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ASSOCIATION BETWEEN THE FAMILY NUTRITION AND
PHYSICAL ACTIVITY (FNPA) SCREENING TOOL AND
CARDIOVASCULAR DISEASE RISK FACTORS IN 10—YEAR
OLD CHILDREN

presented by

KIMBO EDWARD YEE

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ASSOCIATION BETWEEN THE FAMILY NUTRITION AND PHYSICAL
ACTIVITY SCREENING TOOL AND CARDIOVASCULAR DISEASE RISK
FACTORS IN Io-YEAR OLD CHILDREN
By

Kimbo Edward Yee

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE
Kinesiology

2010

ABSTRACT
ASSOCIATION BETWEEN THE FAMILY NUTRITION AND PHYSICAL
ACTIVITY SCREENING TOOL AND CARDIOVASCULAR DISEASE RISK
FACTORS IN 10-YEAR OLD CHILDREN
By

Kimbo Edward Yee

PURPOSE: To examine the association of the Family Nutrition and Physical Activity
(FNPA) screening tool, a behaviorally based screening tool designed to assess the
obesogenic family environment and behaviors, with cardiovascular disease (CVD) risk
factors in 10-year old children. METHODS: One hundred nineteen children were
assessed for body mass index (BMI), percent body fat (%BF), waist circumference (WC),
total cholesterol, HDL-cholesterol, and resting blood pressure. A continuous CVD risk
score was created using total cholesterol to HDL-cholesterol ratio (TCzHDL), mean
arterial pressure (MAP), and WC. The FNPA survey was completed by parents. The
associations between the FNPA score and individual CVD risk factors and the continuous
CVD risk score were examined using correlation analyses. RESULTS: Approximately
35% of the sample were overweight (19%) or obese (16%). The mean FNPA score was
24.6 i 2.5 (range 18 to 29). Significant correlations were found between the FNPA score
and WC (r = -.35, p<.01), BMI percentile (r = -.38, p<.01), %BF (r = -.43, p<.01), and the
continuous CVD risk score (r = —.22, p = .02). No significant association was found
between the FNPA score and TCzHDL (r=0.10, p=0.88) or MAP (r=-O.12, p=0.20).
CONCLUSION: Children from a high-risk, obesogenic family environment as indicated
with a lower FNPA score have a higher CVD risk factor profile than children from a low-

risk family environment.

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. iv
CHAPTER 1

IN TRODUCTION‘ ............................................................................................................... 1

Statement of Purpose ............................................................................................... 2

Significance of the Study ......................................................................................... 2

Hypothesis ................................................................................................................ 3

References ................................................................................................................ 4
CHAPTER 2

LITERATURE REVIEW ..................................................................................................... 6

Cardiovascular Disease Risk Factors and the Metabolic Syndrome ....................... 6

The Development of Cardiovascular Disease Risk Factors in Children ....... 6

History of the Metabolic Syndrome ............................................................ 10

Definitions of the Metabolic Syndrome ..................................................... 11

World Health Organization Definition .......................................... 11

National Cholesterol Education Panel ATP III Definition ............ 11

International Diabetes Federation Definition ................................ ll

Metabolic Syndrome in Children ............................................................... 14

Continuous CVD Risk Score/Metabolic Syndrome Score ......................... 15

Lifestyle Influences and CVD Risk Factors/Metabolic Syndrome in Children ................. 16

Physical Activity and the Metabolic Syndrome in Children .................................. 16

Dietary Behavior and CVD Risk Factors/Metabolic Syndrome in Children ......... 17

Screen Time and CVD Risk Factors/Metabolic Syndrome in Children ................ 20

Family Environment and Obesity/CVD Risk Factors in Children ......................... 20

The Family Nutrition and Physical Activity Screening Tool (FNPA) ............................... 22

Development of the Family Nutrition and Physical Activity Screening Tool ....... 22

Validation of the Family Nutrition and Physical Activity Screening Tool ............ 23

Summary ............................................................................................................................ 25

References .......................................................................................................................... 27
CHAPTER 3

MANUSCRIPT .................................................................................................................. 32

Introduction ............................................................................................................ 32

Statement of Purpose .............................................................................................. 33

Methods .................................................................................................................. 33

Results .................................................................................................................... 35

Discussion .............................................................................................................. 38

REFERENCES ................................................................................................................... 43

iii

APPENDIX ........................................................................................................................ 46

iv

LIST OF TABLES

Table 1: Definitions of the Metabolic Syndrome ............................................................... 13
Table 2: Criteria for Clinical Diagnosis of the Metabolic Syndrome ................................ 14
Table 3: The Ten Domains Used to Create the FNPA ....................................................... 23
Table 4: Descriptive Charactierstics for Males, Females, and the Total Sample .............. 46

Table 5: Correlations Between FNPA Score, Standardized Residuals for

TCzHDL Ratio, WC, MAP, and CVD Risk Score ............................................... 47
Table 6: Weight Category and FNPA Score ...................................................................... 48
Table 7: Body Size and CVD Risk by FNPA Score (525 and >25) .................................. 49

CHAPTER 1
INTRODUCTION
Background and Rationale:

Childhood obesity is a major public health concern given the number of youth
affected and its consequences. Currently, 18% of US. youth are obese and another 17%
are overweight [1-3]. Among the immediate health concerns of obesity is the clustering
of adverse cardiovascular disease (CVD) risk factors (i.e. dyslipidemia, hyperglycemia,
and hypertension), which has been termed as the metabolic syndrome [4]. Recent studies
have shown the emergence of the metabolic syndrome during childhood and adolescence
[5-6] with data from the US. National Health and Nutrition Examination Survey
(NHANES) III (1999-2002) indicating that 10% of adolescents possessed the metabolic
syndrome [5]. Besides the immediate consequences, CVD risk factors track from
childhood and adolescence into adulthood [6-9], which is of importance since the
metabolic syndrome has been shown to be associated with the development of CVD and
Type II diabetes in adulthood [10]. Thus, there is considerable interest in the prevention
of obesity and CVD risk factors beginning in childhood.

It is well established that dietary behavior and physical inactivity are the
two key risk factors influencing obesity and CVD risk factors [11-12]. Among children,
the family environment plays an important role in determining dietary and physical
activity behaviors [13—14]. Children are unique in that their dietary and physical activity
behaviors are influenced directly by parents [13] and indirectly through the construct of
the environment provided to them [15-16]. However, from a methodological standpoint

it has been difficult to capture the shared family and child environment. Recently, the

Family Nutrition and Physical Activity (FNPA) Screening Tool, which assesses family
environmental and behavioral factors (dietary, physical activity, sedentary time, and
sleep) that influence children’s risk for becoming overweight, was developed [17] and
evaluated for its predictive validity of assessing risk for becoming overweight [18].
Results showed that the FNPA could significantly assess changes in BMI over a 1 year
period after accounting for baseline BMI, parent BMI, and other demographic variables.
The FNPA is the first instrument that combines information from a variety of behaviors
(e. g. diet, physical activity and inactivity, sleep patterns, family structure) related to child
obesity to evaluate family environments, and has potential for use by pediatricians,
school nurses, and other health professionals for quickly assessing a child’s family and
home environment and his/her risk for becoming overweight. However, additional
research using the FNPA is warranted to examine its utility in different settings and
populations and other health outcomes besides overweight/obesity (e. g. CVD, metabolic
syndrome, type II diabetes).

Purpose of the thesis:

The purpose of this study was to examine the association of the FNPA with a
continuous CVD risk score in 10 year old children.
Significance of the Study:

The significance of this study will help expand upon the utility of the F NPA
screening tool in assessing not only children’s risk at overweight, but also their CVD risk.
Also, this study will attempt to replicate the same findings of Ihmels et al., that the FNPA
is associated with risk of overweight in children. If significant associations are found in

this study then that could lead to future promising usage of the FNPA in smaller clinical

based settings and school based settings for predicting risk of becoming overweight and

developing CVD in children.

