University of Southern California Dissertations and Theses

The metabolic syndrome in overweight Latino youth: influence of dietary intake and associated risk for Type 2 diabetes

(USC Thesis Other)

The metabolic syndrome in overweight Latino youth: influence of dietary intake and associated risk for Type 2 diabetes

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

THE METABOLIC SYNDROME IN OVERWEIGHT LATINO YOUTH:
INFLUENCE OF DIETARY INTAKE AND
ASSOCIATED RISK FOR TYPE 2 DIABETES

by
Emily Elizabeth Ventura

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL OF THE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE)

May 2009

Copyright 2009 Emily Elizabeth Ventura

ACKNOWLEDGEMENTS

I would like to thank my committee for their guidance and committement to my
learning. I am grateful for my advisor Dr. Michael Goran for introducing me to the field
of obesity research and for giving me a solid understanding of the scientific process. An
excellent mentor, Dr. Goran has continually challenged me, given me enough
independence to allow me to grow, and provided me with unique opportunities to further
my career. I have also been blessed to have Dr. Jaimie Davis as a mentor and friend for
all of my time at USC. I admire her for her perseverance, problem solving skills, and
positive outlook. I have also been fortunate to learn from Dr. Marc Weigensberg, who in
addition to being extremely knowledgeable, is a talented and patient teacher. In addition,
I am grateful for Dr. Stanley Azen for his encouragement and for imparting his statistical
wisdom in a fun and creative spirit, and for Dr. Michael Khoo for his noteworthy ability
to ask helpful, insightful questions.
In addition to my committee members, I have been surrounded by a number of
other wonderful mentors and friends at USC. Specifically I would like to thank Dr.
Donna Spruijt-Metz for teaching me about the complexities of human behavior and for
always letting me know that I am cared about as a student, Dr. Lourdes Baezconde-
Garbanati for modelling excellence in culturally competent research and for providing
such a spark of inspiration, and Dr. Jean Richardson for reassuring me that balance in life
is possible and for encouraging me to pursue my ideas on a larger scale. I am also
thankful for the company and support of the other senior students in the Goran lab,
ii

Claudia Toledo-Corral, Courtney Byrd-Williams, and Katharine Alexander, as well as all
of my fabulous coworkers.
My family and friends have been an incredible source of support and positivity. I
would like to thank my husband Paul for being my haven and keeping me smiling, my
parents Roberta and Greg for loving me selflessly and for being my best role-models, my
best friend Emily D. for helping me remember who I am, and my grandmothers Gwen
and Mary Pauline and my godmother Montena for watching over me. Also thanks to my
Ecuadorian family for inspiring my interest in food and culture. Finally, I would like to
acknowledge the one who held my hand through this process, though at times I did not
recognize it. I am little on my own but “I can do all things through Christ, who
strengthens me” (Phillipians 4:13). I am thankful for this opportunity and hope that I can
use what I have learned to serve others first and foremost.
iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS................................................................................................ ii

LIST OF TABLES.............................................................................................................. v

LIST OF FIGURES ........................................................................................................... vi

ABBREVIATIONS .......................................................................................................... vii

ABSTRACT.....................................................................................................................viii

CHAPTER 1 INTRODUCTION .................................................................................. 1
Background and Significance ......................................................................................... 1
Prevalence of Pediatric Obesity in Latinos and Public Health Implications .............. 1
Metabolic Risk in Overweight Latino Adolescents: Role of Insulin Resistance........ 2
The Metabolic Syndrome: Definition and Prevalence................................................ 3
Utility of the Metabolic Syndrome in Identifying Chronic Disease Risk in Youth:
A Focus on Type 2 Diabetes....................................................................................... 6
Pathophysiology of Type 2 Diabetes and Prevalence in Youth.................................. 7
Dietary Risk Factors for the Metabolic Syndrome ..................................................... 9
Dietary Intervention Studies to Reduce the Prevalence of the Metabolic Syndrome
in Youth .................................................................................................................... 11
Specific Aims and Hypotheses ..................................................................................... 12
Study Samples............................................................................................................... 15

CHAPTER 2 DIETARY INTAKE AND THE METABOLIC SYNDROME IN
OVERWEIGHT LATINO CHILDREN........................................................................... 17

CHAPTER 3 PERSISTENCE OF THE METABOLIC SYNDROME OVER 3
ANNUAL VISITS IN OVERWEIGHT LATINO CHILDREN: ASSOCIATION
WITH PROGRESSIVE RISK FOR TYPE 2 DIABETES............................................... 39

CHAPTER 4 THE EFFECTS OF A RANDOMIZED, CONTROLLED,
MODIFIED-CARBOHYDRATE NUTRITION INTERVENTION ON THE
METABOLIC SYNDROME IN OVERWEIGHT LATINO ADOLESCENTS.............. 60

CHAPTER 5 SUMMARY AND CONCLUSIONS ................................................... 82
Summary of Findings.................................................................................................... 82
Future Research ............................................................................................................ 85
Mechanisms .............................................................................................................. 86
Public Health Strategies to Increase Fiber Intake..................................................... 88
Strengths and Limitations ............................................................................................. 93
Contribution to the Literature ....................................................................................... 94

ALPHABETIZED BIBLIOGRAPHY.............................................................................. 95
iv

LIST OF TABLES

Table 2-1Clinical data of children with and without the metabolic syndrome................. 32

Table 2-2 Dietary data of children with and without the metabolic syndrome ................ 33

Table 2-3 Multiple linear regression of dietary cholesterol intake and systolic blood
pressure ............................................................................................................................. 34

Table 2-4 Multiple linear regression of dietary fiber intake and waist circumference..... 35

Table 3-1 Baseline unadjusted descriptive characteristics and individual metabolic
syndrome features by metabolic syndrome group in overweight Latino children............ 53

Table 3-2 Baseline unadjusted indices of insulin and glucose by metabolic syndrome
group in overweight Latino children................................................................................. 54

Table 3-3 Repeated measures analysis of variance for adiposity measures and
insulin/glucose indices by metabolic syndrome group in overweight Latino children .... 55

Table 4-1 Baseline characteristics of study participants (n=50) by metabolic syndrome
status ................................................................................................................................. 80

Table 4-2 Changes in metabolic syndrome features by randomization group.................. 81

v

LIST OF FIGURES

Figure 2-1 Percentage of participants with zero, one, two, or three or more features of
the metabolic syndrome .................................................................................................... 36

Figure 2-2 Prevalence of each feature of the metabolic syndrome................................... 37

Figure 2-3 Mean soluble fiber intake by number of features of the metabolic syndrome 38

Figure 3-1 Persistence of each metabolic syndrome feature by metabolic syndrome
group ................................................................................................................................. 58

Figure 3-2 Changes in fat mass and insulin/glucose indices over 3 annual visits by
metabolic syndrome group in overweight Latino children............................................... 59

Figure 4-1 Change in metabolic syndrome status by randomization group ..................... 78

Figure 4-2 Changes in waist circumference by fruit increase vs. fruit decrease .............. 79

vi

ABBREVIATIONS

AIR= Acute Insulin Response
BMI= Body Mass Index
DEXA= Dual Energy X-Ray Absorptiometry
DI= Disposition Index
FFA= Free Fatty Acid
FSIVGTT= Frequently Sampled Intravenous Glucose Tolerance Test
GCRC= General Clinical Research Center
HDL= High Density Lipoprotein
MRI= Magnetic Resonance Imaging
OGTT= Oral Glucose Tolerance Test
SI= Insulin Sensitivity
vii

ABSTRACT

One third of overweight Latino youth have the metabolic syndrome, a clustering
of risk factors for diabetes and cardiovascular disease. The objectives of this dissertation
were:1) to examine the association between dietary intake and the metabolic syndrome
with a focus on the quality of carbohydrate intake; 2) to examine whether persistent
metabolic syndrome over 3 annual visits was associated with increased risk for type 2
diabetes; and 3) to test the effects of a randomized, controlled, 16 week, modified-
carbohydrate nutrition education program on metabolic syndrome profiles.
All participants were Latino and had BMI ≥ 85
th
percentile. Data for papers 1 and
2 are from a longitudinal, observational study for children ages 10-17 years, and data for
paper 3 is from a 16 week intervention study for adolescents ages 14-18 years. The
metabolic syndrome was defined by a pediatric adaptation of the Adult Treatment Panel
III report. Body composition was assessed by DEXA, and insulin/glucose kinetics by
OGTT and IVGTT. Dietary intake was assessed by 24 hour recalls in paper 1 and by 3
day diet records in paper 3.
In paper 1, participants with 0 features of the metabolic syndrome ate
significantly more soluble fiber compared to those with 3+ features of the metabolic
syndrome (5g vs. 4g daily). In paper 2, when compared with participants who never had
the metabolic syndrome, participants with persistent metabolic syndrome had a faster rate
of fat mass gain over time and an increasing level of insulin response to oral glucose,
combined with an average of 43% lower insulin sensitivity, and 25% lower beta cell
function. In paper 3, the intervention had few improvements on metabolic syndrome
viii

profiles. However, independent of randomiztion group, those who increased fiber intake
(average of 6g/day) had a decrease in the number of metabolic syndrome features.
In conclusion, cross-sectionally, soluble fiber intake was the only dietary variable
significantly associated with the metabolic syndrome. Longitudinally, persistent
metabolic syndrome was associated with indicators of increased risk for type 2 diabetes.
Finally, in the context of a 16 week intervention, increases in fiber intake are related to
improvements in metabolic syndrome profiles.

ix

CHAPTER 1 INTRODUCTION

Background and Significance

Prevalence of Pediatric Obesity in Latinos and Public Health Implications

Childhood obesity has become a critical public health problem. Children and
adolescents who are overweight are at an increased risk of being overweight as adults and
of suffering from chronic diseases, such as type 2 diabetes.
91
Latinos are
disproportionately affected by obesity. In 2003-2006, 38.9% of Mexican Americans ages
12-19 were at risk of overweight or overweight (BMI percentile ≥ 85
th
percentile for age
and gender), as compared to 33.1% of non-Hispanic whites.
100
Furthermore, the rates of
obesity are increasing faster in Latinos as compared to whites, both in 6-11 year olds and
12-17 year olds.
70

Latinos are the largest minority group in the United States, comprising 13% of the
total population.
15
By 2050, the overall Hispanic population in the United States is
expected to triple as compared to the non-Hispanic white population which is projected to
only increase by 8%.
96
In California, between 1995 and 2005, the Latino teen population
(ages 10-19) grew the most, with a 61% increase, followed by a 45% increase in Asians,
a 22% increase in African Americans, and a 16% increase in Caucasians.
12
Since Latino
youth are the fastest growing segment of the teen population, and a group at increased
risk for obesity and chronic disease, it is particularly important that we assess which
factors contribute to their health.
If Latino youth become increasingly chronically ill, it will not only significantly
reduce their quality of life but will also likely cause a tremendous economic burden on
society as a whole. Latinos in the United States are already at an economic disadvantage
1

as compared to Caucasians, with a higher percentage living in poverty.
96
The percentage
of Mexican-American adults without health insurance is 40% as compared to 10% in
non-Hispanic whites.
96
With increasing illness in this disadvantaged group, overall health
care demands and costs will arguably become unmanageable. In addition, Latino teens in
particular represent a large component of the emerging workforce and are integral to the
future social and economic health of our nation.
Metabolic Risk in Overweight Latino Adolescents: Role of Insulin Resistance
In addition to having a higher prevalence of obesity, Latino children are more
insulin resistant than their Caucasian counterparts, independent of body composition.
48

Insulin is a peptide hormone produced in the pancreas by the beta cells of the islets of
Langerhans and is required for the body to process glucose, which is the main fuel for the
cell. Insulin resistance refers to a state in which there are not enough insulin receptors on
cell membranes, the receptors do not function properly, or there are defects in the post-
receptor signaling.
32
Insulin resistance is the inverse of insulin sensitivity.
In the case of overweight Latino adolescents, the ethnic predisposition for insulin
resistance is combined with the negative effects of adiposity on insulin resistance. In a
study using a multi-ethnic subsample of adolescents from NHANES 1999-2002, Lee et al
found that obesity is the most important risk factor for insulin resistance, independent of
sex, age, or race/ethnicity, and that 52% of obese adolescents were insulin resistant.
78

