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The prevalence and clustering of elevated levels of biomarkers
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Louise van Wyk

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THE PREVALENCE AND CLUSTERING OF ELEVATED LEVELS OF
BIOMARKERS RELATED TO METABOLIC SYNDROME IN BOYS AND GIRLS
12-19 YEARS OLD USING NHANES III, 1988-1994
By

Louise van Wyk

A THESIS

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of

MASTER OF SCIENCE
Department of Food Science and Human Nutrition

2004

ABSTRACT
THE PREVALENCE AND CLUSTERING OF ELEVATED LEVELS OF
BIOMARKERS RELATED TO MS IN CHILDREN AND ADOLESCENTS 12-19
YEARS OLD, NHANES 111 (1988-1994)
By

Louise van Wyk

Metabolic Syndrome (MS) has been estimated to affect 4% of adolescents in the
United States and is believed to be rising steadily due to the dramatic increase in
overweight in adolescents. Currently there has been inadequate information reported on
biomarkers associated with MS in children and adolescents, as well as a clear definition
to understand and define MS in children and adolescents. The first two objectives of our
study were to determine the distribution and prevalence of elevated levels Of biomarkers
associated with MS by age and by sexual maturation stage. The third Objective was to
examine the clustering of these biomarkers among boys and girls l2-l9 years old (using
factor analysis) and the association between demographic variables and identified clusters
of biomarkers. Analyses were conducted using data from NHANES 111 (1988-1994). Our
main findings were that the distribution and prevalence of elevated levels of biomarkers
differed by age and by Tanner stage within gender, depending on the biomarker under
study. Biomarkers that clustered together in boys and girls were waist circumference
levels, triglyceride levels, BMI-for-age percentiles and decreased HDL-cholesterol levels.
The factors obtained suggest that specific subsets of biomarkers associated with MS
occur in adolescents. In addition, these data provide the first insight into the clustering of

specific biomarkers related to MS in adolescents in a nationally representative sample.

ACKNOWLEDGMENTS

I think if I honestly reflect on who I am, how I got here, and what I have
accomplished, I discover a debt to others that I will never be able to repay. I would like to
thank my committee members for their significant contribution to a successful
completion of my Masters thesis. A very Special thanks goes out to Dr. Ellen Velie for
nurturing me every step of the way and for being such an amazing mentor throughout the
two years of my Masters thesis. I would also like to thank her for teaching me the science
behind writing and patience which helped me to achieve my goals. Furthermore I would
like to thank her for the Opportunity that She has given me to work with her in the field of
Nutritional Epidemiology. I will be forever grateful for the experience that I have
obtained from working with her everyday.

I would also like to thank Dr. Norman Hord for acting as my academic advisor,
and for opening doors for me Since the day I enrolled at Michigan State University. I am
thankful to Dr. Kate Claycombe who served on my committee and who Shared her
tremendous knowledge on how to approach the biomarkers related to Metabolic
Syndrome in my study.

I would also like to thank Dr. Leonard Bianchi for his statistical guidance that he
has provided throughout this project, and for spending time on weekends to help me
understand the principals of factor analysis and complex datasets. A very Special thanks
also goes out to Dr. Merlin Hamre for helping me to get familiar with the NHANES
dataset.

I especially want to thank Jacques for “everything” throughout these past two

years. For the motivation every day, and for believing that I was able to accomplish what
I thought I could not. Also for doing everything for me to make my life easier during this
time. I would like to thank my parents and sisters for believing in me as well and for
lending a Shoulder when times were hard. I would especially like to thank my parents
who gave me the opportunity to get the baseline education from which 1 was able to build
on.

And then to the most important of all — My Father in heaven — Almighty God,
without Your grace and blessings I would have never been able to achieve what I
achieved today. Thank you for the guidance and strength You gave me the past two years

— itjust made me realize again that with Your power we can move mountains.

iv

TABLE OF CONTENTS

List Of Tables ..................................................................................................................... ix
List of Figures .................................................................................................................. xiii

Chapter 1 Introduction

1.1 Overview ................................................................................................. l
1.2 Specific aims for this study ..................................................................... 4
1.3 Rationale for study aims .......................................................................... 5

Chapter 2 Background

2.1 Definition of Metabolic Syndrome .......................................................... 9
2.2 Pathogenesis and classification of Metabolic Syndrome ...................... 11
2.2.1 Insulin resistance and hyperinsulinemia ....................................... 14
2.2.2 Dyslipidemia ................................................................................ 16
2.2.3 Blood pressure .............................................................................. 20
2.2.4 C-reaction protein ......................................................................... 23
2.2.5 Impaired glucose tolerance ........................................................... 24
2.2.6 Body fat distribution and waist circumference ............................. 25
2.3 Prevalence of Metabolic Syndrome in adults ........................................ 27
2.4 Adolescence and puberty ....................................................................... 29
2.5 Pubertal development in girls and boys ................................................ 30
2.6 Age at menarche in girls ........................................................................ 32
2.7 Prevalence of Metabolic Syndrome in children .................................... 34

Chapter 3 Methodology

3.1 Study design and population ................................................................. 37
3.2 NHANES 111 design and data collection procedures ............................. 37

3.3 Measurement Of biomarkers associated with Metabolic
Syndrome ............................................................................................... 39
3.3.1 Anthropometric measurements ..................................................... 40
3.3.2 Biomarkers ................................................................................... 41
3.3.2.1 Lipids .............................................................................. 41
3.3.2.2 Serum glucose ................................................................. 42
3.3.2.3 C-reaction protein ........................................................... 43

3.3.2.4 Blood pressure and blood pressure

percentiles ....................................................................... 43

3.4 Covariates
3.4.1 Chronological age, sexual maturation assessment

and age at menarche ..................................................................... 45
3.4.2 Race/ethnicity ............................................................................... 46
3.4.3 Poverty income ratio (PIR) ........................................................... 47

Chapter 4

Chapter 5

3.4.4 Physical activity assessment (Adolescents 17-19

years Old) ...................................................................................... 47
3.4.5 Television viewing (Children 12-16 years old) ............................ 48
3.4.6 Smoking status ............................................................................. 48
3.5 Analytic sample and characteristics ...................................................... 50
3.6 Statistical analysis ................................................................................. 54
3.6.1 Statistical software ....................................................................... 54
3.6.2 NHANES III weighting methodology .......................................... 55
3.6.3 NHANES III variance estimation ................................................. 55
3.6.4 Statistical analysis for aim 1 ......................................................... 55
3.6.5 Statistical analysis for aim 2 ......................................................... 57
3.6.6 Statistical analysis for aim 3 ......................................................... 58
Results
4.1 Description ofNHANES 111 population based sample ......................... 60
4.2 Results for aim l .................................................................................... 61
4.2.1 Distributions of biomarkers by chronological age ....................... 61
4.2.2 Prevalence estimates of markers of Metabolic
Syndrome for boys and girls 12-19 years old by age ................... 68
4.2.3 Conclusion .................................................................................... 70
4.3 Results for aim 2 .................................................................................... 71
4.3.1 Distributions of biomarkers by sexual maturation
stage .............................................................................................. 72
4.3.2 Prevalence estimates of high biomarkers markers of Metabolic
Syndrome for boys and girls by sexual maturation Stage ............. 77
4.3.3 Conclusion .................................................................................... 79
4.4 Results for aim 3 .................................................................................... 80
4.4.1 Correlations .................................................................................. 81
4.4.2 Conclusion .................................................................................... 83

4.4.3 Factor loading matrix for biomarkers related to
Metabolic Syndrome in boys and girls by age and

tanner stage ................................................................................... 83
4.4.4 Demographic characteristics of factor loading
scores for boys and girls ............................................................... 85

Discussion and conclusions

5.1

Comparisons of findings to the literature ............................................ 159
5.1.] Distribution of biomarkers related to Metabolic

Syndrome by age ........................................................................ 160
5.1.2 Prevalence of elevated levels of biomarkers by age ................... 164
5.1.3Distribution of biomarkers related to Metabolic

Syndrome by tanner stage .......................................................... 165
5.1.4 Prevalence of elevated levels of biomarkers by

tanner stage ................................................................................. 167

Vi

5.1.5 Clustering of biomarkers related to Metabolic
Syndrome and the association with demographic

Characteristics ............................................................................ l 68
5.2 Conclusion ........................................................................................... 172
5.3 Strengths and limitations of data sources and methods ....................... 174
5.4 Implications of findings ....................................................................... 176
5.5 Suggestions for future research ........................................................... 177

vii

Chapter 2

Table 2.1
Table 2.2
Table 2.3
Table 2.4
Table 2.5

Chapter 3
Table 3.1

Table 3.2
Table 3.3
Table 3.4

Chapter 4
Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 4.5

Table 4.6

Table 4.7

Table 4.8

LIST OF TABLES

Diagnosis Of Metabolic Syndrome by World Health WHO ...................... 10
Diagnosis Of Metabolic Syndrome by NCEP ATP [11 ............................... 10
Prevalence of Metabolic Syndrome according to NCEP ATP III

Criteria among US Adults age 20 years and older ..................................... 27
Description of pubertal stage as defined by breast, genital and

pubic hair development .............................................................................. 30
Criteria for Metabolic Syndrome in children ............................................. 35

Comparison of clinical measurements between adolescents
(12-19 years old) who fasted for 6-9 hours and adolescents

who fasted for 9 hours or more: NHANES 111, 1988-1994 ....................... 45
Two-by-two table of smoking status and cotinine levels ........................... 49
Analytic Sample of adolescents (12-19 years) in NHANES III ................ 51

Select Characteristics of adolescents (12-19 year Old) in NHANES III
with missing values compared to adolescents with no missing values ...... 53

Select characteristics of total adolescent study population (12-19 years)
who fasted for 6 hours or more, by gender ................................................. 99
BMI-for-age percentiles among US adolescents (12-19 years Old)

by age, within gender, race/ethnicity and poverty index ratio:

NHANES 111, 1988-1994. ........................................................................ 100
Waist circumference (cm) distribution among US adolescents

(12-19 years old) by age, within gender, race/ethnicity and

poverty index ratio: NHANES III, 31988-1994. ....................................... 102
Triglyceride level (mg/dL) distribution among US adolescents

(12-19 years Old by age, within gender, race/ethnicity and poverty

index ratio: NHANES 111, 1988-1994 ...................................................... 104
Total cholesterol level (mg/dL) distribution among US adolescents

(12-19 years old) by age, within gender, race/ethnicity and poverty

index ratio: NHANES 111, 1988-1994 ...................................................... 106
I-IDL-cholesterol level (mg/dL) distribution among US adolescents

(12-19 years old) by age, within gender, race/ethnicity and poverty

index ratio: NHANES III, 1988-1994 ...................................................... 108
LDL-cholesterol level (mg/dL) distribution among US adolescents

(12-19 years old) by age, within gender, race/ethnicity and poverty

index ratio: NHANES III, 1988-1994 ...................................................... 110
Glucose level distribution (mg/dL) among US adolescents

(12-19 years Old) by age, within gender, race/ethnicity and

poverty index ratio: NHANES III, 1988-1994 ........................................ 112

viii

Table 4.9 Systolic blood pressure percentile§ distribution among US

adolescents (12-19 years old) by age, within gender, race/ethnicity

and poverty index ratio: NHANES 111, 1988-1994 .................................. 1 14
Table 4.10 Diastolic blood pressure percentile‘distribution among US

adolescents (12-19 years old) by age, within gender, race/ethnicity

and poverty index ratio: NHANES III, l988-1994 .................................. 116
Table 4.11 Prevalence estimates of markers of Metabolic Syndrome for boys

and girls (12-19 years old) by age group in the population:

NHANES III, l988-1994 ......................................................................... 118
Table 4.12 BMI-for-age percentiler distribution among US adolescents

(12-19 years old) by Tanner Stage, within gender, race/ethnicity

and poverty index ratio: NHANES [11, 1988-1994 .................................. 120
Table 4.13 Waist Circumference (cm) Distribution among US Adolescents

(12-19 years old) by Tanner stage within Gender, Race/ethnicity

and Poverty Index Ratio: NHANES 111, [988-1994 ................................ 122
Table 4.14 Triglyceride level (mg/dL) distribution among US adolescents

(12-19 years old) by Tanner stage, within gender, race/ethnicity and

poverty Index Ratio: NHANES 111, 1988-1994 ....................................... 124
Table 4.15 Total cholesterol level (mg/dL) distribution among US adolescents

(12-19 years old) by Tanner stage, within gender, race/ethnicity and

poverty index ratio: NHANES III, 1988-1994 ........................................ 126
Table 4.16 HDL-Cholesterol Level (mg/dL) distribution among US adolescents

(12-19 years old) by Tanner stage, within gender, race/ethnicity and

poverty index ratio: NHANES III, l988-1994 ........................................ 128
Table 4.17 LDL-cholesterol level (mg/dL) distribution among US adolescents

(12-19 years old) by Tanner stage, within gender, race/ethnicity and

poverty index ratio : NHANES III, 1988-1994 ....................................... 130
Table 4.18 Glucose level (mg/dL) distribution among US adolescents

(12-19 years old) by Tanner stage, within gender, race/ethnicity and

poverty index ratio : NHANES III, l988-1994 ....................................... 132
Table 4.19 Systolic blood pressure percentile distribution among US adolescents

(12-19 years Old) by Tanner stage, within gender, race/ethnicity and

poverty index ratio: NHANES III, l988-1994 ........................................ 134
Table 4.20 Diastolic blood pressure percentile distribution among US adolescents

(12-19 years old) by Tanner stage, within gender, race/ethnicity and

poverty index ratio: NHANES III, l988-1994 ........................................ 136
Table 4.21 Prevalence estimates of markers of Metabolic Syndrome for boys

(12-19 Years Old) by sexual maturation stage (assessed by pubic hair

assessment) in the population: NHANES 111, 1988-1994 ........................ 138
Table 4.22 Prevalence estimates of markers of Metabolic Syndrome for girls

(12-19 Years Old) by sexual maturation stage (assessed by pubic hair

assessment) in the population: NHANES III, 1988-1994 ........................ 140
Table 4.23 Correlation between biomarkers related to MS in adolescent

boys and girls (12-19 years old): NHANES 111, 1988-1994 .................... 142
Table 4.24 Correlation between biomarkers related to MS in adolescent boys

(12-19 years old) by Age: NHANES III, l988-1994 ............................... 143

ix

Table 4.25 Correlation between biomarkers related to MS in adolescent

girls (12-19 years Old) Stratified by age: NHANES III, 1988-1994 ........ 144
Table 4.26 Correlation between biomarkers related to MS in adolescent boys

(12-19 years old) stratified by sexual maturation stage: NHANES 111,

1988-1994 ................................................................................................ 145
Table 4.27 Correlation between biomarkers related to MS in adolescent

girls (12-19 years old) stratified by sexual maturation stage:

NHANES 111, 1988-1994 ......................................................................... 146
Table 4.28 Factor loading matrix for biomarkers related to MS in boys and

girls 12-19 years Old: NHANES 111, 1988-1994 ...................................... 147
Table 4.29 Factor loading matrix for biomarkers related to MS in boys

12-19 years old: NHANES III, 1988-1994 .............................................. 148

Table 4.30 Factor loading matrix for biomarkers related to MS in boys at
Tanner stage 4, Tanner stage 5 and Tanner stage 4 and 5 combined:

NHANES 111, 1988-1994 ......................................................................... 149
Table 4.31 Factor loading matrix for biomarkers related to MS in girls
12-19 years Old: NHANES III, [988-1994 .............................................. 150

Table 4.32 Factor loading matrix for biomarkers related to MS in girls at
Tanner stage 4, Tanner stage 5 and Tanner stage 4 and 5 combined:

NHANES 111, 1988-1994 ......................................................................... 151
Table 4.33 Demographic characteristics of factor loading scores in boys and

girls 12-19 years old using NHANES III: 1988-1994 ............................. 152
Table 4.34 Demographic characteristics of factor loading scores in boys

12-19 years Old using NHANES III: 1988-1994 ..................................... 154
Table 4.35 Demographic characteristics of factor loading scores in girls

12-19 years Old using NHANES III: l988-l994 ..................................... 156

LIST OF FIGURES

Chapter 2
Figure 2.1 Proposed model Of Metabolic Syndrome ................................................... 13
Figure 2.2 Age-adjusted prevalence of Metabolic Syndrome in adults 20 years
and older by race/ethnic group ................................................................... 28
Chapter 3

Figure 3.1 Diagrammatic representation of the identification of factors
(clusters of biomarkers) associated with the risk of Metabolic
Syndrome in adolescents 12-19 years old: NHANES III, l988-1994 ....... 59

xi

Chapter 1: Introduction
1.1 Overview:

Metabolic Syndrome (MS) has been estimated to affect 4% of adolescents in the
United States (1) and is believed to be rising steadily due to the dramatic increase in
overweight in adolescents (2). Until recently it was a condition found almost exclusively
in adults. Results from a recent population-based study also concluded that 25% of the
adult population in the US currently suffers from MS (3).

MS, otherwise known as Syndrome X and Insulin Resistance Syndrome, has been
defined as a clustering of metabolic abnormalities, including resistance to insulin-
stimulated glucose uptake, hyperglycemia, hyperinsulinemia, increased plasma
concentration of very-low-density lipoprotein (VLDL) triglycerides, decreased
concentration of high-density lipoprotein (HDL) cholesterol and hypertension (4). Other
abnormalities associated with this syndrome include abnormal weight distribution
(generally defined as visceral and central adiposity) and increased biomarkers of
inflammation (e.g. C - reactive protein) (5). Prevalence rates of the above metabolic
abnormalities related to this syndrome has been increasing markedly in recent years in
adolescents (6-9), especially among the Obese population (10-13). In addition, few
studies have been conducted to examine the distribution of biomarkers associated with
MS in children and adolescents, particularly in population-based studies (14, 15).

MS is characterized by the co-occurrence of obesity which by itself has been
increasing dramatically in the United States (in particular, among adolescents), and has
become a major public health concern among all age and race/ethnic groups (2, 16). In

2000 the prevalence of overweight (defined as BMI _>_ 95‘h percentile for age on the age-

and sex specific growth charts from the Centers for Disease Control (CDC)(17)) children
(6-11 yr) in the United States was 15.3% and it was 15.5% for adolescents (12-19 years)
and has been predicted to continue to increase (2).

MS has also been shown to predict type 2 diabetes (18), cardiovascular disease
(CVD) (19) and could possibly be associated with increased risk for cancer (20). Type 2
diabetes also is emerging as a major public health concern, especially among adolescents,
and seems to parallel the dramatic increase in obesity (21, 22). Until the early 1990’s
there was a low prevalence of less than 1% of type 2 diabetes in the US adolescent
population (12-19 years old), but within the past decade there has been a rapid increase of
8-45% based on geographical location (23, 24) and socio-economic position (25). CVD
has been shown to be one of the leading causes of mortality and morbidity in adults with
MS (26) and findings from the Bogalusa Heart Study indicated that risk factors for CVD
in children include overweight and obesity, hypertension, dyslipidemia, physical
inactivity and a high-fat diet (27).

Intervention programs to prevent childhood obesity are necessary, Since
overweight during adolescence is also believed to be an important predictor of long-term
morbidity and mortality (28, 29). Research suggests that it is more efficacious to
implement intervention programs geared toward preventing overweight youth rather than
overweight adults (30). Many studies have Shown that overweight children are more
likely to become overweight as an adult compared to normal weight children (31, 32) and
one study concluded that among obese compared to non-obese children there was a four-
fold increased risk of developing MS as an adult (33). The implementation of

appropriate interventions in children and adolescents could potentially decrease elevated

levels of biomarkers related to MS and the development of MS, type 2 diabetes,
cardiovascular disease and possibly cancer.

The etiology of MS is multifactorial where environmental factors as well as
genetic factors are believed to act as interacting determinants. Lifestyle factors, such as
physical inactivity and diet however, seem to be the most closely linked to MS (34-38).
A randomized controlled clinical trial recently Showed that both moderate and vigorous
physical activity levels can decrease the risk for developing MS ((OR= 0.52 (95% CI:
0.40,0.67) for vigorous) and (OR=0.78 (95% CI: 063,096) for moderate)) in adults. This
was significant after adjusting for all other confounding factors (34). The National
Cholesterol Education Program also recommends weight loss and increased physical
activity, as the basis of therapy for MS (39). However, more studies are needed to address
the effect of diet and physical activity on MS especially in children and adolescents.

A recent study by Cook et a1. defined MS in adolescents based on a
predetermined definition of elevated levels of three or more biomarkers related to MS (1 ).
Many studies however, have used a factor analysis approach to identify MS in adults (5,
40-43), and results among studies were all found to be consistent. To our knowledge, no
studies have defined MS in adolescents using factor analysis in a nationally
representative sample. This study will determine the prevalence and clustering of
markers of Metabolic Syndrome in a nationally representative sample of children using
factor analysis. This factor analysis approach will allow us to identify the potential
clustering of biomarkers related to MS in adolescents and to then determine the
relationship of participants with these clusterings to demographic characteristics, such as

race/ethnicity and poverty income ratio, physical activity, television watching and

tobacco use. In chapter 2 below, we will also review the pathophysiological basis of MS

and the relationship of MS to cardiovascular disease and type 2 diabetes mellitus.

1.2 Specific Aims for this Study are:

Aim 1: To examine the distribution and determine the prevalence Of elevated levels of
biomarkers related to Metabolic Syndrome in boys and girls 12-19 years by age within
gender, race/ethnicity and poverty income ratio. Biomarkers to be examined will be:
BMI-for-age Percentiles, Waist Circumference, Total Triglycerides, Total Serum
Cholesterol, Serum HDL-Cholesterol, LDL-Cholesterol, Serum Glucose, Systolic Blood
Pressure, Diastolic Blood Pressure and C - Reactive Protein (which will only be used to

determine the prevalence of elevated levels (> 0.22 mg/dL)).

Aim 2: To examine the distribution and determine the prevalence of elevated levels of
biomarkers related to Metabolic Syndrome in boys and girls 12-19 years by sexual
maturation stage (pubic hair assessment) within gender, race/ethnicity and poverty
income ratio. Biomarkers to be examined will be: BMI-for-age Percentiles, Waist
Circumference, Total Triglycerides, Total Serum Cholesterol, Serum HDL-Cholesterol,
LDL-Cholesterol, Serum Glucose, Systolic Blood Pressure, Diastolic Blood Pressure and
C - Reactive Protein (which will only be used to determine the prevalence Of elevated

levels (> 0.22 mg/dL)).

Aim 3: To examine the clustering of biomarkers that have previously been associated
with Metabolic Syndrome in adults, in boys and girls 12-19 years, using factor analysis,
and the association between demographic variables and identified factor scores.
Variables to be included in factor analysis will be: BMI-for-age Percentiles, Waist
Circumference, Total Serum Cholesterol, Total Triglycerides, Serum HDL-Cholesterol,

LDL-Cholesterol, Systolic Blood Pressure, Diastolic Blood Pressure and Serum Glucose.

1.3 Rationale for Study Aims:

The goals of this study are to determine the distribution and prevalence Of
elevated levels of biomarkers associated with MS, as well as the clustering of these
biomarkers among boys and girls 12-19 years old. We will also examine the association
between demographic variables and identified clusters of biomarkers. To conduct these
analyses we will use population-based data from the USA, using NHANES III (1988-
1994)

To our knowledge only three studies have examined the distribution of
biomarkers that are related to MS in children and adolescents (8, 14, 15). Furthermore,
we are not aware of studies that have presented the distribution of biomarkers by focusing
on MS, or examined the distribution by sociO-economic status. One study examined the
distribution of biomarkers related to MS (triglycerides, HDL-cholesterol, LDL-
cholesterol and total cholesterol) by race/ethnic group (14). The distribution of
biomarkers related to MS in our Study will therefore be able to serve as a reference value
for normal and high levels in adolescents Since our data is representative of the general

US population. In addition, there are very few studies that have looked at the distribution

 

of biomarkers related to MS with regard to pubertal stage of development (44-47).
Pubertal stage of development can possibly influence levels of biomarkers related to MS
(48-50). For example a longitudinal study recently showed that early menarche is
characterized with higher prevalence of clustering of adverse levels of biomarkers related
to MS in young adulthood (48). To our knowledge no studies have examined the
distribution of biomarkers related to MS by age. Since during this period in life,
adolescents can have such fluctuating clinical values, and it is important to determine, at
what age these elevated levels of biomarkers fluctuate the most so that we can stratify or
adjust for age in our analyses.

In the analyses for Aim 1 and Aim 2 we will stratify by age and Tanner stage in
order to determine the age distribution and Tanner stage distribution among each risk
factor related to MS. Results from Aim 1 and Aim 2 will further allow us to decide
whether we Should stratify by age, or Tanner stage in Aim 3 when utilizing the factor
analysis approach. If results from Aim 1 and Aim 2 do not vary substantially, then we
will be able to combine boys and girls in our overall sample as well as stratify by gender
in our analysis, which has more biological meaning.

Current prevalence rates of elevated levels of biomarkers related to MS were
recently presented by Cook et al. in children and adolescents 12-19 years Old using
NHANES III (1). These researchers defined MS, based on an “a priori” approach and
reported the prevalence of elevated levels of biomarkers in adolescents which are
believed to be problematic (discussed in Chapter 2). They then defined MS on the basis
of the definition in adults (39), as having three or more of the clinically problematic

levels of biomarkers. Our analyses strategy will differ from theirs in that they did not

stratify by age or pubertal status when determining the prevalence of elevated levels of
biomarkers related to MS.

In our analysis, we will make use of factor analysis to define MS in adolescents.
This approach has been consistently used in adults and found to produce meaningful
factors that represent the definition of MS as defined by the World Health Organization
(WHO) (51) and the National Cholesterol Education Program (NCEP) (52). To our
knowledge only one Study has used factor analysis to define MS in children and
adolescents and this study is not representative of the general population (53). The factor
analysis procedure will enable us to identify a Single unifying factor that represents the
clustering of biomarkers based on empirical data, instead of restricting the emphasis to
predetermined factors as done by Cook et al. (1). The criteria used to define Metabolic
Syndrome in children and adolescents will differ some what from the criteria to be used
in this study although overall Similar cut-off points will be used. A recent study by Shen
et a1. presented a high-order common factor representing MS in adults by using factor
analysis. Based on their results obtained the authors call for further studies in youth and
minority groups (43)

Our ultimate goal of this study is to identify children and adolescents who are at
risk to develop elevated biomarkers related to MS, since MS is known to be associated
with obesity, type 2 diabetes and cardiovascular disease and also possibly cancer. By
identifying adolescents at risk for MS can pose significant emphasis on prevention and
intervention for future development of MS, type 2 diabetes, cardiovascular disease and
cancer. These data from this work may help to identify unique features of clustering of

biomarkers related to MS in children and adolescents not seen in adults experiencing this

syndrome. This will allow us to focus on intervention programs to control, if possible
Specific components of this syndrome in children and adolescents, through potentially
physical activity and diet. Such early targeted interventions have the potential to decrease
risk for the development of type 2 diabetes, cardiovascular disease and cancer, as well as

MS.