Hypothesis:

It was hypothesized that the FNPA score will be inversely related to a continuous

CVD risk score in 10 year old children.

10.

11.

References

Ogden, C.L., et al., Prevalence of high body mass index in US children and
adolescents, 2007-2008. JAMA, 2010. 303(3): p. 242-9.

Future of Children report: Childhood obesity. , in The Future of Children. 2006.

Koplan, J .P., C.T. Liverman, and V.I. Kraak, Preventing childhood obesity:
health in the balance: executive summary. J Am Diet Assoc, 2005. 105(1): p. 131-
8.

Executive Summary of The Third Report of The National Cholesterol Education
Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High
Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA, 2001. 285(19):
p. 2486-97.

Cook, 8., et al., Metabolic syndrome rates in United States adolescents, from the
National Health and Nutrition Examination Survey, 1999-2002. J Pediatr, 2008.
152(2): p. 165-70.

Eisenmann, J .C., Secular trends in variables associated with the metabolic
syndrome of North American children and adolescents: a review and synthesis.
Am J Hum Biol, 2003. 15(6): p. 786-94.

Eisenmann, J.C., et al., Stability of variables associated with the metabolic
syndrome from adolescence to adulthood: the Aerobics Center Longitudinal
Study. Am J Hum Biol, 2004. 16(6): p. 690-6.

Bao, W., et al., Persistence of multiple cardiovascular risk clustering related to
syndrome X from childhood to young adulthood. The Bogalusa Heart Study. Arch
Intern Med, 1994. 154(16): p. 1842-7.

Katzmarzyk, P.T., et al., Stability of indicators of the metabolic syndrome fiom
childhood and adolescence to young adulthood: the Quebec Family Study. J Clin
Epidemiol, 2001. 54(2): p. 190-5.

Grundy, S.M., et al., Diagnosis and management of the metabolic syndrome: an
American Heart Association/National Heart, Lung, and Blood Institute Scientific
Statement. Circulation, 2005. 112(17): p. 2735-52.

Steinberger, J. and SR. Daniels, Obesity, insulin resistance, diabetes, and
cardiovascular risk in children: an American Heart Association scientific
statement fiom the Atherosclerosis, Hypertension, and Obesity in the Young
Committee (Council on Cardiovascular Disease in the Young) and the Diabetes
Committee (Council on Nutrition, Physical Activity, and Metabolism).
Circulation, 2003. 107(10): p. 1448-53.

12.

13.

14.

15.

16.

17.

18.

Williams, C.L., et al., Cardiovascular health in childhood: A statement for health
professionals from the Committee on Atherosclerosis, Hypertension, and Obesity
in the Young (AHOY) of the Council on Cardiovascular Disease in the Young,
American Heart Association. Circulation, 2002. 106(1): p. 143-60.

Golan, M. and S. Crow, Parents are key players in the prevention and treatment
of weight-related problems. Nutr Rev, 2004. 62(1): p. 39-50.

Welk, G.J., Wood K, Morss G., Parental influences on physical activity in
children: an exploration of potential mechanisms. Pediatric Exercise Science,
2003. 15: p. 19-33.

Spurrier, N.J., et al., Relationships between the home environment and physical
activity and dietary patterns of preschool children: a cross-sectional study. Int J
Behav Nutr Phys Act, 2008. 5: p. 31.

Chen, W., et al., Cardiovascular risk factors clustering features of insulin
resistance syndrome (Syndrome )0 in a biracial (Black— White) population of
children, adolescents, and young adults: the Bogalusa Heart Study. Am J
Epidemiol, 1999. 150(7): p. 667-74.

Ihmels, M.A., et al., Development and preliminary validation of a Family
Nutrition and Physical Activity (FNPA) screening tool. Int J Behav Nutr Phys
Act, 2009. 6: p. 14.

Ihmels, M.A., et al., Prediction of BMI change in young children with the family
nutrition and physical activity (FNPA) screening tool. Ann Behav Med, 2009.
38(1): p. 60-8.

CHAPTER 2
LITERATURE REVIEW

Introduction

The problem of childhood obesity and associated cardiovascular and metabolic
health has gained considerable interest in recent years. With the increasing burden of
obesity and CVD, there is an urgent need to examine the etiology of the metabolic
syndrome in order to improve efforts in its prevention, management, and treatment. Both
genetic and environmental factors influence obesity and CVD risk factors in children;
however, the focus of studies has been predominantly upon environmental or lifestyle
influences (e. g. physical activity and diet). Since environmental/lifestyle factors of
children are controlled primarily by parents, there is a need for screening tools to assess
the family environment. The purpose of this literature review is to inform the reader on
1) CVD risk factors and the metabolic syndrome including its history, definitions, and
prevalence in children and adolescents; 2) associations between physical activity, screen
time, and diet with CVD risk factors/metabolic syndrome in children, and 3) the
development of a screening tool to assess multiple dimensions of lifestyle in children and
families.
1. Cardiovascular Disease Risk Factors and the Metabolic Syndrome
I a. The development of C VD risk factors in children

Even though the clinical manifestations of CVD occur in adulthood,

various studies have shown atherosclerosis has its origins early in life [19-23]. This
evidence stems from autopsy reports of coronary atherosclerotic lesions in children and

adolescents, the prevalence of CVD risk factors in children and adolescents, the tracking

of CVD risk factors from childhood to adolescence to young adulthood, and the
predictability of adult heart disease from childhood and adolescent CVD risk factors.

Much of our knowledge about CVD risk factor development in youth has been the
product of the Bogalusa Heart Study and the Muscatine Study. The following section
provides a brief overview of these research programs that have provided evidence for the
early origins of atherosclerosis.

In the 19705, two major epidemiological studies, the Bogalusa Heart Study and
the Muscatine Study, began to investigate the development of CVD risk factors in
children and adolescents. The Bogalusa Heart Study initially began during the 1973-74
school year in Washington Parish, Louisiana, 3 political ward consisting of a bi-racial
population (63% White, 37% Black). Since the initial cross-sectional survey, several
follow-up surveys have been conducted (1976-77, 1978-79, 1981-82, 1984-85, 1987-
1988, 1988-91, 1993-94, etc.).

The Muscatine Study began during the 1971-72 and 1972-73 academic years and
thereafter included biennial surveys in school-aged children and adolescents and a
follow-up survey between the ages of 20 and 34 yrs (Muscatine Young Adult F ollow-Up
Survey). The initial study population included school children from Muscatine, Iowa.
Like the Bogalusa site, Muscatine was chosen due to the relative stability of the
population and its proximity to the medical examining team (University of Iowa, Iowa
City). In contrast to the Bogalusa Heart Study, the sample in the Muscatine Study
consisted of a majority of Caucasians (approximately 96%).

The first published reports from both studies consisted of the descriptive

epidemiology (age-, sex-, and race-associated variation) of CVD risk factors in preschool

children, school-aged children and adolescents [24-27]. During subsequent follow-up
studies, the tracking of CVD risk factors has been determined over various time periods
(i.e., 3-8 yr intervals [8, 28-30]. In general, tracking coefficients for blood pressure and
blood lipids ranged from 0.30-0.60. However, tracking is often better at the extremes
(i.e., upper quartile). In 2,446 subjects examined for total cholesterol between ages 8-18
yrs and re-examined between 20-30 yrs, 43% remained above the 90th percentile, 62%
remained above the 75‘h percentile, and 81% remained above the 50th percentile of
reference values [31]. Along with the critical issue of the persistence of CVD risk factors
and the predictability of adult CVD, another major component in establishing evidence in
therearly origins of atherosclerosis has been to determine the inter-relationships of CVD
risk factors, environmental factors, and family history. An important finding from such
analyses has been that CVD risk factors cluster in obese children and adolescents [32].
Furthermore, 61% of those subjects in the upper quartile of multiple risk factors during
childhood remained in the upper quartile as young adulthood [29].