Though the mechanisms through which adiposity contribute to insulin resistance are not
fully understood, it has been shown that adipose tissue releases increased amounts of
non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines and other
factors that are involved in the development of insulin resistance.
65
Visceral fat, in
2

particular, is believed to be the most metabolically active fat depot,
101
and there is
evidence to show that visceral fat is associated with insulin resistance in overweight
Latino youth.
24

Insulin resistance is hypothesized to be a central contributor to the development of
chronic disease.
109
In pediatric populations, insulin resistance has been shown to mediate
the relationship between overweight and the following diseases: type 2 diabetes,
cardiovascular disease, polycystic ovarian syndrome, and non-alcoholic fatty liver
disease.
25
Because of the central role of insulin resistance, Reaven coined a syndrome to
define the constellation of metabolic risk factors related to insulin resistance and obesity
in adults.
109, 110
The syndrome was first called the “insulin resistance syndrome” or
“syndrome x,” but is now more commonly referred to as the “metabolic syndrome” and
has been increasingly used to examine risk for cardiovascular disease and type 2 diabetes
in various populations, including pediatric populations.
The Metabolic Syndrome: Definition and Prevalence

Though the metabolic syndrome was coined in 1988, it was not until 2001 that the
National Cholesterol Education Program Adult Treatment Panel (ATP) III report gave the
first standardized definition for adults.
3
The syndrome is characterized by a clustering of
risk factors, and was defined by the ATP III as a combination of 3 or more of the
following conditions: waist circumference >102 cm in males and >88 in females, blood
pressure ≥130/85 mm Hg, fasting glucose ≥110 mg/dl, triglycerides ≥150 mg/dl, or high-
density lipoprotein (HDL) cholesterol ≤40 mg/dl in men and ≤50 mg/dl in women.
According to the specific cutoffs established by the ATP III, approximately 22% of all
US adults have the metabolic syndrome.
40
The ATP definition has since been updated to
3

use a cutoff of fasting glucose ≥100 mg/dl to reflect the current American Diabetes
Association definition of impaired fasting glucose.
1

Experts have yet to come to a consensus on a pediatric definition of the metabolic
syndrome, given that puberty is associated with physiological changes which affect
metabolic profiles.
116
A pediatric definition was proposed by Cook et al in 2003 which
was used to generate the first national report of the metabolic syndrome in a pediatric
population using NHANES III data from 1988-1994.
21
According to the original Cook
definition, the presence of three or more of the following constituted the metabolic
syndrome: triglycerides ≥110 mg/dL, HDL cholesterol ≤40, fasting glucose ≥110 mg/dl,
and blood pressure and waist circumference ≥90
th
percentile. In 2008, Cook et al
published new national prevalence estimates using NHANES data from 1999-2002, in
which the updated cutoff for fasting glucose of ≥100 was used, and found that the
prevalence is 44.2% in overweight adolescents and 9.4% in all U.S. adolescents.
20
In this
same sample, the metabolic syndrome was most common in Mexican Americans
(11.1%), followed by Caucasians (10.7%) and African Americans (5.2%) and was more
common in male adolescents than females (13.2% vs. 5.3%).
Our group has proposed a definition for the metabolic syndrome for overweight
Latino youth which is similar to Cook’s definition but applies age, gender, and ethnic
specific cut-points to the definition proposed by the ATP III.
26
According to our
definition, the presence of 3 or more of the following constitutes the metabolic syndrome:
waist circumference ≥90th percentile for age, gender, and Hispanic ethnicity from
NHANES III data,
38
triglycerides ≥90th percentile of age and gender,
58
HDL cholesterol
≤ 10th percentile for age and gender,
58
systolic or diastolic blood pressure >90th
4

percentile adjusted for height, age, and gender,
2
and a 2hr post oral challenge plasma
glucose value of ≥140 but < 200mg/dl.
5
Using this definition, we found that 30% of our
cohort of overweight Hispanic youth ages 8-13 years (n=126), had the metabolic
syndrome.
26

Comparisons of prevalence rates using the different pediatric definitions have
shown moderate agreement. Our group conducted an analysis with our cohort of
overweight Latino youth in which we compared prevalence estimates using our definition
as well as the definition proposed by Cook and another definition proposed by Weiss.
116,
130
We found that the prevalence of the metabolic syndrome ranged from 26-39% and that
there was moderate to substantial agreement between definitions (kappa of 0.52-0.70).
The variance in estimates by definition can be attributed to the less stringent cutpoints for
HDL and triglycerides in the Cook definition as well as the use of impaired fasting
glucose as opposed to impaired glucose tolerance (2 hour post oral glucose challenge) in
the Cook definition. Although using impaired fasting glucose as a criteria is more
practical as it only requires a fasting blood testing, our group selected impaired glucose
tolerance for our definition because we have found in our cohort of overweight Latino
youth that those with impaired glucose tolerance exhibit normal fasting glucose levels,
suggesting that the fasting measures are not as sensitive in detecting pre-diabetes.
47

Although a pediatric definition has not been agreed upon, there is evidence to
show that the metabolic syndrome does indeed cluster with insulin resistance in cross-
sectional analyses, regardless of which definition is used. Our original cross-sectional
analysis showed that insulin sensitivity was inversely associated with the number of
features of the metabolic syndrome and that those with the metabolic syndrome (3+
5

features) had 62% lower insulin sensitivity than those with no features of the metabolic
syndrome, independent of gender, age, sexual maturation, and body composition.
26
In
the comparative cross-sectional analysis of the same cohort using the 3 separate
definitions,
21, 26, 130
the significant inverse relationship between metabolic syndrome
features and insulin sensitivity was maintained.
116

Utility of the Metabolic Syndrome in Identifying Chronic Disease Risk in Youth: A
Focus on Type 2 Diabetes

Not all experts are convinced that the metabolic syndrome is truly a syndrome and
some question whether the clustering of risk factors is synergistic as opposed to merely
additive.
45, 64
There is evidence to show that the metabolic syndrome is associated with
risk for both cardiovascular disease and type 2 diabetes in adults.
61, 81
It has also been
shown that having the metabolic syndrome in childhood predicts both cardiovascular
disease and type 2 diabetes later in life.
43, 95
However, little is known about the
relationship between the metabolic syndrome and more proximal disease risk within
childhood or adolescence.
Part of the debate related to the utility of the metabolic syndrome relates to the
lack of knowledge regarding the stability of the metabolic syndrome over time,
particularly in youth. There is evidence to show that the metabolic syndrome tracks
moderately well from adolescence to adulthood.
36, 67
Little is known, however, about the
persistence of the metabolic syndrome within childhood or adolescence. To our
knowledge, only two studies to date have assessed the stability of the metabolic
syndrome in a pediatric sample.
45, 130
To our knowledge, no studies have assessed the
persistence of the metabolic syndrome within childhood with more than 2 time points.
Furthermore, no studies have assessed the relationships between the persistence of the
6

metabolic syndrome and the changes in risk factors for type 2 diabetes specifically within
childhood/adolescence.
Given that type 2 diabetes, in particular, is affecting increasingly younger
populations, additional screening tools are needed to identify risk. Risk for type 2
diabetes is difficult to quantify and requires lengthy tests such as an intravenous glucose
tolerance test, which is not practical in a regular clinic setting . If measuring the
metabolic syndrome is found to be effective in assessing more proximal risk for type 2
diabetes within childhood or adolescence, this will be useful for clinicians. While current
consensus statements include screening at-risk children for both type 2 diabetes and
individual features of metabolic syndrome,
6, 54
there is evidence to suggest that the
metabolic syndrome is rarely screened for in youth. Currently, less than 10% of children
with BMI >85th percentile receive metabolic screening when visiting a general pediatric
clinic.
111

Pathophysiology of Type 2 Diabetes and Prevalence in Youth

Type 2 diabetes occurs when either the body does not produce enough insulin or
is not able to use insulin efficiently. As opposed to type 1 diabetes where the pancreatic
beta cells are destroyed by an autoimmune response and do not produce insulin, in type 2
diabetes, the beta cells produce insulin, but either they do not produce enough insulin
and/or the body is insulin resistant. Insulin resistance prompts the beta cells to produce
extra insulin in order to compensate, and the beta cells can eventually become exhausted.
Therefore, the development of type 2 diabetes is a function of the ability of the pancreatic
beta cells to increase insulin secretion in response to insulin resistance. When type 2
diabetes develops, the cells are not able to adequately take in glucose and the level of
7

glucose in the blood stream is chronically elevated. Elevated blood sugar, or
hyperglycemia, can eventually cause severe acute complications such as coma as well as
serious chronic complications arising from vascular damage, particularly in the kidneys,
eyes, and feet. Type 2 diabetes often leads to cardiovascular disease because
hyperglycemia increases the production of free radicals, which are highly reactive
molecules that cause cell damage and subsequent inflammation which promotes
atherosclerosis, or hardening of the arteries.
122
Atherosclerosis then increases risk for
heart attack and stroke. In addition to the role of hyperglycemia, hyperinsulinemia, or
elevated levels of insulin, is highly correlated with insulin resistance,
55
which has
negative metabolic consequences, as described above.
Two independent risk factors for type 2 diabetes are Latino ethnicity and
overweight. In a population-based sample of youth, the prevalence of type 2 diabetes
among Hispanics ages 10-19 years was more than double that of non-Hispanic whites
(0.48 per 1000 as compared to 0.19 per 1000).
80
Although there is little understanding of
why Latinos are at an increased risk for type 2 diabetes, it is believed that genetics play a
role. One hypothesis surrounding this increased prevalence relates to the concept of a
thrifty genotype that was once adaptive in surviving in times of feast or famine when
higher blood glucose levels might have been beneficial.
72
However, now that the food
supply is more constant and processed foods prevail, what may have once been an
adaptive genetic profile may now be maladaptive. In addition to ethnic predispositions,
being overweight increases risk for type 2 diabetes. More than 85 percent of people with
type 2 diabetes are overweight.
97
In our previous work, we have shown that 32% of
8

overweight Latino children ages 8-13 years have pre-diabetes, as defined by impaired
fasting or 2-hour glucose intolerance.
47, 129

Dietary Risk Factors for the Metabolic Syndrome

Diet is one of the main modifiable risk factors for the development of the
metabolic syndrome as well as type 2 diabetes. However, little research has been
conducted in youth to examine specific dietary risk factors associated with the metabolic
syndrome, and even less research has been done with specific, high-risk ethnic groups
such as Latinos. A study examining associations between dietary intake and the
metabolic syndrome in youth using NHANES data from 1999-2002 showed an inverse
association between the prevalence of the metabolic syndrome and quartiles of an overall
Health Eating Index score.
102
An analysis of the subcomponents of the Healthy Eating
Index revealed that the only component associated with the metabolic syndrome was the
fruit score, with those eating the most fruit having the lowest prevalence of the metabolic
syndrome. No other associations between Healthy Eating Index components or individual
nutrients were found. The authors concluded that because this study examined a
nationally-representative population in which the prevalence of the metabolic syndrome
was 3.5%, it may have been difficult to detect additional associations between dietary
intake and the metabolic syndrome. A review of the literature identified only three other
pediatric studies which have explored the relationships between dietary intake and the
metabolic syndrome and risk factors identified were sweetened beverages,
123
a “Western”
dietary pattern,
69
solid hydrogenated fat, and bread made with white flour.
68
In contrast,
the following have been shown to be protective factors: intake of fruits, vegetables, and
dairy products.
68