Chapter 2: Background

2.1 Definition of Metabolic Syndrome

A report from the World Health Organization (WHO) described “metabolic
syndrome” as a “major classification, diagnostic and therapeutic challenge” (51). Meigs
et al., defined MS as the co-occurrence of multiple metabolic and physiologic risk factors
for both type 2 diabetes mellitus and atherosclerotic cardiovascular disease”(5). A study
by Bergstrom et a1. , as well as many other studies, have hypothesized that the clustering
of metabolic risk factors leads to impaired glucose tolerance, hyperglycemia,
hyperinsulinemia, dyslipidemia, and sodium retention that result in MS, Syndrome X (54,
55). Other terms associated with MS are “deadly quartet”, “multiple Metabolic
Syndrome” and “metabolic cardiovascular syndrome”(4, 56-61).

Reaven hypothesized that insulin resistance was the fundamental defect causing
the metabolic abnormalities in this syndrome, and defined it as Insulin Resistance
Syndrome (4). However, recent research has Shown that resistance to insulin may not be
the main cause of the syndrome (62). The World Health Organization (WHO) redefined
this syndrome as MS (51). The prevalence, pathophysiology and appropriate treatment Of
MS have been widely studied in adults, but not as extensively in youth, given until
recently it was only seen in adults. Clear definitions of MS have been established for

adults, by The World Health Organization (WHO) (Table 2.1) (51) and the NCEP ATP

111 (Table 2.2)(39).

Table 2.1 Diagnosis of Metabolic Syndrome in Adults by World Health WHO. (5 1’

 

Presence of insulin resistance or fasting
hyperinsulinemia and 2 or more of:

- Hyperglycemia: Fasting plasma glucose 2 6.1 mmOl/l but
nondiabetic

- Blood pressure: 2 140/90 mm Hg or treated for
hypertension

- Total Triglycerides: Men: 2 2.0 mmol/L and/or HDL-

cholesterol < 1.0 mmol/L
Women: <0.9 mmol/L
- Central obesity (waist circumference): Men: 2 94 cm
Women: _>_ 80cm
And or BMI Z 30 kg/m
- Microalbuminuria: urinary albumin excretion rate >- 20
mcg/min or albumin/creatinine ratio 2

30mg/g.

* WHO: World Health Organization; ’r HDL-C: High density lipoprotein cholesterol; hyperglycemia
defined as: fasting plasma glucose 26.1 mmol/L or impaired fasting glucose in non-diabetic individuals.

Table 2.2 Diagnosis of Metabolic Syndrome in Adults by NCEP ATP III I (39)

 

Three or more of:

- Abdominal obesity Waist circumference: Women: > 88cm; Men >102 cm

- Total Triglycerides: 2 1.7 mM (150 mg/dL)

- HDL-C’: Women: < 1.16 mM (50mg/dL)
Men: < 0.91 mM (40 mg/dL)

- Blood pressure: _>_ 130/85 mmHg

- Fasting plasma glucose 2 6.1 mM

 

* Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection,
Evaluation, And Treatment of High Blood Cholesterol In Adults

“Male patients can develop MS when their WC is only marginally increased e.g. 94-102 cm. They may
have a strong genetic contribution to IR and they will benefit from life habits, similarly to men with
categorical increases in WC” (39)

Because these definitions Of MS differ, the prevalence estimates reported in the
literature for one population could differ from another population. Two studies compared
the prevalence of MS using both definitions. A prospective study done in adults in
Finland determined that the WHO definition was the most sensitive for predicting

prevalent and incident cases, whereas the NCEP definition was less sensitive but had a

much higher specificity (3 7). They also found that adults who were diagnosed with the
defined criteria for MS were also more likely and at a higher risk to develop type 2
diabetes, according to the NCEP definition and the WHO definition. The odds for
developing type 2 diabetes over a 4 year follow-up ranged from 5.0-8.8 for both
definitions (37). A study by Ford et al. using data from NHANES 111 showed that 23.9%
of the participants had MS determined by the ATP III definition and 25.1% had the
syndrome when classified according to the WHO definition (63). The two definitions
were similar for non-Hispanic whites, but differed greatly by other race/ethnic groups.
Therefore we can conclude that a more universally accepted definition is needed in order
to accurately define a population as having MS. This next brings us to the pathogenesis
and classification of biomarkers related to metabolic syndrome in order to better

understand the mechanism so that a more clear definition can be established.

2.2 Pathogenesis and Classification of Metabolic Syndrome

The pathogenesis of this syndrome involves the clustering of risk factors, which
are all associated with type 2 diabetes and cardiovascular disease (4, 59, 61). A few
population studies in adults, such as the Framingham Offspring Study and
Atherosclerosis Risk in Communities Study (AIRC) showed that the metabolic risk
factors co-occur far more than by chance alone. In the Framingham Study low HDL-
cholesterol levels and elevated levels for BMI, systolic blood pressure, triglycerides,
glucose and total cholesterol, clustered at twice the rate that was predicted by chance.

The same results were found in the AIRC Study. In the Framingham Offspring Study,

11

they also showed that subjects with MS had a 1 1-fold increased risk for developing type
2 diabetes, and a 2.5-fold increased risk for developing CVD (64, 65).

We can therefore assume that the interrelated mechanisms among insulin
resistance, elevated lipid levels, increased blood pressure and elevated glucose levels,
indicates that MS risk factors work in synergy and come together to influence the
etiology of chronic disease (cardiovascular disease, type 2 diabetes mellitus) (43) (See
figure 2.1 below). We discussed each risk factor related to MS in detail to get a better

understanding Of the pathophysiology of this syndrome.

Figure 2.1 Proposed model of Metabolic Syndrome (Based on Timar et a1.) (66)

 

Socio-Economic Status

 

 

 

    

 

Genetic

 

 

 

 

Environment

 

 

‘ -

L thsical Activitv I

 

 

 

 

Hyperinsulinemia

/\

 

 

 

Overweight/Obesity
in children

 

  
 

Race/Ethnicity

 

 

 

 

 

Earlier Puberty

 

 

 

 

 

J.

 

Earlier Age at

 

Central Obesity/
Visceral Obesity
Abdominal Obesity

 

Menarche

 

 

 

 

 

 

Insulin Resistance

 

\/

 

 

 

T C - reactive protein

._

T Lipolysis
Jr Glucose Uptake

 

 

 

 

 

 

l HDL-CHOL

 

Dyslipidemia Hypertension

 

 

T LDL-CHOL
T Triglycerides

 

 

 

   
 
    
     
   

Metabolic Syndrome
In Youth: Adults

 

V

 

T Free Fatty Acid Synthesis (FFA)

 

 

 

 

 

 

 

Muscle Liver

T FFA Oxidation T FFA

~1- Glucose Oxidation
oxidation T VLDL-
l- Glycogenesis triglycerides
l- Glucose " T Gluco-
Oxidation Pancreas neogenesis

 

 

 

 

  

\

 

l_.

 

 

Impaired glucose uptake and utilization

 

 

l

 

 

Cardiovascular disease

 

 

 

 

Type 2 diabetes

Hyperglycemia

 

 

 

 

 

 

 

 

l3

2.2.1 Insulin Resistance and Hyperinsulinemia

Insulin Resistance, known as the main cause and a characteristic feature of MS (4)
can be defined as a condition when physiologic concentrations of insulin are unable to
accurately regulate proper glucose and lipid homeostasis (67). Above-mentioned
procedures (glucose and lipid homeostasis) under normal insulin production conditions
include reduced blood glucose concentrations, which involves gluconeogenesis and
glycogenolysis, triglyceride synthesis in the liver and adipose tissues, increased
breakdown of circulating lipoproteins and suppressed lipolysis (the breakdown of
triglycerides into free fatty acids and glycerol) in both adipose tissue and in muscles (67).
Insulin Resistance has also been defined as a common pathophysiologic state in which
target cells fail to respond to ordinary levels of insulin, and therefore produce a
subnormal biologic response (68). It has been hypothesized that the defects that occur
during the insulin action could be related to the failure of insulin to suppress lipolysis
(69).

Children and adolescents experience increased levels of insulin at Specific stages
in their lives. During puberty especially, sensitivity to insulin decreases significantly
between Tanner stages 1 and 2 and then remains stable throughout stages 2, 3 and 4, and
then increases at Tanner stage 5 (70). This idea has first been put forward by Amiel et al
in 1986 (71). They concluded that insulin-stimulated glucose metabolism was about
30% lower in children at Tanner stage 2 and 4 compared to children in Tanner stage 1
and 5. Another study found that during Tanner stage 3, insulin sensitivity decrease almost
25-30%. This is also called “peak reduction”, whereas again sensitivity increases and

stays gradually constant at Tanner stage 5 (72). In a longitudinal study, Goran concluded

14

that the reduction in insulin sensitivity occurs over a very brief period and is not due to
body fat, because body fat varies tremendously during puberty and insulin sensitivity was
similar among Obese and non-obese children (70). It is known that prepuberty is a more
insulin sensitive state compared to puberty, possibly due to the sensitivity of lipoprotein
lipase to insulin during prepuberty, which then leads to increased triglyceride clearance
from the circulation and increased triglyceride storage in adipose tissue (73). It has also
been found that lipid oxidation increases during puberty, which is possibly regulated by
increased growth hormone (GH) secretion, which can then be responsible for the
decreased glucose disposal and insulin resistance (11, 74). This mechanism however is
still not clearly understood.

Insulin resistance, as shown figure 2.1 is said to be one of the consequences of
obesity. AS an adult gains weight, the body becomes resistant to insulin and insulin
sensitivity declines by 30-40%, especially when an individual gains weight 35-50% more
than ideal bodyweight (59). Similar findings were seen in children since 1987 when
Freedman et al. studied 355 black and white school children and found that there was a
significant but weak association (r=0.3-0.4) between central body fat and fasting insulin
(75). A study by Gutin et al. found a stronger correlation (r=0.78) between percentage
body fat and insulin levels in 7-11 year old children (76). Sinaiko et al, also found that
BMI was significantly correlated with fasting insulin and inversely correlated with
insulin sensitivity in 714 children, :1: 12-13 years old (77). In addition, various other
studies have been performed to determine whether visceral adiposity or general adiposity
is related to fasting insulin and insulin resistance. The majority of these studies found that

visceral fat was Significantly related to fasting insulin, and in contrast to the above

15

mentioned Statement, that body fat in general seems to be the predominant factor on
insulin sensitivity (78-80). This hypothesis is supported especially in the younger
population.

BMI along with waist-hip-ratio (WHR) are the most reliable methods for
assessing bodyweight among adults (81). Schmidt MI et al. showed that there is a very
strong correlation between fasting insulin concentrations and body mass index (OR=1.6
to 4.5, p<0.05), and Karter et al. found that insulin sensitivity is inversely related to
WHR, independent of BMI, in both gender groups and in all ethnic groups in the Insulin
Resistance and Arteriosclerosis Study (IRAS) (82, 83). However, among children BMI is
known to not be a very sensitive indicator of body composition, especially during
puberty, because body composition changes dramatically during puberty (73). Insulin
resistance is also known to affect lipid metabolism and glucose metabolism as shown in
figure 2 (84). Ronnemaa et al. showed that serum insulin correlated positively with
serum triglycerides and inversely with HDL-cholesterol in young children within the
highest insulin quartile (85). Furthermore, resistance to insulin as well as
hyperinsulinemia are characteristics of both type 2 diabetes and impaired glucose

tolerance (86).

2.2.2 Dyslipidemia

Dyslipidemia is characterized by a so-called “lipid triad”: increased triglycerides
(Tg), decreased high-density lipoprotein cholesterol (HDL-cholesterol) and elevated low-
density lipoprotein cholesterol (LDL-cholesterol) (66). It is well known in the literature

that elevated levels of above-mentioned lipids and lipoproteins in the bloodstream are

16

associated with increased risk of atherosclerosis, including coronary heart disease and
that most of the pathological processes and risk factors has its onset during childhood
(22).

High serum levels of total cholesterol, especially associated with LDL-cholesterol
are linked to increased coronary risk. With regards to LDL-cholesterol, it is the size and
the density of the particle which is correlated with increased coronary heart disease risk.
The smaller and denser the particle, the easier the particle will oxidize and will cause the
particle to be transported at a slower rate, therefore being more atherogenic (66).
Elevated triglycerides also are associated with increased coronary risk, and are mainly
found in very low density lipoprotein cholesterol (VLDL-cholesterol). VLDL-cholesterol
is responsible for transporting triglycerides in the plasma (87). There is a strong link
between VLDL-cholesterol and HDL-cholesterol, in the sense that HDL-cholesterol
concentrations are usually low when triglyceride (VLDL-cholesterol) concentrations are
high (87). HDL has been termed the “memory box” of triglycerides and can also be used
as a long-terrn indicator of disturbances in triglycerides (87). An inverse association
exists between HDL-cholesterol and coronary heart disease risk (87). HDL-cholesterol is
also known to be lowered by people with an atherogenic lifestyle, including smoking,
obesity and physical inactivity (87).

Findings from the Bogalusa Heart Study suggests that as the number of
cardiovascular risk factors increase, the pathological evidence increases for developing
atherosclerosis in early childhood (88). These results also suggested that almost all
children 2-15 years old have aortic fatty streaks, and another 50% of children 2-15 years

old have fatty streaks in their coronary vessels, of whom 8% of those have raised fibrous

17

plaque. This strongly supports the link to the development of dyslipidemia and
hypertension in children (88).

Lipid levels in adolescents have been Shown to differ by race/ethnic group.
Investigators from the Bogalusa Heart Study found that African Americans have greater
concentrations of serum total cholesterol and HDL-cholesterol, and lower concentrations
of triglycerides and VLDL-cholesterol than whites, and have a greater deposition of fat in
the central region than whites (89). Another study by Hickman et a1. supported the above
findings in that non-Hispanic black children had higher mean total cholesterol, LDL-
cholesterol and HDL-cholesterol levels compared to non-Hispanic whites and Mexican
American children (14). Webber et al. also concluded that clustering of multiple
cardiovascular risk factors occurred greatest among white boys and African Americans
girls (90).

Bodyweight itself may be a significant predictor of elevated lipid levels in
adolescents. Since 1979, the Bogalusa Heart Study has showed that there is a significant
correlation between cholesterol and body weight for 13-14 year old boys and 11-12 year
old girls (90). The Bogalusa Heart Study further reported negative associations between
central adiposity and disturbances in lipid and lipoprotein concentrations, particularly
triglycerides and HDL-concentrations (91 ). Percent body fat is therefore associated with
an atherogenic lipid profile, as reflected by higher levels of LDL during prepuberty, and
higher levels of total cholesterol and LDL-cholesterol during puberty. A study found that
overweight children (defined as BMI > 85th percentile for age and gender) were 2.4 times
as likely as normal weight children to have an elevated level of total cholesterol. They

were also more likely to have higher levels of diastolic blood pressure (OR=2.4), LDL-

cholesterol (OR=3.0), HDL-cholesterol (OR=3.4), systolic blood pressure (OR=4.5),
triglycerides (OR=7.1) and fasting insulin (OR=12.6) (9).

Washington (92) reported that elevated levels of plasma insulin are common in
adults and are frequently associated with coronary heart disease. In an earlier Study
Haffner et al. provided evidence that hypertriglyceridemia and low HDL- cholesterol
concentrations are closely related with insulin concentrations. Their findings were
independent Of age, gender, ethnicity, body mass index and centrality in middle-aged
adults (61). Various studies in both children and adults have shown that hyperinsulinemia
is associated with an adverse pattern of cardiovascular risk factors that include obesity,
dyslipidemia and hypertension. De Fronzo et al. summarized evidence from various
studies that suggest that resistance to insulin and hyperinsulinemia causes increased
synthesis of VLDL particles (Very-low density lipoprotein) and this leads to an increased
production of plasma triglycerides accompanied by insulin resistance (59). This
abnormality occured in normal weight healthy subjects, obese subjects as well as in
NIDDM subjects. Insulin resistance that occurs during dyslipidemia is thought to be due
to the overproduction of VLDL particles which occurs when free fatty acids (FFA) and
glucose from the liver increase as well as through the inhibition of Apo B degradation
and decreased lipoprotein lipase levels (93). Hyperinsulinemia is also shown to be
associated with a decrease in HDL-cholesterol levels (59). This mechanism is complex
and involves the major lipoprotein in HDL-cholesterol, apolipoprotein I. There is an
inverse relationship between apOAl/HDL degradation, which exceeds and enhanced rate
of apoAIlHDL synthesis (59). Sinaiko et al. on the other hand found that fasting insulin

was Significantly correlated with systolic blood pressure in boys and girls. and all lipids

(p<0.0001) except high-density lipoprotein-cholesterol (HDL-Cholesterol) in boys and
triglycerides and HDL-cholesterol in girls. But after adjustment for BMI, fasting insulin
became significantly related to triglycerides (77).

Therefore we conclude that elevated levels of risk factors related to
cardiovascular disease can have its onset early in childhood and will persist into

adulthood leading to major health problems and contribute to the development of MS.

2.2.3 Blood Pressure

Dietz et a1. has reported that overall hypertension in children does not occur very
often (94). In a study of 6622 children 8 to 15 years old in Muscatine, Iowa, only 1% had
persistent elevated blood pressure (Blood pressure > 95‘h percentile for age and sex or
140 mm Hg systolic or 90 mm Hg diastolic) (95). Children classified as having constant
elevated blood pressure were also found to be obese with relative weights in excess of
120% (95). It is important to note that blood pressure during childhood varies widely
throughout the day in children and in adults, because of normal diurnal fluctuation and
changes in physical activity, emotional stress and other factors (96).

The Second National Heart, Lung and Blood Institute Task force developed
definitions for high blood pressure/hypertension in children and adolescents (96).
Normal blood pressure is defined as systolic and diastolic blood pressure being less than
the 90th percentile for age and sex and height. High-normal blood pressure is defined as
average systolic or diastolic blood pressure greater than or equal to the 90‘h percentile but
less than the 95‘h percentile for age, sex and height. Hypertension is defined as the

average systolic or diastolic blood pressure greater than or equal to the 95th percentile for

20

age, sex and height measured on at least three separate occasions (96). Age, race, sexual
maturation stage, height and body mass index (BMI) has all been found to be Significant
predictors of elevated blood pressure levels in longitudinal analyses (97). In addition,
investigators concluded that both systolic and diastolic blood pressures increased with
age in black and white girls of similar ages (97, 98).

Sexual maturation stage is found to be an independent predictor of elevated blood
pressure (98). Black girls had higher blood pressure levels than white girls at each
maturation stage, and were also found to mature earlier than white girls. Blood pressure
is also found to increase with height and BMI in children and adolescents. Even after
adjustment for age, race and height, sexual maturation still remained related to blood
pressure (97). After adjustment for age, weight, height and BMI, the racial differences
remained Significant for systolic blood pressure only, but not for diastolic blood pressure
(97). A study conducted by the National Center for Health Statistics (NCHS) concluded
that children who maintained higher blood pressure than their peers were significantly
taller, heavier had greater bone age and had an earlier sexual maturation. They were also
more likely to be obese, as indicated by skinfold thickness, hip and waist circumferences
and body weight indices (98, 99). Other studies also concluded that body size was found
to be one of the most important determinants of BP in childhood and adolescence (96,
98). From the above evidence, it is therefore important to identify precursors and markers
of hypertension and elevated blood pressure especially in youth.

In adults, hypertension has been found to occur frequently in diabetic individuals,
as well as in individuals with cardiovascular disease. Hypertension in adults has been

Shown to be associated with components of MS, which includes insulin resistance,

21

hyperinsulinemia, glucose intolerance and dyslipidemia (60, 100). Insulin resistance and
hyperinsulinemia has been found to play a causal role in the development of hypertension
in adults (59). In addition, insulin has been shown to be a predictor of incident
hypertension independent of BMI, waist-hip-ratio, weight change and baseline blood
pressure (101). In children, fasting insulin and the acute insulin response were positively
related to systolic blood pressure, but not diastolic blood pressure, and insulin sensitivity
(p<0.001) was negatively associated with systolic and diastolic blood pressure, after
adjustment for body composition (102). One study however, conducted in adults, did not
find insulin resistance to be associated with hypertension (103).

The mechanism through which insulin works in its relationship with hypertension
is complex. Insulin acts as a potent vasodilator, and studies have shown that insulin-
mediated vasodilation is impaired in insulin-resistant states (104, 105). Insulin has been
shown to be responsible for elevation in blood pressure via: Na retention; CNS
activation; enhanced fluxes of Na and Ca into vascular smooth muscle cells, which will
lead to increased vascular sensitivity to the vasoconstrictor effect of pressor amines, and
the proliferation of arteriolar smooth muscle cells (59). Therefore, the causality behind
the association between insulin resistance and hypertension is still a matter of great
controversy, but we can conclude that essential hypertension, just like obesity and
NIDDM, is an insulin-resistant state. However, studies in adults have shown that weight
loss and physical training can improve the body’s sensitivity to insulin, and effectively

lowers blood pressure (106, 107).

22

2.2.4 C-reactive Protein

C-reactive protein (CRP), an acute phase protein produced by the liver can be
described as a sensitive marker for systemic inflammation (108). Recent studies have
suggested that low-grade systemic inflammation may play a role in the pathophysiology
of MS (109-111). CRP have been Shown to be significantly associated with a variety of
cardiovascular risk factors in adult men (Physicians Health Study), including systolic and
diastolic blood pressure, exercise, plasma lipids (Total-cholesterol, triglycerides),
homocysteine, smoking, age and BMI (112). Pannacciulli et a1. studied healthy adult
women and found that factors such as age, insulin resistance, central fat accumulation
and the amount Of total body fat were the most powerful predictors of CRP
concentrations (113). Another study using data from NHANES I II (l988-1994) found
that the prevalence of increased CRP levels was higher in both overweight and obese
adults compared to normal weight adults (114). The same results were seen when
studying children 8-16 years old from a nationally representative sample (NHANES III).
They found that 7.1% of the boys and 6.1% of the girls had elevated levels of CRP and
overweight children were more likely to have elevated CRP levels than normal-weight
children (115). They also found that the odds ratio for having elevated CRP levels was
3.74 for overweight boys and 3.17 for overweight girls, based on BMI (115).

Studies have suggested that the elevated CRP concentrations might be due to
elevated cytokine Interleukine-6 (IL-6) in adipose tissue (108, 116, 117). This is because
increased levels of IL-6 from adipose tissue may induce elevated CRP levels in people
with excess body fat. Recent studies also found that increased levels of CRP correlate

positively with waist circumference and visceral adipose tissue (which will be discussed

23

below) in adults (118-120). Elevated levels of CRP observed in obesity, type 2 diabetes
mellitus, hypertension, CHD and insulin resistance are all critical components of MS and
suggests that the low-grade systemic inflammation plays a significant role in these
conditions (110). We can therefore conclude that inflammation plays an important role in

the development of MS and the diseases that are associated with it.

2.2.5 Impaired Glucose Tolerance

Impaired glucose tolerance (IGT) is defined as a glycemic response to the
standard 75-g oral glucose challenge that is intermediate between normal and diabetic
ranges (121). In adults it has been shown that people with impaired glucose tolerance are
at a higher risk to develop type 2 diabetes mellitus (38). Timar et a1. suggests that both
the etiology of non-insulin dependent diabetes mellitus and impaired glucose tolerance
occurs due to the independent development of impaired pancreatic beta-cell function as
well as insulin resistance. Once the beta-cells from the pancreas fail to maintain the high
rate of insulin secretion and at the same time fail to compensate for insulin resistance,
impaired glucose tolerance occurs, followed by diabetes. Prior to this stage the pancreas
is still able to enhance its secretion of insulin appropriately to compensate for the onset of
insulin resistance, and glucose tolerance still remains normal (66). Therefore once the
loss of blood glucose tolerance begins to emerge, glucose intolerance has its onset.
During the past decade an increase frequency in the occurrence Of type 2 diabetes
mellitus has been reported in adolescents of all race/ethnic groups (122). Diagnosis of
type 2 diabetes in children occurs usually only after the age of 10 years, due to the

physiological insulin resistance that occurs during puberty (22). The pathophysiology

24

however is complex and multifactorial, and parental history plays a big role in the
development of type 2 diabetes.

There are two important anthropometric characteristics which indicate the risk for
MS in children and can be detected in the early stages of development during childhood.

Therefore a review of body fat distribution and waist circumference is discussed.

2.2.6 Body Fat Distribution and Waist Circumference

Due to the rapid increase in overweight and Obesity especially in childhood, it is
important to understand the relationship between body fat distribution and components of
body composition with disease in order to prevent further complications later in life. The
presence of excess fat in the abdomen is shown to be an independent predictor of health
related diseases (e.g. type 2 diabetes, cardiovascular disease) in adults and waist
circumference is highly correlated with abdominal fat content. It has been shown that in
adults, visceral adipose tissue tends to be more highly correlated with MS markers than
subcutaneous abdominal adipose tissue ( 123-125). Visceral adipose tissue, not BMI or
waist-hip-ratio has been shown to be significantly correlated with elevated triglycerides,
low high-density lipoprotein and elevated insulin levels in Obese girls (91).

In adults, waist circumference has been shown to be highly correlated with
plasma lipids, lipoproteins and insulin levels, as well as visceral adipose tissue (126).
Furthermore in children, waist circumference has been found to be highly correlated with
high levels of plasma lipids and lipoprotein levels (127, 128). Freedman et al. found that
in the Bogalusa Heart Study these associations existed independently of weight, height,

and age. The results were also found to be Similar among race and ethnic groups (128).

25

Waist circumference can also be used as an index of both fat distribution and generalized
obesity. A few studies also supported the fact that waist circumference can be used as a
predictor of risk factors for MS/Cardiovascular disease in children (129-131). Savva et al.
reported that both waist circumference and waist-to-height ratio were better predictors of
cardiovascular risk factors than BMI. BMI seemed to be a good predictor for high
systolic and diastolic blood pressures (131). Results from stepwise multiple regression
analysis found that waist circumference was a significant predictor for all risk factors
related to cardiovascular disease, except for triglyceride levels in girls (131).

Morena et al. utilized Receiver Operating Characteristic (ROC) curves to predict
MS from waist circumference measurements. A ROC curve is a way of evaluating the
accuracy of a diagnostic test by summarizing the potential of the test to discriminate
between the absence and presence of a disease i.e. the ability of waist circumference to
identify children with MS. They found that the highest area under the curve corresponded
to waist circumference (0.868) compared to BMI (0.849) and triceps/subscapular
skinfolds (0.834), but this was not statistically significant (130). Waist circumference has
also been found to be a predictor of early pubertal development. One study found that
girls with higher average waist circumference across ages 7 and 9 years (OR= 1.11) as
well as girls who Show a linear increase in waist circumference through 7 and 9 years
(OR=1.12) were more likely to reach puberty and to mature earlier than at 9 years (132).

From the above-mentioned literature we can conclude that waist circumference
seems to be an appropriate, measurement technique to use in children, and relatively easy

to acquire. As mentioned before, MS is known to occur mainly in adults, therefore the

26

prevalence Of MS in adults will be discussed to emphasize again the importance and

rationale of determining the prevalence in youth.