Familial studies have indicated that adverse CVD risk factors are more common
in offspring of parents and relatives with hypertension, diabetes, obesity, hyperlipidemia,
and history of myocardial infarction [33-34]. Besides the associations between familial
history of CVD and CVD risk factors in offspring, genetic studies have also been
conducted to determine candidate genes for CVD risk factors in these two studies [35-
38].

Autopsy reports from the Bogalusa Heart Study have furthered the understanding
of the relationship between childhood CVD risk factors and the extent of fatty streaks and

fibrous plaques in the aorta and coronary arteries [21]. In 204 autopsy cases, 2-39 yrs of

age, 93 cases had data on risk factors. Results indicate that the body mass index (BMI),
blood pressure (BP), total cholesterol (TC), triglycerides (TG), low-density lipoproteins
(LDL), and high-density lipoproteins (HDL), as a group, are strongly associated with the
extent of lesions (canonical correlation, r=0.70). The effect of multiple risk factors, or
clustering of risk factors, on the percentage of intimal surface of the aorta or coronary
arteries covered with fatty streaks indicates that risk factors during childhood or
adolescence are strongly related to the extent of lesions in the aorta and coronary arteries
of young adults, and that as the number of risk factors increases so does the severity of
asymptomatic coronary and aortic atherosclerosis in young people.

Using a noninvasive method known as computerized beam tomography to detect
the presence of atherosclerotic plaque in asymptomatic subjects with CVD, the Muscatine
Study has also shown the relationship between childhood CVD risk factors and coronary
calcification in young adulthood (29 to 37 yrs, mean age = 33 yrs) [22]. Results indicate
that 31% of men and 10% of women have coronary artery calcification. Although CVD
risk factors measured during the previous and most recent visits showed the strongest
associations with coronary artery calcification, childhood (8-18 yrs, mean age=15 yrs)
body weight was a significant predictor of adult coronary artery calcification (Odds ratio
= 3.0). Systolic BP, diastolic BP, TC, and TG measured during childhood were not
significantly different (p>0.05) between young adults showing the presence and absence
of coronary artery calcification. Body weight, the BMI and triceps skinfold thickness
were larger (p<0.01) in males, but not females, during childhood among those who
displayed coronary calcification. Unfortunately, HDL, LDL, or apolipoprotein levels

were not screened during childhood in this sample.

The Bogalusa Heart Study and the Muscatine Study have provided valuable
information regarding the development and persistence of CVD risk factors from
childhood through adolescence into young adulthood. As these studies continue, they
will provide additional information on the long-term persistence of CVD risk factors into

mid- and late-adulthood.

I b. History of the Metabolic Syndrome

The origins of what we now call the metabolic syndrome were first described in
the 19205 by Kylin, as a condition involving hypertension, hyperglycemia, and
hyperuricemia [39]. In the 19405, Jean Vague, a pioneer in abdominal obesity research,
described the terms “android” and “gynoid” obesity and how the distribution of body fat
affects several health problems [40]. Vague particularly found that android obesity was
prominent among patients with diabetes and CVD. In 1988, Gerald Reaven described the
role of insulin resistance in human disease in the Banting Lecture at the American
Diabetes Association conference [41]. In his lecture Reaven described the clustering of
traits as “Syndrome X” and the clinical importance of how this clustering of metabolic
abnormalities was linked to insulin resistance. Important to note in Reaven’s lecture was
the lack of obesity as a central factor in “Syndrome X” due to his argument that there
were a number of non-obese diabetic individuals. Several other names (e. g. Insulin
Resistance Syndrome, The Deadly Quartet, etc.) for this condition arose thereafter, but
the term “metabolic syndrome” still remains today and is now accepted worldwide as the
description of the constellation of metabolic abnormalities that relate to CVD and type II

diabetes [42, 43].

10

1c. Definitions of the metabolic syndrome

As the clinical importance of the metabolic syndrome continued to grow during
the end of the 20th century, health agencies and expert panels attempted to determine a
unifying definition for the metabolic syndrome. Although there is a general consensus as
to the components of the metabolic syndrome, the clinical cutpoints vary among
definitions. The three most well known definitions were produced by the World Health
Organization (WHO), International Diabetes Federation (IDF), and the National
Cholesterol Education Program — Third Adult Treatment Panel (NCEP ATP III) [4, 42-
43]. Table 1 summarizes the definitions from the organizations, and a brief description
follows.

1c]. WHO Definition

The WHO definition was created in 1999 with an emphasis of insulin resistance
being the major underlying factor contributing to the metabolic syndrome [42]. Insulin
resistance was considered as the diagnosis of diabetes mellitus, impaired glucose
tolerance (IGT), or impaired fasting glucose. Besides insulin resistance, two of four
additional risk factors (hypertension, obesity, raised triglycerides, or low HDL-C) are
required for diagnosis.

Ic2. NCEP A TP 111 Definition

Unlike the WHO definition, the ATP III definition did not revolve around the
presence of a single underlying risk factor, but the presence of 3 of the following 5 risk
factors: abdominal obesity, elevated triglycerides, reduced HDL-C, elevated blood
pressure, and elevated fasting glucose [4]. Despite elevated fasting glucose being a

contributing risk factor to having metabolic syndrome, the ATP III definition did not

11

necessarily require the presence of insulin resistance to be diagnosed with metabolic
syndrome.

1C3. IDF Definition

In 2004 the IDF convened a workshop to establish a diagnostic definition of the
metabolic syndrome to be used in clinical practices [43]. The focus of the IDF was much
like the ATP III focus on abdominal obesity rather than insulin resistance. However,
what distinguished the IDF definition from the ATP III definition was that central obesity
needed to be present as the underlying risk factor for metabolic syndrome. Unlike the
ATP III definition that used a set of sex-specific cutpoint for waist circumference to
determine central obesity, the IDF definition uses sex- and —ethnic-group specific cut-
points. Metabolic syndrome diagnosis required the presence of central obesity and two
additional factors: elevated triglycerides, reduced HDL-C, raised blood pressure, and

raised fasting plasma glucose.

12

Table 1. Definitions of the metabolic syndrome.

 

 

 

 

 

 

 

 

 

WHO NCEP ATP III IDF
Glucose intolerance,
IGT, or diabetes 3 of 5 clinically Central obesrty
. . . and/or Insulin . . . together With two or
Underlying Criteria . identifiable risk
reSIStance together . more of the
. factors listed below: .
With two or more of followrng:
the following:
Males: Waist to hip cgcefgfgjdctifilh
ratio > 0.90 Males: WC 3 102 . . .
. FemaleS' Waist to cm ethmcrty spec1fic
Central Obesrty . .' values (if BMI >30
hip ratio > 0.85 Females: WC 3 88 2
kg/m then central
and/or 2 cm obesity can be
BMI > 30 kg/m
assumed)
> 150 mg/dL
Elevated or
Tri l cerides Z 150 mg/dL specific treatment
g y TO 3 150 mg/dL for this lipid
and/or abnormality
Reduced HDL: Males: <40 mg/dL
. . Males - 535 mg/dL Females: <50
LOY.ngh Densrty Females - 539 Males: < 40 mg/dL mg/dL
ipoprotein
mg/dL Females: <50 or
Cholesterol .

(HDL- C) mg/dL specrfic treatment
for this lipid
abnormality

SBP > 130 mmHg
or
DBP > 85 mmHg
Elevated Blood or
Pressure 3 140/90 mmHg 2 130/85 mmHg treatrnen t of
previously
diagnosed
hypertension
> 100 mg/dL
. or
Elevated Fasting N/A 3 100 mg/dL previously
Plasma Glucose .

