DISEASE RISK FACTOR STATUS AND NUTRITION BEHAVIORS: BASELINE AND INTERVENTION EFFECTS By Tyler Brian Becker         A DISSERTATION  Submitted to  Michigan State University in partial fulfillment of the requirements  for the degree of   Human Nutrition- Doctor of Philosophy  2016       ABSTRACT A COMPARISON OF FACTOR STATUS AND NUTRITION BEHAVIORS: BASELINE AND INTERVENTION EFFECTS  By Tyler Brian Becker IMPORTANCE: The prevalence of obesity and other cardiovascular disease (CVD) risk factors has increased among children within the United States (US) over the past 40 years. Overall, national data indicates rural (RU) vs urban (UR) populations have a greater prevalence of obesity and levels of other CVD risk factors, due in part to lower socioeconomic status, less access to healthcare, decreased access to nutrient dense foods (eg, fruits, vegetables), recreational areas, and exercise facilities.  Most cross-sectional research comparing RU and UR obesity rates and nutrition behaviors have used large, national data sets, which may not be representative of smaller regions within the US.  Few nutrition behaviors and CVD risk factors, with most studies addressing obesity prevalence. Fewer studies have compared the effects of school-based nutrition and physical activity (PA) nutrition behavior and CVD risk status.  OBJECTIVE: In a sample of Michigan (MI) children, determine if: 1) mean levels and prevalence of obesity and other CVD risk factors are greater in RU vs UR children; 2a) mean levels of nutrition behaviors and nutrient intakes are more favorable in UR vs RU children, b) fewer % of RU vs UR participants meeting nutrition recommendations, c) relationship of nutrition behaviors and nutrient intakes with CVD risk factors; 3) improvements to nutrition behaviors and CVD risk factors after a school-based nutrition and PA intervention are equivocal between RU and UR participants. DESIGN, SETTING, AND PARTICIPANTS: Objectives 1 and 2: cross-sectional; objective 3: quasi-experimental (RU vs UR comparison).  Participants included RU and UR classification was determined by the Rural-Urban Commuting Area Codes.  MAIN OUTCOMES AND MEASURES: Baseline and post measures were assessed 4-5 months apart. Nutrition behaviors were quantified with a Food Frequency Questionnaire and included food groups, selected macro- and micronutrients, and dietary indices related to cardiovascular health.  CVD risk factors included: anthropometry, blood cholesterol, and resting blood pressure (BP).  Mixed-model ANOVAs evaluated between-group differences adjusted for confounders with school as the random effect. (P < .05). RESULTS: Significant findings included Objective 1: The prevalence of elevated diastolic BP was higher in RU vs UR children. Objective 2: UR vs RU children had higher mean intakes of fruit, dietary fiber per 1000 kilocalories, and vitamin C; no difference for RU and UR children meeting recommendations; observed some anticipated relationships for nutrition variables with CVD risk factors, but also a number of unanticipated results.  Objective 3: Pre to post intervention intakes of dairy and vitamin D increased more in RU vs UR children, and vitamin E increased more in UR vs RU children.  Risk factors: %BF and prevalence of elevated diastolic BP decreased more in RU vs UR children.  CONCLUSIONS AND RELEVANCE: MI RU children are at greater risk for CVD risk factors and consume less nutrient-dense diets vs UR children.  Overall, the prevalence of CVD risk factors was higher in both groups vs national average. (S)Partners improved selected nutrition behaviors and CVD risk factors in both RU and UR children, suggesting this intervention or similar are viable school-based options.iv     This dissertation is gratefully dedicated to my wife Melissa, and my children Liam and Brielle, whose smiles bring one to my face every time.  They have been, and continue to be my inspiration. v  ACKNOWLEDGEMENTS  I would like to acknowledge and express my sincerest gratitude towards my dissertation committee members Drs. Joseph Carlson, Jenifer Fenton, Wei Li, and Joey Eisenmann for their continued expertise, support, and guidance during my three years at Michigan State University.  I would like to express gratitude towards a number of other faculty who have helped me along the way.  Drs. Robyn Anderson from Trinity University (formerly Alma College), Karen Pfeiffer from Michigan State University, and William Saltarelli from Central Michigan University, all who encouraged me to pursue my goal of earning my PhD, especially when I thought it was not possible.  Without their continued support over the years, this would not have been imaginable.  Other faculty and staff who have assisted or inspired me include Dr. David Todem from the Department of Epidemiology and Biostatistics for his extensive statistical knowledge, Drs. Roger Hammer, Jeffery Edwards, Jeffery Betts, Tracy Olrich and the entire Health Professions staff from Central Michigan University, and the faculty and staff from the Departments of Radiology, Food Science and Human Nutrition, and Epidemiology and Biostatistics.  I have to acknowledge a number of my former classmates including Todd Buckingham, RJ Gibbs, Bryan Shagena, and too many others to list.  I express my deepest appreciation for funds I have received from FSHN, MSU Extension, Jerry and Stella Cash, and John Harvey Kellogg.   Lastly, I would like to acknowledge my parents Duane and Sarah Becker, my brother Brad, my sister Erika, and my wife Melissa.   vi  TABLE OF CONTENTS   LIST OF TABLES....................................................................................................................................... x  LIST OF FIGURES ................................................................................................................................... xii  KEY TO ABBREVIATIONS ................................................................................................................ xiii  CHAPTER 1 INTRODUCTION ............................................................................................................... 1               Background/Overview ................................................................................................................ 1 Significance ....................................................................................................................................... 7 Specific Aims and Hypotheses .................................................................................................. 8  CHAPTER 2 REVIEW OF LITERATURE ........................................................................................ 12 Introduction ................................................................................................................................ 12 Prevalence of Cardiovascular Disease Risk Factors in Adults and Children in the United States ............................................................................................................................... 13           Cardiovascular Disease Risk Factor Cutpoints for Children and Adolescents  ................................................................................................................................................ 13               Prevalence of Anthropometry.............................................................................................. 14                            Overweight and Obesity ............................................................................................... 14                            Adults in the United States ................................................................................... 14                            Children and Adolescents in the United States ............................................ 14                            Waist Circumference and Percent Body Fat ......................................................... 16                           Adults in the United States .................................................................................... 16                           Children and Adolescents in the United States ............................................. 17                Dyslipidemia and Hypertension .......................................................................................... 19                           Adults in the United States .................................................................................... 19                           Children and Adolescents in the United States ............................................. 20                Rural Definitions in the United States ............................................................................... 21  Cardiovascular Disease Risk Factors among Rural Children and Adolescents                 from the United States ............................................................................................................. 23                           Overweight and Obesity .............................................................................................. 23                           Waist Circumference and Percent Body Fat ........................................................ 26                           Dyslipidemia and Hypertension ............................................................................... 26                           International Cardiovascular Disease Risk Factor Comparisons Between                           Rural and Urban Children ........................................................................................... 28                 Nutritional Behaviors and Nutrient Intakes among United States Children ....... 30                           Food Groups ..................................................................................................................... 31                           Macronutrients ............................................................................................................... 33                           Micronutrients ................................................................................................................ 34                           Dietary Indices ................................................................................................................ 36                           Nutritional Behaviors and Nutrient Intakes among Rural Adults ............... 37                           Nutritional Behaviors and Nutrient Intakes among Rural Children .......... 38                   Health Behavior and Health Status Barriers among Rural Populations ............... 40  vii                            Socioeconomic Status ................................................................................................... 40                           Access to Nutritious Food ........................................................................................... 42                           Access to Healthcare ..................................................................................................... 43                           Barriers Associated with Physical Activity .......................................................... 43                           Food Insecurity ............................................................................................................... 44                   Physical Activity among Children of the United States.............................................. 45                            Benefits of Physical Activity in Children ................................................................ 45                            Recommendations .......................................................................................................... 45                            Current Physical Activity Behaviors in the United States ............................... 45                            Differences in Physical Activity among Rural and Urban Residents........... 46                    Summary of the Effectiveness of School-Based Nutrition and Physical Activity                    Interventions for Children and Adolescents................................................................... 47 School-Based Nutrition and Physical Activity Interventions for Rural   Children .............................................................................................................................. 49 (S)Partners for Heart Health ...................................................................................... 52            Summary ...................................................................................................................................... 54  CHAPTER 3 PREVALENCE OF OBESITY AND OTHER CARDIOVASCULAR DISEASE  RISK FACTORS BETWEEN RURAL AND URBAN CHILDREN FROM MICHIGAN  ......... 55            Abstract ........................................................................................................................................ 55            Introduction ................................................................................................................................ 56            Materials and Methods ........................................................................................................... 61                   Study Design and Participants ................................................................................... 61                          Residence .................................................................................................................... 62                    Measurements ................................................................................................................. 62  Anthropometry ......................................................................................................... 62  Blood Lipids ............................................................................................................... 63  Blood Pressure ......................................................................................................... 63  Composite Cardiovascular Disease Risk Factor Score .............................. 63  Covariates ................................................................................................................... 64  Statistics ...................................................................................................................... 64            Results........................................................................................................................................... 65            Discussion .................................................................................................................................... 70            Conclusion ................................................................................................................................... 75   CHAPTER 4 COMPARISON OF DIETARY INTAKE BETWEEN RURAL AND URBAN CHILDREN AND THE RELATIONSHIP OF NUTRITION BEHAVIORS TO  CARDIOVASCULAR DISEASE RISK FACTORS ............................................................................ 77            Abstract ........................................................................................................................................ 77            Introduction ................................................................................................................................ 80            Materials and Methods ........................................................................................................... 84                    Study Design and Participants .................................................................................. 84                          Residence .................................................................................................................... 85                    Measurements ................................................................................................................. 85  Nutrition Behaviors ................................................................................................ 85  Dietary Indices .......................................................................................................... 87  viii   Physical Activity ....................................................................................................... 87  Anthropometry ......................................................................................................... 87  Blood Lipids ............................................................................................................... 88  Blood Pressure ......................................................................................................... 88  Covariates ................................................................................................................... 89                          Statistics ...................................................................................................................... 89            Results........................................................................................................................................... 90            Discussion .................................................................................................................................. 102            Conclusion ................................................................................................................................. 116   CHAPTER 5 EFFECTS OF A SCHOOL- AND WEB-BASED NUTRITION AND PHYSICAL ACTIVITY INTERVENTION ON NUTRITION BEHAVIORS AND CARDIOVASCULAR  DISEASE RISK FACTORS IN CHILDREN FROM RURAL AND URBAN SCHOOLS ........ 118            Abstract ...................................................................................................................................... 118            Introduction .............................................................................................................................. 120            Materials and Methods ......................................................................................................... 124                    Study Design and Participants ................................................................................ 124        Intervention .............................................................................................................. 125                          Residence .................................................................................................................. 126                    Measurements ............................................................................................................... 126  Nutrition Behaviors .............................................................................................. 126  Dietary Indices ........................................................................................................ 127  Anthropometry ....................................................................................................... 128  Blood Lipids ............................................................................................................. 129  Blood Pressure ....................................................................................................... 129  Composite Cardiovascular Disease Risk Factor Score ............................ 129  Covariates ................................................................................................................. 129                          Statistics .................................................................................................................... 130            Results......................................................................................................................................... 131            Discussion .................................................................................................................................. 141            Conclusion ................................................................................................................................. 152  CHAPTER 6 SUMMARY .................................................................................................................... 154              Aim 1: Differences for Cardiovascular Disease Risk Factors between Rural and               Urban Children ........................................................................................................................ 157             Aim 2: Differences in Nutrition Behaviors, Physical Activity, and the             Relationship of Nutrients with Cardiovascular Disease Risk Factors among                      Rural and Urban Children .................................................................................................. 159              Aim 3: Effects of (S)Partners for Heart Health, on Nutrition Behaviors, Nutrient               Intakes, and Cardiovascular Disease Risk Factors in Children from Rural and               Urban Schools .......................................................................................................................... 162             Strengths and Weaknesses ................................................................................................. 165              Conclusion ................................................................................................................................. 167  APPENDICES ........................................................................................................................................ 169            Appendix A: (S)Partners Cardiovascular Health Risk Assessment Data Record      ix              .................................................................................................................................................... 170            Appendix B: Block Kids 2004 Food Frequency Questionnaire  ............................ 172            Appendix C: Block Kids 2004 Food Frequency Questionnaire Output .............. 176               Appendix D: Healthy Eating Index 2010 Variables .................................................... 185  BIBLIOGRAPHY ................................................................................................................................... 187                                          x  LIST OF TABLES    Table 1.  Pediatric Cutpoints for At-Risk Cardiovascular Disease Risk Factors .......... 13  Table 2. Prevalence of Overweight and Obesity among Rural Children and Adolescents  at the National and Regional (State) Level within the United States ............................... 24  Table 3. Daily Nutrition Recommendations and Current Intakes of Children ............. 30  Table 4. Demographic Characteristics, Height, and Weight of Participants from Rural  and Urban Schools ............................................................................................................................... 65  Table 5. Cardiovascular Disease Risk Factors in Rural versus Urban Children Adjusted  For Rural/Urban Status, Sex, Ethnicity, Socioeconomic Status, Physical Activity and  School........................................................................................................................................................ 66  Table 6. Odds for At-Risk Cardiovascular Disease Risk Factors in Rural vs Urban  Children (Reference) .......................................................................................................................... 69  Table 7. Demographic Characteristics, Height, and Weight of Participants from Rural and Urban Schools for Aim 2 ........................................................................................................... 90  Table 8. Differences in Nutrition Behaviors, Nutrient Intakes, Physical Activity, and Proportion Meeting Recommendations for MI children by School Location ............... 94  Table 9. Relationship of Nutrition Behaviors and Nutrient Intakes per 1000 kcals with  At-Risk Body Composition among Rural and Urban Children ............................................ 97  Table 10. Relationship of Nutrition Behaviors and Nutrient Intakes per 1000 kcals with Dyslipidemia, and Elevated Blood Pressure among Rural and Urban Children ........ 100  Table 11. Baseline Demographic Characteristics, Height, and Weight of Participants  from Rural and Urban Schools for Aim 3 .................................................................................. 131  Table 12. Baseline Comparison of Nutrition Behaviors, Nutrient Intakes, and Cardiovascular Disease Risk Factors for MI Children by Rural/Urban School .......... 132  Table 13. Pre to Post Differences in Nutrition Behaviors and Nutrient Intakes for MI Children by Rural and Urban Schools ........................................................................................ 135  Table 14. Pre to Post Differences in Cardiovascular Disease Risk Factors for MI  Children by Rural and Urban Schools ........................................................................................ 137   xi  Table 15. Number of and Proportion Change from Baseline for At-Risk Cardiovascular Disease Risk Factors among Rural and Urban Children ..................................................... 140                                              xii  LIST OF FIGURES   Figure 1. Percentage of Rural and Urban Children with At-Risk Body Composition  Measures .......................................................................................................................................................68  Figure 2. Percentage of Rural and Urban Children with Dyslipidemia and Elevated Blood Pressure .........................................................................................................................................................69  Figure 3. Percentage of Rural and Urban Children with At-Risk Body Composition  Measures for Aim 2 ...................................................................................................................................91  Figure 4. Percentage of Rural and Urban Children with Dyslipidemia and Elevated Blood Pressure for Aim 2 ........................................................................................................................92  Figure 5. Change in Percentage of At-Risk Body Composition Measures in Rural and  Urban Children ............................................................................................................................................138  Figure 6. Change in Percentage of Dyslipidemia and Elevated Blood Pressure in Rural and Urban Children ............................................................................................................................................139                            xiii  KEY TO ABBREVIATIONS   %BF: % body fat AMDR: Acceptable Macronutrient Distribution Range AI: Adequate Intake BIA: Bioelectrical Impedance BP: Blood Pressure BMI: Body mass index Ca+: Calcium CHANGE: Creating Healthy, Active, and Nurturing Growing-UP Environments CVD: Cardiovascular disease DRI: Dietary Reference Intake EAR: Estimated Average Requirements FI: Dietary Fiber Index FPL: Federal Poverty Level FFQ: Food Frequency Questionnaire FRL: Free and reduced lunch HEI: Healthy Eating Index Score HDL-C: High-density lipoprotein K+: Potassium kcals: Kilocalories LDL-C: Low-density lipoprotein MetS: Metabolic Syndrome Mg+: Magnesium  xiv  Na+: Sodium NB: Nutrition Behavior NHANES: National Health and Nutrition Examination Survey NMES-S: Nutrition Environment Measures Survey for Stores  Non-HDL-C: non-high-density lipoprotein OMB: 2003 Office of Management and Budget Metropolitan Area Standards  PA: Physical Activity RAE: Retinol Activity Equivalents RU: Rural RUCA: 2010 US Department of Agriculture Economic Research Service Rural-Urban Commuting Areas RUCC: 2003 US Department of Agriculture Economic Research Service Rural-Urban Continuum Codes Documentation  SES: Socioeconomic status TC: Total cholesterol UIC: 2013 US Department of Agriculture Economic Research Service Urban Influence Codes UR: Urban US: United States WC: Waist circumference      1  CHAPTER 1 INTRODUCTION Background/Overview The prevalence of obesity among children 6- to 11-year-olds within the United States (US) has increased from 4.2% in 1963-1965, to 17.5% in 2011-2012.1,2 Additionally, a number of other cardiovascular disease (CVD) risk factors have increased including the cluster of risk factors referred to as metabolic syndrome (MetS),3,4 (defined by  3 or more of the following: hypertriglyceridemia, low high-density lipoprotein [HDL-C], high waist circumference [WC], or high blood pressure [BP]). CVD risk factors during childhood including obesity, blood lipids, and BP tend to track into adulthood increasing risk for premature CVD morbidity and mortality and all-cause mortality.5-7 Although the prevalence of dyslipidemia (eg, high total cholesterol [TC] and non-high-density lipoproteins [non-HDL-C], and low HDL-C) and elevated (borderline high or high) blood pressure (BP) among children and adolescents have decreased in the last 30 years,8 1 in 5 and 1 in 10 children and adolescents have dyslipidemia and elevated BP, respectively.9 The high prevalence of obesity and other CVD risk factors is due in part to increased intakes of foods high in saturated fat, added sugar, and sodium (Na+), coupled with decreased intakes of nutrient-dense foods (eg, fruits, vegetables), and decreases in physical activity (PA), and increases in sedentary behavior.10-12  In a sample of 8815 adults from NHANES (2005-2008), Befort et al13 reported rural (RU) adults have a greater prevalence of obesity compared to their urban (UR) counterparts (39.6% vs 33.4%; P = .006). This difference persisted after controlling for the effects of age, education, income, ethnicity, marital status, diet and PA.  Among 214,000  2  adults from the 2008 Behavioral Risk Factor Surveillance System, the prevalence of coronary artery disease and diabetes was 38.6% and 8.6% higher, respectively in RU vs UR residents.14 Additionally, mortality rates due to CVD and diabetes are greater in RU vs UR residents.15 Similar to RU adults, several studies using national data reveal that RU children have a higher prevalence of overweight and obesity compared to UR children.16-18 Additionally, a meta-analysis on 74 168 children and adolescents aged 2 to 19 years encompassing five studies, by Johnson and Johnson19 found that RU children had 26% greater odds of being obese compared to their UR counterparts.  However, few studies have compared multiple CVD risk factors in addition to overweight and obesity between RU and UR children.20  Within a sample of 962 UR and 1151 RU elementary school children from North Carolina, RU children had significantly greater average systolic BP compared to UR children.20  Many factors contribute to the higher rates of obesity and other CVD risks, morbidity, and mortality among RU adults, and increased CVD risk for RU children.21-25 In RU areas there is a higher prevalence of lower socioeconomic status (SES) including higher rates of poverty vs residents living in UR areas.26  Thus, children from RU areas commonly are from lower SES households,27 which increases their risk for health28,29 and medical care disparities.22 Also RU residents vs UR residents, overall, have less access to nutrient-dense foods,23 and availability of recreational areas and exercise facilities, which is associated with greater levels of sedentary time and lack of engagement in PA.21 National data values of obesity, and data on nutrition behaviors and nutrient intakes for RU children, may not be representative of regional (state) RU populations.  For example,  3  the prevalence of RU overweight and obese children from Kentucky30 and  Georgia31 were 43% and 48%, significantly higher than the RU national average of 39.0%.27 Among Texas RU children aged 6 to 11 years, 16.0% and 18.3% of males and females, respectively were considered obese,32 less than the RU national average of 22.0%.27  There is limited data on how national levels of other CVD risk factors (eg, TC, HDL-C, non-HDL-C, and elevated BP), compare to RU regions within the US.   Rural residents, particularly if low SES,  have less access or the ability to purchase nutrient-dense foods such as fruits and vegetables, and may consume more processed foods that are calorie-dense and higher in Na+, sugar, and saturated fat.16,23 National comparisons contrasting RU and UR nutrition behaviors and intakes reflect this disparity. Among 6563 children aged 2 to 11 years from NHANES (1999-2006), the daily kilocalorie (kcal) intake was significantly greater for the RU compared to the UR group (1935 vs 1844 kcals; P < .05) which may be explained by 21.6% vs 15.6% of RU and UR, respectively, consuming 24 or more oz sweetened beverages daily.16  Furthermore, an analysis of the same data set (NHANES 1999-2006), revealed the percentage of 12- to 19-year-olds consuming  2 or more cups fruit, was greater in the UR compared to the RU group (16.5% vs 12.2%; P < .05),16 and RU children aged 6 to 11 years consume more daily fat than UR children (80.3 vs 73.2 g; P < .05).33 Selected studies on children from RU regions indicate nutritional intakes that are different compared to national intake data on RU children. For example, 4.9% of RU children from Texas reported consuming no fruit per day,34 compared to 14.7% nationally.16  Nationally, RU children consume 5.21 servings of fruits and vegetables  4  combined daily, however, RU White and African-American children from the Mississippi Delta, consume less daily servings (3.3 and 4.2 servings, respectively).35 Overall, little research has been published comparing RU and UR nutrition behaviors and intakes at the regional level, especially in the Midwest. Few studies have been published comparing obesity prevalence between RU and UR children from the Midwest.  A study that included 3416 children aged 8 to 12 years from Iowa (classified as a Midwest state) indicated that obesity prevalence in RU children was significantly greater vs UR children (25.1% vs 19.4%; P < .001).36   Furthermore, with respect to the current study, there have been limited published studies that have addressed obesity and other CVD risk factors in RU Michigan (MI) children, or studies that have compared RU vs UR MI children.  reveals that a slightly greater percentage of RU vs UR 10- to 17-year-olds are overweight or obese (31.6% vs 28.0%).37  A study published in 1996 investigated the prevalence of overweight and obesity among MI children using a sample of primarily RU children and adolescents aged 4 to 17 years (n = 993), from the Upper Peninsula pooled with an UR sample from the Lansing area.38 The prevalence of overweight/obesity were 37% and 26% among males and females aged 6 to 11 years, respectively, which was greater than the national average and MI statewide averages at that time (US: males, 22%, females, 23%; MI: males, 29%, females, 26%).  This relationship was also evident among adolescents aged 12 to 19 years from the same sample. Current data from the Youth Risk Behavior Surveillance Survey,39 reveals  a similar trend, with 32.6% of MI 10- to 17-year-old children and adolescents who are overweight or obese, which is slightly higher than the national average for the same age group (31.3%).40  As with national data trends, different regions  5  and populations in MI have varying rates of obesity. A school-based intervention done with low SES UR children attending elementary schools in Grand Rapids found that 42.5% were overweight or obese.41,42  In MI, there is not a statewide pediatric database on CVD risk factors. A study by Cotts et al43 of 711 sixth graders from three UR middle schools, reported that 8.4% and 3.6% had high systolic and diastolic BP, respectively, which is greater than the national average for total high BP (1.9% for children aged 8 to 12 years).43 With respect to blood lipids and BP, the mean concentrations for TC and HDL-C were slightly higher in the UR MI subset compared to national levels8 (169 vs 160 mg/dL and 55.7 vs 52.2 mg/dL, respectively), as was diastolic BP (63.6 vs 59 and 56.7 mmHg for males and females, respectively).44 Also, Peterson et al45 reported the average diastolic BP was greater among a primarily RU sample of 1486 female and 1390 male MI fifth graders (69 mmHg each) compared to the national average,44 however HDL-C was similar to national averages.  This suggests that children from different regions within MI have CVD risk factor levels higher than the national average, especially diastolic BP. Additional data from MI children is needed to investigate if these relationships exist in other regions of the state.  To prevent obesity and other CVD risk factors among children and adolescents, the American Heart Association recommends implementing primary prevention strategies, such as school-based nutrition and PA interventions, to promote increased intakes of nutrient-dense foods (eg, fruits, vegetables, and whole grains), decreased intakes of calorie-dense foods and beverages, decreased sedentary time, and increased PA.11,46 A recent Cochrane Review47 summarizing 26 studies of school-based interventions that promoted  6  PA and fitness among children and adolescents, revealed little effect on PA rates, BMI, and BP, however, positive effects for increasing PA duration, decreasing sedentary time, and improving blood lipids supports continuation of these programs.  There have been few publications comparing effects from a school-based nutrition and PA intervention between RU and UR children on nutrition behaviors and nutrient intakes and multiple CVD risk factors.  One study that adapted an effective UR multifaceted childhood nutrition intervention designed to modify nutrition behaviors and prevent obesity, on RU populations was the CHANGE (Creating Healthy, Active, and Nurturing Growing-UP Environments) program.48,49  The curriculum was adapted from the Shape-Up-Somerville program,50 and implemented on RU first to sixth graders from California, Kentucky, Mississippi, and South Carolina . Results revealed a significant increase in vegetable consumption vs comparison schools.48  This increase in consumption suggests that some school-based nutrition and PA intervention programs demonstrate usefulness among diverse populations.  More research is needed to compare the effectiveness of interventions between RU and UR children, particularly those from a low SES. A possible option for UR vs RU programming in schools is the (S)Partners for Heart Health program, a school- and web-based intervention designed to improve both dietary and PA behaviors to promote heart and overall health among fifth grade students.51 The program has been delivered in several MI counties in a partnership with MSU Extension Health and Nutrition Institute Staff since 2008.  The programming has included schools from both RU and UR comparisons have been conducted on CVD risk factors, nutrition behaviors, and nutrient intakes from this dataset. The (S)Partners intervention is comprised of eight classroom  7  lessons that promote nutritional and PA recommendations coupled with eight web-based modules, goal setting, and tracking facilitated by college mentors.  Participants and their parents received educational handouts for each lesson.   Significance In summary, little research has compared CVD risk factors between RU and UR children in the US, with the exception of overweight and obesity, and no comparisons have been performed between MI RU and UR children.  Few studies have compared nutrition behaviors and nutrient intakes associated with CVD between RU and UR children in the US and no comparisons have been performed between MI RU and UR children. Furthermore, little research has evaluated the relationship of nutrition behaviors and nutrient intakes with CVD risk factors in RU and UR children.  Lastly, no studies have compared nutrition and CVD risk factor outcomes between RU and UR children after exposure to a school-based nutrition and PA intervention.  This research is significant because it provides insight into potential differences of CVD risk factors and nutrition behaviors and nutrient intakes among RU vs UR MI children and how they compare with national averages.  