2.3 Prevalence of Metabolic Syndrome in Adults

According to the National Cholesterol Education Program UNICEF) ATP [11
guidelines the prevalence of MS was still unknown until recently (39). Timar et al.
described MS as a multifaceted syndrome, which occurs mainly in adults > 50 years Old,
and the syndrome is also more common in men than in women (66). Ford determined the
prevalence of MS in USA adults (3 20 years), by using data from the Third National
Health and Nutrition Examination Survey (NHANES III) (3). The NCEP ATP III criteria
were used to assess the prevalence of MS. Findings from this study were that overall the
unadjusted and age-adjusted prevalence’s of MS was 21% and 23% respectively. A
similar study done on the same population and used the same diagnostic criteria showed
Similar overall prevalence statistics (133). Results showed that MS was present in 4.6%,
22.4% and 59.6% of normal-weight, overweight and obese men, and Similar results were
found for women (133). The study by Ford et al. Showed that the prevalence of MS
increases with age, i.e. for ages 20-29 the prevalence was 6.7%, 60-69 years, 43.5% and
42% for participants 70 or Older Table 2.3 — Exact values for in-between years are not
available).

Table 2.3 Prevalence of Metabolic Syndrome according tO NCEP ATP Ill criteria among
US adults“ age 20 years and Older. (stratified by age)* (3)

 

 

Age in years Prevalence (%)
20-29 6.7

60-69 43.5

70 > 42

 

* Exact values for in between years (30-59) were not
available in the article; # Data from NHANES III

27

The increase in MS with age could be due the simultaneous increase in obesity in
the population, or the fact that insulin sensitivity may decrease with age. Prevalence rates
did not differ much among men and women, but rates were higher for Mexican
Americans (31.9%) and lowest among non—Hispanic whites (23.8%), African Americans
(21.6%) and people reporting an “other race” or ethnicity (20.3%) (Figure 2.2) (3). Park
et al. reported significant differences between Mexican American and non-Hispanic
white men, compared to non-Hispanic black men (p < 0.001 and p <0.006). The latter
mentioned had a much smaller prevalence than the former (13.9% compared to 20.8%
and 24.3%). For women there was no significant relationship between non-Hispanic
white and non-Hispanic black women (22.9% and 20.9%) but Mexican American women
had a significantly higher prevalence (27.2%) (133). The effect of ethnicity and MS could
be due to Mexican Americans adapting to a more westemized lifestyle and lack of
activity. However, this could also be due to genetics. Age-adjusted prevalence rates also
showed that Mexican-American men and women had significantly higher rates for MS
compared to non—Hispanic white and non-Hispanic black men and women (133).

Figure 2.2 Age-adjusted prevalence of Metabolic Syndrome in adults 20 years and older
by race/ethnic group : NHANES III, 1988-1991(3)

 

 

I Prevalence

% of Population

 

 

Mexican White African Other
American American

Race/Ethnic Group

28

Higher body mass index (BMI), current smoking, low household income, high
carbohydrate intake, no alcohol consumption and physical inactivity were also associated
with increased Odds of having MS (133). It was concluded that age-specific prevalence
rates to US census counts from 2000, that 47 million US residents currently suffer from
MS, (3) and Park et al. concluded that approximately one fourth of US adults 20 years or
older meet the diagnostic criteria for MS (133). The numbers mentioned above are likely
to underestimate the current prevalence of MS due to the increasing rise in obesity and its
adverse metabolic effects in the US over the last decade (3). It is now clear the MS is a
big health concern in adults, and only recently with the focus on childhood obesity and
type 2 diabetes, children became a concern as well. However, during growth, both boys
and girls experience puberty and girls experience menarche as well. This all can play a
big role in when and how MS occur, and in our study we will determine the effects of
puberty in girls and boys on biomarkers related to MS. Therefore I would like to discuss
current knowledge of when does puberty and age and menarche occur in the general

population.

2.4 Adolescence and Puberty

According to Marshall and Tanner, puberty can be defined as that time of life
when morphological and physiological changes occur in the growing child (134).
Adolescence on the other hand is defined as a period of physical and psychological
development from the onset of puberty to maturity (135). It is during adolescence in

which lifetime habits and behaviors are established that will likely be maintained into

29

adulthood (136-138). The majority Of studies reporting the development of secondary

sexual characteristics have made use of the five stages of development defined by Tanner

et al. (139). Stage 1 is considered as pre-pubertal and stage 5 as post-pubertal. All 5

stages describe the growth of pubic hair in boys and girls, breast development in girls and

genital development in boys. Table 2.4 describes pubertal stages 1 to 5 for boys and girls,

according to breast development, pubic hair development and genital development (140).

Table 2.4 Description of pubertal stage as defined by breast, genital and
pubic hair development (140)

 

 

 

 

 

Breast Pubic Hair Genital

Stage 1 Pre-adolescent; elevation Pre-adolescent; the vellus Pre-adolescent; Testes,

of papilla only over the pubes is not further scrotum and penis are
developed than that over the about same Size and shape
anterior abdominal wall as in early childhood

Stage 2 Breast bud stage; Sparse growth of long, Scrotum and testes are
elevation of the breast slightly pigmented downy slightly enlarged. Skin of
and the papilla as a small hair, straight, or slightly scrotum is reddened and
mound. Areolar curled, chiefly at the base of changed in texture. Little
diameter is enlarged over the penis or along the labia or no enlargement
stage 1 (Preadolescent)

Stage 3 Breast and areola are both Hair is considerably darker, Penis is slightly enlarged
enlarged and elevated coarser and more curdled. at first mainly in length.
more than in stage 2. No Spreads sparsely over the Testes and scrotum are
separation of their junction of the pubes further enlarged
contours than in stage 2

Stage 4 Areola and papilla form a Hair is now adult type, but (Lower Left) Penis is
secondary mound area covered is still smaller further enlarged, with
projecting above the than in adult. No spread to growth in breadthand
contour of the breast medial surface of the thighs development of glans.

Testes and scrotum are
further enlarged than in
stage 3. Scrotal skin is
darker than in earlier
stages

Stage 5 Mature Stage; Papilla Hair is adult in quantity and (Lower right) Genitalia
only projects with areola type with distribution of the are adult in size and
recessed to general horizontal pattem. Spread shape

contour of breast

is to medial surface of the
thighs, but not up to Iinea
alba or elsewhere above the
base of the inverse triangle

30

2.5 Pubertal Development in Girls and Boys

One of the first studies to report age of onset and sequence of pubertal events was
done by Marshall and Tanner in 1969. They reported that breast and pubic hair
development in girls occurred almost Simultaneously. The mean age for transition from
breast stage 1 to 2 was 11.15 years and for pubic hair, it was 11.69 years. In contrast the
mean age for onset of menarche was 13.47 years (141). Among boys, genitalia stage
began to develop between the ages of 9.5 and 13.5 years, and Tanner stage 5 was reached
between 13 and 17. In contrast to girls, genitalia development in boys has its onset
before pubic hair development (142, 143). At approximately 16.5 years, most US girls
and boys were sexually mature, i.e. they have reached all 5 stages Of Tanner assessment.
The median age at onset of pubic hair development for girls was 9.4 years for non-
Hispanic blacks, 10.6 years for non-Hispanic white girls and approximately 10.4 years
for Mexican American girls (143). Non-Hispanic black girls reached full maturity of
pubic hair development at 14.7 years. Whereas non-Hispanic black girls and non-
Hispanic white girls reached full maturity at approximately 16.3 years. The median age
for the onset of breast development was approximately 9.5 years for non-Hispanic black
girls, 9.8 for Mexican American girls, and 10.4 years for non-Hispanic white girls. Full
breast development (classified as Tanner stage 5) was reached at approximately 14.0
years for non-Hispanic black girls, 14.7 years for Mexican American girls and 15.5 years
for non-Hispanic white girls. For boys the median age for the onset of pubic hair
development was approximately 1 1.2 years for non-Hispanic black boys, and 12.0 years
and 12.3 years for non-Hispanic white and Mexican American boys. All boys reached

full maturity (Tanner stage 5) for pubic hair stage at approximately 15.5 years. In the

31

same study, they also found that the median age for onset of genital development was
approximately 9.2 years for non-Hispanic black boys, 10.0 and 10.3 years for non-
Hispanic white and Mexican American boys. Full genital development for boys was
approximately 15 years for non-Hispanic blacks, 16.0 for non-Hispanic whites and
approximately 15.7 years for Mexican Americans (143).

Herman-Giddens’ results were Similar to the findings mentioned above for boys.
They concluded that the mean age of pubic hair development for non-Hispanic white,
African-American and Mexican American boys was 12, 1 1.2 and 12.3, and full maturity
for pubic hair was reached at 15.7, 15.4 and 15.8 years. The mean age for full
development of genitalia was 15.5 years for non-Hispanic-white, Mexican American and
African American boys (144). It is therefore clear that maturation occurs significantly
earlier in non-Hispanic black girls and boys compared to non-Hispanic white and

Mexican American girls and boys.

2.6 Age at Menarche in Girls

Given that age at menarche occurs relatively later on in the pubertal process, this
cannot be used as an indicator from which age of pubertal onset can be derived (145).
Age at menarche is often influenced by various factors such as genetic parameters, race
and ethnicity, socioeconomic conditions, physical activity and nutritional status (146,
147). According to various studies, age at menarche has changed little over the past few
decades. Most studies have found that the mean age at menarche is between the ranges of
12 to 13 years old. A meta-analysis was done studying 67 countries worldwide. They

found that the mean age at menarche among all countries was 13.53 years (SD i 0.98)

32

(147). Chumlea et al. studied age at menarche on a nationally representative sample,
NHANES III (148). The median age at menarche for all races among US girls was 12.43
years. Non-Hispanic black girls (12.25 years) were more likely to have an earlier age at
menarche than non-Hispanic white girls (12.55 years), and Mexican Americans were in
the middle of the two. Median ages were recorded because ages were recorded as an
integer, and it is therefore not possible to calculate the mean ages (148). Similar results
were found in a cross-sectional/ longitudinal study of non-Hispanic black and non-
Hispanic white girls. They concluded that non-Hispanic black girls were more likely
reach age at menarche 3 months earlier compared to non-Hispanic white girls (12.3 vs.
12.6 years) (149).

AS mentioned above, age at menarche can be influenced and affected by many
other factors. Many studies have examined and supported the association between age at
menarche and adiposity (150, 151). A recent study by Freedman et al. studied black and
white girls in the Bogalusa Heart Study. Their findings were significant in that women of
both race/ethnic groups who reported an age at menarche before 12 years had a higher
body weight and skinfold thickness compared to women who reported having age at
menarche after 13.5 years. A limitation for this study however, was that age at menarche
was self-reported. This again emphasizes that overweight and obesity in the US
adolescent population is becoming a major health problem, Since body fat and earlier age
at menarche both lead to increased risk for several disorders (type 2 diabetes,
cardiovascular disease) later in life. Recently, some further research was done by F rontini
et al., who used the same group of women as mentioned above in the Study by Freedman

et al. They however, examined the longitudinal changes in adiposity and risk factors

33

related to MS in white and black girls in the Bogalusa Heart Study, and their relationship
to early onset of menarche. They concluded in their results that girls who experienced
menarche at an earlier age (less than 12 years) had higher insulin levels and lower
decreases in glucose levels from childhood to adulthood. They also concluded that the
prevalence of multiple risk factors related to MS in adults were higher (1 .8-fold) in
women with an earlier age at menarche (48).

Further population studies are needed to examine the relationship between age at
menarche and risk factors related to MS. Pubertal development is therefore a critical
period in life, and it is therefore important to clearly establish a definition for MS in
youth, so that high risk children can be identified during pubertal deveIOpment or even

sooner. The current prevalence of MS in children and adolescents is warranted.

2.7 Prevalence of Metabolic Syndrome in Children

There is a paucity of studies that have estimated the occurrence of MS in children
and to our knowledge only one study has described the prevalence of biomarkers
associated with MS in a nationally representative sample in youth (1). A Swedish study
(n=1032) concluded that features typical of MS, including elevated serum levels of
insulin, TG, LDL-C, BP values and low HDL-C were already present in adolescents (14
year old and 17 year Old) in the upper BMI quartile, compared to adolescents in the lower
BMI quartile (55). The Bogalusa Heart Study used factor analysis to characterize the
clustering of risk variables related to MS independent of sex and age. They defined the
presence of MS in youth as having high blood pressure, dyslipidemia (high TG, and

decreased HDL-C), hyperinsulinemia, and obesity. Seven risk variables (Ponderal index

34

(weight (kg)/height (m)3), insulin, glucose, triglycerides, and high density lipoprotein
cholesterol, and systolic and diastolic blood pressure were reduced to two independent
factors, which explained 50-59 percent of the variance in the total sample (53). A
limitation however was that they did not define the prevalence of MS in children based
on the known adult definition. Instead they defined the population in the top 25% of each
quartile as having MS.

Cook et al. (1) recently estimated the prevalence and distribution of MS in
adolescents using data from a nationally representative sample of the US population.
They defined MS as Shown in Table 2.5.

Table 2.5 Criteria for Metabolic Syndrome in children (1)

 

 

Criterion Adolescents
High Triglyceride (mg/dL) 3 1 10
Low HDL-C (mg/dL)
Males 540
Females 540

Abdominal Obesity, Waist
circumference (cm)*

Males 3 90th Percentile
Females _>_ 90th Percentile
High fasting glucose (mg/dL) 3 110

High blood pressure (mmHg)l _>_ 90‘h Percentile

* Waist Circumference: All participants at or above the 90th percentile
for age and sex; '1' Systolic and Diastolic BP: At or above the 90‘h percentile
for age, sex, and height

The prevalence of MS as defined above in adolescents 12 -19 years Old was 4.2%.
The syndrome was more common in males (6.1%) than in females (2.1%) and also more
common in Mexican Americans (5.6%) compared to non-Hispanic Whites (4.8%) and
non-Hispanic Blacks (2.0%). They also stratified the subjects on BMI, and found that

28% of overweight adolescents met the criteria for MS. This study also examined the

35

prevalence of MS within Tanner Stage, but did not find any significant differences. Rates
did however increase among Tanner stage 2 (7.0% (0.0-15.4)) and 3 (7.2% (2.5-1 1.9))
and decreased again among Tanner stage 4 (3.5% (0.7-6.3))and 5 (3.4% (1 .5-5.4)) (1).
This could possible be due to the increase in insulin resistance experienced at certain ages
during pubertal development, which will be discussed later in the literature review. The
paucity of detailed studies of the characteristics of children at risk for experiencing
characteristics of MS suggests more work needs to be done in this area. Therefore, our
study will contribute to current knowledge known about Metabolic Syndrome in children,
and results from this study will contribute to the need for screening and intervention

programs in the general population.

36

Chapter 3: Methodology

3.] Study Design and Population

This is a cross-sectional, descriptive study of the prevalence and clustering Of
biomarkers associated with Metabolic Syndrome (MS) among boys and girls 12-19 years
Old in the general US. population from l988-1994. Data for this study came from the
National Health and Nutrition Examination Survey (NHANES III), conducted by the
National Center for Health Statistics (NCHS) Of the Centers for Disease Control and
Prevention (CDC). This study was carried out in 50 states and the District Of Columbia of

the United States in two sets of phases. 1988-1991 and 1991-1994.

3.2 NHANES III Design and Data Collection Procedures

NHANES III is based on a complex, stratified multi-stage sample design
conducted both in-home and in mobile examination centers (MEC)(152). Sampling was
conducted in three stages. To begin with, the United States was divided into a number of
primary sampling units (PSU’S), which were each equivalent to a county (153). A total
sample of 81 PSU’S were selected and in some PSU’S one or more counties was
combined to assure a minimum sample Size. The PSU’S were all selected with probability
proportional to size (i.e. depending on the size of the county), and 13 counties were all
selected with a probability of one (e.g. California was selected as an individual PSU
because of its size). The 13 counties were then divided into 21 survey locations. The
remaining PSU’s (68) where then grouped into 34 strata (two PSU’S per stratum).
Therefore the sample consisted Of 89 survey locations (81 PSU’S) that were randomly

divided into 2 sets or phases of equal length and sample Size. To prevent unbiased

37

estimates from developing, one set was allocated to the first three-year period (1988-
1991) and the second set to the other three year period (1991-1994) (154). The second
stage of sampling involved identifying “secondary sample units”, which included city or
suburban blocks or combinations of blocks. The last stage involved selecting actual
households as well as certain types of group quarters (dormitories). A subsample was
selected from the households for screening and to identify eligible participants.
Participants were selected into the study based on their age, sex, and race or ethnicity.
Older persons, children, Mexican Americans and Black persons were oversampled to
ensure that estimates of the health and nutrition status Of the general US population
could be accurately estimated (152).

In NHANES III, 39, 695 persons were selected over the six years as described
above; of those, 33, 994 (86%) were interviewed in their homes, and the rest were
classified as non-respondents (14%) (152). Approximately 30, 818 (78%) of the
interviewed persons were also examined in NHANES 111 Mobile Examination Centers
(MEC) and an additional 493 persons were given a special limited examination in their
homes if they were not able to make it to the MEC (152). Data was collected in
NHANES separately for children up to 16 years Old and, for adults from 17 + years (152)

Data collection began with a household interview, followed by a physical
examination and questionnaires in the MEC. During the household interview the
following questionnaires were administered (questions asked during the household
questionnaire were administered to a proxy corresponded for children younger than 17
years): Household Screener Questionnaire, Family Questionnaire, Household Adult

Questionnaire, and Household Youth Questionnaire. During the examination at the MEC,

38

five automated questionnaires or interviews were administered: The MEC Adult
Questionnaire, The MEC Youth Questionnaire, The MEC Proxy Questionnaire, a 24-
hour Dietary Recall, and a Dietary Food Frequency Questionnaire. Only data from the
Household interview and MEC examination were used in this study.

The health examination at the MEC included a variety of tests and procedures.
The examinee’s age at the time of the interview and other factors determined which
procedures were administered at the MEC. Subjects (12 years and older) were instructed
to fast for 10-16 hours prior to the morning examination or for six hours before the
afternoon or evening examination. Blood and urine Specimens were Obtained, and a
number of tests and measurements were performed on these biologic samples. A
physician also performed a limited standardized medical examination. Based on the age
of the sample person, the components included body measurements, blood pressure,
spirometry, venipucture, physical function evaluation and a questionnaire to inquire about
infant feeding, selected health conditions, cognitive function, tobacco use, and
reproduction (154). Analyses in this study however, will include only adolescents 12-19

years old who fasted for 6 hours or more before their health examination.

3.3 Measurement of Biomarkers associated with Metabolic Syndrome

Of the data collected in NHANES III, the following were used in this study:
anthropometric measurements (BMI percentiles, waist circumference), biochemical
measures (serum lipids (total cholesterol, HDL-cholesterol, LDL-cholesterol,
triglycerides), c-reactive protein, serum glucose, glycated hemoglobin levels, sexual

development stages and blood pressure measurements (diastolic blood pressure, systolic

39

blood pressure) (154). A description of the collection procedures for these measures

follows.

3.3.1 Anthropometric Measurements

Anthropometric measurements used in this study were waist circumference and
BMI-for-age percentiles obtained from CDC. Measurements were taken in the MEC and
trained examiners were responsible for recording of the body measurements of
participants. In general, standard procedures as defined by NHANES III were followed
for the anthropometric measurements. Height was measured in an upright position with a
stadiometer, and weight was measured at a standing position using a self-zeroing scale
(Mettler-Toledo, Inc, Columbus, Ohio). Height and weight was then used to calculate the
BMI-for-age percentiles developed by CDC. The BMI-for-age growth charts are
developed to screen for nutritional risk in children and are based on data from five
national representative surveys (NHES III, NHES III, NHANES I, NHANES II and
NHANES III), as well as some other supplemental surveys (United Vital Statitics, State
Of Wisconsin Vital Statistics, State of Missouri Vital Statistics, Fels Longitudinal Study
and Pediatric Nutrition Surveillance System). BMI-for-age percentiles of participants in
our study were calculated based on these growth charts and using a SAS program
provided by CDC (17). The waist circumference measurement was made at the midpoint
between the bottom of the rib cage and above the top of the iliac crest. Measurements of

waist circumference were made for each subject at minimal respiration to the nearest

0.1cm (154).

40

3.3.2 Biomarkers

Biomarkers used in this study were serum total cholesterol, serum HDL-
cholesterol, serum LDL-cholesterol, serum triglycerides, serum C - reactive protein,
serum glucose and blood pressure. At the start of the examination in the MEC, a
questionnaire was administered to determine the eligibility of the participant to have a
blood measurement (venipunture) taken as well as other measurements to be done in the
MEC. The questions included were, whether it was safe to perform the venipuncture and
to document and determine fasting compliance of the participant (154). Discussed below
are the technique/measurements used to obtain each marker included for study in these

analyses.

3.3.2.1 Lipids

Serum cholesterol, serum triglycerides and serum HDL-cholesterol was measured
with a Hitachi 704 Analyzer (Boehringer Mannheim Diagnostics, Indianapolis, IN) in
participants 12-19 years old (154). Cholesterol and triglycerides (hydrolyzed to produce
glycerol) were measured enzymatically at the same time. Measurements of total and
HDL-cholesterol and fasting triglyceride levels allows low-density lipoprotein (LDL)
cholesterol levels to be calculated using the equation developed by Friedewald, Levy, and

Fredrickson (154):

 

LDL-cholesterol = (total chol) — (HDL chol) — Triglycerides/5

 

 

 

LDL-cholesterol was calculated only in subjects with triglyceride values less than
<400mg/dL, because as triglyceride levels increase, the proportion of cholesterol to

triglycerides in VLDL-cholesterol decreases, which can lead to overestimation of VLDL-

41

cholesterol and underestimation of LDL-cholesterol, therefore causing errors (155). To
assess accuracy of measurements, CDC prepared quality control pools (one for normal
concentrations and one for elevated concentrations) and assigned reference values for
each pool for total cholesterol, HDL-cholesterol and triglycerides. For cholesterol
measurements, a coefficient of variation (CV) of 5 3% was allowed. Accuracy of
triglyceride measurements was determined with standardization criteria developed by
CDC (e.g. if the concentration was between 89-176 mg/dL a maximum bias of 21:10 and a
maximum standard deviation of 10 was allowed). A Similar approach was used for HDL-
cholesterol (e.g. a concentration of < 40 mg/dL was allowed a i10% variation from the

reference value and a standard deviation of 2.5) (154).

3.3.2.2 Serum Glucose

Serum glucose concentration was measured with Hitachi Model 737 multichannel
analyzer (Boehringer Mannheim, Indianapolis, IN) in participants 12 years and older as
part of a standard sequence of biochemical assessments (154). In analyzing glucose
measurements, two types of quality control systems were used: 1) sample quality control
and 2) batch quality control. The sample quality control was allowed a 5% coefficient of
variation between-assays and within-assays. The batch quality controls were placed in the
calibration rack at the beginning and at the end of the rack of the entire measurement run

(154).

42

3.3.2.3 C-Reactive Protein

C-Reactive Protein was quantified in the Immunology laboratory, Department Of
Medicine, University of Wasington, by latex-enhanced nephelometry using a
modification Of the Behring latex enhanced c-reactive protein assay on the Behring
Nephelometer Analyzer System (BNA) (Behring Diagnostics, Westwood, Massachusetts)
(154). Two types Of quality control measures were used to determine the accuracy of the
measurements. The assay could detect a minimal concentration of 0.21 mg/dL, and
values below this level were classified as undetectable. A majority Of individuals had
values at the minimal detectable concentration, and therefore we categorized C - reactive
protein as a categorical variable (grouped as 5 0.22 mg/dL as normal and > 0.22 mg/dL
as elevated) to determine the prevalence estimates in the population (154). C-reactive
protein was not used in analyses for Aim 3 due to the continuous distribution that it

represents.

3.3.2.4 Blood Pressure and Blood Pressure Percentiles

Three blood pressure measurements were taken in the MEC by a trained physician
using a standard procedure described below (154). The first, fourth, and fifth Korotkoff
sounds (K1, K4, and 1(5) were recorded for those 5-19 years of age. The equipment used
included a baumanometer, blood pressure cuffs, and a Littman-Classic Stethoscope. In
measuring blood pressure the maximum inflation level was determined and three blood
pressure readings were obtained. The participant was requested to be seated at the table
in a relaxed but not Slouchy position, with feet flat on the floor. The right arm of the

participant was placed on the table, and Slightly flexed with palm upward. The arm was

43

supported at heart level; the cuff was applied with bottom edge one inch above crease in
elbow. The blood pressure equipment was positioned so that the tube of the manometer
was away from the participant’s body while the inflation bulb was closer to the body
(154).

Blood pressure percentiles were calculated based on each individual’s age, gender
and height Simultaneously. The method to calculate the blood pressure percentiles was
obtained from the normative blood pressure percentiles determined from nine studies in
the USA (156). They had a total sample of 56, 103 children and adolescents (1-17 years
old) for systolic blood pressure and 41, 335 children and adolescents (1-17 years old) for
diastolic blood pressure of all race and ethnic groups. These percentiles calculated by
Rosner eta1., therefore represent the age-gender-height-specific percentiles for an
“average study” over the nine studies that played a role. They however, only presented
the 90th and the 95th systolic and diastolic blood pressure percentiles for children 1-17
years old in their paper. In this study we calculated all the percentiles for adolescents (12-
19 years old). This was done by calculating the mean normal blood pressure from the
regression coefficients provided by Rosner et al. (156) which came from the 9 studies
that he used in his analysis for each height (height was entered as z-scores into the
regression equations). We then calculated the z- scores of the blood pressure in our
sample subtracted from the mean blood pressure of the nine studies, divided by the
standard deviation, which was also provided by Rosner et al. (156). We then converted
the z-score to percentiles with PROBNORM in SAS. To take into account the 18-19 year

old kids in our analyses, we based the 18-19 year old children’s blood pressure on the 17

44

year old children’s blood pressure, since we didn’t find big differences in the mean blood

pressure levels between 18-19 year old adolescents and 17 year old adolescents.