. diagnosed Type II

diabetes

 

 

 

 

Most recently in 2009, a joint scientific statement was released by the IDF,

National Heart, Lung, and Blood Institute (NHLBI), AHA, World Heart Federation,

13

 

lntemational Atherosclerosis Society, and lntemational Association for the Study of
Obesity on harmonizing the criteria used by individual health organizations to define the
metabolic syndrome [44]. Organization members agreed that waist circumference would
be used to determine central obesity, but that cutpoints would not be set for waist
circumference due to variance in measures between gender and ethnicities along with
organizations that still favor national or regional waist circumference cut points.
However, members agreed upon cutpoints for triglycerides, HDL-C, blood pressure, and
fasting glucose. Table 2 summarizes the definition/criteria from this joint statement.

Table 2. Criteria for clinical diagnosis of the metabolic syndrome.

 

 

 

 

 

 

 

Elevated Waist Circumference* Pop ulation- and country-specific
definitions
Elevated Triglycerides (drug treatment for
elevated triglycerides is an alternate _>_ 150 mg/dL (1.7 mmol/L)
indicatorfl)
Low HDL-C (drug treatment for reduced Males: <40 mg/dL (1.0 mmol/L)
HDL-C is an alternate indicator) Females: <50 mg/dL (1.3 mmol/L)
Elevated blood pressure (antihypertensive Systolic: _>_ 130 mmHg
drug treatment in a patient with a history of and/or
hypertension is an alternate indicatorfl) 285 mmHg
Elevated fasting glucose£ (dru treatment
of elevated glucose is an alternafe indicator) 2 100 mg/dL

 

* It is recommended that the IDF cut points be used for non-Europeans and either the IDF
or AHA/NHLBI cut points used for people of European origin until more data are
available.

1] The most commonly used drugs for elevated triglycerides and reduced HDL—C are
fibrates and nicotinic acid. A patient taking 1 of these drugs can be presumed to have
high triglycerides and low HDL-C. High dose w-3 fatty acids presumes high
triglycerides.

£ Most patients with type 2 diabetes mellitus will have the metabolic syndrome by the
proposed criteria.

Id. The Metabolic Syndrome in Children
With increasing rates of obesity among childhood [1], the cardiovascular and

metabolic complications caused by obesity have led to interest in the metabolic syndrome

14

 

 

of children and adolescents. Several studies have emphasized the importance of the
metabolic syndrome in children and the consequences that metabolic syndrome has on
adult health Status [45-46]. However, the prevalence of metabolic syndrome in children
and adolescents varies between studies due to the lack of a universal definition and thus
makes it difficult to draw conclusions. Several large population studies such as
NHANES, the Bogalusa Heart Study, and the Young Finns study have determined the

prevalence of metabolic syndrome in children [4, 16, 47]. The prevalence of metabolic

 

syndrome has been found to be approximately 10% as compared to approximately 35%
in adults (according to the NCEP ATP III criteria) [5]. In a study of 261 black
preadolescent females and 240 ethnically-diverse preadolescent females, the prevalence
of metabolic syndrome ranged from 0.4% to 24.6% when applying different criteria [48].

As indicated above, comparing results across studies has been difficult due to the
lack of a universal definition of the metabolic syndrome in both children and adults.
Several definitions have been proposed for children and adolescents [44]. 'Additional
studies are needed to establish a universal definition of the metabolic syndrome in
children. Once a universal definition is established, future research is warranted towards
examining the genetic and lifestyle influences that affect the metabolic disorders in
children that lead to an increase in the risk of type II diabetes and CVD.
1 f. Continuous C VD risk factor/metabolic syndrome score:

In light of the lack of a universal definition for the metabolic syndrome in
children, several researchers have devised the concept of using a continuous score that is
representative of a composite CVD risk factor profile (i.e. the metabolic syndrome score)

[7, 9, 49]. Risk factors used in calculating a continuous score have varied both in terms

15

 

of inclusion and assessment method (i.e. to include a measure of glucose or insulin or
not; skinfolds, BMI, or waist circumference as the measure of obesity). Several authors
have recommended using variables that are consistent with those used in the adult criteria
for determining metabolic syndrome [45, 50]. Of those who have used or have developed
a continuous score, Eisenmann [50] is the only one to use criteria that match with the
adult criteria (waist circumference, HDL-C, triglycerides, mean arterial pressure,
indicator of abnormal glucose metabolism). Statistical procedures to create the score
involve standardizing the risk factors/variables chosen and regressing them onto
demographic variables such as age, sex, and ethnicity. The standardized z-scores are then
summed and derived as the continuous metabolic syndrome score with a higher score
representative of an unhealthier metabolic profile. The advantage of using a continuous
score is that it can detect associations of diseases/conditions with low prevalence rates in
small sample sizes [50].
.2. Lifestyle/environmental influences (PA, diet, screen time, family environment),
and CVD risk factors/metabolic syndrome in children
2a. Physical Activity and Metabolic Syndrome in Children

Several authors have reviewed the role of physical activity on CVD risk factors
and the metabolic syndrome in children [49, 51-56]. In general physical activity is
inversely related to metabolic cardiovascular disease risk. For example, results from the
European Youth Heart Study show a graded inverse association between physical activity
measured via accelerometry and the clustering of metabolic risk factors [49]. When
participants were grouped into quintiles based on physical activity counts, those in the

most active quintile had less metabolic risk than those of the other quintiles. This study

16

was unique in that the clustering of risk factors was derived as a composite risk factor
score (i.e. continuous score). Also from the European Youth Heart Study, Brage et al.
examined the association between physical activity and a clustering of metabolic risk
factors also using a composite risk factor score in Danish children [55]. Metabolic risk
was found to be inversely related to physical activity measure via accelerometry (p=
0.008). Butte et. al [52] showed metabolic risk factors were negatively associated with
total physical activity as well as the frequency of bouts of moderate to vigorous intensity
levels of physical activity. However, Eisenmann states that the inverse association is
weak to moderate at best [56], inferring that physical activity is just one of a multitude of
influences on CVD risk factors

Overall, there is a great deal of importance in increasing physical activity levels to
prevent the clustering of metabolic risk in children and to decrease any future burden of
risk in adulthood. However, population studies examining the relationship between
physical activity and clustered metabolic cardiovascular disease risk are limited due to
the use of imprecise methods to measure physical activity in children and the lack of an
absolute definition for the metabolic syndrome including the use of a continuous score.
Future research using more precise, objectively measured physical activity and a
universal definition for metabolic syndrome in youth is needed to further understand the
influence of physical activity on metabolic syndrome and CVD risk factors in children.
2b. Dietary Behavior and C VD Risk F actors/Metabolic Syndrome in Children

Compared to studies of physical activity, fewer studies have examined the

association between dietary behaviors and metabolic syndrome in children. In most

17

cases, the focus has been upon the effects of dietary intervention on metabolic
syndrome/insulin sensitivity [10, 57-58].

ATP 111 recommendations for dietary modification in patients with metabolic
syndrome include low intake of saturated fats and cholesterol, reduced consumption of
simple carbohydrates, and increased intakes of fruits, vegetables, and whole grains (i.e.
low fat, nutrient-dense diet) [10]. In a study by Chen et al. [57], children with the

metabolic syndrome who underwent a 2-week intensive clinical intervention comprised

 

of consuming a high-fiber, low-fat diet coupled with daily aerobic exercise reversed to
not having the metabolic syndrome despite still being obese. The low energy dense,
high-fiber diet showed that dietary modification aids in decreasing the prevalence of
metabolic syndrome in children. Riccardi and Rivellese [5 8] found similar findings and
concluded that the optimal diet for treatment of the metabolic syndrome was one low in
saturated fat and high in fibrous/low-glycemic foods.