9

As compared to the pediatric literature, the adult literature contains more studies
which investigate the associations between dietary intake and the metabolic syndrome.
Studies evaluating overall dietary patterns have shown that prudent dietary patterns
which include principal components such as fruits and vegetables, cereals, legumes, and
fish are protective against the metabolic syndrome.
10, 103, 133
Conversely, other studies
have shown that an empty calorie pattern of eating, characterized by a high consumption
of total fat and sweetened beverages, or a patterns dominated by refined bread or sweets
and cakes, are associated with increased risk for the metabolic syndrome.
118, 134
Studies
which have identified single dietary components related to the metabolic syndrome in
adults have concluded that risk factors include higher consumption of meat and alcohol
intake,
103
fried foods,
86
and consumption of both regular
31
and diet
86
soft drinks. There
is also evidence suggesting that intake of whole grains
37
and intake of dairy products
9

are protective against the metabolic syndrome.
Results from the aforementioned studies that have examined food patterns and
food groups suggest that fiber and sugar intake may be associated with the metabolic
syndrome, though these hypotheses have not been tested and related mechanisms have
not yet been explored. Results related to refined foods, whole grains, fruits and
vegetables, and legumes, suggest that dietary fiber may play a role, perhaps by promoting
satiety and lowering body weight.
117
Results which show associations with sweets or
sweetened beverages suggest that added sugar intake may be related to risk for the
metabolic syndrome, perhaps by increasing triglyceride levels.
119
These particular
nutrients have been hypothesized to play a role in the development of the metabolic
syndrome in high risk populations, but associations have not been directly tested
23, 93
.
10

Although we have not yet assessed the relationship between fiber or sugar intake
and the metabolic syndrome in overweight Latino youth, we have explored associations
with carbohydrate quality and other markers of metabolic risk. In particular, we have
found that that youth in our cohort drink an average of 2.5 servings per day of sugar-
sweetened beverages.
29
Furthermore, we found that intakes of total sugar and sugar-
sweetened beverages are associated with poor beta-cell function, independent of
adiposity. These results suggest that beta-cell compensation is already deteriorating in
subjects with increased intakes of sugar, thus indicating risk for the progression to type 2
diabetes.
Dietary Intervention Studies to Reduce the Prevalence of the Metabolic Syndrome in
Youth
Dietary intervention studies to reduce the prevalence of the metabolic syndrome
in overweight youth are limited. A review of the literature yielded four studies which
examined dietary intervention effects on the metabolic syndrome in youth. All four
interventions also included physical activity components and only one study isolated the
effects of dietary modification as separate from physical activity. The one study which
isolated the effects of dietary change consisted of an eight month intervention with three
arms: lifestyle education every two weeks (which consisted of general instruction
regarding diet as well as physical activity), lifestyle education plus moderate-intensity
physical activity, and lifestyle intervention plus high-intensity physical activity.
66
No
information is provided regarding the specific dietary advice given to participants during
the lifestyle education classes and there was not a control group who did not receive
nutrition education. The other three studies also included a nutrition component in
11

interventions to reduce the prevalence of the metabolic syndrome but did not test its
separate effects. One study reported results from a 12 week intervention with a lifestyle
plus exercise group and a control group and showed significant improvements in systolic
blood pressure, triglycerides, waist circumference, and fasting glucose in the intervention
group.
105
Another showed that a one group, non-controlled, 12 week, family-centered
lifestyle intervention which incorporated nutrition education and physical activity
resulted in a significant improvement in metabolic syndrome status through a decrease in
blood pressure, triglycerides, and glucose levels.
94
The third reported results from a 2
week intensive, residential, diet and exercise program and showed a decrease in
triglycerides and blood pressure and a reversal of the metabolic syndrome status in those
who had it at baseline.
18
There have been no interventions to date which study the effects
of specific, modified carbohydrate nutrition education approach for the reduction of the
prevalence of the metabolic syndrome in youth and furthermore no interventions of this
type in Latino youth.
Specific Aims and Hypotheses
Paper 1: Cross-sectional analysis of dietary intake and the metabolic syndrome
The overall objective of the first study is to investigate the cross-sectional
associations between diet composition and the risk of the metabolic syndrome in a
sample of overweight Latino youth. Specifically, the role of the macronutrient
composition of the diet and the quality of carbohydrate intake, i.e. added sugar and fiber
consumption, will be assessed.

12

Aim 1: to compare the dietary intakes of Latino youth with and without the metabolic
syndrome.
Hypothesis: Participants with the metabolic syndrome will have lower intakes of dietary
fiber and higher intakes of added sugar than those without the metabolic syndrome.
Aim 2: to examine the associations between dietary variables, specifically sugar and fiber
intake, and individual features of the metabolic syndrome.
Hypotheses: Dietary fiber intake will be negatively associated with blood glucose levels
and waist circumference.Added sugar consumption will be positively associated with
triglyceride levels.
Aim 3: to examine differences in dietary intake across the number of features of the
metabolic syndrome.
Hypothesis: Across the increasing number of features of the metabolic syndrome, dietary
fiber intake will decrease and added sugar consumption will increase.
Paper 2: Longitudinal analysis of the metabolic syndrome and risk for type 2 diabetes
The overall objective of the second study is to examine if the persistence of the
metabolic syndrome is associated with changes in risk factors for type 2 diabetes in
overweight Latino children.
Aim 1: to identify how many children in the cohort consistently have the metabolic
syndrome at three annual measurements.
Aim 2: to determine if participants with persistent metabolic syndrome have differences
in insulin and glucose indices, independent of adiposity.
Hypotheses: Participants with persistent metabolic syndrome will have lower insulin
sensitivity and beta cell function over time as compared to participants without the
13

metabolic syndrome, indicating increased risk for type 2 diabetes. Participants with
intermittent metabolic syndrome will also have increased risk for type 2 diabetes as
compared to those who do not have the metabolic syndrome, however their risk will not
be as pronounced as those with persistent metabolic syndrome.
Paper 3: Effects of a randomized, controlled modified-carbohydrate nutrition education
intervention on the metabolic syndrome
The overall objective of the third study is to determine whether a 16-week
nutrition education intervention, with or without a strength training component, is
effective in reducing the prevalence of the metabolic syndrome and its individual
features.
Aim 1: to examine the effects of a nutrition and strength training intervention on the
prevalence of the metabolic syndrome as well as the effects on its individual features.
Hypothesis: The nutrition education only group will show greater improvements in
metabolic syndrome profiles than the control group. The combination group who receives
both nutrition and exercise will show greater improvements in metabolic syndrome
profiles than the nutrition only group, specifically in blood pressure and HDL cholesterol
levels.
Aim 2: to examine whether participants who make the recommended dietary changes,
regardless of their group assignment, have improvements in the metabolic syndrome and
its individual features, as compared to those who did not make the recommended dietary
changes.
Hypotheses: Individuals who achieve the recommended dietary changes of improving
added sugar and fiber intake, relative to caloric intake, will show greater improvements in
14

metabolic syndrome profiles than those who do not make the suggested dietary changes.
Increases in fiber will be associated with decreases in waist circumference and decreases
in sugar will be associated with a decrease in triglyceride levels.
Study Samples
This dissertation draws upon data from two separate research studies conducted
through the University of Southern California (USC) Department of Preventive
Medicine. Both grants are held by Principal Investigator Michael Goran, PhD. Paper 1
and Paper 2 use data from the Study of Latinos at Risk for Diabetes (SOLAR) project, a
NIDDK-RO1 grant. Paper 3 uses data from the Strength and Nutrition Outcomes for
Latino Adolescents (SANO L.A.) study, a NICHD-RO1 grant. Detailed study
methodologies can be found in the individual papers included in subsequent sections. A
brief overview of the two studies is given here.
The SOLAR project is an ongoing longitudinal study of 200 overweight Hispanic
children, ages 8-13 years at study entry in 2000-2002, with a positive family history of
type 2 diabetes. The overall purpose of the project is to examine the natural history and
etiology of the development of type 2 diabetes, with a particular focus on the factors
contributing to insulin resistance and beta-cell compensation during puberty. Participants
are evaluated each year and the project is ongoing.
The SANO-LA study was a 16 week, randomized controlled trial designed to
examine the incremental effects of the following 3 intervention groups on adiposity and
insulin/glucose regulation in overweight Latino adolescents ages 14-18 years: 1) control
(delayed intervention), 2) modified carbohydrate nutrition, and 3) modified carbohydrate
15

nutrition + strength training. Data collection began in February 2006 and ended in June
2007.
16

CHAPTER 2 DIETARY INTAKE AND THE METABOLIC SYNDROME
IN OVERWEIGHT LATINO CHILDREN

Introduction
In 2003-2004, 34.3% of all U.S. children ages 12-19 were either at risk of
overweight or overweight (BMI>85th percentile).
99
As early as childhood, the link
between obesity and disease risk is thought to be explained by insulin resistance.
25

Particularly if they are overweight, children and adolescents may develop the metabolic
syndrome, also known as the insulin resistance syndrome, as a precursor to diabetes and
cardiovascular disease. The NHANES III data showed that 29% of overweight
adolescents ages 12-19 and 4% of all U.S. adolescents have the metabolic syndrome, as
compared to 22% of all US adults.
21
In addition to weight status, ethnicity is associated
with risk for the metabolic syndrome. In the NHANES data, the metabolic syndrome was
most common in Mexican Americans (5.6%), followed by Caucasians (4.8%), and
African Americans (2.0%).
21
The examination of risk factors for the metabolic syndrome
in children and adolescents is warranted, especially in overweight Latino populations.
Studies have identified high intake of total fat and added sugar and low intake of
fiber as risk factors for the metabolic syndrome in adults,
118, 134
but research in youth is
limited. A study examining associations between dietary intake and the metabolic
syndrome in youth using NHANES data from 1999-2002 showed an inverse association
between the prevalence of the metabolic syndrome and quartiles of an overall Health
Eating Index score.
102
An analysis of the subcomponents of the Healthy Eating Index
revealed that the only component associated with the metabolic syndrome was the fruit
score, with those eating the most fruit having the lowest prevalence of the metabolic
17

syndrome. No other associations between Healthy Eating Index components or individual
nutrients were found. A study of 12,441 Korean youth showed that those who ate a more
“Western” diet, had higher waist circumferences, though were not more likely to have the
metabolic syndrome.
69
In a study of 154 Caucasian girls, sweetened beverages were the
only dietary component related to the metabolic syndrome.
123
The aim of the present
study was to investigate the associations between dietary intake, specifically
macronutrient composition and the quality of carbohydrate intake, and the risk of the
metabolic syndrome in a sample of overweight Latino children with a family history of
type 2 diabetes.
Subjects and Methods
Subjects
Participants represent a subset of the University of Southern California (USC)
Study of Latino Adolescents at Risk Diabetes Project (SOLAR), a longitudinal cohort
study aimed to track the incidence of type 2 diabetes in Latino children in Los Angeles
County with a family history of diabetes. Between the years 2000 and 2002, participants
were recruited to the project through clinics, health fairs, newspaper announcements, and
word of mouth, and approximately 230 participants were enrolled. The inclusion criteria
at entry were: 1) Hispanic origin, defined by having all 4 grandparents of Latino ancestry,
as determined by self-report 2) a family history of type 2 diabetes defined as a parent,
grandparent or sibling previously diagnosed with the disease, as determined by self-report
3) age 8-13 years 4) body mass index (BMI) of at least the 85th percentile
16
and 5)
absence of diabetes as confirmed by an oral glucose tolerance test.
6
This study was
approved by the USC Institutional Review Board. Written informed consent was obtained
18