Fasting Blood Biomarkers

This study includes children and adolescents 12-19 years old who fasted for 6
hours or more. In preliminary analyses done we chose only children who fasted for 9
hours or more, since that has been Shown to be more biologically plausible in terms of the
results Obtained from a previous study (157). However, we compared clinical
measurements on children who fasted for 6 to 9 hours with children who fasted for 9
hours or more and the results were found to be Similar in both (Table 3.1)
Table 3.1 Comparison of clinical measurements between adolescents ( 1 2-19 years old)

who fasted for 6-9 hours and adolescents who fasted for 9 hours or more: NHANES 111,
1988-1994

 

Fasted for 9 hours or

 

 

Biomarker Fasted for 6-9 hours more

N Missing Mean N Missing Mean
BMI-for-age Percentiles 651 8 59.52 1642 24 61.92
Waist circumference (cm) 642 17 76.50 1626 40 77.90
Triglycerides (mg/dL) 626 33 88.20 1578 88 86.04
Total cholesterol (mg/dL) 628 3 1 164.73 1580 86 163.90
LDL-cholesterol (mg/dL) 624 35 95.63 1562 104 96.21
HDL-cholesterol (mg/dL) 624 35 51 .33 1569 97 50.62
Serum glucose (mg/dL) 625 34 87.79 1538 128 87.95
C-reactive protein (mg/dL) 627 32 0.28 1556 l 10 0.31
Sy“°"°.B'°°d Press” 639 20 39.93 1602 64 36.41
Percentrle
mam"? mm" "“5“" 608 51 34.88 1537 129 35.58
Percentrle

 

3.4 Covariates

3.4.1 Chronological Age, Sexual Maturation Assessment and Age at Menarche

This study includes children and adolescents age 12-19 of age. Age was

calculated using the birth date Obtained from the Screener Questionnaire (152).

Questions on sexual maturity of the subjects were assessed by trained physicians during
the physical examination that took place in the MEC (154). Tanner sexual maturity
stages were based on the recommendations of Tanner (141, 142). Tanner stage of pubic
hair development, genitalia development in boys and breast development in girls was
assessed for each subject by a trained physician. Tanner stages range from 1 to 5 where
stage 1 represents immaturity, and stage 5 indicates full maturity (154). Age at Menarche
was also Obtained when administering the MEC Proxy Questionnaire. Data were

collected on age of menarche for girls 8-19 years of age (154).

3.4.2 Race/Ethnicity

Race and ethnicity was based on self-report and were categorized as non-Hispanic
White, non-Hispanic Black, Mexican American and Other race/ethnic participants.
Questions asked to adolescents 12-16 were answered by a proxy correspondent. During
the household interview, when administering the family questionnaire, a participant was
proved with a hand card and then asked the following question: “Are any of those groups
------- ‘5 national origin or ancestry? “ The choices on the card were 1) Mexican/ Mexican
American and 2) Other Latin American or Other Spanish — please Specify. The next
question asked “What is the number of the group that best represents ------- ‘5 race? “ And
the options on the card were 1) Aleut, Eskimo, or American Indian, 2) Asian or Pacific

Islander, 3) Black, 4) White, and 5) Another group not listed — Specify” (152).

46

3.4.3 Poverty Income Ratio (PIR)

The Poverty Income Ratio used is based on measures developed by the US
Bureau of the Census (154). The PIR was computed as a ratio of two components. The
numerator was the midpoint of the observed self reported family income category in the
Family Questionnaire. The denominator was the US poverty threshold (produced
annually by the Census Bureau and adjusted for changes caused by inflation), the age of
the family reference person, and the calendar year in which the family was
interviewed(154). Persons who reported having had no income were assigned a zero
value for PIR. There were a substantial proportion (10%, n=223) of participants in our

study who refused to report their income (154).

3.4.4 Physical Activity Assessment (Adolescents 17-19 years old)

Physical activity was assessed during the Household Adult Questionnaire, which
contained questions on usual leisure time physical activity in the past month (152). All
participants were asked if they walked 1 mile or more at a time without stopping or if
they jogged or ran, rode a bicycle, swam, participated in aerobics or aerobic dance, other
dancing, callisthenic or floor exercise, gardening or yard work, or lifted weights during
the past month. Participants were asked how many times they performed the exercise
activity, and based on the type of activity, the metabolic equivalent (MET) intensity
1evel(defined as a ratio of activity of metabolic rate: resting metabolic rate), was then

assigned by NHANES 111 staff for each reported activity (152). We then classified

47

participant’s usual physical activity level based on a recent review by Ainsworth et al

(158) as light (< 3 MET’S), Moderate (3-6 MET’S) and Vigorous (> 6 MET’S).

3.4.5 Television Viewing (Children 12-16 years Old)

Assessment of hours of television watched the previous day was Obtained from
the adolescents when administering the Youth Questionnaire during the MEC visit (154).
The question that was asked of 12-16 years Old was: “How many hours of TV did you
watch yesterday?” Television Viewing was categorized into watching television for 1

hour or less. 2-3 hours, 4-5 hours and 5 hours or more per day (154).

3.4.6 Smoking Status

Assessment Of smoking status was determined by questions asked on tobacco use
to youth ages 8-16 in the MEC during the Youth Questionnaire and Older adolescents 17-
19 during the Household Questionnaire (154). Both questionnaires contained questions
on the use of cigarettes and smokeless tobacco (snuff or chewing tobacco)(152). Self
reported smoking was determined by collapsing variables on both questionnaires that
were asked regarding current tobacco use. A participant was classified as a tobacco user
when he/she reported that he/She was currently smoking cigarettes, cigars or pipes, or if
he/she snuffed tobacco.

Biochemical determination of tobacco exposure was also performed by measuring
serum cotinine levels in blood Specimens Obtained by venipuncture in the MEC (154).
These cotinine levels were used to confirm tobacco use because sometimes people are not

truthful when they answer questions about their smoking behavior. The cotinine assay

48

involved isotope dilution, liquid chromatography and tandem mass spectrometry. We
used cutoff points of higher than lSng/mL and 15ng/mL or less of cotinine in serum to
designate active tobacco users and non-tobacco users. Previous studies using NHANES
data used these cut-off points and have demonstrated a 96% concordance between self
reported smoking status and serum cotinine levels (159, 160)

Smoking status was then categorized into three groups: Smoker, Non-Smoker and
Possibly Smoker (See Table 3.2).

Table 3.2 Two-by-two table of smoking status and cotinine levels

 

 

Self Cotinine Levels

Reported

Smoking

Missing > 15 ng/mL _<_ 15 ng/mL
N * % (of US N * %(of US N * % (of US
population) population) population)

Yes 11 0.64 173 11.74 36 1.44

No 142 56.62 73 3.73 1888 76.84

 

"' N of the sample in the study population

If a participant had cotinine levels > 15 ng/mL and self-reported smoking was yes
then the participant was classified as a smoker. If a participant reported smoking and
cotinine levels were less than 15 mg/dL then he/she was classified as smoking, because
he/She might just not have smoked that day, and cotinine only has an in vivo half life of
24 hours which is still better than the in Vivo half life Of nicotine which is about 30
minutes (161). If a participant reported no smoking and cotinine levels were > 15ng/mL
than that was grouped as possibly smoking. If a participant had missing cotinine levels
and reported smoking then he/she was classified as smoking, and if a participant had
missing cotinine levels and reported a non-smoker then he/she was grouped as a non-
smoker. Finally, if a participant reported no smoking and cotinine levels were classified

as 5 lSmg/dL, then he/She was grouped as a non-smoker.

49

3.5 Analytic Sample and Characteristics

The original adult file (17 years +) consisted of 20050 people, and the original
youth file (2 months to 16 years) had 13994 children (see table 3). The present study
included only 17-19 year old adolescents (n=1225) from the adult file and adolescents
12-16 years old from the youth file (n=2216). Participants were then excluded from the
study if they used drugs such as adrenal corticosteroids, estrogen/progestins, blood
glucose regulators, thyroid/antithyroid drugs, oral contraceptives, or if they had any
disorders of growth hormone secretion, pregnant girls, and girls who were pregnant in the
last 2 years, participants with diabetes mellitus without mention of complication and
cystic fibrosis. These exclusions were made because these conditions or drugs may affect
the biomarkers related to MS.

Participants were considered eligible if they completed both the household
questionnaire and the examination which took place in the MEC (see table 3.3).
Therefore, 222 participants (7% of eligible participants) were excluded because they did
not have information from both assessments. Furthermore, only participants who fasted
for 6 hours or more (23% of participants were missing and not included) were included
in these analyses to ensure stable estimates, which brought the total sample size to 2323
participants for analyses in Aim 1. Aim 2 included participants who also had information
on sexual maturation assessment available, which included only pubic hair assessments.
The total sample for Aim 2 thus includes 2062 participants who also fasted for 6 hours or
more (11% were missing from the sample who fasted for 6 hours or more). The final

sample, which is used to analyze aim 3, included 1931 participants who had complete

50

data on all variables under study (17% were missing from participants who fasted for 6

hours or more).

Table 3.3 Analytic Sample of Adolescents (12-19 years) in NHANES III

 

0
Status and reason for exclusion N A Of total N (Total)

 

sample Sample
Adult File (17 years +) 20050
Adult File (17-19) 1225
Youth File (2 mnths — 16 years) 13944
Youth File(12-16) 2216
Adolescents eligible for study before exclusion 3441
Excluded Participants:
Adrenal Corticosteroids 24
Estrogen/Progestins 6 1
Blood Glucose Regulators 23
Thyroid/Antithyroid 3
Contraceptives 52
Disorders of Growth Hormone Secretion 0
Pregnant now/Pregnant during 2 years 66
- Missing Pregnancy Info 65
Diabetes mellitus without mention of complication 9
Cystic Fibrosis 0
Elevated Glycated Hemoglobin > 8 0
Eligible participants: 3220
Interviewed but not examined” 222 7
Interviewed and examined: 2998
Participants who did not fast I 675 23
Total sample who fasted for 6 hours or more (Aim 1): 2323
Participants with missing puberty measurementsz 261 l 1
Total sample who fasted for six hours or more and who had 2 062
puberty measurements (Aim2):
Participants with missing biomarkers: 392 17
Participants will all biomarkers missing 1 0
Total sample who fasted for six hours or more and who
have all biomarkers for analysis including tanner 1931
measurements (Aim 3):
Total Analytic Sample for factor analysis: 1931

 

* The % of the total sample is calculated from the total eligible sample after exclusion from participants
who were not interviewed or examined; 1' The % of the total sample for participants who did not fast is
calculated from participants who were interviewed and examined; I The % of the total sample is calculated
from the sample who fasted for six or more hours

51

We compared the demographic characteristics of participants (boys and girls) with
missing variables (e.g. participants who did not fast for more than 6 hours (n=1192),
missing Tanner stage variables, or missing risk factor variables related to MS, to
participants who had no variables missing (n=1806) (see table 3.4). We performed chi-
square tests in SUDAAN to determine if there were significant differences between eligible
participants and non-eligible participants by age, race/ethnicity, poverty income ratio,
physical activity (among 17 years and older), television watching (among 16 years and
younger) and tobacco use. Overall, there were only Slight differences among the two
groups. Significant differences were found by age in boys (p<0.01). When looking at
Tanner stage (assessed by pubic hair), significant differences between the two groups were
found for boys (p < 0.01). Thus, the final analytic sample among boys was slightly older
and more mature. This was not seen in girls. There were less younger girls but more
mature. These data indicate, as expected that girls reach puberty much earlier than

boys.

52

Table 3.4 Select Characteristics of adolescents (12-19 year Old) in NHAN ES 111 with
missing values * compared to adolescents with no missing values

 

 

 

 

Sangrle with any missing values Final Analytic Sample

Boys (n=606) Girls (n=586) Boys @889) Girls (n=917)
Characteristic N T521" SE N T;‘?' SE N T22" SE N T12" SE
Age at
Interview
(Years)“
12 100 15.13 1.83 93 12.63 2.09 108 12.77 1.94 111 10.12 1.58
13 79 12.14 1.47 91 12.65 1.94 116 13.68 2.03 130 13.27 1.39
14 84 16.11 2.94 96 20.26 2.82 100 11.67 1.76 114 13.03 1.93
15 74 11.26 2.07 68 12.32 2.44 110 10.46 1.15 105 12.92 1.48
16 70 12.75 2.22 82 14.34 2.00 124 13.61 1.71 125 12.13 1.83
17 84 14.16 2.99 64 9.42 1.85 110 13.12 1.78 117 13.48 1.66
18 70 13.59 2.15 53 11.29 2.76 103 9.89 1.33 102 11.97 1.80
19 45 4.86 1.30 39 7.10 1.92 118 14.81 2.25 113 13.08 2.40

Tanner stage
at Interview

(pubichair)

1 33 4.57 1.27 4 0.90 0.80 61 6.42 1.33 7 1.06 0.49
2 26 3.04 0.94 19 4.70 1.22 46 4.21 1.01 34 3.32 0.89
3 55 8.10 2.31 43 5.63 1.33 87 9.22 1.48 81 7.42 1.44
4 85 16.31 3.29 116 20.98 2.31 183 25.51 2.63 322 35.11 2.52
5 244 37.41 4.13 203 30.92 3.33 512 54.64 3.55 473 53.09 3.02
Missing 163 30.58 6.52 201 36.87 4.64 - - - - - -
Race/

Ethnicity"

$21335an 138 62.33 4.21 161 64.03 3.80 221 69.42 3.22 241 62.60 3.71
SERH'SPM'C 231 17.86 2.33 232 18.03 2.25 307 14.14 1.43 306 16.06 2.06
Mex'fa" 197 7.86 1.32 163 7.31 1.27 331 8.70 1.13 315 9.62 1.35
American

Other 40 11.95 3.16 30 10.62 2.22 30 7.74 3.33 55 11.73 2.72
Poverty

IncomeRatio

<1 215 20.95 2.52 202 23.89 3.02 294 17.85 2.11 293 21.35 2.43
31and<2 141 24.04 3.03 135 21.37 2.92 237 21.96 2.36 240 22.56 2.80
>_2 181 47.35 3.85 189 47.93 4.27 268 49.76 2.78 304 49.99 3.28
Missing 69 7.67 1.30 60 6.81 1.51 90 10.43 2.08 80 6.09 1.47

 

53

Table 3.4 Select Characteristics of adolescents (12-19 year old) in NHANES III with
missing values * compared to adolescents with no missing values (cont’d)

 

 

 

 

Sample with any missing values Final Analytic Sample

Boys (n=606) Girls Q1=586) Boys (n=889) Girls (n=917)
Characteristic N T529 SE N T12? SE N T22" SE N T12" SE
Hours ofTV
watched
yesterday
(12-16 years)“
0-1 84 21.56 3.47 122 30.45 3.69 139 29.66 3.63 140 34.90 3.84
2-3 139 41.32 4.47 154 40.03 3.98 177 32.00 2.67 203 31.43 2.87
4-5 145 27.53 3.22 135 25.49 3.10 209 31.60 3.12 210 26.45 3.13
>5 39 9.59 2.32 19 4.03 1.22 33 6.74 2.00 32 7.22 1.71
Physical
activity level
(17-19years)
Inactive 12 4.70 2.41 214 9.53 2.67 15 3.61 1.53 44 11.38 2.54
Moderate 65 32.21 5.07 73 47.06 4.86 96 30.29 3.95 168 45.40 5.32
Vigorous 120 62.85 5.28 58 42.06 4.30 212 64.42 4.39 114 41.4 5.79
Missing 2 0.24 0.18 1 1.34 1.67 8 1.68 0.84 6 1.78 0.93
Smoking
Statusi
Smoking 63 13.19 2.75 44 13.80 2.39 108 15.20 2.25 66 11.67 1.93
Not Smoking 518 83.43 3.09 529 83.36 2.44 745 80.51 2.45 823 84.07 2.49
P0559" 25 3.38 1.06 13 2.84 0.99 36 4.29 1.12 28 4.26 1.09
Smoking

 

"' Missing values include adolescents who fasted for less than 6 hours or adolescents with missing Tanner stage
measurements or adolescents missing all biomarkers related to MS. I Smoking Status: Smoking: Self-
reported tobacco use and cotinine levels > 15 ng/m;Not Smoking: Self-reported non-tobacco user and
cotinine levels 5 15 ng/mL; Possibly Smoking: Self-reported non-tobacco user and cotinine levels > 15
ng/m; # no missing value

3.6 Statistical Analysis
3.6.1 Statistical Software

All analyses were done using SAS (162) and SUDAAN (163) statistical software.
SUDAAN was used to account for the unequal probability of selection (which resulted
from the cluster design and over sampling of some groups) through sample weights.
SUDAAN also adjusts for non-coverage and non-response bias, and is used to determine

the correct variance estimates (163).

54

3.6.2 NHAN ES 111 Weighting Methodology

Due to the weighted sampling design, each participant did not have the same
probability of being selected, therefore the sample weights were used in statistical
analysis to produce correct population estimates for the population (154). Overall,
sample weight calculations took place in three stages. The first stage involved the
calculation of weights to compensate for unequal probabilities of selection. The second
stage adjusted for non-response. The third stage used post-stratification of the sample
weights to Census Bureau estimates of the United States population (152). We utilized
the weights calculated by NHANES for the total NHANES sample, since all the variables

used in this analyses were determined across all Six years of study.

3.6.3 NHANES III Variance Estimation

Variance estimates were obtained with statistical methods by NHANES III
statisticians to assure that results Obtained weren’t biased and based on a Simple random
sampling assumption. In the present study we used the Taylor serious linearization

approach to obtain the variance estimates (154).

3.6.4 Statistical analysis for Aim 1
To examine the distribution and determine the prevalence of biomarkers related to
Metabolic Syndrome in boys and girls 12-1 9 years by age, within gender, race/ethnicity
and poverty income ratio
The means, percentile estimates, and estimated standard errors by age within

gender, race/ethnicity and poverty income ratio were generated for each of the

55

biomarkers related to MS. These included BMl-for-age percentiles, waist circumference,
triglycerides, total cholesterol, LDL-cholesterol, HDL-cholesterol, serum glucose,
systolic blood pressure, diastolic blood pressure and C-reactive protein. SUDAAN, was
used to take into account the sampling weights and the complex sample design of the
survey. We then determined whether there were any Significant differences between age
groups by conducting t-tests between each age group within each strata in SUDAAN.

We calculated the prevalence estimates of elevated levels of each risk factor by
age, using the cut-off points as suggested by Cook et al. (1). Cook et al. modified the
NCEP’S ATP III definition of Metabolic Syndrome in adults to be used in adolescents 12-
19 years’ old also using data from NHANES 111(1). They Obtained their cut-off points
from various pediatric reference data, and we used similar cut-Off points in our study (see
table 2.5). Elevated blood cholesterol levels for adolescents were determined from the
NCEP Report of the Expert Panel on Blood Cholesterol Levels in Children and
Adolescents (52). According to the NCEP Report of the Expert Panel on Blood
Cholesterol Levels in Children and Adolescents the HDL-cholesterol borderline range for
all sexes is 35 to 45 mg/dL, therefore the midpoint of the range, 40 mg/dL was defined as
the cut-off point fOr low HDL-cholesterol levels. The borderline range for triglyceride
levels in children 10-19 years range from 90-129 mg/dL, therefore the midpoint would be
110mg/dL, which again was used as the cut-off value for triglycerides. The cut-off value
for LDL-cholesterol was > 130 mg/dL and total cholesterol was 2 200 mg/dL (52). The
cut-off value for elevated fasting glucose was determined by the American Diabetes
Association guidelines, which is 1 10mg/dL or higher for all children (164). There are

currently no reference values for waist circumference in children or adolescents, therefore

56

abdominal Obesity was defined as subjects above the 90th percentile value for their age
and sex obtained from this sample population. The same procedure to define waist
circumference at the 90‘'1 percentile was used by Cook et al. (1). C-reactive protein
concentrations were based on two categories: undetectable (< 0.22mg/dL) and elevated
(3 0.22 mg/dL) levels which is based on a previous approach taken by Visser et al. when
analyzing NHANES 111 data in children and adolescents (1 15). High-normal blood
pressure is defined as systolic and diastolic blood pressure greater than or equal to the
90th percentile for age, sex and height, and hypertension is defined as systolic or diastolic

blood pressure greater than or equal to the 95th percentile for age, sex and height (96).

3.6.5 Statistical analysis for Aim 2
T o examine the distribution of biomarkers related to MS in boys and girls
12-19 years by sexual maturation stage (pubic hair assessment) within gender,
race/ethnicity and poverty income ratio

A similar analytic approach as in Aim 1 was taken in Aim 2, (see above). The
mean, estimated standard error and percentiles of all the biomarkers related to MS were
calculated using SUDAAN by stage of sexual maturation within gender, race/ethnicity
and poverty income ratio within stage of sexual maturation. We used pubic hair
assessments as an indicator of sexual maturity, because this measurement was obtained
from both boys and girls and may be less influenced by body fatness than breast
development. A similar approach was taken by Cook et al. (1) but for this analysis we
calculated prevalence estimates of biomarkers related to metabolic syndrome by sexual

maturation stage in boys and girls 12-19 years old. We used t-tests to determine if there

57

were significant differences between mean values for each biomarker related to MS

represented at the different stages within gender, race/ethnicity and poverty income ratio.

3.6.6 Statistical analysis for Aim 3
T o examine the clustering of biomarkers that have previously been associated with
Metabolic Syndrome in adults, in boys and girls 12-19 years using factor analysis, and
the association between demographic variables and these identified factor scores
Principal component factor analysis was used to condense the highly
intercorrelated biomarkers potentially related to MS. A weighted correlation matrix was
created from the biomarkers related to MS using PROC GLM in SAS. This procedure is
ran before the factor analysis procedure, in order to account for the complex survey
design of NHANES III by pooling the variance and covariance within the sampling
stratum. Second, the factor analysis was performed by using the PROC FACTOR
procedure using principal component analysis with varimax rotation. The principal-
component analysis transforms the original variables into a new set Of factor scores or
components, which are independent of each other (53). Varimax rotation is used to obtain
factor loadings that are orthogonal and independent of each other through the factor
procedure. We standardized each variable and then included them into the factor analysis
so that each variable had a mean of 0 and a standard deviation of 1. In order to identify
the most meaningful factors, factors with eigenvalues (“represents the amount of variance
attributable to each component” (53)) greater than 1 were retained. Factor Analysis,
therefore, uses the correlation matrix to identify the factors, which assigned to each

individual in the dataset a factor score for each factor that varied from high to low. In

58

these analyses a person loaded high on a factor if the person had a high score for a certain
factor or loaded low on a factor if a person had a low score on a factor. Therefore
“loading on” a factor is the correlation between each variable and the various factors that
cluster with this variable. To determine which variables loaded on each factor we chose a
minimum absolute loading value of 0.30. Therefore, any value above or equal to an
absolute value of 0.30 would be considered a significant loading on a specific factor.
These factor scores were then divided into quartiles and further analyses were done to
assess the association between specific demographic variables and the factor scores. An
example of the purpose of factor analysis is explained in the diagram (Figure 3.1) below.
We start out with many correlated variables (biomarkers for Metabolic Syndrome) which
are reconstituted into two relatively non-overlapping circles with different variables
(biomarkers in each factor) (165).

Figure 3.1 Diagrammatic representation of the identification of factors (clusters of

biomarkers) associated with the risk of Metabolic Syndrome in adolescents 12-19 years
old: NHANES 111, 1988-1994. Based on Kleinbaum et al. (165)

 

 
 
     
   
   
   

 

Factor 1

 

 

  

BMI

 

WC
HDL-C
TG
SBP DBP
Glucose

Factor analysis

v

 

 

Factor 2

 

 

 

 

 

 

Biomarkers correlated Independent
with MS in adults biomarkers that predict
MS risk in adolescents

 

 

 

 

 

 

 

 

59

BMI: BMI-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles

60

Chapter 4: Results

4.1 Description of NHANES 111 population based sample

The following analyses were completed on data that came from the National
Health and Nutrition Examination Survey (N HANES III), conducted by the National
Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention
(CDC). This study was carried out in 50 states and the District of Columbia of the United
States in two sets of phases, l988-1991 and 1991-1994. Therefore this sample in Table
4.1 (adolescents 12-19 years old) is representative Of the total US adolescent population.

Among girls and boys in this sample who fasted for 6 hours or more, age was evenly
distributed and there were no significant differences in the sample number in girls or boys
across 12-19 years Old. There were slightly younger boys and Slightly less older girls in
the sample.

The majority of the sample has reached Tanner stage 4 and was non-Hispanic
white. There were only a small amount of boys and girls in Tanner stage 1, Tanner stage
2 and Tanner stage 3. The percentage distribution among boys and girls were not
significantly different from each other within Tanner stage.

Girls and boys were distributed evenly among poverty income ratio. The majority
of boys and girls had a poverty income ratio greater than two. From this latter statement,
we can make the assumption that the girls and boys with a poverty income ratio greater
than one are probably mostly non-Hispanic white, Since most of the adolescents in this

sample were non-Hispanic white.

61

Television watching among girls and boys 12-16 years old was evenly distributed
among girls and boys. Girls were more likely to be categorized as inactive (10.45%)
compared to boys (4.42%), and boys (62.18%) were also more likely to have Vigorous
activity levels compared to girls (43.10%). Smoking was evenly distributed among girls
and boys, and the majority of adolescents were categorized as non-smokers. In the
following results for aim 1 and aim 2 we focused primarily on median values than mean

values, since mean values were more likely to be influenced by outliers.

4.2 Results for Aim 1
Aim 1 : T o examine the distribution and determine the prevalence of biomarkers related
to Metabolic Syndrome in boys and girls 12-19 years by age within gender, race/ethnicity
and poverty income ratio.

The first analyses describe the mean and standard error for each biomarker related
to Metabolic Syndrome (MS) as well as the distribution across each biomarker at the 10th,
50th, and 90th percentile. Each biomarker was examined by age, by age within race/ethnic

group and by age within poverty income ratio.

4.2.1 Distributions of biomarkers by chronological age
BMI-for-Age Percentiles

The distribution of BMl-for-age percentiles for boys and girls are described in
Table 4.2. In both boys and girls, older children 18-19 years old tend to have lower
median percentile values on the BMl-for-age growth charts compared to younger boys

and girls.

62

Among boys, Mexican Americans had higher median percentile levels on the
BMI-for-age growth charts compared to non-Hispanic whites and non-Hispanic blacks
across all age groups. Non-Hispanic white boys had Significantly lower median BMI-for-
age percentile values at age 18-19 (48.20) compared to other age groups (12-13 (60.12),
14-15 (69.48) and 16-17 (61.87)). Among girls in all race/ethnic groups, median
percentile values tend to decrease slightly with age on the BMl-for-age growth charts. In
contrast to boys, non-Hispanic black girls and Mexican American girls both had much
higher median percentile values than non-Hispanic white girls across all ages.

Among boys there was no Significant linear trend in BMI-for-age percentiles
across age groups within poverty income ratio. However, boys 18-19 years Old with a
poverty income ratio greater than two had significantly lower median values on the BMI-
for-age growth charts (38.01) compared to other age groups (12-13 (58.36), 14-15
(67.34), 16-17 (65.44)). Girls, on the other hand, with a poverty income ratio less than
one had a much greater median percentile value on the BMI-for-age growth charts
compared to girls with a poverty income ratio greater than two.
Waist circumference

In Table 4.3 waist circumference levels are described by age among boys and
girls, by age within race/ethnic group and by age within poverty income ratio. In both
boys and girls, waist circumference levels increased with age. Waist circumference levels
among girls were only Slightly less compared to those of boys.