Overall, the findings of the aforementioned papers are similar to studies that have
highlighted the beneficial effects of dietary modification on metabolic factors [59-62]
Two of the studies highlighting the benefits from dietary modification focused on the
effects of the Mediterranean diet [62-63]. The Mediterranean diet is one that consists of
high consumption of fruits and vegetables as well as healthy grains. A staple of the
Mediterranean diet is the consumption of large quantities of olive oil and fats in general;
however, the ratio of monounsaturated fats to saturated fats is relatively high. The
ATTICA study showed that Greek adults who had devoted themselves to the
Mediterranean diet had 20% lower odds of having the metabolic syndrome regardless of

age, sex, physical activity, lipids, and blood pressure levels [63].

18

In conclusion, despite the lack of evidence associating dietary behaviors with
metabolic syndrome in children, it is clear that a diet that does not promote excessive
weight gain, dyslipidemia, or insulin resistance represents the dietary lifestyle for both
prevention and treatment of the metabolic syndrome. This diet mainly consists of a low
intake of saturated fat and cholesterol and a high intake of nutrient dense foods such as
fruits, vegetables, and whole grains. The development of the metabolic syndrome is
influenced greatly by the dietary lifestyle of individuals. r
2c. Screen Time and Obesity, C VD Risk F actors/Metabolic Syndrome in Children L-

Several studies have shown the positive association between television viewing
and obesity in children and adolescents [51, 64-65]. Using NHANES data between 1988-
1994, Crespo et al. found that the prevalence of obesity was lowest among children who
reported watching 1 hour or less of television per day while the prevalence of obesity was
highest among children who watched 4 or more hours of television per day [66].
However, television does not account for total screen time as many studies have left out
time spent using the computer and playing video games, which has been found to be
associated with adverse health problems [65]. Thus, it is important to examine not only
the effects of television viewing on obesity, but also computer and video game time as
well.

The American Academy of Pediatrics has established guidelines for screen time
(including television, computer, and video games) that recommend no more than 2
hours/day of screen time for children and adolescents [67]. However, these screen time
guidelines were developed with a focus mainly on obesity and not other outcomes such as

the metabolic syndrome. Only a few studies have examined the effects of television

19

viewing/screen time on metabolic syndrome in children and adolescents [67-68]. Mark
and Janssen found that screen time was positively associated with the metabolic
syndrome in a dose-response manner that was independent of physical activity [68].
However, Ekelund et. al found only a small association between television viewing and
clustered metabolic risk, but determined the significance was due mainly to the
association between television viewing and adiposity after controlling for percent body
fat [69].

In summary, the positive association between the amount of screen time and
obesity is significant, but low. Further studies need to define screen time as the
combined amount of television viewing, computer usage, and video game usage. Due to
the limited number of studies investigating the effects of screen time on metabolic risks
and CVD risk in children, it is suggested that decreasing time spent in sedentary activities
will lead to increased levels of physical activity that will in turn decrease children’s risk
of obtaining metabolic disorders.
2d. Family environment and Obesity/C VD Risk Factors in Children

One general area that has been studied in relation to pediatric overweight and
CVD risk development is the area of the family environments and behaviors. Many
studies have examined family factors such as parental feeding strategies, parental
restriction and reward, family functioning, and household food insecurity in relation to
pediatric overweight and CVD risk development in children [70-72]. In regards to
feeding strategies, the general conclusion is that although there are several feeding
strategies that may increase childhood overweight/obesity risk, there is limited evidence

supporting any consistent conclusions. In a study by Melgar-Quinonez [70] and Kaiser,

20

“cw—u: -

cross-sectional survey analysis in low-income Mexican-American families showed that
biological and socioeconomic factors were more associated with overweight than self-
reported child feeding strategies. However, in a study that assessed the association of
child-feeding practices and child adiposity, 15% of the variance in fat mass was
explained by two practices, pressure to eat and concern for child’s weight [71]. This
study showed that child—feeding practices explained more variance in fat mass than

biological markers such as energy intake.

A clearer area of the family environment’s influence has been parental restriction
foods and its influence on overweight/obesity risk. Several studies have shown that
restriction by parents actually may lead to children to desire forbidden foods [73-74].
One such study by Fisher and Birch [73] evaluated the effects of maternal restriction of
snack foods on children’s intake of the snack foods when made available. In girls, child
and maternal reports of restricting practices predicted snack food intake and higher levels
of restriction predicted higher levels of snack food consumption. However, a majority of
this research supporting this association has been conducted in non-Hispanic, white girls
thus the conclusions may only apply to this population and not others.

Since the evaluation and assessment of family structures and behaviors is
complex due to the variety of constructs involved in one individual family to another, the
determination that there are family influences on pediatric overweight is difficult to
interpret. With that said, is is suggested that parents work to maintain a positive home
and family environment where parental control is balanced with child autonomy.

Management strategies in relation to diet and physical activity should support the

21

development of child psychological and behavioral traits and support a positively
emotional parent-child relationship.
3. The Family Nutrition and Physical Activity Screening Tool (FNPA)
3a. Development of the Family Nutrition and Physical Activity (FNPA) Screening Tool
The FNPA was developed at Iowa State University by Ihmels and colleagues [15]
to assess family environment and behavioral factors that may be associated with
children’s risk of becoming overweight. The basis of the FNPA stems from an Evidence
Analysis project supported by the American Dietetic Association (ADA). The ADA
Evidence Analysis on Childhood Overweight is an ongoing project to determine and
grade the strength of scientific evidence relating physical activity and dietary behaviors
with the risk of becoming overweight and obese. The ADA Evidence Analysis identified
ten primary factors (Table 3) that were positively associated with becoming overweight
and obese; however, twenty-one questions were created for the survey to better assess the
constructs of the FNPA. The questions are coded on a 2, 3, or 4 point Likert scale. The
total scores for each construct are summed to provide an overall summary score. A
higher FNPA summary score is indicative of a favorable, healthy family environment
while a low FNPA summary score is indicative of a family environment that is high risk

for children becoming overweight.

22

rec—'1

Table 3. 10 domains used to create the FNPA. Adopted from Ihmels et. al (2009) [16]

 

Domain

Example of question from the FNPA

 

Breakfast and family meal

Does the child eat breakfast and does the
family eat a meal together?

 

Modeling of nutrition

Does the family watch TV while eating and
do they eat fast food during the week?

 

Nutrient dense foods

Does the family eat prepackaged food or do
they use fresh foods and fruits and
vegetables?

 

High calorie beverages

Does the family drink soda and Kool-Aid
or 100% fruit juices and low fat milk?

 

Restriction and reward

Does the family use food as a reward and
do they restrict unhealthy foods?

 

Parent modeling physical activity

Do the parents participate in physical
activity and does the family participate or
play together?

 

Child’s physical activity

Does the child participate in physical
activity and organized sports?

 

Screen time

How many hours of screen time does the
child get?

 

TV in bedroom

Does the child have a TV in his bedroom
and do the parents monitor the screen time?

 

 

Sleep and schedule

 

How many hours does the child sleep and
is there a bedtime routine?

 

 

3b. Validation of the FNPA

The FNPA was evaluated in an urban midwestern (Des Moines, IA) school

district by examining the association between FNPA scores and BMI of 854 first grade

children. The sample of first grade children were predominantly Caucasian (57.5%),

with smaller percentages of African-Americans (15.3%), Hispanics (16.9%), Asians

(5.6%), and other minorities (4.7%). The targeted schools varied in socioeconomic status

(SES) with 5 schools in high SES (fewer than 33% of the students eligible for free or

reduced lunch prices), 17 schools of middle SES status (33% to 66% of the students

' eligible for free or reduced lunch prices), and 15 schools of low SES (66% of more of the

students eligible for free or reduced lunches). Differences in FNPA scores were also

firm' “A! Li)?
H

 

analyzed across different socio-economic and ethnic populations. School nurses
measured body mass and stature to determine BMI. BMI percentiles were then computed
using the CDC SAS growth chart programs. BMI percentile was used to categorize the
children as normal weight, at risk for overweight, and overweight (terminology used at
that time). FNPA surveys were sent home to parents to be filled out and returned.

