanish boys and girls being leaner (%BF 18.6 and 21.2 , respectively), compared to our study sample. With respect to mean % BF, based on NHANES ( 1999 to 2004 ) smoothed curves ( aged eight to 19 years), Ogden et al. (2011) indicated mean percentage BF in US girls and boys to be roughly 33% and 28 %, respectively, starting from age eight , which is relatively higher than what our results indicate . [231] However, we found a higher overall mean % BF of 23.9 , reported for 281 fifth grade children in Michigan, USA . [211] Moreover, regardless of the lower mean age in 73 our study sample, the mean % BF in our sample was hi gher than ages six to 14 years among Spanish school - going boys (18.6%) and girls (21.2 %) ( Marrodan et al ., 2013) [230] . In summary, the current study sample indicate s that Kuwaiti children h ave risk for measures associated with OB including high W HtR and %BF . The levels in the current study were higher as compared to other child populations. No data have been published on the prevalence of dyslipidemia in Kuwaiti children. In Saudi Arabia, Al - Shehri et al. (2003) reported high risk TG prevalence of ~34.8% for boys and girls aged nine to 12 years (N=1,390 ), which is higher than our findings ; however, the study found a lower overall prevalence of children with at risk levels for TC:HDL ( 21.7 %) , compared to our results. [232] Overall, our findings on the prevalence of being at risk for TC (27%) and TG (45.5%) with 17.6% classified as high risk, are higher than previously reported in global child populations . For example, Ding et al. (2014) reported that 16% of 3,249 Chinese children ( aged six to 18 years) had high risk TG levels. [233] Haroun et al. (2018 ) indicated at risk prevalence for at risk TC at 23% and TG at 13.6% (with 5.5% high risk) among 596 United Arab Emirates (UAE) students 10 to 15.9 years old. [234] Ribas et al. (2012) reported the prevalence at risk for TC at 26% and TG at 21% among 874 Brazilian children six to 13 years old. [235] We detected an overall prevalence of 6.6% for those at high risk for non - HDL , which is relatively similar to recent data from other countries . NHANES (2011 to 2012) data reported the prevalence of being at high risk for non - HDL as 6.9 % among US children aged eight to 12 years. [27] Additionally , i n a slightly younger aged sample in Beijing, Wenqing Ding et al. (2016) reported the prevalence of high risk for non - HDL among 1649 children include aged six t o nine was 7.3% and in 10 to 18 year olds was 6.9%. [233] 74 We found a higher mean level of TG (Tables 1 and 2) in girls compared to boys, which was not anticipated. Higher levels of TG are associated with OB. We expected a higher level of TG in boys versus girls given we expected boys would have a higher prevalence of O B. The current study findings, reveal that the mean TG level increased among Kuwaiti children, particularly for girls, when compared to previous study conducted in Kuwait . Previ ously , a paired - matched study conducted in Kuwait (1995 to 1996) compared mean blood lipids between 460 OB and control (normal weight) individuals ( schoolchildren aged six to 13), and reported higher mean TG among OB boys compared to girls, and a relatively equal mean TG in non - OB girls (n=230) and boys (55 ± 26 m g/dL vs 54 ± 31 mg/dL) . [60] Nonetheless, the mean TG among the girls in our study is higher than what was reported for girls vs . boy s in other studies , among various childhood age ranges. For example, among US sixth graders ( N = 2,866), Peterson et al. (2012) reported significantly higher mean TG in girls compared to boys (96.4 ± 57.8 mg/dL vs 85.6 ± 48.2 mg/dL; P < 0 .001) . [13] Among children aged six to nine (n=1,649) in Beijing, Ding et al. (2016) reported a modestly higher mean TG among girls (73.5 ± 2.6 mg/dL) than in boys (70.8 mg/dL) . [233] Among Brazilian children aged six to 10 years (n=874), Ribas et al . (2012) described higher median TG among girls than in boys (80 mg/dL [65 - 102] vs 76 mg/dL [ 59 - 99 ] ) . [235] Moreover, among Korean children 9.9 years of age (n=770), the mean TG found was slightly higher in girls than in boys (71.1 ± 27.6 mg/dL vs 68.6 ± 25.2 mg/dL) . [174] Finally, Ying Liao et al. (2008) also reported higher me an TG among Chinese girls aged six to 10 (n=177 ), compared to boys (n=312) (74.4 ± 34.5 mg/dL vs 72.6 ± 39.8 mg/dL) . [236] Additionally, compared with other child populations, the level of risk for dyslipidemia among Kuwaiti children is considered to b e high. 75 Girls in our study exhibited an overall greater prevalence of being at risk of high TG and low HDL compared to boys , contrary to our hypothes i s . Our basis for expecting boys to have greater risks than boys , was based on the KNNS [1] data indicating a higher prevalence in OB in boys comparted to girls, in addition to a study during 1995 and 1996 indicated higher mean lipids level among OB boys than girls, whearas, among non - OB, the levels were were similar by gender. [60] ; and thus the boys would likely have greater dyslipidemia. However, several stud ies have reported a higher prevalence of dyslipidemia in girls than boys. [27 233 235] The primary reasons that explain these differences include biological or environmental factors . [85] At a certain age around ten during childhood, girls often have higher adverse blood lipid levels compared to boys . Prebutery is considered a potential factor for adverse blood levels in children. Ruiz et al. (2007) examined the associations of cardiovascular fitness (CVF) with a clustering of metabolic risk factors in 429 boys and 444 girls , aged nine to 10 years of ag e, considering genital ) development classification (Tanner % in girls , 54/44/2/0/0 vs boys , 99/1/0/0/0). The results showed girls with second and third Tanner stages , had significantly higher ( P <0.01) mean TG level s and signifi cantly lower mean HDL level s (1.4 mmol/L vs 1.5 mmol/L ), compared to boys. Also the mean levels of TG ( P =.026) and HDL ( P =.067) in girls were also associated with lower CVF . [237] Stavnsbo et al. (2018) published an international reference value for cardiometabolic risk variables , b ased on cohorts of children ( observation of 11,234 girls and 11,245 boys , aged six to 11 years) from Europe and the United States (51.3%). The mean values of blood lipids such as TC, LDL, declined HDL, and TG become higher in girls than boys beyond age 10 [70] . Moreover, NHANES surveys (1988 to 1994 and 1999 to 2 006 ), combined with the Bogalusa, Fels, Muscatine, and Princet datasets, also indicate serum TG peaks in girls at or after age 11 years [57] . In summary, Kuwaiti girls in the 76 current study, overall were at higher risk of dyslipidemia than boys. Based on previous studies, this is likely due to maturation factors in girls involve higher adverse blood lipid levels as compared to boys. Additionally , it is possible that the signifi cantly higher proportion of girls that were OW and their higher %BF may have contributed to their elevated risks as compared to boys. Our findings on the prevalence of children with BP levels above the healthy normal range indicated that 23% were classifi ed as elevated BP. W e anticipated these to be higher in boys than girls in our study sample (Table 2 ), compared to data gathered earlier about Kuwaiti children ( aged six to 13 years) by Moussa et al. (1999). Moussa et al. reported 5% elevated SBP mHg ) [67] , in addition to 5% of elevated BP, particularly, in OB boys and girls . Overall, our findings , based on current at risk cutpoints for SBP, DBP, and prehypertension , indicate a significant increase in BP among Kuwaiti children. The overall prevalence of BP was significantly higher in girls than in boys (Table 2), in addition to the girls ` higher mean SBP and DBP than boys (Table 1), which was no in a g reement with a previous study in Kuwaiti children . Despite Moussa et al. (1999) reporting a relatively higher mean SBP in boys and girls aged six to 13 years old (110.5 ± 12 mmHg vs 109.5 ± 11.5 mmHg, respectively) compared to our findings, their estimated mean DBP (girls 68 ± 8 vs boys 66.5 ± 7 mmHg) [67] was lower, particularly among girls. In summary, the data indicates no change or decline in SBP and DBP among boys over time; however, the mean DBP has s lightly increased among girls. In the context of our findings , i.e. that girls had a greater prevalence at risk BP than boys , Roland et al. (1980) examined 9,977 public school children aged six to 9 , and found height to be directly related to both SBP and DBP , regardless of age, when accounting for BMI and skinfold 77 [238 239] . Miles - Chan et al. (2013) indicated that height - for age was positively correlated ( r = . 29; P < 0 .001) with SBP and DBP in children aged five to 10 years of age, ( N =2,489 ), of various Asian ethnicities . [240] To better understand Miles - Chan et s findings , we conducted a secondary analysis, using a stepwise multiple linear regression (data not shown), included sex, age, height, weight, BMI, WC, WHtR, and %BF, in addition to MVPA and ST as independent predictors of SBP and DBP. We found that % BF a nd height were directly ( P < 0 .001) related to 1 mmHg increase in SBP with every 0.462% ( r =.482 ) increase in body fat, as well with every 0.351 cm ( r =.378 ) increase in height. On the other hand, with every 0.282 kg ( r =.385 ) increase in body weight, DBP increased ( P < 0 .001) by 1 mmHg. This may explain the higher mean SBP and DBP among the girls in our study , compared to boys. In spite of this, the literature indicates body weight may not be a significant indictor of BP in children , due to the need for dete rmining both maturation and adiposity when compared to height as an independent indicator . [ 241] We found both adiposity (%BF) and stature (height) to be associated with a higher risk of BP among girls , which may be related to maturation factors . The strengths of the current study include recruiting suffic i ent sample size to determine the prevalence at risk of CVD and to evaluate potential gender differences. Also, the comparison by gender controlled for the potential influence of MVPA and ST on CVD risk factors. Furthermore , the study participants were rec ruited from 16 schools within six cities in Kuwait. Measures were performed by a trained research team , according to established pediatric protocols and standards [170] . There were also several potential study limitations. The research wa s a cross - sectional evaluation of as is true for all cross - sectional studies , no cause and effect inference s can be made from the findings. Our study sample was convenience and may not be a representative sample of all Kuwaiti fifth graders. Challenges in Kuwait were related to school 78 collaborations , obtaining parental approvals ( consent ) , restricted time for data collection and research team availability, and The sample size for blood lipids was lower than other variables due to being unable to collect a sufficient blood volume to evaluate theblood lipid variables, technical issues with analyzer, or some children opted out of the blood lipid test. Selected CVD risk factors may have been influenced by varying levels of hydration, or medications taken by some children. Neither of thes e factors were accounted for. Summary and Conclusion This is the first study to determine the prevalence of multiple CVD risk factors among Kuwaiti children, and expected more boys would be at risk of CVD than girls . Our primary findings indicated a higher prevalence of obesity (42%) and elevated BP (23%) in Kuwaiti children than previously reported. Moreover, the alarming preval ence ar risk for CVD risk found to be higher that boys will have greater risks of CVD than girls, the prevalence of OB was not statistically different between bo ys (41%) and girls (38%), though girls had a higher prevalence of OW (27%) compared to boys (15%). Moreover, girls were also at risk for dyslipidemia and elevated BP compared to boys. While we found gender differences in the prevalence of dyslipidemia and BP, it is difficult ( in this age group ) to determine if the primary reason for differences had been related to biological factors that influence risk factors including maturation, and or environmental factors such as dietary and PA behaviors. These finding s suggest the need for follow - up and lifestyle interventional studies or programs to prevent and manage risks of CVD among children in Kuwait. . 79 CHAPTER 4 : PROPORTION OF KUWAIT SCHOOLCHILDREN MEETING US AND WHO/FAO DIETARY RECOMMENDATIONS FOR FOOD GROUPS AND NUTRIENT INTAKE, COMPARED BY GENDER Abdulaziz kh. Al - Farhan 1,4 , Lorraine J. Weatherspoon 1 , Karin A. Pfeiffer 2 , Wei Li 1 , Joseph J. Carlson 1,3 1 Department of Food Science and Human Nutrition , 2 Department of Kinesiology , and 3 Depar tment of Radiology , Michigan State University. 4 The Public Authority for Applied Education and Training, the College of Nursing and the College of Health Sciences, Kuwait Abstract B ackground : There are few studies on nutrition behaviors in Kuwaiti children despite the high prevalence of childhood obesity. The most recent study (2008 - 2009) reported that > 50% of the children exceeded the calorie requirements and only ~20% met the dietary reference intake (DRI) recommendations for key nutrients. Also, data are lacking on gender differences. Objectives: To determine the proportion of Kuwaiti schoolchildren meeting US Dietary Guidelines for Americans (DGA) and DRI and WHO/FAO dietary recommendations and compare differences by gender. Methods : A cross - sectional study of fifth graders (N = 313; 53% girls, 10.4 ± 0.4 years) from Kuwait . S elf - administered Block Kids Food Frequency Questionnaire (Arabic/English) was used to evaluate nutrition intake . Dependent variables: food groups, macronutrients, micronu trients , and healthy eating index (HEI) . Statistics: General linear model and logistic regression with controlling for physical activity (PA) and sceen time (ST) with P < 0 . 05 . Results: The proportion of children who met the recommendations (% US; % WHO) : f ruit (6 9 % DGA; 62% WHO), vegetables ( 29 % DGA; 46% WHO), dairy (3 3 % DGA), whole grains (0% DGA; 1 7 % WHO). T otal kcal (1 7 %), total fat (6 6 % AMDR ; 24% WHO), SFA (31%), TFA (0%), protein (97% AMDR ; 74% WHO), carbohydrates (91% AMDR ; 50.5% 80 WHO), added sugar ( 1 7 %), and fiber (41%) . C alcium ( 20 %), magnesium (41.5%), potassium (1 3 %), sodium (17% D RI ; 13% WHO), and vitamin D (0% D RI ; 1 9 % WHO) . M ore boys than girls met the fruit recommendation (76 % vs . 62%; P = 0 .037 [DGA]) , while more girls than boys met the vegetable s (36.4% vs . 20% ; P = 0 .003 [DGA]) and sodium (18% vs . 8%; P = 0 .022 [WHO]) recommendations. C onclusion : Few children met food group and nutr ient recommendations , or had desirable HEI scores, even though most children exceed ed the ir kcal needs . There were f ew gender differences in meeting the recommendation. The high kcal low nutrient density diet reportedby the majority of Kuwaiti children is related to numerous health risks, and warrants attention . Introduction Poor dietary behaviors, characterized by excessive dietary energy with low nutrient density, are known to adversely affect childhood health; including increases in obesity (OB). [97] In the 1980s economic transitions [81] began behaviors and nutritional status from being under nourished as reflected by < 1% prevalence of OB in 1985 [216] , over nourished state with a OB prevalence of 31% in 2012 . [2] There is evidence for dietary behaviors and patterns t hat promote health and prevent disease risks . [18 - 22] These studies ha ve led to the establishment of nutritional guidelines for promoting health and preventing chronic disease, particularly, the US Dietary Guidelines (DGA) including the dietary reference intake (DRI) for individuals by age and gender . [66] Aligning with US dietary guidelines , t he Joint WHO/FAO Expert Consultation (2002) provided population bas ed dietary recommendations for preventing OB and chronic diseases . [21 64 65] These international nutrition recommendations were developed based on per capita 81 food intake estimated according to nutrition balance sheets . [31 63] Therefore , there are variations between the WHO/FAO and US guideline cutoffs, particularly in the level of carbohydrates (WHO range: 55 75% vs. AMDR: 45 65%), some food groups (fruits, vegetables, and whole grains [lack dairy group]), and micronutrients, particularly vitamin D (WHO: 200 UL vs. DRI: 600 UL). The US DGA and DRI and WHO/FAO guidelines stress on limiting intake of saturated fat , trans - fat, added sugar s, and sodium, while sufficiently consuming essential fatty acids such as n - 3, fiber, fruits and vegetables, whole g rains, in addition to micronutrients such as calcium and vitamin D to prevent childhood OB and associated health risks . Kuwait does not have national nutrition standards and does not u se WHO/FAO nutrition recommendations for children beyond the age of five years . [1] Utilizing US and WHO/FAO understanding of the dietary status of Kuwaiti schoolchildr en to improve their dietary behaviors and prevent OB. Zaghloul et al 2012, involved data collected during 2008 2009 using the USDA 5 - steps 24 - hour recall to describe d the dietary intake of 205 boys (n = 111) and girls (n = 94) aged 9 13 years old. using USDA 5 - steps 24 - hour recall . The results indicated 63.3% of the girls and 43.5% of the boys exceeded their calorie intake . The study also indicated that few er boys tha n girls exceeded micronutrient recommendations including vitamin D (0.7% and 0%), calcium (16.4% and 6.7%), and magnesium (48% and 31%), but not sodium (72% and 61%) . [107] In summary, nutrient density. Similar studies from other countries have reported similar results. Patterson et al. (2010) reported that low intake of fruit, vegetables, fiber and micro - nutrients among 551 82 Swedish schoolchildren (9.6 years, 52% girls) was related to their energy - dense diet . [242] Another study on Irish children ( n = 594) and teenagers ( n = 441) showed that they consumed a high - energy diet which was associated ( P < .001) with lower micronutrient and fiber intake . [243] Also, among Canadian fifth graders (N = 4,966; 51.5% girls), Veugelers et al. (2005) reported the mean daily kcal intake for boys (2256 kcal [>1800 kcal]) and girl s (2077 [>1600 kcal]); their mean daily calcium and fiber intake was lower than recommended levels . [244] Several indices are used to in evaluate th e dietary intake of children including the healthy eating index (HEI). The HEI is a scoring (0 to 100 points) system for monitoring dietary compliance with DGA guidelines [190] ; it has been shown to be a reliable and valid tool . [189] An HEI score at or above 80 points is considered good, while a score of 50 points or lower is considered below the standards . [191] Studies involving children that have used the HEI indicate that the majority are not meeting most recommendations , given the poor HEI score s. For example, NHANES 2011 2012 (N = 2,857; 2 17 years) reported a total HEI - 2010 score of 55 points, which was considered poor in relation to not meeting the recommendations, including high sodium intake . [245] A lso , within same period of time, a study in Michigan USA reported a total HEI - 2010 score of 62 points among fifth graders (N = 210) . [192] Veugelers et al. (2005) reported a mean HEI - 2005 of 62 points among Canadian fifth graders (N = 4,966; 51.5% girls), indicating low vegetable, fruit, and grain intake . [244] Some studies have related the HEI scores to CVD risk factors . Camhi et al. (2015) showed that the HEI score (high fr uit, vegetable, dairy, less solid fats) was related to fewer cardiometabolic risks in children . [16] Another index that has been reported in the literature that i s considered a marker of nutrient density and plant based food intake is gs of dietary fiber intake per 1000 kcal) and has been referred to as fiber index (FI). [199] Several studies have shown that high intake of dietary fiber is inversely related to CVD risk 83 factors . [196 197 246] Carlson. et al. (2011) determined that dietary fiber intake as measured by dietary FI was inversely associated ( P < 0 .001) with the risk of metabolic syndrome (MetS) among adolescents, but found no relationship with the cholesterol index (mg/cholesterol/1000 kcal ( P = 0 .22) or saturated fat index (gs saturated fat/10000 Kcal ) . [199] Ventura et al (2008) examined relationshi p between diet and MetS in 109 overweight Latino children in age 10 to 17 years, and found a lower mean dietary fiber (7.5 ± 2.8 gs/ 1000 kcal) in children (n=24) with MetS, while children without MetS (n=85) had a higher mean fiber intake (8.4 ± 3.1/ 100 0 kcals) [200] determine if they are meeting the DGA and DRI recommendations, for food group and nutrient levels recommended to prom ote health and prevent disease. The FI provides a quick measure nutrient density based on the amount the of dietary fiber /1000 Kcals. In Kuwaiti, there is limited nutrition data pertaining to children and no study has reported int akes in reference to pediatric guidelines for food groups, fats, and added sugars nor have there been studies which reported on the HEI or FI. Also, there is little data to determine if there are significant differences in nutrition intakes by gender . Ther efore, the primary objective s of this study w ere to determine the proportion of Kuwaiti fifth grade children that are meeting nutrition recommendations for food groups, macronutrients , and selected micronutrients according to the DGA and DRI , WHO/ FAO reco mmendations , and two dietary indices (HEI and FI). The secondary objective was to determine if there are differences by gender in meeting the nutrition recommendations , and dietary indices scores and levels . We hypothesized that boys will be less likely to meet the nutrition recommendations and have poorer dietary quality than girls. 