Additionally, this research provides insight in determining if RU and UR cand nutrient intakes and CVD risk factor status change differently following a school-based nutrition and PA intervention.  Ultimately, this study provides valuable insights on the usefulness of (S)Partners for Heart Health and similar programs to help promote the cardiovascular and overall health of RU and UR children.   The overall purpose of this dissertation was to evaluate if baseline measures of overweight and obesity, and other CVD risk factors are greater in RU vs UR children, and if  8  RU children have poorer nutrition behaviors and nutrient intakes.  Additionally, this dissertation will determine if the effects of the (S)Partners are equivocal between RU and UR children. Specific Aims and Hypotheses Aim 1:    Hypothesis 1: Rural children will have a significantly greater BMI and prevalence of overweight and obesity based on BMI percentile as compared to UR children. Hypothesis 2: Rural children will have a significantly higher WC and %BF, prevalence of abdominal obesity, low-risk and high risk %BF compared to UR children.  Hypothesis 3: Rural children will have significantly greater TC and non-HDL-C and lower HDL-C compared to their UR counterparts.  Furthermore, RU children will have a greater prevalence of pediatric dyslipidemia (high TC and non-HDL-C, and low HDL-C) compared to UR children.  Hypothesis 4: Rural children will have a significantly greater systolic and diastolic BP, and mean arterial pressure compared to UR children.  Furthermore, RU children will have a greater prevalence of elevated BP compared to their UR counterparts. Hypothesis 5: Rural children will have a significantly less desirable composite CVD risk factors score compared to UR children.  9  Aim 2: To: 1a) evaluate if mean intakes of food groups and nutrients related to CVD health are lower in MI RU vs UR children; and b) if fewer RU children are meeting national recommendations. 2) Determine if nutrition behaviors and nutrient intakes are related to CVD risk factors among RU and UR children. Hypothesis 1: Rural children will have significantly lower intakes of fruit, vegetables, dairy, and whole grain compared to UR children. Hypothesis 2: Rural children will have significantly greater daily intakes of total kcals, total fat, saturated fat, trans fat, and total sugars compared to UR children. Hypothesis 3: Rural children will have significantly lower intakes of vitamin A, vitamin E, potassium (K+), calcium (Ca+), and greater intakes of Na+ compared to UR children. Hypothesis 4: Rural children will have significantly lower dietary quality compared to UR children based upon the dietary fiber index (FI) (g fiber/1000 kcals) and Healthy Eating Index 2010 (HEI 2010). Hypothesis 5: Intakes of saturated fat, trans fat, total sugars, and Na+ will be positively associated with CVD risk factors [overweight, obesity, abdominal obesity, low- and high-risk %BF, dyslipidemia, and elevated  BP) and intakes of fruit, vegetable, dairy, whole grain, K+, Ca+, FI, and HEI 2010 will be inversely associated with those same CVD risk factors. Aim 3: To: 1) determine if RU and UR have equivocal improvements in nutrition behaviors, nutrient intakes, and CVD risk factors after the (S)Partners for Heart Health program; and  10  to 2) determine if there are equivocal decreases following (S)Partners for the proportion of RU vs UR children identified at baseline with CVD risk factors.   Hypothesis 1: (S)Partners will be equally effective in RU and UR children in significantly increasing intakes of fruit, vegetable, dairy, and whole grain (Food Group Intake). Hypothesis 2: (S)Partners will be equally effective in RU and UR children at significantly decreasing consumption of total kcals, total fat, saturated fat, trans fat, and total sugars (macronutrient Intake). Hypothesis 3: (S)Partners will be equally effective in RU and UR children at significantly increasing vitamin A, vitamin E, K+, and Ca+, and decreasing Na+ intakes (micronutrient intake). Hypothesis 4: (S)Partners will be equally effective in RU and UR children in significantly increasing dietary quality by FI and HEI 2010. Hypothesis 5:  (Anthropometry) (S)Partners will be equally effective in RU and RU children in: a) sustaining the proportion of students that meet cut-points at baseline of BMI for sex and age < 85th percentile, low- and high-risk %BF, and WC for sex and age < 90th percentile; b) significantly improving the anthropometric values of the students not meeting cut- 85th, low- and high-risk %BF 90th). Hypothesis 6: (Blood Lipids) (S)Partners will be as effective in RU and UR children at: a) sustaining students that meet recommended cut-points for TC < 170 mg/dL,  11  HDL-C  40 mg/dL, and non-HDL-C < 145 mg/dL; b)  significantly improving blood lipid levels for the subgroup of students not meeting recommended cut-points at baseline for blood lipids (TC   170 mg/dL, HDL-C < 40 mg/dL, and non-HDL-C  145 mg/dL). Hypothesis 7: (Blood Pressure) (S)Partners will be as effective in RU and UR children in: a) sustaining students that meet cut-systolic and diastolic BP (BP for sex, age, and height percentile < 90th); b) significantly decreasing percentage of participants with systolic and/or diastolic BP for sex, age, and height percentile  90th at baseline. Hypothesis 8: (Composite CVD Risk Score) (S)Partners will be as effective in RU and UR children for decreasing a CVD Risk Composite Score.          12  CHAPTER 2 REVIEW OF LITERATURE Introduction Rates of obesity and cardiovascular disease (CVD) risk factors among adults and children within the United States (US) have significantly increases in the last four decades.2,52-54 National level comparisons reveal that rural (RU) residents have greater obesity and levels of other CVD risk factors compared to urban (UR) residents.13,26,27 A number of contributing factors have been identified for these differences including RU residents more likely to be from a lower socioeconomic status (SES),13,16 have less access to nutrient dense foods because of high cost, access to supermarkets, and travel distance,23,24,34 have less access to healthcare,22 and less availability of recreation areas and facilities.55 To reduce the risk for obesity and other CVD risk factors among children and adolescents, the American Heart Association recommends implementing primary prevention strategies, such as school-based nutrition and physical activity (PA) interventions, to increase nutrient-dense food (eg, fruits and vegetables) intakes, decrease calorie-dense foods and beverage intakes, decrease sedentary time, and increase PA.11,46 The purpose of this literature review will be to: 1) summarize the prevalence of obesity and other CVD risk factors in US adults and children and comparisons between RU and UR populations, 2) summarize nutrition recommendations for food groups, macro- and micronutrients among US children and comparisons between RU and UR populations, and 3) provide an overview for the effectiveness of school-based nutrition and PA interventions in children and adolescents  13  Prevalence of Cardiovascular Disease Risk Factors among Adults and Children in the United States  Cardiovascular Disease Risk Factor Cutpoints for Children and Adolescents  Established cutpoints values of CVD risk factors for children and adolescents aged 2 to 19 years are presented in Table 1. The prevalence for known cutpoints will be described in the following sections. Table 1.   Pediatric Cutpoints for At-Risk Cardiovascular Disease Risk Factors  Risk Factor Low Risk Borderline High Risk High Risk Anthropometry     Overweight/ obesea     Abdominal obesity, percentileb < 90   Body fat percentagec   Males, percentile < 69, < 90  69  90  Females, percentile < 68, < 90  68  90 Dyslipidemiad    Total cholesterol, mg/dL < 170 170-199  200 HDL-C, mg/dL  45 40-45 < 40 non-HDL-C, mg/dL < 120 120-144  145 LDL-C, mg/dL < 110 110-129  Triglycerides, mg/dL    0-9 y < 75 75-99  10-19 y < 90 90-129  130 Elevated BPe   Systolic, percentile; or mmHg < 90  ; or 120/80  Diastolic, percentile; or mmHg < 90 ; or 120/80  Abbreviations:; HDL-C, high-density lipoprotein-C; LDL-C, low-density lipoprotein; BP, blood pressure. a   BMI for sex and age from the 2000 Centers for Disease Control and Prevention Growth Charts56 b Abdominal adiposity: Waist circumference for sex and age from NHANES III (1988-1994)57 c  Body fat percentage: Low-risk %BF (high sensitivity for MetS): %BF for sex and  69th and   68th percentiles for males and females, respectively from NHANES (1999-2004); High-risk  (high specificity for MetS) %BF for sex and age  90th percentile for from NHANES (1999-2004)58,59 d Dyslipidemia: From the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report60 e Elevated BP: BP for age, sex, and height percentile from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents61    14  Prevalence of Anthropometry Overweight and Obesity Adults in the United States.  In the US, 33.9% and 36.5% of adults are considered overweight and obese, respectively.40,62 This prevalence of obesity among adults has not significantly changed since 2009-2010 and may indicate a plateau for obesity rates.63,64   Among adults, a BMI of 25.0 to 29.9 kg/m² is considered overweight, and a BMI at or above 30.0 kg/m² is considered obese.64 Furthermore, among obese adults, a BMI range of 30.0 to 34.9, 35.0-39.9, and at or above 40.0 kg/m² are considered Classes I, II, and III obese, respectively.65  For adults, obesity is a risk factor and risk marker for a number of CVD risk factors (eg, metabolic syndrome [MetS], dyslipidemia, diabetes, hypertension) and CVD development (eg, coronary artery disease, atrial fibrillation, and heart failure).66   Among males, BMI, itself, is positively associated with systolic blood pressure (BP) and triglycerides (r = 0.22 and 0.21, respectively), and negatively associated with high-density lipoprotein (HDL-C) (r = -0.20).67 These relationships are also evident among females (r = 0.30, 0.17, and -0.23, respectively).  Additionally, obesity is associated with an increased risk for CVD and all-cause mortality.68,69 Compared to normal-weight adults, classes I, II, and III obese adults have average decreased years of life by 1.6, 2.7, and 5.0 years, respectively.68  A meta-analysis using 97 articles, globally representing 2.88 million adults and 270 000 deaths, by Flegel et al69 revealed that across obesity classes, obesity increases risk for all-cause mortality by 18% compared to normal-weight adults.  Children and Adolescents in the United States. Currently 17.0% of children and adolescents aged 2 to19 years are obese in the US.2  Additionally, the prevalence of  15  overweight among 6- to 11-year-olds is 17.5%.40 Classes II and III obesity rates for children aged 6 to 11 years are 5.6%2 and 1.1%,respectively.70 BMI percentiles are assessed using BMI for sex and age from the 2000 Centers for Disease Control and Prevention Growth Charts.56 A BMI for sex and age at or above the 85th percentile but less than the 95th percentile is considered overweight, while a BMI for sex and age at or above the 95th percentile is considered obese (class I obese).  Previously, a BMI for sex and age  at or above the 99th percentile was considered severe obesity, now a BMI percentile 120% of the 95th percentile71 and 140% of the 95th percentile72 are considered classes II (extreme) and III obese, respectively. Obesity rates among children and adolescents 2- to 19-years-old had increased significantly from 1999-2014,70 however, obesity rates have not changed  since 2003-2004 suggesting a plateau similar to adults.2 However, rates of obesity among children aged 6- to 11-year-olds have not changed since 2007-2008.2 The prevalence of Classes II and III obesity among children and adolescents has not changed since 2005-20062 and 2009-2010, respectively.72   BMI among children is positively associated with total cholesterol (TC), triglycerides, low-density lipoprotein (LDL-C), non-HDL-C, and inversely associated with HDL-C.73 A meta-analysis consisting of 49 220 children and adolescent 5- to 15-year-olds from 63 studies, revealed that overweight and obese individuals have greater systolic BP (+4.54 and +7.49 mmHg, respectively), diastolic BP (+2.57 and 4.06 mmHg, respectively), TC (+0.02 and +0.15 mmol/L, respectively) , triglycerides (+0.21 and +0.26 mmol/L, respectively), fasting blood glucose (+0.13 and +0.10 mmol/L, respectively), lower HDL-C (-0.17 and -0.22 mmol/L, respectively), and increased left ventricular mass compared to normal-weight children and adolescents of the same age.74   The prevalence of elevated  16  (borderline high or high) BP, dyslipidemia (low HDL-C, high LDL-C and triglycerides), and abnormal glucose are higher among overweight and obese children, and those with a BMI at or above 99th percentile having the highest prevalence for those same CVD risk factors.75   Obesity during childhood typically tracks into adulthood and increases risk for dyslipidemia, hypertension, coronary heart disease, and all-cause mortality.5  Obese children aged 6 to 8 years are more than 10 times as likely to become obese adults compared to children who had BMI percentiles at or below the 50th percentile.76  Furthermore, obese children aged 2 to 5 years are more than 4 times as likely to be overfat adults (high % body fat [%BF]), compared to those with BMI percentiles at or below the 50th percentile. Waist Circumference and Percent Body Fat Adults in the United States. Adult waist circumference (WC) has increased from an average of 95.5 cm in 1999-2000 to 98.5 cm in 2011-2012.77 Furthermore, the age-adjusted prevalence of abdominal obesity in adults is 54.2% and has increased significantly since 1999-2000.77 According to the American Heart Association, abdominal obesity for adults is defined as WC at or above 102 cm for males and at or above 88 cm for females.78  Among adults, WC is positively associated with systolic BP, fasting plasma glucose, triglycerides, and inversely associated with HDL-C.67  Additionally, a higher WC is positively associated with a higher mortality rate across BMI levels from 20 to 50 kg/m².79  Men with a WC at or above 110cm have a 52% greater mortality risk compared to those with a WC lower than 90 cm (HR, 1.52; 95% CI 1.45-1.59).  Women with a WC at or above 95 cm have an 80%  17  greater mortality risk compared to those with a WC lower than 70 cm (HR, 1.80; 95% CI, 1.70-1.89).79  There is not a consensus for percent body fat (%BF) cutpoint values for adults.  The American Association of Clinical Endocrinology/American College of Endocrinology recommends 25% and 35% for males and females, respectively.80  The American College of Sports Medicine, recommends a %BF of 10% to 22% for males and 20% to 32% for females as satisfactory for health.65  Among 12 906 adult males and females from NHANES (1999-2004), Heo et al81 developed %BF cutpoints corresponding to BMI values of 18.5, 25, 30, 35, and 40 kg/m². The corresponding %BF for BMI values of 18.5, 25, 30, 35, and 40 kg/m² for male adults across age and ethnicity were 12.2% to 19.0%, 22.6% to 28.0%, 27.5% to 32.3%, 31.0% to 35.3%, and 33.6% to 37.6%, respectively.  For females across age and ethnicity the corresponding %BF values were 24.6% to 32.3%, 35.0% to 40.2%, 39.9% to 44.1%, 43.4% to 47.1%, and 46.1% to 49.4%. The authors summarized that %BF should be assessed in conjunction with BMI in clinical and research settings.  %BF is positively associated with BP, fasting plasma glucose, TC, triglycerides and inversely associated with HDL-C, and is a better predictor for CVD risk factors compared to BMI.82 A high %BF, independent of BMI, is also associated with increased mortality.83 Although there currently is not published research for the prevalence of at-risk %BF among adults in the US, the average %BF is 28.1% and 40.0% for males and females, respectively.84 Children and Adolescents in the United States.  Currently, 18.9% of children and adolescents are considered abdominally obese, however, the prevalence has not significantly changed since 2003-04 suggesting a plateau similar to obesity rates.57  18  Additionally, the percentage of non-Hispanic White children and adolescents with abdominal obesity has significantly decreased since 2003-04 (-4.63%; P = .04). The current prevalence of abdominal obesity among children aged 6 to 11 years is 17.5%, and mean WC is 64.93 cm.57 Abdominal obesity for children and adolescents is a WC for sex and age at or above the 90th percentile using standards from the National Health and Nutrition Examination Survey (NHANES) III (1988-1994).85  Risk for CVD risk factors increases with WC percentile, however a WC at or above the 90th percentile has low sensitivity for identifying dyslipidemia, elevated  BP, and abnormal blood glucose, suggesting the current cutpoint may be too high.86 A national average %BF for children has not been published within the last 10 years, however data from NHANES (1999-2004) reported a mean %BF of 19.4% and 26.5% among males and females aged 12 to 18.9 years, respectively.59  Furthermore, using data from the same NHANES (1999-2004) sample, Borrud et al87 reported that the average %BF in male children and adolescents aged 8 to 11, 12 to 15, and 16 to 19 years were 28.0%, 25.2%, and 22.9%, respectively.87  For females, the average %BF for corresponding ages were 31.9%, 32.5%, and 34.8%, respectively.  The previous cutpoints for high %BF for males and females were at or above 25% and 32%, respectively.88 New cutpoints for male and female children and adolescents are identified by sex and age, and expressed as percentiles.59  A %BF that is considered low-risk for MetS with high test sensitivity (minimize false negatives) is at or above the 69th percentile and at or above the 68th percentile, for males and females, respectively.59,88  %BF percentiles at high-risk for MetS with high test specificity (minimize false positives) are at or above the 90th percentile for both sexes.  Among 9- to 10-year-old children, %BF is associated with systolic BP, pulse  19  pressure, mean arterial pressure, heart left ventricular mass, and left arterial end-systolic diameter.89 Additionally, children with a sex specific %BF at or above the 75th percentile, which corresponds to a BMI at or above the 85th percentile,90 have a greater prevalence of high TC, triglycerides, LDL-C and low HDL-C compared to children with a %BF below the 75th percentile.91 Dyslipidemia and Hypertension  Adults in the United States.  The prevalence of high TC among adults has decreased from 33.6% to 17.0% during 1960-2000.54  Currently, 11.1% and 14.4% of males and females, respectively, have high TC; and 26.4% and 9.0% have low HDL-C.92 The current mean levels of HDL-C, LDL-C, and triglycerides are 48.0, 120.5, and 127.9 mg/dL, respectively, among male adults, and 58.4, 118.2, and 113.8 mg/dL, respectively, among adult females.93 Dyslipidemia among adults is considered having one or more of the following: high TC: at or above 240 mg/dL, high LDL-C: at or above 160 mg/dL,  high triglycerides: at or above 200 mg/dL, and/or  low HDL-C: lower than 40 mg/dL.94  Dyslipidemia can also be defined as the use of cholesterol lower medications (yes/no) and/or a non-HDL-C of 160 mg/dL or higher.95  Using dyslipidemia criteria from the prior sentence, Saydah et al95 observed a prevalence of 28.6%, 44.2%, 49.7%, and 47.7% among adults classified as normal-weight, overweight, obese, and morbidly obese, respectively for 12 355 adult males and females via NHANES (2007-2010). Furthermore, dyslipidemia is a strong predictor for risk of heart attack and stroke among adults.96 Stage 1 hypertension among adults has decreased from 2003 to 2012 (42.7% to 35.8%).97 Additionally, stage 2 has decreased during the same time period (17.9% to  20  12.3%).  However, prevalent hypertension (awareness of hypertension) has not changed since 1999 among US adults.98 Mean systolic BP has significantly decreased for adults from 135.3 to 131.0 mmHg, while diastolic BP has not changed (71.9 to 71.4 mmHg).97  Prehypertension (systolic BP: 120 to 139 mmHg and/or diastolic BP: 80 to 89 mmHg), stage I hypertension (systolic BP: 140 to 159 and/or diastolic BP: 90 to 99), and stage II hypertension (systolic BP: at or above 160 mmHg and/or diastolic BP: at or above 100 mmHg) are at-risk BP categories.99  Prevalence of hypertension risk is 19.8%, 26.4%, 35.7%, and 44.4% for normal weight, overweight, obese, and morbidly obese adults, respectively, suggesting hypertension is highly associated with obesity.95  Hypertension and hypertension-related disorders are also the leading causes of death worldwide.100  Children and Adolescents in the United States.  The prevalence of dyslipidemia among children and adolescents, has decreased significantly since 1988, although 1 in 5 children and adolescents have dyslipidemia.8,9 The current prevalence of high TC and non-HDL-C, and low HDL-C is 7.0%, 6.9%, and 10.5%, respectively, among children 8- to 12-years-old.  Mean levels of TC, non-HDL-C, and HDL-C were 164, 109, and 54.4 mg/dL, respectively among children aged 9 to 11 years from NHANES (2007-2010).8 Furthermore, mean TC and non-HDL-C levels have significantly decreased since 1988.  High-risk lipid levels of dyslipidemia for children and adolescents are TC: at or above 200 mg/dL or higher, non-HDL-C: at or above 145 mg/dL, triglycerides: at or above 100 mg/dL for ages 0 to 9 years and at or above 130 mg/dL for ages 10 to 19 years, LDL-C: at or above 130 mg/dL, and HDL-C: lower than 40 mg/dL.60  Borderline high-risk lipid levels of dyslipidemia for children and adolescents are TC: 170 to 199 mg/dL, mg/dL, non-HDL: 120 to 144 mg/dL, triglycerides: 75 to 99 mg/dL for ages 0 to 9 and 90 to 129 mg/dL for ages 10 to 19 years,  21  and LDL-C: 110 to129 mg/dL, and borderline low HDL-C: 40 to 45 mg/dL.   Blood lipids during childhood tend to track into adulthood and increase risk for CVD-related morbidity and mortality.6,101  High non-HDL-C and LDL-C levels during childhood are predictive of carotid intima thickness, an indicator for subclinical atherosclerosis, during adulthood 16 to 19 years later.102  Furthermore, quintiles for non-HDL-C and LDL-C during childhood are highly associated with adult obesity and predict adult dyslipidemia.103 The prevalence for elevated (borderline high or high) BP decreased from 1963 to 1988, and increased from 1988 to 1999 in children.53 High BP has significantly decreased from 1999-2000 to 2011-2012, while borderline high BP has remained the same.9 Currently, prevalence for high and borderline high BP, among children aged 8 to 12 years is 1.9% and 4.7%, respectively.9  Another study reported a high BP prevalence of 2.8% and 3.5% for males and females aged 8 to 11 years, respectively for data from NHANES (2003-2010).104  Currently, 1 in 10 children and adolescents aged 2 to 19 years has elevated BP.9  Borderline high BP (or prehypertension) is defined as having a systolic BP or diastolic BP for sex, age, and height percentile at or above the 90th percentile and less than  the 95th percentile, whereas high BP (or hypertension) is defined as systolic or diastolic BP for sex, age, and height percentile at or above the 95th percentile.105  Additionally, adolescents with BP at or above 120/80 mmHg are also considered prehypertensive. The International Diabetes Federation includes a pediatric criteria for a BP at or above 130/85 within its MetS definition.106 A systematic review of 50 studies that investigated the tracking of BP from childhood into adulthood, calculated average correlation coefficients of 0.38 and 0.28 for systolic and diastolic BP, suggesting a moderate relationship.7  22  Rural Definitions in the United States Within the US, 20% of the population lives in the 75% of the counties considered RU.107 There is not a uniform established RU and UR definition in the US, making comparisons using different RU and UR classifications difficult.  These different classifications usually classify rural-urban categorization accounting for population size and density, proximity and relationship to metropolitan areas, urbanity, economic activities, and/or work commutes.107,108 Furthermore, most classification schemes use dichotomous (ie RU or UR) categories, or use a range of tiers to categorize as either RU or UR.  For example, the 2003 Office of Management and Budget Metropolitan Area Standards (OMB) utilizes 3-tiers (ie, Metropolitan Statistical Areas, Micropolitan Statistical Areas, and non-Core Based Statistical Areas), with Metropolitan Statistical areas classified as metropolitan (UR), and the latter two as nonmetropolitan (RU).108  The 2013 US Department of Agriculture Economic Research Service Urban Influence Codes (UIC) further subdivides the OMB classification into 10-tiers, with UIC 1 and 2 classified as metropolitan (UR), and 3 to 12 classified as nonmetropolitan (RU).109  Furthermore, the categorization into dichotomous categories (ie, RU and UR), does not differentiate categories of urbanity, suburbanity, and rurality, as stated in Hart et al,107 the nation, and aggregating rural areas of differing sizes and levels of remoteness may schemes that can be used by researchers studying RU exposure and a few are summarized below; for further information on rural-urban classification systems  refer to papers by Hart et al107 and Hall et al.108  23  Three of the most common rural-urban classification systems include the UIC, the 2013 US Department of Agriculture Economic Research Service Rural-Urban Continuum Codes Documentation (RUCC), and the 2010 US Department of Agriculture Economic Research Service Rural-Urban (RUCA).19,108 The RUCC utilizes a 9-tier classification system with three metropolitan (UR) and six nonmetropolitan (RU), while the RUCA utilizes a 10-tier classification system.  In addition to accounting for population density and urbanity, the RUCA classification uses daily commuting (eg, work, health care, and grocery commutes) to classify rural-urban categorization.110  Most rural-urban classification systems delineate by county, while the RUCA uses zip code. RUCA codes 1 to 3 are considered metropolitan (UR), and 4 to 10 as micropolitian to RU areas (RU).110 Cardiovascular Disease Risk Factors among Rural and Urban Populations in the United States Overweight and Obesity Rural residents on average have a higher prevalence of overweight and obesity compared to their UR counterparts.14,16,20,27,111-114  Rural adults are more likely to be overweight or obese compared to UR adults.14 Data from NHANES (2005-2008) found a significantly greater prevalence of obesity among RU vs UR adults (39.6% vs 33.4%; P = .006).13 A meta-analysis, that included 74 168 children and adolescents from five studies, by Johnson and Johnson19 revealed RU residents had 26% greater odds of being obese compared to UR residents.  Table 2 below summarizes published observations of the prevalence of RU overweight and obese children and adolescents at the national and regional (state) level. 24  Table 2.   Prevalence of Overweight and Obesity among Rural Children and Adolescents at the National and Regional (State) Level within the United States    Overweight or Obese % Obese %  Location  n Age or Grade   Males  Females  Males Females  Reference National       NHANES (1999-2006)a 1229 2-11 yrs 32.9 32.9 16.6 16.6 16  1542 12-19 yrs 38.1 38.1 20.7 20.7  NHANES (2003-2006)a 1028 2-18 yrs 39.0 39.0   27 NSCH (2003)a 12,904 10-17 yrs   20.8 12.1 115 HBSC (2005-2006)a 2663 6th-10th    16.0 12.0 18 Regional        Iowa (2003-2004)a 1025 8-12 yrs 46.9 46.9 25.1 25.1 36 Texas (2010)a 1084 6-11 yrs 35.8 28.3 16.0 18.3 32   12-19 yrs 35.9 43.2 20.2 23.1  Oklahoma (2008)a 237 3rd  39.7 36.9 23.8 19.8 116 Kentucky (1994-1995)b 54 8-12 yrs 48.0 20.7 28.0 10.3 117 Georgia (2002)a 211 2nd ,4,6,8,10,11th  47.0 49.0   31 Georgia (2002)a,d 1152 4th,8,11th  39.9, 39.2 39.9,39.2 23.7, 23.0 23.7, 23.0 118 Mississippia  205 5th  57.0 50 32.0 31.0 119 Ohio (2006-2007)a 5306 6-11 yrs 39.5 36.4 22.6 19.3 120 Louisiana (2006)a 2709 8-15 yrs 42.9 46.6 25.8 28.5 121 Alabama (1996-2005)a 4261 14-19 yrs 43.1 37.7 26.0 21.0 122 North Carolina (2004)a 1121 K-11th  46.5 46.5 29.1 29.1 123 North Carolina (1996)c 1151 3rd, 4th    29.5 29.5 20 West Virginia (1998-2007)a 29,286 9-13 yrs 49.2 45.5 32.1 26.5 124 Pennsylvania (2006-2009)a,e 980,000 K-6th    18.2, 20.5 18.2, 20.5 125 alth Behavior in School-Aged Children. a  BMI for age and sex from the Centers for Disease Control and Prevention 2000 Growth Charts56 b BMI for age and sex from Growth Charts published by Frisancho (1990)126 c BMI for age and sex from the 1977 National Center for Health Statistics Growth Charts d Includes RU growth plus RU decline counties e Includes RU and ultra-RU 25    National level comparisons reveal a greater obesity prevalence among RU vs UR children and adolescents. Using 7882 children and adolescents from NHANES (2003-2006), Davis et al27 observed that 21.8% vs 16.9% of RU and UR children and adolescents, respectively, were obese (P < .05). A similar analysis using NHANES (1999-2006), found a prevalence of 18.6% and 15.1% for obesity among RU and UR children and adolescents, respectively.127 Furthermore, a greater percentage of RU vs UR 2- to 11-year-olds were overweight or obese, although the results were not statistically significant (32.9% vs 27.6%; P = > .05).  The relationship of higher overweight and obesity among RU children and adolescents has persisted nationally since at least 1988.17,115,128 Using data from NHANES (1988-1994) for 6110 children and adolescents aged 6 to 18 years, Wang128 observed a non-significant, slightly higher percentage of RU children aged 6 to 9 years  were obese compared to their UR counterparts (12.1% vs 11.9%); this relationship was also evident among children and adolescents aged 10 to 18 years (11.2% vs 10.2%). Among 47 757 children and adolescents RU children had 1.13 time greater odds of being obese compared to their UR counterparts.115 Regional comparisons at the state level also reveal a greater prevalence of overweight and obesity for RU vs UR children (refer to Table 2).20,36,118 A study by Bailey-Davis et al,125 using BMI data for kindergarten to sixth graders from 501 Pennsylvania school districts (n=980 000), found ultra-RU students followed by RU students had significantly greater obesity prevalence compared to UR and suburban students (20.5% and 18.2% vs 16.2% and 15.7%, respectively).  Moreover, a number of regional observations have revealed a greater prevalence of overweight and obesity among RU children and adolescents compared to national levels.120,122,124  26  Waist Circumference and Percent Body Fat There are few publications on observations for WC and %BF among RU adults, children, and adolescents.  Among 40- to 69-year-old RU women from Nebraska, Hagemen et al129,130 observed that 76.8% and 94.5% of the participants had abdominal obesity and adverse %BF (at or above 30%), respectively. Additionally, WC was observed as a strong predictor for systolic and diastolic BP among RU adolescents aged 15 to 18 years.131 A greater sum of skinfolds (a marker for %BF) was observed among RU vs UR children from North Carolina (27.6 vs 24.0 mm) which coincided with the greater average BMI (18.8 vs 18.0 kg/m²).20  There is some published research reporting %BF observations in RU children and adolescents without an UR comparison. Among a sample of RU fourth to sixth graders from Louisiana (n=2709), the average observed %BF was 25.1%.121  Furthermore, 17.7% and 27.4% were considered overweight and obese, respectively. A comparison of %BF between adolescents aged 12 to 18 years from two Alabama schools, revealed a %BF of about 26% for both.132  Among children 8- to 12-years-old from a RU Kentucky elementary school, the triceps skinfold (a marker for %BF) across ages ranged from 12.9 to 27 mm and 11.1 to 21.9 mm  for males and females, respectively.117  Dyslipidemia and Hypertension There are few publications for blood lipids and BP among RU adults133-136 and children and adolescents.20,131,137  Research comparing blood lipids and BP among adults reveals a greater prevalence for RU compared to UR residents.  Nationally, RU adults have 38.8% and 8.6% greater prevalence for coronary heart disease and diabetes, respectively compared to their UR counterparts.14 Additionally, coronary heart disease mortality rates  27  are greater in RU vs UR populations.136  A subset of men from Georgia found that RU residents were nearly twice as likely to have two or more CVD risk factors compared to UR men.135 The authors attributed this to lower SES, access to health care, and lack of preventative services.  Another study comparing RU and UR men from Pennsylvania, observed significantly higher mean levels of systolic BP (RU:148 mmHg; UR: 140 mmHg; P < .001), TC (RU: 202 mg/dL; UR: 188 mg/dL; P < .001), and triglycerides (RU: 167 mg/dL; UR: 117 mg/dL; P < .001) among RU residents.134  Some research has reported on observations for blood lipids and BP among RU adults with no UR comparison.  Roddy et al133 observed a prevalence of 28% and 15% of high TC and low HDL, respectively, for women aged 40 to 91 years from RU North Dakota, which were greater than national averages at that time. Among a sample of 70 RU, Hispanic women aged 19 to 69 years from Nebraska, 44.3% and 28.6% were considered prehypertensive and hypertensive, respectively, with an average systolic and diastolic BP of 129 and 80 mmHg, respectively.138 Far fewer studies have compared CVD risk factors other than obesity (eg, blood lipids and BP) between RU and UR children.  McMurray et al20 compared CVD risk factors between RU and UR children from North Carolina who were participants from the Cardiovascular Health in Children Study.  The sample consisted of 1151 RU and 962 UR third to fourth graders. Results revealed that TC and diastolic BP were not significantly different, however, systolic BP was significantly greater in the RU compared to the UR group (105.0 vs 103.3 mmHg; P = .0002). There were no significant differences in the prevalence of dyslipidemia or hypertension.  A number of studies have reported on levels  28  of blood lipids and BP for RU children without an UR comparison, with most results revealing greater mean levels and prevalence compared to the national average.123,132  In a sample of 1173 kindergarten to twelfth grade RU students from North Carolina, the mean systolic and diastolic BP was 111 and 69 mmHg, respectively, and the prevalence for elevated BP was 21.6%,123 greater than the national average at that time.9  Among 709 fifth grade children from RU West Virginia, the average TC and HDL-C were 172 and 51mg/dL, respectively, with 59% and 24.5% of those who fasted and non-fasted before blood draw, respectively, identified as having dyslipidemia (TC at or above 200 mg/dL).139  The results were greater than the national average of 10.6% for children and adolescents around the same time (1999-2000).9 Although the primary objective of a study by Moore et al137 was to investigate the interaction of ethnicity (ie, White and African-American) and PA (high and low) with MetS among children and adolescents from RU Georgia, they reported a range for systolic BP (105 to 113 mg/dL), diastolic BP (60 to 64 mmHg), TC (151 to 162 mg/dL), HDL (45 to 56 mg/dL, and triglycerides (74 to 124 mg/dL) among 116 children and adolescent from fourth, sixth, eighth, and eleventh grade.  International Cardiovascular Disease Risk Factor Comparisons Between Rural and Urban Children  Internationally, some countries also report higher rates of obesity and CVD risk factors among RU vs UR children and adolescents. RU Greek children have a higher prevalence of obesity compared to their UR counterparts (12.1% vs 10.7% respectively; P = .01).114,140   Cesani et al141 observed a greater prevalence of RU vs UR children with high central fat distribution (14.1% vs 6.5%; P < .001) among a sample 1368 children from 20  29  kindergartens and elementary school within the Brandsen district of Argentina.  Most research comparing international RU and UR children and adolescents, reveals that UR populations have greater mean levels and prevalence for BMI, obesity, and other CVD risk factors.142  Using data from the Heart Health Promotion from Childhood in Iran, Ahmadi et al143 observed greater hypertension and abdominal obesity for UR compared to RU male adolescents.  Furthermore, UR female adolescents had lower HDL-C and higher WC compared to their RU counterparts. Among 650 children (mean age, 10.8 years) from Mangalore, the average BMI was greater for the UR compared to RU residents (17.0 vs 14.3 kg/m²; P = .001).144 Overall, an increase in westernization and urbanization within developing countries encourages physical inactivity and diets high in fat and sugars contributing to greater adiposity among UR compared to RU residents.145            30  Nutritional Behaviors and Nutrient Intakes among United States Children  Recommendations and current intakes of selected nutrients are presented in Table 3. Table 3.   Daily Nutrition Recommendations and Current Intakes of Children   Nutrient Age Group Recommendationsa,b Age Group Current Intakesc Above (+) or  Below (-) Males  Females Males Females Food Groups        Fruit, cups 9-13 y 1.5 1.5 6-11 y 1.31 (0.01)  1.13 (0.01) - Vegetable, cups 9-13 y 2.5 2 6-11 y 0.87 (0.06)  0.84 (0.05) - Dairy, cups 9-13 y 3  3 6-11 y 2.37 (0.08)  2.09 (0.11) - Whole Grain, oz equivalents 9-13 y 3    3 6-11 y 0.80 (0.07)  0.75 (0.07) - Macronutrients        Total fat, % total kcals 9-13 y 25-35  25-35 6-11 y 32 (0.7) 33 (0.4)  Saturated fat, % total kcals  9-13 y <10%  <10% 6-11 y 11 (0.1) 11 (0.2) + Trans fat, g 9-13 y ND ND 2+, 6-11 yd 1.3, 6.1 1.3, 6.1 ND Total carbohydrate, % total kcals 9-13 y 45-65  45-65 6-11 y 55 (0.4) 54 (0.3)  Total sugars, g     139 (3.5) 120 (2.4)  Dietary Fiber, g 9-13 y 31 26 6- 11 y 15.4 (0.55) 13.9 (0.46)  - Total Protein, % total kcals 9-13 y 10-30  10-30 6-11 y 14 (0.2) 14 (0.2)  Micronutrients        Vitamin A (RAE), µg 9-13 y 600  600 6-11 y 673 (24.2) 560 (21.8)  Males: +, Females: - Vitamin C, mg  9-13 y 45 45 6-11 y 87.7 (4.76) 75.4 (5.14) + Vitamin D, IU or µg 9-13 y 600 600 6-11 y 264 (8.8) 216 (10.0) - Vitamin E -tocopherol), mg 9-13 y 11  11 6-11 y 7.5 (0.29) 6.5  (0.23)  - Sodium, mg 9-13 y < 2300 < 2300 6-11 y 3196 (80.3) 2954 (92.1)  + Potassium, mg 9-13 y 4500 4500 6-11 y 2364 (57.3) 2093 (56.2)  - Calcium, mg 9-13 y 1300 1300 6-11 y 1137 (29.0) 1004 (37.5)  - Magnesium, mg 9-13 y 240  240 6-11 y 249 (6.9) 223 (6.1) Males: +, Females: -  31   Abbreviations: kcals, kilocalories; RAE, retinol activity equivalents. a Recommendations for food groups from the 2015-2020 Dietary Guidelines for Americans146 b Intakes147 c Current nutrient intakes from What We Eat in America, NHANES (2011-2012)148,149 d Current trans fat intake taken from most recent report for ages 2 + by Doell et al150 using NHANES (2003-2006); 6- to 11-year-old specific intake from Kris-Etherton et al151 using NHANES (1999-2002)  Currently, children in the United States are not meeting numerous dietary reference intakes (DRIs) established for certain nutrients.148,149,152 The DRIs include the recommended dietary allowance (RDA) (average daily intake level to meet the needs of 97% to 98% of the population) for a known nutrient dependent on age and sex, or an adequate intake (AI) when an RDA does not exist.147 Some macronutrients do not have a determined RDA or AI but an acceptable macronutrient distribution range (ADMR).147 The AMDR is the % kilocalorie (kcal) range for a particular energy source (ie, protein, carbohydrates, and fat) that is associated with reduced risk of chronic disease while providing intakes of essential nutrients.  What We Eat in America, NHANES 2011-2012 is the most recent report for current nutrient consumption of individuals in the US.148,149,152  Food Groups   The recommendations for intakes of (portions) of fruit, vegetables, dairy and whole grains for children aged 9 to 13 years, are 1.5 cups, 2.5 cups (females: 2 cups), 3 cups, and 3 oz equivalents, respectively (see Table 3).146,147 The food group portions were designed to meet the nutrient requirements for healthy individuals across age and reduce the risk for chronic diseases (eg, obesity, hypertension, dyslipidemia, and type 2 diabetes). Fruit and vegetable intake is associated with decreased risk for CVD, diabetes, and mortality.153 Furthermore, interventions that included a component to increase fruit and vegetable 32  intake, have produced decreases in BP and improvements in vascular function.  