Results revealed that lower income families reported lower FNPA scores
compared to higher income families. Factor analysis Showed positive correlation
between the constructs and child BMI; however, there were greater correlations shown '
between total F NPA score and child BMI. The average pairwise correlation between the
ten constructs of the FNPA was relatively low (1:024, range, 0.07 to 0.66) signifying
relation between the constructs yet a degree of independency between one another. The
positive correlations between constructs represented the tendency of behaviors to group
together in the home environment. Logistic regression was used to evaluate the construct
validity of the FNPA by dividing the FNPA summary scores of the participants into
tertiles to test for differences overweight. In the high risk group (i.e. low FNPA summary
score) the FNPA significantly predicted BMI (p = 0.026). Children who had summary
scores in the lowest tertile had an odds ratio of 1.7 (95% CI = 1.07 - 2.80) compared to
children who had summary scores in the highest tertile; however, when accounting for
parental BMI as a covariate, this association was reduced and no longer significant
(indicating that parental BMI needs to be considered when examining the effectiveness of
the FNPA).

The FNPA is the first instrument that combines information from a variety of

behaviors (e. g. diet, physical activity and inactivity, sleep patterns, family structure)

24

related to child obesity to evaluate family environments. The F NPA has potential for use
by pediatricians, school nurses, and other health professionals for quickly assessing a
child’s family and home environment and his/her risk for becoming overweight.
However, additional research using the FNPA is warranted to validate its usage in
different settings and populations and other health outcomes besides overweight/obesity
(e. g. CVD risk factors, metabolic syndrome)

4. Summary and Conclusions

The clustering of obesity and CVD risk factors, now terms the metabolic
syndrome, is a complex, multifactoral phenotype which is not only affecting adults, but
children and adolescents as well. Lifestyle determinants that influence obesity, CVD risk
factors, and the metabolic syndrome include physical activity behaviors, diet, and screen
time and others.

The associations between these influential lifestyle determinants and CVD risk
have been well studied. Physical activity, especially moderate to vigorous intensity
activity, is inversely related to CVD risk factors in youth. Diets that are high in fat
consumption and low in whole grain, fruit, and vegetable consumption lead to the
promotion of obesity, hypertension, dyslipidemia, and/or insulin resistance. Screen time
is positively associated with children’s risk of developing the metabolic syndrome due to
the possible effects of decreasing time spent in physical activity, disrupting healthy sleep
patterns, and inducing children’s consumption of unhealthy, low density energy foods.

In order to assess these lifestyle and environmental factors that influence
children’s risk of obesity and adverse CVD and risk factors, a screening tool like the

F NPA may prove useful in identifying risk. However, the FNPA has only been validated

25

in predicting risk of overweight and not other adverse health conditions like CVD or the
metabolic syndrome. Future research is warranted to further examine the utility of the

FNPA.

26

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

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31

CHAPTER 3
MANUSCRIPT
Introduction

Childhood obesity is a major public health concern given the number of youth it
affects and its consequences. Currently, 18% of US. youth are obese and another 17%
are overweight [1-3] . Among the immediate health concerns of childhood obesity is the
development of adverse cardiovascular disease (CVD) risk factors (e. g. dyslipidemia,
hyperglycemia, and hypertension) [4]. Besides the immediate consequences, CVD risk
factors track moderately well from childhood and adolescence into adulthood [5-8] and
CVD remains the leading cause of death in the US. Thus, there is considerable interest
in the prevention of obesity and CVD risk factors beginning in childhood [9].

It is well established that diet and physical inactivity are the two determinants of
obesity and CVD risk factors [9, 10]. Among children, the family environment plays an
important role in determining dietary and physical activity behaviors [11, 12]as they can
be influenced directly by parents [11] and/or indirectly through the construct of the
environment provided to them [13]. However, from a methodological standpoint it has
been difficult to capture the shared family and child environment. Recently, the Family
Nutrition and Physical Activity (FNPA) Screening Tool, which assesses family
environmental and behavioral factors that influence children’s risk for becoming
overweight,was developed [14] and evaluated for its predictive validity of assessing risk
for becoming overweight [15]. Results showed that the FNPA was significantly
associated with the prevalence of overweight at baseline [14] and 1 year change in BMI

after accounting for baseline BMI, parent BMI, and other demographic variables [15].

32

The F NPA is the first instrument that combines information from a variety of behaviors
(e. g. diet, physical activity and inactivity, sleep patterns, family structure) related to child
obesity to evaluate family environments, and has potential for use by pediatrician, school
nurses, and other health professionals for assessing a child’s family and home
environment and his/her risk for becoming overweight. However, additional research
using the FNPA is warranted to examine its utility in different settings and populations,
and if it has predictive validity for other health outcomes besides overweight/obesity (e. g.
CVD, metabolic syndrome, type II diabetes). Therefore, the primary purpose of this
study was to examine the association of the FNPA screening tool with CVD risk factors
in 10 year old children. The secondary purpose of this study was to examine the FNPA
by weight classification of children based on BMI percentile.
Methods
Participants.

The participants were fifth grade children that were part of the (S)Partners for
Heart Health intervention, a CVD risk factor prevention and management program [1 6].
Participants were enrolled in one of two waves of data collection (F all 2008 or Fall
2009). Since data collection occurred prior to the intervention, these data are cross-
sectional. A total of 357 surveys (185 from Fall 2008; 172 from Fall 2009) were sent
home to parents of the participants. A total of 171 surveys were returned (return rate of
49%). Six surveys were removed because one or more questions were not answered or
answered incorrectly. Of the remaining 165 surveys returned, 46 participants were
removed from data analysis due to lack of a key measure (35 declined fingerstick, 8

malfunctioned lipid assay, 2 declined waist circumference, I declined blood pressure).

33

Thus, the final sample size was 119 (60 girls, 59 boys). Participants were primarily
Caucasian (>90%). Informed consent and assent were obtained from the participants
and the study protocol was approved by the Michigan State University Institutional
Review Board.

Anthropometry.

Height and body mass were measured according to standard procedures [17].
Height was measured to the nearest 0.1 cm using a portable stadiometer (Shorr Board
stadiometer; Irwin Shorr, Olney, MD). Body mass, measured to the nearest 0.1 kg, and
percent body fat were measured using a foot-to-foot bioelectric impedance device
(Innerscan Body Composition Monitor BC-534, Tanita Corporation, Tokyo, Japan).
Waist circumference was measured using a Gullick anthropometric tape with the anterior
superior iliac spine to the nearest 0.1 cm. Body mass index (BMI) was calculated using
the following equation: body mass in kg/height in m2. Percentiles for height, body mass,
and BMI were determined using SAS growth chart programs available on-line from the
Center for Disease Control (http://www.cdc.gov/growthcharts/).

Blood lipids.

A non-fasting blood sample was obtained by fingerprick and collected in 35
microliter heparinized capillary tubes. The sample was immediately analyzed for total
cholesterol (TC) and high-density lipoprotein-cholesterol (HDL) within 5 minutes by a
portable analyzer according to the protocol of the manufacturer (Cholestech LDX
System, Hayward, CA). Prior to and following daily data collection, the analyzer was

calibrated with standard controls provided by the manufacturer. Previous studies have

34

shown the Cholestech LDX System to be a valid and reliable tool to measure blood lipids
[18-19].
Blood pressure.