84 Methods Study D esign and P articipants A cross - sectional evaluation was conducted with 367 fifth graders ( 10.4 ± 0.4 years of age; 53% girls) in Kuwait. For student recruitmen t, informed parent/guardian consent and child assent forms were distributed to 39 primary schools (19 boys schools, 20 girls schools) within 6 Kuwaiti cities . Data was collected from 16 schools which were supportive of participating during the timeline ava ilable in Spring of 2019. The reasons for schools who did not participate included lack of interest or support from school administrators, and or the inability to participate during the limited timeline. Of the 493 consent forms (boys [258] and girls [235] ) that were collected, 35 parents chose not to have their child participate in the study, and 65 boys and 42 girls were absent on measurement day, resulting in a sample of 367 for this study . Of the 367 study participants who completed th e food frequency q uestionnaire ( FFQ ) , 54 participants (26 boys and 28 girls) FFQs were deemed invalid leaving a total sample of 3 13 participants for the analysis . T he study protocol was approved by the Michigan State University (MSU) Institutional Review Board (IRB) as well by the Ministry of Health Research Ethic Committee, and by the Department of Educational Research in Kuwait. Measurements Self - report surveys were used to obtain participants d ietary intake, and co - variates includ ing m oderate - to - vigorous physical activity (MV PA), and screen time (ST). The surveys were administered by nursing trainers as well as nursing and nutrition students from the Colleges of Nursing and Health Sciences of the Public Authority for Applied Educat ion and Training (PAAET) in Kuwait . The measurement team was trained as per the protocol procedures used by . [283] This included procedures to 85 ensure consistent instructions and guidance for participan ts to accurately complete the self - report FFQ (which used instructions from manufacturer NutritionQuest) and the MVPA and ST survey questions . [210] The following sections will provid e details on the modified and translated Block Kids the procedures used to administer , validate, and process the data prior to analysis. Additiona lly a summary of all variables , includin g co - variates (MVPA and ST) , used in the analysis is provided. Nutritional B ehaviors Block Kids 2004 Food Frequency Questionnaire Originally , t he Block Kids 2004 Food Frequency Questionnaire (FFQ) (Block Dietary D ata Systems, Berkeley, CA ) [247] was an 8 - page FFQ ask ing about the frequency and quantity of 78 foods eate n during the past week which takes approximately 20 to 30 minutes to complete. The results are quantified as daily intake in grams (or milliliters for liquids ) and summarized into daily intake. The FFQ reported equal and high correlation for milk intake ( r = 0.571), 100% juice intake ( r = 0 .550), diary for calcium ( r = 0.515), and vitamin D ( r = 0.512) when compared with the Iowa Fluoride Study targeted nutrient semi - quantitative questionnaire for relative validities . [248] Moreover, it showed reliability intraclass reliability (>.30), when compared with 24 - hour dietary recall, except percen t energy from protein and fruit and vegetable servings . [249] Modified Arabic/English version of Block Kids 2004 FFQ The Block Kids 2004 FFQ was translated by the researcher and staff at NutritionQuest Company during 2016 2017. The process of modify ing the FFQ involved incorporating cultural 86 Continuing Survey of Food Intakes by Individuals (CSFII) 1989 - 91 and the CSFII 1994 - 96, [250] and analysis configuration to account for changes in food questions (added and or omitted). The modified FFQ consisted of eight pages (72 foods), assessing the f requency and quantity of food consumption from food groups and nutrients during the past week. A descriptive list of modifications to the original Block Kids 2004 FFQ food items is illustrated in APPENDIX F. Decisions r egarding selecting cultural foods fro m Kuwait for the modified FFQ were objectively proposed based on data describing Kuwaiti traditional dishes and food contents . [33 177 178] Also, data on consumption of non - traditional foods in Kuwait [251] and the dietary pattern of the Arab Gulf region population living abroad were considered . [252] In the modified FFQ, foods that are restricted for religious reasons were excluded: pork (pork chops, ribs, or cooked ham, slice ham, hamburger, and bacon). Moreover, questions related to foods that were deemed rarely or never consumed by children in Ku wait were deleted : tacos, burritos, and enchiladas. For example, which kind of tacos, burritos, enchiladas do you usually eat? With meat or chicken/Without me at or chicken . Additionally, questions on foods such as h ot dogs and corn dogs, lunch meat like boloney, sloppy Joes, chicken helper, tomato soup, pop tarts, pie, fruit pie, fruit crisp, cobbler, and fruit roll - ups were also deleted . Pinto beans, black beans, chili with beans, or bean burritos were excluded from the beans selections in the FFQ (green beans, string beans or peas, chickpeas [added], and refried beans). eef/chicken), fatayer (pastries), pita bread, sambosa, and lentil soup, in addition to vegetables such as eggplant, zucchini, and okra. Other cultural foods mentioned in the FFQ are rice, white/wheat/bread, hummus, and refried beans. 87 Moreover, some food q uestions were altered like chicken noodle, Cup - a - incl - d beverages, some non - - - - C, Tang, Tampico, Mr. Juicy, Ssips epsi, or 7 - Administering the M odified and T ranslated version of the Block Kids 2004 FFQ The nutrition behaviors of our study sample were assessed by using the translated (Arabic/English) and modified version of the Block Kids 2004 FFQ described earlier. The modified instrument was administered by trained nursing trainers as well as nursing an d nutrition students from the colleges of Nursing and Health Sciences (PAAET) in Kuwait. The completed FFQs were analyzed by NutritionQuest and were validated for outliers, which will be described in the next section s . Nutrition V ariables Data for analysi s were derived from variables including food groups and selected macronutrients and micronutrients . These variables were used to evaluate our study objectives and nutrition recommendations according to the following nutrition guidelines: The US Dietary Gui delines (DGA) and Dietary Reference Intake (DRI) [66] for children (9 to 13 years), and WHO/ FAO ranges of population dietary intake goals [22] and recommended nutrient intake (RNI) . [64] 88 Food G roups Fruit portions (1.5 cups DGA; [>400 g ~1.7 cups WHO]), vegetable portions (males: 2.5 cups; females: 2 cups DGA; [>400 g ~1.7 cups WHO]), dairy portions (2 cups DGA), whole grain (3 oz equivalents DGA ~1 oz WHO). Macronutrients Total calories (girls 1600 2000 kcal; boys1800 2200 kcal), fat (AMDR 25 35%; WHO 15 30% of total kcal /day ), saturated fat (<10% of total kcal/day), trans - fatty acids (less than 1% of tot al kcal/day), linoleic acid (PUFA n - 6, 10 12 g DRIs, [5 8% WHO of total kcal/day]), linolenic acid (PUFA n - 3, 1 1.2 g DRIs, [1 2% WHO of total kcal/day]), and cholesterol (<300 mg). Protein (AMDR 10 30%, [10 15% WHO] of total kcal/day). Carbohydrates (AMDR 45 65%, [55 75% WHO] of total kcal/day) and added sugar (less than 10% of total kcal/day); dietary fiber (boys: 25.2 g; girls: 24.4 g DGA). Micronutrients Sodium ( less than 2 2 00 mg [UL]; less than 2000 mg [RNI] ), potassium (4500 mg [AI/RNI] ), calcium (1300 mg [RDA/RNI] ), and magnesium (240 mg [AI]; 230 mg [RNI] ) and vitamin D (600 IU [RDA], 200 IU [RNI]) . [253] Dietary I ndices Health y Eating Index 2010 A scoring system (0 to 100) based on the USDA Food Patterns and the editions of the DGA was developed to measure the diet quality of the US population. The index examined the overall diet components from food groups and single nutrients in adherence to DGA for chronic disease prevention in adults. The HEI 2010 has 12 components including 9 adequacy and 3 89 moderation components [190] . The components for adequacy are total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy and total protein, s eafood and plant proteins, and fatty acids (PUFAs + MUFAs /SFAs). The components for moderation are refined grains, sodium, and empty calories (component calories from solid fats, alcoholic beverages, and added sugars) . [190] The HEI 2010 uses a density approach to set standards (p er 1,000 calories or as a percentage of calories) and employs least restrictive standards (vary by energy level, sex, and/or age) for determining the maximum score. A high score in the adequacy components (participants maximum points) indicates better qual ity, while in moderation components, a minimum score higher than zero indicates low diet quality maximum are allocated when intakes are at the level of the standard or higher for adequacy components, and when intakes are at the level of the st andard or lower for moderation components. Mean daily consumption frequency from HEI components were scored (0 to 10 points each) and summed for an overall score (range from 0 to 100) . [192 193 254] [255] . A recent update by the National Cancer Center (NIH) on HEI 2010 to become HEI 2015 (13 components) (https://epi.grants.cancer.gov/hei/comparing.html). The update involved separate components. We generated and reported both HEI 2010 and 2015. Calculating t otal Healthy Eating Index ( HEI - 2010 and 2015 ) Based on the FFQ output, the t otal HEI - 2010 scores were generated in SPSS in the following steps. (1) selecting nutrient components: Fatty acid components (Total MUFA + PUFA/Total saturated fat); total protein foods (dry beans + soy beans + just eggs + total meat [red meet/poultry/orga n meet/lunch meat] + nut/seeds + seafood), seafood (fish high in omega - 3 90 + fish low in omega - 3), and plant proteins (dry beans + soy foods + nuts/seeds); total fruit, whole fruit (total fruit [fruit from juice + 100% juice]); total vegetables, greens, an d beans (Leafy vegetables + beans and peas); whole grain; total dairy; refined grains (non - whole grain); empty calories (added sugar g*4 + solid fat g*9 [total saturated + total trans - fat]); sodium. (2) Each value was multiplied by 1000/total calories to b e converted into per 1000 calorie. (3) Each HEI - 2010 component (variables) was ranked based on cut - to 1.1 cup per 10 acids [190] . (4) All the ranked comp onents were summed up to generate the total HEI - 2010 score for the study participants. Calculating HEI 2015 required individually to be incorporated into the moderation com ponents. Additional methods and instructions on calculating the HEI 2010 and 2015 are available at the National Cancer Institute website . [256] Fiber Index Dietary fiber intake is a surrogate marker for nutrient density based on assessing the amount of fiber in grams per 1000 kcal. The intake of dietary fiber - containing foods (e.g. fruits, vegetables, whole grains, legumes, beans, nuts, and se eds) is directly related to dietary fiber level as well as nutrients and phytochemicals associated with cardiovascular health . [196 - 198] For the 91 current study the total daily gs of dietary fiber intake and total kcals of each participants diet was derived from the FFQ. The FI was calculated by coverting the am ount of total dietary fiber gs and total Kcals to fiber intake in gs /1000 kcal. Covariate A ssessment Physical A ctivity (PA) A self - reported question taken from the Youth Risk Behavior Survey (YRBSS) [209] was administered in both Arabic and English (Appendix B). The question was designed to assess the number of days over the preceding week participants engaged in > 60 minutes of moderate - to - vigorous physical activity (MVPA) . [209] The question states: days were you physically active for a total of at least 60 minutes per day (add up all of the time you spend in any type of activity that incr eases your heart rate and makes you breath hard some of the time)? The scale range measured zero to seven days . [211] Screen T ime (ST) Screen time was assessed using the ST questions from the YRBSS [209] and was administered in both Arabic and English (see Appendix B). Participants of the study self - reported the weekly amount of time spent viewing television, playing video games, and using an online comput er, and indicated the number of hours (watch/play) on weekdays and weekends for each of the three screen media types . The average hours of screen time per week was determined using the following formula: (hours of TV time on weekdays * 5 days) + (hours of TV time on weekends * 2 days )/ 7 days + (hours of video game time on weekdays * 5 days) + (hours of video game time on weekends * 2 days )/ 7 days + (hours of computer time on weekdays * 5 days) + (hours of computer time on weekends * 2 days )/ 7 days [212] . Screen time equal to or above two 92 hours per day will be considered high ST. Less than two hours a day will be considered low ST . [213] Statistical A nalysis Identifying O utliers : Verifying and R e - testing the M odified T ranslated version of the Block Kids 2004 FFQ Two verification procedures were carried out to ensure the FFQ data were valid. The participants that reported implausibly low/high total calorie intake ( kcal ) were excluded by two methods. The Exploratory Data Analysis (Tukey 1977) statistical interval fo r labeling extreme kcal were calculated using the interquartile rule formula for determining lower (Q3 - [Q3 [75 th percentile] [Q1 [25 th percentile]* k [1.5 multiplying factor] and upper (Q3 + [Q3 - Q1]* k ) qu artiles. [257 258] Additionally, we contrasted Schofield - HW ([TEE *1.7 moderate activity factor [AF]) [259] , as well the Joint FAO/WHO/UNU Expert Consultation TEE (impeded moderate AF 1.7) . [260] Rodriguez et al. (2000) indicated that Schofield - HW and FAO/WHO/UNU equations for estimating resting energy expenditure (REE) in children and adolescents, OB and non - OB , produced kcal mean differences of - 33.1 to - 35.3 and - 0.62, respectively, when contrasted with the V max calorimeter . [259] The TEE ratio interval (at or below 0.16 to at or above 2 folds) was used to detect over/under reported kcal outliers. The modified FFQ was retested ; it indicated overall moderate absolute agreement (intraclass correlation r = . 674) in crude data, as well as in data without kcal outliers ( intraclass correlation r = .545 ). 93 - test to test for differences between two independent means; it indicted that a minimum of 128 participants were required . Also, Z tests to test for differences between two independent groups indicated that a minimum of 300 participants required (G*Power software). Demographic characteristics and absolute nutrient intakes, HEI, and FI were compared using t - test and Chi - square test. The general linear model controlling for total calories was used to compare the mean nutrient intake between boys and girls. Logistic regression was used to compare the percentages meeting nutrient recommendations, controlling for MVPA and ST. Data analysis was performed using SPSS version 24 (SPSS Inc., Chicago, IL). Results are presented as mean ± SD or SE at a significance level of P The reliability test, wi th intraclass c orrelation c oefficient with t wo - way mixed effects model ( absolute agreement ), was performed to test for consistency between baseline versus re - tested FFQ s estimations. Results The self - administered FFQ data were collected from a total of 174 male and 193 female fifth graders in Kuwait between February and March 2018 . A statistical i nterquartile rule, as well Schofield - HW and FAO/WHO/UNU Kcal predictive equations w ere used for flagging reported total kcal not meeting the cutoff . Of the 367 study participants who completed an FFQ, 54 participants (26 boys [15%] and 28 girls [30%]) were deemed invalid (APPENDIX E ). The FFQs consisted of 313 participants, 53% girls and 47% boys (Table 4). The translated modified FFQ was retested on 26 boys (4 kcal outliers) and 32 girls (3 kcal outliers ) and indicated overall absolute agreement ( intraclass correlation r = .674 ) in crude data (APPENDIX G ) as well as in data without kcal outliers ( intraclass correlation r = .545 ) il l ustrated in Table 3 . 94 Table 3 . Reliability test of the translated (Arabic/English) and modified (cultural food) Block Kids 2004 FFQ completed by Kuwaiti fifth grade boys ( n =22) and girls ( n =29) 1 N=102 FFQs Average Measures Without Kcal outliers Intraclass Correlation 95% Co nfidence Interval F Test with True Value 0 Lower Bound Upper Bound Value df1 df2 Sig All Nutrition Variables Absolute Agreement . 54 5 . 39 8 . 68 2 6. 9 46 50 1 9 50 .000 Consistency . 85 6 . 79 3 . 90 7 6. 9 46 50 1 9 50 .000 Boys Absolute Agreement .605 .421 .779 8.558 21 861 .000 Consistency .883 .801 .943 8.558 21 861 .000 Girls Absolute Agreement .508 .333 .686 6.050 28 1092 .000 Consistency .835 .735 .910 6.050 28 1092 .000 1 Intraclass correlation coefficient statistics for FFQ baseline test versus retest for boys (1 - day interval) and girls (4 weeks interval). Two - way mixed effects model , where children effects are random and measure effects are fixed. This estimate is computed assuming the interaction effect i s absent, because it is not estimable otherwise. Table 4 summarizes the demographic characteristics of t he remaining boys (n = 148) and girls (n = 165) . 95 Table 4 . Demographic characteristics and anthropometrics , and covariates ( m oderate - to - vigorous physical activity [ MVPA ] and screen time [ ST ] ) of Kuwait fifth grade boys versus girls 1 Participants Overall ( N =313) Boys ( n =148) Girls ( n =165) P City Capital 11.8% 9.5% 13.9% 0 .22 Hawalli 38.7% 37.8% 39.4% 0 .77 Farwaniya 5.8% 6.8% 4.8% 0 .47 Mubarak Al - Kabir 22.7% 25.7% 20.0% 0 .23 Ahmadi 10.5% 3.4% 17.0% 0 .001 Jahra 10.5% 16.9% 4.8% 0 .001 Age (years) 10.45 ± 0.38 10.44 ± 0.35 10.46 ± 0.40 0 .572 Height (cm) 142.06 ± 6.62 141.3± 7.07 142.4 ± 6.12 0 .104 Weight (kg) 44.48 ± 13.11 43.93 ± 14.22 44.98 ± 12.05 0 .479 BMI (kg/m 2 ) 21.74 ± 4.99 21.59 ± 5.32 21.87 ± 4.68 0 .624 Covariates MV PA (d/wk) 2.89 ± 2.34 3.37 ± 2.36 2.47 ± 2.24 0 .001 % met MVPA 19.3% 24% 15.2% 0 .054 S T ( h /day) 4.72 ± 2.68 4.97 ± 2.56 4.50 ± 2.77 0 .119 % met ST 17.4% 9.6% 24.2% 0 .001 1 Descriptive statistics presented as mean (SD) or S.E (BMI Z - Score) , P 2 WHO 2007 (World Health Organization) [49] . BMI (Body mass Index), BF% (Percentage body fat), WC (Waist circumference), WHtR (Waist - f or - height ratio [WC/height ]), MVPA ( Moderate - to - vigorous physical activity [ number of days over past week (7 days) meeting > of 60 min]); ST ( Screen time ( h /day). Table 5 summarizes mean nutritional i n takes and FI, in addition to the comparison by gender . The overall mean calorie intake was approximately 2,460 kcal and was statistically ( P < 0.001) higher for boys than girls. Also, the overall mean intake of vegetables, dairy, whole grains, calcium, potassium, and vitamin D, except hig h sodium, were lower than recommended for children 9 to 13 years old . [66] W e hypothesized that boys would have poorer mean dietary intake than girls . T he absolute (crude) mean intakes of macronutrients as well micronutrients except vitamin D and calcium, were significan tly higher in boys than girls. However, after controlling for total kcal , most of boys and girls mean dietary intakes did not statistically differ, except for higher mean intakes of protein, cholesterol, calcium, vegetables, and dairy among boys. The overall mean FI was 8.9 grams, and less than 2% of our study sample consumed the 96 DGA recommendation of 14g per 1000 kcal for children 9 to 13 years old . [66] Also, there were no statistical difference s between boys ` and girls ` FI before or after controlling for total calories. Table 6 illustrates the proportion of Kuwaiti fifth graders meeting DGA and DRIs and WHO/FAO (Range/RNI) recommendations and differences between boys and girls, based on Table 5 . Mean food group, macronutrient, and micronutrient intakes , and fiber index (FI) of fifth grade Kuwaiti boy s versus girls 1 Nutrition Variables Overall ( N =313) Boys ( n =148) Girls ( n =165) P P* Food groups Fruits (cup) 2.36 ± .080 2.56 ± .12 2.19 ± .10 0 .020 0 .487 Vegetables (cup) 1.81 ± .05 1.81 ± .08 1.80 ± .07 0 .963 0 .011 Dairy (cup) 1.67 ± .04 1.70 ± .06 1.65 ± .06 0 .311 0 .027 Whole grains (ounces) 0.63 ± .02 0.68 ± .03 0.59 ± .02 0 .047 0 .785 Macronutrients (g) Total Calories 2460.29 ± 52.2 2652.59 ± 76.9 2287.38 ± 68.5 0 .001 Total Fat 92.08 ± 2.25 99.82 ± 3.34 85.14 ± 2.96 0 .001 0 .892 Saturated fat 29.21 ± .66 31.3 ± .95 27.3 ± .91 0 .002 0 .306 Trans - fat 8.15 ± .23 8.82 ± .33 7.55 ± .33 0 .008 0 .653 n - 6 FAs 2 19.12 ± .62 21 ± .97 17.4 ± .76 0 .004 0 .829 n - 3 FAs 1.67 ± .041 1.82 ± .062 1.53 ± .053 0 .001 0 .377 Cholesterol (mg) 305.1 ± 8.22 338.3 ± 12.07 275.3 ± 10.73 0 .001 0 .029 Carbohydrates 335.7 ± 7.10 359.1 ± 10.64 314.6 ± 9.25 0 .002 0 .414 Total Sugar 162.6 ± 4.04 175.6 ± 6.38 150.9 ± 4.94 0 .002 0 .790 Added Sugar (tsp) 3 22.1 ± .68 24 ± 1.09 20.5 ± .84 0 .012 0 .373 Total Fiber 22.08 ± .55 23.6 ± .82 20.7 ± .72 0 .010 0 .449 Fiber Index 4 8.9 ± .11 8.8 ± 2.00 8.9 ± 2.00 0 .655 0 .418 Protein 83.2 ± 1.85 91.2 ± 2.74 76.2 ± 2.38 0 .001 0 .028 Micronutrients Calcium (mg) 964.04 ± 22.08 1008.8 ± 30.34 923.8 ± 31.58 0 .054 0 .031 Magnesium (mg) 332.8 ± 8,02 357.9 ± 11.97 310.1 ± 10.51 0 .003 0 .527 Potassium (mg) 3010.8 ± 66.7 3201.3 ± 108.41 2840.0 ± 97.15 0 .007 0 .369 Sodium (mg) 3664.2 ± 79.5 3920.3 ± 113.6 3434.5 ± 108.4 0 .002 0 .695 Vitamin D (IU) 5 140.7 ± 4.29 145.8 ± 6.28 136.2 ± 5.87 0 .267 0 .556 1 General linear model, ( p * controlled for total calories), data presented as mean ± S.E, P between sexes. 2 n - 6 FAs (Omega 6, Linoleic acid), n - 3 FAs (Omega 3, linolenic acid). 3 One teaspoon equivalent = 4.2 grams of sugar . [261] 4 Fiber Index (grams fiber/1000 kcal). 5 IU (International Unit), 1 IU of vitamin D = 0.025 µg. 97 binary logistic regression controlled for MVPA and ST. The overall proportion of child ren that met the recommended calorie intake was 17%. Few children met the DGA recommendations for vegetable (28.8%), dairy (33%), while none did for whole grain compared with relatively higher proportion (less than 50%) who met the WHO/FAO recommendations. Around 42% of the children met total fiber intake, while less than 2% met the recommended 14 g per 1000 kcal level . [66] Most children met the recommendations, particularly the AMDR for protein (97%) and carbohydrate (91%), while more than 80% exceeded added sugar intake. More children met the AMDR versus WHO/ FAO ranges for total fat (~66% vs. 24%) and % n - 3 (47% vs. 0.3%). Few children met the saturated fat (31%) while none met the trans - fat recommendations. Less than 40% met the micronutrient recommendations. None of the children met the recommended daily all owance (RDA) of 600 IU for vitamin D, whereas around 19% met the recommended 200 IU of vitamin D by WHO/FAO. We controlled for MVPA and ST using binary logistic regression to determine differences in the proportion of boys versus girls meeting the nutrit ional recommendations . There were more boys met the 1.5 cup ( P = 0 .037) fruit recommendation than girls, while more girls met the 2 2.5 cup ( P = 0 .003) vegetable recommendation. However, no other statistical gender differences were found with regard to mee ting dairy or whole grains recommendations by either nutritional standards. T he results indicated no statistical differences in the proportion of boys versus girls meeting total calorie recommendations. Also, the proportion of boys versus girls meeting th e added sugar, saturated fat, or trans - fat recommendations did not statistically differ . T here were a couple of trends for more girls meeting the WHO/FAO recommendations for % n - 6 ( P = 0 .053) and carbohydrates ( P = 0 .080) as compared to boys . 98 Contrary to our hypothesis that boys would be less likely to meet selected micronutrient recommendations than girls , we found that the proportion of boys and girls meeting calcium, magnesium, potassium, and vitamin D was not statistically differ ent . However, m ore girls met WHO/FAO sodium recommendations (<2 g), in addition to a trend ( P = 0 .074) for meeting the DRI (<2.2g) recommendations . 99 Table 6 . Proportion of Kuwaiti schoolchildren meeting the Dietary Guidelines & Dietary Reference Intakes (DRIs) a nd WHO/FAO (Range/RNI) Recommendations 1 , compared by gender Dietary Recommend ations for Children according to DGA 2 and FAO/WHO 3 Crude Adjusted for MVPA and ST Overall ( N = 313) Boys ( n = 148) Girls ( n = 165) OR (95% CI) P OR (95% CI) P Fruit 1.5 cups DGA 68.7% 75.7% 62.4% 2.008 (1.216 3.314) 0 .006 1.735 (1.034 2.911) 0 .037 400g (1.7 cup) WHO 62.0% 68.9% 55.8% 1.847 (1.152 2.962) 0 .011 1.612 (.990 2.625) 0 .055 Vegetables (cup) 2 - 2.5 cup DGA 28.8 % 20.3 % 36.4 % 2.248 (1.348 3.747) 0 .002 2.210 (1.302 3.750) 0 . 003 400g (1.7 cup) WHO 46.3% 45.9% 47.3% .931 (.593 1.460) 0 .755 .871 (.548 1.384) 0 .559 Dairy (cup) 2 cups/day 32.9% 32.4% 33.3% 1.012 (.629 1.628) 0 .961 .926 (.568 1.511) 0 .759 Whole grains (ounce) 3oz DGA 0% 0% 0% 1oz WHO 16.9% 20.3% 13.9% 1.506 (.823 2.755) 0 .184 1.360 (.729 2.536) 0 .334 Total Calories ( Kcal ) 1600 - 2000 girls , 1800 - 2200 boys 16.9% 18.2% 15.8% .802 (.440 1.465) 0 .473 .801 (.432 1.485) 0 .482 Total fat 25% - 35% AMDR 65.8% 67% 65% 1.142 (.710 1.837) 0 .584 1.055 (.645 1.726) 0 .830 15% - 30% WHO 24.0% 20.3% 27.3% .700 (.410 1.195) 0 .191 .591 (.336 1.037) 0 .067 Saturated fat <10% 31.0% 27.7% 33.9% .783 (.482 1.272) 0 .323 .719 (.435 1.189) 0 .199 Trans - fat <1% 0% 0% 0% n - 6 FAs 4 5% - 10% AMDR 73.5% 68.2% 78.2% 1.823 (1.093 - 3.041) 0 .021 1.679 (.994 - 2.835) 0 .053 5% - 8% WHO 64.2% 59.5% 68.5% 1.575 (.985 - 2.519) 0 .058 1.484 (.917 - 2.401) 0 .108 n - 3 FAs 4 0.6 - 1.2% AMDR 47.3% 50.7% 44.2% .758 (.483 1.190) 0 .229 .736 (.463 1.169) 0 .194 1 - 2% WHO 0.3% 0.7% 0% .000 (.00 000) 0 .996 .000 (.000 .000) 0 .995 Cholesterol <300 mg 55.0% 44.1% 64.8% .424 (.267 .672) 0 .001 .450 (.281 .722) 0 .001 100 Table 6 ( c Dietary Recommend ations for Children according to DGA 2 and WHO/ FAO/ 3 Crude Adjusted for MVPA and ST Overall (N = 313) Boys ( n = 148) Girls ( n = 165) OR (95% CI) P OR (95% CI) P Protein 10% - 30% AMDR 5 97.1% 96.6% 97.6% .685 (.180 2.602) 0 .578 .604 (.150 2.438) 0 .479 10% - 15% WHO 74.4% 70.9% 77.6% .694 (.415 1.160) 0 .163 .642 (.377 1.094) 0 .104 Carbohydrates 45% - 65% AMDR 91.1% 90.5% 91.5% .853 (.392 1.857) 0 .689 .800 (360 1.778) 0 .584 55% - 75% WHO 50.5% 45.5% 55.2% .714 (.455 1.121) 0 .143 .659 (414 1.051) 0 .080 Added sugar <10% 6 16.9% 18.9% 15.2% 1.307 (.713 2.399) 0 .387 1.231 (.660 2.294) 0 .513 Fiber 22.4g girls - 25.2g boys 41.9% 40.5% 43% . 