The average intake for fruit among US children aged 6 to 11 years is 1.31 and 1.13 cups for males and females, respectively,148 slightly less than recommendations.  Additionally, 24.5% of males and females are meeting fruit intake recommendations.154 Average intake for vegetables is 0.87 and 0.84 cups  for males and females 6- to 11-years-old, respectively,148 with 1.6% and 16.3% of males and females aged 9 to 13 years, respectively, meeting  recommendations.154 Some publications have statewide estimates related to fruit and vegetable consumption in MI residents. Among MI adults, 32.1% and 23.9% are consuming fruit two or more times per day and vegetables 3 or more times per day, respectively.155  For MI adolescents from ninth to twelfth grade, 18.7% are consuming an adequate number of fruits and vegetables each day.156 Greater dairy and dairy alternative intakes are associated with better CVD risk factor status.157  From the literature it appears that specific dairy foods (ie, milk, cheese, and yogurt) regardless of fat content, are inversely associated with CVD risk.158  Furthermore, data from two large US cohorts of 3333 adults aged 30 to 75 years, revealed that a greater plasma dairy fatty acid concentration was associated with a lower risk for type 2 diabetes.159 Average intake for dairy and dairy alternative intakes among children aged 6 to 11 years of age, currently are 2.37 and 2.09 cups, respectively.148 The percentage of male and female children aged 6 to 11 years meeting recommendations for dairy and dairy alternatives are 28.0% and 26.0%, respectively.154   The recommendations for grain intake among males and females aged 9 to 13 years are 6 and 5 oz, respectively, with at least 3 oz coming from whole grain.146,147 Whole grains  33  are rich in cereal fiber, and consumption is associated with a reduced risk for obesity, type 2 diabetes, and CVD.160 The average intakes for whole grains among male and female children aged 9 to 13 years are 0.80 and 0.75 oz, respectively.148 Only 0.7% and 0.5% of males and females aged 6 to 11 years, respectively, are meeting recommendations for whole grain intake.154 Macronutrients Among macronutrients, recommendations for total and saturated fat are 25% to 35% and less than 10% of total daily kcals, respectively (see Table 3). 131,132   Trans fatty acids do not have a DRI, because of a lack of need other than as an energy source, however, the daily recommendation for individuals across all ages is to consume as little kcals from trans fat as possible.146,147 Foods high in solid fats (eg, saturated and trans fat) and added sugars  are the major contributers for excess kcals within the diet for US children and adolescents.161,162  Furthermore, a high LDL-C cholesterol level is associated with high daily saturated fat intake.163 Male and female children aged 6 to 11 years, are consuming near the upper end for total fat recommendations (32% and 33% of daily kcals, respectively), and both are consuming about 11% of their daily kcals from saturated fat.149 Daily total fat intakes among children and adolescents aged 8 to 17 years have decreased from 78.0 g in 1999-20002 to 76.3 g in 2009-2012.164 Furthermore, saturated fat intakes have also decreased during that same time period (27.3 to 25.9 g per day; P = .001). Trans fatty acids are associated with adverse health effects (eg, dyslipidemia, inflammation, type 2 diabetes, and coronary heart disease).151,165  From 1999-2002, average trans fat intake for 6- to 11- 34  year-old children was 6.1 g/day151; the current intake for individuals greater than 2 years of age is 1.3 g/day.150  The recommendation for carbohydrate intake for children aged 9 to 13 years is 45% to 65% of daily kcals.146,147 Total carbohydrates among children and adolescents 8- to 17-years-old, have decreased from 1999-2002 to 2009-2012 (291 to 274 g per day; P = .007).164  Within carbohydrate recommendations are sugars and dietary fiber.  Although there is not a recommendation for total sugar intake, added sugars are recommended to contribute less than 10% of daily kcals.146,147  The DRI for dietary fiber among male and female children aged 9 to 13 years are 31 and 26 g, respectively.146,147 A high dietary fiber intake is associated with decreased cardiometabolic risks among adults.166 Benefits from  increased dietary fiber intake and levels of adiposity are less clear in children,167 although diets low in dietary fiber and high in calorie-dense foods increase risk for obesity in later life.168 There are numerous physiological, microbiological, biochemical, and neuro-hormonal effects of high dietary fiber intake that contribute to lower adiposity levels169  US male and females aged 6 to 11 years are consuming 139 and 120 g of total sugar daily, and 15.4 and 13.9 g of dietary fiber.149  Daily dietary fiber intakes among children and adolescents aged 8 to 17 years have increased from 1999-2002 to 2009-2012 (12.9 to 14.5 g per day; P < .001)164 Micronutrients The daily recommendations for vitamins A (retinol activity equivalents), C, D, and E (-tocopherol) are 600 µg, 45 mg, 600 IU, and 11 mg, respectively for children aged 9 to 13 years (see Table 3).146,147 Diets high in the antioxidant vitamins (ie, A, C, and E) are  35  associated with decreased CVD risk due to anti-artherogenic properties including decreasing LDL-C oxidation and nitric oxide breakdown.170,171 Dietary vitamin D intake is associated with decreased inflammation due to a number of immunomodulating effects.172 Overall, the average intake of vitamins A and C among US children aged 6 to 11 years meets recommendations, while average intakes of vitamins D and E are below recommendations.149  Daily recommendations for sodium (Na+), potassium (K+), calcium (Ca+), and magnesium (Mg+), are 1500 (< 2300 mg), 4500, 1300, and 240 mg, respectively for children aged 9 to 13 years.146,147 Dietary Na+ intake is highly associated with hypertension and CVD events, while dietary intakes of K+, Ca+, and Mg+ are associated with lower levels of BP.173 A number of clinical trials have revealed that reductions in Na+ intake leads to decreases in BP in both normotensive and hypertensive individuals, however, salt restriction is also associated with increases in CVD, dyslipidemia, and mortality.173 There have been at least six different mechanisms to explain the association between K+ intake with BP reduction, including inhibiting Na+ reabsorption and increased nitric oxide production.173 The evidence for causal relationships for Ca+ and Mg+ with BP, is not as strong as is with NA+ and MG+ due to a low number of studies and confounding nutritional variables.173  Currently, 96.8%  and 90.1% of males and females aged 9 to 13 years, respectively  are consuming more than the upper limit for Na+ each day.174  The mean intakes for K+ and Ca+ for children aged 6 to 11 years, are below recommendations, while mean Mg+ intake is near recommendations.149 The daily intakes of Na+ and K+ have not changed among children and adolescents aged 8 to 17 years from 1999-2002 to 2009-2012 (3297 to 3354 mg per day; P = .38; and 2280 to 2248 mg per day; P = .20, respectively).164  36  Dietary Indices Several dietary indices are used to assess nutrition behaviors (food groups, dietary patterns and nutrient intakes/density) and nutrients among children.175  An index that is considered to be a marker for nutrient density is dietary fiber intake.  A study that used dietary fiber index (FI) (g fiber/1000 kcal), evaluated adolescents 12- to 19-years-old and reported dietary fiber (lower quintile, 2.9 g; upper quintile, 10.7 g) was inversely related to MetS,176 which is a constellation of three or more adverse CVD risk factors (abdominal obesity, high BP, triglycerides, low HDL-C, and/or abnormal blood glucose).78  Among Latino children, Ventura et al177 found a significant inverse relationship with soluble fiber and MetS, and a non-significant trend with total dietary fiber.  The average FI among US children aged 6 to 11 years is 7.4 and 7.6 g/1000 kcals for males and females, respectively,152 less 14 g/1000 kcals across ages.178   The Healthy Eating Index 2010 (HEI 2010), an index used to assess adherence to the 2010 Dietary Guidelines for Americans,179,180  is associated with a significant reduction in risk for CVD, cancer, type 2 diabetes, and all-cause mortality.181 The HEI 2010 is summed from 12 food components, with a maximum score of 100. The score encourages increased consumption of total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total proteins, seafood and plant proteins, and fatty acids ([monounsaturated fat + polyunsaturated fat]/saturated fat) and decreased consumption of refined grains, Na+, and empty calories (solid fats, alcohol, and added sugars).182  The average HEI 2010 score for US children 2- to 17-years-old is 49.8.183  37  Nutritional Behaviors and Nutrient Intakes among Rural Adults  Few publications have reported on nutrition behaviors and nutrient profiles for RU adults or children, with most reporting on food groups. Some studies have revealed differences in nutrition behaviors and nutrient intakes between RU and UR adults.  Data from the 2009 Behavior Risk Surveillance System Survey (weighted n=219 479 823)  reveals UR adults were 1.16 times more likely to consume 5 or more servings of fruits and vegetables a day compared to RU adults.184 A study using 2556 RU and UR adults from Brazos Valley Texas, found RU residents consumed significantly less daily fruit servings compared to their UR counterparts (1.3 vs 1.6 servings; P < .001), although there was no difference in vegetable serving intakes.185  Furthermore, a greater percentage of UR vs RU adults consumed 5 or more servings for fruit and vegetables per day (26.4% vs 21.8%; P = .02), and a greater prevalence of RU adults consume 3 or more cups of sugar-sweetened beverages per day (17.7% vs 10.5%; P < .001) in the aforementioned study.24 In contrast, Hill et al186 observed no differences in fruit and vegetable intake among RU and UR adults from south-central Virginia and north-central North Carolina (2.9 vs 2.8 cups; P = .19). For macronutrients, among 8815 adults from NHANES (2005-2008), RU residents consumed a slightly greater portion of their kcals from fat than their UR counterparts (34.4% vs 33.6%; P = .02), although daily kcal intakes were not different.13 A number of observational studies on regional RU adult nutrition behaviors and nutrient intakes have been published with no UR comparison.  Among 928 and 889 males and females, respectively, from six RU communities in Idaho, Montana, and Wyoming, sweetened beverage intake was associated with risk for obesity.111  Among RU Nebraska  38  Hispanic women, the daily servings for fruits, vegetables, and dairy were 1.3, 2.4, and 1.4 servings, respectively.138   Rural adults tend to have poorer nutrient intakes compared to national averages. An observation of RU White adults from the Lower Mississippi Delta (ie, Arkansas, Louisiana, and Mississippi), revealed significantly greater intakes of total fat, % kcals from total and saturated fat, added sugars, and significantly lower intakes of fruit, vegetable, and dairy servings, dietary fiber, vitamins A and C, Ca+, Mg+ compared to a national dataset (1994-1996, 1998 Continuing Survey of Food Intakes by Individuals).35 Nutritional Behaviors and Nutrient Intakes among Rural Children Few studies have compared nutrition behaviors and nutrient intakes between RU and UR children and adolescents.  Results for nutritional differences among RU and UR children at the national level, have revealed conflicting results.  Liu et al16 evaluated data for 6563 children and adolescents aged 2 to 19 years from NHANES (1999-2006) and reported that a smaller proportion of RU adolescents (12.2%) consumed 2 or more cups of fruits compared to UR adolescents (16.5%), while a greater proportion of RU children aged 2 to 11 years consumed 2 to 3 cups of dairy compared to UR children of the same age (29.7% vs 22.8%; P < .05). Furthermore, compared to UR children, RU children consumed about 90 more daily kcals (P < .05). Another study using data for children and adolescents aged 2 to 19 years from NHANES (2003-2006) found no differences in fruit, vegetable, or dairy consumption between RU and UR children, however, fruit and vegetable intakes were combined possibly masking differences in either food group.27 Some studies using national level data have compared obesity-related nutrition behaviors between RU and UR children. A national study using 9227 adolescents from the US Health Behaviors in School-Aged  39  Children Survey (2005-2006), observed RU African-Americans were more likely to consume fatty snack foods more than two days per week compared to their UR counterparts, although this relationship was not apparent for White and Hispanic ethnicities.18 At the regional level, some studies have revealed differences for nutrition behaviors and nutrient intakes between RU and UR children. Due to decreased availability of produce in some areas, UR children consume more fruits and vegetables each day compared to their RU counterparts. UR children from Texas consume significantly more types of fruit compared to their RU counterparts, however, nearly identical percentages of children are consuming fruit two or more times per day (RU: 42.3%; UR: 42.9%).34 Additionally, a greater proportion of UR vs RU children were consuming vegetables three or more times per day (14.6% vs 9.3%).   Another study by Davis et al,30 did not observe differences for fruit and vegetable servings between Kansas RU and UR children, although there was a trend for greater servings of junk food for the RU children (P = .09).  Although not directly a RU and UR comparison, Champagne et al,35 compared nutrition behaviors between 485 RU children from the Lower Mississippi delta (Arkansas, Louisiana, and Mississippi) from 2000 and 7756 children from the 1994-1996 CSFII (a primarily UR sample). The researchers observed greater intakes of added sugar for the RU children from the Lower Mississippi and significantly lower intakes of fruit, vegetable, and dairy servings, and dietary fiber compared to the national CSFII sample. Nutrition behaviors and nutrient intakes differ between RU regions across the US. Among a sample of 54 third to fifth graders  from a RU Kentucky elementary school,  the  40  percentage of their daily kcals coming from total and saturated fat were 36.3% and 13%, respectively.117 Furthermore, the children consumed 1.8 and 0.93 daily servings of vegetables and fruit, respectively, with an average dietary fiber intake of 12.4 g per day.  A RU sample of children from Mississippi, reported lower daily intakes for vegetables and fruit (0.5 and 0.8 servings, respectively), although a slightly greater dietary fiber intake (13 g per day).119 In a sample of RU students (n = 98) 6- to 11-years-old from South Carolina, 17.0%, 24.5%, and 58.5% reported consuming no fruit, fruit less than 3 times, and fruit 3 or more times per week, respectively.187  Within RU regions, nutrition differences exist between obese children and their normal-weight counterparts.  For example, obese children from California, Kentucky, Mississippi, and South Carolina are more likely to consume 2 or more servings of vegetables per day and less likely to consume whole milk compared to their normal-weight counterparts.49   Little research has reported on dietary indices among RU children. Among 205 RU children from Mississippi, the observed FI was 7.0 g fiber/1000 kcals.119 From a pooled sample of RU first to sixth grade children from California, Kentucky, Mississippi, and South Carolina, the observed FI was 8.5 g/1000 kcals.48 Health Behavior and Health Status Barriers among Rural Populations Socioeconomic Status Socioeconomic status can be assessed using current income, wealth, education, neighborhood, or a combination of any of these measures.188 A low SES is associated with obesity, hypertension, dyslipidemia, CVD and overall mortality.29,189,190   41    Socioeconomic status is highly associated with obesity among children and adolescents.189,191,192 A number of risk factors associated with obesity are common among low SES children including low fruit and vegetable intake and high intakes of sugar sweetened beverages,193  greater access to media in their bedrooms (eg, TV, DVD players, and video games), and less access to portable play equipment (eg, bikes and jump ropes).194,195  National level comparisons reveal RU children are more likely to be from a lower SES than their UR counterparts..17,27  A larger percentage of RU children are from to UR children (35.2% vs 33.0%), and a smaller percentage of RU children come from households at > 200% of the FPL (21.7% vs 32.0%).17 A similar relationship was observed using data for 7882 children and adolescents from NHANES (2003-2006).27  Another study using NHANES data, (1999-2004), revealed that RU children and adolescents were more likely to be from households < 130% the FPL and parental education less than12 years compared to their UR counterparts.16  Not all national comparisons revealed lower SES for RU children. Using the US Health Behavior in School-Aged Children survey Family Affluence Scale (a proxy for SES), Kenney et al18 observed no difference in SES among  8363 RU and UR  adolescents from  grades sixth to tenth.  However, the Family Affluence Scale is a subjective measure for SES that quantifies using responses to questions inquiring about education, neighborhood.   42  Access to Nutritious Food Rural residents consume less nutrient-dense foods than their UR counterparts due to higher food cost, food quality, and access to food stores.23,25,196 Generally food in RU areas is more expensive than UR areas because of transportation cost and decreased demand.25 This relationship is especially true for nutrient-dense foods.196 The transportation costs to deliver food to RU areas, can lead to lower quality and less variety of foods to make up for shipment costs. The Nutrition Environment Measures Survey for Stores (NMES-S) has been used to assess food availability in RU and UR areas and is scored on food price, availability, and quality.197 More RU areas in Montana, have a lower fruit and vegetable quality, but no differences were observed for fruit and vegetable cost and availability as assessed by NMES-S compared to UR areas.197   However, UR Texas residents consume a greater fruit selection compared to their RU counterparts, suggesting increased availability in UR areas.34  A number of RU populations exist in a food desert, due to less availability of suppliers for nutrient dense foods.198  Geographically disadvantaged areas such as RU areas, tend to have a closer proximity and coverage to fast food restaurants and convenience stores vs supermarkets that supply more nutrient-dense as compared to energy-dense convenience foods.199 Convenience stores offering less availability of more healthful foods outnumber stores that offer more healthful and lower food costs in RU areas.200,201 Furthermore, availability of healthy snack foods and beverages is lower around RU vs UR elementary and middle schools.202  43  The high costs and availability of foods in RU areas have caused some residents to drive to more urbanized areas to purchase food which in itself may act as a hindrance due to travel time and/or fuel costs.23  Fresh produce purchase may not be as abundant in traditional store settings in RU areas, however, markets in these areas offer more opportunities for residents to purchase nutrient dense foods.  In North Carolina, Racine et al203 found that RU families were more likely to shop at UR families. Additionally, the availability of produce may foster development of seasonal eating patterns as individuals in the United States consume less produce in spring as opposed to the fall.204  For example, RU Hispanics consume more fruit and vegetables during the months of September-October (harvest season) compared to June-July (thinning season) and December-January (non-spray season).204    Access to Healthcare   Rural residents tend to be in medically underserved areas and may not have access to health care or health insurance.17,22,184  Merwin et al,22 summarized that RU areas had lower rates for all types of health professionals than UR areas, and that UR areas had nearly twice as many physicians per capita. Additionally, RU residents are less likely to have a personal physician or had a routine medical exam in the last 12 months.184  Overweight RU children are more likely to not have health insurance or have received preventative care in the last 12 months compared to overweight UR children.17   Barriers Associated with Physical Activity There are a number of barriers for PA among RU residents.  Overall, RU counties in the US, have low adequate access to recreational areas and exercise facilities, in contrast to  44  UR counties.55  Individuals living in areas within the vicinity of exercise facilities and parks tend to report higher amounts of vigorous PA compared to individuals who are not.21  Among qualitative studies of RU adults, residents report traffic and crime safety issues, distance to recreation options, and pleasantness of available options as determinants for PA.205,206  Food Insecurity Low food security is associated with higher rates of obesity, dyslipidemia, and hypertension.207 For children 6- to 11-years-old, personal food insecurity is significantly associated with obesity (OR, 1.81; 95% CI, 1.33-2.48; P < .001).208 Food security is usually assessed using the 18 item Household Food Security Module, with individuals considered food insecure if they answer affirmatively to three or more items.209 Little research has compared food insecurity among RU and UR residents. Guerrero et al 210 assessed food security among Wisconsin residents using responses to the question have you been concerned about having enough food for you Using the RUCA classification, participants were classified as either UR core, other UR (suburban), or RU. Food insecurity prevalence for UR core, other UR, and RU were 14.1%, 6.5%, and 10.5% with no statistical differences between groups (P = .13). Dean et al211 measured food insecurity among 1803 Brazos Valley residecounterparts (8.9% vs 4.7%; P = .004).  Some research has reported on observed food insecurity prevalence among RU residents with no UR comparison. A small sample of low- 45  income, RU families from 13 states (n=225), reported 51.1% were food insecure.209 Additionally, 29.4% of respondents reported 3 or more chronic health conditions. Physical Activity among Children of the United States Benefits of Physical Activity in Children The executive summary of the Physical Activity Guidelines Advisory Committee Report, 2008212 states that compared to sedentary children, physically active children have higher levels of cardiorespiratory endurance and  muscular strength, along with reduced %BF, more favorable CVD risk factors, enhanced bone health, and reduced anxiety and depression. Recommendations   According to the 2008 Physical Activity Guidelines for Americans,213 children and adolescents should acquire 60 or more minutes a day (everyday) of moderate and vigorous PA, with at least three days a week composing of vigorous PA.  As part of the 60 minutes of daily PA, muscle and bone strengthening PA should be performed on at least three of those days.   Current Physical Activity Behaviors in the United States  Depending on the type of measure used to quantify PA and database used, either the majority of children in the US are meeting or are not meeting recommendations.  Proxy data from parental reporting via NHANES (2009-2010) reveals that 70.4% of children aged 6 to 11 years are meeting recommendations for PA.214  However, more objective measures to quantify PA, reveal that children on average are not meeting recommendations. Data  46  from NHANES (2003-04) using accelerometers to quantity PA revealed that 48.9% and 34.7% of males and females, respectively, were meeting PA recommendations (average 42%).215  This result is similar to a study using accelerometer data from NHANES (2003-2006).216  Differences in Physical Activity among Rural and Urban Residents Research to date reveals varied results for comparisons of PA rates between RU and UR residents.  Data on 32 440 adults from the 1998 National Health Interview Survey Sample Adult and Adult Prevention Module, found that slightly more White RU vs UR adults were physically active (16.1% vs 13.8%).217  In contrast, more recent data from 8815 adults via NHANES (2005-2008), revealed there were no differences in PA between RU and UR residents.13   Using data for 46 396 children 5- to 18-years-old, from the 2003 National Survey of 17 observed that RU children were 1.5 times more likely to not meet recommendations compared to UR children, using the previous PA recommendations for moderate intensity ( 20 minutes or more at  5 or more days a week). More recent data from NHANES (1999-2006) suggests that this trend has reversed as 20.3% vs 26.2% of RU and UR children aged 2 to 11 years, respectively, are physically inactive. (P < .05).16 However, another study using data overlapping the same time period (NHANES 2003-2006) reported no difference in the number of RU and UR children and adolescents meeting PA recommendations.27    47  Summary of the Effectiveness of School-Based Nutrition and Physical Activity Interventions for Children and Adolescents Overall, reviews of school-based nutrition and PA interventions targeting children and adolescents have had small or moderate success in altering CVD risk factors and/or nutrition behaviors.47,218-223 This is probably due to the difficulty of categorizing intervention programs as they differ on a number of variables including: participant type (eg, age, gender, and ethnicity), study design, individuals performing the intervention, duration of the study, type of intervention, and outcomes measured.47,218-223  Traditionally, changes in anthropometry were indicators of successful school-based nutrition and lifestyle intervention programs. Reviewing changes in anthropometry following a school-based intervention from 25 studies, Doak et al,218 found that 17 of the 25 (68%) interventions significantly decreased BMI or skinfolds in the intervention groups.  Common components of the effective interventions identified by the researchers were decreasing sedentary behavior (television viewing) and/or a combined physical education and nutrition education program.  A meta-analysis using 64 children and adolescent obesity prevention programs with weight gain prevention as the outcome, found programs with the greatest effect sizes were short, targeted weight control vs other health behaviors, and included participant self-selection.221  A Cochrane Review summarizing changes in BMI among 27 946 children from 37 obesity prevention programs by Waters et al,222  found the overall magnitude of change for BMI -0.15 kg/m². Identified components for successful programs included: school curriculums that targeted healthy eating, PA, and body image, increased PA sessions and development of fundamental movement skills, improvement in  48  nutritional quality of food in schools, support for teachers and staff to implement health promotion strategies and activities, and parental support and home activities. Changes in adiposity may not be the best method to assess the effectiveness of a school-based nutrition and PA intervention program.  Females undergo large increases in adiposity around 12 years of age due to increased circulating estrogen during the pubertal stage which would increase BMI, %BF, and WC.224,225  Males usually undergo puberty at a later age and increases in body weight are typically associated with increases in lean body mass attributed to circulating testosterone which also increases BMI and WC.224 Due in part to these discrepancies, most nutrition and PA intervention programs measure additional outcomes along with anthropometry. A Cochrane Review encompassing 26 studies revealed school-based interventions that promoted PA and fitness among children and adolescents had little effect on PA rates, BMI, and BP, however, positive effects including increasing PA duration, decreasing sedentary time, and decreasing blood lipids, encourages the continuation of these programs.47  Furthermore, a meta-analysis by Cai et al,226 observed that 39% of childhood obesity interventions included in the analysis had a significant decrease in BP without corresponding decreases in adiposity measures.  The pooled intervention effect for decreased systolic and diastolic BP were -1.64 mmHg (P = .001) and -1.44 mmHg (P = .001), respectively. Another meta-analysis by Cai et al227 investigating changes in blood lipids from 17 childhood obesity programs, and calculated a pooled intervention effect of -0.97, -6.06, -1.95, and 1.87 mg/dL for TC, LDL-C, triglycerides, and HDL-C, respectively.  The pooled intervention effect was only significant for LDL-C and HDL-C.    49  Changes in nutrition behaviors and nutrient intakes are other important outcomes that can be assessed by school-based nutrition and PA intervention programs.  A meta-analysis consisting of 26 361 children 5- to 12-years-old from 27 school-based programs found a pooled intervention effect of 0.24 fruit (cups) (95%, 0.05-0.43 cups) and 0.07 vegetable cups (95%, -0.03-0.16 cups).228 This suggests that overall school-based nutrition and PA intervention programs are effective for increasing fruit intake, but not vegetable intake. The authors suggested including vegetable tastings and gardening components to help increase vegetable intake.228 Due to the importance of linking social networks to health, the power of social media, and the increased use of internet technologies among children and adolescents, a number of school-based nutrition and PA intervention programs have adopted these technologies into their curricula.229 A systematic review of electronic media interventions for the prevention or treatment of obesity and related behaviors, found 17 out of 20 (85%) studies produced significant changes in anthropometry and obesity-related behaviors (eg, decreased fat intake, increased self-efficacy) compared to comparison groups.220 Another systematic review that summarized results from 12 internet- and school-based obesity prevention interventions targeting adolescents, found programs were effective for improving health behaviors in the short term (less than 3 to 6 months), and 10 out of 12 (83%) improved dietary and/or PA behaviors.223 School-Based Nutrition and Physical Activity Interventions for Rural Children  A number of school-based nutrition and PA interventions in RU children and adolescents with successful outcomes have been published.48,230-232 A 16-week intervention  50  that targeted 205 RU fifth grade students from Mississippi included four in-class educational lessons with content directed towards improving CVD risk factors and a parental night component.233 The results revealed the intervention significantly increased vegetable servings, health knowledge, and decreased the percentage of children with elevated BP, high TC, and LDL-C. There were no changes for BMI or WC during the same time period. Another school-based nutrition and PA intervention program targeting RU children, comprised of 1508 kindergarten to fifth graders from four RU Kentucky elementary schools.234 curriculum, improve health and nutrition education, promote family and community involvement, and realign the schoolAfter the intervention, the percentage of children meeting PA, fruit, and vegetable recommendations significantly increased. A three-year follow-up revealed that the number of children meeting PA, fruit, and vegetable recommendations continued to increase.235 The TEAM Mississippi project, included 450 6- to 10-year-olds from RU communities in Mississippi.236 Schools were separated into controls or intervention, which included family-based nutrition and PA events.  After eight months, %BF significantly decreased for the intervention vs the control group, and dietary habits significantly improvement in the intervention group. Motivating Adolescents with Technology to CHOOSE Health is a school-based nutrition and PA intervention program that targeted seventh grade students from seven RU North Carolina schools.231,237  The intervention included a curriculum that used conceptual knowledge, health skills, individualized tasks, and motivation strategies to promote adoption or maintenance of sound nutrition and PA habits. After the intervention, there  51  was a significant decrease in BMI z-scores.237  After a 3.5 to 4.5 year follow up, the researchers observed a decrease for prevalence of overweight (20% to 12%) for the intervention group and an increase in prevalence (17% to19%) for a comparison group (n = 600 from the National Longitudinal Survey of Youth).231  Living Free of Tobacco, Plus (LIFT+), was a school-based intervention program that focused on preventing tobacco use and increasing fruit and vegetable consumption among five intervention and control schools in RU central Virginia middle schools.232 The intervention included goal setting, peer leaders, and classroom workshops with parental involvement. Immediately following the study, mean fruit and vegetable intake was greater for intervention vs control schools (3.19 vs 2.90 servings, respectively; P = .04) and this relationship still persisted after one year (3.02 vs 2.69 servings; P = .04).  There have been few publications comparing changes for nutrition behavior and CVD risk factor between RU and UR children who underwent the same school-based nutrition and PA intervention program.  Although not a direct comparison, the CHANGE (Creating Healthy, Active, and Nurturing Growing-UP Environments) program,48,49 was designed to adapt, replicate, and evaluate an UR multi-level curriculum (Shape-Up-Somerville)50 on a RU population. The CHANGE program was a two-year randomized, controlled trial that included 432 first to sixth grade students from eight RU communities in California, Mississippi, South Carolina, and Kentucky.48 The multi-level intervention consisted of a daily food service component, and students were exposed to the Shape Up: During and After School curricula, the Eat Well Keep Moving curricula, and the 5-2-1 messages.  Additionally, parental and community outreach components were also implemented. At the end of one year, the students who received the CHANGE intervention  52  improved their diets by consuming significantly more cups of vegetables per 1000 kcals (0.08 cups) and combined fruits and vegetables per 1000 kcals (0.22 cups) compared to the comparison group, although there were no differences observed for intakes of whole grains, dairy, legumes, saturated fat, added sugars, or fiber.  Another study adapted a curriculum originally designed for UR elementary school children from Denver (Integrated Nutrition Education Program), and implemented it on children from RU elementary schools in south-central Colorado and observed favorable outcomes in knowledge, self-efficacy, and attitudes towards nutrition and PA.238  A three- to six-year follow-up, revealed that nutritional-related knowledge and attitudes toward nutrition persisted, however self-efficacy and the percent who were overweight or obese increased, although the researchers did not measure if the trend was significant.239 Both of these studies suggest that intervention programs with a school-based component may be effective in diverse populations (ie, RU and UR) at least in the short term. (S)Partners for Heart Health The (S)Partners for Heart Health intervention has been previously described.51  The ve Theory,240 self-efficacy toward making healthy nutrition and PA-related choices in order to maintain or achieve a desirable CVD risk factor status. The program consists of a classroom-based curriculum, web-based lessons, mentor interaction, and parent handouts The (S)Partners curriculum has been modified from year-to-year, but has consisted of eight lessons with content that emphasizes knowledge and skills for nutrition and PA behaviors to promote heart and overall health.  Prior to implementation, educators  53  received the (S)Partners curriculum with instructions to implement the lessons to improve nutrition and PA knowledge and behaviors, and to engage students in PA for 50% or more of the lessons.  They also were given guidance on working with mentors, and facilitating their students to use the (S)Partner website.   Kinesiology and nutrition students were trained to mentor fifth grade students.  Training included: readings and discussions on nutrition, PA and heart health, principles of goal setting and behavior change, mentoring techniques, and assisting with lesson plans and facilitating break-out groups.  Each mentor was assigned two to three fifth grade students to mentor for 4-months. The web-based protocol was based on previous research,241-245 knowledge gained from focus groups, and a development phase where key intervention components were piloted in one physical education class. A secure website was developed by technology staff (https://www.spartners.msu.edu) with guidance and input from study investigators, graduate students, and college mentors. Communications were monitored by staff and graduate student team leaders.  Nutrition goals targeted a single food group at a time, with emphasis on nutrient-dense foods (ie, fruits, vegetables, whole grains, protein, and low-fat dairy or alternatives). The PA goals included facilitating students to sustain or achieve 60 minutes or more per day of moderate or vigorous PA every day of the week.  Students were encouraged to report their nutrition and PA behaviors during scheduled log-ins.    profile following pre- and post-assessments. Results were contrasted with age appropriate recommendations.  Also parents were given web-links and tips to support their children to meet their PA and nutrition recommendations.   54  Summary  In summary RU children have a greater prevalence of obesity and levels for other CVD risk factors compared to their UR counterparts.16-18,20 Compared to UR children, RU children are more likely to be of a lower SES,27 have less access to healthcare,22,184 less access to recreation areas and facilities,21 and less access to nutrient dense foods because of high cost, access to supermarkets, and travel distance.23,24,34 School-based nutrition and PA interventions that alter nutrition and PA behaviors during childhood, are a primary prevention method to reduce risk of CVD morbidity and mortality during later life.11  Few studies have compared CVD risk factors other than obesity between RU and UR children.  Furthermore, few studies have examined differences in nutrition behaviors and nutrient intakes between RU and UR children, and investigated their relationship with CVD risk factors in both populations.  Lastly, little research has been published comparing changes for nutrition behaviors and CVD risk factors between RU and UR children following a school-based nutrition and PA intervention.  55   CHAPTER 3 PREVALENCE OF OBESITY AND OTHER CARDIOVASCULAR DISEASE RISK FACTORS BETWEEN RURAL AND URBAN CHILDREN FROM MICHIGAN  Abstract  IMPORTANCE:   DESIGN, SETTING AND PARTICIPANTS: Variables included body mass index (BMI), waist circumference, percent body fat, blood lipids (total cholesterol, high density lipoprotein [HDL-C], and non-HDL-C), resting BP, mean arterial pressure (MAP), and a composite CVD risk factor score.  Mixed-model ANOVAs were used to compare between-group differences adjusted for confounders with school as the random effect.  Chi-squares and hierarchal logistic regressions were used to compare the prevalence and odds for adverse CVD risk factors between groups. (P < .05).  56    CONCLUSIONS AND RELEVANCE: In this sample of MI children we expected RU children to have a poorer CVD risk factor status vs UR children.  There were no significant differences in mean levels of CVD risk factors, though RU children did have a greater prevalence and odds for elevated diastolic BP vs UR children.  Also, the prevalence of obesity, low HDL-C, and elevated BP in both RU and UR children were higher than national averages.  This underscores the need for health education policies to support nutrition and PA programs to prevent and reduce CVD risk factors, and promote long-term health in MI children.  