Among race/ethnic groups, non-Hispanic white boys and Mexican American boys
had slightly higher waist circumference levels compared to non-Hispanic black boys

across all age groups. (For e.g. 18-19 year old Mexican American boys (91.55 cm) and

63

non-Hispanic white boys (79.49 cm) vs. non-Hispanic black boys (75.90 cm)). Among
girls, non-Hispanic blacks had higher waist circumference levels when compared to non-
Hispanic whites across all age groups. Mexican American girls also had higher values
compared to non-Hispanic whites, but not quite as high as non-Hispanic black girls.

Median waist circumference levels across all age groups among boys with a
poverty income ratio less than one, and greater than and equal to one and less than two,
were slightly higher compared to median waist circumference levels among boys with a
poverty income ratio greater than two. Waist circumference levels in girls also seemed
to decrease as poverty income ratio increased.

Triglycerides

Triglyceride levels for boys and girls 12-19 years old are described in Table 4.4.
Median triglyceride levels varied by age within boys and girls. Among boys 14-15 years
old, median triglyceride levels were much lower (72.30 mg/dL) compared to 12-13 year
olds (79.84 mg/d), 16-17 year olds (79.69 mg/dL) and 18-19 year olds (88.06 mg/dL).
Whereas among girls 14-15 years old, median triglyceride levels tend to be much higher
(75.63 mg/dL) compared to 12-13 year olds (75.23 mg/dL), 16-17 year olds (71.32
mg/dL) and 18-19 year olds (72.22 mg/dL).

Non-Hispanic white boys, followed by Mexican American boys, had higher
median triglyceride levels compared to non-Hispanic black boys across all age groups.
Among Mexican American girls and non-Hispanic white girls, median triglyceride levels
differed at various ages and there was no significant linear trend. However, both non-
Hispanic white girls and Mexican American girls had much higher median triglyceride

levels compared to non-Hispanic black girls across all age groups.

64

There was no trend seen in median triglyceride levels, when stratifying by age
within poverty income ratio in both boys and girls. Although 18-19 year old boys with a
poverty income ratio less than one seem to have very high median triglyceride levels
(103.36 mg/dL) compared to the younger age groups (12-13 (72.29 mg/dL), 14-15(70.92
mg/dL), 16-17 (81.33 mg/dL). In contrast, compared to boys 18-19 years old, girls with a
poverty income ratio less than one at age 18-19 had a median triglyceride level of 69.84
mg/dL.

Total Cholesterol

Table 4.5 describes the distribution of total cholesterol levels in the adolescent
population 12-19 years old. Overall, there was no linear trend in median total cholesterol
levels within boys and girls across the different age groups. Girls however, seemed to
have slightly higher median total cholesterol levels compared to boys.

Non-Hispanic black boys had higher median total cholesterol levels across all
ages compared to non-Hispanic white boys and Mexican American boys. Mexican
American boys also had higher levels compared to non-Hispanic white boys but these
levels were not as high as non-Hispanic black boys. Among girls Similar results were
found. Non-Hispanic black girls had higher total cholesterol levels compared to non-
Hispanic whites and Mexican Americans across all ages, but in contrast to boys, non-
Hispanic white girls had higher total cholesterol levels compared to Mexican American
boys.

Boys with a poverty income ratio less than one had greater median total
cholesterol levels compared to boys with a poverty income ratio greater than equal to one

and less than two and greater than and equal to two. Among girls, the same results were

65

found, although girls 18-19 years old (176.42 mg/dL), with a poverty income ratio greater
than two, had a much higher total cholesterol levels compared to other girls 18-19 years
old with a poverty income ratio less than one, and greater than and equal to one and less
than two.

HDL-Cholesterol

Table 4.6 describes the distribution of HDL-cholesterol levels in boys and girls
12-19 years old. HDL-cholesterol levels decreased with age in boys across all age groups.
There was no such linear trend in girls, but girls did have higher HDL-cholesterol levels
across all ages compared to boys.

Non-Hispanic white boys had slightly lower median HDL-cholesterol levels
compared to non-Hispanic black boys and Mexican American boys across all ages. Non-
Hispanic black boys had the highest HDL-cholesterol levels compared to the other
race/ethnic groups. Similar results but not as dramatic, were found in girls.

Across all ages boys with a poverty income ratio greater than two had lower
HDL-cholesterol levels compared to boys with a poverty income ratio less than one.
Among girls no significant differences were seen by age within poverty income ratio.
LDL-Cholesterol

In Table 4.7 we reported the distribution of LDL-cholesterol in boys and girls 12-
19 years old by age, by age within race/ethnic group and by age within poverty income
ratio. There was no significant linear trend in median LDL-cholesterol levels across all
age groups in boys and girls. LDL-cholesterol levels were similar among both boys and
girls with only Slight observable differences across age groups. Among older children 14-

15 years (91.01 mg/dL for girls vs. 88.66 mg/dL for boys), 16-17 years (89.02 mg/dL for

66

girls vs. 86.60 mg/dL for boys) and 18-19 years (99.04 mg/dL for girls vs. 92.54 mg/dL
for boys), girls seemed to have slightly higher median LDL-cholesterol levels.

Among both boys and girls across all ages, non-Hispanic blacks had higher
median LDL-cholesterol levels compared to non-Hispanic whites and Mexican
Americans. There were no significant differences across age groups within each poverty
income ratio for boys or girls.

Glucose

Glucose levels for boys and girls 12-19 years old are described in Table 4.8.
Glucose levels remained constant across age groups in both boys and girls. Boys had
higher median glucose levels compared to girls across all ages.

Non-Hispanic white boys and Mexican American boys had slightly higher median
glucose levels compared to non-Hispanic black boys at all ages. Differences however
between non-Hispanic white boys and Mexican American boys were very insignificant.
Among girls similar results were found, in that non-Hispanic white girls and Mexican
American girls had higher median glucose levels compared to non-Hispanic black girls.
No significant differences were found among age groups within poverty income ratio.
Systolic Blood Pressure 1

In table 4.9 the distribution of systolic blood pressure percentiles in boys and girls
12-19 years old are reported. Overall, there was no linear trend in median systolic blood
pressure percentiles across age groups in boys and girls. Girls tend to have slightly lower
median systolic blood pressure percentiles compared to boys.

Both Mexican American boys and non-Hispanic black boys had higher median

systolic blood pressure percentiles compared to non-Hispanic white boys across all ages.

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Among girls, non-Hispanic black girls had higher median systolic blood pressure
percentiles compared to non-Hispanic white and Mexican American girls.

Boys with a poverty income ratio greater than two had lower median systolic
blood pressure percentiles compared to boys with a poverty income ratio less than one,
and boys with a poverty index greater than one and less than two. Among girls no
significant differences were seen in systolic blood pressure percentiles by age within
poverty income ratio.

Diastolic Blood Pressure

In Table 4.10 diastolic blood pressure percentiles are described by age among
boys and girls, by age within race/ethnic group and by age within poverty income ratio.
Diastolic blood pressure percentiles in boys and girls increased with age, but the increase
was very slight. An interesting finding was that among all race/ethnic groups 18-19 year
old boys and girls had significantly higher median diastolic blood pressure percentile
values compared to the other age groups. Older non-Hispanic black boys and girls had
higher diastolic blood pressure percentiles compared to older non-Hispanic white and
Mexican American boys and girls. There was no significant linear trend among boys or

girls by age within poverty income ratio.

4.2.2 Prevalence estimates of elevated levels of biomarkers of MS in boys and girls
12-19 years old by age

In Table 4.11 we estimated the prevalence of elevated biomarkers related to MS
in boys and girls 12-19 years Old by age. Cut-off values for elevated biomarkers were

based on similar cut-off values as used by Cook et al. (1). Among boys 12-13 years old,

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20.43% were classified as being at risk for overweight, where among 18-19 year old boys
only 7% were classified as being at risk for overweight. In contrast to this, 10.43% of
boys 12-13 years old were classified as being overweight and 11.30% 18-19 years olds
were classified as being overweight. Girls on the other hand were more likely to be at
risk for overweight between ages 14-17 (14-15 (18.39%) and 16-17 (17.30%)), and were
more likely to be overweight at younger ages (12-13, 13.37%) compared to older ages
(18-19, 9.24%).

Among boys waist circumference prevalence estimates seemed to be similar
across age groups, and increased Slightly in boys 18-19 years old. Among girls the
prevalence estimates for waist circumference levels seemed to increase very slightly
between ages 14-17, and was lower at ages 12-13 and 18-19. We can make the
assumption that almost 10% of boys and girls in the US adolescent population had
elevated waist circumference levels.

Prevalence estimates for elevated triglyceride levels were very high among boys.
Almost one third of 16-17 year olds and 18-19 year old boys had elevated triglyceride
levels. Whereas among girls prevalence estimates for elevated triglyceride levels seemed
to stay constant throughout ages 12-19 and 20% of female adolescents in the population
according to these results have elevated triglyceride levels.

Low HDL-cholesterol levels were highly prevalent in boys across all age groups.
Boys 12-13 years Old had the lowest prevalence estimates for low HDL-cholesterol
levels. Almost 40% of boys 18-19 years old had low levels of HDL-cholesterol, which
puts them at risk. Among girls prevalence estimates were much lower compared to boys,

although 21% of girls 16-17 years old had low HDL-cholesterol levels.

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Overall the prevalence estimates for glucose levels among boys and girls were
very low compared to the prevalence estimates of other variables in our sample, and there
were no significant differences across age groups. Total-cholesterol prevalence estimates
increased with age in boys and were highest at age 18-19, where almost 15% of boys in
the population had elevated total cholesterol levels according to the NCEP definition.
Among girls the prevalence estimates were also highest for girls 18-19 years old.

Prevalence estimates for elevated LDL-cholesterol levels in boys was
Significantly lower for boys 14-15 years old (4.24%) compared to boys 18-19 years old
(13.23%). Among girls, prevalence estimates for elevated LDL-cholesterol levels seem
to increase with age, and ranged from 7.7% for 12-13 year old girls to 15.24% for 18-19
year old girls.

Prevalence estimates for high normal systolic blood pressure percentiles among
boys were similar across age groups, except 4.57% of boys 18-19 years old were
classified between the 90th and 95th percentiles i.e. they have high-nonnal systolic blood
pressure. Five percent of Boys 12-13 years old were classified as having hypertension (3
95th percentile) whereas prevalence estimates along other age groups were very low.
Among girls prevalence estimates for high-normal blood or hypertension was very low.
Only approximately 2% of girls in the adolescent population had systolic blood pressure
levels above the 85th percentile.

Prevalence estimates for elevated diastolic blood pressure were also very low in
both boys and girls. Almost no adolescents had elevated diastolic blood pressure, when

classifying them by the age-height-sex-Specific percentiles.

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Elevated C-reactive protein levels among boys were more prevalent among 14-17
years old, but this was not found to be significant. Among girls however, prevalence
estimates for C - reactive protein increased by age. Almost 20-25% of girls 18-19 years

old are classified with elevated C-reactive protein levels.

4.2.3 Conclusion

These data indicate that the distribution of biomarkers varied in the population by
age, gender, race/ethnicity and poverty income ratio. BMI-for-age percentiles decreased
by age in boys and girls and were higher in Mexican American boys and non-Hispanic
black and Mexican American girls. Waist circumference levels increased by age and
were higher in non-Hispanic white and Mexican American boys and higher in non-
Hispanic black girls. Triglyceride levels were highest in non-Hispanic white and Mexican
American boys and girls, and there was no significant trend by age. Total cholesterol
levels and LDL-cholesterol levels were higher among non-Hispanic black boys and girls.
HDL-cholesterol levels decreased with age in boys and increased with age in girls.
Among boys and girls non-Hispanic whites had lower HDL-cholesterol levels compared
to non-Hispanic black and Mexican American boys and girls. Glucose levels were fairly
equally distributed among age and non-Hispanic white boys and Mexican American and
non-Hispanic white girls had higher glucose values across all ages. Systolic and diastolic
blood pressure percentiles were higher among non-Hispanic black boys and girls, but
among boys, Mexican Americans also had higher systolic blood pressure percentiles

Prevalence estimates of elevated levels of biomarkers related to MS varied by age

for each biomarker. High levels of BMI-for-age percentiles ((_>_ 85‘h and < 95th BMI-for-

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age percentiles) and (3 95th BMI-for-age percentiles)), waist circumference, triglycerides
and HDL-cholesterol were found in approximately 10% or more of boys and girls across
all age groups. High total cholesterol, LDL-cholesterol and C-reactive protein prevalence
estimates also occurred in a significant amount of adolescents in the population.
Prevalence estimates of high glucose levels, systolic and diastolic blood pressure
percentiles were very low. We therefore can conclude that prevalence estimates of

elevated levels of biomarkers related to MS varied substantially by age.

4.3 Results for Aim 2
Aim 2: To examine the distribution of biomarkers related to MS in boys and girls 12-19
years old by sexual maturation stage (pubic hair assessment) within gender,
race/ethnicity and poverty income ratio

The second analyses describe the mean and Standard error for each biomarker
related to Metabolic Syndrome (MS) as well as the distribution across each biomarker at
the 10th, 50‘“, and 90th percentile. Each biomarker was examined by Tanner stage, by
Tanner stage within race/ethnic group and by Tanner stage within poverty income ratio.
The sample size for girls at Tanner stage one was very small, therefore those results were

found to be insignificant and will not be discussed.

4.3.1 Distributions of Biomarkers by Sexual Maturation Stage
BMI-for-age Percentiles
Table 4.12 describes the BMI-for-age percentiles for boys and girls by Tanner

stage. At Tanner stage 1, boys were more likely to be below the 50th percentile on the

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BMl-for-age growth charts. At Tanner stage 2 there was a significant increase from
39.25 for boys at Tanner stage 1 to 67.18 for boys at Tanner stage 2. The median
percentile values then tend to decrease at Tanner stage 3 (58.42) and 4 (52.11) and then
increased again at Tanner stage 5 (59.99). Girls followed a Similar pattern as boys and
had higher median percentile values on the BMI-for-age growth charts compared to boys.

BMI-for—age percentiles displayed considerable differences among race/ethnic
groups and there was no linear trend from Tanner stage 1 to Tanner stage 5 for boys or
girls. Non-Hispanic black girls had higher median BMI-for-age percentiles on the
growth charts compared to non-Hispanic black boys. There was no Si gnificant trend
found among poverty income ratio, although girls with a poverty income ratio less than
one had Slightly higher median BMI-for-age percentile levels compared to girls with a
poverty income ratio greater than two.

Waist Circumference

The distribution of waist circumference levels are reported in Table 4.13. Median
waist circumference levels in boys were slightly higher at Tanner stage 2 (75.65 cm)
compared to the other Tanner stages, and then increased again at Tanner stage 5 (77.23
cm). Among girls the same trend was found.

Overall among boys, non-Hispanic white boys and Mexican American boys had
higher median waist circumference levels compared to non-Hispanic black boys at all
Tanner stages. Mexican American boys at Tanner stage 5 had significantly higher
median levels compared to the other stages. Among girls, Mexican Americans had
higher median waist circumference levels at all Tanner stages compared to non-Hispanic

whites and non-Hispanic blacks although the differences were not extreme. Overall, boys

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and girls with a poverty income ratio less than one had slightly higher median waist
circumference levels, compared to boys and girls with a poverty income ratio greater than
and equal to two.

Triglycerides

In Table 4.14 the triglyceride levels for boys and girls by Tanner stage, by Tanner
stage within race/ethnic group and by Tanner stage within poverty income ratio are
reported. There was no Significant trend for triglyceride levels across sexual maturation
stages for boys or girls. Boys had slightly higher median triglyceride levels compared to
girls at all Tanner stages except Tanner stage 4, where median values among boys and
girls were found to be equal.

Non-Hispanic white boys at Tanner stage 2, 3, 4 and 5 had higher median
triglyceride levels compared to non-Hispanic black boys. Mexican American boys also
had higher median triglyceride levels compared to non-Hispanic black boys. Mexican
American girls had higher median triglyceride levels compared to non-Hispanic white
and non-Hispanic black girls. There was no trend among sexual maturation stages within

poverty income ratio for boys or girls.

Total Cholesterol

In Table 4.15 total cholesterol levels are described by Tanner stage among boys
and girls, by Tanner stage within race/ethnic group and by Tanner stage within poverty
income ratio. Boys at Tanner stage 1 (170.94 mg/dL) and Tanner stage 2 (172.09 mg/dL)
had median total cholesterol levels that were much higher than Tanner stage 3 (162.19

mg/dL), Tanner stage 4 (148.07 mg/dL) and Tanner stage 5 (155.27 mg/dL). Median

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total cholesterol levels at Tanner stage 1 and Tanner stage 2 were also higher for boys
compared to median total cholesterol levels for girls at Tanner stage 1 (150.60 mg/dL)
and Tanner stage 2 (158.09 mg/dL).

Non-Hispanic black boys had higher median total cholesterol levels at all sexual
maturation stages compared to non-Hispanic white boys and Mexican American boys.
Similar results were found for girls. Almost at all sexual maturation stages it seemed like
girls had slightly higher median total cholesterol levels compared to boys across all race
ethnic groups.

Among boys median total cholesterol levels were slightly lower if they had a
poverty income ratio greater than and equal to two, compared to boys with a poverty
income ratio less than one. Among girls there were no significant differences among
Tanner stage within poverty income ratio.

HDL—Cholesterol

A description of HDL-cholesterol levels are reported in Table 4.16 within boys
and girls by Tanner stage. Among boys median HDL-cholesterol levels decreased with
increasing Tanner stage. Among girls no significant differences were found. Girls tend to
have higher median HDL-cholesterol levels compared to boys, except at Tanner stage 2
where boys (50.00 mg/dL) had a higher median HDL-cholesterol level compared to girls
(46.74 mg/dL).

Non-Hispanic white boys and Mexican American boys had slightly lower median
HDL-cholesterol levels compared to non-Hispanic black boys at all sexual maturation
stages. Among girls, non-Hispanic white girls had lower median HDL-cholesterol levels

compared to non-Hispanic black girls and Mexican American girls.

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Boys with a poverty income ratio less than one had higher median HDL-
cholesterol levels at Tanner stage 1 through Tanner stage 4 compared to boys with a
poverty income ratio greater than and equal to two. Among girls no significant
differences were found within poverty income ratio by Tanner stage.

LDL-Cholesterol

Table 4.17 describes the LDL-cholesterol levels for boys and girls by Tanner
stage. Median LDL-cholesterol levels were higher in boys than in girls at Tanner stage 1
(105.27 mg/dL vs. 75.55 mg/dL), Tanner stage 2 (103.45 mg/dL vs. 88.47 mg/dL) and
Tanner stage 3(94.69 mg/dL vs. 93.20 mg/dL). At Tanner stage 4 girls had higher
median LDL-cholesterol levels than boys (91.98 mg/dL vs. 83.20 mg/dL), and at Tanner
stage 5 median LDL-cholesterol levels were equal among boys and girls (90.07 mg/dL
for boys vs. 90.08 mg/dL for girls).

Among boys, non-Hispanic blacks had higher median LDL-cholesterol levels
compared to non-Hispanic white and Mexican Americans across all Tanner stages. In
girls median LDL-cholesterol levels varied across race/ethnic groups by sexual
maturation stage, and there was no significant trend. For both boys and girls, there were
no significant differences between sexual maturation stages within poverty income ratio.
Glucose

In Table 4.18 glucose distribution among boys and girls 12-19 years old are
discussed. Overall levels remained constant for boys and girls from Tanner stage 1
through to Tanner stage 5. Median glucose levels for boys were higher than for girls at

all Tanner stages expect for Tanner stage 3 where girls had slightly higher median

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glucose levels compared to boys. Median levels also stayed constant between sexual
maturation stages among race/ethnic groups and poverty income ratio.
Systolic Blood Pressure Percentiles

The distribution of systolic blood pressure percentiles are reported in tables 4.19
among boys and girls. Median systolic blood pressure percentiles were higher among
boys compared to girls but did not decrease nor increase with increasing Tanner stage.

Non-Hispanic black boys and non-Hispanic black girls tend to have higher
median systolic blood pressure percentiles compared to non-Hispanic white boys and
non-Hispanic white girls. Overall Mexican American boys and girls also had higher
median systolic blood pressure percentile levels compared to non-Hispanic white boys
and girls, but not as high as non-Hispanic black boys and girls.

There was no significant trend between sexual maturation stages in each poverty
income ratio. Boys with a poverty income ratio less than one had higher median systolic
blood pressure percentile values at Tanner stage one and two compared to boys with a
poverty income ratio greater than and equal to two.

Diastolic Blood Pressure Percentiles

The distribution of diastolic blood pressure percentiles are reported in tables 4.20 among
boys and girls. Median diastolic blood pressure percentiles values were higher for boys
than girls at all sexual maturation stages, and for girls from Tanner stage 3 median
diastolic blood pressure percentiles seemed to increase with increasing Tanner Stage.

Non-Hispanic black boys and girls had higher median diastolic blood pressure
percentiles at Tanner stage 3, Tanner stage 4, and Tanner stage 5 when comparing to non-

Hispanic white boys and girls. Non-Hispanic black girls also had higher median diastolic

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blood pressure percentiles compared to Mexican American girls, but Mexican American
boys had higher values than non-Hispanic black boys at Tanner stage 2 and Tanner stage
5. There were no significant differences between sexual maturation stages when

stratifying by gender within poverty income ratio.

4.3.2 Prevalence estimates of elevated levels of biomarkers related to MS in boys
and girls by sexual maturation stage
Boys

Table 4.21 describes the prevalence estimates of elevated levels of biomarkers of
MS in boys 12-19 years old by sexual maturation stage. Cut-off values for elevated
biomarkers were again based on similar cut-off values as used by Cook et al. (1). Among
boys, 15% and more were classified as being at risk for overweight from Tanner stage 1
through Tanner stage 5. The prevalence estimate for overweight boys tend to increase by
Tanner stage, and then decrease dramatically at Tanner stage 4 after which it increases
again at Tanner stage 5. Elevated waist circumference levels showed a Similar pattern as
overweight in boys. Prevalence estimates increased from Tanner stage 1 (7.99 %)
through to Tanner stage 3 (17.86 %), and then decreased at Tanner stage 4 (5.67 %), after
which it then increased again in Tanner stage 5 (11.94%).

Prevalence of elevated triglyceride levels increased at Tanner stage 2 (28.16 %),
Tanner stage 3 (25.55 %) and Tanner stage 5 (26.10%) and was lower at Tanner stage 1
(9.36%) and Tanner stage 4 (16.47%). Prevalence estimates for low HDL-cholesterol

levels increased Significantly with increasing sexual maturation Stage.

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Boys at Tanner stage 2 were more likely to have elevated glucose levels
compared to other stages. Overall the prevalence estimates for elevated glucose levels
was low across all Tanner stages. Prevalence estimates for elevated total cholesterol
levels in boys varied by Tanner stage, and there was no significant increasing or
decreasing trend. Boys at Tanner stage 5 had the highest prevalence estimate for elevated
total cholesterol levels.

Similar results were found for elevated LDL-cholesterol levels. Prevalence
estimates for elevated LDL-cholesterol levels were highest in boys at Tanner stage 5.
Prevalence estimates for high-normal blood pressure and hypertension were very low at
all Tanner stages. Elevated C - reactive protein levels in boys increased slightly from
Tanner stage l(3.70%) and Tanner stage 3 (11.78%) and then stayed fairly constant at
Tanner stage 4 (9.80%) and 5 (10.34%).

Girls

Table 4.22 describes the prevalence estimates of elevated levels of biomarkers
of MS in girls 12-19 years old by sexual maturation stage. Cut-off values for elevated
biomarkers were also based on similar cut-off values as used by Cook et al. (1). Girls
were more likely to be at risk for overweight at Tanner stage 2 (29.45%) and again at
Tanner stage 5 (17.91%). Whereas they were more likely to be overweight at Tanner
stage 3 (13.35%) and Tanner stage 4 (11.56%) compared to Tanner stage 2(6.14%) and
Tanner stage 5 (8.66%). Almost a third of the female adolescent population was at risk
for overweight at Tanner stage 2 (29.45%).

Prevalence estimates for elevated waist circumference levels were greater at

Tanner stage 3 (17.75%) and at Tanner stage 5 (12.17%). Elevated triglyceride level

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prevalence estimates were higher at Tanner stage 2(29.12%) and Tanner stage 3 (27.91%)
and decreased significantly at Tanner stage 4 (17.92%).

Prevalence estimates for decreased HDL-cholesterol levels seemed to be higher
at Tanner stage 2 (20.19%) compared to Tanner stage 3 (18.79%), Tanner stage 4
(13.38%) and Tanner stage 5 (16.39%). Prevalence estimates for elevated total
cholesterol and elevated LDL-cholesterol levels were highest at Tanner stage 3 and
Tanner stage 5. The prevalence of norrnal-high blood pressure as well as hypertension
was low for girls at all sexual maturation stages.

Prevalence estimates of elevated C-reactive protein levels were higher among
girls than boys. Seventeen percent (17%) of girls at Tanner stage 2 had elevated C -
reactive protein levels compared to 9% of girls at Tanner stage 3, 1 1% of girls at Tanner

Stage 4 and 12% of girls at Tanner stage 5.

4.3.3 Conclusion

These data indicate that the prevalence of biomarkers related to metabolic
syndrome fluctuated by Tanner stage in boys and girls depending on the biomarker
studied. BMI-for-age differed by Tanner stage in boys and girls as did waist
circumference levels. Non-Hispanic white boys and Mexican American boys and
Mexican American girls had higher waist circumference levels at all Tanner stages. Also
boys with a poverty income ratio less than one had higher waist circumference levels.
Triglyceride levels showed no differences between Tanner stage for boys or girls, and
non-Hispanic white and Mexican American girls had higher levels. Total cholesterol

levels in boys were higher at Tanner stage 1 and Tanner stage 2 and stayed fairly constant

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in girls. Both non-Hispanic black boys and girls had higher total cholesterol levels across
all Tanner stages. In boys HDL-cholesterol levels decreased with increasing Tanner stage
was lower in non-Hispanic white boys and Mexican American boys and non-Hispanic
white girls. LDL-cholesterol levels varied in boys and girls within Tanner stage and non-
Hispanic black boys and girls had higher LDL-cholesterol levels compared to other
race/ethnic groups. Glucose levels remained constant at all Tanner stages and systolic
blood pressure percentiles were higher in boys than in girls, and higher in non-Hispanic
black boys and girls. Diastolic blood pressure percentiles differed by Tanner stage and
race/ethnicity and no linear trend was found.

We also conclude that extreme levels Of biomarkers related to MS fluctuated by
Tanner stage in terms of their prevalence in both boys and girls. It does seem as though
boys in Tanner stage 2 and girls in Tanner stage 3 were more likely to have the highest
prevalence estimates for all elevated biomarkers. However, there is no clear pattern of

when elevated levels of biomarkers related to MS occur.

4.4 Results for Aim 3

Analyses to address Aim 3 of this study are described below. The overall aim
was to examine the clustering of biomarkers of boys and girls 12-19 years that have been
associated with Metabolic Syndrome (MS) in adults. We used factor analysis to identify
patterns of biomarkers (represented as factor loading scores). We examined the
association between demographic variables and the identified overall factor scores both

within strata of chronological age and Tanner stage. The first analyses done in aim 3 were

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to look at the correlations between biomarkers related to MS in boys and girls 12-19

years old by chronological age and by Tanner stage.