927 (.588 1.463) 0 .745 .822 (.512 1.321) 0 .418 14g per 1000 kcal 7 1.3% 1.4% 1.2% .896 (.125 6.440) 0 .913 1.301 (.166 10.220) 0 .802 Calcium ( 1300 mg ) 19.8% 22% 17.6% 1.409 (.806 2.465) 0 .229 1.317 (.742 2.337) 0 .347 Magnesium ( 350 mg ) 41.5% 43.2% 40% 1.184 (.751 1.866) 0 .467 1.102 (.690 1.760) 0 .683 Potassium ( 4500mg ) 12.8% 15% 10.3% 1.671 (.854 3.272) 0 .134 1.608 (.808 3.202) 0 .176 Sodium (mg) <2200 UL 8 17.3% 12.2% 21.8% .484 (.258 .905) 0 .023 .556 (.292 1.057) 0 .074 <2000 WHO 13.4% 8.1% 18.2% .375 (.180 .780) 0 .009 .418 (.198 .882) 0 .022 Vitamin D (IU) 9 600 AI 10 0% 0% 0% 0 (.000 00) - 0 (.000 00) - 200 WHO 18.8% 19.6% 18.2% 1.146 (.649 2.024) 0 .638 .987 (.547 1.780) 0 .966 1 Chi - square test for proportion differences between boys and girls ( ref = reference) . Logistic regression adjusted for MVPA and ST , P between sexes. 2 Dietary Guidelines for Americans (DGA 2015 - 2020) - Eight Edition and DRI (Dietary Reference Intake) [66] . 3 The Joint WHO/FAO Expert Consultation on diet, nutrition, and the prevention of chronic diseases. Public Health Nutrition. 20 04 [63] . FAO/WHO expert consultation on human vitamin and mineral requirements. FAO 2001 [64] . 4 n - 6 FAs (Omega 6, Linoleic acid), n - 3 FAs (Omega 3, Linolenic acid). 5 AMDR (Acceptable macronutrients distribution range). 6 One teaspoon equivalent = 4.2 grams of sugar [261] . 7 Fiber recommendations for children ( 9 to 13 years old) [66] . 8 UL (upper limit). 8 UL (Upper limits), 9 IU (Inte rnational Unit, 1 IU of Vitamin D = 0.025 µg.), 10 AI (Adequate Intake). 101 Table 7 summarizes the results of determining dietary indices based on the H EI - 2010 total score (0 to 100 points) of the study sample, and differences between total and components scores . The overall mean total HEI - 2010 score s was 58 points , meaning the cutoff point of 80 points or above was not met . [255] Approximately , 14% of the sample scored 50 points or lower , which is considered poor . We anticipated that boys would have lower total HEI - 2010 score than girls , though there were no significant statistical differences between boys and girls. Also, there were no significant differences in the proportion of boys (12.2%) vs. gi rls (15.8%) who scored poorly ( 50 points or below). With respect to adequacy compo nents, girls had significantly higher scores for significantly higher for whole grains and protein components than girls. For moderation t score was statistically higher than that of girls. The results of the analysis using HEI - 2015 are summarized in Table 8. The overall mean HEI - 2015 score of 52 points was lower than the standard . [191] No child had total scores that were classified as good (80 points or above), but many children (36.4%) had poor total scores ( 50 or below ) . No statistical differences were found in the total HEI - 2015 score between boys and girls. There was a trend ( P boys (31.1%). With respect to adequacy components, girls scored significantly higher points in total vegetables, dairy and fatty acid intake than boys . Boys maintai ned significantly higher scores in whole grains and protein intake than girls . With moderation components, the sodium component score was significantly higher for boys than girls . 102 Table 7 . Healthy Eating Index - 2010 (HEI - 2010) 12 - Components (0 - 100 points) of Kuwa it 5th grade boys (n = 148) vs . girls (n = 165) 1 Component Maximu m points Standard for maximum score Standard for minimum score of zero Participants Standard Scores per 1000 kcal Maximum Points Adequacy: Boys Girls p Boys Girls p Total Fruit 2 5 1,000 kcal No fruit 0.99 ± .045 0.98 ± .043 .911 3.33 ± .07 3.29 ± .07 .655 Whole Fruit 3 5 1,000 kcal No whole fruit 0.54 ± .036 0.53 ± .031 .921 3.09 ± .096 3.12 ± .090 .841 Total Vegetables 4 5 1,000 kcal No vegetables 0.69 ± .028 0.78 ± .026 .016 2.30 ± .069 2.61 ± .061 .001 Greens and Beans 4 5 1,000 kcal No dark leafy vegetables or greens and beans 0.20 ± .016 0.23 ± .016 .161 2.56 ± .161 2.64 ± .153 .717 Whole Grains 10 1,000 kcal No Whole Grains 0.26 ± .011 0.26 ± .011 .823 4.09 ± .170 3.63 ± .151 .042 Dairy 5 10 1,000 kcal No dairy 0.66 ± .020 0.72 ± .024 .036 4.56 ± .123 4.96 ± .136 .034 Total Protein Foods 6 5 1,000 kcal No Protein foods 2.44 ± .070 2.19 ± .067 .011 3.23 ± .058 2.99 ± .065 .006 Seafood and Plant Proteins 7 5 1,000 kcal No Seafood or Plant Proteins 1.49 ± .083 1.33 ± .070 .131 3.58 ± .059 3.49 ± .061 .291 Fatty Acids 8 10 (PUFAs+MUFAs)/SFAs >2.5 (PUFAs+MUFAs)/SFAs 0.81 ± .029 0.96 ± .037 .001 1.26 ± .099 1.73 ± .149 .010 Moderation: Refined Grains 10 1,000 kcal 1,000 kcal 2.80 ± .050 2.83 ± .050 .620 7.27 ± .15 7.15 ± .16 .578 Sodium 10 1.49 ± .018 1.50 ± .018 .683 9.27 ± .202 8.36 ± .271 .009 Empty Calories 9 20 of energy 28.56 ± .46 28.69 ± .44 .845 13.98 ± .723 13.86 ± .254 .734 1 Independent t - test and Chi - square test, data presented as mean ± S.E or SD (Total HEI Score), P 0 .05 for differences between sexes 2 Includes fruit juice. 3 Includes all forms except juice. 4 Includes any beans and peas (called legumes in HEI - 2005) not counted as total protein foods (called meat and beans in HEI - 2005). 5 Includes all milk products, such as fluid milk, yogurt, and cheese, and fortified soy beverages. 6 Beans and peas are i ncluded here (and not with vegetables) when the total protein foods (called meat and beans in HEI - 2005) standard is otherwise not met. 7 Includes seafood, nuts, seeds, soy products (other than beverages) as well as beans and peas counted as total protein fo ods. 8 Ratio of polyunsaturated fatty acids (PUFAs) and monounsaturated fatty acids (MUFAs) to saturated fatty acids (SFAs). 9 Calories from solid fats, alcohol, and added sugars; threshold for counting alcohol is >13 g/1,000 kcal. Guenther P. et al. J Acad Nutr Diet . 2013;113:569 - 580 [190] Total HEI Score 58.54 ± 7.02 57.86 ± 7.81 . 429 0 % 0 % 12.2 % ( 18/147 ) 15.8 % ( 26/165 ) . 374 103 Table 8. Healthy Eating Index - 2015 (HEI - 2015) 13 - Components (0 - 100 points) of Kuwait fifth grade boys (n = 148) vs . girls (n = 165) 1 Component Maximu m points Standard for maximum score Standard for minimum score of zero Participants Standard Scores per 1000 kcal Participants Maximum Points Adequacy: Boys Girls p Boys Girls p Total Fruit 2 5 1,000 kcal No fruit 0.99 ± .045 0.98 ± .043 .911 3.33 ± .07 3.29 ± .07 .655 Whole Fruit 3 5 1,000 kcal No whole fruit 0.54 ± .036 0.53 ± .031 .921 3.09 ± .096 3.12 ± .090 .841 Total Vegetables 4 5 cup equivalents per 1,000 kcal No vegetables 0.69 ± .028 0.78 ± .026 .016 2.30 ± .069 2.61 ± .061 .001 Greens and Beans 4 5 1,000 kcal No dark leafy vegetables or greens and beans 0.20 ± .016 0.23 ± .016 .161 2.56 ± .161 2.64 ± .153 .717 Whole Grains 10 1,000 kcal No Whole Grains 0.26 ± .011 0.26 ± .011 .823 4.09 ± .170 3.63 ± .151 .042 Dairy 5 10 1,000 kcal No dairy 0.66 ± .020 0.72 ± .024 .036 4.56 ± .123 4.96 ± .136 .034 Total Protein Foods 6 5 1,000 kcal No Protein foods 2.44 ± .070 2.19 ± .067 .011 3.23 ± .058 2.99 ± .065 .006 Seafood and Plant Proteins f 7 5 1,000 kcal No Seafood or Plant Proteins 1.49 ± .083 1.33 ± .070 .131 3.58 ± .059 3.49 ± .061 .291 Fatty Acids 8 10 (PUFAs+MUFAs)/SFAs >2.5 (PUFAs+MUFAs)/SFAs 0.81 ± .029 0.96 ± .037 .001 1.26 ± .099 1.73 ± .149 .010 Moderation: Refined Grains 10 1,000 kcal 1,000 kcal 2.80 ± .050 2.83 ± .050 .620 7.27 ± .15 7.15 ± .16 .578 Sodium 10 1.49 ± .018 1.50 ± .018 .683 9.27 ± .202 8.36 ± .271 .009 Added Sugars 10 14.98 ± .47 15.17 ± .43 .768 4.43 ± .197 4.46 ± .189 .901 Saturated Fats 9 10 10.63 ± .104 10.62 ± .113 .954 3.47 ± .111 3.44 ± .120 .850 1 Independent t - test and Chi - square test, data presented as mean ± S.E or SD (Total HEI Score), at P 0 .05 for differences between sexes 2 Includes fruit juice. 3 Includes all forms except juice. 4 Includes any beans and peas (called legumes in HEI - 2005) not counted as total protein foods (called meat and beans in HEI - 2005). 5 Includes all milk products, such as fluid milk, yogurt, and cheese, and fortified soy beverages. 6 Beans and peas are included here (and not with vegetables) when the Total Protein Foods (called meat and beans in HEI - 2005) standard is otherwise not met. 7 Includes seafood, nuts, seeds, soy products (other than beverages) as well as beans and peas counted as Total Protein Foods. 8 Ratio of polyunsaturated fatty acids (PUFAs) and monounsaturated fatty acids (MUFAs) to saturated fatty acids (SFAs). 9 In 2005, the Sodium and Saturated Fats components had three standar ds each, corresponding to scores of 0, 8, and 10 points. https://epi.grants.cancer.gov/hei/comparing.html Total HEI Score 52.50 ± 5.68 51.92 ± 6.17 .385 0% 0% 31.1% (48/148) 41.2% (68/165) .063 104 Discussion Limited studies have reported dietary intake and meeting nutrition recommendation in Kuwaiti schoolchildren or compared by gender. which is known to affect childhood nutrition al status and health [262] and promote obesity and chronic diseases in children [15] . Few children met vegetable , whole grains, dairy, kcal , saturated fat, trans - fat (none), n - 3 FAs, added sugar, calcium, potassium, sodium, and vitamin D recommendations. We expected boys to be less likely in meeting nutrition recommendations and to have poorer total HEI and FI than girls, but we found no gender differences. The unexpected gender d ifference of boys meeting the fruit recommendations can be explained by their higher crude mean fruit intake which was found to be correlated ( r = .245; P < 0 .001) with mean MVPA and found to be associated ( P = 0 .002) with meeting the fruit intake cutoff p oint. The proportion of children meeting kcal levels (20%) was lower in our study sample than in a previous study (2008 2009), which reported around 50% Kuwaiti children aged 9 to 13 years met the kcal recommendation. Moreover, macronutrient levels were me t by a higher proportion in our study sample, while findings related to micronutrient levels were similar with previous findings . [36] Nevertheless, when contrasted with several si milar studies from other countries, our findings showed higher kcal and macronutrient, but lower micronutrient intake, except sodium. Otilia Perichart - Perera et al. (2010) reported that Mexican schoolchildren (9 13 years old, n = 228, 48.2% girls) had low er overall intake of total calories (2,088 kcal/day), cholesterol (270 ± 116 mg/day), but not fiber (19.6 ± 8.1g) . [263] Among Canadian fifth graders ( n = 4 ,966; 51.5% girls), Veugelers et al. (2005) reported lower mean total kcals (2256 kcal [boys] vs . 2077 kcal [girls]), but better mean calcium (1157 mg) and vitamin D (242 IU), except sodium (2626 mg) [244] intake than our findings. More studies reported lower mean nutrition intakes than 105 our estimations. Lauren Au et al. (2012) reported mean kcal and macronutrient intake of racially diverse students from the fourth to eighth grade in Boston: USA (n = 148), White, (n = 57), Hispanic (n = 48), Black (n = 12), Asian (n = 15), and multiracial (n = 16). Accordingly, the study reported mean energy intake (1,277 kcal; 1570 kcal; 1767 kcal; 1315 kcal; and 1775 kcal), mean total fat (50.7g; 46.1g; 55.7g; 47.5g; and 45.8g), saturated fat (17.8g; 16.2g; 17.0g; 15.7g; and 15.4g), and carbohydrate intake (211.2g; 222.2g; 203. 2g; 224g; and 226.3g) [55] which were lower than our estimated intakes. Moreover, in their Korean Child - Adolescent four - year c ohort Study (n = 770, 48% girls, 9.9 ± 0.3 years old), Yang - Im - Hur et al. (2016) reported boys vs. . [174] Moreover, we contrasted our findings on the overall mean nutrition al intakes with the more recent NHANES 2015 - 2016 (6 - 11 years old) findings: What we eat in America . [261 264] Our findings revealed relatively higher intakes of solid fats (37g vs. 35g), added sugar (22 tsp. vs. 16.6 tsp.), and sodium (3.6 g vs. 3 g), and slightly lower intakes of whole grains (0.6 oz vs. 1 oz), dairy (1.6 cup vs. 2 c up, calcium (900 mg vs. 1000 mg) and vitamin D (3.5 µg vs. 5.4 µg) than NHANES. vs. 14g), and potassium (3000 mg vs. 2100 mg) intakes were higher than those fo r US - based children. In summary, the mean nutritional intake of our study sample is considered worse than the average consumption o f children in other countries. stat nutrition intake and behavior . [1] We included the WHO/FAO nutritional recommendations [22 64] and the DGA and DRIs [66] to better understand variations between international and US nutrition that there were considerable 106 variations in meeting the AMDR versus WHO ranges for total fat (66% vs. 24%) and carbohydrates (91% vs. 50%); on the other hand, few children met only WHO/FAO cutoff points for vegetables, whole grains and vitamin D, but not t he DGA. With respect to our findings on dietary indices, we calculated both the HEI 2015 as well as the previous HEI 2010 version. Although HEI 2015 was recently released which no studies available, we contrasted with studies used HEI 2010 . W e estimated a n overall total HEI - 2010 score of 58 points and no participant possessed a good HEI total score of 80 points or above. A study during 2012 involving fifth graders (N = 210) in Michigan, USA reported a better HEI - 2010 total score of 62 points, where 2.5% ha d good HEI scores . [192] On the other hand, among 2 17 - year - olds (N = 2,857), NHANES 2011 - 2012 reported a slightly lower total HEI - 2010 score (55 points) . [245] than the HEI score from the NHANES sample . With regard to recen tly released HEI - 2015, there are no published data by NHANES or other studies available on children . We found that the overall HEI - 2015 score of 52 points was lower than the total score 58 points of HEI - 2010 in our - 2010 and HEI - 2015 scores were similar and considered lower than the standa rds [191] , contrary to our hypothesis. Also, unexpectedly, more children, especially girls, had poorer total scores with HEI - 2015. This can be explained by their excessive satura ted fat and added sugar intake, indicating that few children met the recommendations. With regard to FI, the overall mean level of FI was not statistically different by gender. The mean FI in the current study ( 8.9 ± .11 g/1000 kcals) was similar to what Ventura et al. (2008) reported (8.4 ± 3.1 g s ) in a sample ( n =85) of OW Latino children 10 - 17 years old. [200] 107 The strengths of the study included using DGA, DRI, and WHO/FAO recomme ndations for assessing nutritional intakes in a sufficient sample of fifth grade boys and girls based on power analysis considering the potential influence of kcals, MVPA and ST f actors. In addition, the study also utilized dietary indices such as HEI and FI for evaluating Kuwaiti children dietary pattern . We used a valid [248 249] which was translated (Arabic/English) and modified (cultural food) a nd even re - tested to facilitate accurate responses by Kuwait schoolchildren. Nutrition data collection were performed by a trained research team following research protocols and FFQ instructions by NutritionQuest, also used by the (S)Partners for Heart Hea lth schoolchildren program [210] . We used valid statistical methods [258] and energy expenditure equations [259] for detecting kcal outliers and flagged 26 boys and 28 girls from the original sample (174 boys and 193 girls). However, there were limitations to our st udy, such conducting cross - sectional comparison and not an intervention or a follow - up study. The study consisted of a convenien ce sample and not generalizable to all Kuwaiti fifth graders. Several studies have indicated that it can be difficult to obtain accurate nutrition reporting by children owing to issues with memory or proportion estimation . The type of dietary assessment tool that is used (FFQ or recalls ) [265] can influence results. C hildren using FFQ may over - or underestimate energy intake and some micronutrients related to the length of the questions . [266] S ummary and Conclusion This was the first study to report on the the proportion of Kuwaiti schoolchildren meeting food group and nutrient intake recommendations , and to report on the HEI and FI , compared the results by gender. The overall proportion of chi ldren meeting the recommendations was poor. Less than 50% of the children met food group and less than 40% of children met the 108 micronutrients recommendations. Around 80% of the children exceeded calorie, added sugar, saturated fat, and sodium, while none m et trans - fat recommendations. Also, none of the children some participants met the WHO/FAO but not the DGA recommendations for whole grains and vitamin D. Nevertheles s, for most other nutrients and some food group recommendations, the DGA and DRI were found to be more applicable than WHO/FAO in estimating Kuwait fifth girls met vegetable and sodium intake than boys and more boys met the fruit intake. The current dietary pattern of Kuwaiti children indicates that the majority of children are eating a high caloric low nutrient dense diet and likely contribute to the alarming r ates of OB and other CVD risk factors. The findings of the study suggest the need for intervention programs to improve 109 CHAPTER 5: PREVALENCE OF CARDIOVASULAR DISEASE RISK FACTORS AND MEETING US AND WHO/FAO DIETARY RECOMMENDATIONS IN KUWAITI SCHOOLCHILDREN BY WEIGHT STATUS Abdulaziz kh. Al - Farhan 1,4 , Lorraine J. Weatherspoon 1 , Karin A. Pfeiffer 2 , Wei Li 1 , Nabil K. Badawy 4 , Nayef Y . Bumarium 4 , Joseph J. Carlson 1,3 1 Department of Food Science and Human Nutri tion, 2 Department of Kinesiology, and 3 Department of Radiology , Michigan State University. 4 t he Public Authority for Ap plied Education and Training , College of Nursing and College of Health Sciences, Kuwait Abstract Background: Overall data suggests OB children tend to have more CVD risk s than non - OB children however a portion of OB children have limited risks factors, while some normal weight children have significant CVD risks. C oncern s with Kuwait i children include high rate s of childhood OB and energy dense diets. There is no data on Kuwaiti children and nutrition intakes by weight status . Objective: In a sample of OB, OW, NW and UW Kuwaiti children determine if there are differences between weight categories in: 1) prevalence of CVD risks 2) meeting US and WHO/FAO nutrition recommendations. Method : A cross - sectional evaluation on fifth graders (N=313; age 10.4 ± 0.4) in Kuwait. Assessments included CVD risk factors and a self - administered Food Freq uency Questionnaire (FFQ) . Independent variables: BMI - for - age weight categories. Dependent variables : at risk TC, LDL, HDL, non - HDL, TC:HDL, and TG; systolic blood pressure (SBP ) and diastolic (DBP ); food groups, macronutrients, and micronutrients. Analy ses included ANCOVA and logistic regression controlling for kcals and gender, with P . 110 Results: The prevalence of at risk: TG (OB 59.4% vs. OW 40.7%, NW 29.6%, UW 42.9%; P =0.002) ; TC:HDL (OB 43% vs. OW 22.6%, NW 8.2%, UW 11.1%; P <0.001) ; HDL (OB 52.9% vs. OW 35.1%, NW 22.2%, UW 11.1%; P =0.0 13 ), and BP (OB 37.5% vs. OW 17.1%, NW 16.1%, UW 13.3%; P <0.002). The only significant differences in meeting nutrition recommendation between categories were: 82% of OB met protein versus OW (63%), NW (77%), and UW (53%) ( P =0.004) ; carbohydrate s recommendations were met by UW (100% vs.NW 91.6%, OW 80%, OB 95.5%; P =0.008) and sodium by OW (30% vs. UW 20%, NW 9.5%, OB 16%) ; P =0.024) . Conclusion: These findings warrant CVD risk assessments for Kuwaiti c hildren regardless of weight status, and i ntervention s to improve dietary behaviors to prevent CVD risks indicated that OB children had the highest risk levels overall, however NW versus OB children had a higher risks for several lipid variables. Overall, few met nutrition recommendation, and few differences between weight categories. These findings warrant CVD risk assessments for Kuwaiti children regardless of weight status, and i ntervention s to improve dietary behaviors. . Introduction Cardiovascular disease (CVD) is the leading cause of death worldwide; it is responsible for 17.5 million deaths (31%) annually, [267] and 41% of total deaths in Kuwaiti adults. [29] CVD risk factors are known to track from childhood into adulthood . O verweight ( OW ) and obese ( OB ) children are known to have higher risks for CVD . Nevertheless, some OB individuals are found to be metabolically healthy [268] whereas, non - OB individuals can be at risk for one or more CVD risk factors. [17] S tudies that have investigated CVD risk factors in Kuwaiti children other than weight status and blood pressure (BP) are limited . A pair - matched study from 1995 111 1996 reported familial and environmental factors associated with childhood OB in 460 OB versus normal weight (NW) schoolchildren (from 6 to 13 years old). The prevalence of at - risk schoolchildren in terms of SBP was 8.3% in OB and 0.7% in non - OB , and overall prevalence of at risk BP was 5%. [60] Poor dietary beha viors, characterized by excessive dietary energy with low nutrient density, are known to adversely affect childhood health; including increases in obesity (OB). [97] In the 1980s economic transitions [81] began behaviors and nutritional status from being under nourished as reflected by < 1% prevalence of OB in 1985 [216] , over nourished state with a OB prevalence of 31% in 2012 . [2] The data on Kuwaiti The only recent study available during 2008 2009 described the dietary intake of 205 children aged 9 13 years old using USDA 5 - steps 24 - hour recall. The results indicated 50% of the children exceeded their calorie intake. The study also indicated that around 20% of children were meeting micronutrient recommendations including vitamin D ( ~ 0.7%), calcium ( ~12 %), and magnesium ( ~ 4 0 %), but not sodium ( ~66 %) . [107] In low nutrient density. There is evidence for dietary behaviors and patterns that promote health and prevent disease risks . [18 - 22] These studies ha ve led to the establishment of nutritional guidelines for promoting health and preventing chronic disease, particularly, the US Dietary Guidelines (DGA) including the dietary reference int ake (DRI) for individuals by age and gender . [66] Aligning with US dietary guidelines, t he Joint WHO/ FAO Expert Consultation (2002) provided population based dietary recommendations for preventing OB and chronic diseases . [21 64 65] Kuwait does not have national nutrition standards and does not use WHO/FAO nutrition recommendatio ns 112 for children beyond the age of five years . [1] Utilizing US and WHO/FAO nutritional guidelines die tary status of Kuwaiti schoolchildren to improve their dietary behaviors and prevent OB. In Kuwait, there is limited data regarding the prevalence of CVD risk factors and meeting nutrition recommendations by weight status, among schoolchildren. Data does not exist for the prevalence of dyslipidemia and other CVD risk factors, nor does it exist for the proportion of Kuwaiti children meeting food groups, solid fat s , and added sugar recommendations by weight status. Also, with the exception of using US Dietary Reference Intake (DRI), no published studies have reported on WHO/FAO recommendations or dietary indices, such as HEI and FI in Kuwaiti schoolchildren. There is no data on the prevalence of CVD risk factors and the proportion that are meeting nutrition recommendations in non - OB Kuwaiti children . Therefore, using a sample of Kuwaiti fifth grad ers according to weight categories (underweight [UW], normal weight [NW], OW, and OB), the objectives of the study were to determine the prevalence of CVD risk factors and if there are significant differences between weight categories , Therefore, in a samp le of OB, OW, NW and UW Kuwaiti children determine if there are differences between weight categories in: 1) prevalence of CVD risks and determine if risks are greatest in OB children 2) meeting US and WHO/FAO nutrition recommendations and determine if f ewer OB children meet the recommendations (food groups, macronutrients, and selected micronutrients). 