from parents and youth assent from participants. Other findings relevant to either diet or
the metabolic syndrome in this cohort have been previously published
26, 29, 114, 115
,
however the present analysis is the first in which associations between dietary intake and
the metabolic syndrome have been examined.
The present, cross-sectional study was conducted with all 113 participants who
had annual hospital visits between February 2005 and June 2006 and for whom dietary
and clinical data for the 5 features of the metabolic syndrome were obtained. Participants
had been followed for 3-6 years at the time that this data was collected. None of the 113
participants included in the present analysis were diabetic. All but 7 of the 113
participants still had a BMI above the 85th percentile and the mean percentile value for
those who had fallen below the 85th percentile was 80.6.
Protocol
Participants completed two consecutive clinic visits, one outpatient visit and one
inpatient visit, as occurs each year. For the outpatient visit, children arrived at the
General Clinical Research Center (GCRC) at approximately 0800 h after an overnight
fast. Height and weight were recorded to the nearest 0.1cm, 0.1 kg, respectively. Body
mass index (BMI) and BMI percentiles for age are determined based upon established
Centers for Disease Control normative curves
16
using the EpiInfo 2000 computer
software, version 1.1. Sitting blood pressure was measured in triplicate using the right
arm after the participant rested quietly for 5 minutes. Three readings of blood pressure
were obtained, and the average was recorded. Tanner stage was assessed based on breast
stage and pubic hair during a history and physical exam conducted by a licensed pediatric
health care provider using established guidelines.
121
For the oral glucose tolerance test,
19

participants ingested 1.75g of oral glucose solution /kg body weight (to a maximum of
75.0g). Blood samples were taken via antecubital vein catheter for measurement of
glucose before (fasting) and 2 h after the glucose load. Impaired glucose tolerance was
defined as a 2-h post challenge plasma glucose value of at least 140 and less than
200mg/dl.
5
Blood samples taken during the oral glucose tolerance test were separated for
plasma and immediately transported on ice to the Los Angeles County-USC Medical
Center Core Laboratory where glucose was analyzed on a Dimension clinical chemistry
system and an in vitro hexokinase method (Dade Behring, Deerfield, IL).
For the inpatient visit, participants were admitted to the GCRC in the afternoon
and only given water after 2000 h until testing the following morning. Height and weight
were again recorded and averaged with the readings from the outpatient visit. Sitting
blood pressure was taken in triplicate and averaged with the readings from the outpatient
visit. Fasting blood samples were measured for triacylglycerol, and total and HDL
cholesterol using the Vitros chemistry DT slides (Johnson and Johnson Clinical
Diagnostics Inc., Rochester, NY). Waist circumference, measured at the umbilicus, was
recorded to the nearest 0.1cm. Body composition was measured by a whole-body dual-
energy x-ray absorptiometry (DXA) scan by a certified Radiological Technologist using a
Hologic QDR 4500W (Hologic, Bedford, MA.) A urine pregnancy test was given to all
female participants prior to the DXA scan. The upper weight limit for the DXA scan is
300 lbs, therefore 6 of the participants from this present analysis were not scanned. All
six of these participants had the metabolic syndrome. Their data was included in the
analyses in all statistical models with the exception of the linear regression of cholesterol
intake and blood pressure, as body composition was a covariate in this model. This model
20

was also tested without controlling for body composition to assure that the results were
the same with and without the inclusion of these participants.
Definition of the metabolic syndrome
The metabolic syndrome is a newly defined construct and the validity of its use as
a clinical measure is still debated. In 2001, the National Cholesterol Education Program
Adult Treatment Panel (ATP) III report proposed a definition of the metabolic syndrome
for adults which has been widely employed.
3
To date, no standard definition exists for
children/adolescents. For the present study, the metabolic syndrome was categorized
using the criteria proposed by Cruz et al,
26
which applies pediatric cutoffs to the ATP III
definition. In order to classify as having the metabolic syndrome, participants had to have
3 or more of the following : 1) abdominal obesity (waist circumference ≥90th percentile
for age, gender, and Hispanic ethnicity from NHANES III data),
38
hypertriglyceridemia
(triacylglycerol ≥90th percentile of age and gender),
58
low HDL cholesterol (HDL
cholesterol ≤ 10th percentile for age and gender),
58
elevated blood pressure (systolic or
diastolic blood pressure >90th percentile adjusted for height, age, and gender),
2
and
impaired glucose tolerance as described in the protocol section above.
Dietary intake
Dietary intake was assessed with two 24-hr recalls from each participant using the
multiple pass technique, which has been validated against the doubly labeled water
method in children.
62
One recall was administered in person by a bilingual dietary
technician with the use of three-dimensional food models, and the second recall was
administered via an unscheduled phone call by the same technician in the following
week. Nutrition data were analyzed using the Nutrition Data System for Research (NDS-
21

R version 5.0_35), a software program developed by the University of Minnesota, and all
entries were checked for accuracy by a second technician. This study focuses on
macronutrients and does not investigate micronutrients or dietary patterns. The NDS
program defines added sugars as sugars/syrups added to foods during preparation or
processing, such as high fructose corn syrup, but not including naturally occurring sugars
like lactose and fructose.
During the collection of the 24 hour recalls, when a participant’s intake seemed
excessively low or high, the technician asked if he/she considered the day’s intake to be
usual. When a participant indicated that the day’s intake was not usual, the recall was not
included in the analysis. Of the 113 participants, three were excluded from the sample
because both days of intake were said to not be usual. Of the remaining 110 participants,
21 commented that one day of intake was not usual. This left 89 participants with two
recalls and 21 with one recall. There were no statistically significant differences in
clinical characteristics, dietary characteristics, or metabolic syndrome status between
participants with one vs. two recalls. Subsequently, the dietary data were examined for
plausibility of caloric intake by assessing the residuals of the linear regression of caloric
intake and body weight. One participant was excluded due to a residual which was over
three standard deviations from the mean, leaving 109 participants in the sample.
Statistical Analyses
Prevalence of individual components of the metabolic syndrome was calculated as
the total number of participants with each component expressed as a percentage of the
total sample. For the preliminary analyses, participants were dichotomized into two
groups: those with three or more symptoms (i.e. those with the metabolic syndrome) and
22

those with less than three features (i.e. those without the metabolic syndrome.)
Independent t-tests were used to compare normally distributed clinical and dietary
variables of interest. Variables that were not normally distributed, as assessed by the
Shapiro-Wilk test, were compared using a Wilcoxon rank sum test, as log transforming
the variables did not consistently improve the normality of the data. A chi-square test was
performed to compare gender distributions. Effect sizes of trends were calculated by
(µ ∆)/(SD
pooled
), where µ ∆ is the difference in the two means and SDpooled is the pooled
standard deviation.
Subsequently, simple Pearson correlations were employed to assess the potential
relationships between each dietary variable of interest and each of the separate features of
the metabolic syndrome. Multiple linear regression was used to explore the associations
between features of the metabolic syndrome and dietary variables that were statistically
significantly correlated. The normality and homoscedasicity of residuals in each
regression model were evaluated with the Shapiro-Wilk test and by examining scatter
plots. Non-normally distributed variables were then log transformed and the distribution
of the residuals was reassessed to assure normality. The following variables were log
transformed for the multiple regression analyses: waist circumference, systolic blood
pressure, dietary cholesterol, total dietary fiber, soluble dietary fiber, insoluble dietary
fiber, total dietary fat, and energy (kcals). To examine differences in intake of specific
dietary variables across groups of participants who had zero, one, two, or three or more
features of the metabolic syndrome, analysis of covariance (ANCOVA) was used with a
Tukey adjustment for multiple comparisons, as sample sizes were close to equal. The
distributions of the residuals of the ANCOVA models were also checked for normality.
23

In the multiple regression and ANCOVA models, sex, age, and Tanner stage were
added as covariates. Although age and Tanner stage are statistically significantly
correlated, there are a priori reasons to include both in the models as they do not
necessarily measure the same construct in terms of their respective associations with the
metabolic health
51
. Total energy intake was controlled for in all regression and
ANCOVA models. In addition to energy intake, non-carbohydrate macronutrient intake
(grams of protein and fat) was controlled for in the dietary fiber models and non-fat
macronutrient intake (grams of carbohydrate and protein) was controlled for in the
dietary cholesterol models, in order to prevent colinearity. However, the direct
relationships of total fat intake as well as total carbohydrate intake with the metabolic
syndrome features were independently tested in the multivariate models. Body
composition (i.e. total fat mass and total lean tissue mass) was controlled for when
appropriate. For example, when waist circumference was the outcome variable, we did
not find it necessary to control for body composition as these variables were highly
correlated. Data were analyzed with SAS software (version 9.1, SAS Institute, Cary, NC)
and type 1 error was set at p<0.05.
Results
The clinical characteristics of the study sample are found in Table 2-1. The
prevalence of the metabolic syndrome in the sample was 22%. The metabolic syndrome
was more prevalent in males than in females (29% vs. 13%, p=0.042). Despite similar
height and lean tissue mass, participants with the metabolic syndrome were significantly
younger (13.7 vs. 14.5 years, p=0.040) and had lower Tanner scores (3.2 vs. 3.8,
p=0.032) than those who did not have the metabolic syndrome. In addition, the
24

participants with the metabolic syndrome weighed more (90.9 vs. 81.5 kg, p=0.044), had
a higher BMI (33.0 vs 30.7, p=0.010) and had more total fat mass (33.9 vs. 27.1 kg,
p=0.004) than those without the metabolic syndrome. The distribution of the participants
by number of features is shown in Figure 2-1. The most prevalent components of the
syndrome are low HDL cholesterol (52%) and high waist circumference (48%).
The overall dietary intake of participants with the metabolic syndrome was
similar to those without the metabolic syndrome when compared with independent t-
tests/Wilcoxon rank sum tests, as shown in Table 2-2. There were no statistically
significant differences in dietary intake between the two groups. However, there was a
trend toward significance in total fiber intake (p=0.096); those who did not have the
metabolic syndrome ate more fiber per 1,000 calories than those with the metabolic
syndrome. This trend showed a small to moderate effect size of 0.34
19
.
In terms of the simple Pearson correlations between the dietary variables and the
individual components of the metabolic syndrome, there were significant negative
correlations between the three measures of dietary fiber (total, soluble, and insoluble) and
waist circumference with coefficients -0.24 (p=0.012), -0.27(p=0.005), and -
0.21(p=0.029), respectively. There was a significant positive correlation between dietary
cholesterol and systolic blood pressure (r=0.22, p=0.022). None of the other features of
the metabolic syndrome (HDL cholesterol, triacylglycerol, diastolic blood pressure, or 2-
hour glucose) were correlated with any of the dietary variables. Only the dietary variables
(fiber and cholesterol) that were statistically significantly correlated with individual
feature(s) of the metabolic syndrome were examined further.
25