Resting blood pressure was assessed using a manual blood pressure device with
the appropriate sized cuff on the subject's upper right arm following standardized
procedures and recommendations [20]. Resting systolic (SBP) and diastolic (DBP) blood
pressures were measured by auscultation after the subject was seated for 5 minutes.

Mean arterial pressure (MAP) was calculated'as DBP + (SBP-DBP/3). Measurements
were taken in triplicate at 1-minute intervals. The mean of the three measurements was
used for data analysis.

Determination of continuous cardiovascular disease risk score

The continuous CVD risk scOre was derived by first standardizing the individual
CVD risk factors (waist circumference, MAP, and TC:HDL ratio) by regressing them
onto age and gender to account for age and gender- related differences in these CVD risk
factors (Z_WC, Z_MAP, and Z_TCzHDL). The standardized residuals (z scores) for the
individual variables were then summed to create the continuous CVD risk score. A higher
CVD risk score indicates a less favorable CVD risk profile.

Family Nutrition and Physical Activity Screening Tool.

The FNPA screening tool was developed by Ihmels and colleagues [14] through a
comprehensive Evidence Analyses supported by the American Dietetic Association
designed to determine the strength of evidence linking physical activity and diet
behaviors with overweight/obesity. The Evidence Analyses identified ten primary factors

(breakfast and family meals, modeling of nutrition, nutrient dense foods, high calorie

35

beverages, restriction and reward, parent modeling physical activity, child’s physical
activity, screen time, TV in the bedroom, sleep and routine schedule) that were positively
associated with becoming overweight and obese. For this study the revised version of the
original FNPA was used. The revised version consists of 10 questions assessing the ten
constructs mentioned above (http://adaf.eatright-fnpa.org/public/partner.cfm). The
revised version is considered more user-friendly and was developed as a result of the
initial study as well as from feedback provided by parental focus groups. The score
ranges from 10 to 30 with lower scores indicating an adverse, obesogenic family
environment.

Statistical analysis.

Descriptive statistics were calculated for all variables and sex differences were
examined by independent t-tests for continuous variables. The associations between the
FNPA score and the individual CVD risk factor residuals (Z_WC, Z_MAP, and
Z_TCHDL), and the continuous CVD risk score (Z_CVD were examined using Pearson
correlation coefficients. Differences in the CVD risk factors were also examined by
median split of the FNPA by independent t-tests. Differences in the FNPA score by
weight status (normal weight, overweight, and obese) were examined using analysis of
variance (ANOVA). Statistical analyses were conducted using SPSS version 17.0
(Chicago, IL).

Results

Prior to data analysis of the entire sample, differences between respondents and

non-respondents to the survey, and those with incomplete data were examined. There

were no significant differences in body mass, BMI, BMI percentile, TC, HDL, TC:HDL,

36

MAP, WC, or self-reported PA and fruit and vegetable consumption between subjects
who returned the FNPA and subjects who did not return it. Subjects that did not return
the FNPA had a significantly higher amount of total screen time compared to those who
did return the FNPA (4.1 hours vs. 3.0 hours, respectively). There were no significant
differences in body mass, BMI, BMI percentile, TC, HDL, TC:HDL, MAP, WC, or self-
reported PA and fruit and vegetable consumption between the subjects removed from
data analysis (due to an incorrect/incomplete F NPA or from lacking a CVD risk factor
measure) and subjects who returned the F NPA.

Table 4 provides the descriptive characteristics of the sample. Mean values for
body size and composition variables were not significantly different between sexes
(p>0.05); however, body mass and BMI were slightly higher in males that resulted in a
significantly higher mean BMI percentile and percent of males classified as combined
overweight and obese (p<0.05). There were no significant sex differences in CVD risk
factors or the F NPA score. The percent of subjects classified as overweight or obese was
32.0%, and the percent of subjects with adverse TC, HDL-C, TC:HDL-C, and BP were
25.2%, 28.6%, 39.5%, and 10.1%, respectively.

Pearson correlations between the FNPA score, the standardized residuals of
individual CVD risk factors and the continuous CVD risk score are shown in Table 5.
The correlations between the FNPA score and Z_TCHDL, Z_WC, and Z_MAP were
0.014, -0.355, and -0. 123, respectively, with the correlation between the FNPA score and
Z_WC found to be significant (p<0.05). The correlations between the FNPA score and
other indicators of adiposity — BMI, BMI percentile, and percent body fat (-0.425, -O.377,

and -0.430, respectively) — were significant (p<0.05) and of similar magnitude as found

37

with WC. When controlling for Z_WC, the correlations between the FNPA score and
Z_TCHDL and Z_MAP were 0.146 and -0.006, respectively.

To further explore the association between the FNPA and CVD. risk factors, the
sample was split based on the median FNPA score (325 and >25) (Table 6). The
prevalence of overweight and obese and the mean values for BMI, and percent body fat
were significantly higher in children with a F NPA score _<_25 when compared to those
with a FNPA score >25 (p<0.05). The continuous CVD risk score was not significantly
different between these two groups. The mean FNPA score by weight classification is
shown in table 7. Overweight and obese children had a significantly lower mean FNPA
score when compared to normal weight children (p<0.05).

Discussion

This study shows the association between the FNPA screening tool for assessing
modifiable home environments and behaviors and CVD risk factors in children. The
FNPA score explained about 5% of the total variance in the continuous CVD risk factor
score of 10-year old children. The results were stronger for adiposity measures alone.

This study is the first to report the use of the FNPA since the original
investigations by Ihmels and colleagues [14, 15], which included cross-sectional and 1-
year follow-up of first grade students from Des Moines, IA, USA and examined BMI as
the outcome. Compared to the sample in the original investigators, our sample size was
considerably smaller and older (119 fifth graders vs. 854 first graders). The magnitude of
the correlation found between the FNPA score and child BMI was higher in our study (-
0.425 vs. -0.173). When comparing children’s FNPA score by median split (525 or >25),

the prevalence of overweight and obese was greater in children with a score 525

38

compared to children with a score of >25 (43.1% vs. 14.9%). We also found that
overweight and obese children had a significantly lower FNPA score compared to normal
weight children. Likewise, Ihmels et al. [14] found that children who had a FNPA score
in the lowest tertile had an increased odds of overweight (1.7, 95% CI = 1.07 — 2.80)
compared to children who had a FNPA score in the highest tertile. On the other hand, the
FNPA was not significantly related to TC:HDL or MAP in the current study; therefore,
the association with the continuous CVD risk score is probably due to WC as it was
significantly correlated with the other CVD risk factors included in the CVD risk score.
Several studies have examined the association between the individual
lifestyle factors included in the FNPA (i.e. physical activity, diet, screen time, etc.) and
CVD risk factors [21-24]. For example, results from the European Youth Heart Study
[21] showed an inverse correlation (r=-0. 18) between moderate-to-Vigorous physical
activity and a continuous CVD risk score. In a study examining the influence of physical
activity and total screen time on childhood overweight [22] the correlations between
physical activity and total screen time with BMI were -0.25 and 0.22, respectively. A
study by Frank et al. [23] investigating the association between diet and CVD risk factors
in 10-year old children found low correlations [range: -0.19 to 0.18] between dietary
components (protein, fat, carbohydrate, sodium, cholesterol, etc.) and individual CVD
risk factors (total cholesterol, systolic blood pressure, skinfolds, etc.). Laurson et al. [24]
have also investigated behavioral lifestyle factors using a composite score and its
association with BMI, with results showing low correlations (r <0.20) between individual
behavioral factors (PA, screen time, dietary variables, family eating) and BMI as well as

the composite behavioral score and BMI (r=—0.019). Thus, our results are similar to the

39

general consensus that behavioral lifestyle factors show low correlations (r<0.30) with
adipoSity and CVD risk factors in children.