113 Methods Study D esign and Participants A cross - sectional evaluation was conducted on 313 fifth grade children (10.4 ± 0.4 years of age) in Kuwait. For student recruitment, informed parent/guardian consent and child assent forms were distributed to 39 primary schools (19 boys schools, 20 girls schools) within six Kuwaiti cities (Capital 11.8% , Hawalli 38.7 %, Farwania 5.8 % , Mubarak Al - Kab i r 22.7 %, Ahmadi 1 0.5 %, and Jahra 10.5 %) . Data was collected from 16 schools which were supportive of participating during the timeline available in Spring of 2019. The reasons for schools who did not participate included lack of interest or support from school administrato rs, and or the inability to participate during the limited timeline . Of the 493 consents and assents (boys [258] and girls [235]) that were collected, 35 parents chose not to have their child participate in the study, and 65 boys and 42 girls were absent o n measurement day, resulting in a sample of 367 for this study . Of the 367 study participants who completed the food frequency questionnaire (FFQ) , 54 participants (26 boys and 28 girls) FFQs were deemed invalid leaving a total sample of 3 13 participants f or the analysis . T he study protocol was approved by the Michigan State University (MSU) Institutional Review Board (IRB) as well by the Ministry of Health Research Ethic Committee, and by the Department of Educational Research in Kuwait. Details of measurement variables are summarized below. Measurements Data collection occurred in Kuwait between February 2018 and March 2018. It included CVD risk factor assessments (anthropometric and biometric measures) and self - report surveys to obtain participant - to - vigorous physical 114 activity (MVPA) and screen time (ST). Data collection was performed by nursing trainers as well as nursing and nutrition students from the Colleges of Nursing and Health Sciences of the Public Authority for Applied Education and Training (PAAET) in Kuwait. The measurement school program in MI, USA. [21 0] The protocol includes following pediatric CVD risk factor assessment procedures from the American Academy of Pediatrics guidelines. [20] Nutrition data collection was performed according to the dietary survey instructions by the company, NutritionQuest. Anthropometric A ssessments Several anthropome tric measures were used in this study to derive outome measures . Measures included height and weight , which was used to calculate body mass index (BMI), body fat percentage (% BF), waist circumference (WC ), and derived waist - to - height ratio (WHtR). Each of the anthropometric measurements are summarized below . Standing height was measured using a ShorrBoard (Shorr Production, Olney, MD) or wall mounted, or calibrated stadiometer (210 Holtain Limited, Dyfed, UK ), to the nearest 0.1cm , without shoes. Body weight (to the nearest 0.1 kilogram) and BF percentage were measured using a calibrated electronic scale ( Tanita BC - 534 ), which employs foot - to - foot bioelectrical impedance (BIA) ( Tanita Corporation, Tokyo, Ja pan). Height and weight were used to calculate BMI , which was converted to Z - scores based on methods devised by Cole et al. (1992 , Z= ). [219] Waist circumfer ence (WC) was measured to the nearest 0.1 centimeter using a Gulick measuring tape (Gulick Co., Tokyo, Japan). The Gulick tape was positioned in a horizontal plane around the abdomen at a level 1cm above the superior border of the iliac crest . 115 [144 220] Waist - to - height ratio (WHtR) was derived from , according to Ashwell et al. (2005) . [153] Independent V ariables BMI - for - age z - score was classified using the WHO 2007 cut - - 1, NW > - 1 to [54] Dependent V ariables CVD R isk F actors Dyslipidemia Assessment of blood lipids involved the following cutpoints from the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents . [89] At risk cut point values : total cholesterol (TC) at or above 170 mg/dL, LDL at or above 110 mg/dL, HDL at or lower than 45 mg/dL, TG at or above 90 mg/dL, non - HDL at or above 120 mg/dL, and total cholesterol: HDL ratio at or above 3.5. High risk cut point values : total cholesterol (TC) at or above 200 mg/dL, LDL at or above 130 mg/dL, HDL at or lower than 40 mg/dL, TG at or above 1 3 0 mg/dL, non - HDL at or above 145 mg /dL. A blood sample was collected from participants in a non - fasted state by finger prick (40 µL) , using heparinized capil lary tubes. The blood sample was analyzed using CardioCheck Plus (version 1.09; Maria Stein, OH). CardioCheck is a portable analyzer that was calibrated prior to testing at each school site. Per protocol , each blood sample was placed on a multi - lipid panel cassette to obtain analysis of total cholesterol (TC), HDL cholesterol, and TG wi thin 90 seconds. 116 Levels of LDL were calculated based on the Friedewald formula (LDL = TC - (HDL + TG/5) . [222] Additionally , the TC:HDL ratio, and non - HDL cholesterol were calcu lated. Regarding CardioCheck Plus accuracy, Whitehead et al. (2013) evaluated CardioCheck and Cholestech LDX accuracy using laboratory methods. CardioCheck exhibited higher intra and inter - batch imprecision and external quality assessment (EQA) scheme bet ween - analyzer variation for the measurement of TC, HDL , compared to the Cholestech LDX cholesterol analyzer in Li Hep whole blood and plasma . [223] Steiner et al. indicated that differences between non - fasted and fasted measures of pe diatric lipids LDL and TG were minor and clinically acceptable . [224] Moreover, it has been determin ed that non - fasting TC and HDL measurements are appropriate and in strong agreement ( intra - class correlation .92) with fasting values . [225] Non - HDL was calculated by subtracting HDL from TC and deemed acceptable fo r using non - fasting samples . [232] Non - HDL is used clinically as a marker for atherogenic apolipoprotein B containing lipoproteins [226 227] , a nd considered an effective predictor for dyslipidemia and subclinical atherosclerosis in adulthood . [227] Resting B lood P ressure Manual resting systolic and diastolic blood pressure (BP) were assessed following standardized procedures [164] , using a stethoscope and a standard BP aneroid , with an , using a Professional Aneroid Sphygmomanometer (AllHeart, Louisiana, MO). Once a participant ha d been seated for five minutes, two measures were taken at one minute intervals to determine an average. If the first two measures differed beyond parameters (4 mmHg), a third measure was taken. The blood pressure values were classified using the 2017 Clin ical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents using cutpoints fo r children 117 ages 1 to <13 years . [218] The cutpoint for determining the prevalence of children at risk corresp ( derived from a comprehensive review of almost 15 , 000 published articles between January 2004 and July 201 6 page 1 ) [88] which corresponds to 90 th to < 95 th percentile by sex, age, and height. Levels > 95 th percentile are de fined as hypertension w hich includes stage I and II. The prevalanece of also determined individually based on same cutpoints (SBP >120 mmHg and DBP > 80 mmHg ) . Nutritional B ehaviors Block Kids 2004 Food Frequency Question naire Originally, the Block Kids 2004 Food Frequency Questionnaire (FFQ) (Block Dietary Data Systems, Berkeley, CA) [247] was an 8 - page FFQ asking about t he frequency and quantity of 78 foods eaten during the past week which takes approximately 20 to 30 minutes to complete. The results are quantified as daily intake in grams (or milliliters for liquids) and summarized into daily intake. The FFQ reported equ al and high correlation for milk intake ( r = 0.571), 100% juice intake ( r = 0.550), diary for calcium ( r = 0.515), and vitamin D ( r = 0.512) when compared with the Iowa Fluoride Study targeted nutrient semi - quantitative questionnaire for relative validities . [248] Moreover, it showed reliability intraclass reliability (>.30), when compa red with 24 - hour dietary recall, except percent energy from protein and fruit and vegetable servings . [249] Modified Arabic/English version of Block Kids 2004 FFQ The Block Kids 2004 FFQ was translated by the rese archer and staff at NutritionQuest Company during 2016 2017. The process of modifying the FFQ involved incorporating cultural 118 Continuing Survey of Food Intakes by Indiv iduals (CSFII) 1989 - 91 and the CSFII 1994 - 96, [250] and analysis configuration to account for changes in food questions (added and or omitted). The modified FFQ consisted of eight pages (72 foods), assessing the frequency and quantity of food consumption from food groups and nutrients during the past week. A descriptive list of modifications to the original Block Kids 2004 FFQ food it ems is illustrated in APPENDIX F. Decisions regarding selecting cultural foods from Kuwait for the modified FFQ were objectively proposed based on data describing Kuwaiti traditional dishes and food contents . [33 177 178] Also, data on consumption of non - traditional foods in Kuwait [251] and the dietary pattern of the Arab Gulf region population living abroad were considered . [252] In the modified FFQ, foods that are restricted for religious reasons were excluded: pork (pork chops, ribs, or cooked ham, slice ham, hamburger, and bacon). M oreover, questions related to foods that were deemed rarely or never consumed by children in Kuwait were deleted : tacos, burritos, and enchiladas. For example, which kind of tacos, burritos, enchiladas do you usually eat? With meat or chicken/Without mea t or chicken. Additionally, questions on foods such as hot dogs and corn dogs, lunch meat like boloney, sloppy Joes, chicken helper, tomato soup, pop tarts, pie, fruit pie, fruit crisp, cobbler, and fruit roll - ups were also deleted. Pinto beans, black be ans, chili with beans, or bean burritos were excluded from the beans selections in the FFQ (green beans, string beans or peas, chickpeas [added], and refried beans). (pastries), pita bread, sambosa, and lentil soup, in addition to vegetables such as eggplant, zucchini, and okra. Other cultural foods mentioned in the FFQ are rice, white/wheat/bre ad, hummus, and refried beans. 119 Moreover, some food questions were altered soup like chicken noodle, Cup - a - rolls, including sandwiches or - some no n - - Its, Ritz Bits, - - C, Tang, Tampico, Mr. Juicy, - Adm inistering the M odified and T ranslated version of the Block Kids 2004 FFQ The nutrition behaviors of our study sample were assessed by using the translated (Arabic/English) and modified version of the Block Kids 2004 FFQ described earlier. The modified in strument was administered by trained nursing trainers as well as nursing and nutrition students from the colleges of Nursing and Health Sciences (PAAET) in Kuwait. The completed FFQs were analyzed by NutritionQuest and were validated for outliers, which wi ll be described in the next sections. Nutrition V ariables D ata for analysis were derived from variables including food groups and selected macronutrients and micronutrients. These variables were used to evaluate our study objectives and nutrition recommen dations according to the following nutrition guidelines: The US Dietary Guidelines (DGA) and Dietary Reference Intake (DRI) [66] for children (9 to 13 years), and WHO/FAO ranges of population dietary intake goals [22] and recommended nutrient intake (RNI) . [64] 120 F ood G roups Fruit portions (1.5 cups DGA; [>400 g ~1.7 cups WHO]), vegetable portions (males: 2.5 cups; females: 2 cups DGA; [>400 g ~1.7 cups WHO]), dairy portions (2 cups DGA), whole grain (3 oz equivalents DGA ~1 oz WHO). Macronutrients Total calories (girls 1600 2000 kcal; boys1800 2200 kcal), fat (AMDR 25 35%; WHO 15 30% of total kcal/day), saturated fat (<10% of total kcal/day), trans - fatty acids (less than 1% of total kcal/day), linoleic acid (PUFA n - 6, 10 12 g DRIs, [5 8% WHO of total kcal/day]), linolenic acid (PUFA n - 3, 1 1.2 g DRIs, [1 2% WHO of total kcal/day]), and cholesterol (<300 mg). Protein (AMDR 10 30%, [10 15% WHO] of total kcal/day). Carbohydrates (AMDR 45 65%, [55 75% WHO] of total kcal/day) and added sugar (less than 10% of total kca l/day); dietary fiber (boy s: 25.2 g; girls: 24.4 g DGA). Micronutrients Sodium ( less than 2 2 00 mg [UL]; less than 2000 mg [RNI] ), potassium (4500 mg [AI/RNI] ), calcium (1300 mg [RDA/RNI] ), and magnesium (240 mg [AI]; 230 mg [RNI] ) and vitamin D (600 IU [RDA], 200 IU [RNI]) . [253] Covariate A ssessment Physical A ctivity (PA) A self - reported question taken from the Youth Risk Behavior Survey (YRBSS) [209] was administered in both Arabic and English (Appendix B). The question was designed to assess the number of days over the preceding week pa rticipants engaged in > 60 minutes of moderate - to - vigorous physical activity (MVPA) . [209] The question states: 121 days were you physically active for a total o f at least 60 minutes per day (add up all of the time you spend in any type of activity that increases your heart rate and makes you breath hard some of the time)? The scale range measured zero to seven days . [211] Screen T ime (ST) Screen time was assessed using the ST questions from the YRBSS [209] and was administered in both Arabic and English (see Appendix B). Participants of the study self - reported the weekly amount of time spent viewing television, playing video games, and u sing an online computer, and indicated the number of hours (watch/play) on weekdays and weekends for each of the three screen media types . The average hours of screen time per week was determined using the following formula: (hours of TV time on weekdays * 5 days) + (hours of TV time on weekends * 2 days )/ 7 days + (hours of video game time on weekdays * 5 days) + (hours of video game time on weekends * 2 days )/ 7 days + (hours of computer time on weekdays * 5 days) + (hours of computer time on weekends * 2 d ays )/ 7 days . [212] Screen time equal to or above two hours per day is considered high ST. Less than two hours a day is considered low ST . [213] Statistical A nalysis Identifying outliers : Verifying and re - testing the modified translated version of the Block Kids 200 4 FFQ Two verification procedures were carried out to ensure the FFQ data were valid. The participants that reported implausibly low/high total calorie intake (kcal) were excluded by two methods. The Exploratory Data Analysis (Tukey 1977) statistical int erval for labeling extreme formula for determining lower (Q3 - [Q3 [75th percentile] [Q1 [25th percentile]* k [1.5 122 multiplying factor] and upper (Q3 + [Q3 - Q1]* k ) quartiles . [257 258] Additionally, we contrasted nditure by Schofield - HW ([TEE *1.7 moderate activity factor [AF]) [259] , as well the Joint FAO/WHO/UNU Expert Consultation TEE (impeded moderate AF 1.7) . [260] Rodriguez et al. (20 00) indicated that Schofield - HW and FAO/WHO/UNU equations for estimating resting energy expenditure (REE) in children and adolescents, OB and non - OB, produced kcal mean differences of - 33.1 to - 35.3 and - 0.62, respectively, when contrasted with the V max c alorimeter . [259] The TEE ratio interval (at or below 0.16 to at or above 2 folds) was used to detect over/under reported kcal outliers. The modified FFQ was retested ; it indicated overall moderate absolute agreement (intraclass correlation r = .674) in crude data, as well as in data without kcal outliers (intraclass correlation r = .545). Analysis of covariance ( ANCOVA ) with Bonferroni post - hoc test analysis was used for comparing the mean differences of CVD risk factors and dietary intake between BMI - for - age (WHO 2007) [49] weight categories ( UW, NW, OW, and OB ) with controlling for gender (random effect ) and total calories. Chi - proportions at risk for CVD and meeting nutrition recommendations, and binary logistic regression for odds and differences in proportions between weight categories with controlling for gender , MVPA, and ST . Data analysis was performed using SPSS version 24 (SPSS Inc., 2016, Chicago, IL). Results presented as mean ± SD or S.E. at significance level P Results Table 9 summarizes the demographic characteristics of par ticipants from a total sample of Kuwait fifth graders ( N =367), who were measured for CVD risk factors and who completed self - 123 weight (unable to assess %BF) (0.3%), three res WC sample variables varied due to participants who opted out from the test (n=64), either from an inability to obtain adequat e blood samples and technical or assay issues (n=30). The missing blood lipid data included TC (28%, n=106), HDL (26%, n=96), LDL (36%, n=133), TC:HDL (29%, n=108), non - HDL (29%, n=109), and TG (33%, n=123). Of the 367 study participants who completed an F FQ, 54 participants were deemed invalid. The FFQs included in the analysis consisted o f 313 participants (Table 1). According to BMI - for - age categories, our total study sample consisted of fewer than 4.8% UW children, most of the participants were deemed O B at 42.3% and for NW, 30.4% compared with children in the OW category, 22.4%. 124 Table 9 . Mean anthrop ometrics , blood lipids, BP, covariates ( moderate - to - vigorous physical activity [ MVPA ] and screen time [ ST ] ) 1 of Kuwait fifth graders by BMI - for - age categories 2 Variables Overall N =31 3 Underweight (UW) - 1 SD) n =15 Normal Weight (NW) (> - n =95 Overweight (OW) n =70 Obese (OB) (>+2 SD) n =13 3 P Age 10.45 ± .38 10.39 ± .30 10.48 ± .42 10.50 ± .36 10.42 ± .37 0 .395 Anthropometry Height (cm) 142 ± 6.63 133.8 ± 4.94 139.1 ± 5.73 142.4 ± 5.17 144.9 ± 6.48 0 .001 Post - hoc test Ref P <. 0 001 P < 0 .001 P < 0 .001 Weight (kg) 44.4 ± 13.1 25.8 ± 2.06 33.0 ± 4.17 42.6 ± 4.52 55.8 ± 10.8 0 .00 1 Post - hoc test Ref P = 0 .0 09 P < 0 .001 P < 0 .001 BMI Z - Score 1.42 ± 1.37 - 1.44 ± .460 0.042 ± .578 1.54 ± .285 2.67 ± .521 0 .001 Post - hoc test Ref P < 0 .001 P < 0 .001 P < 0 .001 Blood Lipids 3 TC (mg) 152.7 ± 33.4 147.7 ± 19.2 148.9 ± 32.2 153.2 ± 31.6 155.3 ± 36.1 0 . 586 Post - hoc test Ref P = 0 . 967 P = 0 . 822 P = 0 .5 89 TG (mg) 98.7 ± 48.3 88.4 ± 32.4 83.7 ± 36.1 88.3 ± 32.2 113.7 ± 57.9 0 . 001 Post - hoc test Ref P = 0 .798 P = 0 . 503 P = 0 . 025 HDL (mg) 51.2 ± 14.3 64.0 ± 18.4 55.8 ± 12.2 52.1 ± 13.1 46.9 ± 14.3 0 .0 35 Post - hoc test Ref P = 0 .0 78 P = 0 .0 39 P < 0 .00 8 LDL (mg) 82.7 ± 25.9 72.6 ± 6.49 79.3 ± 28.3 83.2 ± 28.0 85.0 ± 24.0 0 . 584 Post - hoc test Ref P = 0 . 486 P = 0 . 612 P = 0 .2 19 TC:HDL 3.06 ± .811 2.46 ± .507 2.69 ± .561 3.0 ± .850 3.37 ± .819 0 .0 46 Post - hoc test Ref P = 0 . 218 P = 0 . 146 P = 0 .00 4 non - HDL (mg) 100.8 ± 26.5 90.7 ± 9.33 93.4 ± 27.5 100.6 ± 29.2 106.4 ± 24.3 0 .008 Post - hoc test Ref P = 0 . 398 P = 0 . 461 P = 0 .0 65 Blood Pressure 4 SBP mmHg 106.3 ± 12.1 99.3 ± 11.7 100.8 ± 12.0 105 ± 10.3 111.8 ± 10.8 0 .00 3 Post - hoc test Ref P = 0 . 722 P = 0 . 135 P = 0 .00 3 DBP mmHg 68.6 ± 8.64 63.6 ± 8.64 64.9 ± 10.5 67.3 ± 8.92 71.2 ± 8.68 0 .00 8 Post - hoc test Ref P = 0 .5 94 P = 0 . 193 P = 0 .0 14 MAP mmHg 151.6 ± 17.2 141.7 ± 16.6 144.1 ± 17.9 149.9 ± 14.6 159.2 ± 14.7 0 .001 Post - hoc test Ref P = 0 .579 P = 0 .063 P < 0 .001 125 Table 9 ( c Covariate 5 MV PA ( days /wk) 2.89 ± 2.34 2.0 ± 2.36 3.42 ± 2.45 2.86 ± 2.42 2.64 ± 2.17 0 .0 51 Post - hoc test Ref P = 0 . 119 P = 0 . 236 P = 0 . 600 ST (hrs/day) 4.72 ± 2.68 4.65 ± 1.89 4.98 ± 2.63 4.42 ± 2.84 4.70 ± 2.72 0 . 924 Post - hoc test Ref P = 0 . 964 P = 0 . 417 P = 0 . 861 1 ANCOVA with Bonferroni post - hoc test [UW reference] controlled for gender (random effect) , data presented as mean ± S.D, P ). 2 WHO 2007. [49] 3 Blood lipids: TG (Triglycerides), LDL - C (Low density - lipoprotein cholestero l), HDL - C (High density - lipoprotein cholesterol), none - HDL - C (Total cholesterol HDL - C), TC:HDL - C ratio (TC/HDL - C). 4 Blood pressure: SBP (Systolic blood pressure ), DBP (Diastolic blood pressure), MAP (Mean arterial pressure ([( SBP - DBP / 3) + DBP]). Bloo d lipids and BP cut - points for children according to the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report [89] and the Clinical P ractice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. [218] 5 Lifestyle behaviors : MVPA (Moderate - to - vigorous physical activity [number of days over past week (7 days) meeting > of 60 min]); ST (screen time) 126 Table 10 summarizes the overall prevalence of children at risk of CVD across weight categories using binary logistic regression with controlling for gender , MVPA, and ST . We estimated overall prevalence of at risk for elevated TG 46.4 %, TC 27.4%, LDL 13.9 %, non - HDL 24 %, TC:HDL 27.1%, low HDL 35.6 %, elevated SBP 18.5% and DBP 15.7%, and elevated BP 25% . The results indicated that OB children had twice the at risk of elevated TG than other weight categories. They were also six times as likely to have elevated TC:HDL, nine times as likely to be at risk of low HDL, six times as likely to be at risk of elevated SBP, four times as likely to be at risk for elevated DBP as well as elevated BP than counterparts in other weight categories. 127 Table 10 . Prevalence at risk for dyslipidemia and BP, and covariates ( moderate - to - vigorous physical activity [ MVPA ] and screen time [ ST ] ) 1 in Kuwait fifth graders by BMI - for - age categories 2 CVD Risk Factors 3 Overall Prevalence ( n =31 3 ) Underweight * - 1 SD) n =15 Normal Weight (> - n =95 Overweight n =70 Obese (>+2 SD) n =13 3 P At risk 46.4% 42.9% 29.6% 40.7% 59.4% 0 .002 OR (95% CI) .486 ( .093 2.551 ) .8 01 (. 156 - 4. 101 ) 2. 001 ( .410 9.775 ) 3 0 mg/dL 17.1 % 14.3 % 9.3 % 11.1 % 25 % 0 .0 29 OR (95% CI) .548 ( .052 5.753 ) .697 ( .069 7. 067 ) 2. 119 ( .236 19. 046 ) At risk 27.4% 22.2% 29.5% 24.1% 28.3% 0 . 789 OR (95% CI) 1.418 (.267 7.528) 1.067 (.196 5.817) 1.369 (.267 7.021) 5.8% 0% 8.2% 7.4% 4% 0 . 652 OR (95% CI) 168544245.8 (.000) 114730792.3 (.000) 64661485.64 (.000) At risk LDL - 13.9% 0% 15.4% 12% 15% 0 .9 09 OR (95% CI) 315440531.2 (.000) 211833153.9 (.000) 300167983.8 (.000) High LDL - 4.5% 0% 3.8% 8.0% 3.3% 0 . 754 OR (95% CI) 81467720.35 (.000) 124618548.3 (.000) 51374324.31 (.000) At risk HDL - 35.6% 11.1% 22.2% 35.1% 52.9% 0 .001 OR (95% CI) 2.189 (.250 - 19.133) 4.092 (.474 - 35.311) 9.034 (1.084 - 75.316) Low HDL - C <40 mg/dL 18.5% 11.1% 7.9% 14.0% 27.9% 0 .0 13 OR (95% CI) .6 00 ( .059 6. 086 ) 1. 248 ( .131 11.869 ) 2 . 891 (.3 33 2 5 . 068 ) At risk Non - HDL 24% 0% 21.3% 18.9% 30.6% 0 . 249 OR (95% CI) 457051950.8 (.000) 339003218.2 (.000) 761827425.5 (.000) High Non - 6.3% 0% 4.9% 7.5% 7.1% 0 .9 56 OR (95% CI) (.000) (.000) (.000) 27.1% 11.1% 8.2% 22.6% 42.9% 0 .001 OR (95% CI) . 738 (. 075 7.305 ) 2.342 ( .260 21.093 ) 6. 328 ( .749 53.430 ) Elevated BP 25.2% 13.3% 16.1% 17.1% 37.5% 0.00 2 OR (95% CI) 1.514 (. 293 7.838 ) 1.387 ( .264 7.291 ) 4. 394 ( .907 21.283 ) El e vated SBP 18.3% 6.7% 8.6% 10.0% 31.3% 0.001 128 Table 10 ( c OR (95% CI) 1.488 ( .165 13.452 ) 1.617 ( .179 14.631 ) 6. 926 (.8 55 56.086 ) Elevated SBP 15.7% 6.7% 10.8% 12.9% 21.9% 0 . 181 OR (95% CI) 1.885 (. 214 16.626 ) 2.075 (. 235 18.311 ) 3.900 (. 477 31.918 ) Covariates 4 MVPA 60 min/day - 7 days 19.3% 13.3% 26.1% 20.3% 14.7% 0.190 OR (95% CI) 2.274 (.474 10.901) 1.753 (.351 8.763) 1.110 (.230 5.356) 17.4% 13.3% 11.8% 27.1% 16.7% 0.165 OR (95% CI) .903 (.175 4.652) 2.274 (.458 11.293) 1.356 (.279 6.586) 1 Chi - square or exact test for calculating proportions. Logistic regression controlled for gender , MVPA , and ST for comparing between groups ( * underweight: reference), P 2 WHO 2007 . [49] 3 Variables at risk: BF% (Percentage body fat); WC (Waist circumference); WHt R (Waist - for - height ratio [WC/ht ]); TG (Triglycerides ); LDL - C (Low density - lipoprotein cholesterol ); HDL - C (High density - lipoprotein cholesterol ); non - HDL - C (Total cholesterol HDL - C ); TC:HDL - C ratio (TC/HDL - C ); SBP (Systolic blood pressure); DBP (Diastolic blood pressure ) . 4 Covariates: MVPA (Moderate - to - vigorous physical activity [number of days over past week (7 days) meeting > of 60 min]); ST (screen time) . Blood lipids and BP cut - points for children according to the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report [89] and Clinical Practice Guideline for Screening and Management of High Blood Pressure in Child ren and Adolescents. [218] between weight categories based on ANCOVA with controlling for gender and total kcals. A fter controlling for total kcals, no statistical difference was found in mean food groups, macronutrients, and micronutrients between weight categories . 