The relationships between dietary cholesterol intake and systolic blood pressure
and also between fiber intake and waist circumference were assessed with multiple linear
regression (Tables 2-3 and 2-4). Dietary cholesterol intake was positively and
significantly associated with systolic blood pressure, adjusting for sex, age, Tanner stage,
energy intake, non-fat macronutrient intake, total fat mass, and total lean tissue mass
(p=0.017), Table 2-3. (Total dietary fat intake was not significantly associated with
systolic blood pressure, adjusting for sex, age, Tanner state, energy intake, and non-fat
macronutrient intake, p=0.27.) The adjusted multiple regression model of total dietary
fiber and waist circumference showed a trend toward statistical significance (Table 2-4,
model 1) in the negative direction (p=0.071). The adjusted multiple regression model
with insoluble fiber was not statistically significant (p=0.109; data not shown). The only
multiple regression model of fiber intake and waist circumference that remained
statistically significant when the covariates of interest (sex, age, Tanner stage, energy
intake, and non-carbohydrate macronutrient intake) were added was with soluble dietary
fiber (Table 4, model 2), and this association was also negative (p=0.036). (Total
carbohydrate intake was not significantly associated with waist circumference, adjusting
for sex, age, Tanner stage, energy intake, and non-carbohydrate macronutrient intake,
p=0.73.)
When the ANCOVA was performed to test differences in mean cholesterol intake
in participants grouped by number of features of the metabolic syndrome, the results were
not significant (p=0.105 for comparison between 0 and 3+ features; data not shown.)
When testing dietary fiber intake in the same manner, we found that soluble fiber intake
was significantly higher in those with zero features of the metabolic syndrome as
26

compared to those with 3 or more features of the metabolic syndrome (5.2 vs. 4.1g,
p=0.046) when sex, age, Tanner stage, energy intake, and non-carbohydrate
macronutrient intake were included in the model (Figure 2-3). (Total carbohydrate intake
did not differ between feature groups, adjusting for sex, age, Tanner stage, energy intake,
and non-carbohydrate macronutrient intake, p=0.62 for comparison between those with
zero and 3 or more features.)
Discussion
To our knowledge, this study is one of the first to examine associations between
dietary factors and the metabolic syndrome in children. In this thorough analysis, the only
dietary factor that was significantly associated with the number of features of metabolic
syndrome was intake of soluble dietary fiber. The participants with zero features of the
metabolic syndrome ate 21% more fiber than those who had the metabolic syndrome,
which was a difference of about 1 gram of soluble fiber per day (5.2 vs. 4.1g). Looking at
the individual components of the metabolic syndrome also proved useful since grouping
the features together might overlook some of the potential relationships between
important dietary variables and individual clinical measures. Soluble fiber intake was
significantly negatively associated with waist circumference, and dietary cholesterol
intake was significantly positively related to systolic blood pressure, after adjusting for
covariates.
The observed association between dietary cholesterol intake and systolic blood
pressure may be caused indirectly via an atherosclerotic precursor. Excess intake of
dietary cholesterol is a known contributor to the adherence of plaque to the artery walls,
which may narrow blood vessel diameter and increase blood pressure. However, the
27

effect of dietary cholesterol intake in this study sample was limited to an association with
blood pressure and was not related to the number of features of the metabolic syndrome.
Unlike dietary cholesterol, dietary fiber intake was significantly associated with
the number of features of the metabolic syndrome, in addition to being associated with
waist circumference. Results from prospective and cross-sectional studies in adults
support our findings in that they link low dietary fiber intake with an increase in the
metabolic syndrome.
90, 92
To our knowledge, our study is the first to show a negative
association between dietary fiber intake and the number of features of the metabolic
syndrome in children. However, there are other studies that have found relationships
between fiber intake and individual variables associated with the metabolic syndrome in
youth. In a study using data from the CARDIA study, Ludwig et al found that fiber
consumption was a better predictor of fasting and 2 hour insulin levels and waist-to-hip
ratio than total or saturated fat consumption in young adults.
84
The authors conclude that
high-fiber diets may protect against obesity and cardiovascular disease by lowering
insulin levels. In another study, Steffen et al showed that increased whole grain
consumption, resulting in an increased intake of dietary fiber, was associated with lower
body mass and greater insulin sensitivity among adolescents.
120

While a number of studies have shown that dietary fiber intake is inversely
associated with adiposity, few studies distinguish between the effects of soluble and
insoluble fiber intake. The consumption of soluble fiber delays gastric emptying and
promotes satiety as opposed to foods rich in insoluble fiber which decrease
gastrointestinal transit time by increasing fecal bulk.
7, 117
Increased satiety could lead to
decreased adiposity and thus lower waist circumference. Although Ludwig et al suggest
28

the potential intermediate role of insulin levels in the relationship between fiber intake
and obesity, they do not discuss the separate roles of soluble fiber vs. insoluble dietary
fiber.
84

In general, children in the U.S. do not consume the recommended amount of
fiber. On average, boys (ages 9-13) eat 15g of fiber per day, and girls of the same age eat
13g per day, whereas the Adequate Intake reference values for boys and girls are 31g and
26g, respectively.
60
Participants in our study without the metabolic syndrome ate 14.8g
per day and those with the metabolic syndrome ate 13.6g, roughly half of what they
should consume. As daily caloric intake may vary widely, the Institute of Medicine
established fiber intake guidelines based on calories consumed; 14g of fiber per 1000
calories of overall caloric intake is recommended.
60
Our participants consumed, on
average, 8.2 grams of fiber per 1000 calories, which is again only about half of the
recommended amount. If youth can increase their consumption of fiber, they may be able
to reduce risk for the metabolic syndrome and associated diseases.
Our results are encouraging in that they suggest that modest increases in soluble
dietary fiber could reduce central adiposity and metabolic risk. The one gram difference
in soluble fiber intake between participants with and without the metabolic syndrome
equates to one extra apple or orange a day or one extra serving of beans to increases
soluble fiber intake from 4.1 grams per day to 5.2 grams. To gain a better understanding
of the sources of soluble fiber in our participants’ diets, we looked at the frequency of
consumption of various individual helpings of foods eaten in a given sitting with 1 gram
or more of soluble fiber. The 5 most common sources which contained 1 gram or more
were as follows: fresh fruit (orange, apple, mango, and pear were most common), beans
29

(both boiled and refried), potatoes of various forms including French fries, bread
products, and chocolate milk. (Carageenan stabilizers are added to chocolate milk and
add 1.45g of soluble fiber per 8 fluid ounces of chocolate milk). Considering that not all
of these are considered healthy foods, the quantity and frequency of consumption as well
as the combination of different sources of soluble fiber may be important.
There are several limitations which should be considered regarding this study.
The small sample size limits statistical power and may hinder the ability to detect
associations between diet and the metabolic syndrome. In addition, the cross-sectional
nature of the study prevents conclusions about causality. There may be a cohort effect, as
participants had been retained in the study for 3-6 years. Potential contributors to
measurement error include the age of participants, the use of 24-hr recalls, the availability
of only one recall for 21 subjects, and the focus on macronutrients as opposed to dietary
patterns. Finally, overweight participants may be especially prone to underreporting
dietary data
59
. The impact of measurement error would be to attenuate the observed
relationships and to contribute to weak or null findings.
This paper is the first that we know of to investigate the relationship between
dietary intake and the metabolic syndrome in overweight Latino adolescents. In addition,
we are the first to show that soluble dietary fiber intake is associated with the metabolic
syndrome in this population. Our results suggest that dietary interventions aimed at
increasing soluble fiber intake by as little as 1g per day could potentially reduce central
adiposity and improve metabolic health in overweight Latino adolescents. Instead of
focusing on a more general message of reducing junk food and the consumption of empty
30

calories, we suggest a more targeted, positive message focusing specifically on adding
foods rich in soluble fiber to the diet.
31

Table 2-1Clinical data of children with and without the metabolic syndrome

Children
without the
Metabolic
Syndrome
(n=85)
Children with
the Metabolic
Syndrome
(n=24)
P-value
Gender (Male/Female)
a
44/41 18/6 0.042
Age (yrs)
b
14.5 ± 1.8 13.7 ± 1.4 0.040
Tanner stage
b
3.8 ± 1.2 3.2 ± 1.3 0.032
Height (cm)
c
162.4 ± 8.8 165.2 ± 10.3 0.187
Weight (kg)
c
81.5 ± 20.4 90.9 ± 17.8 0.044
BMI (kg/m
2
)
b
30.7 ± 6.2 33.0 ± 4.5 0.010
BMI Z score
b
1.9 ± 0.5 2.3 ± 0.3 0.0002
Total fat mass (kg) (n=103)
c,d
27.1 ± 10.5 33.9 ± 7.7 0.004
Total lean tissue mass(kg) (n=103)
c,d
48.7 ± 10.5 51.4 ± 15.2 0.323
Waist circumference (cm)
b
90.8 ± 13.2 98.1 ± 7.8 0.0018
Systolic blood pressure (mm Hg)
c
112.6 ± 8.4 122.8 ± 9.8 <0.0001
Diastolic blood pressure (mm Hg)
b
63.8 ± 5.5 66.9 ± 7.7 0.080
Cholesterol total (mg/dl)
c
142.7 ± 24.4 147.2 ± 30.4 0.453
LDL cholesterol (mg/dl)
c
85.6 ± 21.6 85.3 ± 22.2 0.952
HDL cholesterol (mg/dl)
b
38.6 ± 8.1 32.4 ± 4.7 0.0002
Triacylglycerol (mg/dl)
b
92.9 ± 41.8 147.5 ± 68.0 0.0001
2-h glucose (mg/dl)
c
117.8 ± 20.6 131.0 ± 20.9 0.007
a
Chi square test ran for gender.
b
Wilcoxon rank sum test used for variables that were not normally distributed but
means ± std dev are presented for ease of interpretation
c
Independent t-tests used to compare means and data are means ± std dev
d
Six participants were unable to complete DEXA scan due to upper weight limit.

32

Table 2-2 Dietary data of children with and without the metabolic syndrome

Children without the
Metabolic
Syndrome (n=85)
Children with the
Metabolic Syndrome
(n=24)
P-value
Energy (kcal)
a
1776.6 ± 513.9 1839.9 ± 566.3 0.735
Protein
(g/day)
b
70.0 ± 21.8 68.6 ± 18.6 0.766
(%of kcals)
a
16.2 ± 4.2 15.5 ± 3.5 0.423
Total dietary fat
(g/day)
a
64.0 ± 23.9 67.5 ± 26.1 0.503
(%of kcals)
b
31.8 ± 5.5 32.6 ± 7.4 0.563
Saturated fat
(g/day)
a
22.1 ± 8.4 23.1 ± 8.6 0.425
(%of kcals)
b
11.0 ± 2.4 11.2 ± 2.7 0.798
Polyunsaturated fat
(g/day)
a
13.1 ± 6.5 13.7 ± 6.8 0.701
(%of kcals)
a
6.6 ± 2.2 6.5 ± 2.0 0.927
Cholesterol
(mg/day)
a
206.5 ± 119.0 256.6 ± 158.9 0.153
(%of kcals)
a
0.11 ± 0.07 0.14 ± 0.11 0.318
Carbohydrate
(g/day)
b
235.7 ± 71.6 244.9 ± 87.0 0.601
(%of kcals)
b
53.2 ± 6.7 53.1 ± 8.4 0.909
Added sugar
(g/day)
a
66.9 ± 39.3 75.8 ± 46.5 0.440
(%of kcals)
b
15.0 ± 7.4 16.2 ± 7.7 0.491
Total dietary fiber
(g/day)
a
14.8 ± 6.2 13.6 ± 5.9 0.304
(g/1,000 kcals)
a
8.4 ± 3.1 7.5 ± 2.8 0.096
Soluble fiber
(g/day)
a
4.6 ± 1.9 4.3 ± 1.9 0.576
(g/1,000 kcals)
a
2.6 ± 0.9 2.3 ± 0.7 0.118
Insoluble fiber
(g/day)
a
9.9 ± 4.6 9.1 ± 4.5 0.343
(g/1,000 kcals)
a
5.7 ± 2.4 5.1 ± 2.3 0.187
a
Wilcoxon rank sum test used for variables that were not normally distributed but means ± std dev are
presented for ease of interpretation.
b
Independent t-tests used to compare means and data are means ± std dev

33

Table 2-3 Multiple linear regression of dietary cholesterol intake and systolic blood
pressure

Dependent variable Independent variable ß P for ß
Systolic blood
pressure
(mm Hg)
a
Sex -0.050 0.009
Age
a
-0.071 0.297
Tanner stage
a
-0.011 0.607
Energy (kcal)
a
0.066 0.326
Dietary protein (g) -0.0005 0.287
Dietary carbohydrate (g) -0.0001 0.635
Total fat mass (kg) 0.000002 0.025
Total lean tissue mass (kg) 0.000003 0.002
Dietary cholesterol (mg)
a
0.034 0.017
a
Variables were log transformed.

34

Table 2-4 Multiple linear regression of dietary fiber intake and waist circumference

Dependent
variable Independent variable ß P for ß
Model
1
Waist
circumference
a
Sex -0.049 0.062
Age
a
0.295 0.016
Tanner stage
a
0.012 0.723
Energy (kcal)
a
-0.0002 0.999
Dietary fat (g)
a
-0.018 0.785
Dietary protein (g) 0.0001 0.904
Total dietary fiber (g)
a
-0.060 0.071

Model
2
Waist
circumference
a
Sex -0.051 0.051
Age
a
0.311 0.011
Tanner stage
a
0.006 0.863
Energy (kcal)
a
-0.002 0.988
Dietary fat (g)
a
0.002 0.979
Dietary protein (g) 0.00002 0.982
Soluble dietary fiber (g)
a
-0.069 0.036

a
Variables were log transformed.