Introduction Approximately 75% of counties in the United States (US) are classified as rural (RU), and 20% of the US population resides within these areas.107  Rural populations typically have lower population size and density, lower urbanity, and are further away from metropolitan areas than urban (UR) populations.107,108 Nationally, young and old RU inhabitants have an increased prevalence for obesity compared to their UR counterparts.13,16-18,27  Additionally, the prevalence of coronary heart disease among adults is 38.8% greater in RU vs UR areas.14  A recent meta-analysis that pooled 74 168 children and adolescents from five studies found that those from RU areas have a 26% greater odds of being obese compared to their UR counterparts.19  Besides obesity, few studies have compared other CVD risk   57   factors between RU and UR children.  Among third and fourth grade children from North Carolina, McMurray et al20 observed RU children had a higher mean sum of skinfolds (a marker for percent body fat [%BF]) and systolic BP vs UR children. Furthermore, the RU children had 50.4% greater odds of being obese compared to the UR children.   Although, being obese during childhood increases risk for dyslipidemia (high total cholesterol [TC] and low-density lipoprotein [LDL-C] and low high-density lipoprotein [HDL-C]) and elevated (borderline high or high) blood pressure (BP),74 independent of body weight, low physical activity (PA) and high saturated fat intake increases the risk for those same CVD risk factors.11,105 Ice et al124 reported that a sample of 29 436 RU fifth grade Appalachian children (mean age, 10.9) had greater prevalence of selected CVD risk factors vs the national average from the  National Health and Nutrition Examination Survey (NHANES) 2011-2012,9  including  higher prevalence of elevated BP (systolic BP: 12.3% and diastolic BP 11.7%; vs 6.5% [systolic and/or diastolic BP]), high TC: 11.6% vs 7.0%, and low HDL-C: 18.2% vs 10.5%.  The reasons for higher rates of obesity and other CVD risk factors in RU areas are multi-factorial.21-25 Children from RU areas typically are from households with a lower socioeconomic status (SES) and low parental education level.27 A lower SES is attributed to an increased risk for numerous CVD risk factors including obesity and metabolic syndrome (MetS) (a cluster of risk factors defined by three or more of the following: hypertriglyceridemia, low HDL-C, high blood glucose, waist circumference [WC], and/or high BP)246 , which is linked to increases in morbidity and mortality.28,247,248  Also, low SES contributes to health and medical care disparities.22 Rural residents are less likely to have a personal physician or have had a routine medical exam in the last 12 months.184   58   Additionally, RU residents typically have less access to nutrient-dense foods because of high cost, access to supermarkets, and travel distance.23,24,34 Other factors contributing to increased risk for CVD among RU populations include less availability of recreation areas and facilities,21 and lower likelihood to engage in high intensity exercise compared to their UR counterparts.249  However, there are some conflicting reports.  For example, Liu et al16 reported 2- to 11-years-old RU children are less likely to be physically inactive compared to their UR counterparts (20.3 vs. 26.2%; P < .05).16 Another contributing factor is psychosocial stressors which may be greater among RU vs UR residents, as suicide rates among RU adolescents are nearly twice as high compared to UR adolescents,250 and RU adults are more likely to report a positive depression screen compared to UR adults.251 Both child and parental stress increase the risk for obesity in later life.252 Being a racial minority, particularly African-American, in RU areas may have a compounded effect for an increased risk of obesity.  Kenney et al18 reported that RU obese African-American children were more likely to exercise less than daily, eat fatty snack foods  two or more days per week, and spend more than  two or more hours in screen time each day as compared to UR obese African-American children.18   The majority of research that indicates the prevalence of overweight and obesity rates are greater in RU vs UR children have used large data sets that include a sampling of children throughout the US.16,27  However, these findings may not be generalizable of each geographic area in the US due to regional differences (eg, culture, traditions, weather, landscape). For example, among 3416 Iowa fourth to sixth grade children, a greater prevalence of RU children were overweight or obese compared to their UR counterparts, similar to national RU and UR differences.36  However, a similar study on children from   59   Kansas reported no difference in the prevalence of overweight and obesity between RU and UR children.30    The state of Michigan (MI) (a Midwest state in the US), has consistently had higher rates of childhood obesity compared to national averages over the past 20 years.  However, there has been little data published on comparisons between RU and UR.  A statewide comparison using 10- to 17-year-olds, revealed that a slightly greater percentage of RU vs UR residents were overweight or obese (31.6% vs 28.0%).37  Another study of primarily RU children and adolescents aged 4 to 17 years (n = 993), from the Upper Peninsula and an UR sample from the Lansing area,38 found the prevalence of overweight/obesity were 37% and 26% among males and females aged 6 to 11 years, respectively, which was greater than the national average and MI statewide averages at that time (US: males, 22%, females, 23%; MI: males, 29%, females, 26%).  This relationship was also evident among adolescents aged 12 to 19 years from the same sample.  Currently, data from the Youth Risk Behavior Surveillance System survey, reveals  32.6% of MI 10- to 17-year-old children and adolescents are overweight, which is slightly higher than the national average for the same age group (31.3%).253  Different regions in MI may have different rates. For example, a school-based study performed with low income UR children from Grand Rapids, found that 42.5% of these children were considered overweight or obese.42 There is not a statewide pediatric database for CVD risk factors, other than obesity, in MI, however, a few studies have reported results in different regions of MI.  A study by Cotts et al43 of 711 sixth graders from three UR middle schools reported that 8.4% and 3.6% had high systolic and diastolic BP (BP for sex, age and height percentile at or above   60   the 95th percentile)  respectively, which is greater than the national average for total high BP (1.9% for children aged 8 to 12 years).43 With respect to blood lipids and BP the mean concentrations for TC and HDL-C were slightly higher in that UR MI subset compared to national levels8 (169 vs 160 mg/dL and 55.7 vs 52.2 mg/dL, respectively), as was diastolic BP (63.6 vs 59 and 56.7 mmHg for males and females, respectively).44 Additionally, Peterson et al45 reported the average diastolic BP was greater among a primarily RU sample of 1486 female and 1390 male MI fifth graders (69 mmHg each) compared to the national average,44 however HDL-C was similar to national averages.   More data from MI children is needed to investigate if these relationships exist in other regions and populations including RU and UR children.  Overall, there is a paucity of research comparing the prevalence of CVD risk factors between RU and UR children other than the prevalence of overweight and obesity in the US.  Moreover, no RU and UR comparisons of multiple CVD risk factors have been published on MI RU and UR children.  There is currently a lack of knowledge on potential differences for CVD risk factor status between RU and UR children from low income school districts in MI.  status compare to national data, as well as insights to tailor interventions to address CVD risk in these populations.  We hypothesized that MI children from RU schools will have greater levels of CVD   61   risk factors, and greater prevalence and odds for at-risk CVD risk factors compared to children from UR schools.   Materials and Methods Study Design and Participants This study was a cross-sectional analysis of baseline data from fifth grade public school children residing in both RU and UR areas of MI. Participants were recruited from 11 schools who participated in the (S)Partners for Heart Health program from 2008-2012.51 The design and rationale of the (S)Partners for Heart Health program has been reported elsewhere.51  The protocol was approved by the Michigan State University Institutional Review Board.  All participating school principal or superintendent were required to sign an agreement to allow their fifth grade classrooms to participate. Inclusion criteria included: > 50% eligibility for Free and Reduced Lunch (FRL) (with the exception of 2008-2009 and 2009-2010 which included four schools with < 50% eligibility), and within 15 miles of Michigan State University, East Lansing, MI or partnering colleges or Universities including Alma College, Macomb Community College, Saginaw Valley State University, and Oakland University (with the exception of 2008-2009 where three schools were greater than 15 miles). Participants were recruited from physical education class, or their home room in each school, with all students invited to participate.  To participate in measurement, students were required to assent and have parental consent. Participants could opt out of any measurement at any time. There were 2644 total eligible males and females (RU: 800; UR: 1844), with 1013 providing both assent and parental consent (RU:   62   310; UR: 703).  Eighteen students were absent or did not have data at baseline leaving 995 total for this sample (RU: 307; UR: 688). Residence. Rural and UR residence was classified using the 2010 Rural-Urban Commuting Area (RUCA) codes developed by the University of Washington and the Economic Research Service.107  The classification uses a 10-teir system that incorporates population density, distance from metropolitan areas, and commuting information.107,108  Other classification schemes such as the Urban Influence Codes and Rural-Urban Commuting Codes designates residence by county, however, RUCA designates by zip code.  Zip codes areas with RUCA codes from 1 to 3 were classified as UR, and from 4 to 10 as RU.110  zip code was used as a proxy for the neighborhood of the students at the school. Using the RUCA classification, two and nine schools were considered RU and UR, respectively. Measurements  Participants were invited to participate in CVD risk factor assessments including anthropometry, blood lipids, BP, and lifestyle surveys including food frequency questionnaires (FFQ) and PA. All CVD risk factor measures were conducted by personnel who were trained on standard pediatric specific procedures, and were required to demonstrate reliable and valid technique before being approved to perform measurements.  The measurement protocol for this study has been previously described and each measure is summarized below (see Appendix A).51   Anthropometry.  Height and weight were measured according to standard procedures and was used to calculate BMI (weight in kg/ height in m²).  BMI percentiles were assessed using the 2000 CDC Growth charts. 254  Stature of the participants were measured without   63   shoes using a Shorrboard stadiometer (Shorr Production, Olney, MD) or similar to the nearest 0.1cm.  Weight (kg) and %BF was measured using foot-to-foot bioelectrical impedance (BIA) via a Tanita BC-534 InnerScan Body Composition Monitor (Tanita, Tokyo, Japan). BIA has been shown to have good accuracy for %BF, fat free mass, and fat mass (ICC 0.82).255  WC was obtained using a Gullick measuring tape superior to the iliac crest, and below the navel.256  WC is a valid indicator for abdominal obesity and has demonstrated strong correlations with subcutaneous, visceral, and total adipose tissue (r = 0.94, 0.85, and 0.94, respectively) among both normal-weight and obese children.257 Blood Lipids. Blood samples were collected in a non-fasted state by fingerstick using 35 µL heparinized capillary tubes.  The blood samples were analyzed by a Cholestech LDX (Alere, Waltham, MA), which strongly correlates with core laboratory measures for TC, LDL-C, HDL-C, and triglycerides (r = 0.91, 0.88, 0.77, and 0.93, respectively).258  Lipid panels collected included: TC, HDL-C, and TC:HDL-C.  Non-HDL-C was calculated as TC minus HDL-C.259  Prior to data collection, the analyzer was calibrated by controls via the manufacturer. Blood Pressure. Blood pressure was measured in a resting state manually following standard research procedures and was previously describe for the current study.51,260  In brief, after five minutes of rest, two sitting BP measures were taken at 1-min intervals, and averaged.  If the first two measurements differed by 4 or more mmHg, a third measurement was taken, and the two closest values were averaged.   Composite Cardiovascular Disease Risk Factor Score. A composite CVD risk factor score was calculated as: (WC z-score + mean arterial pressure [MAP]  [1/3 (systolic BP  diastolic BP)   64   + diastolic BP] + non-HDL-C z-score + HDL-C z-score + TC z-score + %BF z-score)/6 based on methods by Eisenmann for calculating MetS.261   Covariates. Ethnicity was determined based on participant responses to a survey question on the FFQ asking for ethnicity identification (see Appendix B).  Socioeconomic status was quantified using the percentage of children who were eligible for grade for that particular year, which has been used as an indirect measure of SES.262    PA was assessed using one self-reported question from the Youth Risk Behavior Surveillance System survey.263  The question stphysically 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 breathe hard some of scale range was 0 to 7 days.   Statistics. Demographic characteristics were compared using t-tests and chi-square tests. To adjust for possible confounders, CVD risk factors were compared using a mixed-model ANOVA with fixed factors as residence, sex, ethnicity, SES, PA, and school as the random effect. Chi-square tests were performed to compare prevalence of adverse CVD risk factors between each group. Variables deemed at-risk were defined as: BMI for sex and age at or above 85th percentile and less than the 95th  at or above the 95th 56 %BF for sex and age: males at or above the 69th percentile and females  at or above the 68th percentile, -sensitivity,88 %BF for sex and age at or above the 90th percentile for for MetS with high specificity,88 and WC for sex and age  at or above the 90th percentile 57 For blood lipids pediatric cutpoints were used and included, TC of   65   170 mg/dL or higher,60 HDL-C lower than 40 mg/dL, 60 and high non-HDL-C of 145 mg/dL or higher.60 Elevated systolic and diastolic BP was determined by BP for sex, age, and height percentile at or above the 90th percentile.61   To compare odds for adverse CVD risk factors, hierarchal logistic models were performed with UR as the reference group. Two logistic models were performed: 1) crude model with no covariate adjustments; and a 2) model adjusted for sex, ethnicity, SES, and PA. Means are presented as means (SD) or means (standard error). Data analysis was performed using SPSS version 21 (SPSS Inc., Chicago, IL) and SAS (version 9.4, 2003, SAS Institute) with significance set at P < .05.   Results Participant demographic characteristics including those classified as RU or UR are presented in Table 4. Among sex differences, 39.1% and 43.9% of the RU and UR groups respectively, were male, although they were not statistically different.  There were significantly more White children in the RU group and significantly more African-American children in the UR group. There was a trend (P = .07) for difference in % eligibility for FRL between RU schools (41.7%) and UR schools (63.7%), with an overall of 59%. There was a trend for a greater mean age in the RU group (10.7 and 10.6 years; P = .06). There were no significant differences for height or weight. Table 4. Demographic Characteristics, Height, and Weight of Participants from Rural and Urban Schools  Characteristic Total Rural Urban P value Total 995 307 688  Sex, No. (%)a     Males 422 (42) 120 (39) 302 (44) .16 Females 573 (58) 187 (61) 386 (56)  Ethnicity, No. (%)a        66        White 472 (51) 186 (63) 286 (46) <.001 African-American 150 (16) 11 (4) 139 (22) <.001 Hispanic 43 (5) 16 (5) 27 (4) .44 Other 257 (28) 81 (28) 176 (28) .88 Schools, No. 11 2 9  Free/Reduced Lunch, mean (SD), % 58.8 (19.4) 47.7 (17.4) 63.7 (18.7) .07 Age, mean (SD), y  (No.)a 10.6 (0.57) (n=989) 10.7 (0.45) (n=307) 10.6 (0.62) (n=681)  .06 Height, mean (SD), cm  (No.)a 144 (6.97) (n=977) 144 (6.62) (n=307) 144 (7.13) (n=670)  .74 Weight, mean (SD), kg  (No.)a 42.4 (11.8) (n=962) 41.9 (10.8) (n=301) 42.6 (12.3) (n=657)  .33 a Subgroup sample sizes do not add up to the total sample size because of missing data  Table 5 includes a summary of the results for our primary objective to compare CVD risk factor differences between RU and UR children. The number of children in either group varies based on level of participation for measures. There were no differences in anthropometry (BMI, BMI percentile, low- and high-risk %BF, or WC) or blood lipids.  Systolic BP and the composite CVD risk factors score were not different between groups. There was a trend for a greater diastolic BP (P = .06) and MAP (P = .07) for the RU compared to the UR group.  Table 5. Cardiovascular Disease Risk Factors in Rural versus Urban Children Adjusted for Rural/Urban Status, Sex, Ethnicity, Socioeconomic Status, Physical Activity, and School Risk Factor Rurala Participants, No. Urbana Participants, No. Adjusted Differenceb P Value Anthropometry, mean (SE)      BMI, kg/m² 20.4 (1.01) 305 20.9 (0.44) 657 -0.46 (1.16) .69    67          BMI Percentile 70.6 (6.00) 305 69.4 (2.64) 642 1.17 (6.89) .86 Body Fat, % 24.5 (1.58) 305 25.2 (0.72) 652 -0.70 (1.83) .88 Waist Circumference, cm 69.6 (3.96) 303 69.9 (1.70) 658 -0.38 (4.50) .93 Blood Lipids, mean (SE)      TC, mg/dL 149 (4.16) 229 148 (2.20) 464 0.54 (4.82) .91 HDL-C, mg/dL 44.5 (1.98) 231 48.2 (1.04) 466 -3.76 (2.29) .10 non-HDL-C, mg/dL, 105 (4.34) 229 100 (2.30) 455 5.03 (5.02) .32 TC:HDL-C Ratio 3:59 (0.18) 229 3:26 (0.10) 458 0.33 (0.22) .14 Blood Pressure, mean (SE)      Systolic BP, mmHg 108 (3.14) 307 103 (1.34) 664 4.95 (3.56) .17 Systolic BP Percentile 65.1 (9.61) 307 54.7 (4.09) 664 10.4 (10.9) .34 Diastolic BP, mmHg 72.6 (2.90) 307 66.3 (1.24) 664 6.27 (3.29) .06 Diastolic BP Percentile 78.0 (8.08) 307 62.7 (3.44) 664 15.3 (9.18) .10 Mean Arterial Pressure, mmHg 84.6 (2.88) 307 78.6 (1.23) 664 5.93 (3.26) .07 CVD Composite, mean (SE), z-score -0.94 (0.20) 229 -0.81 (0.09) 455 -0.12 (0.22) .58 Abbreviations: BMI, body mass index; TC, total cholesterol; HDL-C, high density lipoprotein; BP, blood pressure; CVD, cardiovascular disease.  a Calculated using least-squares means regression adjusting for school location, sex, ethnicity, SES, PA, and school.   b Adjusted difference in values between Rural group and Urban after adjustments for school location, sex, ethnicity, SES, PA,  and school.   68   The mean difference for the prevalence of RU and UR participants with adverse CVD risk factors was determined by chi-square tests, and presented in Figures 1 and 2. There were no differences in the prevalence of adverse anthropometry and dyslipidemia between groups, although a number of trends were observed.  There was trend for a greater prevalence of obesity and high-risk %BF among the UR children (P = .08 and P = .06, respectively).  There were no group differences in the prevalence of elevated systolic BP, however, the RU children had a greater prevalence of elevated diastolic BP compared to the UR children (27.7% vs. 19.5%; P = .01).   The crude and adjusted odds-ratios revealed no significant group differences for adverse levels of anthropometry measures, blood lipids, and systolic BP are summarized in Table 6.  However, crude and adjusted odds-ratios revealed RU children had 63% (OR, 1.63; 95% CI, 1.18-2.25) and 74% (OR, 1.74; 95%, 1.25-2.43) greater odds for elevated diastolic BP compared to their UR counterparts.  Abbreviations: %BF, body fat a  Overweight: BMI for sex and  85th and < 95th percentiles of the 2000 CDC Growth Charts; Obese: BMI for sex and age  95th percentile of the 2000 CDC Growth Charts bLow-risk %BF (high sensitivity for MetS): %BF for sex and  69th  68th percentiles for males and females, respectively from NHANES (1999-2004); High-risk (high specificity for MetS) %BF for sex and age  90th percentile for from NHANES  (1999-2004) c Abdominal obesity: Waist circumference for sex and  90th percentile from NHANES III (1988-1994)    c a b b a d   69    dFrom NHANES (2011-2012) for children aged 6 to 11 years40,57   Abbreviations: TC, total cholesterol; HDL-C, high density lipoprotein; BP, blood pressure a -C < 40 mg/dL, non-HDL-Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report b Elevated BP: BP for age, sex, and height percentile  90th percentile from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents cFrom NHANES (2011-12) for children aged 8 to 12 years9 dSignificant P < .05  Table 6. Odds for At-Risk Cardiovascular Disease Risk Factors in Rural vs Urban Children (Reference)  OR (95% CI)  OR (95% CI)  Risk Factor  Crude  P Value Adjusteda P Value Anthropometry, %     Overweightb 1.12 (0.76, 1.63) .54 1.18 (0.80, 1.74) .41 Obeseb  0.78 (0.54, 1.13) .19 0.85 (0.58, 1.25) .40 Low-Risk %BFd 1.07 (0.81, 1.41) .64 1.08 (0.80, 1.46) .62 High-Risk %BFd 0.73 (0.49, 1.07) .11 0.79 (0.52, 1.20) .27 Abdominal Obesityc 0.81 (0.56, 1.16) .24 0.82 (0.56, 1.20) .30 Dyslipidemia, %     e 1.00 (0.67, 1.49) .99 1.01 (0.66, 1.54) .96 HDL-C <40 mg/dLe 1.15 (0.79, 1.65) .47 1.06 (0.72, 1.55) .79 non-HDL-e 1.09 (0.57, 2.10) .79 0.92 (0.46, 1.81) .80 TC:HDL > 3:5 1.24 (0.88, 1.75) .22 1.15 (0.81, 1.64) .44 Elevated BP, %    Systolic BPf 0.74 (.50, 1.11) .15 0.82 (0.54, 1.23) .33 Diastolic BPf 1.63 (1.18, 2.25) .00 1.74 (1.25, 2.43) .00 Abbreviations: %BF, body fat; TC, total cholesterol; HDL-C, high density lipoprotein; BP, blood pressure. Urban as reference group. aAdditionally adjusted for sex, ethnicity, SES, and physical activity b  Overweight: BMI for sex and  85th and < 95th percentiles of the 2000 CDC Growth Charts; Obese: BMI for sex and age  95th percentile of the 2000 CDC Growth Charts   a b b a a c d   70    c Low-risk %BF (high sensitivity for MetS): %BF for sex and  69th  68th percentiles for males and females, respectively from NHANES (1999-2004); High-risk (high specificity for MetS) %BF for sex and age  90th percentile for from NHANES  (1999-2004) d Abdominal obesity: Waist circumference for sex and  90th percentile from NHANES III (1988-1994) e -C < 40 mg/dL, non-HDL-Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report                     f Elevated BP: BEvaluation, and Treatment of High Blood Pressure in Children and Adolescents   Discussion  In the present study there were no differences in BMI or prevalence of overweight in either group, however, there was a trend for a greater percentage of UR children being obese and having high-risk %BF.  Previous studies with large samples have revealed that RU children in the US are more likely to be obese compared to their UR counterparts.  Liu et al264 found that a greater proportion of RU children 2- to 11-years-old were overweight and obese compared to UR children (32.9% vs. 27.6% and 16.6% vs. 14.1%, respectively).    71   Furthermore, this trend was also observed in adolescents 12- to 19-years-old.  Data from the 2003-children were 1.25 times more likely to live in RU versus UR areas.17 This discrepancy in results from our current study could be attributed to the smaller sample size (N=995), as opposed to 14 332 16 and 46 396 17 participants. Additionally, numerous studies comparing regional (state) differences in obesity prevalence (eg, Iowa, Pennsylvania, Georgia) have revealed a greater obesity prevalence for RU vs UR children.36,118,125  To date, only a few studies have compared an anthropometric measure other than BMI between US RU and UR children.   McMurray et al20 found that RU children had a significantly higher skinfold thickness compared to UR children (27.6 mm vs 24.0 mm; P < .001).20 Furthermore, the RU participants had 50% greater odds of being obese compared to the UR children.  Due to the limited number of studies and little data published, more direct RU vs UR comparisons are needed to determine if RU and UR children differ for other anthropometry measures (eg, %BF and WC) nationally and in other regions of the US. Several studies on US adults have reported higher levels of BP in RU vs UR residents Nationally, data for 6896 adults from NHANES (1999-2006), revealed RU residents had a significantly greater prevalence of high BP compared to UR adults (45.0% vs 39.5%; P < .05).26 Among a small sample of adults (n=388) from Pennsylvania, RU residents had a higher mean systolic BP compared to their UR counterparts (148 vs 140 mmHg; P < .001).134  The result from the current study, is one of the few studies to report RU children have a greater prevalence and odds for elevated diastolic BP compared to UR children in the US. Diastolic BP is a better predictor of premature mortality risk (< 50 years of age) than systolic BP.265 Furthermore, there was a trend for a greater mean diastolic BP and   72   MAP in the RU vs UR children.  Although, not significant MAP, which is the average arterial pressure during one cardiac cycle, is strongly predictive of CVD risk among young men.266   Few studies have compared BP between RU and UR children. McMurray et al20  compared BP and hypertension in a sample of 1151 RU and 962 UR children in North Carolina, and found no significant differences in diastolic BP (68.4 vs 67.9 mmHg; P = .22), however, systolic BP was significantly greater in the RU vs UR group (105 vs. 103 mmHg; P < .001).20 Although, there were no differences for the prevalence of hypertension between groups (RU: 11.3%; UR: 9.3%).   Our results and the findings summarized from the preceding studies suggest that RU children may be at a greater risk for high BP. In the prior study by McMurray et al,20 obesity increased the odds for hypertension in RU and UR children by more than 200 percent, suggesting BMI is related to the discrepancies for BP among that group of RU and UR children. Although our study displayed no significant group differences for BMI, overweight, or obesity, a trend emerged for a greater proportion of UR children being obese and high-risk %BF, which was in contrast to the McMurray et al study.20  In another regional study with a sample of 277 RU children aged 6 to 11 years from southeastern Ohio (a Midwest state), there were no difference in the odds for hypertension among normal-weight and children with a BMI at or above the 85th percentile.120  This suggests that BMI may not be associated with risk for high BP in all RU populations.   Although not assessed for the current study, numerous dietary factors can contribute to increases in BP and hypertension including high intakes of sodium and low intakes of potassium, calcium, and magnesium.173 Another potential contributor to the observed difference in diastolic BP, is the potentially higher rate of psychosocial stressors in RU vs UR populations,250,251 which increases risk for hypertension.267 This suggests a   73   need to further investigate the role of nutrition and psychosocial factors in relation to adverse CVD risk factors (ie, BP) among RU and UR MI children and to control for these factors as was done with PA in the current study.   With respect to blood lipids, to our knowledge few studies have directly compared RU and UR children. McMurray et al20 also reported no differences between RU and UR children, but only assessed TC levels.20  Overall, these studies suggest that blood lipids may not differ among  RU and UR children, however, more research is need to determine if RU and UR differences in blood lipids are evident at the national level and are true for regional areas of the US.  The composite CVD risk factor was not different between either groups which was probably due to the lack of differences for most of the CVD risk factors (ie, TC, HDL-C, and non-HDL-C) factored into the score.  There is little published research comparing CVD composite scores (eg, MetS) between RU and UR residents. In a study using 6896 adults from NHANES (1999-2006), the MetS prevalence was greater for RU vs UR residents (39.9% vs 32.8%; P < .01).26 Furthermore, a greater percentage of RU adults had four or more MetS components compared to UR adults (17.8% vs 15.0%; P < .05). Although we were unable to locate any studies that compared CVD composite scores between RU and UR children, among 116 RU fourth, sixth, eighth, and eleventh graders from Georgia, children and adolescents who reported low levels of PA had greater odds (OR, 3.02; 95% CI, 1.02-8.95; P < .05) for MetS than children and adolescents who reported high levels of PA.137 The lack of studies comparing CVD risk factor composite scores between RU and UR   74   children, suggests a need to investigate at the national level and other regional areas in the US.   In general, the prevalence of at-risk CVD risk factors is greater in this study compared to national figures.  In this study, 35.1% and 38.6% of the RU and UR children respectively, were considered overweight or obese which is slightly greater than the national average of 34.2% among 6- to11-year-olds.40  Furthermore, the UR children in our study had a greater prevalence of abdominal obesity (21.2%) compared to the national average of 17.5% for 6- to 11-year-olds.57  The proportion of low HDL-C was greater in both groups (RU: 26.8%; UR: 24.4%) than the national average for children aged 8 to 12 years (10.5%).9  The prevalence of elevated BP in both groups was greater in this study than the national average of 6.5% for this age group.9    This suggests that both RU and UR MI children may be at a greater risk for some CVD risk factors and not others compared to the national average. There are several strengths with the current study. This is one of the few studies to compare CVD risk factors other than BMI in RU and UR children and the first such study in MI. Another strength is including a good portion of the population at risk for health disparities based on eligibility for FRL and a high obesity prevalence.   However, there were several limitations that need to be addressed. Unequal sample size with respect to school location may have biased the conclusions because of a loss of statistical power, however, hierarchal analyses were performed to account for the clustering of students within schools. Another limitation is both groups were not balanced on ethnicity, however, 4% of RU children identified themselves as African-American in this   75   study which is similar to the statewide average of 2.1% of RU residents classified as African-American.268 An additional limitation is that blood lipids levels and BP may have been affected by medications (eg, antihyperlipidemics, attention-deficit/hyperactivity disorder [ADHD] medications) used by the participants as the prevalence of use for these medications has increased.269,270 However, the prevalence is still low ranging from 29.5 per 1000 for antiasthmatics to 0.27 per 1000 for antihyperlipidemics,270 with 23.5% of children and adolescents under 18-years-old  having taken at least one prescription drug in the past 30 days.271 However, in MI the prevalence of children aged 4- to 17-years-old taking medications for ADHD has increased from 5.1% to 8.3% from 2007 to 2011.272  Stimulate class medications for ADHD can increase BP and heart rate in the long term.273 Another limitation is the method in which residency was categorized.  The RUCA delineates by zip code which may not differentiate neighborhoods within such as suburban areas. address, which may not be representative of all students attending that school. The results may not be generalizable to other RU and UR populations in the US because the sample consisted of MI children only.  Furthermore, the results may not be representative of other RU populations in MI, (ie, Northern Lower Peninsula and Upper Peninsula) as the two RU schools were from Central and Southern Michigan. Conclusion In this sample of RU and UR MI children overall more than 50% had an at-risk CVD risk factor.  We expected CVD risk factors would be greater in RU children, though we found no significant differences in mean levels of CVD risk factors between groups.  The only   76   significant differences in prevalence and odds for at-risk CVD risk factors was RU children for elevated diastolic BP compared to their UR counterparts.  Regardless if students were from RU or UR schools, MI children have a number of CVD risk factors greater than the national average (ie, overweight and obesity, low HDL-C, elevated BP). There is a need to increase the implementation of nutrition and PA intervention programs at the child level to reduce the burden of CVD related health in later life.  Future research is needed to investigate the role of nutrition in CVD risk for RU and UR MI children to provide insights for intervention strategies.        77  CHAPTER 4 COMPARISON OF DIETARY INTAKE BETWEEN RURAL AND URBAN CHILDREN AND THE RELATIONSHIP OF NUTRITION BEHAVIORS TO CARDIOVASCULAR DISEASE RISK FACTORS  Abstract IMPORTANCE:  The prevalence of obesity CVD risk factors, has increased in the last 40 years among adults and children in the United States (US). This is due in part to decreased consumption of nutrient-dense foods, increases in calorie-dense foods, and decreases in physical activity (PA).  Compared to urban (UR) populations, data that is available indicates rural (RU) populations are at a greater risk for obesity and other cardiovascular disease (CVD) risk factors, and have a lower life expectancy.  Additionally, Michigan (MI) adults and adolescents have higher obesity and lower intakes of fruits and vegetables compared to national averages. Little research has compared differences between RU and UR children nutrition behaviors and PA in the US and none have been performed on MI children. Furthermore, there is limited data on the relationship of nutrition behaviors with CVD risk factors in children overall. OBJECTIVES/HYPOTHESES: 1a) Evaluate if mean intakes of food groups and nutrients related to CVD health and PA are lower in MI RU vs UR children; and b) if fewer RU children are meeting national recommendations; and  2) determine if nutrition behaviors and nutrient intakes are related to CVD risk factors among RU and UR children. DESIGN, SETTING AND PARTICIPANTS: Cross-sectional study on 670 (RU: n=226; UR: n=444) Michigan fifth graders (mean age, 10.6 years) from public schools with  30% eligibility for free and reduced lunch who participated in the baseline assessment for a school-based nutrition and PA intervention during the Fall 2008-2012.  Rural and UR        78  classification was determined by the Rural-Urban Commuting Area Codes.  MAIN OUTCOMES AND MEASURES:  Nutrition behaviors and nutrient intakes were assessed with a Food Frequency Questionnaire (FFQ) and included daily intakes of food groups (fruit, vegetables, dairy, and whole grains), macronutrients (kilocalories [kcals], total fat, saturated, and trans fat; total carbohydrate, sugar, dietary fiber, and protein), and micronutrients (vitamins A, C, D, and E), and minerals (Na+, K+, Ca+, and Mg+). Also, two dietary indices: the Dietary Fiber Index (FI) (g fiber/1000 kcal) and the Healthy Eating Index (HEI) 2010. PA was assessed using a self-reported question. CVD risk factors included: BMI (overweight/obesity), waist circumference (WC) (a marker for abdominal obesity), percent body fat (%BF), blood lipids (total cholesterol, high density lipoprotein [HDL-C], non-HDL-C), and resting blood pressure (BP).  Mixed-model ANOVAs evaluated between-group differences adjusted for confounders.  Chi-squares evaluated % meeting or not meeting nutrition recommendations.   Logistic regressions assessed relationships for nutrition behaviors and nutrient intakes (per 1000 kcals) with CVD risk factors. (P < .05).  RESULTS: Overall, there were few RU and UR children meeting nutrition and PA recommendations, and over 60% had at least one CVD risk factor.  For objective 1a) UR children reported higher fruit (P = .01) and vitamin C intakes (P = .04), and a higher FI (P = .02). There were no other differences in food groups, nutrients, or PA; 1b) there were no group differences in the proportion meeting recommendations.  Objective 2) in RU children, increases in whole grain were associated with a decreased likelihood for elevated systolic BP (P = .03); and K+ and Ca+ intakes, and higher HEI scores were associated with a decreased likelihood for elevated diastolic BP (all P < .04). There were several unanticipated associations with nutrition and risk factors including: in UR children, fruit        79  and vegetable increases were associated with increased likelihood for overweight/obesity, and low- and high-risk %BF (all P < .02); and saturated fat increases were associated with a decreased likelihood for increased WC (abdominal obesity) and low- and high-risk %BF (all P < .04).  CONCLUSIONS AND RELEVANCE: We expected RU children would have poorer nutrition behaviors, nutrient intakes, and PA. This was true based on the reported UR childrens greater intakes of fruit, vitamin C, and higher FI, which suggests a more nutrient-dense diet vs RU children.   However for the hypothesis that fewer RU vs UR children were meeting recommendations; there were no group differences, although a large percentage of both groups were not meeting nutrition and PA recommendations.   Regarding associations between nutrition and CVD risk factors.  Only a few associations were found as hypothesized including whole grain, K+, Ca+ and HEI were associated with one outcome (BP), and several associations were in contrast to the hypotheses and previous studies.  Possible explanations include: the FFQ included items classified as fruit and vegetables with varying nutrient and caloric-density that could either reduce or increase CVD risk, participants and in particular overweight and obese children may have recently changed their habitual nutrition behaviors (independently, or via external factors: [eg, parents, public health messages]), and/or misreported intakes.  In summary, RU children had poorer nutrient density vs UR children, however, overall RU and UR MI children were meeting nutrition and PA recommendations, and their high prevalence of CVD risks.  These findings underscore a need to adopt primary prevention programs to encourage nutrition and PA behaviors to promote the cardiovascular and overall health of both RU and UR MI children.         80  Introduction The prevalence of overweight and obesity in children 6- to 11-years-old from the United States (US) has increased from 13% to 33% over the last four decades,40,274 while other cardiovascular disease (CVD) risk factors including dyslipidemia, high blood pressure (BP), and metabolic syndrome (MetS) have also emerged among children.275,276  Significant contributors to these increases include less consumption of nutrient-dense foods such as fruits and vegetables, and greater consumption of convenience foods that are calorie-dense (eg, high saturated fat and  total sugars),12,161,277 coupled with low physical activity (PA) and increased sedentary time.46,215  Additionally, most US children are not meeting recommendations for fruits, vegetables, dairy, and whole grains.154 In general, there is a greater prevalence of obesity and other CVD risk factors in rural (RU) children and adults vs their urban (UR) counterparts.13,16,26 Nutrition-related behaviors that have been identified as contributors to  include less access to nutrient-dense foods such as fruits and vegetables, and greater access to foods that are calorie-dense and higher in sodium (NA+) and saturated fat.16,23,200 Other contributing factors include health disparities due to lower socioeconomic status (SES),27 and less availability of recreational areas and exercise facilities in RU vs UR regions.21 Few studies have been published comparing differences in nutrition behaviors and nutrient intakes between RU and UR children.16,27,30 Liu et al16 evaluated 6563 children and adolescents aged 2 to 19 years via the 1999-2006 National Health and Nutrition Examination Survey (NHANES).  