4.4.1 Correlations
Overall Sample — Boys and Girls

Overall there were no substantial differences in correlations of biomarkers related
to MS between boys and girls within age or within Tanner stage. Boys and girls in this
sample were remarkably similar. Some slight differences in correlations (Table 4.23)
between boys and girls were that systolic blood pressure percentiles in boys (r=0.26)
were more correlated with triglyceride levels than in girls (r=0.05). Systolic blood
pressure percentiles in boys (r=0.23) were also more correlated with waist circumference
levels compared to girls(r=0.01). All other correlations among variables were similar in
both boys and girls.
Boys by Age

Table 4.24 list the correlations between biomarkers related to MS for boys within
each age group. Among boys there were no significant differences in correlations by age.
Some interesting observations were that in younger boys 12-13 years old, total
cholesterol levels were negatively correlated with BMI-for-age percentiles (r=-0.l3). As
boys became older, the correlation between BMI-for-age and total cholesterol became
more strongly and positively correlated (14-15 (r=-0.04), 16-17 (r=0.12), 18-19 (r=0.25)).
Similar results among boys were found for LDL-cholesterol levels and BMI-for-age.

BMI-for-age percentiles in older boys tend to develop a strong positive

correlation with glucose levels whereas in the younger boys this association was quite

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weak (18-19 (F025), 16-17 (r=-0.00), 14-15 (r=0.12), 12-13 (r=0.12). The strong
correlation between total cholesterol and HDL-cholesterol seem to decrease with age.
Triglyceride levels were strongly negatively correlated with HDL-cholesterol levels at all
ages (12-13 (r=-0.36), 14-15(r=-0.25), 16-17(r=-0.45), 18-19(r=-0.30)).
Girls by Age

Results for girls (table 4.25) were similar to boys, i.e. there were no substantial
differences in correlations between biomarkers related to metabolic syndrome by age.
However, triglyceride levels were negatively correlated to BMl-for-age in younger
adolescents, compared to a positive correlation between BMI-for-age and triglyceride
levels at an older age (12-13 (r=-0.03), 14-15 (r=0.30), 16-17(r=0.15), 18-19(r=0.31)).

An interesting finding was that waist circumference levels were negatively
correlated with total cholesterol at all ages, except 16-17 year old girls who had a positive
correlation with total cholesterol (F021). No other significant differences were found.
Boys and Girls by Tanner stage

Table 4.26 and Table 4.27 presents the correlations between biomarkers related to
MS in girls and boys 12-19 years old by Tanner stage 4 and Tanner stage 5, as well as
Tanner stage 4 and 5 combined. We combined Tanner stage 4 and 5 because correlations
were similar in Tanner stage 4 and Tanner stage 5 in both boys and girls. We anticipated
that factor loading scores would be Similar among boys and girls in Tanner stage 4 and

Tanner stage 5, but this result was not found (discussed below).

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4.4.2 Conclusion

In summary from the results above we conclude that correlations between
biomarkers of MS were Similar for boys and girls by age and Tanner stage, and there
were no substantial differences. In the following analyses which involve the factor
loading matrices we therefore combined boys and girls in our sample (which would be
referred to as overall sample) based on the assumption that correlations between

biomarkers did not produce any Significant differences.

4.4.3 Factor loading matrix for biomarkers related to MS in boys and girls by age
and Tanner stage

Tables 4.28 through Table 4.32 represent the factor loading matrices for
biomarkers related to MS in boys and girls 12-19 years old. First we utilized the factor
analysis procedure including boys and girls in the overall sample based on the conclusion
that the correlations were so similar among boys and girls. Second we utilized the factor
analysis procedure by gender because it was more biologically plausible. We then also
stratified by age and Tanner stage to see if there were any significant differences by these
covariates. Overall, the factors obtained varied considerably by both age and Tanner
stage in boys and girls. We believe that the sample sizes by age and Tanner stage were
too small to produce stable estimates. Therefore we identified the most meaningful
factors in boys and girls were when we pooled the total sample for each gender (boys and
girls) and also when broken down by gender (referred to as total sample for boys and

total sample for girls).

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In these results we will therefore discuss only the factors obtained in the overall
sample and the total sample for boys and total sample for girls presented in Table 6.
Adolescents in factor 1 of the overall sample and Boys in factor 1 of the total sample
loaded high and positive on BMI-for-age percentiles, waist circumference levels and high
but negative on HDL-cholesterol levels. They also loaded slightly high on triglycerides
but not as high. This factor is the most closely representative of the definition for
Metabolic Syndrome in adults.

Factor 2 in the overall sample and factor 2 in the total sample in boys loaded high
on total cholesterol and LDL-cholesterol. Factor 3 in the overall sample differed from
factor 3 in the total sample for boys. Factor 3 in the overall sample loaded high on
systolic blood pressure percentiles and diastolic blood pressure percentiles. Whereas
factor 3 in the total sample for boys loaded high on HDL-cholesterol and LDL-
cholesterol.

Girls, however, showed a very different pattern than boys. Factor 1 in girls was
identical to Factor 2 in the overall sample and the Factor 2 in the total sample for boys.
Factor 1 in girls also loaded high on total cholesterol and LDL-cholesterol. Factor 2 in
girls loaded high on BMI-for-age percentiles and high on waist circumference levels, and
high but negatively on glucose levels. Factor 3 in girls loaded high on glucose, systolic
blood pressure percentiles and diastolic blood pressure percentiles, and factor 4 in girls
loaded high and positive on triglycerides and glucose and high but negative on HDL-
cholesterol.

The first three factors obtained for the overall sample which included boys and

girls explained 60% of the variance out of all the other factors that were not included in

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the results since they did not have eigenvalues greater than one. The first three factors in
the total sample for boys as well as the first three factors in the total sample for girls also

accounted for 60% of the variance explained.

4.4.4 Demographic characteristics of factor loading scores for boys and girls

Demographic characteristics of the factor loading scores for Factor 1, Factor 2
and Factor 3 are presented in Table 4.33, Table 4.34 and Table 4.35. Physical activity
was assessed only in 17-19 year Old and television watching only in 12-16 year olds,
therefore quartiles was no longer evenly distributed among those 2 groups. This is due to
the fact that circulation of quartiles was based on the overall sample, the total sample for
boys and the total sample for girls.

Factor I —- Overall Sample (Boys and Girls)

We described the demographic characteristics of the overall sample for boys and
girls combined in Table 4.33. Factor I loaded high and positively on BMI-for-age
percentiles, waist circumference and negatively but high on HDL-cholesterol. Factor 1
also loaded high and positively on triglycerides but less significantly. Adolescents in the
lowest quartile of factor 1 were more likely to be younger. Adolescents in the highest
quartile of factor 1 were more likely to be older (16-19) compared to adolescents in the
lowest quartile.

Adolescents in lowest quartile of factor 1 were more likely to be in Tanner Stage 1
and adolescents in the highest quartile were more likely to be at Tanner stage 2. Non-
Hispanic white and Mexican American adolescents were more likely to be in the highest

quartile of factor 1 compared to non-Hispanic blacks. Also adolescents with a poverty

86

income ratio less than one were more likely to be in the highest quartile of factor 1, but
these differences were not significant. Adolescents who were in the highest quartile of
factor one were more likely to smoke (41.13% vs. 15.05% in lowest quartile)), be
inactive (46.23% vs. 14.93 in the lowest quartile) and watch 4-5 hours of television the
day before the interview (31.17% vs. 32.13% in the lowest quartile who watched only 0-1
hour).
Factor 2 — Overall sample (Boys and Girls)

Demographic characteristics of Factor 2 for the overall sample were discussed in
Table 4.33. Overall adolescents in the lowest quartile of factor 2 seemed to be between
the ages of 14-17, where as adolescents in the highest quartile seemed to be between 18-
19 years old. In contrast to this, adolescents in the highest quartile of factor 2 were more
likely to be in Tanner stage 1 (36.92 %) and Tanner stage 2 (33.28 %), where as
adolescents in the lowest quartile were more likely to be in Tanner stage 4 (35.52%).

Non-Hispanic blacks (28.24 %) were more likely to be in the highest quartile of
factor 2 compared to non-Hispanic whites (21.67%) and Mexican Americans (22.99%).
Adolescents in the highest quartile of factor 2 were more likely to have a poverty income
ratio less than one (25.29%), but adolescents in the lowest quartile of factor 2 had a
Similar percentage distribution (28.64%) as adolescents in the highest quartile.

Adolescents in the highest quartile of factor 2 where more likely to smoke, be
categorized as moderate physical activity and watch 4-5 hours of television the day
before the interview. And the opposite was found for participants in the lowest quartile.

They were more likely to be non-smokers, and be classified as having Vigorous physical

87

activity levels. Television watching was the same among all groups in the lowest quartile
of factor 2.
Factor 3 — Overall sample (Boys and Girls)

In Table 4.33, the demographic characteristics of adolescents in factor 3 are
reported. Adolescents in factor 3 loaded high on systolic and diastolic blood pressure.
Adolescents in the highest quartile of factor 3 were more likely to be 12-13 years old
(28.05%) and 18-19 years old (27.47%), whereas adolescents in the lowest quartile were
more likely to be 16-17 years old. Adolescents in the lowest quartile of factor 3 were
more likely to be in Tanner stage 2, and we did not see a clear distribution of adolescents
in the highest quartile of factor 3. Again the percentage distribution among the highest
and lowest quartile was evenly distribution among race/ethnic group as well as poverty
income ratio, therefore no considerable differences were found.

Adolescents in the highest quartile of factor 3 were more likely to be classified as
smokers and possible smokers, but these differences were not significant. Also
adolescents in the highest quartile were more likely to have vigorous levels of physical
activity (28.15%) VS. adolescents in the lowest quartile of factor 3 were more likely to
have inactive physical activity levels (34.77%). In contrast to the physical activity
levels, adolescents in the highest quartile of factor 3 were more likely to watch television
for 5 hours or more the day before the interview.

Summary of F actor 1, Factor 2 and Factor 3 — Overall Sample

We can conclude from the results in table 4.33 that factor 1 and factor 2 seemed

to be the most meaningful and interpretable factors and accounted for 48% of the total

variance explained. Adolescents in the highest quartile of factor 1 were more likely to be

88

Older, be at Tanner stage 2 and were more likely to be non-Hispanic white and Mexican
American. The were also more likely to have a poverty income ratio of less than one, be
classified as smokers, have a inactive physical activity level and watch 4-5 hours of
television per day.

Adolescents in the highest quartile of factor 2 were more likely to by younger,
and be in Tanner stage 1 and Tanner stage 2. They were also more likely to be non-
Hispanic black and have a poverty income ratio less than one. Furthermore they were
more likely to be classified as smokers, have moderate physical activity levels and watch
4-5 hours of television per day.

AS mentioned above the last factor (factor 3) was the least interpretable factor in
that adolescent the highest quartile were younger (12-1 3) and older (18-19). They were
also more likely to be in Tanner stage 1. In the lowest and highest quartile they were
evenly distributed among race/ethnic groups and poverty income ratio. They were more
likely to be classified as smokers and possible smokers, have vigorous physical activity
levels and watch more than 5 hours of television per day.

Factor 1 — Total Sample for Boys

Table 4.34 represents the demographic characteristics by factor loading scores in
boys. Boys in factor I loaded high on BMI-for-age percentiles, waist circumference
levels, and high but negative on HDL-cholesterol levels. They also loaded slightly high
on triglyceride levels. Boys 18-19 years old were more likely to be in the highest
quartile of factor 1, and younger boys 12-13 years old were more likely to be in the

lowest quartile of factor 1.

89

When comparing pubertal measurements, boys at Tanner stage 1 were more likely
to be in the lowest quartile for factor 1, where as the percentage of boys at Tanner stage 2
(29.55%), Tanner stage 3 (27.86%) and Tanner stage 5 (30.29%) were almost equally
distributed in the highest quartile of factor one. Therefore approximately 30% of boys in
Tanner stage 2, Tanner stage 3 and Tanner stage 5 were in the highest quartile of factor 1.

Mexican American boys (33%) were more likely to be in the highest quartile for
factor one followed by non-Hispanic white boys (28.29 %). Non-Hispanic black boys
(14.82 %) were the least likely to be in the highest quartile for factor 1. Boys with a
poverty income ratio less than one were more likely to be in the highest quartile for factor
1, but differences were not very large when comparing it to the boys with a poverty
income ratio greater than or equal to one. Boys (33%) who smoked were in the highest
quartile for factor 1, compared to 25% who reported being a non-smoker and 18% of
boys who possibly smoked in the highest quartile of factor 1.

Boys in the highest quartile of factor 1 reported moderate physical activity
(43.26%) during the past month. 38% of boys in the highest quartile for factor 1 reported
being inactive during the past month. 32% of boys in the highest quartile for factor 1
reported watching television for 4-5 hours the day before the interview, compared to 31%
of boys in the lowest quartile who reported watching only 0-1 hours of television the day
before the interview.

Factor 2 — Total Sample for Boys

In Table 4.34 we also reported the demographic characteristics of the factor

loading scores on factor 2 in boys 12-19 years old. Factor 2 loaded high on total

cholesterol levels and LDL-cholesterol levels and was Similar to factor 2 in the overall

90

sample. The percentage of boys in the highest quartile of factor 2 seemed to be
approximately equally distributed among all age groups. However, boys 14-15 years and
16-17 years had the highest percentages in the lowest quartile of factor 2. Older boys 18-
19 years old had the highest percentage (25.55%) on the fourth quartile of factor 2 (1 2-1 3
(23.81%), 14-15 (17.52 %), 16-17 (18.12%)).

Boys in Tanner stage 1 (41.19%) and Tanner stage 2 (41.40%) had very high
percentages in the highest quartile of factor 2, compared to boys in Tanner stage 3
(14.54%), Tanner stage 4 (14.78 %) and Tanner stage 5 (23.51 %). 41% of boys in
Tanner stage 4 were in the lowest quartile of factor 2.

Non-Hispanic black boys (29.90%) had the highest percentage in the top quartile
of factor 2 compared to the other race/ethnic groups (non-Hispanic white (21.05 %) and
Mexican American (23.21) and Other (6.81%) ) and non-Hispanic white boys had the
highest percentage in the lowest quartile of factor 2.

Boys with a poverty income ratio less than one were more likely to be in the
highest quartile of factor 2, whereas boys with a poverty income ratio greater than and
equal to one and less than 2, were more likely to be in the lowest quartile of factor 2,
followed by boys with a poverty income ratio greater than two. 29% of boys in the
highest quartile of factor 2 reported smoking. Only 19% in the highest quartile of factor
2 reported non-smoking.

Boys in the highest quartile of factor 2 also reported moderate physical activity
during the past month, where as 75% of boys in the lowest quartile of factor 2 reported

being inactive during the past month. There were no big differences in the percentage of

91

hours of television watching in the highest quartile of factor 2. However, 34% in the
lowest quartile of factor 2 reported watching 4-5 hours per week.
Factor 3-Total Sample for Boys

Factor 4.34, which is also presented in Table 12, loaded positive on LDL-
cholesterol and HDL-cholesterol. Boys in the highest quartile of factor 3 were more
likely to be between the ages of 18-19 years old (33.96 %), and boys in the lowest
quartile of factor 3 were more likely to be 16-17 years old (37.31%). There were no
substantial differences among boys within Tanner stage at the highest quartile of factor 3.
The majority of boys in Tanner stage 2 (57.39%), Tanner stage 3 (34.28 %) and Tanner
stage 4 (33.36 %) were more likely to be in the lowest quartile of factor 3.

Similar findings as in factor 2 where seen in factor 3, in that non-Hispanic black
boys were more likely to be in the highest quartile of factor 3 (28.52% ), and non-
Hispanic white boys were more likely to be in the lowest quartile of factor 3 (32.59 %).
The majority of boys with a poverty income ratio less than one were in the lowest two
quartiles of factor 3. 27% of boys with a poverty income ratio less than one were in the
highest quartile of factor 3 compared to 27.40% with a poverty income ratio greater than
and equal to one, and less than two, and a poverty income ratio greater than and equal to
two (22.50%). However, there were no significant differences within poverty income
ratio among quartiles in factor 3.

Differences with regards to smoking status were also non-significant among
quartiles in factor 3. Boys who reported possibly smoking had the highest percentage in
the fourth quartile of factor 3. Similar results as in factor 1 and factor 2 were found for

factor 3 in terms of physical activity. Boys in the highest quartile of factor 3 reported

92

moderate physical activity levels during the past month. Boys in the highest quartile of
factor 3 were more likely to watch television for more than 5 hours a day. Boys who only
watched television for 1 hour or less were more likely to be in the lowest quartile of
factor 3.
Summary of F actor 1, Factor 2 and Factor 3 - Boys

In summary, boys who loaded high on factor 1, which is the most closely related
to metabolic syndrome, were more likely to be older (18-19 years old), Mexican
American and have a poverty income ratio of less than one. They were also more likely
to be categorized as smokers, reported moderate physical activity levels and watched
between 4-5 hours of television per day. Factor 1 in this sample presented 28% of the
variance explained all three factors combined presented 61% of the variance explained.

Factor 2 and factor 3 in boys 12-19 years old were Similar, in that both of them
loaded high on total cholesterol and LDL-cholesterol, but factor 3 also loaded high and
positively on HDL-cholesterol. Boys who were high in these two factors were more
likely to be older (18-19 years old), and non-Hispanic black. Boys in factor 2 were more
likely to be smokers and reported moderate physical activity. Factor 3 also reported
moderate physical activity levels, and reported that they watched more than 5 hours of
television per day.
Factor I — Total Sample for Girls

We analyzed these data to include all girls 12-19 years since stratified by age and
Tanner stage did not yield interpretable results (Table 4.3 5). Factors that were produced
from this sample were not as clearly interpretable as the overall sample and the total

sample in boys 12-19 years. Girls in factor 1 had high levels of LDL-cholesterol and total

93

cholesterol and this factor was similar to factor 2 in boys. Girls in the highest quartile of
factor 1 were more likely to be older (18-19 years old) (12-13 (20.06%), 14-15 (19.91%),
16-17 (24.98%), 18-19 (30.02%) and girls in the lowest quartile of factor 1 were more
likely to be between 16 and 17 years old (33.47 % VS. (12-13 (25.98%), 14-15 (27.77%),
18-19 (17.55%)). Girls in the highest quartile of factor 1 were also more likely to be at
Tanner stage 2 in terms of sexual maturation.

Findings in girls were similar to findings in boys who had high values of LDL—
cholesterol and total cholesterol. Girls who were non-Hispanic black were more likely to
be in the highest quartile of factor 1 (29.14 %) compared to non-Hispanic white
(22.67%), and Mexican American (19.22 %) girls. In contrast to boys, girls in the highest
quartile of factor 1 were more likely to have a poverty income ratio greater than and
equal to two. Girls in the lowest quartile of factor 1 were also more likely to have a
poverty income ratio greater than and equal to two. Girls in the highest quartile of factor
1 were more likely to smoke and report moderate physical activity levels in the past
month. Thirty two percent (32%) of girls in the lowest quartile of factor 1 reported no
physical activity during the past month. One third of girls in the lowest quartile of factor
1 reported that they watched more than 5 hours of television per day, whereas 26% in the
highest quartile reported watching television 4-5 hours per day.

Factor 2 — Total Sample for Girls

In Table 4.35, the demographic characteristics of the factor loading scores for
girls in Factor 2 are presented. In the highest quartile of factor 2 for girls, there were no
big differences in the percentage distribution of age. Girls in the lowest quartile were

more likely to be younger (12-1 3 years (40.46%) and 14-15 years (30.58%)). There were

94

also 28% of 18-19 year old girls in the lowest quartile of factor 2. We found an equal
distribution of girls in Tanner stage one in the lowest (54%) and the highest quartiles
(40.18%). The majority of girls were in the lowest quartile of factor 2 within Tanner
stage, especially Tanner stage 3 and Tanner stage 4. Among the highest quartile in factor
2, race/ethnic groups were equally distributed. However, among the lowest quartile there
were 30.94% non-Hispanic white and girls, 24.46% non-Hispanic girls, 19.22 Mexican
American girls and 28% girls from other race/ethnic groups.

Girls in the highest quartile of factor 2 were more likely to have a poverty income
ratio of less than one and girls in the lowest quartile were more likely to have a poverty
income ratio greater than two. Girls in the highest quartile of factor 2 were classified as
smokers and 54% reported being inactive during the past month. Also among girls in the
highest category, 29% reported watching television 4-5 hours the day before the
interview, whereas 20% reported watching television for less than 1 hour the day before
the interview
Factor 3 — Total Sample Girls

Girls in factor 3 (Table 4.35) had higher levels of glucose, systolic blood pressure
and diastolic blood pressure. Age was evenly distributed among girls in the highest and
lowest quartile of factor 3. The majority of girls in the highest quartile of factor 3 were in
Tanner stage 3 (25.74 %), Tanner stage 4 (23.66%) and Tanner stage 5 (23.87%), and
only a very small percentage was in Tanner stage 1 and Tanner stage 2.

There were no significant differences among race/ethnic groups within quartiles
of factor 3, although non-Hispanic blacks were the most prevalent in quartile 4 compared

to other race/ethnic groups (non-Hispanic white (22.75%), non-Hispanic black (27.31%),

95

and Mexican American (25.05%)). There were also no Significant differences seen in
poverty income ratio among quartiles of factor 3 in girls.

Girls in the highest quartile of factor 3 were more likely to be categorized as non-
smokers, whereas girls in the lowest quartile were more likely to be categorized as
smokers. An interesting finding was that girls in the highest quartile of factor 3, were
more likely to report a Vigorous physical activity level during the past month, and were
less likely to watch television. Where as girls in the lowest quartile reported watching 2-3
hours of television the day before the interview.

Factor 4 — Total Sample for Girls

Girls in factor 4 had high levels of triglycerides and glucose and low levels of
HDL-cholesterol and systolic blood pressure percentiles. Similar results as in factor 3
were found in factor 4, in that girls had similar age distributions in the highest quartile,
and in the lowest quartile of factor 4 girls tend to be older than (12-13 (16.04%), 14-15
(19.95%), 16-17 (22.49%), 18-19 (28.98%)). Girls in the highest quartile of factor 4
were more likely to be at Tanner stage 1 and Tanner stage 3. Girls in the lowest quartile
were more likely to be at Tanner stage 1.

There were more non-Hispanic white (30.43%) and Mexican American (27.80%)
girls in the highest quartile of factor 4 (vs. non-Hispanic black (12.54%)), and more non-
Hispanic black girls in the lowest quartile of factor 4. Within poverty income ratio, no
substantial differences were found among quartiles in factor 4. Girls in the highest
quartile of factor 4 were also more likely to be smokers and reported being inactive

during the past month. Where as girls in the lowest quartile most likely reported having

96

Vigorous activity levels during the past month. Girls in the highest quartile of factor 4,
watched between 2 and 5 hours of television the day before the interview.
Summary of F actor 1, Factor 2, Factor 3 and Factor 4 -Girls

In summary, girls 12-19 years old had a different factor loading pattern compared
to boys. There was no clear pattern of metabolic syndrome among girls in the total
sample, but each factor did produce interesting results when compared to demographic
factors. The four factors in the total sample for girls presented 72% of the variance
explained.

Girls in factor 1 with high LDL-cholesterol and high total cholesterol levels were
more likely to be older (18-19 years) and non-Hispanic black. It is notable that girls in the
highest quartile of factor I appeared to have a higher poverty income ratio compared to
other adolescent girls. However, they were also more likely to be categorized as smokers
and watched 4-5 hours of television per day.

Girls in factor 2 who were classified as being overweight or at risk for overweight
with high waist circumference levels were more likely to have a poverty income ratio less
than one. They were classified as smokers, being inactive and watched television for 4-5
hours per day. Among the girls in factor 2 there were no Significant differences in age or
race/ethnic group.

Girls in factor 3 had high levels of glucose, systolic blood pressure and diastolic
blood pressure. They were more likely to have elevated levels at Tanner stage 3, Tanner
stage 4 and Tanner stage 5 and be classified as non-smokers, with Vigorous physical

activity levels and watch television for less than one hour per day.

97

Factor 4 loaded high on triglycerides and glucose and low on HDL-cholesterol.
Girls with high scores on factor 4 were more likely to be non-Hispanic white and
Mexican American. They were also classified as smokers, being inactive and watched

television the day before the interview for 2 to 5 hours.