35

22% 23%
33%
22%
0
20
40
60
80
100
01 2 3+
Number of features of the metabolic syndrome
Percentage of sample

Figure 2-1 Percentage of participants with zero, one, two, or three or more features of the
metabolic syndrome

36

52% 48%
20% 17%
16%
0
20
40
60
80
100
Low HDL
cholesterol
High waist
circumference
High blood
pressure (systolic
or diastolic)
Impaired glucose
tolerance
High triacylglycerol
Percentage of sample

Figure 2-2 Prevalence of each feature of the metabolic syndrome

37

5.2g
4.4g 4.4g 4.1g
3
4
5
6
01 2 3+
Number of features
Soluble fiber (g)
P<0.05

Figure 2-3 Mean soluble fiber intake by number of features of the metabolic syndrome

ANCOVA is adjusted for age, sex, Tanner stage, non-carbohydrate macronutrient intake, and energy
intake.

38

CHAPTER 3 PERSISTENCE OF THE METABOLIC SYNDROME OVER
3 ANNUAL VISITS IN OVERWEIGHT LATINO CHILDREN: ASSOCIATION
WITH PROGRESSIVE RISK FOR TYPE 2 DIABETES

Introduction
Obesity and Latino ethnicity are two independent risk factors for the development
of type 2 diabetes in youth. Even in childhood, there is a linear relationship between
increased body fat and lower insulin sensitivity.
24, 46, 49, 78
Furthermore, independent of
body composition, Latino children are more insulin resistant than white children.
48

NHANES III data showed that the metabolic syndrome, a clustering of risk factors for
diabetes and cardiovascular disease,
54
was more common in Latino adolescents than in
whites or blacks.
21

Previously, our research group found that 30% of the Study of Latino Adolescents
at Risk for Diabetes Project (SOLAR) cohort of overweight Latino youth had the
metabolic syndrome.
26
This cross-sectional analysis showed that insulin sensitivity was
inversely associated with the number of features of the metabolic syndrome and that
those with the metabolic syndrome (3+ features) had 62% lower insulin sensitivity than
those with no features of the metabolic syndrome, independent of gender, age, sexual
maturation, and body composition. However, we have not yet evaluated this relationship
over time. To our knowledge, no studies have evaluated the persistence of the metabolic
syndrome and risk for type 2 diabetes within childhood in this population.
The overall objective of this study was to therefore examine if the persistence of
the metabolic syndrome was associated with changes in risk factors for type 2 diabetes
within childhood in overweight Latino youth. The first aim was to identify how many
39

children in the cohort consistently have the metabolic syndrome at three annual
measurements. The second aim was to determine if participants with persistent metabolic
syndrome had differences in insulin and glucose indices over time, independent of
adiposity.
Methods
Subjects
Participants were a subset of the University of Southern California SOLAR
(Study of Latino Adolescents at Risk) for Diabetes Project, a longitudinal cohort study
aimed to track the incidence of type 2 diabetes. Study inclusion criteria were: 1) Latino
origin as defined by all four grandparents being Latino as determined by parental self-
report; 2) family history of type 2 diabetes in at least one grandparent, parent, or sibling;
3) age 8-13; 4) body mass index (BMI) of at least the 85
th
percentile for age;
16
and 5)
absence of diabetes as confirmed by an oral glucose tolerance test (OGTT).
5
Participants
(n=73) in the present analysis were selected because they had complete data for the
metabolic syndrome parameters for each of the first 3 annual study visits. Mean age of
the sample was 11.0 ±1.7 years at baseline. This sample (n=73) did not differ at baseline
from the rest of the larger initial cohort (n=182) in key characteristics such as age,
gender, Tanner stage, BMI, body composition, fasting glucose, 2 hour glucose, or insulin
sensitivity (p>0.05 as assessed with independent t-tests and chi-square tests.) None of the
participants were diabetic. This study was approved by the university Institutional
Review Board. Written informed consent was obtained from parents and youth assent
from participants.

40

Protocol
Detailed methods for the longitudinal study have been previously published.
26, 47,
129
Briefly, the design involves a set of yearly clinical assessments, consisting of an
outpatient visit in which an OGTT is conducted, and an overnight, inpatient visit, in
which a frequently sampled intravenous glucose tolerance test (FSIVGTT) is conducted.
Outpatient visit: Children fasted overnight and came to the General Clinical
Research Center (GCRC) at 0800 h. Participants changed into hospital gowns and height
and weight were recorded in triplicate to the nearest 0.1cm and 0.1 kg, respectively. BMI
and BMI percentiles for age were calculated using the EpiInfo 2000 software, version
1.1, based upon established CDC normative curves.
16
Sitting blood pressure was
measured in triplicate.
2
Tanner stage was coded to assess sexual maturation based on
breast stage in girls and pubic hair in boys during a history and physical exam conducted
by a licensed pediatric care provider.
121
For the OGTT, subjects were given 1.75g of oral
glucose solution /kg body weight (to a maximum of 75.0g). Blood was collected and
assayed for glucose and insulin at –5min and 15, 30, 45, 60, and 120 min relative to
glucose ingestion. Impaired glucose tolerance was defined as a 2-h post challenge plasma
glucose value of at least 140 and less than 200mg/dl.
5
Two-hour insulin and glucose area
under the curve (AUC) and incremental area under the curve (IAUC) were calculated
from the OGTT data, in mg/min/dl for glucose and µU/min/ml for insulin. Glucose and
insulin AUC are calculated as the sum of the area of each time segment by insulin or
glucose concentration, and IAUC as the sum of the same area adjusted for the starting
point.
41

Inpatient visit: Participants were admitted to the GCRC in the afternoon and
fasted from 2000 h until testing the following morning, which began at 0730 h. Sitting
blood pressure was again taken in triplicate, and the values from the two visits were
averaged. A flexible iv catheter was placed in each arm and the FSIVGTT was
conducted. At time zero, subjects were given a 0.3 g/kg body weight dose of glucose
(25% dextrose) with sampling at 2, 4, 8, 19, 22, 30, 40, 50, 70, 100, and 180 minutes. At
20 minutes, a 0.02 U /kg body weight dose of Humulin® R insulin (regular insulin for
human participants; Eli Lilly, Indianapolis, USA) was injected. In order determine insulin
sensitivity (SI) and the acute insulin response to glucose (AIR), values for glucose and
insulin were entered into the MINMOD MILLENIUM 2002 program (Version 5.16,
Richard N. Bergman). The disposition index (DI), an index of the compensatory
adaptation to insulin resistance, was calculated as the product of SI and AIR, and is used
to approximate beta cell function. Fasting blood samples were also measured for
triglycerides, and total and HDL cholesterol using the Vitros chemistry DT slides
(Johnson and Johnson Clinical Diagnostics Inc., Rochester, NY).
After the FSIVGTT, body composition was measured by a whole-body dual-
energy x-ray absorptiometry (DEXA) scan by a certified Radiological Technologist using
a Hologic QDR 4500W (Hologic, Bedford, MA.) A urine pregnancy test was given to all
female participants prior to DEXA. In addition, waist circumference, measured at the
umbilicus, was recorded to the nearest 0.1cm. Central fat distribution was measured by
magnetic resonance imaging (MRI) at the LAC/USC Imaging Science Center using a GE
1.5 Signa LX-Ecospeed with a GE 1.5 Tesla magnet and a single slice at the level of the
42

umbilicus. This procedure measures intra-abdominal adipose tissue (IAAT) and
subcutaneous abdominal adipose tissue (SAAT).
Definition of the Metabolic Syndrome
No standard definition of the metabolic syndrome has been agreed upon for
children/adolescents.
63, 116
For this analysis, the metabolic syndrome was categorized
using a definition we have previously proposed
26
which applies pediatric cutoffs to the
Adult Treatment Panel III definition.
3
The metabolic syndrome was defined as having ≥3
of the following: 1) abdominal obesity (waist circumference ≥90th percentile for age,
gender, and Hispanic ethnicity from NHANES III data),
38
hypertriglyceridemia
(triglycerides ≥90th percentile of age and gender),
58
low HDL cholesterol (HDL
cholesterol ≤ 10th percentile for age and gender),
58
elevated blood pressure (systolic or
diastolic blood pressure >90th percentile adjusted for height, age, and gender),
2
and
impaired glucose tolerance, as described above.
Statistical Analyses
Participants were classified in 3 groups: NEVER (negative for metabolic
syndrome at all 3 annual visits); INTERMITTENT (positive for metabolic syndrome at 1
or 2 annual visits); and PERSISTENT (positive for metabolic syndrome at all 3 annual
visits).
Baseline characteristics of the 3 groups were compared using chi-square tests and
analysis of variance (ANOVA) with Bonferroni corrections. All participants had
complete data for the 5 features of the metabolic syndrome at all 3 time points. However,
subjects were still included if they were missing data for MRI , DEXA, IAUC from the
OGTT, or IVGTT (SI, AIR, or DI) measurements. At baseline, 1 subject had missing data
43

for SI, DI, and AIR and 6 were missing MRI. Two year changes in adiposity as well as
insulin dynamics, measured by SI, AIR, and DI, were analyzed with repeated measures
analysis of covariance (ANCOVA). For the ANCOVA analyses, the following are the
numbers for missing data: 2 DEXA, 9 SAAT, 10 IAAT, 8 glucose IAUC, 4 insulin
IAUC, and 5 SI, DI, and AIR. The class variable was metabolic syndrome group and the
time variable was annual visit number (1-3). For ANCOVA, the following covariates
were included in all models: gender, baseline age and baseline Tanner stage. Baseline
lean tissue mass was also controlled for when fat mass, SAAT, or IAAT was the
outcome. In the IAAT models, baseline SAAT was controlled for, and vice-versa. In
models where insulin and glucose indices were evaluated, baseline body composition (fat
mass and lean tissue mass) were controlled for. Models were also run with the inclusion
of Tanner stage and body composition at all time points but these covariates were not
significant, so models including baseline values were used. For repeated measures
ANCOVA, Mauchly’s test of sphericity was used to assess the form of the common
covariance matrix. When the sphericity assumption was not met, the Huynh-Feldt
correction was used. Data were analyzed with SPSS version 13.0 (SPSS Inc, Chicago,
IL), and type 1 error was set at α<0.05.
Results
Of the 73 children, 35 (48%) did not have the metabolic syndrome at any of the 3
annual visits and were classified into the NEVER group. Twenty four (33%) of the
participants were in the INTERMITTENT group, as they had the metabolic syndrome at
one or two visits. Fourteen children (19%) had the metabolic syndrome at all three visits
and were classified as PERSISTENT. The persistence of each individual feature,
44

displayed by metabolic syndrome group, is shown in Figure 3-1. The most commonly
persistent features were high waist circumference, followed by low HDL cholesterol and
high triglycerides. The percentage of participants displaying persistent metabolic
syndrome features was incremental by metabolic syndrome group, with the lowest
percentage in the NEVER group, followed by the INTERMITTENT group, and the
PERSISTENT group. The number of features met by each of the metabolic syndrome
groups was also incremental, with the NEVER group having an average of 1.02 features,
the INTERMITTENT group having an average of 2.18 features, and the PERSISTENT
group having an average of 3.48 features (data not shown).
Baseline unadjusted descriptive characteristics of the participants by metabolic
syndrome group are shown in Table 3-1. Age did not differ across groups. The NEVER
group had fewer male participants (p<0.05) and lower BMI than the other groups
(p<0.05). In addition, the NEVER group had lower fat mass (p<0.05) and lean mass
(p<0.01) when compared to the INTERMITTENT group. There was an overall group
difference in SAAT p<0.05, but no differences in IAAT (p>0.05). Participants in the
PERSISTENT group had a lower mean Tanner stage than those in the other groups,
indicating that they were less sexually mature (p<0.05). Comparisons of the features of
the metabolic syndrome at baseline are also shown in Table 1. As expected by group
definitions, the NEVER group had lower waist circumference, blood pressure, and
triglycerides as well as higher HDL cholesterol than the other groups (p<0.05), but 2 hour
glucose values did not differ by group.
Baseline unadjusted insulin and glucose related indices of the three groups are
found in Table 3-2. The INTERMITTENT group had higher fasting insulin than the
45