129 Table 11 . Mean food group, macronutri ent, and micronutrient intakes 1 in Kuwaiti fifth graders by BMI - for - age categories 2 Nutrition Variables Overall N =31 3 Underweight (UW) - 1 SD) n =15 Normal Weight (NW) (> - n =95 Overweight (OW) n =70 Obese (OB) (>+2 SD) n =13 3 P Total calories per day 2462.2 ± 52.5 2353.8 ± 219.8 2463.6 ± 74.3 2168.8 ± 106.9 2619 ± 90.7 0 .016 Mean ( ± SE) food group and nutrient intakes with controlling for total kcals P* Fruit (cup) 2.40 ± .098 2.38 ± .320 2.55 ± .127 2.51 ± .150 2.17 ± .109 0 .103 Vegetables (cup) 1.86 ± .066 2.07 ± .215 1.79 ± .085 1.76 ± .10 1.82 ± .073 0 .638 Dairy (cup) 1.64 ± .050 1.51 ± .164 1.70 ± .065 1.68 ± .077 1.66 ± .056 0 .772 Whole grains (oz) .624 ± .029 .581 ± .86 .660 ± .034 .632 ± .040 .622 ± .029 0 .772 Total fat (g) 92.4 ± 1.09 93.7 ± 3.44 92.9 ± 1.36 90.8± 1.60 92.1 ± 1.16 0 .756 Saturated fat (g) 29.2 ± .283 29.2 ± .930 29.4 ± .369 28.7 ± .435 29.3 ± .315 0 .586 Trans - fat (g) 8.25 ± .189 8.64 ± .621 8.08 ± .247 8.08 ± .291 8.20 ± .211 0 .846 n - 6 FAs (g) 19.1 ± .531 19.2 ± 1.74 19.4 ± .692 19.1 ± .815 18.9 ± .591 0 .946 n - 3 FAs (g) 1.66 ± .026 1.64 ± .085 1.66 ± .034 1.67 ± .040 1.68 ± .029 0 .965 Cholesterol (g) 310.6 ± 9.09 327.7 ± 29.8 319.1 ± 11.8 298.3 ± 13.9 297.1 ± 10.1 0 .418 Protein (g) 83.9 ± .970 86.3 ± 3.18 83.6 ± 1.26 83.1 ± 1.48 82.8 ± 1.07 0 .755 Carbohydrate (g) 334.7 ± 3.11 329.5 ± 9.19 334.2 ± 3.65 339.2 ± 4.29 336.1 ± 3.11 0 .729 Total sugar (g) 162.3 ± 3.12 157.9 ± 10.2 160.2 ± 4.07 169.3 ± 4.79 161.8 ± 3.47 0 .471 Added sugar (tsp) 3 21.9 ± .665 20.8 ± 2.18 20.8 ± .867 23.1 ± 1.02 22.8 ± .740 0 .225 130 Table 11 ( c Fiber (g) 22.3 ± .411 22.8 ± 1.34 22.7 ± .535 22.2 ± .630 21.5 ± .457 0 .348 Sodium (mg) 3679.7 ± 43.7 3792.4 ± 143.4 3638.8 ± 57.0 3563.7 ± 67.0 3724.1 ± 48.6 0 .196 Potassium (mg) 3052.2 ± 42.4 3150.6 ± 139.1 3074.8 ± 55.2 3047.8 ± 65.0 2935.9 ± 47.1 0 .164 Calcium (mg) 953.6 ± 15.7 913.9 ± 51.5 983.5 ± 20.4 960.6 ± 24.0 956.6 ± 17.4 0 .562 Magnesium (mg) 334.6 ± 4.36 335.7 ± 14.3 342.6 ± 5.68 335.5 ± 6.69 324.4 ± 4.85 0 .108 Vitamin D (IU) 4 140.6 ± 5.3 136.5 ± 17.3 145.9 ± 6.90 145.7 ± 8.12 134.3 ± 5.89 0 .537 1 ANCOVA ( p *controlled for total calories and gender ), data presented as Mean S.E ., P 2 WHO 2007 [49] n - 6 FAs (Omega 6, Linoleic acid); n - 3 FAs (Omega 3, Linolenic acid). 3 One teaspoon equivalent = 4.2 grams of sugar . [261] 4 International Unit (UI ), 1 IU of Vitamin D = 0.025 µg. 131 Table 12 summarizes the proportion of children meeting the US and WHO/FAO nutrition recommendation across weight categories. The overall proportion of children meeting food group recommendations, except fruits, is low (<50%), with saturated fat (~31%), added sugars (17%), fiber (42%), and micronutrients (<42%). W e observed that OB children met the WHO/FAO ranges for protein compared to other weight categories . We also observed few differences between weight categories meeting the AMDR for carbohydrates by the UW children. More children in the OW category met sodium recommendatio ns (DRIs) than counterparts in other weight categories. 132 Table 12 . Proportion of Kuwaiti fifth graders meeting the US Dietary Guidelines (DGA ), Dietary Reference Intake (DRI ), and WHO/FAO (Range/RNI) recommendations by BMI - for - age categories 1 DGA 2 and WHO/ FAO 3 Dietary Guideline Cut points Overall Proportion N=31 3 Underweight (UW) ref - 1 SD) n =15 Normal Weight (NW) (> - n =95 Overweight (OW) n =70 Obese (OB) (>+2 SD) n =13 3 P - value Fruit 1.5 cups DGA 68.9% 60.0% 71.6% p = 0 . 643 65.7% p = 0 . 834 69.7% p = 0 . 529 0 . 856 400g (1.7 cup) WHO 62.2% 60.0% 67.4% p = 0 . 887 55.7% p = 0 . 598 62.1% p = 0 . 990 0 .6 67 Vegetables (cup) 2 - 2.5 cup DGA 28.8% 33.3% 27.4% p = 0 . 219 24.3% p = 0 . 376 31.8% p = 0 . 676 0 . 410 met 400g (1.7 cup) WHO 46.5% 46.7% 47.4% p = 0 . 991 37.1% p = 0 . 455 50.8% p = 0 . 836 0 . 354 Dairy (cup) 2 cups/day 32.7% 26.7% 36.8% p = 0 . 501 27.1% p = 0 . 984 33.3% p = 0 . 616 0 . 610 Whole grains (ounce) 3oz DGA 0% 0% 0% 0% 0% - 1oz WHO 17% 0% 17.9% p = 0 . 999 12.9% p = 0 . 999 20.5% p = 0 . 998 0 . 468 Total Calories ( kcal ) 1600 - 2000 girls , 1800 - 2200 boys 16.7% 20.0% 16.8% p = 0 . 714 22.9% p = 0 . 853 12.9% p = 0 . 468 0 . 428 Total fat 25% - 35% AMDR 4 65.7% 53.3% 67.4% p = 0 . 371 74.3% p = 0 . 162 61.4% p = 0 . 634 0 .2 96 15% - 30% WHO 23.7% 26.7% 21.1% p = 0 . 408 35.7% p = 0 . 777 18.9% p = 0 . 363 0 . 109 Saturated fat <10% 30.8% 20.0% 27.4% p = 0 . 642 40.0% p = 0 . 196 29.5% p = 0 . 460 0 .2 99 133 Table 12 ( c Trans - fat <1% 0% 0% 0% 0% 0% - n - 6 FAs 5% - 10% AMDR 73.4% 73.3% 70.5% p = 0 . 912 64.3% p = 0 . 531 80.3% p = 0 . 494 0 . 101 5% - 8% WHO Range 64.1% 66.7% 60.0% p = 0 . 651 58.6% p = 0 . 686 69.7% p = 0 . 755 0 . 307 n - 3 FAs 0.6 - 1.2% AMDR 47.4% 40.0% 45.3% p = 0 . 654 45.7% p = 0 . 605 50.8% p = 0 . 406 0 . 782 1 - 2% WHO Range 0.3% 0% 0% p =1.000 0% p =1.000 0.8% p = 0 .999 1.00 0 Cholesterol <300 mg 54.8% 46.7% 51.6% p = 0 . 624 67.1% p = 0 . 170 51.5% p = 0 . 633 0 . 281 Protein 10% - 30% AMDR 97.1% 100% 97.9% p = 0 .999 91.4% p = 0 .999 99.2% p = 0 .999 0 .0 46 10% - 15% WHO 74.7% 53.3% 76.8% p = 0 .0 86 62.9% p = 0 . 704 81.8% p = 0 . 021 0 .004 Carbohydrates 45% - 65% AMDR 91.0% 100% 91.6% p = 0 .999 80.0% p = 0 .999 95.5% p = 0 .999 0 .0 08 55% - 75% WHO Range 50.3% 33.3% 51.6% p = 0 . 213 60.0% p = 0 . 102 46.2% p = 0 . 369 0 . 269 Added sugar <10% 5 17.0% 13.3% 21.1% p = 0 . 643 21.4% p = 0 . 555 12.1% p = 0 . 879 0 . 423 Fiber 22.4 girls - 25.2g boys 42.0% 40.0% 45.3% p = 0 . 839 35.7% p = 0 . 627 43.2% p = 0 . 872 0 .61 8 134 Table 12 ( c Calcium (1300 mg) 19.9% 13.3% 15.8% p = 0 . 873 18.6% p = 0 . 629 24.2% p = 0 . 364 0 .3 50 Magnesium (350 mg) 41.7% 40.0% 45.3% p = 0 . 725 32.9% p = 0 . 605 43.9% p = 0 . 803 0 .4 47 Potassium (4500mg) 12.8% 26.7% 11.6% p = 0 . 114 10.0% p = 0 . 104 13.6% p = 0 . 183 0 .3 88 Sodium (mg) <2200 UL 6 17.3% 20.0% 9.5% p = 0 . 379 30.0% p = 0 . 390 15.9% p = 0 . 743 0 .0 24 <2000 WHO 13.5% 20.0% 8.4% p = 0 . 262 20.0% p = 0 . 974 12.9% p = 0 .4 62 0 . 315 Vitamin D (IU) 7 600 RDA 8 0% 0% 0% 0% 0% - 200 WHO 18.6% 13.3% 20.0% p = 0 . 712 17.1% p = 0 . 830 18.9% p = 0 . 656 0 .9 51 1 Chi - gender , MVPAand ST to compare between weight categories at P . 2 Dietary Guidelines for Americans 2015 2016 - Eight Edition & DRI (Dietary Reference Intake ). [66] 3 The joint WHO/FAO Expert Consultation on diet, nutrition and the prevention of chronic diseases , [22] P ublic Health Nutrition , 2004. FAO/WHO expert consultation on human vitamin and mineral requirements . [65] 4 AMDR, n - 6 FAs (Omega 6, Linoleic acid ), n - 3 FAs (Omega 3, Linolenic acid). 5 O ne teaspoon equivalent = 4.2 grams of sugar . [261] 6 Upper Limit ( UL ), Adequate Intake (AI). 7 International Unit (IU ), 1 IU of Vitamin D = 0.025 µg. 8 RDA (Recommended dietary allowance) . 135 Discussion Limited studies have assessed multiple CVD risk factors in Kuwaiti children, particularly non - OB children. In a sample of OB, OW, NW, and UW, the primary objective of the study was to determine the prevalence of CVD risks, and if OB children will have greater risks than non - OB children. Our secondarily objective was determining the proportion of children meeti ng nutrition recommendations, and whether OB children will be less likely to meet the recommendations than other weight categories. The overall findings indicated more CVD risk factors among OB children; however, contrary to our hypotheses, risks for dysli pidemia were also detected among the non - OB children. On the other hand, the overall proportion of children across weight categories met the nutrition recommendations was low. Also, except for a higher proportion of OB children met protein recommendations, no other differences between weight categories in meeting the recommendations were found, which we were not expecting. Each of the primary outcome variables is discussed below. With respect to dyslipidemia , no available studies have reported the prevalenc e of dyslipidemia in Kuwaiti schoolchildren. Moreover, we found higher risks for TG and low HDL in OB children of our study than estimated prevalence of elevated TG (30%) and low HDL (11%) in OW and OB Mexican American children (N=128, 11.9 ± .06 years old ). [26 9] Also, our findings on the prevalence of high risk for low HDL was higher than reported on 8 - 17 year olds from NHANES 2011 - 12 which reported prevalences in NW 6.7% and OW 12.5% . [27] Despite our expectations of higher CVD risks in OB than in non - OB children, we found that the prevalence at risk as well as high risk of elevated TC and LDL did not statistic ally differ across weight categories, though it was relatively higher in NW children than in OB children. Also OW children had a higher prevalence of LDL levels classified as high risk as compared to 136 OB children. This indicates that it is important to ass ess Kuwaiti children for dyslipidemia regardless of weight status. Our findings were not in agreement when contrasted with a study a mong 7 to 14 years old (N=937) in Salvador. Nutrição et al. (2012) reported a higher prevalence (26.4%) of dyslipidemia (ele NW (20.2%) categories [26] , however this study also indicates that a significant proportion of noNW children had dyslipidemia. [26] Our findings on at risk TG and TC indicated a higher prevalence of in OB (15%) than compared to OW (7.9%) and NW (8.1% ) children. With regard to BP in Kuwaiti children, limited studies report the prevalence of BP beyond 1999. We estimated an overall higher prevalence ( 25 %) of elevated BP than previously reported (5%) in Kuwaiti schoolchildren (6 to 13 years) by Moussa et al. (1999). [67] Also, in prevalence of BP ( 37 %) compared with NHANES data. The 2017 American Academy of Pediatric G uidelines reclassified BP in 15,647 children aged 5 to 18 years from NHANES (1999 to 2014) and reported an increased prevalence of elevated BP from 11.8% to 14.2% mostly in [28] In essence, the risk of BP in Kuwaiti children has increased and may be considered among the highest globally. Our secondary objective was to determine the proportion of Kuwaiti children meeting nutrition recommendations across weight cate gories, and if there are differences between weight categories. The overall dietary pattern of our study sample across weight categories was characterized as excessive kcals, added sugar, saturated fat, trans - fat, n - 6 FAs, cholesterol, and sodium, while lo w in vegetables, dairy, whole grains, fiber, n - 3 FAs, calcium and vitamin D (Tables 1, 2). Moreover, more children in our study were not meeting nutrition recommendations, particularly kcals (80%), when compared with a previous study on Kuwaiti 137 children. Z aghloul et al. (2012) found that around 50% of Kuwaiti school children (9 to 13 years, n=205) exceeded kcals, carbohydrate, and protein intake, while around 80% did not meet micronutrients recommendations including calcium and vitamin D, or n - 3 FAs and fib er recommendations. [36] Other studies produced similar findings that diet high in kcals is related to an inability to meet the food group and micronutrient recommendations. Petterson et al. (2010) examined dietary energy density with dietary quality in 551 children (mean age 9.6 years, 52% girls) and found an adverse relationship between total food intake including fruit, vegetables, fiber, micronutrient intake, and increased energy density. [242] 2013 ) observed that % total energy from fat, saturated fat and added sugars increased ( P < 0 .001) with high energy densi ty, meanwhile, % total protein, fiber, and micronutrient intake decreased among children ( N =594, 5 to 12 years). [243] We hypothesized that OB children would be less likely to mee t the recommendations than other weight categories. However, after controlling for covariates (MVPA and ST), we found fewer exceptions with macronutrients. Meeting the WHO/FAO ranges for protein by OB children, while meeting the AMDR for carbohydrates by U W children related to their mean intake level. Likewise, meeting the DRIs recommendations for sodium by the OW category, who had a lower mean sodium intake when compared with other weight categories (Table 13). With respect to using the DGA and DRIs as we ll as WHO/FAO recommendations, some children in our study met the WHO/FAO cut - points for vegetables (46.5%), whole grains (17%), and vitamin D (18.6%), but did not meet the US cut - points, except for 28% of children meeting the DGA recommendations for veget ables. On the other hand, meeting the AMDR and DRIs for macronutrients and sodium seemed more applicable for Kuwaiti children when compared with WHO/FAO ranges and RNI. 138 The strengths of the current study included recruiting suffic i ent sample size of fifth grade boy and girls to determine the prevalence at risk of CVD as well as the proportion meeting the nutrition recommendations . Also, the comparison by weight status controlled for the potential influence of kcals , gender , MVPA, and ST . Data colle ction was performed by a trained research team according to research protocols, using valid instruments and standardized procedures in pediatric research for assessing CVD risk factors and dietary status in children. [20 89 164] We utilized a valid [248 249] nutritional assessment instrument, Block Kids 2004 FFQ, which was translated (Arabic/English) and modified (cultural food) , and retested to facilitate accurate Dietary Guidelines and DRIs and WHO/FAO recommendations, using dietary indices such as HEI and FI, which is not available from literature in Ku wait . We used valid statistical methods [258] a nd energy expenditure equations [259] for detecting kcals outliers. However, t here were also several potential study limitations. The research was a cross - sectional evaluation of as is true for all cross - sectional studies , no cause and effect inference s can be made from the fin dings. Our study sample was convenience and may not be a representative sample of all Kuwaiti fifth graders. The sample size for blood lipids was lower than other variables due to being unable to collect a sufficient blood volume to evaluate theblood lipid variables, technical issues with analyzer, or some children opted out of the blood lipid test. Also the UW group sample size was small and all results involving comparisons with this group should be taken with caution. The selected CVD risk factors may b e affected by dehydration or medications taken by some and is related to poor memory or portion estimation or the type and design of the dietary 139 instrumen t used. [265] For example, children using FFQ may over or underestimate energy intake and some micronutrients related to the length of questions. [266] Summary and Conclusion This study determined the prevalence of CVD risk factors, and the proportion meeting nutrition recommendations in a sample of Kuwaiti fifth graders that were categorized by weight status. The overall prevalence of dyslipidemia and BP was higher than report ed in similar aged children populations and may be considered among the highest globally. Although the prevalence of at risk TG, TC:HDL, non - HDL, SBP, and DBP was the highest in OB children, risks for elevated TC and LDL were higher in NW and OW than in OB children. With respect to meeting nutrition recommendations, while we expected that OB children would be less likely to meet nutrition recommendations than non - OB children, there were few differences between weight categories and overall the proportion of children meeting nutrition recommendations was poor. These findings warrant CVD risk assessments for Kuwaiti children regardless of weight status, and interventions to improve dietary behaviors. . 140 CHAPTER 6 : SUM MA RY AND CONCLUSIONS This chapt er includes is a summary of the of three study aims (from Chapters 3, 4, and 5). and include a brief background and rationale for conducting this work. This is followed by a summary of the results for each aim, and a global summary of the study strengths and weaknesses, conclusion, and future directions . In Kuwait, there is little published data on the prevalence of children classified at risk for cardiovascular disease (CVD) risk factors, with the exception of OB. Also, limited studies have reported on the proportion of children meeting food group and nutrition recommendations by gender. Moreover, there are no studies that have compared the prevalence of CVD risk factors and the proportion of Kuwaiti children meeting nutrition recommendations between w eight status categories ( UW, NW, OW, OB). Using a sample of Kuwaiti fifth graders, the purpose of this dissertation was to: 1) Determine the prevalence of CVD risks for - , and whether there were differences in the prevalence of CVD risk factors by gender . Secondarily, determine if there are differences in mean CVD risk factors levels by gender. 2) Determine the proportion of children meeting US Dietary Guidelines (DGA), Dietary Referen ce Intake (DRI), and WHO/FAO recommendations for food groups (fruit, vegetables, dairy, and whole grains), macronutrients (total calorie, total fat, saturated fat, trans - fat, n - 6 and n - 3 fatty acids [FAs], protein, carbohydrates, added sugar, and fiber), s elected micronutrients (calcium , potassium, magnesium, sodium, and vitamin D ), and two dietary indices (Healthy Eating Index [HEI] and Fiber Index [FI ) ]), thereby determining whether there are gender differences in dietary intake and meeting nutrition reco mmendations; 3 ) i n a sample 141 of OB, OW, NW and UW children determine if there are differences between weight categories in: 1) prevalence of CVD risks and determine if risks are greatest in OB children 2) meeting US and WHO/FAO nutrition recommendations an d determine if fewer OB children meet the recommendations for food groups , macronutrients, and selected micronutrients. Aim 1: Prevalence of Cardiovascular Disease Risk Factors in Kuwaiti Schoolchildren, Compared by Gender This cross - sectional study was c onducted to determine the prevalence of CVD risks in Kuwaiti children and whether there are differences in the prevalence and mean levels between genders. Secondarily, determine if there are differences in mean CVD risk factors levels by gender. We anticip ated that boys would have a higher prevalence and mean levels of CVD risk factors than girls. Our primary findings indicated a high prevalence of CVD risk factors in Kuwaiti children that are concerning. This includes high rates of OB and OW, elevated rest ing BP and dyslipidemia. The OB prevalence of 39% indicates the OB rate is increasing in Kuwait when compared to KNSS in 2011 - 2012 data Kuwaiti schoolchildren ( aged 10 to <15 years (N = 4561) reported a 31% OB rate. Although the prevalence of OW was higher in girls (27.1%) than boys (15.5%), however, the OW prevalence among girls remained similar to the previously reported prevalence of 28%. [1] We estim ated an overall prevalence at risk for dyslipidemia and found that our study sample had a greater prevalence of at risk for TC and TG as well as high risk for TG as compared to children from other countries. [26 233 234] With respect to BP, the overall prevalence of elevated BP 23.3% was higher than previously reported in 6 to 13 years Kuwaiti schoolchildren BP 5%) by Moussa et al. (1999). [67] This shows that the risk of BP in Kuwaiti children has increased in the last two decades. Overall CVD risk factor prevalence rates 142 of childen at risk were higher or equ al to similar aged populations globally that are considered to have a high prevalence of CVD risks. We expected boys would have higher rates of CVD risk factors than girls because boys had higher rates of obesity based on the KNSS 2012 - 13 report. Contra ry to our hypotheses, girls were more likely to be at risk of dyslipidemia compared to boys. Girls also had a higher mean TG than boys, also higher than reported in other studies with various childhood age ranges. [13 174 233 235 236] S everal studies have reported a higher prevalence of dyslipidemia in girls than boys. [27 233 235] Girls had greater risks for elevated BP, elevated SBP and DBP than boys, also greater risks of BP than previously reported in girls aged between 6 and 13 years old (by Moussa et al. (1999) two decades ago [60] Some studies found that stature was directly related to BP in children. [238 - 240] We found that %BF and height, which both were relatively higher in girls than boys, were directly ( P <0.001) related to the increases in SBP, whereas, body weight was found to be related ( P <0.001) to the increases in DBP. The findings of AIM 1 indicate the need for follow - up and lifestyle interve ntional studies or programs to prevent and manage risks of CVD among children in Kuwait. Aim 2: Proportion of Kuwait schoolchildren meeting US and WHO/FAO Dietary Recommendations for Food Groups and Nutrient Intakes, compared by Gender In this cross - sectio nal study to determine the proportion of children meeting nutrition recommendations and dietary indices (HEI and FI), and whether there are differences by gender. We hypothesized that boys would be less likely to meet nutrition recommendations and would ha ve a poorer quality of diet than girls. Overall, 16.7% of children met kcals, less than 50% met food groups (except fruits), and less than 40% met micronutrients. None of the children had a good HEI (a total score of 80 points or above), and only 1.3% met fiber recommendations 143 (14g/1000 kcal) for children aged between 9 and 13 years old. The crude and adjusted (calorie, MVPA, and ST) comparison of proportions meeting the recommendations indicated few differences between boys and girls. More boys met the DGA recommendations for fruit than girls (75.7% vs. 62.4%; P =0.037), which we did not anticipate. Other findings supported our hypothesis, as more girls than boys met the DGA and DRI recommendations for vegetables (36.4% vs. 20.3%; P =0.003), sodium (18.2% vs. 8.1%; P =0.022), and cholesterol (64.8% vs. 44.1%; P <0.001). Our findings are in contrast with a study from 2008 - 2009, which described the dietary intake and meeting of dietary recommendations of Kuwaiti children between the ages of 9 to 13. The study indicated that 50%, including around 60% of girls and around 40% of boys, exceeded kcals intake, which was lower than our findings. However, the study also indicated that fewer boys than girls exceeded micronutrient recommendations, including vitamin D (0.7% and 0%), calcium (16.4% and 6.7%), and magnesium (48% and 31%) , but not sodium (72% and 61%) similar to our findings. [36] Our findings showed a higher mean kcals and macronutrient intakes, but a lower mean micronutrient intake, except for sodium, when contrasted with several similar studies from other countries. [55 174 261 263 270] With respect to gender differences in dietar scores were similar and considered lower than the standard. [191] This was contrary to our hypothesis that boys would have a poor er dietary quality. Furthermore, more children, especially girls, had unexpectedly poorer total HEI scores, while, no participant possessed a good HEI total a nd girls (overall mean 8.9 ± .11g /1000 kcals). 144 The current dietary pattern of Kuwaiti children indicates that the majority of children are eating a high caloric low nutrient dense diet and likely contribute to the alarming rates of OB and other CVD risk factors. The findings of aim 2 suggest the need for intervention programs to Aim 3: Prevalence of Cardiovascular Disease Risk Factors and meeting US and FAO/WHO Dietary Recommendations in Kuwaiti Schoolchil dren by Weight Status This cross - sectional study i n a sample of OB, OW, NW, and UW Kuwaiti children determined if there are differences between weight categories in 1) prevalence of CVD risks and determine if risks are greatest in OB children 2) meeting US and WHO/FAO nutrition recommendations and determine if fewer OB children meet the recommendations for food groups, macronutrients, and selected micronutrients. The overall findings indicated more CVD risk factors among OB children; however, contrary t o our hypotheses, risks for dyslipidemia were also detected among the non - OB children. Our findings regarding risks for BP indicated a higher prevalence (25.2%) of elevated BP than previously reported (5%) in Kuwaiti schoolchildren (6 to 13 years) by Mouss a et al. (1999). [67] Our findings were also higher than reported in a sample of OW and OB US children that had a prevalence of 14.2% . [28] Overall our findings indicate th e risk of elevated BP in Kuwaiti children has increased and appears to be amongst the highest globally. With respect to risks for dyslipidemia, we found higher risks for TG and HDL in OB children as compared to a sample of OW and OB Mexican American chil dren [269] Also, among NW and OW children, ou r findings on the prevalence of high risk for HDL was higher than reported in US children aged 8 to 17 by NHANES 2011 2012. [27] 145 We hypothe sized that OB children would have a greater risk of CVD than non - OB children. The findings of our study indicated a significantly greater prevalence at risk of CVD in OB versus non - OB children, including TG, TC:HDL, HDL, and BP. On the other hand, the prev alence of at risk for TC and LDL was higher than in NW than OB children, but was not statistically different. Additionally, the prevalence of high risk LDL was higher in OW than in OB children. These findings indicate that Kuwaiti children have dyslipid emia regardless of their weight status. Our secondary objective for Aim 3 was to determine the proportion of Kuwaiti children meeting nutrition recommendations across weight categories, and if there are differences between weight categories. We found tha t few children across weight categories met the recommendations; 17% for kcal, <50% for food groups, and <40% for micronutrients, except sodium. We observed a higher proportion of children in our study were not meeting kcal recommendations than previously described in a sample of 205 Kuwaitie children 9 to 13 years old. [36] Moreover, our findings indicate a high - energy density and low - nutrient density, which is similar to what was reported by studies from other countries. [242 - 244 263] Contrary to our hypotheses, a few significant differences between weight categories were detected in meeting recommendations for selected nutrients. Groups that had higher prevalence of meeting included protein by OB, sodium by OW and carbohydrates by UW. These findings warrant CVD r isk assessments for Kuwaiti children regardless of weight status, and interventions to improve dietary behaviors. 