98

Table 4.1 Select Characteristics of Total Adolescent Study Population (12-19 years) who
fasted for 6 hours or more, by Gender

 

 

Boys (n=1142) Girls (n=1181)
Characteristic N T529 SE N T12?" SE
Age at Interview (Yea rs)“
12 158 14.18 1.52 146 10.37 1.12
13 161 14.00 1.69 171 12.96 1.01
14 135 11.93 1.44 163 15.68 1.91
15 145 10.99 1.28 131 12.41 1.13
16 144 12.80 1.33 179 13.62 1.72
17 140 13.42 1.39 144 12.28 1.31
18 128 10.06 1.17 124 11.41 1.16
19 131 12.63 1.62 123 11.28 2.01
Tanner stage at Interview
(pubic hair)
1 76 6.88 1.32 7 0.94 0.44
2 57 4.39 1.00 39 3.85 0.86
3 1 10 9.97 1.40 95 7.31 1.23
4 211 25.11 2.36 356 35.15 2.57
5 574 53.66 3.07 537 52.75 2.65
Missing 114 10.84 3.34 147 13.51 2.41
Race/Ethnicity“
Non-Hispanic White 290 68.49 2.53 322 63.28 3.20
Non-Hispanic Black 388 14.42 1.26 405 16.06 1.74
Mexican American 420 8.48 0.96 388 8.79 1.10
Other 44 8.61 2.09 66 1 1.86 2.34
Poverty Income Ratio
< 1 382 18.27 1.67 379 22.08 2.27
_>_] and < 2 294 21.76 1.97 302 22.68 2.41
1 2 348 49.42 2.42 395 49.32 2.66
Missing 118 10.56 1.84 105 5.92 1.30
Hours of TV watched yesterday
(12-16 years) “
0-1 179 27.91 3.32 198 33.89 3.18
2-3 240 33.59 2.80 272 32.17 2.76
4-5 276 31.17 2.88 278 27.64 3.22
>5 48 7.34 1.72 42 6.30 1.32
Physical activity level (17-19 years)
Inactive 19 4.42 1.56 51 10.45 2.07
Moderate 122 32.00 3.31 195 44.53 5.02
Vigorous 250 62.18 3.73 138 43.01 5.18
Missing 8 1.40 0.70 7 2.01 0.94
Smoking Status" “
Smoking 135 15.21 1.92 85 12.27 1.67
Not Smoking 966 80.98 1.96 1064 84.1 1 1.95
Possibly Smoking 41 3.82 0.82 32 3.62 0.87

 

” Smoking Status: Smoking: Self-reported tobacco use and cotinine levels > lSng/mL: Not Smoking: Self-
reported non-tobacco user and cotinine levels _<_ 15 ng/mL: Possibly Smoking: Self-reported non-tobacco
user and cotinine levels > 15ng/mL: 1 Column percents add to 100; # No missing values

99

 

 

 

 

 

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Table 4.11 Prevalence Estimates of Markers of Metabolic Syndrome for Boys and Girls
(12-19 Years old) by Age Group in the Population: NHANES Ill, l988-1994

 

 

 

 

Boys
Biomarkers 12-13 14-15 16-17 18-19
% SE ”/6 SE % SE % SE

BMI-for-Age
Percentiles“

a 85th - < 95‘h 20.43“ 3.85 23.04+ 4.99 11.72 3.28 6.97“ 2.03

1 95th 10.38 2.05 8.26 2.05 13.60 3.08 11.30 4.03
Waist Circumference
(cm)

Z 90‘’1 Percentile 10.77 2.27 10.78 2.27 10.38 2.74 13.58 4.30
Triglycerides (mg/dL)

3 110 mg/dL 23.05 4.30 14.04” 3.67 27.35 6.11 33.32” 6.06
HDL Cholesterol (mg/dL)

g 40mg/dL 2155*" 4.58 32.69 5.67 35.15"+ 4.35 37.58 3.67
Serum Glucose (mg/dL)

_>_ 110 mg/dL 4.26 2.76 0.08 0.08 2.82 1.72 2.05 1.62
Total Cholesterol
(mg/6L)

3 200 mg/dL 8.17 2.31 3.37““? 1.38 11.16“ 3.26 14.11§§ 4.25
LDL-Cholesterol
(mg/dL)

z 130 mg/dL 8.50 2.37 4.24““ 1.71 12.15 3.29 13.23”“ 4.35
Systolic Blood Pressure
Percentiles’

_>_ 90‘h - < 95th 1.34 0.76 1.10 0.61 1.74 1.42 3.57 1.64

2 95‘h 5.17 2.68 0.06 0.07 1.07 0.77 2.11 1.59
Diastolic Blood Pressure
Percentilesg

_>_ 90th - < 951h 1.33 0.94 2.03 1.57 0.91 0.92 0.06 0.06

2 95‘h 0.00 0.00 0.00 0.00 0.19 0.19 0.22 0.13
C-reactive Protein
(mg/dL)

> 0.22 mg/dL 8.01 2.60 9.72 2.96 10.59 3.02 7.95 3.73
" BMI-for—Age Percentiles calculated from the CDC growth charts developed by National Center

for Health Statistics. If a child is between the 85th and 95‘h percentile, the child is then classified as
at risk for overweight, If the child is above the 95‘h percentile, the child is classified as overweight

§ Age-sex-height specific blood pressure percentiles calculated from nine U.S studies used in the
Report of the Second Task Force on Blood Pressure Control in Children (96)

# p < 0.01 Significant difference between 12-13 year old and 18-19 year old boys ;

1' p < 0.01 Significant difference between 14-15 year old and 18—19 year old boys ;

§ p < 0.05 Significant difference between 14-15 year old and 18—19 year old boys;

11' p < 0.01 Significant difference between 14 — 15 year old boys and 18-19 year old boys;
11 p < 0.05 Significant difference between 14-15 and 16-17 year old boys;

§§ p < 0.05 Significant difference between 14-15 year old and 18-19 year old boys;

## p < 0.05 Significant difference between 14-15 and 18-19 year old boys;

HT p < 0.001 Significant difference between 12-13 and 16-17 year old boys;

118

Table 4.11 Prevalence Estimates of Markers of Metabolic Syndrome for Boys and Girls
(12—19 Years old) by Age Group in the Population: NHANES III, l988-1994 (cont’d)

 

Girls
Biomarkers 12-13 14-15 16-17 18-19
% SE °/o SE °/o SE % SE

 

 

BMl-for-Age

Percentiles * *

285‘“-<95"‘ 13.72 2.21 18.39§ 3.22 17.30 2.99 10.04§ 2.65
_>_95‘h 13.37 2.62 10.94 3.13 9.87 1.71 9.24 2.69
Waist Circumference

(cm)

390‘hPercenti1e 10.64 2.23 12.1 3.18 12.36 2.66 10.99 3.23
Triglycerides

(mg/(1L)

ZIIOmg/dL 18.92 4.04 21.08 4.28 20.35 3.78 19.81 3.71
HDL Cholesterol

(mg/dL)

f40mg/dL 12.83 3.47 13.60 2.84 21.43 4.16 12.27 2.75
Serum Glucose

(mg/dL)

leOmg/dL 0.24 0.15 0.53 0.33 0.33 0.20 0.33 0.19
Total Cholesterol

(mg/(1L)

ZZOOmg/dL 10.80 2.65 8.08 2.22 10.36 2.08 15.86 3.51
LDL-Cholesterol

(mg/dL)

Zl30mg/dL 7.77 2.16 10.53 3.17 12.12 2.24 15.24 3.59
Systolic Blood

Pressure Percentiles‘

290"‘-<95‘h 1.96 0.85 2.34 1.68 0.25 0.16 0.20 0.20
295‘h 1.56 1.39 0.29 1.15 0.15 0.09 0.11 0.11
Diastolic Blood

Pressure Percentiles‘i

 

3 90th - < 95'h 0.10 0.08 0.21 0.21 0.00 0.00 0.87 0.86
2 95"‘ 0.00 0.00 0.00 0.00 0.25 0.20 0.24 0.17
C-reactive Protein
(mg/dL)
> 0.22 mg/dL 8.72m 2.82 7.40m 2.92 11.35 2.47 20,25m 3.47
** BMI-for-Age Percentiles calculated from the CDC growth charts developed by National Center

for Health Statistics. If a child is between the 85'h and 95'h percentile, the child is then classified as
at risk for overweight, If the child is above the 95‘h percentile, the child is classified as overweight
§ Age-sex-height specific blood pressure percentiles calculated from nine U.S studies used in the
Report of the Second Task Force on Blood Pressure Control in Children (96)
in p < 0.01 Significant difference 12-13 years old, 14-15 years old and 18—19 year old girls

119

 

 

 

 

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I41

Table 4.23 Correlation between biomarkers related to MS in Adolescent Boys and Girls
(12-19 years old): NHANES III, l988-l994

 

BM] WC TC LDL-C HDL-C TG Glucose SBP DBP

 

Boys (n=938)

BM]

WC 0.72

TC 0.07 0.10

LDL-C 0.10 0.13 0.91

HDL—C -O.23 -0.33 0.23 -0.05

TG 0.23 0.32 0.31 0.12 -0.34

Glucose 0.16 0.19 0.06 0.07 -0.08 0.09

SBP 0.29 0.23 0.16 0.14 -0.12 0.26 0.09

DBP -0.0l 0.07 -0.06 -0.06 0.00 -0.03 0.03 0.20
Girls (n=993)

BMI

WC 0.74

TC 0.02 0.02

LDL-C 0.08 0.07 0.92

HDL-C -O.3l -0.33 0.20 -0.08

TG 0.21 0.26 0.26 0.10 -0.34

Glucose 0.03 0.25 0.01 0.02 -0.05 0.03

SBP 0.29 0.01 0.02 0.04 -0.08 0.05 0.] l

DBP 0.05 0.1 l 0.06 0.06 -0.06 0.09 0.06 0.29

 

BMI: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles.

I42

Table 4.24 Correlation between biomarkers related to MS in Adolescent Boys (12-19
years old) stratified by Age: NHANES III, l988-l994

 

BMI wc TC LDL-C HDL-C TG Glucose SBP DBP
12-13 (n=242)

 

 

 

BMl

wc 0.80

TC -0.13 -0.16

LDL-C -0.10 -0.08 0.91

HDL-C -0.32 -0.42 0.27 0.00

TG 0.24 0.24 0.12 -0.08 -O.36

Glucose 0.12 0.15 0.10 0.06 -0.05 0.18

SBP 0.19 0.17 0.05 0.03 -0.08 0.15 0.06

DBP -O.18 -0.09 0.06 0.12 -003 -0.12 0.08 0.13
l4-15(n=215)

BM!

wc 0.76

TC -004 0.11

LDL-C 0.05 0.20 0.91

HDL-C -025 -O.26 0.34 -001

TO 0.08 0.20 0.32 0.20 -025

Glucose 0.12 0.09 0.02 0.00 -009 0.23

SBP 0.18 0.15 0.14 0.16 -012 0.19 0.07

DBP 0.04 0.11 -0.01 -003 0.02 -000 0.15 0.20
l6-l7(n=252)

BM]

wc 0.71

TC 0.12 0.09

LDL-C 0.13 0.08 0.90

HDL-C -0.25 -027 0.13 -0.18

TG 0.33 0.39 0.27 0.13 -0.45

Glucose -0.00 0.12 0.10 0.12 -0.01 -002

SBP 0.34 0.14 -002 -0.03 -0.18 0.26 011

D8? 0.06 0.09 -002 -0.10 0.07 0.11 -0.08 0.39
18-l9(n=229)

BM]

wc 0.84

TC 025 0.12

LDL-C 0.23 0.12 0.91

HDL-C -0.1 1 -023 0.07 -0. 16

TO 0.19 0.22 0.34 0.08 -0.30

Glucose 025 0.27 0.04 0.09 0.00 -0.12

SBP 0.40 0.36 0.27 0.26 -0.23 0.28 0.09

DBP 0.08 0.07 -0.13 -0.13 0.07 -0.08 -0.00 0.10

 

BM]: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles.

I43

Table 4.25 Correlation between biomarkers related to MS in Adolescent Girls (12-19
years old) by stratified Age: NHANES III, l988-l994

 

 

 

 

BMI WC TC LDL-C HDL-C TG Glucose SBP DBP
12-13
(n=254)
BM]
WC 0.82
TC -0.06 -0.08
LDL-C 0.07 0.04 0.89
HDL-C -0.28 -0.31 0.36 0.04
TG -0.03 0.02 0.26 0.02 -O.l9
Glucose -0. 16 -0.18 0.09 0.04 0.00 0.15
SBP 0.23 0.22 -0.04 0.0] -0.06 -0.08 0.02
DBP 0.10 0.15 0.06 0.10 -0.09 0.03 -0.15 0.21
14-15
(n=240)
BM]
WC 0.77
TC -0.02 -0.09
LDL-C 0.06 0.03 0.9]
HDL-C -O.38 -O.44 0.25 -0.07
TG 0.30 0.34 0.24 0.15 -0.42
Glucose 0.01 0.19 -0. IO -0.l l -0.03 0.06
SBP 0.41 0.27 -0.0| 0.02 -0.15 0.14 -0.02
DBP 0.11 -0.02 0.01 -0.01 -0.09 0.20 0.07 0.28
16-17
(n=270)
BMl
WC 0.69
TC 0.23 0.2]
LDL-C 0.26 0.21 0.95
HDL-C -0.16 -0.28 0.05 -O.18
TG 0.15 0.40 0.30 0.20 -0.45
Glucose -0.0] -0.01 -0.04 -0.02 -0.l6 0.15
SBP 0.25 0.24 -0.04 -0.04 -0.06 0.07 0.06
DBP -0.01 -0.04 0.08 0.02 0.16 0.03 0.10 0.31
18-19
(n=229)
BMl
WC 0.84
TC -0.06 -0.00
LDL-C -0.07 -0.04 0.89
HDL-C -0.27 -0.23 0.24 -0.07
TC 0.3] 0.33 0.20 -0.06 -0.29
Glucose 0.16 0.24 -0.04 0.02 -0.05 -O.12
SBP 0.29 0.2] -0.0l -0.03 -0.04 0.10 0.07
DBP 0.07 0.14 0.07 0.06 -0.15 0.20 -0.02 0.47

 

BMl: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL): LDL-C: LDL-
cholesterol;HDL-C: HDL-cholesterol: TG: Triglycerides: SBP: Systolic blood pressure percentiles; DBP: Diastolic

Blood Pressure Percentiles.

144

Table 4.26 Correlation between biomarkers related to MS in Adolescent Boys (12-19
years old) Stratified by Sexual Maturation Stage: NHANES III, l988-l 994

 

BMI WC TC LDL-C HDL-C TG Glucose SBP

 

Tanner stage 4

 

 

(n=183)
BM!
WC 0.63
TC -0. l 8 -0.24
LDL-C -0.15 -0. l 6 0.92
HDL-C -0. l 7 -0.36 0.29 -0.01
TG 0.06 0.14 0.23 0.12 -0.32
Glucose -0.02 0.08 0. 16 0.19 -0.16 0.20
SBP 0.19 0.03 0.05 0.02 0.2] -0.04 0.08
DBP -0.07 -0.03 0.07 0.10 0.06 -0.l6 -0.03 0.35
Tanner stage 5
(n=512)
BM]
WC 0.75
TC 0. l6 0.23
LDL-C 0.17 0.23 0.93
HDL-C -0.l8 -0.22 0.17 005
TC 0.2] 0.32 0.40 0.23 -0.36
Glucose 0.17 0.21 0.10 0.10 -0.01 0.03
SBP 0.36 0.36 0.25 0.24 -0.24 0.36 0.08
DBP -0.05 -0.0l -0.l 1 -0.13 0.08 -0.07 0.03 0.14
Tanner stage 4
and Tanner
Stage 5 (n=695)
BM]
WC 0.72
TC 0.09 0. l 5
LDL-C 0.11 0.16 0.92
HDL-C -0.21 -0.27 0.20 -0.05
TG 0.23 0.33 0.36 0.21 -0.37
Glucose 0.13 0.18 0.09 0.09 -0.04 0.07
SBP 0.34 0.26 0.18 0.16 -0.15 0.28 0.08
DBP -0.00 0.05 -0.05 -0.05 0.02 -0.03 0.01 0.19

 

BMI: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles.

I45

Table 4.27 Correlation between Biomarkers related to MS in Adolescent Girls (12-19
Years old) Stratified by Sexual Maturation Stage: NHANES III, l988-l994

 

BM] WC TC LDL-C H Dl..-C TG Glucose SBP

 

 

 

Tanner stage 4
(n=322)
BM]
WC 0.79
TC -0.08 -0.06
LDL-C -0.03 -0.02 0.93
HDL-C -0.3l -0.34 0.34 0.11
TC 0.2] 0.27 0.14 0.00 -0.40
Glucose 0.08 0.04 0.07 0.07 -0.06 0.0]
SBP 0.22 0.17 -0.04 -0.04 -0.18 0.09 0.15
DBP -0.10 0.10 0.05 0.10 -0.18 0.15 -0.01 0.22
Tanner stage 5
(n=473)
BM]
WC 0.73
TC 0.03 0.01
LDL-C 0.08 0.06 0.91
HDL-C -0.27 -0.32 0.24 -0.07
TC 0.2] 0.26 0.32 0.14 -0.29
Glucose 0.0] -0.06 -0.03 -0.0] -0.05 -0.01
SBP 0.28 0.19 0.02 0.02 -0.01 0.00 0.01
DBP 0.09 0.04 0.08 0.06 0.00 0.08 0.] l 0.28
Tanner stage 4
and Tanner
stage 5 (n=795)
BM]
WC 0.77
TC 0.02 0.0]
LDL-C 0.06 0.05 0.92
HDL-C -0.28 -0.32 0.23 -0.05
TG 0.22 0.27 0.27 0.10 -0.34
Glucose 0.08 -0.01 -0.02 0.01 -0.05 0.00
SBP 0.25 0.19 0.0] 0.02 -0.07 0.05 0.10
DBP 0.01 0.06 0.05 0.03 -0.05 0.12 0.07 0.28

 

BM]: BMI-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C: LDL-
cholestero|;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles; DBP: Diastolic
Blood Pressure Percentiles.

146

Table 4.28 Factor loading matrix for biomarkers related to MS in boys

and girls 12-19 years old: NHANES II], 1988-1994

 

 

 

 

Biomarker Factor 1 Factor 2 Factor 3 Factor 4*

Total Sample (n=1931)

Overall "
BM] 0.383 -0.012 -0.003 -
WC 0.392 -0.018 -0.005 -
TG 0.252 0.105 -0.034 -
HDL-C -0.325 0.1 16 0.279 -
LDL-C -0.008 0.506 0.065 -
Tot-C -0.052 0.506 -0.001 -
Glucose -0.003 -0.008 0.279 -
SBP 0.012 0.021 0.516 -
DBP -0.184 -0.040 0.678 -

Total Sample

Boys (n=938)’
BM] 0.348 -0.032 0.019 -
WC 0.367 -0.027 0.025 -
TG 0.251 0.095 0.013 -
HDL-C -0.326 0.145 0.755 -
LDL-C -0.013 0.463 0.494 -
Tot-C -0.054 0.498 0.187 -
Glucose 0.1 11 0.014 -0.038 -
SBP 0.080 0.059 -0.074 -
DBP -0.124 -0.074 0.093 -

Total Sample

Girls (n=993)z
BM] -0.005 0.477 0.006 -0.065
WC -0.008 0.464 -0.007 -0.019
TG 0.095 -0.017 -0.107 0.527
HDL-C 0.099 -0.070 0.1 19 -0.527
LDL-C 0.48] 0.006 -0.012 0.007
Tot-C 0.508 -0.026 0.002 -0.034
Glucose -0.056 -0.372 0.355 0.506
SBP -0.020 0.130 0.553 -0.178
DBP 0.008 -0.074 0.572 -0.020

 

BMI: BMI-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles.

"‘ Eigenvalues for factor 4 were not greater than 1 therefore no factor loadings are displayed

- Variance explained for all the factors in each sample: #60%; 1' 60%; I60

147

Table 4.29 Factor loading matrix for biomarkers related to MS in boys 12-19 years old:

NHANES III, l988-1994

 

 

 

 

 

Biomarker Factor 1 Factor 2 Factor 3 Factor 4*
12-13 (n=242)‘
BM] 0.365 0.012 -0.087 -
WC 0.368 -0.001 -0.021 -
TG 0.250 0.093 -0.001 -
HDL-C -0.249 0.058 -0. 109 -
LDL-C 0.035 0.488 -0.002 -
Tot-C 0.020 0.51 1 -0.045 -
Glucose 0.1 12 0.088 0.201 -
SBP 0.077 -0.017 0.527 -
DBP -0. 142 -0.070 0.699 -
14—15 (n=215)?
BM] 0034 0.520 -0.1 16 0.005
WC 0.045 0.502 -0.084 0.006
T6 0.1 19 -0.104 0.585 0.017
HDL-C 0.170 -0. 129 -0.466 0.737
LDL-C 0.444 0.045 -0.002 0.404
Tot-C 0.482 -0.039 -0.047 0.208
Glucose -0.066 -0. 150 0.422 -0.061
SBP 0.052 0.037 0.085 -0.062
DBP -0.068 -0.034 -0. 143 0.289
16-17 (n=252)1
BM] 0028 0.438 -0.014 0.085
WC 0063 0.522 0.012 -0.019
TG 0.076 -0.023 -0.420 0.063
HDL-C 0.076 0.096 0.617 0.184
LDL-C 0.491 -0.066 -0.053 -0.042
Tot-C 0.523 -0.026 0.081 0.067
Glucose 0.01 3 0.47 7 0.351 -0.244
SBP 0.000 -0.027 -0.070 0.489
DBP 0.031 0.011 0.229 0.590
18-19 (u=229)§
BM] 0.396 0.007 -0.012 0.089
WC 0.417 -0.095 0.065 0.009
TG -0.100 0.304 0.520 0.041
HDL-C -0.033 0.169 -0.499 0.270
LDL-C -0.023 0.477 -0.076 -0.064
Tot-C -0.070 0.514 -0.045 0.044
Glucose 0.402 -0.039 -0.350 -0.352
SBP 0.1 16 0.086 0.216 0.260
DBP 0.005 -0.01 1 -0.090 0.825

 

BM]: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides;

SBP: Systolic blood pressure percentiles;DBP: Diastolic Blood Pressure Percentiles

"' Eigenvalues for factor 4 were not greater than 1 therefore no factor loadings are displayed

- Variance explained for all the factors in each sample: #61%; 1' 73%; 175%; §75

148

Table 4.30 Factor loading matrix for biomarkers related to MS in boys at Tanner
stage 4, Tanner stage 5 and Tanner stage 4 and 5 combined: NHANES II], 1988-1994

 

 

 

 

Biomarker Factor 1 Factor 2 Factor 3 Factor 4

Tanner stage 4 (n=183)“
BM] 0.108 0.596 -0.161 0.004
WC 0.022 0.490 0.049 -0.004
TG 0.095 0.029 0.404 -0.090
HDL-C 0.161 -0.002 -0.494 0.057
LDL-C 0.476 0.062 0.022 -0.032
Tot-C 0.513 0.062 -0.037 0029
Glucose -0.025 -0.164 0.53 1 0.226
SBP 0.008 0.1 12 -0.012 0.573
DBP -0.069 -O. 107 0.101 0.583

Tanner stage 5 (n=512)1'
BM] -0.089 0.484 0.001 -0.080
WC -0.049 0.447 0.042 -0.035
TG 0.120 -0.137 0.448 0.019
HDL-C 0.172 0.085 -0.544 0.137
LDL-C 0.457 -0.042 -0.053 -0.042
Tot-C 0.496 -0.054 -0.071 0.014
Glucose -0.005 0.416 -0.315 0.026
SBP 0.068 0.027 0.284 0.392
DBP -0.023 -0.041 -0.050 0.860

Tanner stage 4 and 5

combined (n=695):1:
BM] 0073 0.480 -0.001 0.005
WC -0.041 0.453 -0.048 -0.009
TG 0.133 -0.127 -0.495 0.010
HDL-C 0.129 0.1 1 1 0.601 0.083
LDL-C 0.461 -0.032 0.068 0.005
Tot-C 0.492 -0.037 0.009 -0.031
Glucose 0.01 0 0.454 0.323 -0. 1 1 5
SBP 0.048 0.023 -0.1 16 0.492
DBP -0.051 -0. 100 0.136 0.779

 

BM]: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles

- Variance explained for all the factors in each sample: #73%; 1' 75%; 173%

149

Table 4.31 Factor loading matrix for biomarkers related to MS in girls 12-19 years old:

NHANES [I], 1988-1994

 

 

 

 

 

Biomarker Factor 1 Factor 2 Factor 3 Factor 4
12-13 (n=254)“
BM] 0.460 0.061 -0091 0077
WC 0.457 0.043 -0.052 -0.048
TG 0.059 0.041 0.636 -0.017
HDL-C -0.251 0.175 -0.392 -0.015
LDL-C 0.074 0.475 -0.010 -0003
Tot-C -0.014 0.495 0.042 0014
Glucose -0.123 -0025 0.485 0.042
SBP -0054 -0042 0.007 0.630
DBP -0.108 0.015 0.023 0.683
14.15 (u=240)*
BM] 0.319 -0.014 0.101 -0195
wc 0.330 -0.043 0.088 -0. 185
TC 0.263 0.178 -0.108 0.403
HDL-C -0.417 0.038 0.289 -0.180
LDL-C 0.037 0.471 -0047 -0.048
Tot-C -0.073 0.493 0.054 -0002
Glucose -0.041 -0.040 0.035 0.725
SBP —0.026 -0.021 0.565 -0.192
DBP -0172 0.021 0.623 0.309
16-17 (n=270):
BM] 0016 0.491 -0132 0.001
wc -0050 0.465 0.029 0036
TC 0.069 0.004 0.447 -0.006
HDL-C 0.090 -0049 -0.489 0.181
LDL-C 0.481 -0.043 0.001 -0.022
Tot-C 0.507 -0.056 -0.048 0.039
Glucose -0.024 -0.261 0.482 0.251
SBP -0.128 0.231 -0.022 0.482
DBP 0.082 -0.1 13 -0.009 0.645
18-19 (n=229)§
BM] 0.408 -0.005 0.120 0042
we 0.429 0.026 0.097 0045
TC 0067 0.062 0.560 -0.021
HDL-C -0.017 0.065 -0399 0.065
LDL-C 0.037 0.498 -0.032 -0.021
Tot-C 0.003 0.516 0.013 .0001
Glucose 0.452 0.02 1 -0.471 -0.01 0
SBP 0.042 -0035 -0.152 0.592
DBP -0.139 0.011 0.041 0.597

 

BM]: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles

- Variance explained for all the factors in each sample: #72%; 1' 75%; 1,76%; §76%

150

Table 4.32 Factor loading matrix for biomarkers related to MS in girls at Tanner stage 4,
Tanner stage 5 and Tanner stage 4 and 5 combined: NHANES III, l988-1994

 

Biomarker by Factor 1 Factor 2 Factor 3 Factor 4

 

Tanner stage 4 (n=322)7

 

 

BM] 0.021 0.496 -0.168 0.042
WC 0.037 0.452 -0.028 -0.022
TG 0.085 0.121 0.432 -0.049
HDL-C 0.122 -0.1 18 -0.356 0.134
LDL-C 0.477 0.032 0.017 0.573
Tot-C 0.500 0.025 0.025 0.021
Glucose 0.020 -0.003 -0.180 0.033
SBP -0.041 -0.043 0.162 -0.266
DBP 0.027 -0.247 0.576 0.679

Tanner stage 5

(u=473)*
BM] 0.345 -0.024 0.196 0168
WC 0.386 -0.029 0.091 -0. 199
TC 0.283 0.176 -0.204 0.231
HDLC -0.375 0.073 0.261 -0.295
LDL-C 0.003 0.463 -0.018 -0.004
Tot-C -0.057 0.495 0.023 0048
Glucose -0.010 -0.019 0.043 0.764
SBP -0.020 -0.040 0.596 -0.099
DBP -0.080 0.330 0.490 0.350

Tanner stage 4 and 5

combined (n=795)
BM] 0.001 0.522 -0.086 -0.079
WC 0004 0.486 -0.008 -0.099
TG 0.093 -0.122 0.617 0.045
HDL—C 0.1 19 0.037 -0.594 -0.005
LDL-C 0.478 0.012 0.004 -0.023
Tot-C 0.505 -0.012 -0.033 -0.009
Glucose -0.037 -0.128 0.107 0.386
SBP 0.005 0.241 -0.277 0.499
DBP 0.019 -0.099 0.062 0.609

 

BM]: BMl-for-age percentiles; WC: Waist Circumference (cm); TC: Total cholesterol (mg/dL); LDL-C:
LDL-cholesterol;HDL-C: HDL-cholesterol; TG: Triglycerides; SBP: Systolic blood pressure percentiles;
DBP: Diastolic Blood Pressure Percentiles

- Variance explained for all the factors in each sample: #74°/o; '1‘ 73%; 172%

151

 

 

 

 

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Chapter 5: Discussion and Conclusion

This study utilized data from NHANES III (1 988-1994) to examine the distribution
and determine the prevalence of elevated biomarkers related to MS in boys and girls 12-
19 years by age and sexual maturation stage within gender, race/ethnicity and poverty
income ratio, and to examine the clustering of these biomarkers that have previously been
associated with Metabolic Syndrome in adults, in boys and girls 12-19 years, using factor
analysis.