NEVER group (p<0.01), but fasting glucose, glucose IAUC, or insulin IAUC did not
differ by group. SI was higher in those who NEVER had the metabolic syndrome
compared to the other 2 groups (p<0.05) but AIR or DI did not differ by group.
Results from the repeated measures ANCOVA are shown in Table 3-3. Although
overall BMI percentile did not change significantly over time, the NEVER group
maintained a lower adjusted BMI percentile as compared to the other groups (p<0.05).
The PERSISTENT group gained fat mass at a faster rate than the NEVER group (Figure
3-2a, 20% gain of baseline value by visit 3 vs. a 15% gain, p=0.024 for time*group
interaction). The PERSISTENT group also maintained a significantly higher level of
SAAT than the NEVER group (p=0.048), but this difference was stable over time. IAAT
was not significantly affected by time, group status, or an interaction of time*group
status.
Repeated measures ANCOVA results also revealed longitudinal differences in
indices related to insulin and glucose, independent of covariates including body
composition. The PERSISTENT group maintained higher fasting glucose, 2 hour
glucose, and glucose IAUC levels than the other two groups (p<0.05), but these
differences were stable over time. However, changes in 2 hour insulin and insulin IAUC
over time were significantly associated with metabolic syndrome group; the
PERSISTENT group increased by over 70% in both while the other groups had an overall
decrease (Figure 3-2b, p<0.05 for time*group interaction). In addition, although all
participants, regardless of group, declined in adjusted SI over time (p=0.001), the
PERSISTENT group remained 43% less insulin sensitive, on average, as compared to
those in the NEVER group (Figure 3-2c, p=0.006). Furthermore, despite no significant
46

differences in baseline DI, by visit 2, the PERSISTENT group had a 25% lower adjusted
DI than the NEVER group, and this difference was maintained through visit 3 (Figure 3-
2d, p=0.02). However, the rates of change did not differ by group for DI or for AIR
(time*group interaction>0.05) and there were no group differences in AIR.
Discussion
The primary objective of this study was to evaluate whether persistence of the
metabolic syndrome over 3 annual measurements in overweight Latino children was
associated with risk factors for type 2 diabetes and whether these risk factors worsened
over time. Of the 73 participants, 19% had persistent metabolic syndrome and 48% never
had it. At baseline, there were no significant differences in total fat mass between the
NEVER and PERSISTENT groups, but over time, the PERSISTENT group gained more
fat than the NEVER group (20% vs. 15% of baseline value, adjusted for fat free mass and
other covariates). At baseline, participants in the PERSISTENT group had 37% lower SI
as compared to the NEVER group, and over time, the PERSISTENT group maintained an
average of 43% lower adjusted SI, independent of covariates including body
composition. Although there were no significant differences in baseline DI, the
PERSISTENT group had a 25% lower adjusted DI by visit 2, compared to the NEVER
group, and this difference was maintained through visit 3. Finally, although there were no
group differences in insulin IAUC at baseline, the PERSISTENT group had an adjusted
70% increase over time while the other two groups showed a relative decline.
This study is one of few to examine the persistence of the metabolic syndrome
over time within childhood, and the first to focus specifically on associations with risk for
type 2 diabetes in overweight Latino youth. To our knowledge, there have only been two
47

other studies which have evaluated the persistence of the metabolic syndrome in a
pediatric sample. In a study by Goodman et al., 1,098 adolescents (52% white, 47%
black, and 2% Latino) were evaluated for the metabolic syndrome at baseline (average
age 15 years) and three years later.
45
Through factor analysis of 8 metabolic risks, the
authors found that factor structures were identical at both time points. However, clinical
categorization of the metabolic syndrome was not stable and the authors concluded that
the syndrome has limited clinical utility for adolescents.
45
However, only two time points
were included and associations with insulin sensitivity and beta cell function were not
evaluated. In another study, by Weiss et al, the persistence of the metabolic sydrome was
also assessed at two time points, an average of 22 months apart, in a sub-sample of 77
youth who were ages 4-20 years at baseline.
130
Though demographic information is not
given for the sub-sample, the larger study sample is 27% Latino. The authors found that
71% of the subjects who had the metabolic syndrome at the first assessment also had it at
the second assessment. In addition, eight subjects who had impaired glucose tolerance
and the metabolic syndrome at the first assessment had developed type 2 diabetes by the
second assessment.
In addition to the two studies which evaluated the persistence of the metabolic
syndrome, several other studies have evaluated either predictors of the metabolic
syndrome in childhood or associations between childhood metabolic syndrome and risk
for type 2 diabetes later in life. In a study of 154 white girls who were measured for
adiposity at ages 5, 7, 9, 11, and 13 years, as well as measured for the metabolic
syndrome at age 13 only, Ventura et al found that increases in fat mass and BMI across
childhood were predictive of metabolic syndrome risk at age 13.
123
In a study of 1,604
48

American Indians, Franks et al found that a composite score of metabolic syndrome
features in 5-19 year olds was a predictor of the development of type 2 diabetes.
43

Morrison et al also found that in a sample of youth (72% white and 28% black, ages 5-19
years at baseline), the metabolic syndrome in childhood was a significant predictor of
type 2 diabetes 25 to 30 years later.
95
As far as we know, our study is the first to show
that progressive risk for type 2 diabetes is evident with persistent metabolic syndrome
over just three consecutive annual measurements within childhood.
The underlying pathophysiology for the progressive risk for type 2 diabetes in the
PERSISTENT group, particularly as compared to the NEVER group, is comprised of
increasing adiposity, consistently lower SI, as well as a lesser ability of the beta cells to
compensate by increasing insulin secretion, i.e. DI, which was lower by visit 2 and stayed
consistently lower through visit 3. Other research has shown that insulin resistance and
impaired insulin secretion are independent predictors of the development of type 2
diabetes in adult Pima Indians and Latinos.
11, 56, 131
Though all of the participants in our
analysis showed an overall decline in SI across the 3 annual visits, the PERSISTENT
group showed a dramatically accelerated increase in insulin response to oral glucose,
which supports the conclusion that they are becoming progressively more insulin
resistant than the other two groups.
It is important to note that all of the participants in our analysis, regardless of their
metabolic syndrome group status, have lower SI than normal weight children of their
same age. To put our results into context, in a previous analysis with a multi-ethnic group
of children (mean age 9.6 yrs), Goran et al showed an inverse relationship between fat
mass and SI, independent of family history of type 2 diabetes.
50
This group of children,
49

which had an average BMI of around 20, had an average SI of around 6. In comparison,
the NEVER participants in the current analysis had an average baseline SI of 2.7,
followed by an average of 1.7 and 1.6 in the INTERMITTENT and NEVER groups.
Therefore, though all of our participants are relatively insulin resistant, the groups with
more metabolic syndrome features are more dramatically so. It is also worthwhile to note
that at baseline the INTERMITTENT and PERSISTENT groups were fairly comparable
in terms of adiposity and insulin/glucose indices, including SI. However, by following
these groups across 3 visits, we found that the PERSISTENT group emerged as the group
which showed stronger associations with progressive diabetes risk, even when compared
to the INTERMITTENT group.
Considering that the PERSISTENT group was less sexually mature, as indicated
by Tanner staging, at baseline compared to the other two groups, one important question
that arises is whether the PERSISTENT group progressed through puberty at a different
rate than the other groups, which could have influenced their metabolic health. To answer
this question, we assessed the changes in Tanner stage of the 3 groups by repeated
measures ANOVA (data not shown). We found that although the PERSISTENT group
started at a lower Tanner stage of 1.4, as compared to a 2.4 and 2.5 in the other groups,
all groups progressed in Tanner stage at the same rates, i.e we did not see a time*group
interaction in Tanner stage over time. However, we did include Tanner stage as a
covariate in all of the adiposity and glucose/insulin models presented in this paper, in
order to make sure that our results are not driven by differences in pubertal development.
It is interesting to explore the mechanisms for the combination of progressive risk
factors exhibited in the PERSISTENT group. One potential explanation for the increased
50

insulin IAUC in the PERSISTENT group may be increased hepatic insulin resistance and
decreased hepatic extraction of insulin caused by fatty liver.
27, 74
Although we did not
directly measure liver fat, we did measure visceral fat by single slice MRI, and it has
been shown that visceral fat is associated with hepatic steatosis.
104
Furthermore, both
visceral fat and liver fat are associated with hepatic insulin resistance.
44
In our analysis,
the PERSISTENT group has higher IAAT than the NEVER group at all 3 time points, but
this difference was not statistically significant. In a separate study conducted by our
group with data from the same cohort, we found that persistent pre-diabetes is associated
with a faster accumulation of IAAT over time.
52
In the context of the present analysis, it
could be that a more direct measure of hepatic fat would show significant group
differences. For example, it has recently been shown that obese children of various
ethnicities with the metabolic syndrome are more likely to have non-alcoholic fatty liver
disease, as measured either by biopsy or ultrasound, than obese children without the
metabolic syndrome.
83, 113

Our findings have several important implications for clinical screening of
overweight youth for associated metabolic co-morbidities. While current consensus
statements include screening for both type 2 diabetes and features of metabolic
syndrome,
6, 54
the value of repetitive assessment for metabolic syndrome in youth has
been unclear. We show that assessing overweight Latino children in our sample for the
metabolic syndrome on a yearly basis is useful in identifying those at particularly
heightened risk for type 2 diabetes. Yearly metabolic syndrome assessment is achievable
and relatively inexpensive, whereas conducting a FSIVGTT, which takes 3 hours to
complete and requires the injection of glucose and insulin, is not a practical screening
51

tool. Logistical consideration needs to be given to which definition of the metabolic
syndrome is used, however. For example, in a clinic setting, it would be easier to measure
fasting blood glucose rather than 2 hour glucose from an OGTT, as we used in this study.
To address this concern, our group conducted a cross-sectional comparison in our cohort
using three published pediatric definitions for the metabolic syndrome, including one
definition
21
that used fasting glucose rather than 2 hour glucose, and found that
regardless of which was used,
21, 26, 130
the inverse relationship between metabolic
syndrome features and SI was maintained.
116

A limitation that should be considered for the present study is the relatively small
overall sample size and the uneven sample sizes between metabolic syndrome groups,
which may preclude additional findings. In addition, the generalizability of these findings
is limited to Latino youth in a similar age range (~8-13 years) with a family history of
diabetes. However, these limitations are offset by the strength of the longitudinal study
design, along with the well-defined study population and the use of rigorous measures of
metabolic status, such as DEXA for adiposity and FSIVGTT for SI.
In conclusion, in our cohort of overweight Latino children, persistent metabolic
syndrome was associated with indicators of progressive risk for type 2 diabetes,
specifically with increasing body fat accumulation, increasing insulin response to oral
glucose, and lower insulin sensitivity and beta cell function. Annual determination of the
presence of the metabolic syndrome in overweight Latino adolescents could help
clinicians to identify those individuals most at risk for progression to type 2 diabetes.