146 Strengths and Weaknesses The strengths of the current study included recruiting a suffic i ent sample size s of fifth grade boy s and girls to determine the ir prevalence of at risk CVD , and the proportion meeting the nutrition recommendations. Also, the comparisons by gender and by weight status controlled for the potential influence of kcals, MVPA and ST, and gender . Data coll ection was performed by a trained research team according to research protocols, using valid instruments and standardized procedures in pediatric research for assessing CVD risk factors and dietary status in children. [20 89 164] We utilized a valid [248 249] nutritional assessment instrument, Block Kids 2004 FFQ, which was translated (Arabic/English) and modified (cultural food) , and retested to based on US Dietary Guidelines and DRIs and WHO/FAO recommendations, using dietary indices such as HEI and FI, which is not available from literature in Ku wait . We used valid statistical methods [258] a nd energy expenditure equations [259] for detecting kcals outliers. However, our study encountered several limitations related to the following factors. However, there were also several potential study limitations. The research was a cross - sectional evaluation of as is true for all cross - sectional studies , no cause and effect inferences can be made from the findings. O ur study sample was convenience and may not be a representative sample of all Kuwaiti fifth graders. The sample size for blood lipids was lower than other variable s due to being unable to collect a sufficient blood volume to evaluate the blood lipid variables, tech nical issues with analyzer, or some children opted out of the blood lipid test. Also the UW group sample size was small and all results involving comparis ons with this group should be taken with caution. The selected CVD risk factors may be affected by dehydration or medications taken by some children. Previous studies have 147 difficult to obtain and is related to poor memory or portion estimation or the type and design of the dietary instrument used . [265] For example, children using FFQ may over or underestimate energy intake and some micronutrients related to the length of questions. [266] Conclusion In summa ry, Aim 1 determined that the overall prevalence of OB and BP in Kuwaiti schoolchildren was higher than the reported literature. Unexpectedly, boys and girls had similar levels of OB, whereas girls were more likely to be at risk of OW, dyslipidemia, and BP than boys. Aim 2 determined that the overall proportion of children meeting nutrition recommendations and having a good dietary quality was low in Kuwaiti schoolchildren. With respect to gender. We hypothesized more girls would meet dietary recommendatio ns than boys. There were few differences which included more girls met vegetable and sodium intake recommendations, while contrary to our hypothesis, more boys met fruit recommendations. Other unexpected differences were that boys had a higher mean intake of vegetables, dairy, protein, cholesterol, and calcium than girls. Aim 3 determined that OB children had a higher prevalence of CVD risk factors. However, the non - OB children had a higher risk of elevated TC and LDL than OB children, but this was not sta tistically significant. Regardless of weight category , most of the children were not meeting their nutrition recommendations, and they had a poor quality diet. Study Significance and Future Directions This work has provided valuable insights into the pre valence of CVD risk factors and nutritional recommendations among Kuwaiti schoolchildren by gender and weight. Based on the high prevalence of CVD morbidity and mortality in Kuwaiti adults, this study may ultimately help to provide a basis for the developm ent of intervention programs for children to improve 148 their dietary and lifestyle behaviors to prevent CVD risk factor development and reduce premature morbidity. Additionally, we found the rates of OB, and other CVD risk factors , and poor dietary behavio rs were high but rates were similar in boys and girls. It is therefore recommended that officials in Kuwait establish strategic plans for the regular assessment and tracking of the schoolchildren for CVD risks. Future studies should include intervention programs to promote healthful dietary and PA behaviors. Furthermore, the investigation of other factors including the role of parental and family support for healthful nutrition and other lifestyle behaviors, the influence of school and community environm ent on childrens health. Also, should be considered. . 149 A PPENDICES 150 APPENDIX A : Research Study Approvals 151 152 153 15 4 155 156 157 158 159 APPENDIX B : Parent concent and child assent 160 161 162 163 164 165 166 APPENDIX C : Measurement battery 167 168 169 170 APPENDIX D : Translated modified Block 2004 Kids food frequency questionnaire (FFQ) 171 172 173 174 175 176 177 APPENDIX E : Addendum A to Cost Proposal for Modified Arabic/English Version of Block Kids 2004 FFQ List of Changes to Food Questions 178 179 180 181 182 183 184 185 186 APPENDIX F : F FQ Kcals Outliers Table 13. Study participants excluded from analysis based on predictive equation of Schofield - HW and FAO/WHO/UNU for n Sex AGE BMI (kg/m2) Ht cm Wt kg FFQ Total Kcal 1 REE (Schofield) 1 REE (Schofield) Kcal Ratio 1 TEE 1.7 (Schofield) 1 TEE 1.7 (Schofield) Kcal Ratio 2 TEE FAO/WHO/UNU 2 TEE FAO/WHO/UNU Kcal Ratio 3,4 Kcal Interquartile Range 1 Male 10 15.5 135 28.3 6968.07 1160.60 6.00 1973.01 3.53 1890.96 3.68 1168 6487 2 Male 10.6 15.9 135 29 6113.41 1171.97 5.22 1992.35 3.07 1924.72 3.18 1168 6487 3 Male 10 29.2 143.5 60.2 5817.13 1690.63 3.44 2874.07 2.02 3167.74 1.84 1168 6487 4 Male 10.3 16.3 129 27.2 4837.08 1134.49 4.26 1928.63 2.51 1837.38 2.63 1168 6487 5 Male 9.11 14.3 130 24.2 5480.2 1087.11 5.04 1848.09 2.97 1688.04 3.25 1168 6487 6 Male 10.6 18.1 135 33 6576.6 1236.97 5.32 2102.85 3.13 2112.69 3.11 1168 6487 7 Male 10 16.2 129 26.9 3828.01 1129.61 3.39 1920.34 1.99 1822.66 2.10 1168 6487 8 Male 10.11 21.8 134 39.1 408.7 1334.72 0.31 2269.03 0.18 2383.15 0.17 1168 6487 9 Male 10.5 23.6 146 50.4 5248.95 1534.81 3.42 2609.18 2.01 2832.46 1.85 1168 6487 10 Male 10.9 15.4 130 26 7706.65 1116.36 6.90 1897.81 4.06 1778.21 4.33 1168 6487 11 Male 10.3 15.2 139 29.4 5099.9 1183.96 4.31 2012.73 2.53 1943.89 2.62 1168 6487 12 Male 10 15.9 131.5 27.5 3845.66 1142.79 3.37 1942.75 1.98 1852.06 2.08 1168 6487 13 Male 10.4 15.6 141.6 31.2 6480.54 1216.78 5.33 2068.52 3.13 2029.15 3.19 1168 6487 14 Male 10.2 22.2 152 51.3 6806.93 1557.67 4.37 2648.04 2.57 2865.36 2.38 1168 6487 15 Male 10.7 16.5 137 31 5309.91 1207.21 4.40 2052.26 2.59 2019.76 2.63 1168 6487 16 Male 10 16.7 128 27.3 4631.7 1134.74 4.08 1929.06 2.40 1842.28 2.51 1168 6487 17 Male 10.2 23.1 141 45.9 6302.85 1454.83 4.33 2473.21 2.55 2661.58 2.37 1168 6487 18 Male 10.3 18.6 141 36.9 4963.45 1308.58 3.79 2224.58 2.23 2287.87 2.17 1168 6487 19 Male 10.3 41 145 86.2 6815.59 2115.19 3.22 3595.82 1.90 3812.45 1.79 1168 6487 20 Male 10.3 22.2 151.5 51 7400.15 1552.11 4.77 2638.58 2.80 2854.44 2.59 1168 6487 21 Male 12 16.5 145.5 35 5480.45 1283.88 4.27 2182.59 2.51 2203.53 2.49 1168 6487 22 Male 10 15.5 125.6 24.5 6557.17 1085.95 6.04 1846.11 3.55 1703.18 3.85 1168 6487 23 Male 10.7 15.2 139 29.4 4311.86 1183.96 3.64 2012.73 2.14 1943.89 2.22 1168 6487 24 Male 10.5 17 145 35.7 5080.17 1294.57 3.92 2200.76 2.31 2234.82 2.27 1168 6487 25 Male 10.7 24.4 142 49.3 6326.64 1511.45 4.19 2569.46 2.46 2791.67 2.27 1168 6487 26 Male 10 15.7 135 28.6 6610.67 1165.47 5.67 1981.30 3.34 1905.46 3.47 1168 6487 1 Female 10 20.5 139 39.7 4468.45 1178.44 3.79 2003.35 2.23 2140.27 2.09 770 5782 2 Female 10 25.1 149 55.7 4977.46 1358.78 3.66 2309.93 2.15 2492.08 2.00 770 5782 3 Female 10.1 17 133 30 4654.01 1069.40 4.35 1817.98 2.56 1813.80 2.57 770 5782 4 Female 10.3 15 138 28.6 3884.37 1080.94 3.59 1837.60 2.11 1759.63 2.21 770 5782 5 Female 11 25.4 138 48.3 363.74 1245.73 0.29 2117.74 0.17 2358.26 0.15 770 5782 6 Female 10.3 20.9 129 34.8 668 1090.95 0.61 1854.62 0.36 1986.03 0.34 770 5782 7 Female 10.8 18 151.5 41.4 5225.45 1250.79 4.18 2126.34 2.46 2188.68 2.39 770 5782 8 Female 10.3 23.9 141 47.5 4856.71 1252.99 3.88 2130.08 2.28 2340.81 2.07 770 5782 9 Female 9.11 19.6 141 38.9 4800.74 1181.05 4.06 2007.78 2.39 2116.57 2.27 770 5782 10 Female 10.4 20.5 139 39.7 6931.58 1178.44 5.88 2003.35 3.46 2140.27 3.24 770 5782 11 Female 9.11 21.9 143 44.8 4614.63 1239.70 3.72 2107.49 2.19 2277.64 2.03 770 5782 12 Female 10.7 18.9 145 39.7 6126.39 1206.34 5.08 2050.78 2.99 2140.27 2.86 770 5782 13 Female 10.4 24.2 140 47.5 5015.53 1248.34 4.02 2122.17 2.36 2340.81 2.14 770 5782 14 Female 10.7 19.9 141 39.6 4636.1 1186.90 3.91 2017.74 2.30 2137.34 2.17 770 5782 187 Table 13 (c 15 Female 10.9 29.2 150 65.6 505.53 1446.24 0.35 2458.61 0.21 2593.35 0.19 770 - 5782 16 Female 10.5 15.3 134 27.4 4617.09 1052.30 4.39 1788.91 2.58 1711.77 2.70 770 - 5782 17 Female 10.4 24 142.5 48.8 5036.72 1270.84 3.96 2160.42 2.33 2368.87 2.13 770 - 5782 18 Female 10.7 20.2 138 38.4 5093.99 1162.92 4.38 1976.96 2.58 2101.47 2.42 770 - 5782 19 Female 10 14.6 129.5 24.5 4651.73 1007.12 4.62 1712.10 2.72 1590.74 2.92 770 - 5782 20 Female 10 17.7 134 31.7 4039.02 1088.27 3.71 1850.06 2.18 1877.19 2.15 770 - 5782 21 Female 10 13.9 131 23.9 3491.01 1009.07 3.46 1715.42 2.04 1564.74 2.23 770 - 5782 22 Female 10 13.1 138.5 25.2 6931.63 1054.82 6.57 1793.20 3.87 1620.65 4.28 770 - 5782 23 Female 10.3 28.3 140 55.4 711.05 1314.42 0.54 2234.52 0.32 2487.62 0.29 770 - 5782 24 Female 10.11 12.9 140 25.3 3663 1062.63 3.45 1806.48 2.03 1624.89 2.25 770 - 5782 25 Female 10 21.2 141 42.3 8779.29 1209.49 7.26 2056.13 4.27 2213.25 3.97 770 - 5782 26 Female 10.5 18.6 147.5 40.5 681.86 1224.66 0.56 2081.92 0.33 2163.38 0.32 770 - 5782 27 Female 10.2 17.3 138 32.9 4792.64 1116.91 4.29 1898.74 2.52 1920.36 2.50 770 - 5782 28 Female 10.2 13.9 134 25 5399.52 1032.23 5.23 1754.78 3.08 1612.15 3.35 770 - 5782 1 RodrÍGuez, G. Moreno et al. Clinical Nutrition (2002) [259] 2 Human energy requirements Report of a Joint FAO/WHO/UNU Expert Consultation, Rome, 17 - 24 October 200. http://www.fao.org/docrep/007/y5686e/y5686e00.htm 3 J.W. TUKEY. EXPLORATORY DATA ANALYSIS: The Future of Data Analysis (1977) [257] 4 Hoaglin, David C. et al. (1987) Fine - Tuning Some Resistant Rules for Outlier Labeling [258] 188 APPENDIX G : Reliability Test Table 14. Reliability (test - retest) of the translated (Arabic/English) and modified (cultural food) Block Kids 2004 FFQ performed on 5th grade boys (n=26) and girls (n=32) in Kuwait N=116 FFQs Average Measures Crude Data Intraclass Correlation 95% Confidence Interval F Test with True Value 0 Lower Bound Upper Bound Value df1 df2 Sig All Variables .674 .547 .779 8.667 57 2223 .000 Fruit .679 .458 .810 3.097 57 57 .000 Boys .843 .651 .929 6.789 25 25 .000 Girls .369 - .316 .695 1.569 31 31 .108 Vegetables .751 .581 .852 4.067 57 57 .000 Boys .858 .684 .936 6.912 25 25 .000 Girls .532 .059 .769 2.163 31 31 .018 Dairy .804 .669 .884 5.086 57 57 .000 Boys .958 .887 .983 29.311 25 25 .000 Girls .625 .222 .818 2.614 31 31 .005 Whole grains .551 .248 .733 2.251 57 57 .001 Boys .593 .111 .816 2.873 25 25 .005 Girls .536 .042 .774 2.131 31 31 .019 Kcals .822 .700 .895 5.608 57 57 .000 Boys .930 .797 .972 18.139 25 25 .000 Girls .573 .118 .792 2.312 31 31 .011 Protein .830 .714 .899 5.902 57 57 .000 Boys .934 .826 .972 18.376 25 25 .000 Girls .606 .183 .809 2.493 31 31 .007 Total fat .798 .659 .880 4.926 57 57 .000 Boys .906 .783 .959 11.848 25 25 .000 189 Table 14 (c Girls .550 .064 .782 2.184 31 31 .017 n - 6 fatty acids .668 .441 .803 3.018 57 57 .000 Boys .849 .667 .932 6.762 25 25 .000 Girls .215 - .644 .621 1.267 31 31 .257 n - 3 fatty acids .795 .655 .879 4.871 57 57 .000 Boys .904 .771 .958 11.738 25 25 .000 Girls .569 .105 .791 2.282 31 31 .012 Saturated fat .814 .687 .890 5.351 57 57 .000 Boys .909 .777 .961 12.699 25 25 .000 Girls .653 .286 .831 2.843 31 31 .002 Trans - fat .799 .662 .881 4.965 57 57 .000 Boys .867 .697 .941 8.312 25 25 .000 Girls .633 .244 .821 2.687 31 31 .004 Cholesterol .812 .684 .889 5.397 57 57 .000 Boys .844 .607 .933 7.674 25 25 .000 Girls .740 .467 .873 3.800 31 31 .000 Carbohydrates .808 .676 .886 5.180 57 57 .000 Boys .926 .768 .971 18.022 25 25 .000 Girls .534 .043 .773 2.130 31 31 .019 Added sugar .798 .659 .881 4.895 57 57 .000 Boys .924 .768 .970 17.197 25 25 .000 Girls .487 .003 .743 2.063 31 31 .024 Fiber .784 .636 .872 4.629 57 57 .000 Boys .906 .791 .957 10.936 25 25 .000 Girls .527 .016 .771 2.081 31 31 .023 Calcium .796 .656 .879 4.901 57 57 .000 Boys .918 .810 .964 13.327 25 25 .000 Girls .634 .241 .822 2.678 31 31 .004 Potassium .813 .685 .889 5.321 57 57 .000 Boys .938 .849 .973 18.514 25 25 .000 Girls .554 .073 .784 2.204 31 31 .016 190 Table 14 ( c Magnesium .766 .605 .861 4.279 57 57 .000 Boys .909 .795 .960 11.934 25 25 .000 Girls .427 - .196 .723 1.723 31 31 .068 Sodium .836 .724 .903 6.247 57 57 .000 Boys .921 .818 .965 13.871 25 25 .000 Girls .672 .327 .840 3.012 31 31 .001 Vitamin D .822 .699 .895 5.549 57 57 .000 Boys .955 .883 .981 26.248 25 25 .000 Girls .630 .239 .820 2.669 31 31 .004 Intraclass Correlation Coefficient statistics for FFQ baseline test versus follow up (retesting) in boys (1 - day interval) and girls (4 weeks interval). Two - way mixed effects model (Absolute agreement) where children effects are random and measures effects are fixed. This estimate is compu ted assuming the interaction effect is absent, because it is not estimable otherwise. 191 BIBLIOGRAPHY 192 BIBLIOGRAPHY 1. MOH. Kuwait Nutrition Surveillanc - 2012 official Report. 2012 2. Elkum N, Al - Arouj M, Sharifi M, Shaltout A, Bennakhi A. Prevalence of childhood obesity in the state of Kuwait. Pediatr Obes 2016 doi: 10.1111/ijpo.12090[published Online First: Epub Date]|. 3. Lloyd E. Chambless GH, Aaron R. Folsom, Wayne Rosamond, Moyses Szklo, A. Richey Sharrett, and Limin X. Clegg. Association of Coronary Heart Disease Incidence with Carotid Arterial Wall Thickness and Major Risk Factors The Atherosclerosis Risk in Communities (ARIC) Study, 1987 1993. American Journal of Epidemiology 1997; 146 (6 ) 4. Keaven M. Anderson P, Patricia M. Odell, PhD, Peter W. F. Wilson, MD, William B. Kannel, MD, MPH Framingham and Boston, Mass. Cardiovascular disease risk profiles. Journal 1991 5. William B. Kannel M, Daniel L. McGee, PhD. Diabetes and Cardiovascular Disease. Jama 1979; 241 :2035 - 38 6. Jeremiah Stamler M, Olga Vaccaro, M D , James D. Neaton, Ph D , Deborah Wentworth, M P h . Diabetes, Other Risk Factors, and 12 - Yr Cardiovascular Mortality for Men Screened in the Multiple Risk Factor Interve ntion Trial. Diabetes care 1993; 16 (2) 7. Daniel Levy MD, Robert J. Garrison, M.S., Daniel D. Savage, M.D., Ph.D.*, William B. Kannel, M.D., M.P.H., and William P. Castelli, M.D. Prognostic Implications of Echocardiographically Determined Left Ventricular Mass in the Framingham Heart Study. N E ngl J Med 1990; 322 :1561 - 66 8. Peter W.F. Wilson RDA, and William P. Castelli. High density lipoprotein cholesterol and mortality. The Framingham Heart Study. Arteriosclerosis 1988; 8 :737 - 41 9. Gerald S. B Erenson Md, S Athanur R. S Rinivasan , P h .D., W Eihang B Ao , P h .D., W i lliam P. N Ewman Iii, M.D.,, R Ichard E. T Racy Md, P h .D., And W Endy A. W Attigney , M.S., For The Bogalusa Heart Study . Association between Multiple Cardiovascular Risk Factors and Atherosclerosis in Children and Young Adults. The New England Journal of Medicine 1998; 338 (23):1650 - 56 10. Emelia J. Benjamin M, ScM; Daniel Levy, MD; Sonya M. Vaziri, MD, MPH. Independent Risk Factors for Atrial Fibrillation in a Population - Based Cohort. Jama 1994; 271 (11):840 - 44 11. Peter W.F. Wilson MRBDA, PhD; Daniel Levy, MD; Albert M. Belanger, BS; Halit Silbershatz, PhD; William B. Kannel, MD. Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation 1998; 97 :1837 - 47 12. J a S. Canadian guidelines for cardiac rehabilita tion and cardiovascular disease prevention: 3rd Edition. 2009 193 13. Peterson MD, Liu D, IglayReger HB, Saltarelli WA, Visich PS, Gordon PM. Principal component analysis reveals gender - specific predictors of cardiometabolic risk in 6th graders. Cardiovascul ar diabetology 2012; 11 :146 doi: 10.1186/1475 - 2840 - 11 - 146[published Online First: Epub Date]|. 14. Williams CL. Cardiovascular Health in Childhood: A Statement for Health Professionals From the Committee on Atherosclerosis, Hypertension, and Obesity in the Young (AHOY) of the Council on Cardiovascular Disease in the Young, American Heart Association. Circulation 2002; 106 (1):143 - 60 doi: 10.1161/01.cir.0000019555.61092.9e[published Online First: Epub Date]|. 15. Melanson KJ. Dietary Factors in Reducing Risk of Cardiovascular Diseases. American Journal of Lifestyle Medicine 2007; 1 (1):24 - 28 doi: 10.1177/1559827606294793[published Online First: Epub Date]|. 16. Camhi SM, Whitney Evans E, Hayman LL, Lichtenstein AH, Must A. Healthy eating index and metabolically healthy obesity in U.S. adolescents and adults. Preventive medicine 2015; 77 :23 - 7 doi: 10.1016/j.ypmed.2015.04.023[published Online First: Epub Date]|. 17. Sims EA. Are there persons who are obese, but metabolically healthy? Metabolism 2001; 50 (12):1499 - 50 4 doi: 10.1053/meta.2001.27213[published Online First: Epub Date]|. 18. Pearson TA. AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: 2002 Update: Consensus Panel Guide to Comprehensive Risk Reduction for Adult Patients Without C oronary or Other Atherosclerotic Vascular Diseases. Circulation 2002; 106 (3):388 - 91 doi: 10.1161/01.cir.0000020190.45892.75[published Online First: Epub Date]|. 19. Gidding SS, Dennison BA, Birch LL, et al. Dietary recommendations for children and adolesce nts: a guide for practitioners: consensus statement from the American Heart Association. Circulation 2005; 112 (13):2061 - 75 doi: 10.1161/CIRCULATIONAHA.105.169251[published Online First: Epub Date]|. 20. Gidding SS, Dennison BA, Birch LL, et al. Dietary rec ommendations for children and adolescents: a guide for practitioners. Pediatrics 2006; 117 (2):544 - 59 doi: 10.1542/peds.2005 - 2374[published Online First: Epub Date]|. 21. Consultation JWFE. DIET, NUTRITION AND THE PREVENTION OF CHRONIC DISEASES. 2003 22. Nishida C, Uauy R, Kumanyika S, Shetty P. The Joint WHO/FAO Expert Consultation on diet, nutrition and the prevention of chronic diseases: process, product and policy implications. Public health nutrition 2007; 7 (1a) doi: 10.1079/phn2003592[published Online First: Epub Date]|. 23. Organization WH. Global recommendations on physical activity for health. 2010 24. Lear SA, Hu W, Rangarajan S, et al. The effect of physical activity on mortality and cardiovascular - income, middle - income, and low - income countries: the PURE study. The Lancet 2017 doi: 10.1016/s0140 - 6736(17)31634 - 3[published Online First: Epub Date]|. 25. Andersen LB, Harro M, Sardinha LB, et al. Physical activity and clustered cardiovascular risk in children : a cross - sectional study (The European Youth Heart Study). The Lancet 194 2006; 368 (9532):299 - 304 doi: 10.1016/s0140 - 6736(06)69075 - 2[published Online First: Epub Date]|. 26. NetoI ODdA, SilvaI RdCR, AssisI AMO, PintoI EdJ. Factors associated with dyslipidemia in children and adolescents enrolled in public schools of Salvador, Bahia. Rev Bras Epidemiol 2012; 15 (2):335 - 45 27. Kit BK, Kuklina E, Carroll MD, Ostchega Y, Freedman DS, Ogden CL. Prevalence of and trends in dyslipidemia and blood pressure among US chi ldren and adolescents, 1999 - 2012. JAMA pediatrics 2015; 169 (3):272 - 9 doi: 10.1001/jamapediatrics.2014.3216[published Online First: Epub Date]|. 28. Sharma AK, Metzger DL, Rodd CJ. Prevalence and Severity of High Blood Pressure Among Children Based on the 2 017 American Academy of Pediatrics Guidelines. JAMA pediatrics 2018; 172 (6):557 - 65 doi: 10.1001/jamapediatrics.2018.0223[published Online First: Epub Date]|. 29. WHO. Noncommunicable Diseases Country Profiles 2014. 2014 30. Al - Nafisi AAaH. Public knowled ge of cardiovascular disease and its risk factors in Kuwait: a cross - sectional survey. BMC Public Health 2014 2014; 14 (1131) 31. Al - Hooti SN. Food consumption pattern for the population of the state of Kuwait based on food balance sheets. Ecology of Food and Nutrition 2002; 41 (6):501 - 14 doi: 10.1080/03670240214731[published Online First: Epub Date]|. 32. Al - Shawi AN. Nutrient Intakes of University Women in Kuwait. The Journal of the Royal Society for the Promotion of Health 1992; 112 (3):114 - 18 doi: 10.1177/ 146642409211200302[published Online First: Epub Date]|. 33. B.H. Dashti * FA - A, M.S. Khalafawi, S. Al - Zenki, W. Sawaya. Nutrient contents of some traditional Kuwaiti dishes. Food Chemistry 2001; 74 : 169 75 34. Golzarand M, Mirmiran P, Jessri M, Toolabi K, Mojarrad M, Azizi F. Dietary trends in the Middle East and North Africa: an ecological study (1961 to 2007). Public health nutrition 2012; 15 (10):1835 - 44 doi: 10.1017/S1368980011003673[published Online First: Epub Date]|. 35. S. Zaghloul CW, M. Al Somaie, and P. Prakash Low adherence of Kuwaiti adults to fruit and vegetable dietary guidelines. EMHJ 2012; 18 (5) 36. Zaghloul S, Al - Hooti SN, Al - Hamad N, et al. Evidence for nutrition transition in Kuwait: over - consumption of macronutrients and obesity. Public health nutrition 2013; 16 (4):596 - 607 doi: 10.1017/S1368980012003941[published Online First: Epub Date]|. 37. Ibrahim El - Bayoumy M, DrPH, Ibrahim Shady, MPH, DrPH, and Hesham Lotfy, MBBCh, MPH. Prevalence of Obesity Among Adolescents (10 to 14 Years ) in Kuwait. Asia - Pacific Journal of Public Health 2009; 21 (2) 38. Ramadan J, Barac - Nieto M. Reported frequency of physical activity, fitness, and fatness in Kuwait. American journal of human biology : the official journal of the Human Biology Council 2003 ; 15 (4):514 - 21 doi: 10.1002/ajhb.10190[published Online First: Epub Date]|. 39. KNSS. Kuwait Nutrition Surveillanc - 2012 official. 2013 195 40. Ng SW, Zaghloul S, Ali HI, Harrison G, Popkin BM. The prevalence and trends of overweight, obesity and nutrition - re lated non - communicable diseases in the Arabian Gulf States. Obesity reviews : an official journal of the International Association for the Study of Obesity 2011; 12 (1):1 - 13 doi: 10.1111/j.1467 - 789X.2010.00750.x[published Online First: Epub Date]|. 41. A.N. Al - Isa LT. Body mass index of Kuwaiti adolescents aged 10 14 years: reference percentiles and curves. Eastern Mediterranean Health Journal 2008; 14 (2) 42. Abdulwahab Naser Al - Isa;Thalib L. Body mass index of Kuwaiti children aged 3 - 9 years: reference per centiles and curves. The Journal of the Royal Society for the Promotion of Health 2006; 126 (1):41 43. Ahmed Bayoumi and Mohamed A. A. Moussa . Kuwait nutritional survey: comparison of the nutritional status of Kuwaiti children aged 0 - 5 years with the NCHS/ CDC reference population Bulletin of the World Health Organization 1985; 63 (3):521 - 26 44. Kelishadi R. Childhood overweight, obesity, and the metabolic syndrome in developing countries. Epidemiologic reviews 2007; 29 :62 - 76 doi: 10.1093/epirev/mxm003[publish ed Online First: Epub Date]|. 45. Naser Al - Isa MAAMA. Nutritional status of Kuwaiti elementary school children aged 6 10 years: comparison with the NCHS/CDC reference population. International journal of food sciences and nutrition 2000; 51 (4):221 - 28 doi: 10.1080/09637480050077103[published Online First: Epub Date]|. 46. P. Mirmiran RS - K, S. Jalali - Farahani and F. Azizi Childhood obesity in the Middle East: a review. EMHJ 2010; 16 (9) 47. Cole TJ, Lobstein T. Extended international (IOTF) body mass index c ut - offs for thinness, overweight and obesity. Pediatr Obes 2012; 7 (4):284 - 94 doi: 10.1111/j.2047 - 6310.2012.00064.x[published Online First: Epub Date]|. 