They reported that a smaller proportion of RU adolescents (12.2%) consumed  two or more cups fruits compared to UR adolescents (16.5%), while a        81  larger portion of RU 2- to 11-year-old children consumed two to three cups of dairy vs UR children (29.7 vs. 22.8%; P <  .05). However, a similar study using NHANES (2003-2006) from children and adolescents aged 2- to 19-years-old found no differences for reported intakes of fruit and vegetables combined or dairy intake between RU and UR residents.27 Furthermore, there was no difference in the percent of RU vs UR children meeting PA recommendations. However, the aforementioned Liu et al16 reported a lower percentage of RU vs UR children were performing 60 minutes or more of PA less than 5-days per week (20.3% vs 26.2%; P < .05). In contrast, an earlier study reported UR children were more likely to meet PA recommendations than RU children.17   A consideration for national studies comparing health in RU vs UR, is that RU regions in the US are different from one another (eg, culture, traditions, weather, and landscape).  Studies often include data from many RU regions, and likely are not generalizable to all RU populations. For example, 4.9% of RU children from Texas reported consuming no fruit per day,34 compared to 14.7% of RU children nationally.16  Rural White and African-American children from the Mississippi Delta reported consuming 3.3 and 4.2 combined daily servings of fruit and vegetables, respectively,35 much less than the reported RU national fruit and vegetable aggregate of 5.2 daily servings.27 In Michigan (MI), statewide data reveals a lower percentage of adolescents have been consuming adequate fruits and vegetables compared to the national average since 2001.156 It is unknown if there are differences for reported nutrition behaviors, nutrient intakes, and PA in RU vs UR MI children.        82  There are numerous dietary indices used to assess nutrition behaviors (food groups, dietary patterns and nutrient intakes/density) and nutrient intakes among children.175  An index that is considered to be a marker for nutrient density and surrogate marker for plant-based food intake is dietary fiber intake.  Dietary fiber intake is inversely associated with numerous CVD risk factors for adults and children.12,166 Additionally, there was an NHANES (1999-2002) analysis on adolescents aged 12 to 19 years that evaluated the associations of three dietary indices (g fiber/1000 kcals, g saturated fat/1000 kcals, and mg cholesterol/1000 kcals) with MetS, a constellation of  three or more CVD risk factors78 (waist circumference [WC], high BP, high triglycerides, low high-density lipoprotein [HDL-C], and/or hyperglycemia).176 The Dietary Fiber Index (FI) (lower quintile, 2.9 g; upper quintile, 10.7 g) was inversely related to MetS, while there were no relationships with the other two indices. A similar study that evaluated the relationship of diet, including dietary fiber, and MetS in overweight Latino children,177 found a significant inverse relationship with soluble fiber and MetS, and a non-significant trend with total dietary fiber.  Another commonly used index, is the Health Eating Index 2010 (HEI 2010), which is used to assess adherence to the 2010 Dietary Guidelines for Americans.179,180 Scoring is a summation from 12 nutrition components with a maximum score of 100.  Higher scores of the HEI have been associated with a significant reduction in risk for CVD, cancer, type 2 diabetes, and all-cause mortality.181  Nationally, there is no significant difference in the HEI score for 6- to 11-year-old children (46.7 vs 48.1), however, RU children 2- to 5-years and adolescents aged 12- to 19-years-old have lower HEI scores than their UR counterparts (49.7 vs 51.8; P < .05; and 45.5 vs 46.8; P < .05, respectively).33 No comparisons using dietary indices have been performed between MI RU and UR children.        83  In contrast to studies using indices of nutrient density or those meeting guidelines (eg, HEI), some studies in adolescents have reported an association between a healthy diet or components of a diet (eg, high fruit, vegetable, and whole grain intake; low sugar and solid fat intake) with healthy CVD risk factors.278  For example, Davis et al27 identified that intake of meat and sugar-sweetened beverages are associated with odds of being obese in UR children, but not RU children. Identifying which nutrition behaviors differ between RU and UR children, including those that are associated with obesity and other CVD risk factors may offer insights for intervention programs promoting a healthy diet. Limited research has investigated the relationship of multiple CVD risk factors with nutrition behaviors and nutrient intakes among RU and UR children in the US18,27; and no studies have been published on MI children.  Therefore, our objectives (stated as active research hypotheses) were to evaluate 1a) if mean intakes of food groups and nutrients related to CVD health and PA are lower in MI RU vs UR children; and b) if fewer RU children are meeting national recommendations. 2) Determine if nutrition behaviors and nutrient intakes are related to CVD risk factors among RU and UR children. More specifically for objective 1) we hypothesized that MI RU children will have poorer reported daily intakes of food groups (fruit, vegetables, dairy, and whole grains), macronutrients (kilocalories [kcals], total fat, saturated, and trans fat; total carbohydrate, sugar, dietary fiber, and protein), and micronutrients vitamins A, C, D, and E, and minerals (Na+, potassium [K+], calcium [Ca+], and magnesium [Mg+]) lower FI and HEI 2010 scores, and lower PA.  Furthermore, we hypothesized that a greater proportion of RU vs UR children would not be meeting recommendations for aforementioned food groups, macro- and micronutrients, and PA. Secondarily we hypothesized that higher intakes of the        84  aforementioned food groups, and the macro- and micronutrients that are recommended to be consumed in higher levels (vitamins- A, C, D, and E; minerals- K+, Ca+, and Mg+) would be associated with a decreased likelihood for CVD risk factors, while increased intakes of saturated and trans fat, total sugars and Na+ would be associated with an increased likelihood for CVD risk factors among both groups.   Materials and Methods Study Design and Participants This study was a cross-sectional analysis of baseline data from fifth grade public school children residing from both RU and UR areas of MI. Participants were recruited from 11 schools who participated in the (S)Partners for Heart Health program from 2008-2012.51 The design and rationale of the program has been reported elsewhere.51  The protocol was approved by the Michigan State University Institutional Review Board.  All participating school principal or superintendent were required to sign an agreement to allow their fifth grade classrooms to participate. Inclusion criteria included: > 50% eligibility for Free and Reduced Lunch (FRL) (with the exception of 2008-2009 and 2009-2010 which included four schools with < 50% eligibility), and within 15 miles of Michigan State University, East Lansing, MI or partnering colleges or Universities including Alma College, Macomb Community College, Saginaw Valley State University, and Oakland University (with the exception of 2008-2009 where three schools were greater than 15 miles). Participants were recruited from physical education class, or their home room in each school, with all students invited to participate.  To participate in measurement, students were required to assent and have parental consent. Participants could opt out of any measurement at any        85  time. There were 2644 total eligible males and females (RU: 800; UR: 1844), with 1013 providing both assent and parental consent (RU: 310; UR: 703).  Eighteen students were absent or did not have data at baseline leaving 995 total for this sample (RU: 307; UR: 688). Residence. Rural and UR residence was classified using the 2010 Rural-Urban Commuting Area (RUCA) codes developed by the University of Washington and the Economic Research Service.107  The classification uses a 10-teir system that incorporates population density, distance from metropolitan areas, and commuting information.107,108  Other classification schemes such as the Urban Influence Codes and Rural-Urban Commuting Codes designates residence by county, however, RUCA designates by zip code.  Zip codes areas with RUCA codes from 1 to 3 were classified as UR, and from 4 to 10 as RU.110  zip code was used as a proxy for the neighborhood of the students at the school. Using the RUCA classification, two and nine schools were considered RU and UR, respectively. Measurements Participants were invited to complete lifestyle surveys including food frequency questionnaires (FFQ), and participate in CVD risk factor assessments including anthropometry, blood lipids, and resting BP. All CVD risk factor measures were assessed by personnel who were trained on standard pediatric specific procedures, and were required to demonstrate reliable and valid technique before being approved to perform measurements.  The measurement protocol for this study was previously described and each measure is summarized below (see Appendix A).51   Nutritional Behaviors. The 2004 Block Kids FFQ (NutritionQuest, Berkeley, CA) was used to evaluate dietary intake (see Appendix B).279 After corrections for measurement error,        86  compared to 24-hr recalls, the FFQ has moderate correlations for energy from fat (%), sodium, grains (servings), and fruit (servings), except for some food groups in children less than 12-years-old.280  Compared to three-day food logs, the FFQ provides reasonable estimates for milk, 100% fruit juice, Ca+, and vitamin D consumption (r = 0.57, 0.55, 0.46, and 0.49, respectively ).281  The participants were guided by staff to complete the FFQs.  After completion of the FFQs, they were sent to NutritionQuest for processing which included an electronic flagging system to identify potentially invalid FFQs, including those with an unrealistic number of foods reported (too few or too many), and unrealistic kcal intakes (too low or too high). Two registered dietitians reviewed the flagged FFQs to questions that asked if they reported they were trying to lose weight to determine if the reported food pattern and or the kcal level was plausible for the flagged participants.  In the current data set, significantly more RU vs UR children had FFQs deemed valid (73.6% [226/307] and 64.5% [444/688], respectively; P = .02), and were analyzed. Further analysis, revealed that children who identified themselves as African-American, had 2.39 times greater odds of having an invalid FFQ compared to children who identified themselves as White (P < .001) (data not shown).  The food group and nutrient intake data used included daily intakes of: food groups in cups: fruit, vegetable, and dairy, whole grain (ounce equivalents), total kcals, macronutrients in g, included:  total fat, saturated fat, trans fat, total carbohydrate, total sugars, dietary fiber, and protein.  Micronutrients included vitamins A (retinol activity equivalents in µg), C (mg), D (IU), and E (-tocopherol) (mg); and minerals in mg: Na+, K+, Ca+, and Mg+.          87  Dietary Indices. The FI and HEI 2010 were selected since they reflect key components of dietary patterns and nutrition recommendations to prevent or reduce CVD risk among children.11 The FI has been used as a surrogate for nutrient density and plant-based food intake.176 Increased dietary fiber intake is associated with decreased risk for CVD risk factors (eg, metS, inflammation, and obesity),166 and high dietary fiber intake patterns during childhood reduce the risk for obesity during adulthood.168  The HEI 2010, assesses dietary compliance to the 2010 Dietary Guidelines for Americans and is the sum of 12 nutrient components with a 100 point maximum.180,182 These primary prevention guidelines are designed to promote health and decrease risk for CVD, cancer, and reduce morbidity and all-cause mortality.282  The score encourages increased consumption of total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total proteins, seafood and plant proteins, and fatty acids ([monounsaturated fat + polyunsaturated fat]/saturated fat) and decreased consumption of refined grains, Na+, and empty calories (solid fats, alcohol, and added sugars).182 Variables included in the analysis and calculations for the HEI 2010 are described in Appendices C and D. Physical Activity. PA was assessed using one self-reported question adopted from the Youth Risk Behavior Surveillance System survey.263  how many 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 rate and makes  Anthropometry.  Height and weight were measured according to standard procedures and was used to calculate BMI (weight in kg/ height in m²).  BMI percentiles were assessed        88  using the 2000 CDC Growth charts. 254  Stature of the participants were measured without shoes using a Shorrboard stadiometer (Shorr Production, Olney, MD) or similar to the nearest 0.1cm.  Weight (kg) and %BF was measured using foot-to-foot bioelectrical impedance (BIA) via a Tanita BC-534 InnerScan Body Composition Monitor (Tanita, Tokyo, Japan). BIA has been shown to have good accuracy for %BF, fat free mass, and fat mass (ICC 0.82).255  WC was obtained using a Gullick measuring tape superior to the iliac crest, and below the navel.256  WC is a valid indicator for abdominal obesity and has demonstrated strong correlations with subcutaneous, visceral, and total adipose tissue (r = 0.94, 0.85, and 0.94, respectively) among normal-weight and obese children.257 Blood Lipids. Blood samples were collected in a non-fasted state by fingerstick using 35 µL heparinized capillary tubes.  The blood samples were analyzed by a Cholestech LDX (Alere, Waltham, MA), which strongly correlates with core laboratory measures for total cholesterol (TC), low-density lipoprotein, HDL-C, and triglycerides (r = 0.91, 0.88, 0.77, and 0.93, respectively).258  Lipid panels collected included: TC and HDL-C.  Non-HDL-C was calculated as TC minus HDL-C.259  Prior to data collection, the analyzer was calibrated by controls via the manufacturer.  Blood Pressure. Blood pressure was measured in a resting state manually following standard research procedures and was previously describe for the current study.51,260  In brief, after five minutes of rest, two sitting BP measures were taken at 1-min intervals, and averaged.  If the first two measurements differed by 4 or more mmHg, a third measurement was taken, and the two closest values were averaged.          89  Covariates. Ethnicity was determined based on participant responses to a survey question on the FFQ asking for ethnicity identification.  Socioeconomic status was quantified using the percentage of children who were eligible for particular year, which has been used as an indirect measure of SES.262   Statistics.  Demographic characteristics were compared using t-tests and Chi-square tests. A mixed-model analysis of variance was used to compare between-group differences for the dependent variables using SAS (version 9.4, 2003, SAS Institute) while controlling for residence, sex, ethnicity, and SES, with school as the random effect.  Chi-square tests were used to compare the percentage of subjects meeting nutrition recommendations using SPSS version 21 (SPSS Inc., Chicago, IL).  Daily nutrition recommendations for 9 to 13 year old children146,147 included: fruit portions (1.5 cups), vegetable portions (males: 2.5 cups; females: 2 cups), dairy portions (3 cups), whole grain (3 oz equivalents), 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 (males: 31 g; females: 26 grams), total protein (10% to 30% of daily kcals) vitamin A (retinol activity equivalents) (600 µg), vitamin C (45 mg), vitamin D (600 IU), vitamin E (-tocopherol) (11 mg), Na+ ( less than 2300 mg), K+ (4500 mg), Ca+ (1300 mg), and Mg+ (240 mg).  Meeting PA recommendations was performing 60 minutes or more of moderate and vigorous PA every day of the week.212 To assess the relationship of CVD risk factors with nutrition behaviors and nutrient intakes, a logistic regression adjusted for sex, ethnicity, SES, and PA was applied, with each nutrition variable adjusted per 1000 kcals (except HEI 2010). Variables deemed at-risk were defined as: BMI for sex and age at or above 85th percentile and less than the 95th  BMI for sex and age at or above the 95th 56 %BF for sex and age: males  at        90  or above the 69th percentile, females  at or above the 68th -with high sensitivity,88 %BF for sex and age at or above the 90th percentile for both sexes 88 and WC for sex and age  at or above the 90th 57 For blood lipids pediatric cutpoints were used and included, TC of 170 mg/dL or higher,60 HDL-C lower than 40 mg/dL, 60 and high non-HDL-C of 145 mg/dL or higher.60 Elevated systolic and diastolic BP was determined by BP for sex, age, and height percentile at or above the 90th percentile.61    Data are presented as mean (SD) or mean (SE) and significance was set at P < .05.  Results Demographic data are presented in Table 7.  Both groups had similar proportions of males and females (40% and 60%, respectively). A greater percentage of the RU group was White compared to their UR counterparts (62.5 vs. 49.2%; P = .001), and a greater percentage of UR children was African-American (17.7 vs. 3.6%; P < .001).  A higher proportion of the UR sample was eligible for FRL, trending for significance (P = .07). There were no significant differences for age, height, weight, or BMI between groups.  A greater percentage of UR vs RU children had 0 CVD risk factors (37% vs 30%; P = .03), and a greater percentage of RU vs UR children had 1 CVD risk factor (24% vs 18%; P = .03).  There were no differences in the percentage of RU and UR children with either 2 or 3 or more CVD risk factors. Table 7. Demographic Characteristics, Height, and Weight of Participants from Rural and Urban Schools  for Aim 2 Characteristic Total Rural Urban P value Total 670 226 444  Sex, No. (%)a     Males 267 (40) 88 (39) 179 (40) .61 Females 393 (60) 137 (61) 256 (60)  Ethnicity, No. (%)a     White 354 (54) 140 (63) 214 (49) .00 African-American 85 (13) 8 (4) 77 (18) < .001 Hispanic 34 (5) 15 (7) 19 (4) .20        91        Other 189 (28) 61 (26) 125 (29) .69 Schools, No. 11 2 9  Free/Reduced Lunch, mean (SD), % 58.8 (19.4) 47.7 (17.4) 63.7 (18.7) .07 Age, mean (SD), y  (No.)a 10.6 (0.61) (n=666) 10.6 (0.44) (n=226) 10.6 (0.68) (n=440)  .11 Height, mean (SD), cm  (No.)a 144 (7.01) (n=662) 144 (6.69) (n=225) 144 (7.18) (n=437)  .93 Weight, mean (SD), kg  (No.)a 42.4 (11.9) (n=655) 42.0 (11.0) (n=224) 42.6 (12.3) (n=431)  .49 BMI, mean (SD), kg/m² (No.)a 20.1 (4.45) (n= 655) 20.0 (4.14) (n= 224) 20.2 (4.60) (n= 431)  .40 CVD risk factors, No. (%)     0 230 (34) 67 (30) 163 (37) .03 1 133 (20) 55 (24) 78 (18) .03 2 99 (15) 33 (15) 66 (15) .91  208 (31) 71 (31) 137 (30) .73 Abbreviations: BMI, body mass index. CVD, cardiovascular disease  a Subgroup sample sizes do not add up to the total sample size because of missing data  The prevalence of at-risk CVD risk factors in this sample are presented in Figures 3 and 4.  There were no differences in the prevalence of at-risk body composition measures between groups except a greater prevalence of UR vs RU children had a high-risk %BF (19.2% vs 12.8%; P = .03).  Additionally, there were no significant differences in the prevalence of dyslipidemia and elevated systolic BP between groups, although a greater percentage of RU vs UR children had elevated diastolic BP (28.4% vs 19.3%; P = .006).  Abbreviations: %BF, body fat  b a c c d        92   a  Overweight: BMI for sex and  85th and < 95th percentiles of the 2000 CDC Growth Charts; Obese: BMI for sex and age  95th percentile of the 2000 CDC Growth Charts b Abdominal obesity: Waist circumference for sex and  90th percentile from NHANES III (1988-1994) cLow-risk %BF (high sensitivity for MetS): %BF for sex and  69th  68th percentiles for males and females, respectively from NHANES (1999-2004); High-risk (high specificity for MetS) %BF for sex and age th percentile for from NHANES  (1999-2004) dSignifcant P < .05   Abbreviations: TC, total cholesterol; HDL-C, high density lipoprotein; BP, blood pressure a -C < 40 mg/dL, non-HDL-Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report b Elevated BP: BP for age, sex, and height percentile  90th percentile from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents cSignificant P < .05  Results for our primary objective to evaluate reported nutrition behaviors, nutrient intakes, and PA between RU and UR children, are presented in Table 8.  We hypothesized that RU children would report poorer nutrition and PA behaviors than their UR counterparts.  This included between group comparisons for intakes of food groups, selected macro- and micronutrients, and PA, including the percentage meeting recommendations. For food groups, the mixed-model ANOVA revealed reported fruit intake was higher in the UR than in the RU children (2.24 vs 1.51 cups; P = .01).  The proportion of children a a c a b b        93  meeting fruit recommendations was also higher in UR (56.3%) vs RU (50.0%), but was not statistically significant (P = .12).  There were no group differences for vegetable, dairy, or whole grain intakes, or the proportion meeting recommendations. Only 6.7% to 6.9% of children in both groups were meeting daily vegetable recommendations.  The proportion of RU and UR children meeting dairy recommendations was 9.3% and 10.1%, respectively while no children (0%) from either group were meeting whole grain recommendations.  The comparison of macronutrient intakes and the proportion meeting recommendations revealed there were no significant between group differences for reported intakes of total kcals, fat (ie, total, saturated, and trans), total carbohydrates, sugar, dietary fiber and protein.   Furthermore, there were no significant differences in the percentage of both groups meeting nutrition recommendations for those nutrients. For total fat 61% to 62% of both groups met total fat recommendations, and slightly more UR than RU children were meeting saturated fat recommendations (27.3% vs 24.3%).  For total carbohydrate, roughly 80% of both groups were meeting carbohydrate recommendations. There was a trend for UR children consuming more dietary fiber per day than RU children (15.3 vs 11.6 g; P = .06), however, only 4.1% and 5.4%, respectively were meeting recommendations.   With respect to micronutrients, there were no between group differences for the reported intake of vitamins A, D and E, while UR children consumed significantly more vitamin C than RU children (P = .04), although 80.0% of both groups were meeting vitamin C recommendations (Table 2).  While it was not statistically significant, a slightly greater proportion of UR vs RU children met vitamin A and E recommendations, however, neither group met recommendations for vitamin D. For minerals, there were no significant        94  differences in reported mean intakes or the percentage meeting recommendations between RU and UR children. Slightly more RU than UR children were meeting recommendations for Na+, K+, and Ca+, while slightly more UR children were meeting Mg+ recommendations.  The analyses for the calculated dietary indices revealed the FI was significantly higher in the UR children compared to their RU counterparts (9.14 vs. 7.89 g/1000 kcals; P = .02) as hypothesized, however, the evaluation of HEI 2010 was not different amongst groups.  Furthermore, there was no difference in the mean or percentage meeting recommendations for PA. Table 8. Differences in Nutrition Behaviors, Nutrient Intakes, Physical Activity, and Proportion Meeting Recommendations for MI children by School Location  Rurala  (n=226) Urbana (N=444) Adjusted Differenceb P Value Nutrition Behavior         Food Groups, mean (SE)     Fruit Portion, cups 1.51 (0.25) 2.24 (0.12) -0.73 (0.30) .01 1.5 cups, % meeting 50.0 56.3  .12 Vegetable Portion, cups 0.47 (0.36) 1.03 (0.15) -0.57 (0.40) .16 M: 2.5 cups; F: 2 cups, % meeting 6.7 6.9  .92 Dairy Portions, cups 1.61 (0.15) 1.47 (0.08) 0.14 (0.19) .45 3 cups, % meeting 9.3 10.1  .73 Whole Grain, oz  0.45 (0.09) 0.54 (0.04) -0.09 (0.10) .39 3 oz equivalents, % meeting 0 0   Macronutrients, mean (SE)     Total Kilocalories 1506 (145) 1707 (67.0) -200 (171) .24 Total Fat, g 52.0 (8.74) 61.3 (3.79) -9.26 (10.1) .36 25-35% total kcals, % meeting 61.5 61.3  .95 Saturated Fat, g 18.5 (2.24) 21.0 (1.00) -2.50 (2.61) .34 <10% total kcals, % meeting 24.3 27.3  .38 Trans Fat, g 5.66 (1.21) 5.59 (0.52) 0.07 (1.39) .96 Total Carbohydrate, g 212 (17.4) 238 (8.61) -25.2 (21.0) .23 45-65% total kcals, % meeting 80.1 80.0  .97 Total Sugar, g 114 (11.4) 131 (5.60) -16.8 (13.7) .22 Dietary Fiber, g 11.6 (1.65) 15.3 (0.73) -3.67 (1.93) .06 M: 31 g; F: 26 g, % meeting 5.4 4.1  .48 Total Protein, g 49.2 (6.77) 59.2 (2.98) -9.95 (7.88) .21 10-30% total kcals, % meeting 95.6 93.2  .23 Micronutrients, mean (SE)     Vitamin A, µg 495 (84.0) 533 (36.2) -38.78 (96.6) .69 600 µg, % meeting 30.4 32.2  .58        95        Vitamin C, mg 106 (16.6) 147 (8.21) -40.8 (20.0) .04 45 mg, % meeting 80.0 80.0  .97 Vitamin D, IU 157 (15.2) 146 (7.48) 10.7 (18.2) .56 600 IU, % meeting 0 0   Vitamin E, mg 4.96 (1.23) 5.81 (0.53) -0.86 (1.42) .55 11 mg, % meeting 8.0 8.6  .79 Sodium, mg 2024 (318) 2543 (138) -520 (368) .16 < 2300 mg, % meeting 57.5 52.2  .20 Potassium, mg 1790 (245) 2266 (108) -476 (285) .10 4500 mg, % meeting 3.5 3.4  .91 Calcium, mg 769 (96.3) 761 (42.1) 7.29 (112) .95 1300 mg, % meeting 11.1 10.1  .71 Magnesium, mg 186 (24.1) 221 (10.6) -34.8 (28.0) .22 240 mg, % meeting 28.3 33.3  .19 Dietary Indices, mean (SE)     Dietary Fiber (g fiber/1000 kcals) 7.89 (0.45) 9.14 (0.22) -1.24 (0.54) .02 HEI 2010c 62.3 (2.46) 65.1 (1.06) -2.84 (2.82) .31 PA, mean (SE), No.  4.90 (0.30) (n=222) 4.32 (0.15) (n=434) 0.59 (0.38) .12 7 days, % meeting 32.4 27.9  .23 Abbreviations: M, male; F, female; kcals, kilocalories; HEI, Health Eating Index.  a Calculated using least-squares means regression adjusting for school location, sex, ethnicity, SES, and school. b Adjusted difference in values between rural and urban group after adjustments for school location, sex, ethnicity, SES, and school c HEI 2010 is summative from 12 nutrition components with a maximum score of 100  The results from our secondary objective to evaluate the relationship of selected food groups and nutrient intakes with CVD risk factors are presented in Tables 9 and 10. We hypothesized that selected food groups (fruits, vegetables, dairy, and whole grains), selected micronutrients (vitamins- A, C, D, and E; minerals- K+, Ca+, and Mg+), and dietary fiber indices scores (FI and HEI 2010) would be associated with desirable CVD risk factor status, while those that prudent intake is recommended (saturated and trans fat, total sugars, and Na+) would be associated with increased CVD risk status The relationship of food groups and nutrient intakes/1000 kcals with body composition (overweight/obese, abdominal obesity, and %BF) among RU and UR children are presented in Table 9. No food groups were associated with any of the three obesity-related measures for RU children.  Unexpectedly, increasing fruit intakes were associated        96  with increased likelihood for overweight/obesity, and low- and high-risk %BF among UR children (OR, 1.32 [95% CI, 1.05-1.67]; OR, 1.37 [95% CI, 1.08-1.74]; and OR, 1.35 [95% CI, 1.03-1.77], respectively). Another unexpected result was increasing vegetable intakes were associated with an increased likelihood for overweight/obesity, abdominal obesity, and low- and high-risk %BF among UR children (OR, 1.77, [95% CI, 1.08-2.91]; OR, 2.00 [95% CI, 1.20-3.43]; OR, 2.10 [95% CI, 1.27-3.47]; and OR, 2.00 [95% CI, 1.18-3.40], respectively).  Dairy and whole grain intakes were not associated with body composition measures among RU or UR children.  Unexpectedly, increasing saturated fat intake was associated with a decreased likelihood for abdominal obesity, and low- and high-risk %BF among UR, but not RU children.  Trans fat and total sugar intakes were not associated with any obesity-related measures for both groups, but surprisingly increasing total sugar intake  was associated with a decreased likelihood of  high-risk %BF for RU children (OR, 0.978; 95% CI, 0.957-0.999; P = .04). Increased vitamins A and E intakes were unexpectedly associated with an increased likelihood of low-risk %BF for UR children (OR, 1.002 [95% CI, 1.00-1.003]; and OR, 1.15 [95% CI, 1.02-1.30], respectively), however, they were not associated with overweight/obesity, or abdominal obesity among RU and UR children.  Another unanticipated result was increasing K+ intakes were also associated with an increased likelihood for low-risk %BF among UR children (OR, 1.001; 95% CI, 1.000-1.001; P = .03).  No other micronutrient and anthropometry measures were related in either group.        97  Unexpectedly, FI was associated with an increased likelihood of abdominal obesity for RU children, and overweight/obese, abdominal obesity, and low- and high-risk %BF for UR children.  Furthermore, increasing HEI 2010 score was associated with an increased likelihood for overweight/obesity and low-risk %BF among UR children (OR, 1.03 [95% CI, 1.01-1.06]; and OR, 1.04 [95% CI, 1.01-1.07], respectively). Table 9. Relationship of Nutrition Behaviors and Nutrient Intakes per 1000 kcals with Body Composition among Rural  and Urban Children   OR (95% CI) Nutrition Behavior per 1000 kcalsa Overweight/ Obeseb P Value Abdominal Obesityc P Value Low-Risk %BFd  P Value High-Risk %BFd  P Value Food Groups         Fruit Portions (cups) Rural 0.99 (0.70, 1.41) .96 1.25  (0.84-1.86) .27 1.02 (0.73, 1.42) .92 1.14 (0.74, 1.77) .56 Urban 1.32 (1.05, 1.67) .02 1.12 (0.86, 1.46) .41 1.37 (1.08, 1.74) .01 1.35 (1.03, 1.77) .03 Vegetable Portions   (cups)                      Rural 1.32 (0.65-2.67) .44 1.56  (0.71, 3.47) .27 1.11 (0.54, 2.28) .79 0.97 (0.35, 2.73) .96 Urban 1.77 (1.08, 2.91) .02 2.00 (1.20, 3.43) .01 2.10 (1.27, 3.47) .00 2.00 (1.18, 3.40) .01 Dairy Portions (cups) Rural 0.86 (0.54, 1.37) .53 0.65 (0.35, 1.21) .17 1.14 (0.73, 1.77) .57 0.88 (0.47, 1.67) .70 Urban 0.78 (0.55, 1.12) .17 0.65 (0.42, 1.01) .06 0.83 (0.59, 1.17) .29 0.70 (0.44, 1.11) .13 Whole Grain (oz)    Rural 0.47 (0.15, 1.53) .21 0.78 (0.20, 3.09) .72 0.75 (0.24, 2.31) .61 1.23 (0.29, 5.35) .77 Urban 1.51 (0.67, 3.42) .33 1.47 (0.60, 3.59) .40 1.12 (0.49, 2.57) .79 2.08 (0.78, 5.53) .14 Macronutrients         Saturated Fat (g)     Rural 0.99 (0.87, 1.12) .99 0.97 (0.83, 1.13) .69 1.05 (0.93, 1.18) .46 1.03 (0.87, 1.22) .71  Urban 0.93 (0.85, 1.02) .10 0.89 (0.80, 0.99) .03 0.90 (0.83, 0.99) .02 0.89 (0.80, 1.00) .04 Trans Fat (g)           Rural 0.96 (0.75, 1.23) .75 1.05 (0.78, 1.41) .76 1.01 (0.80, 1.29) .92 1.09 (0.78, 1.51) .63 Urban 0.93 (0.79, 1.08) .33 0.90 (0.74, 1.10) .31 0.88 (0.75, 1.04) .12 0.90 (0.74, 1.10) .30 Total Sugar (g)        Rural 1.00 (0.98, 1.01) .52 0.99 (0.97, 1.00) .10 0.99 (0.98, 1.01) .40 0.97 (0.96, 1.00) .04 Urban 1.00 (1.00, 1.01) .49 1.00 (1.00, 1.02) .49 1.00 (1.00, 1.01) .58 1.01 (1.00, 1.02) .33 Micronutrients                98    Abbreviations: %BF, percent body fat; kcals, kilocalories; HEI, Health Eating Index.  a Adjusted for sex, ethnicity, SES, and PA b  Overweight: BMI for sex and  85th and < 95th percentiles of the 2000 CDC Growth Charts; Obese: BMI for sex and age  95th of the 2000 CDC Growth Charts c Abdominal obesity: Waist circumference for sex and  90th percentile from NHANES III (1988-1994) d Low-risk %BF: %BF for sex and   69th  68th for males and females, respectively from NHANES (1999-2004); High-risk %BF: %BF for sex and age  90th percentile from NHANES (1999-2004)        e Not adjusted per 1000 kcals  The relationship of nutrition food groups and nutrient intakes per 1000 kcals with blood lipids and BP among RU and UR children are presented in Table 10. Surprisingly, increased fruit intakes were associated with increased likelihood of adverse non-HDL-C (OR, 2.30; 95% CI, 1.24-4.27; P = .01) for RU children. No other food group intakes were related to CVD          Vitamin A (µg)         Rural 1.00 (1.00-1.00) .29 1.00 (1.00, 1.00) .84 1.00 (1.00, 1.00) .86 1.00 (1.00, 1.00) .43 Urban  1.00 (1.00, 1.00) .29 1.00 (1.00, 1.00) .81 1.00 (1.00, 1.00) .03 1.00 (1.00, 1.00) .74 Vitamin E (mg)        Rural 1.03 (0.91, 1.17) .63 0.95 (0.79, 1.14) .58 1.06 (0.93, 1.22) .37 0.83 (0.59, 1.17) .29 Urban 1.10 (0.98, 1.24) .11 1.06 (0.93, 1.21) .40 1.15 (1.02, 1.30) .03 1.03 (0.90, 1.19) .65 Sodium (mg)           Rural 1.49 (0.49, 4.59) .48 1.80 (0.46, 7.04) .40 1.40 (0.46, 4.23) .55 1.04 (0.22, 4.89) .96 Urban 0.98 (0.47, 2.07) .96 1.10 (0.45, 2.66) .84 1.06 (0.47, 2.14) .99 0.92 (0.37, 2.28) .86 Potassium (mg)      Rural 1.00 (1.00, 1.00) .95 1.00 (1.00, 1.00) .26 1.00 (1.00, 1.00) .62 1.00 (1.00, 1.00) .46 Urban 1.00 (1.00, 1.00) .13 1.00 (1.00, 1.00) .96 1.00 (1.00, 1.00) .03 1.00 (1.00, 1.00) .26 Calcium (mg)          Rural 1.00 (1.00-1.00) .73 1.00 (1.00, 1.00) .19 1.00 (1.00, 1.00) .30 1.00 (1.00, 1.00) .40 Urban 1.00 (1.00, 1.00) .35 1.00 (1.00, 1.00) .05 1.00 (1.00, 1.00) .48 1.00 (1.00, 1.00) .16 Dietary Indices         Fiber (g)/1000 kcals                                  Rural               1.04 (0.94, 1.16) .43 1.14 (1.01, 1.28) .04 1.06 (0.95, 1.17) .29 1.12 (0.97, 1.26) .13 Urban 1.13 (1.05, 1.22) .00 1.10 (1.02, 1.20) .01 1.15 (1.07, 1.24) <.001 1.15 (1.06, 1.25) .00 HEI 2010e                        Rural -0.99 (0.95, 1.03) .59 1.00 (0.95,1.05) .98 1.00 (0.96, 1.04) .94 1.01 (0.95, 1.06) .85 Urban 1.03 (1.01, 1.06) .02 1.02 (1.00, 1.06) .17 1.04 (1.01, 1.07) .00 1.03 (1.00, 1.07) .06        99  risk factors, however, as expected increasing whole grain intake was associated with a decreased likelihood of elevated systolic BP (OR, 0.12; 95% CI, 0.02-0.84; P = .03) for RU children. Saturated and trans fat intakes were not associated with CVD risk factors for either groups.  Unexpectedly, increasing total sugar intake was associated with a decreased likelihood of elevated diastolic BP for RU children (OR, 0.983; 95% CI, 0.968-0.998; P = .03).  No other macronutrient intakes were associated with adverse blood lipids or BP. Vitamin A intakes were not associated with adverse blood lipids or BP.  Unpredictably, increasing intakes of vitamin E were associated with increased likelihood for elevated systolic BP among UR children (OR, 1.17; 95% CI, 1.02-1.34; P = .02).  Another unanticipated result was increased Na+ intake was associated with a decreased likelihood of adverse non-HDL-C for RU children (OR, 0.051; 95% CI, 0.003-0.862; P = .04). As expected, increasing K+ and Ca+ intakes were associated with a decreased likelihood of elevated diastolic BP for RU children (OR, 0.999 [95% CI, 0.998-1.000]; and OR, 0.998 [95% CI, 0.996-1.000], respectively).  No other micronutrients were associated with adverse blood lipids or BP. Unexpectedly, a higher FI was unexpectedly associated with an increased likelihood of elevated systolic BP for UR children (OR, 1.11; 95% CI, 1.02-1.20; P = .01), however, as expected increased HEI 2010 score was associated with a decreased likelihood of elevated diastolic BP for RU children (OR, 0.958; 95% CI, 0.919-0.998; P = .04).  No other relationships with dietary indices and blood lipids or BP variables associations were significant.       100  Table 10. Relationship of Nutrition Behaviors and Nutrient Intakes per 1000 kcals with Dyslipidemia, and Elevated  Blood Pressure among Rural Children  OR (95% CI)   Nutrition Behavior per 1000 kcalsa Total Cholesterol b P Value HDL-C (< 40 mg/dL)b P Value Non-HDL-C mg/dL)b P Value Systolic Blood 90th)c P Value   Diastolic Blood 90th)c   P Value Food Groups            Fruit Portions (cups)                          Rural 0.99 (0.64, 1.53) .96 0.93 (0.61, 1.43) .75 2.30 (1.24, 4.27) .01 0.67 (0.37, 1.20) .17  0.71 (0.48, 1.06)   .09 Urban 1.21 (0.89, 1.64) .22 0.89 (0.47, 1.68) .72 1.08 (0.80, 1.45) .61 1.31 (1.00, 1.72) .05 1.25 (0.95, 1.63)  .11 Vegetable Portions   (cups)           Rural 1.32 (0.54, 3.18) .54 0.70 (0.25, 1.93) .49 0.74 (0.12, 4.62) .75 0.65 (0.20, 2.12) .47  0.50 (0.20, 1.26)   .14 Urban 0.91 (0.47, 1.78) .78 0.33 (0.05, 2.17) .25 1.04 (0.56, 1.94) .91 1.66 (0.97, 2.84) .06 0.77 (0.41, 1.46)  .42 Dairy Portions (cups)             Rural 0.92 (0.50, 1.69) .79 1.66 (0.97, 2.85) .06 0.95 (0.35, 2.62) .93 0.77 (0.39, 1.49) .43  0.66 (0.40, 1.09)   .10 Urban 0.91 (0.57, 1.48) .71 0.85 (0.35, 2.10) .73 0.85 (0.54, 1.33) .47 0.86 (0.54, 1.38) .53 0.88 (0.56, 1.37)  .57 Whole Grain (oz)     Rural 0.32 (0.06, 1.76) .19 1.18 (0.29, 4.90) .82 0.21 (0.01, 4.03) .30 0.12 (0.02, 0.84) .03  1.18 (0.37, 3.73)   .78 Urban 0.33 (0.09, 1.23) .10 0.06 (0.00, 1.33) .08 2.19 (0.77, 6.23) .14 0.99 (0.35, 2.82) .98 1.36 (0.52, 3.53)  .53 Macronutrients           Saturated Fat (g) Rural 1.02 (0.87, 1.20) .82 1.09 (0.93, 1.27) .28 0.89 (0.69, 1.15) .37 1.20 (1.00, 1.44) .05  1.10 (0.97, 1.26)   .14 Urban 0.97 (0.87, 1.09) .66 1.06 (0.86, 1.30) .61 0.93 (0.83, 1.04) .19 0.98 (0.88, 1.09) .72 0.93 (0.84, 1.04)  .19 Trans Fat (g)  Rural 0.97 (0.69, 1.35) .84 0.77 (0.55, 1.07) .12 0.83 (0.47, 1.45) .51 1.19 (0.86, 1.67) .30 1.28 (0.99, 1.65)  .06 Urban 0.91 (0.73, 1.13) .37 0.91 (0.60, 1.40) .67 0.86 (0.69, 1.06) .16 1.07 (0.88, 1.29) .52 1.05 (0.87, 1.26)  .62 Total Sugar (g)  Rural 1.00 (0.98, 1.01) .61 1.00 (0.98, 1.02) .85 1.02 (0.99, 1.05) .18 0.98 (0.96, 1.00) .07  0.98 (0.97, 1.00)   .03         101             Urban 1.01 (1.00, 1.02) .21 1.02 (1.00, 1.04) .17 1.01 (1.00, 1.02) .28 1.00 (0.99, 1.01) .67 1.01 (1.00, 1.02)  .23 Micronutrients           Vitamin A (µg)  Rural 1.00 (1.00, 1.00) .42 1.00 (1.00, 1.00) .60 1.00 (0.99, 1.00) .11 1.00 (1.00, 1.00) .54  1.00 (1.00, 1.00)   .08 Urban 1.00 (1.00, 1.00) .86 1.00 (1.00, 1.00) .23 1.00 (1.00, 1.00) .89 1.00 (1.00, 1.00) .18 1.00 (1.00, 1.00)  .98 Vitamin E (mg) Rural 1.01 (0.86, 1.19) .91 0.79 (0.58, 1.07) .13 0.88 (0.56, 1.37) .57 0.97 (0.80, 1.17) .75  0.89 (0.74, 1.07)   .21 Urban 1.07 (0.92, 1.25) .39 0.60 (0.33, 1.09) .10 0.94 (0.79, 1.11) .44 1.17 (1.02, 1.34) .02 1.03 (0.90, 1.19)  .65  Sodium (mg)  Rural 0.88 (0.19, 3.99) .86 0.97 (0.25, 4.30) .97 0.05 (0.00, 0.86) .04 2.26 (0.48, 10.7) .31 1.65 (0.53, 5.16)  .39 Urban 0.55 (0.19, 1.56) .26 0.45 (0.06, 3.13) .42 0.74 (0.28, 1.99) .56 1.16 (0.45, 3.01) .76 0.54 (0.89, 1.13)  .06 Potassium (mg)  Rural 1.00 (1.00, 1.00) .79 1.00 (1.00, 1.00) .53 1.00 (1.00, 1.00) .27 1.00 (1.00, 1.00) .15  .999 (.998, 1.00)   .02 Urban 1.00 (1.00, 1.00) .34 1.00 (1.00, 1.00) .16 1.00 (1.00, 1.00) .82 1.00 (1.00, 1.00) .07 1.00 (1.00, 1.00)  .81   Calcium (mg)           Rural 1.00 (1.00, 1.00) .46 1.00 (1.00, 1.00) .24 1.00 (1.00, 1.00) .85 1.00 (1.00, 1.00) .15 .998 (.996, 1.00)  .02 Urban 1.00 (1.00, 1.00) .70 1.00 (1.00, 1.00) .35 1.00 (1.00, 1.00) .32 1.00 (1.00-1.00) .33 1.00 (1.00, 1.00)  .87 Dietary Indices           Fiber (g/1000 kcals) Rural       0.99 (0.87, 1.14) .93 1.02 (0.90, 1.16) .73 1.19 (0.95, 1.48) .13 0.89 (0.74, 1.07) .21  0.95 (0.85, 1.07)   .39 Urban 1.01 (0.92, 1.10) .91 .86 (0.68, 1.07) .18 1.00 (0.91, 1.09) .95 1.11 (1.02, 1.20) .01 1.07 (0.99, 1.16)  .10 HEI 2010d      Rural 1.01 (0.96, 1.06) .71 1.00 (0.95, 1.05) .99 1.04 (0.96, 1.13) .34 0.96 (0.91, 1.01) .13 0.96 (0.92, 1.00)               .04 Urban 1.01 (0.98, 1.05) .56 .95 (0.89, 1.02) .13 1.01 (0.97, 1.04) .78 1.03 (1.00, 1.07) .07   1.01 (0.98, 1.05)             .49 Abbreviations: HDL-C, high-density lipoprotein; kcals, kilocalories; HEI, Health Eating Index.  a Adjusted for sex, ethnicity, SES, and PA b  -HDRisk Reduction in Children and Adolescents: Summary Report c Elevated Blood Pressure: BP for sex, age, and height percentile  90th percentile from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents d Not adjusted per 1000 kcals        102  Discussion This is one of the few studies that have compared reported nutrition behaviors, nutrient intakes, and PA between RU and UR children, and one of the few to do so in MI children. For our primary objective we expected RU children would have poorer reported nutrition and PA behaviors compared to UR children, and fewer RU children would be meeting recommendations.  