To our knowledge, this is the first study to estimate the clustering of biomarkers
related to Metabolic Syndrome (MS) using factor analysis in a nationally representative
sample in adolescents. Elevated levels of these identified clusters of biomarkers in a
population representative of the general population of adolescents, may describe early
markers of risk for MS and thus potentially help to identify adolescents to target for early
interventions in other populations. Findings from this study suggests that the presence of
MS in adolescents, which is similar as MS in adults, further accentuates the need for

prevention and interventions to take place early in life rather than in adulthood.

5.1 Comparisons of findings to the literature

In the section below we summarize our findings for each of the Aims in our study

and compare these findings to the existing literature

160

 

5.1.1 Distribution of biomarkers related to MS by age
Aim 1: T o examine the distribution and determine the prevalence of biomarkers related
to Metabolic Syndrome in boys and girls 12-19 years by age within gender, race/ethnicity
and poverty income ratio.

These results provide reference data on the distribution of biomarkers related to
MS in boys and girls 12-19 years old by age, and by age within race/ethnic group and
poverty income ratio for the years l988-l994. We are aware of only one population-
based study that has described the distribution of several specific biomarkers (HDL-
cholesterol, LDL-cholesterol, total cholesterol and triglycerides) related to MS by age in
a similar way as we did (14). These authors used the same dataset we used but grouped
their ages slightly different than we did and also had a somewhat different sample due to
different exclusions. Therefore, we expected similar results as in their study to appear in
our study. Future studies may compare their results to this data, since our results are
representative of the general US adolescent population.
Overall Age

The distribution of biomarkers related to MS in boys and girls by overall age
varied depending on the biomarker studied. Our main findings related to the distribution
of biomarkers related to MS in boys and girls were that overall triglycerides, total
cholesterol, LDL-cholesterol, glucose, systolic blood pressure percentiles and diastolic
blood pressure percentiles did not differ considerably by age, and no linear trend was
found among these variables by age. These results (which include triglycerides, total
cholesterol and LDL-cholesterol) are comparable to findings by Hickman et al. using the

same NHANES [11 population, but slightly different age groupings (14). We noted that

l6l

BMl-for-age percentiles decreased by age for boys 18-19 years and slightly for girls of
the same age. Given that BMI-for age percentiles were already standardized for age, we
did not expect to see large variation for this biomarker. Waist circumference levels
increased by age for both boys and girls, but more so for boys. One cross-sectional study
conducted on 5-17 year old children and adolescents had similar findings for waist
circumference levels by age for boys (12- l 7 years) (128), but girls in our study had
higher median waist circumference levels. This study however, was conducted in a bi-
racial rural community that was relatively poor and poverty has been associated with
higher BMI and waist circumference, and therefore their results are not generalizable to
the US population. Our study also showed that HDL-cholesterol levels decreased by age
in boys and increased by age in girls, again consistent with findings obtained by Hickman
et al. using the same population (14). To our knowledge, this study is the first to estimate
the distribution of BMl-for-age, waist circumference, systolic blood pressure percentiles,
diastolic blood pressure percentiles and glucose levels by age in the general US
adolescent population, though similar studies have been done in sub-populations (166).
Age within Race/Ethnic Group

Our findings of patterns of biomarkers related to growth in non-Hispanic white,
non-Hispanic black and Mexican American boys were also similar to the reported
literature. In our study Mexican American boys were more likely to have higher median
percentile levels by age on the BMl-for-age growth charts compared to both non-
Hispanic white boys and non-Hispanic black boys. Non-Hispanic black girls, however,
were more likely to have higher median percentile levels by age on the BMl-for-age

growth charts compared to both non-Hispanic white girls and Mexican American girls.

162

Overall, girls had higher median percentiles on the BMl-for-age growth charts than boys.
Other studies that have used similar data (NHANES III and NHANES IV) have identified
the same patterns in terms of race/ethnic group when they examined the prevalence of
overweight (based on same BMl-for-age percentiles), and trends in overweight for
children and adolescents in the US population (2, 21 ). The NHLBI Growth and Health
study also indicated that non-Hispanic black girls at age 9 had a 37% higher overweight
prevalence compared to non-Hispanic white girls (166). Waist circumference levels in
our study were similar to BMl-for-age percentiles, in that Mexican American boys and

non-Hispanic black girls had the highest levels among race/ethnic groups.

 

The report from the NCEP Expert Panel on blood cholesterol levels in children
and adolescents stated that recommended levels for normal total cholesterol is <17O
mg/dL and recommended levels for LDL-cholesterol levels is < 1 10 mg/dL in all children
and adolescents in the US (52). The median total cholesterol levels by age within
race/ethnic group in our study were generally within normal ranges across all ages, with a
few exceptions in non-Hispanic black boys and girls. Non-Hispanic black boys and girls
had higher values of total cholesterol, HDL-cholesterol and LDL-cholesterol at all ages
compared to non-Hispanic white and Mexican American boys and girls. Among non-
Hispanic black boys l2-l3 years old, the median total cholesterol levels were l75 mg/dL
and for non-Hispanic black girls, l8-l9 years the median total cholesterol levels was 172
mg/dL. Hickman et al. found similar results (14).

The median LDL-cholesterol levels in our study were all well within the accepted
recommendation of < l 10 mg/dL among boys and girls. These findings were supported

by Hickman et al. (I 4). Hickman et al. (14) and Freedman et al. (I 67) both found that

163

non-Hispanic black boys and girls had higher levels of total cholesterol, LDL-cholesterol
and HDL-cholesterol compared to Mexican American and non-Hispanic white boys and
girls. The study population used by Freedman et al. is similar as mentioned above, in that
they came from a bi-racial poorer community in Louisiana. In contrast to the latter
results, triglyceride levels in our study as well as in Hickman’s study were higher among
non-Hispanic white and Mexican American boys and girls compared to non-Hispanic
black boys and girls (14).

Among race/ethnic group, glucose levels, systolic and diastolic blood pressure
percentiles did not vary a great deal by age. Non-Hispanic white and Mexican American
boys and girls had slightly higher median glucose levels compared to non-Hispanic black
girls and boys, but levels remained relatively constant throughout 12-19 years. Among
boys, non-Hispanic black and Mexican American boys had somewhat higher median
systolic blood pressure percentiles, and among girls non-Hispanic black girls had slightly
higher median systolic blood pressure percentiles.

Age within Poverty income ratio

We examined the distribution of biomarkers related to MS by age within poverty
income ratio and found that the biomarkers did not vary considerably. However, we saw
some variation by body size characteristics and total cholesterol levels. BMl-for-age
percentiles and waist circumference levels among girls tended to increase as poverty
income ratio decreased. Trends among boys were not as significant. Among both boys
and girls total cholesterol levels were significantly inversely associated with poverty
income ratio. We saw little variation or linear trend, however, by age within poverty

income ratio for triglycerides, LDL-cholesterol, glucose, systolic blood pressure

164

percentiles and diastolic blood pressure percentiles in both boys and girls. To our
knowledge, there is currently no other literature available that describes the distribution
of biomarkers related to MS by age within poverty income ratio for comparison of

findings.

5.1.2 Prevalence of Elevated Levels of Biomarkers by Age

In the second analyses of aim 1 we presented the prevalence estimates of elevated
levels of biomarkers related to MS in boys and girls 12-19 years old by age. Cook et al.
also presented prevalence estimates of individual biomarkers related to MS in boys and
girls 12—19 years old using data from NHANES III (l988-1994) (1). A limitation to their
study was that they did not stratify or adjust for age, which makes it difficult to determine
if these biomarkers fluctuated by age in adolescents. In our study we found that boys
were more likely to be at risk for overweight at younger ages (12-13 years) and girls were
more likely to be at risk for overweight between ages 14-17. Similar to Cook et al. (1),
we also saw that elevated triglyceride levels (_>_ 1 10 mg/dL) and low HDL-cholesterol
levels (5 40 mg/dL) were the most prevalent across all age groups, and elevated glucose
levels (3 110 mg/dL) and systolic and diastolic blood pressure percentiles (3 90th
percentile) were the least prevalent. Our findings for elevated C-reactive protein levels
were similar to those of Ford et al. (15) (who used NHANES IV data) and Visser et al.
(114) (who used NHANES [II data), where girls were more likely to have elevated levels
compared to boys with increasing age (for example in our study 20.25% of girls 18-19

years had elevated levels vs. 7.95% of 18-19 year old boys). Visser et al. also determined

165

that in children 8-16 years old elevated C-reactive protein levels were present in 7.1% of

boys and 6.1% of girls in the population (1 14).

5.1.3 Distribution of Biomarkers related to MS by Tanner stage
Aim 2: To examine the distribution of biomarkers related to MS in boys and girls 12-19
years by sexual maturation stage within gender, race/ethnicity and poverty income ratio.
Our results provide data on the distribution of biomarkers related to MS in boys
and girls 12-19 years old by sexual maturation stage. We are not aware of any other
studies that have estimated the distribution of biomarkers related to MS by sexual
maturation stage, or Tanner stage in particular, in the general US adolescent population,
and few studies have estimated the relationship between biomarkers related to MS with
pubertal development in children and adolescents in other populations (46, 49, 50, 168).
We also know of no other population-based studies that examined the distribution
of biomarkers related to MS by Tanner stage within race/ethnic group and poverty
income ratio in a nationally representative sample. We believe it is important to examine
biomarkers related to MS by Tanner stage because various studies have shown that levels
of biomarkers (e.g. BMI, fasting insulin, and total cholesterol levels) that are related to
MS can be influenced by pubertal stage of development (48-50). Early puberty has been
shown to be related to earlier occurrence of hyperinsulinemia, for example, that could

possibly lead to an increased risk for the development of type 2 diabetes (169).

I66

Overall Tanner stage

We found no substantial differences by Tanner stage for each biomarker related to
MS in boys and girls. This could be due to the small sample sizes in Tanner stage 1,
Tanner stage 2 and Tanner stage 3, since the majority of our sample was in Tanner stage
4 and Tanner stage 5. As such, the majority of these subjects were likely to have reached
mature pubertal status. However, in our study we did see that total cholesterol levels
were decreased slightly in boys by Tanner stage, but not in girls. Results from the
Bogalusa Heart Study and other studies showed that total cholesterol levels in boys
decreased markedly with increasing Tanner stage (49, 50, 168, 170). Our results
obtained for triglyceride levels were not consistent with other smaller population studies
in terms of pubertal development. We did not see any linear trend for triglyceride levels
by Tanner stage. In a Finnish study in children (3-18 years old), triglyceride levels in
boys increased with increasing Tanner stage (50). HDL-cholesterol levels in boys
decreased with increasing Tanner stage, and in girls HDL-cholesterol levels increased
with increasing Tanner stage. This finding was consistent with findings from the
Bogalusa Heart Study (50).
Tanner stage within Race/Ethnic Group

Patterns of the distribution of biomarkers related to MS by Tanner stage did not
vary substantially by race/ethnic group and thus were similar to patterns observed for
overall Tanner stage. Overall, non-Hispanic white boys and Mexican American boys
were more likely to have higher median values of triglycerides and waist circumference
levels, as well as lower levels of HDL-cholesterol. Among girls, however, Mexican

Americans were more likely to have higher median values of triglycerides and waist

167

circumference among all sexual maturation stages. In contrast to these results, non-
Hispanic black boys and girls were more likely to have higher median values of total
cholesterol levels compared to Mexican American and non-Hispanic white boys and
girls. No linear trend by Tanner stage was seen for glucose, systolic and diastolic blood
pressure percentiles.
Tanner stage within poverty income ratio

In contrast to the distribution of biomarkers related to MS within poverty index
ratio by age, the distribution of biomarkers within poverty income ratio by Tanner stage
did differ to some extent by each Tanner stage in boys and girls. Girls with a poverty
income ratio less than one tended to have higher BMl-for-age percentiles on the BMl-for-
age growth charts compared to girls with a poverty income ratio greater and equal to two
at Tanner stage 3, 4 and 5. Boys and girls with a poverty income ratio less than one had
higher waist circumference levels compared to boys and girls with a poverty income ratio
greater than and equal to two at all Tanner stages. Only boys with a poverty income ratio
less than one had higher total cholesterol levels and higher systolic blood pressure
percentiles compared to boys with a poverty income ratio greater than and equal to two at
Tanner stage 1,3,4 and 5 for total cholesterol and at Tanner stage 1,2,4 and 5 for systolic
blood pressure percentiles. We are not aware of any studies that have examined the

distribution of biomarkers related to MS by Tanner stage within poverty income ratio.

5.1.4 Prevalence estimates of elevated levels of biomarkers by Tanner stage

As mentioned previously, to our knowledge no other studies have examined the

prevalence of elevated levels of biomarkers related to MS by sexual maturation stage in a

168

population-based sample like NHANES III. Prevalence estimates of elevated levels of
biomarkers related to MS in boys and girls were higher at Tanner stage 2 for both boys
and girls, and higher at Tanner stage 5 for boys and Tanner stage 3 for girls. Boys in
Tanner stage 2 were most likely to have elevated levels of waist circumference levels,
triglyceride levels, and total cholesterol levels. They were also more likely to be at risk
for overweight. Boys in Tanner stage 5 were more likely to have low levels of HDL-
cholesterol, high levels of LDL-cholesterol and high normal systolic blood pressure.
Prevalence estimates of C-reactive protein levels were highest among boys in Tanner
stage 3. Girls were more likely to have elevated levels of biomarkers related to MS
(overweight, high waist circumference, high triglyceride levels, low HDL-cholesterol
levels and high total cholesterol levels), at Tanner stage 3. Similar to boys, girls in
Tanner stage 2 were more likely to be at risk for overweight and have high levels of C -

reactive protein.

5.1.5 Clustering of biomarkers related to MS and their association with
demographic characteristics
Aim3: T o examine the clustering of biomarkers that have previously been associated with
Metabolic Syndrome in adults, in boys and girls 12-19 years using factor analysis, and
the association between demographic variables and these identified factor scores.

Again, this is the first study to have demonstrated the clustering of biomarkers
related to MS in adolescents 12-19 years old in a nationally representative sample using
factor analysis. This factor analysis approach has been extensively used in adults to

determine the clustering of biomarkers related to MS (40, 43). A recent study by Ford et

169

 

al defined MS by using factor analysis in the NHANES 111 adult population and found
that regardless of age, sex or race/ethnic group, levels of waist circumference, fasting
insulin, triglycerides, and HDL-cholesterol clustered together in one factor (171). This
study verifies that this approach may be valuable to identify clusters of biomarkers
related to MS among adolescents in a nationally representative sample that could be
applied to other populations and used to identify children at risk for the development of
Type 2 diabetes (40).

In our analyses correlations between biomarkers for girls and boys were similar
which allowed us to combine boys and girls in our factor analysis procedure. We then
stratified by gender, age and Tanner stage and found that by age and Tanner stage factors
were not meaningful due to a small sample sizes in each group. Therefore we believe that
the combined analyses produced the most stable factors.

From our results we can conclude that MS, as defined by the NCEP criteria for
adults (157), was not identifiable as a prevalent factor in these adolescents, i.e. the
biomarkers did not group similarly as they would group in adults according to the NCEP
criteria. Blood pressure and glucose also were not part of the clustering of biomarkers in
adolescents. We did, however, identify interpretable clusters of biomarkers related to MS
— particularly in our overall sample and in our sample of boys. In our findings, factor one
of the overall sample (boys and girls) and factor one of the total sample in boys were
similar and the most comparable to the definition of adult MS. This factor loaded high
on BMI-for-age percentiles, waist circumference and high but negative on HDL- .
cholesterol levels. It also loaded somewhat high on triglyceride levels. The clustering of

these biomarkers in different forms allows us to conclude that a subset of children was at

170

risk for elevated levels of this particular clustering of biomarkers when this study was
conducted. Factor 2 in the overall sample and the total sample for boys as well as factor

1 in the total sample for girls loaded high on LDL-cholesterol and total cholesterol.
Factor 3 in the total sample for boys loaded high on HDL-cholesterol and LDL-
cholesterol. From these factors we can conclude that the cholesterol biomarkers are
highly derived from one another. Factor 3 in the overall sample loaded high on systolic
and diastolic blood pressure, and factor 3 in the total sample for girls loaded high on
glucose, systolic blood pressure and diastolic blood pressure. Factor 4 in girls loaded high
on triglycerides and high but negative on HDL-cholesterol. Glucose did not load onto any
factors in boys, but was present in girls in three factors, but as we have mentioned before,
glucose levels did not vary much in the distribution by age and Tanner stage, and the
range overall was fairly small too.

Our results differ slightly from the only other study we are aware of that used
factor analysis to determine the clustering of variables and their relationship to MS in
adolescents (53). Chen et al. examined a biracial population of children (5-11 years),
adolescents (12-17 years) and young adults (18-38 years), in the Bogulasa Heart Study.
In their analyses adjusted for age and sex, they identified two factors. The first was
characterizied by: hyperinsulinemia/insulin resistance, dyslipidemia, and obesity and the
second by hypertension (systolic blood pressure and diastolic blood pressure). These
investigators utilized a larger sample size (n=4522) relative to the sample size in our
study (n=193 l ). Another study in Sweden examining biomarkers related to MS in
adolescents (l4 and 17 year olds) using partial correlation analysis, but not factor

analysis, concluded that in the subset of adolescents with high BMI (> 30 kg/m2 ), high

171

serum insulin, triglycerides, LDL-cholesterol and systolic blood pressure and low HDL-
cholesterol levels clustered together. They also examined the clustering of biomarkers
stratified by insulin quartiles, and didn’t find any significant differences in the clustering
in the clustering of biomarkers when stratifying by insulin (55).
Demographic characteristics of factor loading scores

In further analyses we divided each factor into quartiles to examine the
association of the factor scores in each factor with demographic characteristics.
Adolescents in the highest quartile of factor 1 in the overall sample and factor I in the
total sample for boys were more like to be Mexican American and have less healthy
habits in terms of smoking, physical activity and television watching. Adolescents in the
highest quartile of factor 2 in the overall sample and the total sample for boys, had
similar health habits to the previous factor, but were more likely to be non-Hispanic
black. Similar results were found for girls in the highest quartile of factor 1. Adolescents
in the highest quartile of factor 3 in the overall sample for boys, and girls in the highest
quartile of factor 3 both loaded high on systolic and diastolic blood pressure but had
different demographic characteristics. Girls in the highest quartile of factor 3 had
healthier habits compared to adolescents in the overall sample. Girls in the highest
quartile of factor 2 who had higher BMl-for age percentiles and higher waist
circumference levels were more likely to be smokers, have inactive physical activity
levels and watch television for 4-5 hours per day. Girls in the highest quartile of factor 4
had similar health habits compared to girls in factor 3. Overall factors did not vary
substantially by age, Tanner stage and poverty index ratio. Results in terms of race/ethnic

group and poverty income ratio as well as age and Tanner stage were consistent with

172

results obtained in Aim 1 and Aim 2. For example, in Aim 1 and Aim 2 we found that
non-Hispanic black boys and girls were more likely to have higher total cholesterol,
LDL-cholesterol and HDL-cholesterol levels compared to the other race/ethnic groups. In
Aim 3 we concluded that non-Hispanic black adolescents were more likely to be in the
highest quartile of factors that all loaded high on total cholesterol and LDL-cholesterol

levels.

5.2 Conclusion

In summary, this study examined the distribution and clustering of biomarkers related
to MS, as well as determined the prevalence of elevated biomarkers related to MS by age
and Tanner stage in a nationally representative sample. Our major findings related to our
first objectives (Aims 1 and 2) were that the distribution and prevalence of elevated levels
of biomarkers differed by age and by Tanner stage, within gender, depending on the
biomarker under study, though there were few biomarkers that increased or decreased in
either a linear or curvilinear manner by age or Tanner stage. The distribution of some of
the biomarkers did, however, differ between non-Hispanic whites, non-Hispanic blacks
and Mexican Americans where for example non-Hispanic white and Mexican American
boys and girls were more likely to have higher levels of triglycerides and lower levels of
HDL-cholesterol compared to non-Hispanic blacks. Overall, there were fewer
differences in distributions or prevalences of elevated levels of biomarkers between age
and Tanner stage within poverty income ratio. These findings point out that adolescents

may be at risk for elevated levels of biomarkers related to MS at different ages and

173

maturation stages and also depending on their race/ethnic group and somewhat on their
poverty income ratio status.

We then examined correlations and clusters of biomarkers using factor analyses. In
these analyses we found that correlations of biomarkers related to MS were remarkably
similar in boys and in girls by age and Tanner stage. We then combined boys and girls in
the factor analysis procedure. We also stratified by gender, since boys and girls are
shown to be biologically different at this stage in life. We identified 3 factors in the
overall sample for boys and girls from this approach (Factor 1: BMI-for-age percentiles,
waist circumference levels, triglyceride levels and HDL-cholesterol levels; Factor 2: total
cholesterol levels and LDL-cholesterol levels; Factor 3: systolic and diastolic blood
pressure percentiles).

In the total sample for boys we identified 3 factors that were somewhat similar to
factors in the overall sample for boys and girls (Factor 1: BMI-for—age percentiles, waist
circumference levels, triglyceride levels and HDL-cholesterol levels; Factor 2: LDL-
cholesterol and total cholesterol; Factor 3: HDL-cholesterol and LDL-cholesterol). Last,
we identified 4 factors in the total sample for girls (Factor 1: LDL-cholesterol and total
cholesterol; Factor 2: BMI-for-age percentiles, waist circumference levels and negative
glucose levels; Factor 3: Glucose, systolic and diastolic blood pressure percentiles; Factor
4: Triglycerides, Glucose and HDL—cholesterol)

These factors suggest that specific subsets of biomarkers associated with MS occur in
adolescents. In addition, these data provide the first insight into the clustering of specific
biomarkers related to MS in adolescents in a nationally representative sample. According

to the NCEP ATP llI definition of MS in adults, not all biomarkers are equally predictive

174

of developing MS, as found by a population-based study in adults (NHANES III) (3).
Therefore in our study, even the subsets of biomarkers that clustered may still put these
adolescents at risk to develop MS. Like obesity, MS could emerge as a critical risk factor
for chronic diseases in adolescents. It is therefore necessary to understand clusters of

biomarkers related to MS in children and adolescents in our population.

5.3 Strengths and Limitations of Data Sources and Methods

There are several important considerations that we have to take into account when
interpreting the results of this study. The strengths are that this is a population-based
study that is representative of the general US adolescent population, and therefore the
results obtained are generalizable. The response rate of the whole NHANES III study
population study was 86% which is considered to be very high for such a nationwide
cross-sectional study. This study is also significant in that no other studies have examined
the clustering of biomarkers related to MS in a nationally representative sample using
factor analysis or somehow identifying clusters of specific markers. Furthermore, all
measurements that were collected in NHANES III were rigorously collected according to
standardized procedures in a clinical setting by trained staff and physicians.

The limitations of the present study must also be considered. This study utilized
cross-sectional data, i.e. the data was collected at one point in time. We, therefore, cannot
make causal inferences from this data. However, this is a national study and the results
obtained from this study will provide a baseline to monitor trends in biomarkers related to

MS in adolescents in the USA. Another potential limitation to our study was that the

175

 

NCEP Adult Treatment Panel recommendations include only examinees who have fasted
for 9 hours or more (157).

In addition, selection bias could have resulted from our study since we excluded
adolescents who did not fast for 6 hours or more, who had tanner stage variables missing,
or had other missing biomarker data. However, we did compare demographic
characteristics of adolescents who were missing from our study, to adolescents who had
complete data, and they did not differ on age, tanner stage, race/ethnic group, poverty
income ratio, smoking status or physical activity. Potential measurement error could also
have occurred, since we included participants who fasted for 6 hours or more in our
sample, though including only those who fasted for 9 hours is optimally recommend. We
did compare biomarkers of those who fasted 6-9 hours to those who fasted 9 hours plus,
and since values were similar we didn’t want to include additional selection bias by
excluding these participants. A further limitation in our study was that when stratifying
by age and Tanner stage, we believe sample sizes became too small to obtain stable factor
analysis results.

A valuable contribution to the biomarkers we would have liked to include in our
study would have been plasma insulin levels, since previous studies have shown that
elevated plasma insulin levels have been a strong component of MS (43, 48, 53, I72) and
the previous study (53) that examined the clustering of biomarkers related to MS using
factor analysis in adolescents, included insulin levels and observed it to be an important
component of the MS related factor. Insulin is known to play a very important role in the

mechanism of MS.

176

Furthermore, several of the covariates used in this study, (poverty income ratio,
physical activity measurements and television watching) also had some limitations to this
data. Many participants did not report their income for unknown reasons and those who
didn’t report may have been different, thus potentially introducing some selection bias.
In addition, different measures of physical activity were available by age. Physical
activity was measured only in adults 17 years and older, and television watching was
measured only in children and adolescents younger than 17 years. It would have been
interesting to look at the association between the identified factors and the above

mentioned characteristics in the adolescents who did not have these variables measured.

5.4 Implications of Findings

Since this data is representative of the general population, these results provide
reference values for future studies, in the distribution and clustering of biomarkers related
to MS as well as the prevalence of elevated levels of biomarkers related to MS.
Furthermore, future analyses of NHANES IV or studies could use these results as
baseline measures to compare this cross-sectional sample (1988-1994) to what has
occurred more recently (1999-2004). Interesting results might come from such an
approach since overweight has increased significantly in US adolescents since NHANES
III (2) and overweight is also related to MS. The prevalence of risk factors may have
increased and clusters of patterns may have changed and potentially appear more similar
to adult patterns. Our results are also significant in that we incorporated information on

sexual maturation stage. race/ethnic group and poverty income ratio in all our analyses to

177

 

determine what impact these covariates may have on the biomarkers related to MS in
adolescents.

Due to the increase in overweight in youth further studies may find similar factors
as we did, and they may also find adult MS at a younger age due to this increase in
overweight. From our results we can also conclude that the clusters that we obtained
from each factor especially in the overall sample (boys and girls) and the total sample for
boys, may possibly be seen as early predictors of “pre-metabolic syndrome”, and can be

used to monitor adolescents at risk for further increased levels.

5.5 Suggestions for future research

The results obtained from our study sets the ground work for future studies to
build on. Future studies should aim for a larger sample size in order to produce stable
estimates to allow for stratification by age, Tanner stage and potentially race/ethnicity
and poverty income ratio in the factor analysis procedure. Future studies are also needed
to determine the effect of diet and physical activity on the clustering of risk factors
related to MS in adolescents.

It would be ideal if further prospective epidemiological studies could be
conducted in order to follow children and adolescents over time. This will enable us to
determine if the biomarkers that clustered at this point in time will cluster differently at
different ages. In addition, longitudinal studies of children and adolescents are important
to see if these clusters of biomarkers in children predict the development of type 2
diabetes, cardiovascular disease or cancer, since all of them have been shown to be

related to MS. In addition, since our sample size by overweight status was too small,

178

future studies should stratify by overweight status (2 90th percentile for age on the BMI-
for-age growth charts) in their factor analysis procedure to determine whether these

children and adolescents have a higher risk to develop MS.

179

10.

ll.

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