48. Tim J Cole MCB, KatherineMFlegal, William H Dietz. Establishing a standard definition for child over weight and obesity worldwide: international survey. Bmj 2000; 320 (6) 49. de Onis M. Development of a WHO growth reference for school - aged children and adolescents. Bulletin of the World Health Organization 2007; 85 (09):660 - 67 doi: 10.2471/blt.07.043497[published Online First: Epub Date]|. 50. Mercedes de Onis CG, 3 Adelheid W. Onyango,2 and Elaine Borghi2. Comparison of the WHO Child Growth Standards and the CDC 2000 Growth Chart. American Society for Nutrition 2007 51. Voss LD, Metcalf BS, Jeffery AN, Wilkin TJ. IOTF thresholds for overweight and obesity and their relation to metabolic risk in children (EarlyBird 20). Int J Obes (Lond) 2006; 30 (4):606 - 9 doi: 10.1038/sj.ijo.0803187[published Online First: Epub Date]|. 52. Must A, Anderson SE. Body mass index in children and adolescents: considerations for population - based applications. Int J Obes (Lond) 2006; 30 (4):590 - 4 doi: 10.1038/sj.ijo.0803300[published Online First: Epub Date]|. 196 53. Magnhild L Pollestad Kolsgaard GJ, Cathrine Brunborg, Sigmund A Anderssen, Serena Tonstad, Lene Frost Andersen Reduction in BMI z - score and improvement in cardiometabolic risk factors in obese children and adolescents. The Oslo Adiposity Intervention Study - a hospital/public health nurse combined treatment. BMC Pediatrics 2011; 11 (47) 54. Hebestreit A, Bornhorst C, Barba G, et al. Associations between energy intake, daily food intake and energy density of foods and BMI z - score in 2 - 9 - year - old Europea n children. European journal of nutrition 2014; 53 (2):673 - 81 doi: 10.1007/s00394 - 013 - 0575 - x[published Online First: Epub Date]|. 55. Au LE, Economos CD, Goodman E, et al. Dietary intake and cardiometabolic risk in ethnically diverse urban schoolchildren. J ournal of the Academy of Nutrition and Dietetics 2012; 112 (11):1815 - 21 doi: 10.1016/j.jand.2012.07.027[published Online First: Epub Date]|. 56. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk -- a review of the literature. European journal of clinical nutrition 2010; 64 (1):16 - 22 doi: 10.1038/ejcn.2009.68[published Online First: Epub Date]|. 57. Cook S, Auinger P, Huang TT. Growth curves for cardio - metabolic risk factors in children and adolescents. The Journal of pediatrics 2009; 155 (3):S6 e15 - 26 doi: 10.1016/j.jpeds.2009.04.051[published Online First: Epub Date]|. 58. Agredo - Zuniga RA, Aguilar - de Plata C, Suarez - Ortegon MF. Waist:height ratio, waist circumferenc e and metabolic syndrome abnormalities in Colombian schooled adolescents: a multivariate analysis considering located adiposity. The British journal of nutrition 2015; 114 (5):700 - 5 doi: 10.1017/S0007114515002275[published Online First: Epub Date]|. 59. Mar theAfad - A Classification. PloS one 2013; 8 (2):e55849 doi: 10.1371/[published Online First: Epub Date]|. 60. M.A.A. Moussa;Shaltout AAN - D, D;Mourad, M;AlSheikh, N;Agha , N;Galal, D O. Factors associated with obesity in Kuwaiti childrenEuropean Journal of Epidemiology; 1999; 15 (1):41 61. AlSumaie M. The Kuwait nutrition surveillance system (KNSS): an example for the region. MOH/KUWAIT 2011 62. Blössner MdOaM. WHO Global Database on Child Growth and Malnutrition. WHO/NUT 1997; 4 63. FAO JW. Dietary Recommendations in the Report WHO Technical Report Series 2003; 916 64. Nations FaAOotU. FAO/WHO expert consultation on human vitamin and mineral requirements. 2001 65. Consultation JFWE. Vitamin and mineral requirements in human nutrition : report of a joint FAO/WHO expert: 2 nd Edition. 1989 66. USDA. 2015 - 2016 Dietary Guidelines for Americans - Eight Edition. 2015 67. Mohamed A.A. MAA, Shaltoutb Nashami Al - Sheikhc, Nizam Aghac. Prevalence of Obesity among 6 - 13 Kuwaiti children. Med Principles Pract 1999; 8 :272 - 80 197 68. Health MO. Kuwait Nutrition Surveillance System 2014 Annual Report. Food and Nutrition Administration Kuwait 2015. 69. E.A. Saleh AARM. Hypertension a nd its determinants among primary school children in Kuwait an epidemiological study. Eastern Mediterranean Health Journal 2000; 6 (2 - 3):333 - 37 70. Stavnsbo M, Resaland GK, Anderssen SA, et al. Reference values for cardiometabolic risk scores in children a nd adolescents: Suggesting a common standard. Atherosclerosis 2018; 278 :299 - 306 doi: 10.1016/j.atherosclerosis.2018.10.003[published Online First: Epub Date]|. 71. Spear BA, Barlow SE, Ervin C, et al. Recommendations for treatment of child and adolescent o verweight and obesity. Pediatrics 2007; 120 Suppl 4 :S254 - 88 doi: 10.1542/peds.2007 - 2329F[published Online First: Epub Date]|. 72. Philip B Mellen ADL, Janet A Tooze, Mara Z Vitolins, Lynne E Wagenknecht, and David M Herrington. Whole - grain intake and carotid artery atherosclerosis in a multiethnic cohort: the Insulin Resistance Atherosclerosis Study. Am J Clin Nutr 2007 2007; 85 (1):495 - 502 73. State USDo. Background Note: Kuwait. Bureau of Public Affairs 1988 74. Information TPAFC. Population Public ation_Kuwait. PACI 2015 75. Robert T. Jackson ZA - M. Iron deficiency is a more important cause of anemia than hemoglobinopathies in Kuwaiti adolescent girls. The American Society for Nutritional Sciences 2000; 130 (5):1212 - 16 76. Ahmed F, Waslien C, Al - Sumaie MA, Prakash P. Secular trends and risk factors of overweight and obesity among Kuwaiti adults: National Nutrition Surveillance System data from 1998 to 2009. Public health nutrition 2012; 15 (11):2124 - 30 doi: 10.1017/S1368980011003685[published Onl ine First: Epub Date]|. 77. Mohamed A.A. Moussaa AMES. Mortality in Kuwait Pattern and Seasonality. Med Principles Pract 1998; 7 :18 - 27 78. Ghaida'a F Al - Mutairi AAA - B, Ibrahim M Abdul - Halim, Huda K Alabdul - Jalil, Samer A Karraz, Seham A Ali. PREVALENCE OF STATIN USE IN TYPE2 DIABETES MELLITUS AND/OR HYPERTENSIVE PATIENTS AT RISK OF CORONARY HEART DISEASE IN SHAMIYA FAMILY PRACTICE HEALTH CENTER - KUWAIT. Bull. Alex. Fac. Med 2009; 45 (4) 79. AL - O waish RA. Descriptive epidemiology of acute myocardial infar ction in Kuwait, 1978. Bulletin ofthe WorldHealth Organization 1983; 61 (3):509 - 16 80. Alarouj M, Bennakhi A, Alnesef Y, Sharifi M, Elkum N. Diabetes and associated cardiovascular risk factors in the State of Kuwait: the first national survey. Int J Clin P ract 2013; 67 (1):89 - 96 doi: 10.1111/ijcp.12064[published Online First: Epub Date]|. 81. Farrag OL. The status of child nutrition in the Gulf Arab States. Journal of Tropical Pediatrics 1983; 29 (6):325 - 9 198 82. James A. Stone HMA, Neville G. Suskin. Canadian Guidelines for Cardiac Rehabilitation and Cardiovascular Disease Prevention Canadian Association of Cardiac Rehabilitation 2009; 3rd Edition 83. Al - Hazzaa HM, Al - Sobayel HI, Musaiger AO. Convergent validity of the Arab Teens Lifestyle Study (ATLS) physica l activity questionnaire. International journal of environmental research and public health 2011; 8 (9):3810 - 20 doi: 10.3390/ijerph8093810[published Online First: Epub Date]|. 84. WHO. GLOBAL STATUS REPORT on noncommunicable diseases 2014. 2014 85. Friend A, Craig L, Turner S. The prevalence of metabolic syndrome in children: a systematic review of the literature. Metabolic syndrome and related disorders 2013; 11 (2):71 - 80 doi: 10.1089/met.2012.0122[published Online First: Epub Date]|. 86. Morandi A, Miragl ia Del Giudice E, Martino F, Martino E, Bozzola M, Maffeis C. Anthropometric indices are not satisfactory predictors of metabolic comorbidities in obese children and adolescents. The Journal of pediatrics 2014; 165 (6):1178 - 83 e2 doi: 10.1016/j.jpeds.2014.07 .004[published Online First: Epub Date]|. Rong Wei, PhD*; Laurence M. Grummer - MD, PhD, ScD§; and Clifford L . Johnson, MSPH. Centers for Disease Control and Prevention 2000 Growth Charts for the United States: Improvements to the 1977 National Center for Health Statistics Version. Pediatrics 2002; 109 (1) 88. Joseph T. Flynn M, MS, FAAP, a David C. Kaelber, MD, PhD, MPH, FAAP, FACP, FACMI, b. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics 2017; 140 (3) 89. Expert Panel on Integrated Guidelines for Cardiovascular H, Risk Reduction in C, Adolescents, National Heart L, Blood I. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics 2011; 128 Suppl 5 :S213 - 56 doi: 10.1542/peds.2009 - 2107C[published Online First: Epub Date]|. 90. Al - Isa AN. Changes in body mass index (BMI) and prevalence of obesity among Kuwaitis 1980 - 1994. International Journal of Obesity 1997; 21 :1093 - 99 91. Ahmed F, Waslien C, Al - Sumaie M, Prakash P. Trends and risk factors of hypercholesterolemia among Kuwaiti adults: National Nutrition Surveillance Data from 1998 to 2009. Nutrition 2012; 28 (9):917 - 23 doi: 10.1016/j.nut.2011.12.012[published Online First: Epub Date]|. 92. Kamel El - Rashed RA - O, Abdullah Diab. Hypertension in Kuw ait: the Past, Present and Future. Saudi J Kidney Dis Transplant 1999; 10 (3):357 - 64 93. Ibrahim Al Rashdan YAN. Prevalence of Overweight, Obesity, and Metabolic Syndrome Among Adult Kuwaitis: Results From Community - based National Survey. Angiology 2010; 61 (1):42 - 48 94. Karageorgi S, Alsmadi O, Behbehani K. A review of adult obesity prevalence, trends, risk factors, and epidemiologic methods in Kuwait. Journal of obesity 2013; 2013 :378650 doi: 10.1155/2013/378650[published Online First: Epub Date]|. 199 95. Fa ruk Ahmed1* CW, Mona A Al - Sumaie3, Prasanna Prakash3 and Ahmad Allafi. Trends and risk factors of hyperglycemia and diabetes among Kuwaiti adults: National Nutrition Surveillance Data from 2002 to 2009. BMC public health 2013; 13 (103) 96. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes research and clinical practice 2010; 87 (1):4 - 14 doi: 10.1016/j.diabres.2009.10.007[published Online First: Epub Date]|. 97. Malik VS, Willett WC, Hu FB. Global obesit y: trends, risk factors and policy implications. Nature reviews. Endocrinology 2013; 9 (1):13 - 27 doi: 10.1038/nrendo.2012.199[published Online First: Epub Date]|. 98. 2000 RoaWc. WHO Technical Counsultation. Obesity: preventing and managing the global epide mic. 2000:0512 - 3054 99. Kopelman PG. Obesity as a medical problem. NATURE 2000; 404 (6) 100. Ebbeling CB, Pawlak DB, Ludwig DS. Childhood obesity: public - health crisis, common sense cure. The Lancet 2002; 360 (9331):473 - 82 doi: 10.1016/s0140 - 6736(02)09678 - 2[published Online First: Epub Date]|. 101. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 2010; 92 (5):1257 - 64 doi: 10.3945/ajcn.2010.29786[published Online First: Epub Date] |. 102. Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta - analysis. Bmj 2012; 345 :e4759 doi: 10.1136/bmj.e4759[published Online First: Epub Date]|. 103. Angelopoulos PD, Milionis HJ, Moschonis G, Manios Y. Relations between obesity and hypertension: preliminary data from a cross - sectional study in primary schoolchildren: the children study. European journal of clinical nutr ition 2006; 60 (10):1226 - 34 doi: 10.1038/sj.ejcn.1602442[published Online First: Epub Date]|. 104. May AL, Kuklina EV, Yoon PW. Prevalence of cardiovascular disease risk factors among US adolescents, 1999 - 2008. Pediatrics 2012; 129 (6):1035 - 41 doi: 10.1542/peds.2011 - 1082[published Online First: Epub Date]|. 105. Ronald M. Krauss MMW, EdD. Obesity Impact on Cardiovascular Disease. Circulation 1998; 98 :1472 - 76 106. Al - Kandari YY. Prevalence of obesity in Kuwait and its relation to sociocultural varia bles. obesity reviews 2006; 7 :147 - 54 107. Zaghloul S, Al - Hooti SN, Al - Hamad N, et al. Evidence for nutrition transition in Kuwait: over - consumption of macronutrients and obesity. Public health nutrition 2012; 16 (4):596 - 607 doi: 10.1017/S1368980012003941[published Online First: Epub Date]|. 108. Ahmad R. Al - Haifi MAA - F, Buthaina I. Al - Athari, Fahhad A. Al - Ajmi, Ahmad R. Allafi, Hazzaa M. Al - Hazzaa, and Abdulrahman O. Musaiger. Relative Contribution of Physical Activity, 200 Sedentary Behaviors, and Dietary Habits to the Prevalence of Obesity among Kuwaiti Adolescents. Food and Nutrition Bulletin 2013; 34 (1) 109. Al - Isa A, Akanji AO, Thalib L. Prevalence of the metabolic syndrome among female Kuwaiti adolescents using two different cr iteria. The British journal of nutrition 2010; 103 (1):77 - 81 doi: 10.1017/S0007114509991425[published Online First: Epub Date]|. 110. Al - Isa AN. Body mass index, overweight and obesity among Kuwaiti intermediate school adolescents aged 10 - 14 years. European journal of clinical nutrition 2004; 58 (9):1273 - 7 doi: 10.1038/sj.ejcn.1601961[published Online First: Epub Date]|. 111. Amuna P, Zotor FB. Epidemiological and nutrition transition in developing countries: impact on human health and development. The Procee dings of the Nutrition Society 2008; 67 (1):82 - 90 doi: 10.1017/S0029665108006058[published Online First: Epub Date]|. 112. A hmed B ayoumi . Kuwait nutritional survey: comparison of the nutritional status of Kuwaiti children aged 0 - 5 years with the NCHS/CDC re ference population*. Bulletin of the World Health Organization 1985; 63 (3):521 - 26 113. Ahmed Bayoumi, Mohamed A. A. Moussa . Kuwait Nutritional Survey Comparison of the Nutritional Status of Kuwaiti Children Aged 6 to 9 Years with the NCHS CDC Reference Population. International journal of epidemiology 1985; 14 (3) 114. Dietz MCBaWH. Workshop on childhood obesity: summary of the discussion. m J Clin Nutr 1999 1999; 70(suppl) :173S - 5S 115. Chinn S, Rona RJ. International definitions of overweight and obes ity for children: a lasting solution? Ann Hum Biol 2002; 29 (3):306 - 13 doi: 10.1080/03014460110085340[published Online First: Epub Date]|. 116. Keke LM, Samouda H, Jacobs J, et al. Body mass index and childhood obesity classification systems: A comparison o f the French, International Obesity Task Force (IOTF) and World Health Organization (WHO) references. Rev Epidemiol Sante Publique 2015; 63 (3):173 - 82 doi: 10.1016/j.respe.2014.11.003[published Online First: Epub Date]|. 117. Cynthia L. Ogden PRJK, DrPH*; K PhD§; Rong Wei, PhD*; Laurence M. Grummer - Roche M, PhD, ScD§; and Clifford L. Johnson, MSPH*. Centers for Disease Control and Prevention 2000 Growth Charts for the United States: Improvements to the 1977 National Center for Health Statistics Version. Pediatrics 2002; 109 (1) 118. Nancy F. Butte CG, 3 and Mercedes de Onis4. Evaluation of the Feasibility of InternationalGrowth Standards for School - Aged Children and Adolescents. J. Nutr 2007; 137 :153 - 57 119. WHO. A Growth Chart for International Use in Maternal and Child Health Care: Guidelines for Primary Health Care Personnel. Geneva, Switzerland. 1978 120. Cynthia L. Ogden PRJK, DrPH*; Katherine M. F PRW, PhD*; Laurence M. Grummer - PhD, ScD§; and Clifford L. Johnson, MSPH*. Centers for Disease Control and Prevention 2000 201 Growth Charts for the United States: Improvements to the 1977 National Center for Health Statistics Version. Pediatrics 2002; 109 (1) 121. Cole TJ, Flegal KM, Nicholls D, Jackson AA. Body mass index cut offs to define thinness in children and adolescents: international survey. Bmj 2007; 335 (7612):194 doi: 10.1136/bmj.39238.399444.55[published Online First: Epub Date]|. 122. Organization WH. Nutrition Landscape Information System (N LIS) Country Profile Indicators Interpretation Guide WHO Library Cataloguing - in - Publication Data 2010:1 - 39 123 . J Kain1* RU, F Vio1 and C Albala1. Trends in overweight and obesity prevalence in Chilean children Comparison of three definitions. European journal of clinical nutrition 2002; 56 :200 - 04 Defining Body Fatness in Adolescents: A Proposal of the Afad - A Classification. PloS one 2013; 8 (2) doi: 10.1371/[published Online First: Epub Date]|. 125. Fu WP, Lee HC, Ng CJ, et al. Screening for childhood obesity: international vs population - specific def initions. Which is more appropriate? International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity 2003; 27 (9):1121 - 6 doi: 10.1038/sj.ijo.0802385[published Online First: Epub Date]|. 12 6. de Onis M, Onyango A, Borghi E, et al. Worldwide implementation of the WHO Child Growth Standards. Public health nutrition 2012; 15 (9):1603 - 10 doi: 10.1017/S136898001200105X[published Online First: Epub Date]|. 127. de Onis M, Onyango AW, Borghi E, Garz a C, Yang H. Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: implications for child health programmes. Public health nutrition 2007; 9 (07) doi: 10.101 7/phn20062005[published Online First: Epub Date]|. 128. Monasta L, Lobstein T, Cole TJ, Vignerova J, Cattaneo A. Defining overweight and obesity in pre - school children: IOTF reference or WHO standard? Obesity reviews : an official journal of the Internati onal Association for the Study of Obesity 2011; 12 (4):295 - 300 doi: 10.1111/j.1467 - 789X.2010.00748.x[published Online First: Epub Date]|. 129. Jackson RT, Al Hamad N, Prakash P, Al Somaie M. Waist circumference percentiles for Kuwaiti children and adolescen ts. Public health nutrition 2010; 14 (1):70 - 6 doi: 10.1017/S1368980010002600[published Online First: Epub Date]|. 130. Kilpi F, Webber L, Musaigner A, et al. Alarming predictions for obesity and non - communicable diseases in the Middle East. Public health nu trition 2013; 17 (5):1078 - 86 doi: 10.1017/S1368980013000840[published Online First: Epub Date]|. 131. Shields M, Tremblay MS. Canadian childhood obesity estimates based on WHO, IOTF and CDC cut - points. International journal of pediatric obesity : IJPO : an official journal of the International Association for the Study of Obesity 2010; 5 (3):265 - 73 doi: 10.3109/17477160903268282[published Online First: Epub Date]|. 132. Ni Mhurchu C, Rodgers A, Pan WH, Gu DF, Woodward M, Asia Pacific Cohort Studies C. Body mass index and cardiovascular disease in the Asia - Pacific Region: an overview of 33 cohorts 202 involving 310 000 participants. International journal of epidemiology 2004; 33 (4):751 - 8 doi: 10.1093/ije/dyh163[published Online First: Epub Date]|. 133. The presen tation and use of height and weight data for comparing the nutritional status of groups ot children under the age of 10 years. Bulletin of the World Health Organization 1977; 55 (4):489 - 98 134. Latham NS . Nutritional Anthropometry in the Identification of Malnutrition in Childhood. Environmental Child Health 1971 PhD§; Rong Wei, PhD*; Laurence M. Grummer - Roche, MD, PhD, S cD§; and Clifford L. Johnson, MSPH*. Centers for Disease Control and Prevention 2000 Growth Charts for the United States: Improvements to the 1977 National Center for Health Statistics Version. Pediatrics 2002; 109 :45 - 60 136. Flegal KM, Wei R, Ogden CL, Fr eedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index - for - age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin Nutr 2009; 90 (5):1314 - 20 doi: 10.3945/ajcn.2009.28335[published Online First: Ep ub Date]|. 137. SERVICES DOHAH. CDC Table for Calculated Body Mass Index Values for Selected Heights and Weights for Ages 2 to 20 Years. Centers for Disease Control and Prevention 2000 138. Organization WH. BMI - for - age BOYS 5 to 19 years (z - scores). 200 7 139. Organization WH. BMI - for - age GIRLS 5 to 19 years (z - scores). 2007 140. Inokuchi M, Matsuo N, Takayama JI, Hasegawa T. BMI z - score is the optimal measure of annual adiposity change in elementary school children. Ann Hum Biol 2011; 38 (6):747 - 51 doi: 10.3109/03014460.2011.620625[published Online First: Epub Date]|. 141. T. J. Cole . SMOOTHING REFERENCE CENTILE CURVES: THE LMS METHOD AND PENALIZED LIKELIHOOD STATISTICS IN MEDICINE 1992; 11 :1305 - 19 142. Cole KMFaTJ. Construction of LMS Parameters fo r the Centers for Disease Control and Prevention 2000 Growth Charts. National Health Statistics Reports 2013; 63 143. Al - Isa AN, Campbell J, Desapriya E. Factors Associated with Overweight and Obesity among Kuwaiti Elementary Male School Children Aged 6 - 1 0 Years. International journal of pediatrics 2010; 2010 doi: 10.1155/2010/459261[published Online First: Epub Date]|. 144. Bosy - Westphal A, Booke CA, Blocker T, et al. Measurement site for waist circumference affects its accuracy as an index of visceral an d abdominal subcutaneous fat in a Caucasian population. The Journal of nutrition 2010; 140 (5):954 - 61 doi: 10.3945/jn.109.118737[published Online First: Epub Date]|. 145. Zimmet P AKGM, Kaufman F, Tajima N, Silink M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S. The metabolic syndrome in children and adolescents an IDF consensus report. Pediatric Diabetes 2007; 8 :299 - 306 203 146. SC Savva MT, ME Savva, Y Kourides, A Panagi, N Silikiotou, C Georgiou, A Kafatos. Waist circumference and waist - to - height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. International Journal of Obesity (2000) 24, 2000; 24 :1453 - 58 147. Xi B, Mi J, Zhao M, et al. Trends in abdominal obesity among U.S. children and adolescents. Pediatrics 2014; 134 (2):e334 - 9 doi: 10.1542/peds.2014 - 0970[published Online First: Epub Date]|. 148. McCarthy HD, Ashwell M. A study of central fatness using waist - to - height ratios in UK children and adolescents over two decades supports the simple message -- 'keep your waist circumference to less than half your height'. Int J Obes (Lond) 2006; 30 (6):988 - 92 doi: 10.1038/sj.ijo.0803226[published Online First: Epub Date]|. 149. Rachael W Taylor IEJ, Sheila M Williams, and Ailsa Goulding. Evaluation of waist cir cumference, waist - to - hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual - energy X - ray absorptiometry, in children aged 3 19 y1,2,3. Am J Clin Nutr 2000; 72 :490 - 5 150. Li C, Ford ES, Mokdad AH, Cook S. Recent t rends in waist circumference and waist - height ratio among US children and adolescents. Pediatrics 2006; 118 (5):e1390 - 8 doi: 10.1542/peds.2006 - 1062[published Online First: Epub Date]|. 151. David S Freedman HSK, Zuguo Mei, Laurence M Grummer - Strawn, William H Dietz, Sathanur R Srinivasan, and Gerald S Berenson. Relation of body mass index and waist - to - height ratio to cardiovascular disease risk factors in children and adolescents the Bogalusa Heart Study1,2,3. Am J Clin Nutr 2007; 86 :33 - 40 152. Mitsuhiko H ara ES, Fujihiko Iwata, Tomoo Okada, Kensuke Harada. Waist - to - height Ratio is the Best Predictor of Cardiovascular Disease Risk Factors in Japanese Schoolchildren. Journal of Atherosclerosis and Thrombosis 2002; 9 (3):127 - 32 153. Ashwell M, Hsieh SD. Six r easons why the waist - to - height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. International journal of food sciences and nutrition 2005; 56 (5):30 3 - 7 doi: 10.1080/09637480500195066[published Online First: Epub Date]|. 154. M.H. Slaughter , R.A. Boileau , C.A. Horswill , R.J. Stillman , M.D. Van Loan, D.A. Bemben . Skinfold Equations for Estimation of Body Fatness in Children and Youth. Human Biology 1988; 60 (5):709 - 23 155. Laurson KR, Eisenmann JC, Welk GJ. Body fat percentile curves for U.S. children and adolescents. American journal of preventive medicine 2011; 41 (4 Suppl 2):S87 - 92 doi: 10.1016/j.amepre.2011.06.044[published Online First: Epub Date] |. 156. Laurson KR, Eisenmann JC, Welk GJ. Development of youth percent body fat standards using receiver operating characteristic curves. American journal of preventive medicine 2011; 41 (4 Suppl 2):S93 - 9 doi: 10.1016/j.amepre.2011.07.003[published Online First: Epub Date]|. 157. Dencker M, Thorsson O, Linden C, Wollmer P, Andersen LB, Karlsson MK. BMI and objectively measured body fat and body fat distribution in prepubertal children. Clin Physiol Funct Imaging 2007; 27 (1):12 - 6 doi: 10.1111/j.1475 - 097X.200 7.00709.x[published Online First: Epub Date]|. 204 158. Zeng Q, Dong S - Y, Sun X - N, Xie J, Cui Y. Percent body fat is a better predictor of cardiovascular risk factors than body mass index. Brazilian Journal of Medical and Biological Research 2012; 45 (7):591 - 60 0 doi: 10.1590/s0100 - 879x2012007500059[published Online First: Epub Date]|. 159. Al Zenki S, Al Omirah H, Al Hooti S, et al. High prevalence of metabolic syndrome among Kuwaiti adults -- a wake - up call for public health intervention. International journal o f environmental research and public health 2012; 9 (5):1984 - 96 doi: 10.3390/ijerph9051984[published Online First: Epub Date]|. 160. AlMajed HT, AlAttar AT, Sadek AA, et al. Prevalence of dyslipidemia and obesity among college students in Kuwait. Alexandria Journal of Medicine 2011; 47 (1):67 - 71 doi: 10.1016/j.ajme.2010.12.003[published Online First: Epub Date]|. 