Our secondary aim was to determine if nutrition behaviors and nutrient intakes related to cardiovascular health were associated with CVD risk factors among RU and UR children.  For our primary objective, RU did have poorer nutrition s reported higher FI, and higher vitamin C intake which was likely driven by the higher intakes of fruit, as compared to UR children.  However, there were no group differences in the percentage of RU vs UR meeting nutrition and PA recommendations.  Furthermore, a small portion of children from both groups were meeting food group and nutrition intake recommendations.   With respect to the secondary objective to evaluate the relationship of nutrition behaviors and nutrient intakes with CVD risk factors in RU and UR children, only a few associations of the hypothesized associations were found. In RU children, increasing whole grain intake was associated with a decreased likelihood for systolic BP, and K+, Ca+, and HEI 2010 score were associated with a decreased likelihood for diastolic BP.  Numerous unanticipated relationships between nutrition behaviors and nutrient intakes with CVD risk factors were observed which also did not coincide with previous literature. These included increased fruit and vegetable intakes were associated with likelihood for overweight/obesity, and low- and high-risk %BF for UR children.  Also, increases in saturated fat intake was associated with a decreased likelihood of abdominal obesity and low- and high-risk %BF for UR children, and        103  total sugar intake was associated with a decreased likelihood of high-risk %BF for RU children. Also, in contrast to the hypothesis, higher FI and HEI 2010 scores were associated with increased likelihood of overweight/obesity and low-risk %BF for UR children.  Higher vitamin E intake was associated with an increased likelihood for elevated systolic BP among UR children. With respect to the food group and nutrient intakes that coincided with our primary hypothesis, only three nutrition variables (ie, fruit, vitamin C, and FI) were reportedly lower in RU vs UR children.  It is noteworthy that the FI is considered to be a marker for nutrient density and higher levels have been inversely associated with CVD risks.176,177,283 UR children were consuming 0.73 more cups of fruit than RU children. The higher fruit intake for UR children likely explains the higher intakes of vitamin C and higher FI. Despite these mean group differences, there were no differences in the proportion of both groups meeting nutrition recommendations for fruit, vitamin C, and dietary fiber. Related to the current study findings, recently two studies using large national data sets have produced conflicting results for fruit intake comparisons between RU and UR children. In the NHANES (1999-2006) study by Liu et al,16 a greater percentage of UR vs RU and adolescents aged 12 to 19 years were consuming two or more cups of fruit per day; however, there were no significant difference in the percentage of UR and RU children aged 2 to 11 years consuming two or more cups of fruit per day (18.6% vs 14.8%; P > .05).  Another study using data analyzed from a sample of 7882 children and adolescents aged 2 to 19 years via NHANES (2003-2006) by Davis et al27 found a combined total of fruit and vegetable servings of 5.21 and 5.34 servings in RU and UR residents, respectively, with no group differences.27 However, the researchers combined the parental proxy reporting of        104  in either food group.  The results in the current study may indicate that UR children have a greater access to fruit compared to RU children similar to other published studies.23,34  Another explanation for the greater fruit intake in the UR vs the RU group, is that RU children may have been more aware of the importance of fruit consumption due to environmental influences from the school and community. A lack of nutrition education and access to nutritionists are some barriers listed by the Rural Healthy People 2010 that partially explain higher obesity rates and poorer nutrition in RU areas.284  More research is needed to compare nutritional education influences in the built environment between RU and UR children. At the regional level, few studies have compared fruit intake between RU and UR children. Overall, it appears to be variable by region. For example, Davis et al30 reported  no significant difference for daily fruit servings (1.53 vs 1.81 servings, respectively; P = .38) in 35 RU vs 97 UR children from Kansas.30  However, that study used a very small sample size and the lack of observed differences may be due to a loss of statistical power given a 0.3 greater serving for UR intake is about 20% higher than the RU intake.  In another study which was not a direct RU and UR comparison, Champagne et al,35 compared nutrition behaviors between 485 RU White and African-American children from the Lower Mississippi delta (Louisiana, Arkansas, and Mississippi) in 2000 and 7756 White and African-American children from the 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII) (a primarily UR sample).  The researchers noted that White RU children consumed significantly less fruit servings than White children from CSFII sample (1.1 vs 1.7 servings; P < .001), and less than their RU African-American counterparts (1.1 vs 1.6        105  servings; P = .004). A potential reason why there are discrepancies in the studies summarized that compared RU and UR fruit intake, is they all relied on some form of parental proxy measure which may not be as accurate as the ch-reported energy intake.285 These results in the current study and previous studies suggest that interventions to promote nutrient-dense diets in RU populations by state, should not rely on RU national data, since they may not be reflective of nutrition behaviors and nutrient intakes for all RU regions In the current study, UR children reported consuming 41 mg more vitamin C than the RU children (147 vs 106 mg; P =.04). This observed result is most likely due to the greater fruit intake observed in the UR group.  While there are no studies that have reported on a direct RU and UR comparison for vitamin C, Champagne et al35 observed White children from CSFII, had a greater intake of vitamin C vs White RU children from the Mississippi Delta (100 vs. 65 mg; P < .001), similar to the result observed in the current study.  However, in the aforementioned study the national data set was from years 1994-1996 and 1998 vs 2000 in the RU sample, which may have been enough of a time period where cross-sectional comparisons may have produced erroneous results.  For example, the researchers described how folic acid fortification probably produced the greater folate intakes observed across the RU subgroups.  MI children, especially those residing in UR areas, may have greater intakes of vitamin C compared to other populations in the US. Both samples in the Champagne et al study35 had lower mean vitamin C intakes than what was reported in the current study. Additionally, reported vitamin C intake in our RU sample was also greater than the vitamin C intake reported in a sample of 194 children from Mississippi (74 mg).119 Furthermore, both groups in the current study were consuming a        106  greater amount of vitamin C compared to the national average of 87.7 and 75.4 mg for males and females 6- to 11-years-old, respectively.149  These results may indicate that RU and UR MI children may have a greater preference or availability for vitamin C rich foods (eg, fruits, vegetables, or foods and beverages fortified with vitamin C). Nationally, male and female children aged 6 to 11 years are consuming a FI of 7.4 and 7.6 g/1000 kcals, respectively,152 which is significantly less than the Institute of across ages.178  In our study, the FI among RU and UR children were 7.9 and 9.1 g/1000 kcals, respectively. Furthermore, there was a trend for a greater dietary fiber intake for UR vs RU children in the current study (15.3 vs 11.6 g; P = .06). These results are most likely due to the paralleled greater fruit intake in the UR group.  The FI in our RU group was 1 g higher than the Dietary Fiber Index (7 g/1000 kcals) for the RU children reported in the Davy et al,119 and 0.5 g less than the FI reported in a sample of 432 children from California, Kentucky, Mississippi, and South Carolina.48  However, the lower FI in the current study and others, suggests that nutrient-density is low in US children, especially in RU children.  The American Heart Association and the United States Department of Agriculture recommends intakes of foods high in dietary fiber and micronutrients (ie, fruits, vegetables, and whole grains) to promote general health and reduce the risk for obesity and other CVD risk factors.146,286 In contrast to our hypotheses, there were no other food group differences which is NHANES (1999-2006),16 the only significant differences for food groups were a greater proportion of UR vs RU adolescents 12- to 19-years-old consuming two or more cups of fruit per day (16.5% vs 12.2%; P < .05), and more RU vs UR children aged 2 to 11 years        107  were consuming two to three cups of dairy per day (29.7% vs 22.8%; P < .05), however, there were no difference for daily intakes of fruit in RU vs UR children aged 2 to 11 years, or daily intakes of dairy between RU and UR adolescents aged 12 to 19 years.  There were also no reported differences in whole grain or dairy intakes for RU and UR children and adolescents 2- to 19-years-old.  Among 7882 children and adolescents from NHANES (2003-2006) Davis et al30 observed no differences in daily dairy servings between RU and UR children similar to our findings.30  Although, not a direct RU and UR comparison, the Champagne et al study35 observed that White RU children consumed significantly less vegetable servings (2.6 vs. 2.2 servings; P = .04), and dairy servings (2.2 vs. 2.0 servings; P = .0469) than White children from the primarily UR national CSFII sample. However, these relationships were not evident among the African-American children from the aforementioned study. RU  and UR children in our sample were consuming 0.47 and 1.03 cups of vegetable per day, respectively, with the RU intake being lower than the national average (0.87 and 0.84 cups per day for males and females aged 6 to 11 years, respectively),148 recommendations for 9- to 13-year-olds a greater percentage of RU and UR children were meeting vegetable intake recommendations compared to the national average for males of the same age (1.61%), but less than national average for females (16.3%).154 For dairy recommendations, both  RU and UR children were consuming less than the national average of 2.37 and 2.09 cups for 6- to 11-year-old males and females, respectively,148 and a  smaller percentage of both RU and UR were meeting dairy recommendations compared to the national average of 28.0% and 26.0% for males and females, respectively.154 For whole grain, RU and UR children were consuming 0.45 and 0.54 oz, respectively, less than        108  the national average for male and females aged 6 to 11 years (0.80 and 0.75 oz, respectively).148 Although, no RU or UR were meeting whole grain recommendations, their intakes were similar to the  national average of 0.7% and 0.5% for males and female 9- to 13-year-olds, respectively.154  Overall, a large percentage of RU and UR children were not meeting nutrition recommendations underscoring a need for nutrition education interventions. There were no other significant differences for macro- and micronutrient intakes in our sample in contrast to our hypotheses.  Very few studies have compared intakes of macro- and micronutrients between RU and UR children. At the national level RU children aged 6 to 11 years  are consuming more total fat than their UR counterparts (80.3 vs 73.2 g; P < .05).33  Although we found no significant difference in the intake of daily kcals between RU and UR groups (1506 vs 1707 kcals, P = .24), at a national level RU children 2- to 11-years-old are consuming +91 extra kcals daily more than their UR counterparts (1935 vs 1844 kcals; P < .05).16  In contrast the Champagne et al study35 reported no significant differences for daily kcal intakes between RU White children and White children from the primarily urban CSFII sample (2107 and 2081 kcals).  Furthermore, there were no differences in daily kcal intakes between the RU African-American children and the African-American children from the CSFII sample (2099 and 1976 kcals). However, total kcals in the RU White and African-American children in the aforementioned study were greater than the 1506 total kcals observed in the RU group from the current study, as was total fat (White: 79.2 g and African-American: 84.1 g vs 52 g) and saturated fat (White: 27.9 g and African-American: 29.2 g vs 18.5 g).  Additionally, in a RU sample of 97 children from Mississippi, Harrell et al233 observed daily intakes of kcals, total fat, and saturated fat of        109  1811 kcals, 67 g, and 23 g, respectively, which were also higher than the total kcals, total fat, and saturated fat intakes reported in our RU sample.  Rural Healthy People 2010  states a higher dietary fat and kcal consumption among RU populations have been identified as some of the cultural limitations to maintaining a healthy weight and reduction for risk of CVD.284  The results in our RU sample indicate that total kcals, total fat, and saturated fat intakes are lower than other RU areas in the US, suggesting that MI RU children may respond favorably to nutrition and PA interventions. For our secondary objective, we evaluated relationships of selected food groups and nutrients recommended for cardiovascular health with CVD risk factors. There were numerous relationships that were evaluated which were hypothesized based on previous studies showing plausible relationships (for example: high saturated intake and high TC).12 There were a number of anticipated results in regards to nutrition behaviors and nutrient intakes and their associations with decreased likelihood for CVD risk factors. To date, little research has evaluated these relationships among RU and UR children and we were unable to locate many others to compare with our results.  Increasing whole grains intake was associated with a decreased likelihood of elevated systolic BP for RU children, which is consistent with other literature in that whole grain consumption is associated with a decreased risk of CVD in adults.160  However, data from 4928 adolescents via NHANES (1999-2004), by Hur et al287 did not notice an association between whole grain intake and systolic BP, but did with BMI and WC.  Although we did not observe a relationship of whole grain intakes with decreased likelihood for levels of obesity-related measures, Choumenkovitch et al288 observed that whole grain intake was inversely associated with BMI z-scores in RU third to sixth graders, even after adjustment for other food groups (ie,        110  fruit, vegetable, and dairy).  This was in contrast to what was observed by from the Hur et al study,287 as the relationship of whole grain intake with BMI and WC disappeared after adjustment for other food groups.  However, the Hur et al287 consisted of primarily UR children, and these results suggest that whole grain intake may have more of an inverse relationship with obesity and other CVD risk factors in RU regions across the US.   Nutrition interventions in RU settings should also include a whole grain component. Another anticipated result observed in the current study, was increasing K+ and Ca+ intakes and HEI 2010 score were associated with decreased likelihood of elevated diastolic BP for RU children, similar to the literature.173,181  However, the food groups high in K+ and Ca+ (fruits and vegetables, and dairy, respectively) were not associated with a decreased likelihood for diastolic BP in RU children, although there was a trend for increasing fruit intakes and  reduced likelihood of diastolic BP (OR, 0.71; 95% CI, 0.48-1.06).  The relationship with HEI 2010 and decreased diastolic BP, was anticipated as the score is based on adherence to the Dietary Guidelines for Americans179  which is based on a number of food components associated with BP.12   A number of findings were unanticipated and inconsistent with the current literature. Increasing fruit and vegetable intakes were associated with increased likelihood for overweight/obesity and low- and high-risk %BF for UR children. However, these relationship were not observed in RU children, and none of the other food groups (dairy and whole grain) were associated with any of the obesity-related measures. A dietary pattern with a high consumption of fruits and vegetables is associated with lower levels of coronary heart disease, and a lower risk of all-cause mortality, particularly CVD mortality, in adults.289,290  A recent systematic review by Ledoux et al,291 noted that the longitudinal        111  relationship between consumption of fruits and vegetables and adiposity among children is still unclear, with some studies revealing an inverse association while others do not.  However, the researchers pointed out that a number of differences within the studies included in the review (eg, small sample sizes, not controlling for energy expenditure, and short study durations), probably contributed to the inconsistent findings.  Although, startling, similar relationships or null observations with fruit and vegetable intake with obesity have been observed in RU and UR children.  Tovar et al49 observed that obese RU children are more likely to consume 2 or more servings of vegetables per day compared to normal-weight children, which was in contrast to the results from our RU sample. The authors inferenced that the adults of the because the parents of these children were also more likely to have been told by a physician that their child is overweight compared to parents of normal-weight children. The aforementioned Davis et al study27 using NHANES (2003-2006) data did not notice any differences for number of daily fruit and vegetable servings combined in RU or UR normal-weight vs obese children.  The researchers speculated that it may be due to the use of the dietary assessment methods (ie, 24-hr recalls) in large populations.  The relationship with fruit and vegetable intake with obesity-related measures likely explains the paralleled observation for relationships of key nutrients within fruit and vegetables with the same obesity-related measures.  Our results revealed that increasing FI was associated with a greater likelihood of abdominal obesity for RU children, and overweight/obesity, abdominal adiposity, and low- and high-risk %BF for UR children.  These results are in contrast to research on adults, in whom high dietary fiber intake is        112  associated with decreased cardiometabolic risks.166 Although the benefit of increased dietary fiber intake and lower levels of adiposity is less clear in children (mostly due to a lack of studies),167 diets low in dietary fiber and high in calorie-dense foods increase risk for obesity in later life.168 There are numerous physiological, microbiological, biochemical, and neuro-hormonal effects of dietary fiber that may explain decreased adiposity levels of those with high dietary fiber intakes.169 The observed association for increased likelihood of obesity-related measures for UR children with FI is probably the result of similar relationships observed in fruit and vegetable intake with those same obesity-related measures (overweight/obesity, low- and high-risk %BF).  Additionally, increasing FI was related to increased likelihood of elevated systolic BP among UR children, which is probably due to the overweight and obese children reporting large intakes of specific food types high in dietary fiber (eg, fruits and vegetables) since prevalence of hypertension is associated with obesity.98  One possible explanation for the results observed in the current study is the types of fruits and vegetables included in either variable.  Intakes of 100% fruit juice (which is lumped into the total fruit category on the FFQ and is calorie-dense, creates a different post-prandial metabolic response, with respect to insulin and satiety) and French fries (considered a vegetable on the FFQ although it has greater calorie-density and lower nutrients as compared to a non-fried potato and most other vegetables) are higher in overweight US children292 which may explain the association of increasing fruit and vegetable intakes with greater likelihood of obesity-related measures for UR children.  This observation may explain some of the relationship for increased HEI 2010 score with an increased likelihood for overweight/obesity, and low-risk %BF in UR children, since there        113  is an emphasis on fruits and vegetables for calculation of that score.182 However the HEI 2010 score also utilizes other food components that were not associated with overweight/obesity including whole grains, dairy, and Na+.   Another alternative to the preceding results is that overweight and obese children over-reported their fruit and vegetable intake, which is consistent with studies in which biomarkers were used to validate (and discredit) self-reporting of fruits and vegetables.293  However, this is unlikely, as there were not significant differences in the prevalence of overweight/obese between groups, however, significantly more UR children were high-risk %BF.  Additionally, if over-reported fruit and vegetable intake by overweight and obese children was the likely explanation we would also have observed a similar relationship in the RU children.    Another unexpected finding was that increasing total sugar intake was associated with a decreased likelihood of high-risk %BF for RU children, and increasing saturated fat intake was associated with a decreased likelihood of abdominal obesity and both low- and high-risk %BF in UR children.  Sugar intake from sugar-sweetened beverages had been consistently associated with increased WC in children.277  Furthermore, added sugar and solid fat (including saturated fat) intakes are key sources of excess calorie consumption in US children and increase the risk for obesity-related measures.162  Although the results were in contrast to what was anticipated, Davis et al27 found a similar result to ours in that normal-weight UR children consumed significantly more desserts/sweets than obese UR children. The researchers hypothesized that obese children were likely underreporting their sugar and fat intakes.  This may also explain the results observed in the current study as there is literature to suggest this. Among 14 044 children from NHANES (2003-2012), being overweight or obese was a significant predictor for under-reporting of energy (daily        114  kcals) intake.294   Another possibility for these observations is overweight or obese children were highly aware of their weight status and were motivated to recently modify nutrition behaviors (eg, increase fruit and vegetable consumption, and decrease consumption of foods high in saturated fat and sugars), either independently or due to external factors (eg, parents/guardians and public health messages). This is possible as obesity and abdominal adiposity rates have stabilized in children across the US,2,57 suggesting that national media and community efforts have had an effect on obese children and their families. Another unanticipated finding was increasing vitamin E intake was associated with an increased likelihood of elevated  systolic BP for  UR children, which is in contrast to other studies that have revealed a higher vitamin E intake is associated with a lower prevalence of hypertension.295  Vitamin E acts as an antioxidant and inhibitor of unsaturated fatty acid oxidation, which ultimately may reduce oxidative damage in the arterioles, thereby reducing BP.295 We were unable to locate a study or an observation that revealed an association or mechanism for vitamin E and high BP in UR children, or why this relationship was not evident in RU children. Although we did not look at the relationship of specific vegetable types with CVD risk factors, intakes of spinach, which is high in vitamin E,296 are greater in UR  and suburban vs RU areas (32% and 47% vs 21%, respectively).297  Another unexpected result was that increasing Na+ consumption was associated with a decreased likelihood of non-HDL-C for RU children.  Although this relationship may not appear comprehendible at first glance there are literature and potential mechanisms for this observation. In a study of 65 healthy men, dietary Na+ restriction for one week decreased HDL-C by 4% without an effect on TC, thereby increasing non-HDL-C.298 The researchers hypothesized that renal hemodynamic factors due to Na+ may have        115  contributed to the decrease in HDL-C. In contrast a Cochrane Review299 that summarized the effects from intakes of low Na+ vs high Na+ on BP, blood lipids, renin, aldosterone, and catecholamines, found a pooled effect of a +2.5% increase in TC with no effects on HDL-C from low Na+ intakes.  This would indicate an increase in non-HDL-C due to Na+ restriction. We are unsure of why increased Na+ decreased the likelihood for non-HDL-C, as research investigating this relationship in children is lacking. A strength of this study is that it is one of the few to compare nutrition behaviors and nutrient intakes related to CVD between RU and UR children in the US and examine their relationship with CVD risk factors among both populations. Another strength is including a good portion of the population at risk for health disparities based on eligibility for FRL and a high obesity prevalence.   This study had several limitations. The comparisons were cross-sectional, precluding inferences with respect to causal factors. Unequal sample size with respect to school location may have biased the conclusions because of a loss of statistical power, however, hierarchal analysis was performed to account for the clustering of students within schools. Another limitation is we were unable to determine individual level SES so we utilized an area-level marker of SES instead, which is may not be an accurate proxy especially in RU and UR areas.300 Our data is  based on self-reported FFQs which may be fallible to misreporting301 and as a result the sample size decreased due to invalid FFQs. However, we used a protocol to enhance the ability to determine if flagged invalid FFQs were invalid. Additionally, compared to multiple 24-hour recalls, the FFQ may under-report dairy and grain servings, and dietary fiber in children less than 12-years-old, however, fruit intakes are nearly identical.280 Another limitation is both groups were not        116  balanced on ethnicity, however, 4% of RU children identified themselves as African-American in this study which is similar to the statewide average of 2.1% of RU residents classified as African-American.268 An additional limitation is that blood lipids levels and BP may have been affected by medications (eg, antihyperlipidemics, attention-deficit/hyperactivity disorder [ADHD] medications) used by the participants as the prevalence of use for these medications has increased.269,270 However, the prevalence is still low ranging from 29.5 per 1000 for antiasthmatics to 0.27 per 1000 for antihyperlipidemics,270 with 23.5% of children and adolescents under 18-years-old  having taken at least one prescription drug in the past 30 days.271 However, in MI the prevalence of children aged 4- to 17-years-old taking medications for attention-deficit/hyperactivity disorder has increased from 5.1% to 8.3% from 2007 to 2011.272  Stimulate class medications for ADHD can increase BP and heart rate in the long term.273 Another limitation is the method in which residency was categorized. The RUCA delineates by zip code which may not differentiate neighborhoods within such as suburban areas. Additionally, resiindividual address, which may not be representative of all students attending that school. Although the results in the current study may not be generalizable to other RU and UR populations in the US, this illustrates there are differences at the regional vs national level. Conclusion The current study provides an insight into nutrition behaviors and nutrient intakes, PA, and CVD risk factors among RU and UR MI fifth grade children.  We found that UR children reported consuming a more nutrient-dense diet than RU children based on a greater FI        117  which included fruit and vitamin C.  In contrast to our hypotheses, there were no differences for the percentage of RU and UR children meeting national nutrition recommendations.  Few children overall were meeting nutrition and PA recommendations, and more that 60% had at least one CVD risk factor.   Only a few anticipated associations for nutrition behaviors and nutrient intakes with CVD risk factors were observed as anticipated and there were numerous unanticipated relationships in contrast to the literature.  Possible considerations for the unexpected relationships are that the FFQ included foods in fruits and vegetables that have varying nutrient and caloric-density, overweight and obese children may have recently changed their habitual nutrition behaviors (independently, or via external factors: [eg, parents, public health messages]), and possible under- and over-reporting. These findings underscore a need to adopt primary prevention programs to encourage nutrition and PA behaviors to promote the cardiovascular and overall health of both RU and UR MI children.        118  CHAPTER 5 EFFECTS OF A SCHOOL- AND WEB-BASED NUTRITION AND PHYSICAL ACTIVITY INTERVENTION ON NUTRITION BEHAVIORS AND CARDIOVASCULAR DISEASE RISK FACTORS IN CHILDREN FROM RURAL AND URBAN SCHOOLS  Abstract IMPORTANCE: In the last 40 years, obesity and other cardiovascular disease (CVD) risk factors have increased among children. Furthermore, obesity and other CVD risk factors tend to track into adulthood.  As a result there are national recommendations for nutrition and physical activity (PA) to prevent or reduce pediatric health risks.  The American Heart Association recommends school-based programs to promote healthful nutrition and PA behaviors as primary prevention to reach children of all socioeconomic status (SES).  Children at greatest risk for poor lifestyle behaviors and CVD risk are of low SES, particularly rural (RU) populations.  Overall, school-based nutrition and PA interventions which target children and adolescents of all body sizes, have had moderate improvements for nutrition behaviors and CVD risk factors.   Few studies have compared the effects of school-based nutrition and PA interventions between RU and urban (UR) children. OBJECTIVE: To compare the effects of a school- and web-based nutrition and PA intervention, (S)Partners for Heart Health, on nutrition and CVD risk factors between participants from RU and UR schools. Hypotheses: 1) RU and UR children will have equivocal improvements in nutrition behaviors, nutrient intakes, and CVD risk factors; 2) RU and UR children identified with CVD risk factors at baseline will have equivocal improvements from pre-post. DESIGN, SETTING, AND PARTICIPANTS: A quasi-experimental design included 634 fifth grade children (mean age, 10.6 years) (n = 200 from        119  2 RU schools; n = 434 from 5 UR schools) who participated in the (S)Partners intervention during a school year (2009-2013).  Rural or UR classification used the Rural-Urban Commuting Area Codes. MAIN OUTCOMES AND MEASURES: Were conducted at baseline (late fall) and 4 months later. Nutrition behaviors and nutrient intakes were assessed with a Food Frequency Questionnaire including: daily intakes of food groups (fruit, vegetables, dairy, and whole grains), selected macronutrients (kilocalories [kcals], total fat and carbohydrate and subtypes, dietary fiber, and protein), and micronutrients (vitamins A, C, D, and E); minerals (sodium [Na+], potassium [K+], calcium, and magnesium).  CVD risk factors included BMI, % body fat (%BF), waist circumference (WC), total cholesterol, high-density lipoprotein (HDL-C), non-HDL-C, resting BP and a composite CVD risk factor score.  Pediatric cutpoints were used to classify children at-risk.  Between group differences were analyzed with a mixed-model ANOVA and a Generalized Estimating Equation for determining between group changes for proportions of children classified at-risk. (P < .05). RESULTS:  At baseline, few RU or UR children were meeting nutrition recommendations intake (P = .04), % meeting Na+ recommendations (P = .047), diastolic BP (P = .001), and diastolic BP percentiles (P = .003) in RU vs UR children.  Significant pre-post differences: Hypothesis 1: Food Groups- After intervention, dairy intake increased more in RU vs UR group (0.31 vs -0.07 cups; P = .04). Macro- and Micronutrients- Vitamin D increased more in RU vs UR children (25.9 vs -18.3 IUs; P = .01), and vitamin E increased more in UR vs RU (1.25 vs -1.28 mg; P = .01).  In the overall sample, vegetable intake, total kcals, fat, saturated fat, vitamin A, Na+ and K+ decreased after intervention. CVD Risk Factors: %BF decreased significantly more in RU vs UR (-1.26% vs 0.63%; P < .001); and diastolic BP decreased        120  significantly within the UR group with no group differences. In the overall sample, BMI, %BF, WC, and HDL-C significantly increased, while systolic and diastolic BP significantly decreased after intervention. Hypothesis 2: The proportion of children with low-risk %BF and elevated diastolic BP decreased significantly more in RU vs UR (P = .007 and P < .001, respectively), while the proportion of RU and UR children with elevated systolic BP decreased within both groups with no group differences.  CONCLUSIONS AND RELEVANCE: In this sample of RU and UR children we hypothesized there would be equivocal pre to post improvements in outcomes.  The results indicate for most variables, there was a modest to no change, with only a few group differences in nutrition and nutrient intakes (ie, dairy, vitamins D and E), and CVD risk factors (%BF and BP). The greater increase in dairy and vitamin D intakes in RU vs UR, likely contributed to decreases in elevated BP in RU; greater increase in vitamin E intake in UR vs RU may have contributed to decreases in elevated systolic BP in UR children. Overall, the (S)Partners intervention improvements were modest, suggesting this or similar interventions are appropriate for both RU and UR schools, and should include methods to promote improvements in fruit, vegetable, and whole grain intake. Introduction Rural (RU) individuals in the US have greater age-adjusted mortality rates compared to their urban (UR) counterparts, even after adjusting for socioeconomic status (SES), sex, and race.15  This difference is largely attributed to the increased cause-specific death rates of chronic diseases including cardiovascular disease (CVD) and cancer among RU residents.  Additionally, obesity and other CVD risk factors (eg, dyslipidemia, hypertension, type 2        121  diabetes), that contribute to CVD-related events and some forms of cancer, are greater in RU vs UR adults, children and adolescents.13,16,302  Obesity, blood lipids (total cholesterol [TC], high-density lipoprotein [HDL-C], low-density lipoprotein [LDL-C]), and blood pressure (BP) tend to track into adulthood and increase the risk for CVD-related morbidity and mortality during adulthood.5-7  To reduce the risk for obesity and other CVD risk factors among children and adolescents, the American Heart Association recommends implementing primary prevention strategies to increase nutrient-dense food (eg, fruits and vegetables) intakes, decrease calorie-dense foods and beverage intakes, decrease sedentary time, and increase physical activity (PA).11,46  School-based nutrition and PA interventions are a common method for obesity and CVD prevention among youth.  A recent Cochrane Review47 of 26 school-based interventions that promoted PA and fitness among children and adolescents, revealed little effect on PA rates, BMI, and BP, however, increased PA duration, decreased sedentary time, and improved blood lipids supported the continuation of these programs.47  Changes in adiposity may not be the only method to assess the effectiveness of a school-based nutrition and PA intervention program.  Although changes in adiposity measures were not examined in a meta-analysis by Evans et al,228 the researchers found that  school-based interventions produced a pooled increase of 0.32 portions of fruits and vegetables among 26 361 children aged 5 to 12 years from 26 studies.  Among adults, fruit and vegetable consumption is an effective primary prevention strategy to decrease risk for CVD.303 Furthermore, a meta-analysis by Cai et al,226 observed that 39% of childhood obesity prevention programs with significant reductions in BP did not have corresponding decreases for adiposity measures. Based on these results, the researchers concluded there        122  is a need to include other outcome measures (eg, nutrition, blood lipids, BP) in addition to changes in BMI, when evaluating school-based nutrition and PA interventions.   Although not a direct comparison of results from a school-based nutrition and PA program between RU and UR children, the CHANGE (Creating Healthy, Active, and Nurturing Growing-UP Environments) program,48,49 was designed to adapt, replicate, and evaluate an UR multi-level curriculum (Shape-Up-Somerville)50 in a RU population. The CHANGE program was a two-year randomized, controlled trial that included 432 first to sixth grade students from eight RU communities in California, Mississippi, South Carolina, and Kentucky.48 The multi-level intervention consisted of a daily food service component, and students were exposed to the Shape Up: During and After School curricula, the Eat Well Keep Moving curricula (both curricula incorporated social-cognitive theory components), and the 5-2-1 messages (5 servings of fruit and vegetables per day; less than 2 hours of screen time per day; and 1 hour of PA per day).  Additionally, parental and community outreach components were also implemented. At the end of one year, the students who received the CHANGE intervention improved their diets by consuming significantly more cups of vegetables per 1000 kilocalories (kcals) (0.08 cups) and combined fruits and vegetables per 1000 kcals (0.22 cups) compared to the comparison group.  Another study adapted an UR school-based nutrition and PA curriculum designed for elementary school children from Denver (Integrated Nutrition Education Program), for elementary schools in RU south-central Colorado and observed favorable outcomes in knowledge, self-efficacy, and attitudes towards nutrition and PA.238          123  School-based nutrition and PA programs implemented in Michigan (MI) UR children have produced positive outcomes.41,43 The Project Healthy Schools program was designed to improve CVD risk factors utilizing educational and environmental change among 711 sixth grade students from a city with a population of around 100,000 residents.43 After the intervention, mean TC, LDL-C, and diastolic BP significantly decreased (169 to 154 mg/dL, 86 to 84 mg/dL, and 64 to 62 mmHg, respectively).  However, little research has been published on RU MI children after a school-based nutrition and PA intervention. To date, no research has compared outcomes (eg, nutrition, obesity, blood, lipids, BP) between RU and UR children after the same intervention. One possible outlet for RU vs UR education is the (S)Partners for Heart Health program, a school- and web-based intervention designed to improve both dietary and PA behaviors to promote heart and overall health among fifth grade students.51  The objectives of this study were to 1) determine if RU and UR have equivocal improvements in nutrition behaviors, nutrient intakes, and CVD risk factors after the intervention; and to 2) determine if there are equivocal decreases following the intervention for the proportion of RU vs UR children identified at baseline with CVD risk factors.  