Since, the FNPA score explained only 5% of the total variance in CVD risk score,
it is important to consider other factors not measured in this study (e. g. genetics, race, and
stress) that influence CVD risk factors. Several review papers [25, 26] have outlined the
importance of genetic polymorphisms in the pathogenesis of CVD. Using data from the
Framingham Heart Study, McQueen et al. [27] found the heritability of a composite
metabolic score to be 0.61. Additionally, the general x environmental interaction on
CVD risk factors is a topic of great interest [28]. Future studies should examine the
combined influence of genetic factors and shared behavioral environment factors
determined by the F NPA on CVD risk factors in children.

Although this study showed the association between the F NPA screening tool and
CVD risk factors in children, WC appears to driving the association in terms of the
continuous CVD risk score. The only significant association between the CVD risk
factors and the FNPA score was with WC (r=-0.355). Also, when examining the
associations amongst the selected CVD risk factors, both TC:HDL and MAP were found
to be significantly correlated with WC. These results along with those of Ihmels et. al
suggest that the utility of the FNPA is best suited for predicting a child’s risk for
overweight/obesity. However, since obesity is closely related to CVD risk factors in
youth [29], the F NPA screening tool can be used to identify CVD risk by determining
risk for overweight. Additional work is needed to further refine and improve the FNPA

in both clinical and research settings.

40

A limitation of this study was the small sample size. Another limitation of the
study was the cross sectional design. Similar to the study by Ihmels et al. [15] that used
the FNPA to predict change in BMI over 1 year, future studies should use the F NPA to
predict change in CVD risk factors using a longitudinal design. Despite the validation of
the FNPA screening tool, no studies have yet shown the reliability of the FNPA. Future
studies are in development to address this issue. FNPA scores in this study ranged from
18-29 with more than half of the scores >25. Given the distribution of scores on the
higher end, the evaluation of higher risk environments (i.e. a higher percentage of FNPA
scores ranging from 10-18) was not possible. In comparison, the only other usage of the
revised version of the FNPA had scores ranging from 11-30 with a mean FNPA score of
16 in a sample of obese children seeking treatment [30]. Some FNPA surveys were
returned shortly after the start of the intervention; however, no differences in body size,
CVD risk factors, FNPA score, or self-reported physical activity, nutrition, and screen
time were found when comparing children who returned surveys after the start of the
intervention to those who returned surveys before the start of the intervention. A
limitation with the continuous CVD risk score used in this study was that it was created
using only 3 risk factors (TC:HDL, WC, and MAP) and did not include a measure of
insulin resistance. With the use of a continuous CVD risk score, it should be noted that
the score itself is sample specific and thus any comparison with another study cannot be
made unless demographic variables are similar. Another limitation of this study was the
low prevalent rates of CVD risk factors as indicated by the small percentage of children

with adverse CVD risk factors.

41

In conclusion, the role of the family environment on CVD risk factors in children
is important to consider due to the influence that the family environment has on
children’s behaviors (e. g. physical activity and dietary behaviors, sedentary time) related
to CVD risk. The FNPA screening tool has the potential to identify children that may be
at risk for overweight and the development of CVD risk factors. For clinical and health
professionals, the FNPA screening tool can be used for a quick assessment of a child’s
family environment and behaviors and their risk for becoming overweight and
developing adverse CVD risk factors. Interventions designed to modify the family home
environment can use the FNPA to determine what changes to implement to improve
obesity and CVD risk factors in children. Future research is needed to further examine
the utility of the FNPA screening tool in other populations and in longitudinal studies
examining both child overweight and CVD risk.

ACKNOWLEDGEMENTS

This project was funded through grants from the Blue Cross Blue Shield of
Michigan Foundation and MSU Families and Communities Together Coalition (FACT)
as well as from the MSU Food, Nutrition, and Chronic Disease Graduate Student

Professional Development Fellowship Fund.

42

10.

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45

@3213.

Table 4. Descriptive characteristics for males, females, and the total sample.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Males (n=59) Females (n=60) Total (N=119)
Age (yrs) 10.5 (0.4) 10.5 (0.4) $12: ($4;
Height (cm) 143.9 (6.9) 143.1 (6.4) 1241361866638
Weight (kg) 42.1 (11.5) 39.0 (9.8) :23 £1366;
BMI (kg/m2) 20.0 (4.1) 18.9 (3.7) 113954329 )1
BMI Percentile 71.1 (26.0)* 59.3 (29.1) 62403_(29%::)
%Overweight (%) 16.0% 23.2% 20.2%
%Obese (%) 20.0% 5.8% 1 1.8%
WC (cm) 68.5 (12.6) 65.4 (11.1) 5%6i7f1111'fl3
Percent Body Fat 22.4 (7.9) 23.3 (7.5) 5299}ng
TC (mg/dL) 155.5 (27.5) 152.1 (23.6) 13669223)
%Adverse - TC 30% 21.7% 25.2%
HDL-C (mg/dL) 48.0 (14.4) 47.8 (16.3) 43°19 fig?)
0 _
/° 33:? 28% 29% 28.6%
TC:HDL Ratio 3.6 (1.5) 3.4 (1.0) '1’: 9;;
%Adverse - o o o
TC:HDL Ratio 36" 4M 39'5 /"
SBP (mmHg) 102.7 (12.0) 102.0 (9.9) $3253 (11%?)
DBP (mmHg) 67.6 (8.9) 67.6 (8.0) 233%?
%Adverse — BP 12% 8.7% 10.1%
MAP (mmHg) 79.4 (8.9) 79.1 (8.0) 513902—(9; )3
Z_CVD -000 (2.6) -0.00 (1.8) 3209525)
FNPA Score 24.7 (2.5) 24.5 (2.5) 2413395)

 

 

 

 

 

Values are mean (SD) for boys and girls. Minimum and maximum (range) also shown
for the total sample. * P<0.05 for sex difference

%Adverse TC: 2200 mg/dL; %Adverse HDL-C: <40 mg/dL; %Adverse TC:HDL Ratio:
>3.5; %Adverse BP: :95 age-, sex-, and height-specific percentile for SBP or DBP

46

Table 5. Correlations between FNPA score, standardized residuals for TC:HDL ratio,
WC, MAP, and CVD risk score in 10 year old children.

 

 

 

 

 

 

 

 

 

 

 

 

F NPA Z_TCHDL Z_WC Z_MAP Z_CVD
Score
FNPA Score - 0.014 -0.355** -0.123 -0.216*
Z_TCHDL - - 0.325” 0.149 0.686M
Z_WC - - - 0.330“ 0.771**
Z_MAP - - - - 0.689“
Z_CVD - - - - -

 

** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)

47

 

Table 6. Differences in body size and CVD risk factors by median split of the F NPA
score (_<_25 and >25) in 10 year old children.

 

 

 

 

 

 

 

 

 

FNPA >25 FNPA g
Overweight (%) 10.6% 26.4%*
Obesity (%) 4.3% 16.7%*
Overweight and Obesity (%) 14.9% 43.1%*
BMI (kg/m2) 18.0 (2.7) 20.3 (4.3)*
Percent Body Fat 20.1 (5.7) 24.8 (8.3)
Z CVD -0.51 (1.9) 0.33 (2.3)

 

 

* Significanfdifference between groups (p<0.05)

48

 

Table 7. Differences in the FNPA by weight in 10 year old children.

 

 

 

 

 

 

Classification Percentage Of FNPA score
sample
Normal weight 68.0% 25.1 (2.2)
Overweight 20.2% 23.9 (2.5)*
Obese 11.8% 22.5 (2.7)*

 

 

 

* Significantly different from normal weight category (p<0.05).

49

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