161. Alfredo Halpern MC, Mancini, Maria Eliane C Magalhães, Mauro Fisberg, Rosana Radominski, Marcelo C Bertolami,Adriana Bertolami, Maria Edna de Melo, Maria Teresa Zanella,MarciaSQueiroz, Marcia Nery. Metabolic syndrome, dyslipidemia, hypertension and type 2 diabetes in youth: from diagnosis to treatment. Diabetology & Metabolic Syndrome 2010; 2 (55) 162. Rosner B, Cook N, Portman R, Daniels S, Fal kner B. Blood pressure differences by ethnic group among United States children and adolescents. Hypertension 2009; 54 (3):502 - 8 doi: 10.1161/HYPERTENSIONAHA.109.134049[published Online First: Epub Date]|. 163. Lazarou C, Panagiotakos DB, Matalas AL. Lifest yle factors are determinants of children's blood pressure levels: the CYKIDS study. Journal of human hypertension 2009; 23 (7):456 - 63 doi: 10.1038/jhh.2008.151[published Online First: Epub Date]|. 164. U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES NIoH, Nati onal Heart, Lung, and Blood Institute. THE FOURTH REPORT ON THE Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents. NIH Publication 2005; 05 - 5267 165. Chobanian AV, Bakris GL, Black HR, et al. Seventh report of the Joi nt National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42 (6):1206 - 52 doi: 10.1161/01.HYP.0000107251.49515.c2[published Online First: Epub Date]|. 166. MD B. The Seventh Report of the Joint Natio nal Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. 2004 167. Falkner B, Daniels SR. Summary of the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents. Hypert ension 2004; 44 (4):387 - 8 doi: 10.1161/01.HYP.0000143545.54637.af[published Online First: Epub Date]|. 168. Lee Hooper CDS, JulianPTHiggins, Rachel L Thompson, Nigel E Capps, George Davey Smith, Rudolph A Riemersma, Shah Ebrahim. Dietary fat intake and prev ention of cardiovascular disease systematic review. Bmj 2001; 322 (31) 169. Prevention CfDCa. State - Specific Trends in Fruit and Vegetable Consumption Among Adults United States, 2000 2009. MMWR Morbidity and Mortality Weekly Report 2010; 59 (35) 205 170. Gidding SS, Daniels SR, Kavey RE, Expert Panel on Cardiovascular H, Risk Reduction in Y. Developing the 2011 Integrated Pediatric Guidelines for Cardiovascular Risk Reduction. Pediatrics 2012; 129 (5):e1311 - 9 doi: 10.1542/peds.2011 - 2903[published Online Firs t: Epub Date]|. 171. Musaiger AO, Takruri HR, Hassan AS, Abu - Tarboush H. Food - based dietary guidelines for the arab gulf countries. Journal of nutrition and metabolism 2012; 2012 :905303 doi: 10.1155/2012/905303[published Online First: Epub Date]|. 172. FA O. Kuwait Nutrition Profile. 2006 173. Howard BV, Wylie - Rosett J. Sugar and cardiovascular disease: A statement for healthcare professionals from the Committee on Nutrition of the Council on Nutrition, Physical Activity, and Metabolism of the American He art Association. Circulation 2002; 106 (4):523 - 7 174. Hur YI, Park H, Kang JH, et al. Associations between Sugar Intake from Different Food Sources and Adiposity or Cardio - Metabolic Risk in Childhood and Adolescence: The Korean Child - Adolescent Cohort Stud y. Nutrients 2016; 8 (1) doi: 10.3390/nu8010020[published Online First: Epub Date]|. 175. Johnson RK, Appel LJ, Brands M, et al. Dietary sugars intake and cardiovascular health: a scientific statement from the American Heart Association. Circulation 2009; 120 (11):1011 - 20 doi: 10.1161/CIRCULATIONAHA.109.192627[published Online First: Epub Date]|. 176. Ronald M. Krauss RHE, Barbara Howard, Lawrence J. Appel, Stephen R. Daniels, Richard J. Deckelbaum, John W. Erdman, Jr, Penny Kris - Etherton, PhD, RD; Ira J. G oldberg, MD; Theodore A. Kotchen, Alice H. Lichtenstein, William E. Mitch, Rebecca Mullis, Killian Robinson, Judith Wylie - Rosett, Sachiko St. Jeor, John Suttie, Diane L. Tribble, Terry L. Bazzarre. AHA Dietary Guidelines Revision 2000: A Statement for Hea lthcare Professionals From the Nutrition Committee of the American Heart Association. Circulation 2000; 102 :2284 - 99 177. Wajih N. Sawaya FA - A, Ismail Naeemi,, Ali Al - Sayegh NA, and M. Sherif Khalafawi. Dietary Fat Profiles of Composite Dishes of the Arabia n Gulf Country of Kuwait JOURNAL OF FOOD COMPOSITION AND ANALYSIS 1998; 11 :200 - 11 178. Dashti B, Al - Awadi F, AlKandari R, Ali A, Al - Otaibi J. Macro - and microelements contents of 32 Kuwaiti composite dishes. Food Chemistry 2004; 85 (3):331 - 37 doi: 10.1016/j .foodchem.2003.05.001[published Online First: Epub Date]|. 179. McNutt S, Zimmerman TP, Hull SG. Development of food composition databases for food frequency questionnaires (FFQ). Journal of Food Composition and Analysis 2008; 21 :S20 - S26 doi: 10.1016/j.jfc a.2007.05.007[published Online First: Epub Date]|. 180. Garcia RA, Taren D, Teufel NI. Factors associated with the reproducibility of specific food items from the Southwest Food Frequency Questionnaire. Ecology of Food and Nutrition 2000; 38 (6):549 - 61 doi: 10.1080/03670244.2000.9991596[published Online First: Epub Date]|. 181. Gladys Block AMHaDN. A Reduced Dietary Questionnaire Development and Validation. Epidemiology 1990; 1 (1):58 - 64 182. WC. W. Nutrition Epidemiology Oxford Unuversity Press 1998; 3 206 183. Gladys Block AMH, Connie M. Dresser, Margaret D. Carroll, Jane Gannon, Lilly Gardners. A DATA - BASED APPROACH TO DIET QUESTIONNAIRE DESIGN AND TESTING AMERICAN JOURNAL OF EPIDEMIOLOGY 1986; 124 (3) 184. Kipnis V. Structure of Dietary Measurement Erro r: Results of the OPEN Biomarker Study. American Journal of Epidemiology 2003; 158 (1):14 - 21 doi: 10.1093/aje/kwg091[published Online First: Epub Date]|. 185. Kumanyika S, Tell GS, Fried L, Martel JK, Chinchilli VM. Picture - Sort Method for Administering a F ood Frequency Questionnaire to Older Adults. Journal of the American Dietetic Association 1996; 96 (2):137 - 44 doi: 10.1016/s0002 - 8223(96)00042 - 9[published Online First: Epub Date]|. 186. Jj C, Ka P. (S)Partners for Heart Health: A School - and Web - Based Intervention Pilot: Effects on Nutrition, Physical Activity, Screen Time and Cardiovascular Risk Factors in 5th Grade Children. Journal of Community Medicine & Health Education 2015; 05 (05) doi: 10.4172/2161 - 0711.1000376[published Online First: Epub Date]|. 187. Nicklas TA, Baranowski T, Cullen KW, Berenson G. Eating Patterns, Dietary Quality and Obesity. Journal of the American College of Nutrition 2001; 20 (6):599 - 608 doi: 10.1080/07315724.2001.10719064[published Online First: Epub Date]|. 188. Waijers PM, Feskens EJ, Ocke MC. A critical review of predefined diet quality scores. The British journal of nutrition 2007; 97 (2):219 - 31 doi: 10.1017/S0007114507250421[published Online First: Epub Date]|. 189. Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: a systematic review and meta - analysis of cohort studies. Journal of the Academy of Nutrition and Dietetics 2015; 115 (5):78 0 - 800 e5 doi: 10.1016/j.jand.2014.12.009[published Online First: Epub Date]|. 190. Guenther PM, Casavale KO, Reedy J, et al. Update of the Healthy Eating Index: HEI - 2010. Journal of the Academy of Nutrition and Dietetics 2013; 113 (4):569 - 80 doi: 10.1016/j. jand.2012.12.016[published Online First: Epub Date]|. 191. Hazel A.B. Hiza P, RD; Patricia M. Guenther, PhD, RD; Colette I. Rihane, MS, RD. Diet Quality of Children Age 2 - 17 Years as Measured by the Healthy Eating Index - 2010. Nutrition Insight 2013; 52 1 92. Drenowatz C, Carlson JJ, Pfeiffer KA, Eisenmann JC. Joint association of physical activity/screen time and diet on CVD risk factors in 10 - year - old children. Frontiers of medicine 2012; 6 (4):428 - 35 doi: 10.1007/s11684 - 012 - 0232 - 4[published Online First: E pub Date]|. 193. Feskanich D, Rockett HR, Colditz GA. Modifying the Healthy Eating Index to assess diet quality in children and adolescents. J Am Diet Assoc 2004; 104 (9):1375 - 83 doi: 10.1016/j.jada.2004.06.020[published Online First: Epub Date]|. 194. Pan Y, Pratt CA. Metabolic syndrome and its association with diet and physical activity in US adolescents. J Am Diet Assoc 2008; 108 (2):276 - 86; discussion 86 doi: 10.1016/j.jada.2007.10.049[published Online First: Epub Date]|. 207 195. Robson SM, Couch SC, Peugh JL, et al. Parent Diet Quality and Energy Intake Are Related to Child Diet Quality and Energy Intake. Journal of the Academy of Nutrition and Dietetics 2016; 116 (6):984 - 90 doi: 10.1016/j.jand.2016.02.011[published Online First: Epub Date]|. 196. Theuwissen E, Mensink RP. Water - soluble dietary fibers and cardiovascular disease. Physiol Behav 2008; 94 (2):285 - 92 doi: 10.1016/j.physbeh.2008.01.001[published Online First: Epub Date]|. 197. Galisteo M, Duarte J, Zarzuelo A. Effects of dietary fibers on disturbanc es clustered in the metabolic syndrome. J Nutr Biochem 2008; 19 (2):71 - 84 doi: 10.1016/j.jnutbio.2007.02.009[published Online First: Epub Date]|. 198. David S. Ludwig MAP, Candyce H. Kroenke, Joan E. Hilner, Linda Van Horn, Martha L. Slattery, David R. Jaco bs, Jr, PhD. Dietary Fiber, Weight Gain, and Cardiovascular Disease Risk Factors in Young Adults. Jama 1999; 282 (16) 199. Carlson JJ, Eisenmann JC, Norman GJ, Ortiz KA, Young PC. Dietary fiber and nutrient density are inversely associated with the metabol ic syndrome in US adolescents. J Am Diet Assoc 2011; 111 (11):1688 - 95 doi: 10.1016/j.jada.2011.08.008[published Online First: Epub Date]|. 200. Ventura EE, Davis JN, Alexander KE, et al. Dietary intake and the metabolic syndrome in overweight Latino childre n. J Am Diet Assoc 2008; 108 (8):1355 - 9 doi: 10.1016/j.jada.2008.05.006[published Online First: Epub Date]|. 201. Carl J. Caspersen P , Kenneth E. Powel, Gregory M. Christenson . Physical Activity, Exercise, and Physical Fitness: Definitions and Distinctions for Health - Related Research. Public Health Reports 1985; 100 (2):126 - 30 202. Kohl HW, Fulton JE, Caspersen CJ. Assessment of Physical Activity among Children and Adolescents: A Review and Synthesis. Preventive medicine 2000; 31 (2):S54 - S76 doi: 10.1006/pmed. 1999.0542[published Online First: Epub Date]|. 203. Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth. J Appl Physiol (1985) 2008; 105 (3):977 - 87 doi: 10.1152/japplphysiol.00094.2008[published Online First: Epub Date]|. 204. Russell R. Pate BJL, and Greg Heath Descriptive Epidemiology of Physical Activity in Adolescents. Pediatric Exercise Science 1994; 6 :434 - 47 205. Kelli L. Cain JFS, Terry L. Conway, Delfien Van Dyck, and Lynn Calhoon. Using Accelerometers in Youth Physical Activity Studies A Review of Methods. Journal of Physical Activity and Health 2013; 10 :437 - 50 206. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 2003; 37 :197 - 206 207. Beets MW, Born stein D, Beighle A, Cardinal BJ, Morgan CF. Pedometer - measured physical activity patterns of youth: a 13 - country review. American journal of preventive medicine 2010; 38 (2):208 - 16 doi: 10.1016/j.amepre.2009.09.045[published Online First: Epub Date]|. 208 208. Sallis JF, Saelens BE. Assessment of physical activity by self - report: status, limitations, and future directions. Research quarterly for exercise and sport 2000; 71 Suppl 2 :1 - 14 doi: 10.1080/02701367.2000.11082780[published Online First: Epub Date]|. 209. Prevention CfDCa. Youth Risk Behavior Surveillance United States, 2013. MMWR Morbidity and Mortality Weekly Report 2014; 63 (4) 210. Carlson JJ, Eisenmann JC, Pfeiffer KA, et al. (S)Partners for Heart Health: a school - based program for enhancing physica l activity and nutrition to promote cardiovascular health in 5th grade students. BMC public health 2008; 8 :420 doi: 10.1186/1471 - 2458 - 8 - 420[published Online First: Epub Date]|. 211. Suton D, Pfeiffer KA, Feltz DL, Yee KE, Eisenmann JC, Carlson JJ. Physical activity and self - efficacy in normal and over - fat children. American journal of health behavior 2013; 37 (5):635 - 40 doi: 10.5993/AJHB.37.5.7[published Online First: Epub Date]|. 212. Montoye AH, Pfeiffer KA, Alaimo K, et al. Junk food consumption and scree n time: association with childhood adiposity. American journal of health behavior 2013; 37 (3):395 - 403 doi: 10.5993/AJHB.37.3.12[published Online First: Epub Date]|. 213. Krebs NF. Prevention of pediatric overweight and obesity. (Policy Statement). Pediatri cs 2003; 112 (2):424 214. Moriarty - Kelsey M, Daniels SR. Childhood Obesity Is the Fuel That Fires Adult Metabolic Abnormalities and Cardiovascular Disease. Childhood Obesity 2010; 6 (5):250 - 56 doi: 10.1089/chi.2010.0504[published Online First: Epub Date]|. 215. Wang Y, Lobstein T. Worldwide trends in childhood overweight and obesity. International Journal of Pediatric Obesity 2006; 1 (1):11 - 25 doi: 10.1080/17477160600586747[published Online First: Epub Date]|. 216. Ahmed Bayoumi and Mohamed A.A. Moussa. Kuwai t Nutritional Survey: Comparison of the Nutritional Status of Kuwaiti Children Aged 6 - 9 Years with the NCHS/CDC Reference Population. International journal of epidemiology 1985; 14 (3) 217. Bagchi1 K. Nutrition in the Eastern Mediterranean Region of the World Health Organization. Eastern Mediterranean Health Journal 1998; 14 (Special Issue) 218. Joseph T. Flynn M, MS, FAAP, a David C. Kaelber, MD, PhD, MPH, FAAP, FACP, FACMI, b Carissa M. Baker - Smith, MD, MS,, MPH F, FAHA, c Douglas Blowey, MD, d Aaron E. Carroll, MD, MS, FAAP. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics 2017; 140 (3):e20171904 219. T. J. Cole . SMOOTHING REFERENCE CENTILE CURVES: THE LMS METHOD AND PENALIZED LIKELI HOOD. STATISTICS IN MEDICINE 1992; 11 :1305 - 19 220. Linda B Houtkooper TGL, Scott B Going, and Wanda H Howell. Why bioelectrical impedance analysis should be used for estimating adiposity. Am J C/in Nutr 1996; 64 :436S - 48S 209 221. Zimmet P AKGM, Kaufman F, Ta jima N, Silink M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S. The metabolic syndrome in children and adolescents an IDF consensus report. Pediatric Diabetes 2007; 8 : 299 - 306 222. William T. Friedewald RIL, and Donald S. Fredrickson. Estimation of the Concentration of Low - Density Lipoprotein Cholesterol in Plasma, Without Use of the Preparative Ultracentrifuge. CLINICAL CHEMISTRY 1972; 18 (6) 223. Whitehead SJ, Ford C, Gama R. A combined laboratory and field evaluation of the Cholestech LDX and Card ioChek PA point - of - care testing lipid and glucose analysers. Annals of clinical biochemistry 2014; 51 (Pt 1):54 - 67 doi: 10.1177/0004563213482890[published Online First: Epub Date]|. 224. Steiner MJ, Skinner AC, Perrin EM. Fasting might not be necessary befo re lipid screening: a nationally representative cross - sectional study. Pediatrics 2011; 128 (3):463 - 70 doi: 10.1542/peds.2011 - 0844[published Online First: Epub Date]|. 225. Steven R. Craig M, Rupal V. Amin, MD, Daniel W. Russell, PhD, Norman F. Paradise, Ph D. Blood Cholesterol Screening Influence of Fasting State on Cholesterol Results and Management Decisions. J GEN INTERN MED 2000; 15 :395 - 99 226. Steven R. Craig RVA. Blood Cholesterol Screening Influence of Fasting State on Cholesterol Results and Managem ent Decisions. J GEN INTERN MED 2000; 15 :395 - 99 227. Dai S, Yang Q, Yuan K, et al. Non - high - density lipoprotein cholesterol: distribution and prevalence of high serum levels in children and adolescents: United States National Health and Nutrition Examinat ion Surveys, 2005 - 2010. The Journal of pediatrics 2014; 164 (2):247 - 53 doi: 10.1016/j.jpeds.2013.08.069[published Online First: Epub Date]|. 228. Mercedes de Onis RM, Cutberto Garza and Anna Lartey. WHO Child Growth Standards based on length height weight and age. Acta Pædiatrica 2006; 450 :76 - 85 doi: 10.1080/08035320500495548[published Online First: Epub Date]|. 229. Prevention CoDCa. Modified z - scores in the CDC growth charts. National Center for Health Statistics 2009 230. Marrodan M, Alvarez JM, de E spinosa MG, et al. Predicting percentage body fat through waist - to - height ratio (WtHR) in Spanish schoolchildren. Public health nutrition 2014; 17 (4):870 - 6 doi: 10.1017/S1368980013000888[published Online First: Epub Date]|. 231. Ogden CL. Smoothed Percentage Body Fat Percentiles for U.S. Children and Adolescents, 1999 2004. National Center for Health Statistics 2011 232. Sulieman N. Al - Shehri M, PhD; Zayed A. Saleh, MD; Mohamed M. Salama, MD; Yehia M. Hassan, MD. Prevalence of Hyperlipidemia among Saudi School Children in Riyadh. Annals of Saudi Medicine 2004; 24 (1) 233. Ding W, Cheng H, Yan Y, et al. 10 - Year Trends in Serum Lipid Levels and Dyslipidemia Among Children and Adolescents From Several Schools in Beijing, China. J Epidemiol 2016; 26 (12):637 - 45 doi: 10.2188/jea.JE20140252[published Online First: Epub Date]|. 210 234. Haroun D, Mechli R, Sahuri R, et al. Metabolic syndrome among adolescents in Dubai, United Arab Emirates, is attributable to the high prevalence of low H DL levels: a cross - sectional study. BMC public health 2018; 18 (1):1284 doi: 10.1186/s12889 - 018 - 6215 - x[published Online First: Epub Date]|. 235. Ribas SA, Santana da Silva LC. Anthropometric indices: predictors of dyslipidemia in children and adolescents fr om north of Brazil. Nutr Hosp 2012; 27 (4):1228 - 35 doi: 10.3305/nh.2012.27.4.5798[published Online First: Epub Date]|. 236. Liao Y, Liu Y, Mi J, Tang C, Du J. Risk factors for dyslipidemia in Chinese children. Acta Paediatr 2008; 97 (10):1449 - 53 doi: 10.1111/j.1651 - 2227.2008.00946.x[published Online First: Epub Date]|. 237. Ruiz JR, Ortega FB, Rizzo NS, et al. High cardiovascular fitness is associated with low metabolic risk score in children: the European Youth Heart Study. Pediatr Res 2007; 61 (3):350 - 5 doi: 10.1203/pdr.0b013e318030d1bd[published Online First: Epub Date]|. 238. Roland J. Prineas , Richard F. Gillum , Hiroshi Horibe , Peter J. Hannan . The Minneapolis Children's Blood Pressure Study: Supplement 1. Hypertension 1980; 2 (4) 239. Roland J. Pr ineas, Richard F. Gillum, Hiroshi Horibe, Peter J. Hannan. The Minneapolis childrn's blood pressure study. Part 2 multiple determinants of children's blood pressure. Hypertension 1980; 2 (4) 240. Miles - Chan JL, Joonas N, Joganah S, et al. BMI and cardiov ascular function in children and adolescents of Mauritius Island. J Nutr Sci 2013; 2 :e3 doi: 10.1017/jns.2012.26[published Online First: Epub Date]|. 241. B. Rosner P, R. J. Prineas, MB,BS, PhD J. M. H. Loggie, MD, and, S. R. Daniels M, PhD. Blood pressure nomograms for children and adolescents, by height, sex, and age, in the United States. The Journal of pediatrics 1993; 123 (6) 242. Patterson E, Warnberg J, Poortvliet E, Kearney JM, Sjostrom M. Dietary energy density as a marker of dietary quality in Swe dish children and adolescents: the European Youth Heart Study. European journal of clinical nutrition 2010; 64 (4):356 - 63 doi: 10.1038/ejcn.2009.160[published Online First: Epub Date]|. 243. O'Connor L, Walton J, Flynn A. Dietary energy density and its asso ciation with the nutritional quality of the diet of children and teenagers. J Nutr Sci 2013; 2 :e10 doi: 10.1017/jns.2013.8[published Online First: Epub Date]|. 244. Paul J. Veugelers ALF, Elizabeth Johnston, Ph. Dietary Intake and Risk Factors for Poor Die t Quality Among Children in Nova Scotia. Canadian Journal of Public Health 2005; 96 (3) 245. Survey NHaNE. HEI - 2010 Total and Component Scores for Children, Adults, and Older Adults During 2011 - 2012. 2016 246. David S. Ludwig M, PhD, Mark A. Pereira P, Candyce H. Kroenke M, et al. Dietary Fiber, Weight Gain, and Cardiovascular Disease Risk Factors in Young Adults. JAMA pediatrics 1999; 282 (16):1539 - 46 211 247. Gladys Block P, Patricia Wakimoto, DrPH, RD, Christopher Jensen, PhD, MPH, Shelly Mandel, Robin R. Green, PsyD, LPC. (block)Validation of a Food Frequency Questionnaire for Hispanics. Prev Chronic Dis 2006; 3 (3) 248. Marshall TA, Eichenberger Gilmore JM, Broffitt B, Stumbo PJ, Levy SM. Relative validity of the Iowa Fluoride Study targeted nutrient sem i - quantitative questionnaire and the block kids' food questionnaire for estimating beverage, calcium, and vitamin D intakes by children. J Am Diet Assoc 2008; 108 (3):465 - 72 doi: 10.1016/j.jada.2007.12.002[published Online First: Epub Date]|. 249. Cullen KW , Watson K, Zakeri I. Relative reliability and validity of the Block Kids Questionnaire among youth aged 10 to 17 years. J Am Diet Assoc 2008; 108 (5):862 - 6 doi: 10.1016/j.jada.2008.02.015[published Online First: Epub Date]|. 250. Cecilia Wilkinson Enns M, RD; Sharon J. Mickle, BS; Joseph D. Goldman, MA. Trends in Food and Nutrient Intakes by Children in the United States. Family Economics and Nutrition Review 2002; 14 (2) 251. Musaiger AO. Consumption, health attitudes and perception toward fast food among Arab consumers in Kuwait: gender differences. Global journal of health science 2014; 6 (6):136 - 43 doi: 10.5539/gjhs.v6n6p136[published Online First: Epub Date]|. 252. Kh Al - Farhan A. Changes in Dietary Behavior of Arab International Students in the US. Foo d Science and Nutrition 2018; 4 (2):1 - 14 doi: 10.24966/fsn - 1076/100033[published Online First: Epub Date]|. 253. Jennifer J. Otten JPH, Linda D. Meyers. Dietary Reference Intakes: The Essential Guide to Nutrient Requirements. National Academy of Sciences 20 05 254. Kennedy ET, Ohls J, Carlson S, Fleming K. The Healthy Eating Index. Journal of the American Dietetic Association 1995; 95 (10):1103 - 08 doi: 10.1016/s0002 - 8223(95)00300 - 2[published Online First: Epub Date]|. 255. Guo X, Warden BA, Paeratakul S, Bray GA. Healthy Eating Index and obesity. European journal of clinical nutrition 2004; 58 (12):1580 - 6 doi: 10.1038/sj.ejcn.1601989[published Online First: Epub Date]|. 256. Institute NC. The Healthy Eating Index. Division of Cancer Control & Population Sci ences 2018; https://epi.grants.cancer.gov/hei/uses.html 257. Tukey JW. EXPLORATORY DATA ANALYSIS: The Future o f Data Analysis. 1977 258. Hoaglin DCI, Boris. Fine - Tuning Some Resistant Rules fo r Outlier Labeling. Journal of the American Statistical Association 1987; 82 (400):1147 - 49 doi: 10.1080/01621459.1987.10478551[published Online First: Epub Date]|. 259. RodrÍGuez GM, L. A. SarrÍA, A. Fleta, J. Bueno, M. Resting energy expenditure in children and adolescents: agreement between calorimetry and prediction equations. Clinical nutrition 2002; 21 (3):255 - 60 doi: 10.1054/clnu.2001.0531[published Online First: Epub Date]|. cal Report series 1. http://www.fao.org/docrep/007/y5686e/y5686e00.html . 2004 212 261. Shanthy A Bowman PJCC, MS; James E Friday, BS;, Natalia Schroeder P, RD; Miyuki Shimizu, MS; RD;, Randy P LaComb MaAJM, MS, RD. Food Patterns Equivalents Intakes by Americans: What We Eat in America, NHANES 2003 - 2004 and 2015 - 2016. Food Surveys Research Group Dietary Data Brief 2018; 20 262. Ogata BN, Hayes D. Position of the Academy of Nutrition and Dieteti cs: nutrition guidance for healthy children ages 2 to 11 years. Journal of the Academy of Nutrition and Dietetics 2014; 114 (8):1257 - 76 doi: 10.1016/j.jand.2014.06.001[published Online First: Epub Date]|. 263. Perichart - Perera O, Balas - Nakash M, Rodriguez - C ano A, Munoz - Manrique C, Monge - Urrea A, Vadillo - Ortega F. Correlates of dietary energy sources with cardiovascular disease risk markers in Mexican school - age children. J Am Diet Assoc 2010; 110 (2):253 - 60 doi: 10.1016/j.jada.2009.10.031[published Online Firs t: Epub Date]|. 264. Shanthy A Bowman PJCC, MS; James E Friday, BS; Krystal L Lynch, PhD; Randy P LaComb, MS; and Alanna J Moshfegh, MS, RD. Food Patterns Equivalents Intakes by Americans What We Eat in America, NHANES 2003 - 2004 and 2013 - 2014. Food Surve ys Research Group 2017; 17 265. McPherson RS, Hoelscher DM, Alexander M, Scanlon KS, Serdula MK. Dietary Assessment Methods among School - Aged Children: Validity and Reliability. Preventive medicine 2000; 31 (2):S11 - S33 doi: 10.1006/pmed.2000.0631[published O nline First: Epub Date]|. - term food frequency questionnaires compared with recalls and records. J Clm Epidemiol 1995; 49 (10):1195 - 200 267. WHO. Cardiovascular d iseases (CVDs) Sheet 2017 268. Karelis AD, St - Pierre DH, Conus F, Rabasa - Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? The Journal of clinical endocrinology and metabolism 2004; 89 (6):2569 - 75 doi: 10.1210/jc.2004 - 0165[published Online First: Epub Date]|. 269. Breslin WL, Johnston CA, Strohacker K, et al. Obese Mexican American children have elevated MCP - 1, TNF - alpha, monocyte concentration, and dyslipidemia. Pediatrics 2012; 129 (5):e1180 - 6 doi: 10.1542/peds.2011 - 2477[published Online First: Epub Date]|. 270. Paul J. Veugelers ALF, Elizabeth Johnston. Dietary Intake and Risk Factors for Poor Diet Quality Among Children in Nova Scotia. 96, 2005; 3