We hypothesized that (S)Partners will be equally effective in both groups at increasing intakes of food groups (fruit, vegetables, dairy, and whole grains), dietary fiber, micronutrients (vitamins [A, C, D, and E]; minerals (potassium [K+], calcium [Ca+], and magnesium [Mg+]), dietary indices (dietary fiber index [g fiber/1000 kcals] [FI] and Healthy Eating Index [HEI] 2010), and decreasing intakes of total fat, saturated and trans fat, and sodium (Na+). Furthermore, we believe mean BMI, percent body fat (%BF), waist circumference (WC), TC, non-HDL-C , and a composite CVD risk factor score will decrease,        124  and HDL-C will increase similarly for RU and UR children after intervention.  Lastly, we believe the (S)Partners program will be equally as effective at decreasing the proportion of RU and UR children classified as overweight, obese, high %BF (low- and high-risk), abdominally obese, having dyslipidemia (high TC and non-HDL-C, and low HDL-C), and elevated BP. Materials and Methods Study Design and Participants The study design was quasi-experimental as treatments were not randomized among seven MI schools. Baseline and follow-up measures were obtained from fifth graders who participated in (S)Partners for Heart Health51 from 2009-2013.  The design and rationale of the (S)Partners program has been reported elsewhere.51  The protocol was approved by the Michigan State Unprincipal or superintendent were required to sign an agreement that allowed their fifth grade classrooms to participate. Inclusion criteria included: > 50% eligibility for Free and Reduced Lunch (FRL) (with the exception of 2009-2010 which included four schools with < 50% eligibility), and within 15 miles of Michigan State University, East Lansing, MI or partnering colleges or Universities including Alma College, Macomb Community College, Saginaw Valley State University, and Oakland University. Participants were recruited from physical education class, or their home room in each school, with all students invited to participate.  To participate in measurement, students were required to assent and have parental consent. Participants could opt out of any measurement at any time. There were 1530 total eligible males and females (RU: 449; UR: 1081), with 645 providing both assent        125  and parental consent (RU: 201; UR: 444).  Eleven students were absent or did not have data at baseline leaving 634 total for this sample (RU: 200; UR: 434). Intervention. The rationale for and specific components of the (S)Partners intervention have been published elsewhere.51 The primary behavioral aims of the program are to sustain or achieve nutrition and PA behaviors in reference to national recommendations to support heart and general health. The primary content of the (S)Partners for Heart Health51 program curriculum from 2009-2013, consisted of eight lessons about the role of nutrition and exercise in heart health and preventing CVD.   Lessons were facilitated by the physical education teacher or a trained educator at the school.  Following the completion of each of the lessons, undergraduate (mentors) and graduate students conducted small group break-out sessions.  Additionally, the college mentors interacted with the students via a web-based goal setting, tracking, and education program.  Goal setting included setting specific food group goals and minutes of PA.  All physical education teachers and trained educators underwent instruction on how to teach the curriculum and were given a facilitator guide to assist with the lesson.  All college mentors went through a training protocol.  The web-based component was monitored by study coordinators, graduate students, and faculty.  The web-based platform allowed for facilitation of behavorioal goal setting for nutrition and PA using a behaviorial tracking tool, and links to educational modules to promote the acheivement of heart healthy behavioral and educational goals while reinforcing classroom educational efforts.  The fifth grade students were required to log on weekly at school and set their nutrition and PA goals for the week based on national guidelines for nutrition and PA.147,213  Parental involvement and support included receiving lesson newsletters with        126  links to the website and materials, a  Residence. Rural and UR residence was classified using the 2010 Rural-Urban Commuting Area (RUCA) codes developed by the University of Washington and the Economic Research Service.107  The classification system uses a 10-teir system that incorporates population density, distance from metropolitan areas, and commuting information.107,108  Other classification schemes such as the Urban Influence Codes and Rural-Urban Commuting Codes designates residence by county, however, RUCA designates by zip code.  Zip code areas with RUCA codes from 1 to 3 were classified as UR, and from 4 to 10 as RU.110  at the school. Using the RUCA classification, two and five schools were considered RU and UR, respectively. Measurements Participants were invited to complete lifestyle surveys including food frequency questionnaires (FFQ), and participate in CVD risk factor assessments including anthropometry, blood lipids, and BP. All CVD risk factor measures were conducted by personnel who were trained on standard pediatric specific procedures, and were required to demonstrate reliable and valid technique before being approved to perform measurements.  The measurement protocol for this study has been previously described and each measure is summarized below (see Appendix A).51   Nutritional Behaviors. The 2004 Block Kids FFQ (NutritionQuest, Berkeley, CA) was used to evaluate dietary intake (see Appendix B).279 After corrections for measurement error,        127  compared to 24-hr recalls, the FFQ has moderate correlations for energy from fat (%), Na+, grains (servings), and fruit (servings), except for some food groups in children less than 12-years-old.280  Compared to three-day food logs, the FFQ provides reasonable estimates for milk, 100% fruit juice, Ca+, and vitamin D consumption (r = 0.57, 0.55, 0.46, and 0.49, respectively).281  The participants were guided by staff to complete the FFQs.  After completion of the FFQs, they were sent to NutritionQuest to be proccessinng using an electronic flagging system to identify potentially invalid FFQs, including those with an unrealistic number of foods reported (too few or too many), and unrealistic kcal intakes (too low or too high). Two registered dietitians reviewed the flagged FFQs to determine if asked if the participant reported they were trying to lose weight to determine if the reported food pattern and/or the kcal level was plausible for the flagged FFQs.  At baseline there was not significant difference in the percentage of completed valid FFQs between groups (RU: 70.7% [133/188]; UR: 66.7% [260/390]; P = .33).  The food groups and nutrient intake data used included daily intakes of: food groups in cups, included: fruit, vegetable, and dairy, whole grain (ounce equivalents), total kcals, macronutrients in g, included:  total fat, saturated fat, trans fat, total carbohydrate, total sugars, dietary fiber, and protein.  Micronutrients included vitamins: A (retinol activity equivalents in µg), C (mg), D (IU), and E (-tocopherol) (mg); and minerals in mg: Na+, K+, Ca+, and Mg+.   Dietary Indices. The FI and HEI 2010 were selected since they reflect key components of dietary patterns and nutrition recommendations to prevent or reduce CVD risk among children.11 The FI has been used as a surrogate for nutrient density and plant-based food intake.176 Increased dietary fiber intake is associated with decreased risk for CVD risk        128  factors (eg, metabolic syndrome [MetS], inflammation, and obesity),166 and high dietary fiber intake patterns during childhood reduce the risk for obesity during adulthood.168  The HEI 2010, assesses dietary compliance to the 2010 Dietary Guidelines for Americans and is the sum of 12 nutrient components with a 100 point maximum.180,182 These primary prevention guidelines are designed to promote health and decrease risk for CVD, cancer, and reduce morbidity and all-cause mortality.282  The score encourages increased consumption of total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total proteins, seafood and plant proteins, and fatty acids ([monounsaturated fat + polyunsaturated fat]/saturated fat) and decreased consumption of refined grains, Na+, and empty calories (solid fats, alcohol, and added sugars). 182 Variables included in the analysis and calculations for the HEI 2010 are described in Appendices C and D. Anthropometry.  Height and weight were measured according to standard procedures and was used to calculate BMI (weight in kg/ height in m²).  BMI percentiles were assessed using the 2000 CDC Growth charts. 254  Stature of the participants were measured without shoes using a Shorrboard stadiometer (Shorr Production, Olney, MD) or similar to the nearest 0.1cm.  Weight (kg) and %BF was measured using foot-to-foot bioelectrical impedance (BIA) via a Tanita BC-534 InnerScan Body Composition Monitor (Tanita, Tokyo, Japan). BIA has been shown to have good accuracy for %BF, fat free mass, and fat mass (ICC 255  WC was obtained using a Gullick measuring tape superior to the iliac crest, and below the navel.256  WC is a valid indicator for abdominal obesity and has demonstrated strong correlations with subcutaneous, visceral, and total adipose tissue (r = 0.94, 0.85, and 0.94, respectively) among both normal-weight and obese children.257        129  Blood Lipids. Blood samples were collected in a non-fasted state by fingerstick using 35 µL heparinized capillary tubes.  The blood samples were analyzed by a Cholestech LDX (Alere, Waltham, MA), which strongly correlates with core laboratory measures for TC, LDL-C, HDL-C, and triglycerides (r = 0.91, 0.88, 0.77, and 0.93, respectively).258  Lipid panels collected included: TC and HDL-C.  Non-HDL-C was calculated as TC minus HDL-C.259  Prior to data collection, the analyzer was calibrated by controls via the manufacturer. Blood Pressure. Blood pressure was measured in a resting state manually following standard research procedures and was previously describe for the current study.51,260  In brief, after five minutes of rest, two sitting BP measures were taken at 1-min intervals, and averaged.  If the first two measurements differed by 4 or more mmHg, a third measurement was taken, and the two closest values were averaged.   Composite Cardiovascular Disease Risk Factor Score. A composite CVD risk factor score was calculated as: (WC z-score + mean arterial pressure [1/3 (systolic BP  diastolic BP) + diastolic BP] + non-HDL-C z-score + HDL-C z-score + TC z-score + %BF z-score)/6 based on methods by Eisenmann for calculating MetS.261   Covariates. Ethnicity was determined based on participant responses to a survey question on the FFQ asking for ethnicity identification.  Socioeconomic status was quantified using particular year, which has been used as an indirect measure of SES.262  PA was assessed using one self-reported question from the Youth Risk Behavior Surveillance System survey.263  st 7 days, how many 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        130  The scale range was 0 to 7 days.   Statistics.  Demographic characteristics were compared using t-tests and chi-square test.  Baseline outcome variables were compared using t-tests and chi-squares to compare the percentage of each group meeting recommendations.  Daily nutrition recommendations for 9 to 13 year old children146,147 included: fruit portions (1.5 cups), vegetable portions (males: 2.5 cups; females: 2 cups), dairy portions (3 cups), whole grain (3 oz equivalents), 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 (males: 31 g; females: 26 grams), total protein (10% to 30% of daily kcals) vitamin A (retinol activity equivalents) (600 µg), vitamin C (45 mg), vitamin D (600 IU), vitamin E (-tocopherol) (11 mg), Na+ ( less than 2300 mg), K+ (4500 mg), Ca+ (1300 mg), and Mg+ (240 mg).   To compares changes from baseline for nutrition and CVD variables between groups, a mixed model ANOVA with schools as the random effect within the condition while controlling for the baseline value of the dependent variable, sex, ethnicity, SES, and PA was performed. Only subjects with both baseline and post-intervention values were compared for between group differences.  To compare changes from baseline to follow-up and the difference between the two groups for the proportion of RU and UR with at-risk CVD risk factors, Generalized Estimating Equations with sandwich estimators for their variance covariance matrix were performed. Variables deemed at-risk were defined as: BMI for sex and age at or above 85th percentile 56 %BF for sex and age: males  at or above the 69th percentile, females  at or above the 68th percentile,  -88 %BF for sex and        131  etS with high specificity,88 57 For blood lipids pediatric cutpoints were used and included, TC of 170 mg/dL or higher,60 HDL-C lower than 40 mg/dL, 60 and high non-HDL-C of 145 mg/dL or higher.60 Elevated systolic and diastolic BP was determined by BP for sex, age, and height percentile at or above the 90th percentile.61 Means are presented as mean (SD) or mean (SE) and significance set at P < .05. Data analysis were performed using SPSS version 21 (SPSS Inc., Chicago, IL) and SAS (version 9.4, 2003, SAS Institute).  Results The baseline characteristics of the 201 RU and 434 UR children are presented in Table 11.  Both groups had more females than males (38% and 44% of the RU and UR groups, respectively).  The percentage of participants who identified as White was significantly greater in the RU than the UR group (56% vs 25%), while the percentage of those who identified as African-American was significantly lower (4% vs 23%) in the RU group.  There were no significant differences in age, height, or weight at baseline  Table 11. Baseline Demographic Characteristics, Height, and Weight of Participants from Rural and Urban Schools for Aim 3 Characteristic Total Rural Urban P value Total 634 200 434  Sex, No. (%)a     Males 272 (43) 77 (38) 195 (44) .16 Females 367 (57) 123 (62) 244 (56)  Ethnicity, No. (%)a     White 263 (42) 111 (56) 152 (35) <.001 African-American 107 (17) 8 (4) 99 (23) <.001 Hispanic 27 (4) 9 (5) 18 (4) .83 Other 235 (37) 71 (35) 164 (38) .60 Schools, No. 7 2 5  Free/Reduced Lunch, mean (SD), % 64.8 (18.2) 55.6 (16.6) 68.8 (18.3) .25         132       Age, mean (SD), y  (No.)a 10.6 (0.46) (n=627) 10.6 (0.46) (n=200) 10.6 (0.46) (n=427)  .26 Height, mean (SD), cm  (No.)a 145 (6.94) (n=618) 144 (6.87) (n=200) 145 (6.98) (n=418)  .46 Weight, mean (SD), kg  (No.)a 42.4 (11.7) (n=607) 42.0 (11.1) (n=198) 42.6 (11.9) (n=409)  .56 a Subgroup sample sizes do not add up to the total sample size because of missing data  The summary of the analysis to determine if there were baseline differences in nutrition behaviors, nutrient intakes, and CVD risk factors are presented in Table 12. For food groups, dairy intakes were significantly greater for the RU compared to the UR group (1.72 vs 1.51 cups; P = .04). There were no significant differences between groups for the other food groups, except there was a trend for a greater fruit and vegetable intake in the UR vs RU group (fruit: 2.22 vs 1.88 cups; P = .05; and vegetable: 0.99 vs 0.84 cups; P = .08).  For macronutrients there were no differences in intakes of total kcals, fat, saturated fat, trans fat, carbohydrate, sugar, dietary fiber, and protein at baseline.  For micronutrients, a greater percentage of RU vs UR children were meeting Na+ recommendations (62.1% vs 51.5%; P = .047) while there were no other significant differences for the other micronutrients.  The FI and HEI 2010 were not different between groups.  Table 12 also includes baseline levels of CVD risk factors.  The only significant differences between RU vs UR children included a greater diastolic BP and diastolic BP percentile in RU children (69.5 vs 67.3 mmHg; P = .001; and 72.4 vs 66.6 centile; P = .003, respectively).   Table 12. Baseline Comparison of Nutrition Behaviors, Nutrient Intakes, and Cardiovascular Disease Risk Factors for MI Children by Rural/Urban School    Rural  Urban      Participants, No.  Participants, No. P Value Nutrition Behavior                   133        Food Groups, mean (SD)           Fruit Intake, cups  1.88 (1.50) 132 2.22 (1.65) 260 .05  1.5 cups, % meeting 24.6 132 40.0 260 .11 Vegetable Intake, cups 0.84 (0.89)  132 0.99 (0.79) 260 .08  M: 2.5 cups; F: 2 cups, % meeting  5.3  132  8.9  260  .21 Dairy Intake, cups  1.73 (1.00) 132 1.51 (0.96) 260 .04  3 cups, % meeting 9.1 132 10.0 260 .77 Whole Grain, oz  0.51 (0.34)  132 0.55 (0.40) 260 .43  3 oz equivalents, % meeting 0  0   Macronutrients, mean (SD)         Total Kilocalories  15071 (696) 132 1682 (813) 260 .16  Total Fat, g 57.2 (27.3)  132 60.9 (32.7) 260 .23  25-35% total kcal, % meeting 59.8 132 61.2 260 .80 Saturated Fat, g 20.1 (11.0)  132 20.8 (11.0) 260 .49  <10% total kcals, % meeting 22.0 132 28.1 260 .19 Trans Fat, g 5.46 (3.78)  132 5.41 (4.32) 260 .91  Total Carbohydrate, g 216 (104)  132 232 (116) 260 .16  45-65% total kcals, % meeting 80.3 132 78.8 260 .74 Total Sugar, g 116 (70.7) 132 128 (75.8) 260 .12  Dietary Fiber, g 13.8 (7.02) 132 15.0 (7.50) 260 .13 M: 31 g; F: 26 g, % meeting 3.8 132 4.3 260 .82 Total Protein, g 54.9 (25.6)  132 58.7 (30.8) 260 .20  10-30% total kcals, % meeting 94.7 132 91.9 260 .31 Micronutrients, mean (SD)         Vitamin A, µg 529 (319)  132 541 (295) 260 .69  600 µg, % meeting 28.8 132 34.6 260 .25 Vitamin C, mg 118 (108)  132 139 (114) 260 .09  45 mg, % meeting 80.3 132 85.8 260 .16 Vitamin D, IU 168 (102)  132 149 (93.3) 260 .07  600 IU, % meeting 0  0   Vitamin E, mg 5.62 (4.37)  132 5.91 (3.81) 260  .51 11 mg, % meeting 7.6 132 9.6 260 .50 Sodium, mg  2317 (1120) 132 2500 (1246) 260 .16  < 2300 mg, % meeting 62.1 132 51.5 260 .047 Potassium, mg 2081(964)  132 2234 (1071) 260 .17  4500 mg, % meeting 2.3 132 3.8 260 .41 Calcium, mg  821 (390) 132 390.2 (374) 260 .15  1300 mg, % meeting 10.0 132 10.6 260 .85 Magnesium, mg  208 (92.3) 132 220 (102) 260 .25 240 mg, % meeting 30.3 132 36.2 260 .25 Dietary Indices           Dietary Fiber Index (g fiber/1000 kcals) 8.90 (3.04)   132 9.17 (3.09) 260   .41 HEI 2010 64.9 (7.72)  132 66.0 (7.66) 260  .20 Cardiovascular Disease Risk Factors         Anthropometry, mean (SD)     BMI, kg/m² 20.0 (4.13) 198 20.2 (4.56) 409 .59 BMI Percentile 68.1 (26.6) 198 68.4 (26.7) 402 .88 Body Fat, % 24.0 (8.35) 198 24.3 (9.17) 406 .67 Waist Circumference, cm 67.8 (12.5) 197 68.3 (12.7) 410 .68 Blood Lipids, mean (SD)      Total Cholesterol, mg/dL 151 (23.9) 141 148 (25.2) 289 .42 HDL-C, mg/dL 47.4 (12.1) 142 48.4 (13.0) 297 .42 non-HDL-C (mg/dL) 103 (25.1) 141 100 (26.4) 287 .21 TC:HDL 3.34 (1.11) 141 3.25 (1.17) 289 .26 Blood Pressure, mean (SD)            134          Systolic BP, mmHg 103 (10.3) 200 103 (11.3) 412 .62 Systolic BP Percentile 51.6 (29.4) 200 53.6 (31.4) 406 .46 Diastolic BP, mmHg 69.5 (8.71) 200 67.3 (7.77) 406 .00 Diastolic BP Percentile 72.4 (23.3) 200 66.6 (22.7) 406 .00 CVD Composite, mean (SD) z-score  0.01 (0.63)  140  -0.43 (0.65)  276  .46 Abbreviations: M, male; F, female; HEI, Health Eating Index; CVD, cardiovascular disease; BMI, body mass index; HDL-C, high density lipoprotein; BP, blood pressure.  Results to address the hypothesis that children from RU and UR schools will have equivocal changes in nutrition behaviors and nutrient intakes after the (S)Partners intervention are presented in Table 13.  Dairy intakes increased more for the RU vs UR group (0.31 vs -0.07 cups; P = .04) with no significant within change in either group.  Fruit, vegetable, and whole grain intakes did not change significantly within or between groups. In the overall sample, vegetable portions significantly decreased after intervention (-0.11 cups; P = .02).  There were no significant within group changes or between group differences in macronutrients at follow-up for total kcals, fat, saturated fat, trans fat, total carbohydrate, total sugar, dietary fiber, and protein.  In the overall sample, total kcals, total fat, and saturated fat decreased significantly after intervention (-94 kcals, P = .048; -4.17 g, P = .04; and -1.57 g, P =.02, respectively).  For micronutrients, vitamin D significantly decreased within the UR group (-18.3 IUs; P = .03), with a non-significant increase within the RU group (26 IUs; P = .07) resulting in a group difference of 44 IUs with a significant group effect favoring the RU vs UR group (P = .01).  Vitamin E increased significantly within the UR group (1.25 mg; P = .04), with a non-significant decrease within the RU group (-1.28 mg; P = .17) resulting in a group difference of -2.53 mg with a significant group effect favoring the UR vs RU group (P = .01).  No other micronutrients significantly changed between and within either group following the intervention, although vitamin A, Na+ and K+ decreased significantly in the        135  overall sample (-39.7 µg, P = .02; -198 mg, P = .009; and -126 mg, P = .047, respectively). Neither dietary index significantly changed after the intervention. Table 13. Pre to Post Differences in Nutrition Behaviors and Nutrient Intakes for MI Children by Rural and Urban Schools   Overall Change, +/- Rural,a +/- (n=106) Urban,a +/- (n=184) Adjusted Differenceb P Value Nutrition Behavior      Food Groups, mean (SE)          Fruit Intake, cups -0.19 (0.10) -0.39 (0.28) -0.12 (0.18) -0.27 (0.30) .37 Vegetable Intake, cups -0.11 (0.05)c -0.11 (0.11) +0.01 (0.07) -0.12 (0.12) .33 Dairy Intake, cups +0.97 (0.06) +0.31 (0.17) -0.07 (0.11) -0.38 (0.18) .04 Whole Grain (oz)  +0.01 (0.03) +0.03 (0.35) +0.01 (0.17) 0.02 (0.41) .96 Macronutrients, mean (SE)         Total Kilocalories -94.0 (47.4)c -29.2 (140) +52.7 (90.3) -81.8 (149) .58 Total Fat, g -4.17 (2.04)c -2.99 (5.78) +3.59 (3.73) -6.58 (6.14) .29 Saturated Fat, g -1.57 (0.65)c +0.05 (1.86) +0.48 (1.20) -0.43 (1.98) .83 Trans Fat, g -0.75 (0.24) -0.57 (0.68) -0.28 (0.44) -0.29 (0.71) .68  Total Carbohydrate, g -11.42 (6.57)  -7.74 (17.7)  -1.31 (10.6)  -6.43 (20.5)  .75 Total Sugar, g -5.16 (4.09) +3.64 (12.2) +4.14 (7.87) -0.49 (13.0) .97 Dietary Fiber, g -0.91 (0.47) -1.08 (2.15) +0.67 (1.15) -1.75 (2.43) .47 Total Protein, g -2.99 (1.82) +1.23 (4.60) -1.76 (2.75) 2.99 (5.35) .58 Micronutrients, mean (SE)          Vitamin A, µg -39.7 (17.16)c  -5.60 (72.2)  -16.3 (39.1)  10.7 (81.4)  .90 Vitamin C, mg -5.69 (6.31) -12.7 (15.6) 2.35 (9.39) -15.1 (18.2) .41 Vitamin D, IU +0.78 (5.13) 25.9 (14.0) -18.3 (8.37)c 44.12 (16.3) .01 Vitamin E, mg -0.15 (0.34) -1.28 (0.92) +1.25 (0.59)c -2.53 (0.98) .01 Sodium, mg -198 (75.2)c -157 (210) -36.2 (135) -121 (223) .59 Potassium, mg -126 (63.1)c -84.6 (178) -50.2 (114) -34.4 (189) .86 Calcium, mg -29.8 (22.8) +73.0 (83.4) -15.8 (49.2) 88.8 (91.6) .33 Magnesium, mg -7.11 (6.80) -7.92 (17.53) +6.35 (10.5) -14.3 (20.4) .48 Dietary Indices, mean (SE)      Dietary Fiber Index (g fiber/1000 kcals)  -0.91 (8.03)  -0.36 (0.92)  +0.21 (0.50)  -0.58 (1.03)  .58 HEI 2010 +0.52 (0.49) -0.44 (2.27) +0.96 (1.25) -1.40 (2.53) .58 Abbreviations: HEI, Health Eating Index.  aCalculated using least-squares means regression adjusting for school location, baseline value of the dependent variable, sex, ethnicity, SES, PA, and school. bAdjusted difference in values between Rural and Urban group after adjustments for school location, baseline value of the dependent variable, sex, ethnicity, SES, PA, and school c Significant within change (P < .05) Results for our hypothesis that children from RU and UR schools will have equivocal improvements in CVD risk factors following the (S)Partners intervention are presented in Table 14. BMI did not significantly change between groups, however, BMI significantly        136  increased within the UR group (0.63 kg/m²; P = .001).  %BF significantly increased in the UR group (0.63 %BF; P = .04), and decreased in the RU group (-1.26 %BF; P = .01), with a between group of -1.89% favoring the RU vs UR group (P = .001). Although there was not a significant between group change, WC significantly increased within the UR group (4.39 cm; P < .001).  In the overall, sample after intervention BMI, %BF, and WC significantly increased (0.47 kg/m², P = < .001; 0.45%, P = .005; and 3.18 cm, P < .001, respectively).  TC, HDL-C, non-HDL-C did not change between groups, although HDL-C significantly increased overall in both samples (1.06 mg/dL; P = .03).  There were no between group differences for changes in systolic and diastolic BP after intervention, however, diastolic BP significantly decreased within the UR group (-2.88 mmHg; P = .001).  Additionally, both systolic and diastolic BP decreased significantly in the overall sample (-1.49 mmHg, P = .01; and -1.70 mmHg, P < .001, respectively).         137  Table 14. Pre to Post Differences in Cardiovascular Disease Risk Factors for MI Children by Rural and Urban Schools  Risk Factor Overall Change, +/- Rural,a +/- Participants, No. Urban,a +/- Participants, No. Adjusted Differenceb P Value Anthropometry, mean (SE)      BMI, kg/m² +0.47 (0.08)c +0.33 (0.29) 194 +0.63 (0.20)c 345 -0.30 (0.33) .36 Body Fat, %c  +0.45 (0.16)c -1.26 (0.49)c 194 +0.63 (0.31)c 360 -1.89 (0.55) .00 Waist Circumference, cm  +3.18 (0.26)c +3.21 (2.00) 144 +4.39 (1.19)c 191 -1.18 (2.43) .63 Blood Lipids, mean (SE)      Total Cholesterol, mg/dL +0.86 (1.06) +2.69 (3.07) 117 +3.81 (1.93) 222 -1.12 (3.39) .74 HDL-C, mg/dL +1.06 (0.48)c +1.29 (4.92) 116 +1.44 (2.37) 224 -0.14 (5.71) .98 non-HDL-C, mg/dL, -0.43 (0.99) +1.57 (3.06) 114 +2.71 (1.96) 217 -1.14 (3.34) .73 TC:HDL-C -0.77 (0.04) -0.11 (0.13) 116 -0.04 (0.08) 222 -0.08 (0.15) .58 Blood Pressure, mean (SE)      Systolic BP, mmHg -1.49 (0.45)c -4.77 (3.10) 194 -1.32 (1.55) 372 -3.45 (3.58) .34 Diastolic BP, mmHg -1.70 (0.45)c -0.37 (1.58) 194 -2.88 (0.88)c 372 2.51 (1.80) .16 CVD Composite, mean (SE), z-score  -0.03 (0.03) -0.08 (0.09) 85 -0.01 (0.06) 105 -0.07 (0.10) .51 Abbreviations: BMI, body mass index; HDL-C, high density lipoprotein; BP, blood pressure; CVD, cardiovascular disease.  a Calculated using least-squares means regression adjusting for school location, baseline value of the dependent variable, sex, ethnicity, SES, PA, and school.   b Adjusted difference in values between Rural group and Urban after adjustments for school location, baseline value of the dependent variable, sex, ethnicity, SES, PA,  and school. c Significant within change (P < .05)  The results from the analyses to determine if RU and UR children had equivocal improvements in the proportion identified with at-risk CVD risk factors at baseline are presented in Figures 5 and 6 and Table 15.   Although there were no significant changes in the proportion of RU and UR children with low-risk %BF (Figure 5), the proportion of children in the RU group with low-risk %BF decreased significantly more than the UR group (OR, 0.93; 95% CI, 0.88- 0.98) (Table 15).  The proportion of children with elevated systolic BP significantly decreased in both groups (RU: 16.0% to 4.1%; P < .001; and UR: 18.2 to        138  12.2%; P = .02) (Figure 6), with no significant between group changes (Table 15). The proportion of RU children with elevated diastolic BP significantly decreased (32.5% to 14.9%; P < .001) (Figure 6).  Furthermore, the proportion of children in the RU group with elevated diastolic BP decreased significantly more than in the UR group (OR, 0.85; 95% CI, 0.78-0.93) (Table 15).   There were no other significant changes in CVD risk factors from pre- to post-testing.  Abbreviations: %BF, body fat a  th and < 95th percentiles of the 2000 CDC Growth Charts; Obese:  95th percentile of the 2000 CDC Growth Charts b  90th percentile from NHANES III (1988-1994) cLow-th th percentiles for males and females, respectively from NHANES (1999-2004); High- 90th percentile for from NHANES  (1999-2004) a a b c c        139   Abbreviations: TC, total cholesterol; HDL-C, high density lipoprotein; BP, blood pressure. a  non-Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report b th percentile from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescent c Significant within change P < .05 c a a a b b c c    140   Table 15. Number of and Proportion Change from Baseline for At-Risk Cardiovascular Disease Risk Factors among Rural and Urban Children   Rural Urban          OR (95% CI)  Risk Factor  Developed Maintained Improved Developed Maintained Improved Between Group Change P value Anthropometry, No.                   Overweighta  10 177 7 10 194 18 1.02 (0.96, 1.07) .80 Obesea  3 189 2 12 339 7 0.99 (0.96, 1.02) .57 Abdominal Obesity b 9 135 0 8 180 2 1.03 (0.98, 1.08) .24 Low-Risk %BFc 5 177 16 23 358 21 0.93 (0.88, 0.98) .00 High-Risk %BFc 5 188 1 11 333 11 1.02 (0.98, 1.06) .30 Dyslipidemia, No.             d 12 95 10 9 195 18 1.06 (0.96, 1.15) .21 HDL-C < 40 mg/dLd 11 96 9 10 195 19 1.05 (0.96, 1.14) .31 non-HDL-mg/dLd 7 101 6 8 205 4 1.00 (0.94, 1.07) .89 TC:HDL > 3.5 9 122 10 11 264 14 1.04 (0.93, 1.07) 1.00 Elevated Blood Pressure, No.            Systolic BPe 6 158 30 35 253 53 0.94 (0.87, 1.02) .14 Diastolic BPe 14 134 46 40 257 44 0.85 (0.78, 0.93) < .001 Abbreviations: BF, body fat; TC, total cholesterol; HDL-C, high density lipoprotein; BP, blood pressure. a  Overweight: BMI for sex and  85th and < 95th percentiles of the 2000 CDC Growth Charts; Obese: BMI for sex and age  95th of the 2000 CDC Growth Charts b Abdominal obesity 90th percentile from NHANES III (1988-1994) c Low- 69th  68th for males and females, respectively from NHANES (1999-2004); High- 90th percentile from NHANES III (1999-2004) d -or Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report e Borderline high and high blood pressure: BP for sex, age, and height percentile  90th percentile from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescent   141  Discussion In the current study we compared the effects of a school- and web-based nutrition and PA intervention on nutrition and CVD risk factors between participants from RU and UR schools.  With respect to our primary objective and hypothesis that RU and UR children will have equivocal improvements in nutrition behaviors, nutrient intakes, and CVD risk factors, our results indicated this was true for nearly all variables.  For nutrition behaviors and nutrient intakes the only significant between groups (RU vs UR) differences from baseline to post for our primary objective included dairy and vitamin D intakes which increased significantly more in the RU vs UR group, and vitamin E increased significantly more in the UR vs RU group.  In the overall sample, vegetable intake, total kcals, total fat, saturated fat, vitamin A, Na+, and K+ decreased from pre to post. For CVD risk factors the only between group difference observed was %BF decreased significantly more in the RU vs UR group.  Additionally, diastolic BP decreased significantly within the UR group, with no between group differences.  In the overall sample, BMI, %BF, WC, and HDL-C significantly increased after intervention, while systolic and diastolic BP significantly decreased.  For our secondary objective and hypothesis that RU and UR children identified with CVD risk factors at baseline will have equivocal improvements from pre to post; there were between group differences including decreases in the proportion of RU children identified with low-risk %BF and elevated diastolic BP compared to UR children. The proportion of children with elevated systolic BP decreased significantly in both groups following the intervention, with no between group effects.      142  After the (S)Partners for Heart Health program, there were only a few changes for nutrition variables that differed between groups (ie dairy, vitamin D and E).  In the current study, dairy intakes significantly increased more for the RU vs UR group after intervention, which likely explains the parallel increased intake of vitamin D for the RU vs UR group.  The finding is important as both dairy and vitamin D intake are associated with CVD health12,157,304; and around 89% of US children 9- to 18-years-old  are not meeting the recommendations for dairy and dairy alternatives.305 The result in the current study is in contrast to the results observed by Cohen et al48 who compared changes in nutrition variables among 432 children from eight RU communities in California, Kentucky, and Mississippi, and South Carolina, and observed no significant changes in dairy intake following intervention.   Inability to observe improvements in dairy consumption in the aforementioned vs the current study, may be due to how dietary intakes were measured.  The former utilized a food screener to assess nutrition intake in the previous 24 hours,306 while the current study used an FFQ to assess usual intake over the previous week.279   Drapeau et al307 assessed nutrition behaviors in 404 UR fifth and sixth graders from Quebec, before, during, immediately post, and after a 10-week follow-up from an 8-week intervention vs a control group. The researchers found significant increases in dairy servings in the intervention compared to the control group from baseline to follow-up similar to the results observed in the current study.307 It is unclear as to why a similar change for the UR group was not observed in our current study as (S)Partners utilized similar goal-tracking and reinforcements to promote behavior similar to the aforementioned study.307 A possible explanation may be that multicomponent interventions are not as successful at increasing dairy intakes as those targeting solely    143  dairy intakes.  A review article by  Hendrie et al308 reported that 50% of interventions with multiple nutrition components increased intake of dairy, vs 100% of interventions that targeted solely dairy intake.308  However, only five included in the review targeted specifically increasing milk or dairy product intakes. Perhaps, the failure to observe change for dairy intake in the UR group is due to availability or a preference for dairy and dairy alternatives among RU youth, as a greater proportion of RU vs UR children 2- to 11-year-olds nationally are consuming two to three or more cups dairy (53.2% vs 47.8%).16  Furthermore, RU children in the current study at baseline were consuming greater intakes of dairy compared to UR children further supporting a preference for dairy intake among RU vs UR children. However, only 9.1% and 10.0% of RU and UR children in the current sample were meeting dairy recommendations (three or more cups per day).146,147 These results suggest that some RU populations may respond more favorably to school-based nutrition interventions with increased dairy consumption compared to UR populations.  The only other observed between group change after intervention for nutrition variables, was vitamin E intake in favor of UR vs RU children. However the change difference is small and probably clinically insignificant. It is possible UR children had increased their consumption of foods high in vitamin E (eg, nuts, seeds, leafy greens),296 more than RU children.  For example, spinach intakes are greater in UR  and suburban vs RU areas (32% and 47% vs 21%, respectively),297 which suggests that UR populations already have an increased preference or availability of these foods compared to RU populations, and thus more likely to increase their intake of them. Foods high in vitamin E are also high in other nutrients that are associated with lower BP in adults and children including dietary fiber, omega-3 fatty acids, K+, Ca+, and Mg+.173 More research is needed    144  to see if there are differences in other particular food types that are high in vitamin E between RU and UR children. There were few improvements observed for nutrition behaviors and nutrient intakes (ie, decreased total kcals, fat, saturated fat, and Na+) in the overall sample.  This is not an uncommon finding.  A review that examined the effect of school-based nutrition interventions on fruit and vegetable intake by Aloia et al309 found only six of the 14 studies that met the inclusion criteria, had positive effects on fruit and/or vegetable consumption.  Also, many studies that report nutrition effects, are often a subset of variables. Cohen et al study48 observed significant positives changes for three (ie, increased intake of vegetable and fruit/vegetable combination, and decrease in glycemic index)  of 11 nutrition variables evaluated after intervention.  Additionally, among a sample of 205 RU fifth graders from Mississippi, Harrell et al233 only observed significant improvements for three of 23 nutrition behaviors and nutrient intakes after a school-based nutrition and PA intervention.  Another possible explanation for the inability to observe many significant improvements is changes in seasonal dietary patterns due to decreased availability of produce.  There is less availability of fruits and vegetables in MI during the early spring (post-intervention) compared to the fall (pre-intervention).310   Another explanation for the inability to observe positive changes for nutrition availability, particularly in low income populations.  Parental eating habits311 and the school environment312 have the greatest effect on food available for consumption among children.  Although, the (S)Partners intervention did have a parental component (lesson    145  goal tracking), it may not have had an influence on parental eating behaviors or food purchases.  Parents may not have read or applied recommendations that were encouraged, while others may have had financial or food accessibility barriers. Furthermore, there was not a component to increase the accessibility of more nutrient-dense foods, while decreasing the availability of more calorie-dense foods in the schools themselves.  This suggests that school-based nutrition and PA intervention programs, particularly those delivered to low SES populations, should include school and parental involvement to decrease barriers for food availability and accessibility to foster behavior change in children.  To date no direct comparisons between RU and UR US children have been made for CVD risk factors following a school-based nutrition and PA intervention. The current study indicated some improvements in a few of several CVD risk factors that were assessed.   Also, contrary to our hypothesis the improvements were not equivocal for both RU and UR students. For, %BF there was a modest but significant decrease in RU children, and a significant increase in UR children overall, but the between group difference favored the RU vs UR group. Furthermore, the proportion of RU children with low-risk %BF decreased more than their UR counterparts following the intervention. This result is important as a high %BF is associated with other CVD risk factors, morbidity, and all-cause mortality.83,91 To our knowledge there are not published studies in the US that have compared %BF changes between RU and UR children following a school-based nutrition and PA program, however, an analogous study was conducted in the Waikato Region of New Zealand.313  The study, Project Energize, was designed to improve child health with school-based    146  interventions for children aged 5 to 10 years of age from 124 schools during 2004-2006.  After a two-year follow-up in a sample of 926 5- to 7-year-olds, there was a trend for a more favorable %BF standard deviation score change for RU vs UR children.314 Although not significant, the researchers theorized that rurality had an effect that could be of public health importance.  Due to the limited number of studies comparing %BF between RU and UR children following a school-based nutrition and PA intervention,  additional research will help provide insights for intervention effect on %BF and determine if RU children respond more favorably or different than UR children. The reduction in %BF for RU children in the current study is similar to results reported after a comparable school-based intervention program in RU middle schools in Mississippi.233  In that study, mean %BF significantly decreased from 27% to 26% in the intervention vs control group, however, WC significantly increased in both groups (73 to 75 cm; and 72 to 73 cm, respectively; P < .05), with no between group differences. There were no other between group (RU vs UR) differences for obesity-related measures (eg, BMI, WC) observed in the current study, although BMI, %BF, and WC all increased modestly but significantly in the UR group following intervention. The likely explains the increase in BMI, %BF, and WC in the overall sample because there were more UR than RU children. Maturation of the subjects may have accounted for the increases obesity-related measures for UR children and perhaps the inability to observe changes in BMI and WC in the RU group.  UR children typically mature at a younger age compared to those living in RU areas.315,316  Although maturation stages were not assessed in this study, females may undergo large increases in adiposity around the average age of the participants (10.6 years at baseline and 11 years at post) used in the current study.  During this age there tends to    147  be increased circulating estrogen that influences increases in adiposity.224  However, males usually undergo puberty at a later age and increases in body weight are associated with increased lean body mass attributed to increased circulating testosterone.224  These results further emphasize the need to measure additional CVD risk factors in conjunction with obesity for school-based nutrition and PA intervention outcomes in RU and UR schools.  Another desirable outcome of the (S)Partners program was the significant decrease in the proportion of children with elevated systolic BP in both groups, and decrease in elevated diastolic BP in the RU group. This is important as elevated BP during childhood increases the risk of hypertension during adolescence and ultimately into adulthood.7,317 The reduction in the proportion of RU children in the current study with elevated systolic and diastolic BP is similar to a 16-week school-based intervention by Harrell et al233 among RU fifth graders from Mississippi.  The intervention group from that study revealed a decrease of 9% and 3% for the proportion of RU children with high systolic and diastolic BP, respectively.  Overall, childhood nutrition and PA interventions have had moderate success for reducing BP.47  A recent meta-analysis of 23 childhood obesity prevention studies by Cai et al,226 on BP noted significant pooled intervention effects of  -1.64 mmHg (P = .001) and -1.44 mmHg (P = .001) for systolic and diastolic BP, respectively.  BP is an established independent risk factor for CVD, but is also associated with a number of risk factors including obesity.318,319 With respect to possible reasons for the BP decreases in the current study, there were not changes in the proportion of children who were overweight or obese in either group, however, there were significant decreases in %BF for the RU group.  %BF in prepubescent children is associated with levels of systolic and diastolic BP.89  The decrease in %BF in the RU group may explain some of the observed decreases    148  for BP, but does not explain the decrease in systolic BP for the UR group as BMI, %BF, and WC increased.  High dietary Na+ intakes and low K+ intakes common in the Western diet also contribute to elevated BP levels.320  Furthermore, higher intakes of K+, Ca+ and Mg+, and vitamins D and E containing foods are associated with lower levels of BP and have been identified as key factors for blood pressure improvements in intervention studies in children and adults.173,321  There was not a significant decrease for Na+ intakes observed between or within groups in the current study, nor was there a reported increase in K+, Ca+, or Mg+ intakes.  However, there was a significant increase in dairy and vitamin D intakes for the RU children, which may have contributed to the favorable decreases in elevated BP, as both are associated with decreased BP in children.12  This does not explain the within decrease for diastolic BP in UR children, nor their decreased proportion with elevated BP, however, their increased vitamin E intake may have had an effect given that both vitamin E, other components found in high vitamin E containing foods (eg, dietary fiber and K+), and supplemental forms of vitamin E have been shown to reduce BP in adults.12,173,321  Perhaps, a combination of significant and non-significant changes and interactions with  multiple risk factors and nutrient intakes associated with high BP contributed to reducing the proportion of children with elevated BP. In a meta-analysis by Cai et al,226 the researchers reported that interventions that included a combined nutrition and PA component were most effective at reducing BP, similar to our intervention. However, since we controlled for reported PA, the results supports the likelihood that changes were related to dietary or other factors mentioned. This not only adds credence to the importance of monitoring changes in other CVD risk factors along with obesity, but    149  suggests that multi-component school-based health programs should emphasize nutrition and PA in combination. Blood lipids and the composite CVD risk factor did not change between or within groups after the (S)Partners intervention.  For blood lipids the lack of change was not surprising when considering at baseline the mean levels for all lipid values were well within recommended levels based on pediatric cutpoints.  Also less than 28% of the sample had one or more lipid value that was classified at risk.  Research on changes in blood lipids following childhood health interventions are mixed.  Among 13 childhood obesity prevention studies Cai et al,227 calculated a non-significant pooled intervention effect of -0.97 mg/dL (95% CI, -3.26-1.32; P = .408) for TC, while a meta-analysis by HO et al322 using five studies calculated a significant decrease in TC for short- and long-term studies. Furthermore, Cai et al227 calculated a pooled intervention effect of 1.87 mg/dL (95% CI, 0.39-3.34; P = .013) among 10 studies for HDL-C, however Ho et al322 did not find an intervention effect for HDL-C among four studies.  Although, there were no changes in the prevalence of dyslipidemia in RU children in our sample, Harrell et al233 observed a decrease of 4% and 8% for high and borderline (moderate) TC, respectively, and a decrease of 3% and 5% for high and borderline (moderate) LDL-C, respectively.   Compared to the (S)Partners intervention, that study  increased the number of fruits and machines, which both increased the availability of foods that may alter blood lipids.11  The inability to observe a change for the composite CVD risk factor score was likely due to the -C, and non-HDL-C) after intervention.  Given the current prevalence of dyslipidemia is 18.1% among US    150  children aged 8 to 12 years,9 future studies or analysis of existing pre-post data  should also evaluate the change among those children that are classified at-risk at baseline. To our knowledge this is the first study to directly compare outcomes between RU and UR US children who were exposed to the same school-based nutrition and PA intervention. A strength is both groups consisted of similar participants, with no differences in mean age, height, and weight.  Another strength was the sample included a significant proportion of children from low SES families which increases their risk for health disparities.  This study is subject to a number of limitations.  A number of students did not complete all measures at baseline and after the intervention, thereby decreasing the number of comparisons that could have been performed, and it is possible those that did not attend both measurements had different levels of risk than those who participated. Unequal sample size with respect to school location may have biased the conclusions because of a loss of statistical power, however, hierarchal analyses were performed to account for the clustering of students within schools. Another limitation is both groups were not balanced on ethnicity, however, given  the study was a RU and UR comparison the low level of African American participants in the RU group was expected given that only 2.1% of RU residents in MI are classified as  African-American.268 An additional limitation is that selected CVD risk factors including blood lipids levels and BP may have been affected by medications used by children. The prevalence for the use of medications for both BP and dyslipidemia among youth has increased in the US.269 Additionally, medications to treat attention-deficit/hyperactivity disorder and other medications in children have increased    151  and some may have independent effects on CVD risk measures, or alter behaviors that influence CVD risk.270-273 Furthermore, our data for nutrition behaviors and nutrient intakes is based on self-reported FFQs, which may be subject to misreporting,301 and further reduced our sample size when comparing changes in the nutrition variables.  However, we used a protocol to enhance the ability to determine if flagged invalid FFQs were invalid.   Additionally, compared to multiple 24-hour recalls, the FFQ may under-report dairy and grain servings, and over-report vegetable servings in children less than 12-years-old, however, fruit intakes are nearly identical.280 Although the intervention and staff training were similar year to year, different modifications were implemented to improve programming and some components of the program may not have proceeded as planned at each of the participating sites. The quality and effectiveness of lesson delivery by facilitators and college students may have been different between schools and years which may have had an impact on health status.  Furthermore, some children may have been absent during lessons.  Children may also have had similar programming either prior to or concurrently, which may affect the results. Additionally, children may have had different levels of access to the website while in school or at home. The results may not be generalizable to other RU and UR populations because of the small number of schools involved (two and five, respectively); and different regions within MI and the US will have variable cultural customs, SES, and level of support for health promoting behaviors.   Another limitation is the use of the RUCA to categorize residency, which may not differentiate different neighborhoods within areas (ie, suburbs vs city). Also areas that are classified as RU include a great variance where people live (in a town or country).  Furthermore, using school as a proxy for residency may not be    152  representative of all students attending the school as children may live in a RU area and attend school in a UR area, and vice versa. Despite these limitations, (S)Partners for Heart Health and similar interventions may be promising methods to improve nutrition behaviors and nutrient intakes, and CVD risk factor status among children attending schools in different residencies.   Conclusion School-based nutrition and PA intervention programs are a method to encourage youth to adopt a healthy lifestyle that includes eating a nutrient-dense diet and increasing PA, which may ultimately reduce morbidity and mortality in later life.  The (S)Partners program is designed to increase the number of fifth grade children meeting nutrition and PA recommendations for heart and general health, and has shown utility when implemented in diverse populations (eg, RU and UR schools).  Our results were in contrast to our hypothesis that nutrition behaviors and CVD risk factors would change equivocally in RU and UR children.  The (S)Partners intervention improved dairy and vitamin D in children from RU vs UR schools, and vitamin E intake in children from UR vs  RU schools, which may have influenced declines in BP for both groups.  After the intervention, the proportion of children with low-risk %BF and elevated diastolic BP decreased significantly more in RU vs UR children. However, the proportion of children with elevated systolic BP significantly decreased in both groups.  These results support using the (S)Partners intervention in both RU and UR schools to improve nutrition behaviors and CVD risk factor status.  More research is needed to determine if similar school-based programs that utilize nutrition and PA components are effective for other RU and UR populations.  Lastly, follow-up studies a    153  few years after the (S)Partners for Heart Health program should be performed to see if the participants maintained behaviors and CVD risk factor profiles.                   154  CHAPTER 6 SUMMARY  Therefore the purpose of this dissertation was to determine if: 1) mean levels and prevalence of obesity and other CVD risk factors are greater for RU vs UR children; 2a) mean levels of nutrition behaviors, nutrient intakes, and PA are more favorable in UR vs RU children; 2b) fewer RU vs UR participants are meeting nutrition and PA recommendations; 2c) relationship of nutrition behaviors and nutrient intakes with CVD risk factors; 3) the improvement to nutrition behaviors and CVD risk factors after a school-based nutrition and PA intervention are equivocal between RU and UR participants.     155   The specific objectives of Aim 2 (Chapter 4) were to evaluate 1a) if mean intakes of food groups and nutrients related to CVD health and PA are lower in MI RU vs UR children; and 1b) if fewer RU children are meeting national recommendations; and to 2) determine if nutrition behaviors and nutrient intakes are related to CVD risk factors among RU and UR children. For our primary objective we anticipated RU children would have poorer nutrition and PA behaviors compared to UR children, and fewer RU children would be meeting recommendations.  For our secondary objective we anticipated that poor nutrition behaviors would be associated with an increased likelihood for CVD risk factors among both groups. Results for our primary objective revealed UR children had a greater intake of fruit, vitamin C, and a greater dietary fiber index (FI) (g fiber/1000 kilocalories [kcals]) compared to their RU counterparts, with no significant differences in other food groups, macro- and micronutrients, Healthy Eating Index (HEI) 2010, or PA.  Additionally, there    156  were no differences in the percentage of RU vs UR children meeting nutrition and PA recommendations. For our secondary objective, anticipated results included: in RU children increasing whole grain intake was associated with a decreased likelihood for systolic BP, and potassium (K+), calcium (Ca+), and the HEI 2010 score were associated with a decreased likelihood for diastolic BP.  A number of unanticipated relationships between nutrition behaviors and nutrient intakes with CVD risk factors were observed. Increasing fruit and vegetable intakes were associated with an increased likelihood for overweight/obesity, and low- and high-risk %BF for UR children. Increasing saturated fat intake was associated with a decreased likelihood of abdominal obesity and low- and high-risk %BF for UR children, and total sugar intake was associated with a decreased likelihood of high-risk %BF for RU children. An increasing FI and HEI 2010 score were associated with increased likelihood of overweight/obesity and low-risk %BF for UR children.  Lastly, increased vitamin E intakes were associated with an increased likelihood for elevated systolic BP among UR children. The specific objectives of Aim 3 (Chapter 5) were to: 1) determine if RU and UR have equivocal alterations in nutrition behaviors, nutrient intakes, and CVD risk factors after the (S)Partners for Heart Health intervention; and to 2) determine if there are equivocal decreases following (S)Partners for the proportion of RU vs UR children identified at baseline with CVD risk factors.  We hypothesized that (S)Partners would be equally as effective at improving nutrition behaviors and nutrient intakes, and improving levels and prevalence of CVD risk factor among RU and UR children. Results for our primary objective revealed there were few significant between group changes, however, reported dairy and vitamin D intakes increased significantly more in the RU vs UR group,    157  while vitamin E intake increased significantly more in the UR group.  For our secondary objective, after intervention, mean %BF decreased significantly more in the RU vs UR group, systolic BP decreased in the UR group with no between group differences, the proportion of children with elevated systolic BP decreased similarly in both groups, and the proportion of RU children with elevated diastolic BP and low-risk %BF decreased significantly more than for the UR children.  Aim 1: Differences for Cardiovascular Disease Risk Factors between Rural and Urban Children For this cross-sectional comparison we hypothesized that MI children from RU schools would have greater levels of CVD risk factors, and greater prevalence and odds for at-risk CVD risk factors compared to children from UR schools.  Our results revealed there was a greater prevalence of elevated diastolic BP for RU vs UR children (27.7% vs 19.5%; P = .01).  Furthermore, the crude and adjusted odds ratios revealed that RU children had 63% and 74% greater odds respectively, of having elevated diastolic BP compared to their UR counterparts. There are limited studies published that compared CVD risk factors other than BMI (obesity) between RU and UR children.  Murray et al20 found no difference in prevalence of high BP between RU and UR children from North Carolina, and no difference for mean diastolic BP levels, however, systolic BP levels were significantly greater for the RU group (105.0 vs 103.3 mmHg; P < .001).  The aforementioned study, found obesity increased the odds for hypertension in RU and UR children by more than 200 percent, however, we observed no differences in BMI between groups. This suggests that other factors may explain BP differences observed in the current study.  Although not assessed    158  for the current study, numerous dietary factors can contribute to increases in BP and hypertension.173 Another potential contributor is the potentially higher rate of psychosocial stressors in RU vs UR populations,250,251 which increases risk for hypertension.267 More research is needed to examine the role of nutrition and psychosocial factors in relation to adverse CVD risk factors among RU and UR MI children. Furthermore, more research is needed comparing BP between RU and UR children at the national level and different regions in the United States (US). We observed no significant differences for overweight and obesity between RU and UR children, which is in contrast to other studies using national and regional level data.16,27,36 The differences may have been related to participants being from fifth grade classrooms  with a higher percentage eligible for Free and Reduced Lunch (FRL) (58.8%), as this is a proxy for socioeconomic status (SES).262 Low SES t is highly associated with obesity.189,191,192 Also, both RU and UR groups in the current study had greater obesity rates than the national average.40 Additionally, MI children and adults have consistently had a higher prevalence of obesity rates vs national average regardless of SES.38,39,323    With respect to MI children vs national averages, in addition to having a higher prevalence of obesity  in our sample of fifth grade children, other CVD risk factors were greater than the national average including , low HDL-C, and elevated BP,9,40 underscoring the need for effective primary prevention programs that include nutrition and PA to prevent and or reduce risks, in MI youth.     159  Aim 2: Differences in Nutrition Behaviors, Physical Activity, and the Relationship of Nutrients with Cardiovascular Disease Risk Factors among Rural and Urban Children  For this cross-sectional comparison we hypothesized that mean levels of nutrition behaviors and nutrient intakes would be better in UR vs RU children; however, there were few between group (RU vs UR ) differences in nutrition behaviors and nutrient intakes  though several differences did support our hypothesis. There was a greater fruit intake observed in UR vs RU children (2.24 vs 1.51 cups; P = .01), which is consistent with national and other regional comparisons.16,34 However, not all research has revealed a difference for fruit consumption between RU and UR children.27 The higher reported fruit intake likely explains the greater vitamin C and contributed to the higher FI for the UR vs RU children (147 vs 106 mg/dL; P = .04; and 9.14 vs 7.89 g fiber/1000 kcals; P = .02, respectively).  These results suggests UR MI children in our sample suggests UR MI children have a more nutrient-dense diet than RU MI children. However, there were no differences in the percentage or RU and UR children meeting nutrition recommendations. A greater percentage of RU and UR from the current sample were meeting fruit intake recommendations and vitamin C intakes than national and other regional observations, suggesting that RU and UR MI children may respond positively to nutrition interventions that emphasize fruits and other foods high in vitamin C, because of increased preference or access.  There were also no differences in reported PA or the percentage meeting recommendations between groups. There were a number of significant results for the relationships of nutrition behaviors and nutrient intakes with CVD risk factors, however, comparing our results to    160  other studies was difficult to the lack of published studies.  For Aim 1, the analysis indicated RU children had a greater prevalence of elevated diastolic BP vs UR children.  Results from Aim 2 revealed increasing whole grains intake was associated with a decreased likelihood of elevated  BP for RU children, which is consistent with other literature in that whole grain consumption is associated with a decreased risk for CVD.160  It was also observed that that K+ and Ca+ intakes and a higher HEI 2010 among RU children were associated with a decreased likelihood for elevated diastolic BP, which coincides with the current literature.12,173,181 These results suggest the importance of integrating methods to increase intakes of whole grains and other food groups high in K+ and Ca+ (eg, fruits, vegetables, and dairy). There were a number of unanticipated results in regards to relationships of nutrition behaviors and nutrient intakes with CVD risk factors for children from both groups.  For example, fruit and vegetable intakes were associated with an increased likelihood for overweight/obesity and low- and high-risk %BF for UR children.  Furthermore, a higher FI increased the likelihood for overweight/obesity, abdominal obesity, and low- and high-risk %BF among UR children, however, this is likely explained by their fruit and vegetable consumption.  Overall, the literature on this indicates that fruit and vegetable intakes are associated with lower levels of obesity-related measures in adults, although this relationship is not as clear in children.291 Others have also reported unexpected relationships. For example, Tovar et al49 found a greater vegetable intake was associated with odds for RU obesity,49 and an evaluation of data from 2003-2006 National Health and Nutrition Examination survey data indicated there was not a relationship with combined fruit and vegetables with obesity in RU and UR children and adolescents.27 Other    161  surprising findings were saturated fat intake was associated with decreased likelihood for abdominal obesity, and low- and high-risk %BF among UR children, and total sugar intake was associated with increased likelihood of high-risk %BF for RU children.  Both of these relationships are in contrast to published studies, as most studies indicate that saturated fat and sugar intakes are associated with cardiometabolic risks including obesity, elevated BP, and dyslipidemia in children and adults.12,319,324,325 It is unclear as to why most of the unanticipated observations between nutrition behaviors and nutrient intakes with CVD risk factors were observed in the UR vs the RU group, however, there are a number of possible explanations. For example, the food frequency questionnaire (FFQ) included foods in the fruit and vegetable variables that vary in nutrient- and calorie-densities, some of which can increase CVD risks.292 Also overweight and obese children are more likely than their normal-weight counterparts to over-report their fruit and vegetable intake and under-report their intakes of foods high in kcals from saturated fats and total sugar.293,294   Another possibility for these observations is overweight or obese children that were aware of their weight status and motivated (by themselves or by parent/guardian) and may have acutely improved their nutrition behaviors.  Additionally, several studies have reported that a significant proportion of children that are classified as normal weight, are not satisfied with their body weight, and are often are trying to lose weight.326,327  A study by Banner et al (MSU Thesis) reported on a sample of elementary children (including children from the current sample) who answered survey questions if they were content with their current weight and body size, and if they were trying to lose weight. Among the children that were classified as normal-   162  weight, over a third reported they were discontent with their weight and that they were trying to lose weight. In summary, there were few differences for nutrition behaviors and nutrient intakes between RU and UR MI children, except UR reported a greater fruit and vitamin C intake, and higher FI which suggests a more nutrient-dense diet.  Among RU children whole grains, K+, and Ca+ intakes and greater HEI 2010 score were associated with decreased likelihood for elevated BP.  This suggests nutrition and PA interventions that increase intakes of foods high in these nutrients (and food components that increase HEI 2010 score),182 may have a positive effect on BP in RU children. A number of unanticipated results for food groups and nutrient intakes with CVD risk factors were observed, which may have resulted from a myriad of factors including the aforementioned factors previously discussed. Regardless, few children were meeting recommendations for food groups and nutrient intakes, and > 50% had at least one CVD risk factor, illustrating the need to include effective programs including school-based nutrition and PA programs to promote cardiovascular and overall health. Aim 3: Effects of (S)Partners for Heart Health, on Nutrition Behaviors, Nutrient Intakes, and Cardiovascular Disease Risk Factors in Children from Rural and Urban Schools For this pre to post intervention comparison we hypothesized that RU and UR groups would have equivocal improvements in nutrition behaviors, nutrient intakes, and CVD risk factors after the (S)Partners for Heart Health intervention, and there would equivocal decreases following the intervention for the proportion of RU vs UR children    163  identified at baseline with CVD risk factors.  However, there were few between group differences observed after intervention, including dairy and vitamin D intakes increased significantly more for the RU vs UR children, while the opposite was observed for vitamin E.  The increased vitamin D intake for RU children is likely due to the paralleled increase in dairy intake. Although, it is unclear as to why there was not a concurrent increase in dairy intake in the UR group, our baseline results revealed a greater dairy intake in the RU vs UR group.  This observation and others,16 suggests that RU children in general have an increased preference or increased availability of dairy products. This may indicate that RU children may respond more favorably to school-based nutrition and PA interventions in regards to dairy intake.  We are unsure as to why vitamin E intake increased more in the UR group after intervention, however, it may be due to an increased consumption of foods high in vitamin E (eg, nuts, seeds, leafy greens),296 more than RU children.  For example, spinach intakes are greater in UR  and suburban vs RU areas (32% and 47% vs 21%, respectively),297 which suggests that UR populations already have an increased preference or availability of these foods compared to RU populations, and thus more likely to increase their intake of them.   There were few changes in nutrition behaviors and nutrient intakes between and within groups, which is similar to results observed from a number of nutrition and lifestyle intervention programs for children and adolescents.228,328 For example, among 432 RU children, there was an increase in vegetable intake and decrease in glycemic index following intervention, however, no significant changes were observed for fruits, whole grains, legumes, dairy, potatoes, % of daily kcals from saturated fat, added sugars, or the FI.48  More research is needed to observe if long-term changes for nutrition    164  behaviors and nutrient intakes are apparent in RU and UR children following an intervention.  For changes in CVD risk factors following (S)Partners, %BF difference was favored in the RU vs UR group, and the proportion of RU children with low-risk %BF decreased significantly more than UR children.  This could be the result of RU responding more favorably to a school-based nutrition and PA intervention, however, evidence for this is scarce.314  Another explanation for the non-equivocal findings for %BF between RU and UR, is that the UR children may have had a pubertal influence, as this may appear around the same age for females in the current study,224 and UR children typically undergo puberty at an earlier age than RU children.316 Diastolic BP decreased significantly for the UR group, however, the change was not significantly different than the change for the RU group.  The proportion of RU children with elevated diastolic BP, decreased significantly more than the UR group after the intervention, however, both groups observed significant decreases in the percentage with elevated systolic BP. The reduction in levels of BP without a corresponding decrease in obesity-related measures is similar to the literature.226 The more pronounced changes in the percentage of elevated BP in RU children, may be explained by their corresponding increases in dairy and vitamin D intake.12  The decreases in BP observed in UR children may have been due to their increased vitamin E intake, as foods high in vitamin E are also high in other nutrients that are associated with lower BP in adults and children including dietary fiber, omega-3 fatty acids, K+, Ca+, and Mg+.173 More research is needed to see if    165  there are differences for intakes of particular food types that are high in vitamin E between RU and UR children.  Results were in contrast to our hypothesis that nutrition behaviors and nutrient intakes, and CVD risk factors would change equivocally in RU and UR children.  These results support using the (S)Partners for Heart Health program and similar school-based nutrition and PA interventions in both RU and UR schools to improve nutrition behaviors and CVD risk factor status.  Lastly, follow-up studies a few years after intervention should be performed to see if the participants maintained behaviors and CVD risk factor profiles. Strengths and Weaknesses There are a number of strengths in this dissertation.  This is one of the few studies to examine differences for CVD risk factors other than obesity between US RU and UR children, and the first to do so in MI.  Furthermore, this is one of a few studies to compare nutrition behaviors and nutrient intakes related to cardiovascular health between RU and UR children, and examine their relationship with CVD risk factors among both populations.  This is also the first study to investigate changes in nutrition behaviors and nutrient intakes, and CVD risk factors between RU and UR children following the same school-based nutrition and lifestyle intervention. Each aim consisted of similar participants, with no differences in mean age, height, and weight.  Another strength is including a good portion of the population at risk for health disparities based on eligibility for FRL (proxy for SES) and a high prevalence of obesity and other CVD risk factors.   An additional strength is that all CVD measures were performed using standard procedures by trained    166  staff in both groups and the results were analyzed while accounting for variables that may affect CVD measures (eg, sex, ethnicity, SES, PA).9,40,213,329 However, there were several limitations that need to be addressed.  Aims 1 and 2 comparisons were cross-sectional, precluding inferences with respect to causal factors. Unequal sample size with respect to school location may have biased the conclusions for each of the aims because of a loss of statistical power, however, hierarchal analyses were performed to account for the clustering of students within schools. Another limitation is both groups were not balanced on ethnicity, however, given  the study was a RU and UR comparison the low level of African American participants in the RU group was expected given that only 2.1% of RU residents in MI classified as  African-American.268 An additional limitation is that selected CVD risk factors including blood lipids levels and BP may have been affected by medications used by children. The prevalence of the use of medications for both BP and dyslipidemia has increased.269 Additionally, medications to treat attention-deficit/hyperactivity disorder and other medications in children has increased and some may have independent effects on CVD risk measures, or alter behaviors that influence CVD risk.270-273 Our data is  based on self-reported FFQs which may be fallible to misreporting301 and as a result the sample size decreased due to invalid FFQs.  However, we used a protocol to further evaluate the flagged FFQs deemed at risk for being invalid.  Another limitation is the method in which residency was categorized.  The 2010 US Department of Agriculture Economic Research Service Rural-Urban delineates by zip code which may not differentiate neighborhoods within such as suburban areas. Additionally, residence was determined by    167  students attending that school. The results may not be generalizable to other RU and UR populations in the US because the sample consisted of MI children only.  Furthermore, the results may not be representative of other RU populations in MI, (ie, Northern Lower Peninsula and Upper Peninsula) as the two RU schools were from Central and Southern Michigan. There were some additional limitations pertaining to Aim 3. Although the intervention and staff training were similar year to year, different modifications were implemented to improve programming and some components of the program may not have proceeded as outlined at each of the participating sites.  Other factors that may have affected fidelity of the intervention is  the quality and effectiveness of lesson delivery by facilitators and college students may have varied between schools and years which may have impacted the proportion of children that modified nutrition and other behaviors that impacted CVD risk and health status.  Furthermore, some children may have been absent during lessons, and the level of support by schools for having a supportive environment for lesson delivery and access the (S)Partners website at school varied from school to school.   Also the level of parental support including access to the (S)Partners website at home was variable.   Additionally, children may also have had similar programming either prior to or concurrently, which may affected their behaviors and the results.  Conclusion In summary, Aim 1 determined that the prevalence of CVD risk factors in this sample of RU and UR children were similar, though RU children had a greater prevalence of    168  elevated diastolic BP.  Overall, both groups had a greater prevalence for CVD risk factors than the national average.  Aim 2 determined that the RU children had a lower nutrient density as compared to their UR counterparts. Furthermore, few children in either group were meeting nutrition or PA recommendations.  Aim 3 determined the (S)Partners for Heart Health intervention was modest in modifying some nutrition behaviors, nutrient intakes, and CVD risk factors in both RU and UR children.  However, the sample used for Aim 3 did not indicate a less nutrient-dense diet in the RU vs UR children as observed in Aim 2, which may be due to the smaller number of UR schools in Aim 3 vs Aim 2 (five vs nine).  The Aim 3 results suggest the (S)Partners intervention and similar programs may be effective methods of primary prevention strategies to promote CVD and overall health in RU and UR children.  More research is needed to investigate if other RU and UR CVD and nutrition differences exist at the national level, and in other regions across the US.  Longer (more than 1 year) follow-up studies, are need to determine if nutrition behaviors and CVD risk factors maintained or changed for RU and UR children who were former participants in (S)Partners. Overall, these results suggest a need to implement primary prevention strategies to promote healthy nutrition and PA behaviors.         169   APPENDICES   170   APPENDIX A (S)Partners Cardiovascular Health Risk Assessment Data Record  CARDIOVASCULAR HEALTH RISK ASSESSMENT DATA RECORD   Pre-test  First Name_______________ ID #_____DOB  _________   Age______ Gender _____  Blood Data (ask the following questions before starting)      Last time you ate? _____hrs ago.  What did you eat/ drink?______________________________  Have you had a cold or other infection in the last two weeks? ______________________   Total Cholesterol______mg/dL             HDL Cholesterol______mg/dL              Total Chol:HDL Ratio _______         Triglycerides________mg/dL            LDL Cholesterol______mg/dL (calculated)     hs-CRP ___________ mg/L                Clinician(s) Full Name _______________________ _______________________  Anthropometrics  Height     Meas. 1_____cm 2_____cm 3_____cm 3rd measure required if 2nd measure not w/in 0.4 cm of 1st measure  Sitting ht.  (Stool ht._____)  Meas. 1_____cm 2_____cm 3_____cm 3rd measure required if 2nd measure not w/in 0.4 cm of 1st measure  Weight     Meas. 1_____kg 2_____kg 3_____kg  BIA (BF = body fat)  Meas. 1_____%BF 2_____%BF 3_____%BF  Waist Circumference   Meas. 1_____cm 2_____cm 3_____cm 3rd measure required if 2nd measure not w/in 0.4 cm of 1st measure   Clinician(s) Full Name _______________________ _______________________    171   Blood Pressure (systolic/diastolic) **2nd measurement MUST be within 4mm/hg** Blood Pressure        Meas. 1____/___mmHg      2____/____mmHg      3____/____mmHg  Do you have any food allergies? ___ If yes, please explain_________________________  Do you take any medication(s)? ___If yes, please list______________________________           ______________________________________       OVER   Acanthosis Nigricans  NONE/NOT PRESENT   Measurement Clinician(s) _______________________ _______________________             172  APPENDIX B Block Kids 2004 Food Frequency Questionnaire        173         174           175             176  APPENDIX C Block Kids 2004 Food Frequency Questionnaire Output     177      178      179      180      181      182       183      184      185  APPENDIX D Health Eating Index 2010 Variables Total Fruit -Total Fruit; 0.8 cups/1000 kcals Whole Fruit  -Total Fruit  100% Fruit Juice 0.4 cups/1000 kcals Total Vegetables  - 1.1 cups/1000 kcals Greens and Beans  -Green Vegetables (beans and peas not counted as total protein); 1: 0, 2: 0.067, 3: 0.133, 4:  0.2 cups/1000 kcals Whole Grains -Whole grains; 1: 0, 2: 0.1875, 3: 0.375, 4: 0.563, 5: 0.75, 6: 0.938, 7: 1.125, 8: 1.313, 9:  1.5 ounces/1000 kcals Dairy -Total Dairy; 1: 0, 2: 0.163, 3: 0.325, 4: 0.488, 5: 0.65, 6: 0.813, 7: 0.975, 8: 1.138, 9: 1.299,  1.3 cups/1000 kcals Total Protein -Eggs, Meat (Fish and Poultry), Lunch Meats, Red Meat, Dry Beans, Nuts/Seeds 1: 0, 2: 0.834,  2.5 ounces/1000 kcals -Specific Variables from Block output: JUSTEGGS + FISH_HI + FISH_LO + LNCHMEAT + REDMEAT + NUTSEEDS + DRYBEANS + POULTRY + SOYFOODS + ORGMEAT Seafood and Plant Proteins -Seafood/Fish (Both Hi and Low in Omega-3s), Dry Beans, Nuts/Seeds, Soy Protein; 1: 0, 2:  0.8 ounces/1000 kcals - Specific Variables from Block output: FISH_HI + FISH_LO + DRYBEANS + NUTSEEDS + SOYFOODS Fatty Acids    186  -(MUFAs + PUFAs) / Saturate 2.5 Refined Grains -Non- 4.3, 2: 4.299, 3: 3.988, 4: 3.675, 5: 3.36, 6: 3.05, 7: 2.738, 8: 2.425, 9:  1.8 ounces/1000 kcals Sodium -Total S 2, 2: 1.999, 3: 1.888, 4: 1.775, 5: 1.663, 6: 1.55, 7: 1.438, 8: 1.325,  1.1 grams/1000 kcals Empty Calories -Added sugars (Converted to Grams to Calories) + Solid Fats (Saturated Fat + Trans Fat, Converted to Calories) / Total K 50, 2: 49.999, 3: 48.277, 4: 46.556, 5: 44.833, 6: 43.111, 7: 41.389, 8: 39.667, 9: 37.944, 10: 36.222, 11: 34.5, 12: 32.778, 13: 31.056, 14:  19% of total kcals          187  BIBLIOGRAPHY    188  BIBLIOGRAPHY  1. 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