52

Table 3-1 Baseline unadjusted descriptive characteristics and individual metabolic
syndrome features by metabolic syndrome group in overweight Latino children

Variable Pearson
Chi-Square
Never (N)
(n=35)
Intermittent (I)
(n=24)
Persistent (P)
(n=14)

Male gender (%) * 31.40% 62.50% 64.30%

Omnibus
Test Significant Comparisons

N vs. I N vs. P I vs. P
Tanner stage * 2.43 ± 1.20 2.54 ± 1.50 1.43 ± 1.09 *
*
Age (years) 11.0 ± 1.7 11.6 ± 1.8 10.4 ± 1.4

BMI (kg/m
2
) ** 25.7 ± 4.5 29.6± 6.1 29.7 ± 5.0 * *

BMI percentile *** 95.2 ± 3.9 97.8 ± 2.1 98.7 ± 1.0 ** **

Total fat mass (kg) * 20.5 ± 8.1 27.1 ± 11.3 26.8 ± 10.9 *

Total lean tissue
mass (kg)
** 33.0 ± 7.8 41.5 ± 11.9 35.8 ± 9.6 **

Waist
circumference
(cm)
** 81.4 ± 11.5 91.1 ± 12.3 91.0 ± 14.6 *

SAAT (cm
2
) * 280.4 ± 111.2
(n=31)
347.2 ± 131.3
(n=23)
372.1 ± 144.6
(n=13)

IAAT (cm
2
) 44.7 ± 20.0
(n=31)
46.2 ± 18.7
(n=23)
54.8 ± 19.3
(n=13)

Systolic blood
pressure (mm Hg)
*** 105.1 ± 9.1 110.6 ± 8.5 116.5 ± 6.4 * ***

Diastolic blood
pressure (mm Hg)
** 60.8 ± 4.6 61.9 ± 5.2 65.9 ± 3.7 ** *
HDL cholesterol
(mg/dl)
*** 44.6 ± 10.5 36.2 ± 6.6 32.6 ± 4.8 ** ***

Triglycerides
(mg/dl)
*** 90.6 ± 39.9 126.5 ± 43.9 143.0 ± 53.9 ** **

2 hour glucose
(mg/dl)
124.0 ± 17.6 123.0 ± 16.3 131.1 ± 15.2

Never=negative for metabolic syndrome at all 3 annual visits, Intermittent=positive for metabolic
syndrome at 1 or 2 visits. Persistent = positive for metabolic syndrome at all 3 visits.
SAAT=subcutaneous adipose tissue, IAAT=intra-abdominal adipose tissue.
Chi-square test used for gender and Tanner stage comparisons and data are percentages or medians.
ANOVA performed to compare means with Bonferroni corrections for multiple comparisons and data
are means ± standard deviations.
*P<0.05, **P<0.01, ***P<0.001
53

Table 3-2 Baseline unadjusted indices of insulin and glucose by metabolic syndrome
group in overweight Latino children

Significant
Comparisons
Variable Ombibus
Test
Never (N)
(n=35)
Intermittent (I)
(n=24)
Persistent (P)
(n=14)
N
vs. I
N vs. P I vs.P
Fasting glucose
(mg/dl)
89.9 ± 6.9 91.5 ± 6.3 93.6 ± 6.3
2 hour glucose (mg/dl) 124.0 ± 17.6 123.0 ± 16.3 131.1 ± 15.2
Glucose IAUC
(mg/min/dl)
87.4 ± 36.1 74.5 ± 31.9 94.5 ± 45.3
Fasting insulin
(µU/ml)
** 11.8 ± 6.7 20.9 ± 11.8 18.0 ± 10.4 **
2 hour insulin (µU/ml) 120.0 ±94.2 164.8 ± 142.4 157.4 ± 110.8
Insulin IAUC
(µU/min/ml)
237.7 ± 144.8 285.1 ± 226.5 299.8 ± 132.4
Insulin sensitivity
(X10-4 min -1/ µU/ml
)
** 2.7 ± 1.5
(n=34)
1.7 ± 1.0 1.6 ± 0.6 ** *
Acute insulin response
(µU/ml X 10 min)
1357± 1020
(n=34)
2192 ± 1709 1736 ± 941
Disposition index
(X10-4 min -1)
2660± 1341
(n=34)
2597 ± 1067 2387 ± 915
Never=negative for metabolic syndrome at all 3 annual visits, Intermittent=positive for metabolic
syndrome at 1 or 2 visits. Persistent = positive for metabolic syndrome at all 3 visits.
IAUC=incremental area under the curve, SI=insulin sensitivity, AIR=acute insulin response,
DI=disposition index (a measure of beta cell function).
ANOVA performed to compare means with Bonferroni corrections for multiple comparisons and
data are means ± standard deviations.
*P<0.05, **P<0.01, ***P<0.001
54

Table 3-3 Repeated measures analysis of variance for adiposity measures and insulin/glucose indices by metabolic syndrome group in
overweight Latino children

Time Metabolic syndrome group Time*Group
Adjusted values
Omnibus
test Group contrasts

Baseline
(Visit 1)
1 year follow-
up (Visit 2)
2 year follow-
up (Visit 3) N vs. I N vs. P I vs. P
BMI (kg/m
2
)
a
0.642 0.001 * *** 0.445
Never (n=35) 25.7 ± 3.3 26.8 ± 3.2 28.8 ± 3.6
Intermittent (n=24) 29.6 ± 3.4 31.3 ± 3.5 31.5 ± 3.6
Persistent (n=14) 29.7 ± 2.8 32.0 ± 2.8 33.2 ± 2.9
BMI percentile
a
0.312 <0.001 ** *** 0.481
Never (n=35) 95.2 ± 0.9 95.2 ± 0.7 94.0 ± 0.9
Intermittent (n=24) 97.8 ± 0.9 98.0 ± 0.8 97.1 ± 1.0
Persistent (n=14) 98.7 ± 0.9 99.0 ± 0.7 98.9 ± 0.8
Fat mass(kg)
a
0.066 0.106 * 0.024
Never (n=35) 20.5 ± 6.7 22.5 ± 6.2 23.6 ± 5.7
Intermittent (n=24) 27.1 ± 9.2 30.5 ± 8.6 30.6 ± 7.5
Persistent (n=12) 27.2 ± 8.5 30.7 ± 7.6 32.8 ± 6.7
SAAT (cm
2
)
a
0.059 0.138 * 0.542
Never (n=31) 280.4 ± 90.2 307.2 ± 91.9 320.8 ± 85.3
Intermittent (n=21) 356.1 ± 118.5 416.1 ± 117.4 412.4 ± 105.0
Persistent (n=12) 370.1 ± 113.9 422.1 ± 114.1 442.6± 101.5
IAAT (cm
2
) 0.053 0.342 0.268
Never (n=31) 44.7 ± 9.7 36.4 ± 7.2 36.4 ± 11.0
Intermittent (n=21) 46.5 ± 11.2 48.9 ± 8.4 55.7 ± 13.0
Persistent (n=11) 51.6 ± 11.3 47.9 ± 6.9 55.2 ± 11.5

55

Fasting glucose (mg/dl)

0.408

0.012

**

*

0.481
Never (n=35) 89.9 ± 2.9 89.7 ± 0.7 91.3 ± 1.5
Intermittent (n=24) 91.5 ± 3.1 92.3 ± 0.9 93.2 ± 2.5
Persistent (n=14) 93.6 ± 2.5 96.2 ± 0.6 98.6 ± 1.7
2-hour glucose (mg/dl) 0.045 <0.001 * *** * 0.251
Never (n=35) 124 ± 4.8 122.7 ± 4.3 116.5 ± 5.4
Intermittent (n=24) 123.0 ± 6.1 129.3 ± 6.7 127.2 ± 5.5
Persistent (n=14) 131.1 ± 6.1 140.0 ± 4.9 136.8 ± 4.6
Glucose IAUC (mg/min/dl) 0.461 0.005 ** * 0.238
Never (n=33) 85.7 ± 13.8 81.7 ± 12.0 69.9 ± 9.2
Intermittent (n=20) 72.4 ± 20.0 81.7 ± 16.2 79.6 ± 12.5
Persistent (n=12) 94.6 ± 19.2 102.3 ± 18.0 103.0 ± 13.9
Fasting insulin (µU/ml) 0.031 0.146 0.196
Never (n=34) 11.5 ± 3.5 15.2 ± 3.1 16.1 ± 5.1
Intermittent (n=23) 20.1 ± 5.1 20.3 ± 4.5 16.8 ± 5.8
Persistent (n=14) 18.0 ± 5.0 25.6 ± 3.7 25.7 ± 4.5
2-hour insulin (µU/ml) 0.828 0.007 ** * 0.029
Never (n=34) 125.1 ± 40.9 159.5 ± 44.7 93.8 ± 44.5
Intermittent (n=23) 171.0 ± 48.5 171.6 ± 57.6 157.4 ± 52.3
Persistent (n=14) 157.4 ± 40.2 286.9 ± 40.6 281.2 ± 43.0
Insulin IAUC
a
(µU/min/ml) 0.025 0.025 * * 0.012
Never (n=34) 234.3 ± 68.1 293.8 ± 69.1 210.6 ± 83.8
Intermittent (n=22) 289.9 ± 92.0 311.2 ± 94.4 277.7 ± 104.8
Persistent (n=13) 302.6 ± 74.2 449.4 ± 76.1 516.0 ± 98.9
SI
a
(X10-4 min -1/ µU/ml ) 0.001 0.019 ** 0.872
Never (n=32) 2.78 ± 0.7 2.04 ± 0.5 1.94 ± 0.3
Intermittent (n=23) 1.71 ± 0.9 1.43 ± 0.6 1.37 ± 0.4
Persistent (n=13) 1.7 ± 0.8 1.11 ± 0.5 1.06 ± 0.3

Table 3-3 (cont)

56

Table 3-3 (cont)
AIR (µU/ml X 10 min) 0.106 0.506 0.302
Never (n=32) 1336.4 ± 466.5 1493.5 ± 475.8 1519.0 ± 296.8
Intermittent (n=23) 2211.3 ± 684.7 2132.5 ± 634.7 1882.3 ± 381.0
Persistent (n=13) 1568.0 ± 740.1 1973.1 ± 649.2 1930.2 ± 410.7
DI (X10-4 min -1) 0.419 0.044 * * 0.482
Never (n=32) 2606.7 ± 491.3 2454.5 ± 562.5 2319.0 ± 410.8
Intermittent (n=23) 2607.6 ± 634.9 2313.9 ± 714.3 2066.8 ± 435.8
Persistent (n=13) 2368.3 ± 404.7 1845.3 ± 361.1 1669.6 ± 233.1
Never=negative for metabolic syndrome at all 3 annual visits, Intermittent=positive for metabolic syndrome at 1 or 2 visits. Persistent =
positive for metabolic syndrome at all 3 visits. SAAT=subcutaneous adipose tissue, IAAT=intra-abdominal adipose tissue,
IAUC=incremental area under the curve, SI=insulin sensitivity, AIR=acute insulin response, DI=disposition index (a measure of beta cell
function).
Repeated measures ANOVA used to compare changes in insulin and glucose dynamics over visits 1-3 and data are means ± standard
deviations.
*P<0.05, **P<0.01, ***P<0.001
All analyses are adjusted for gender and baseline age, and Tanner stage. Total lean tissue mass was also controlled for in the total fat mass
model, and total lean and total fat were controlled for in all insulin and glucose indices models.
a Sphericity assumption violated and huynh-feldt correction used.

57

Figure 3-1 Persistence of each metabolic syndrome feature by metabolic syndrome group.

0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Persistent
IGT
Persistent
HBP
Persistent
TAG
Persistent
HDL
Persistent
WAIST
Percent
Never (n=35)
Intermittent (n=24)
Persistent (n=14)

Never=negative for metabolic syndrome at all 3 annual visits, Intermittent=positive for
metabolic syndrome at 1 or 2 visits. Persistent = positive for metabolic syndrome at all 3
visits.
58

Figure 3-2 Changes in fat mass and insulin/glucose indices over 3 annual visits by
metabolic syndrome group in overweight Latino children

Figure 3-2c. Insulin sensitivity
0.5
1
1.5
2
2.5
3
123
Vis it
Insulin Sensitivity
(X10-4 min -1/ µU/ml )
Figure 3-2d. Disposition index
1500
1700
1900
2100
2300
2500
2700
12 3
Vis it
Disposition index
(X10-4 min -1)

Figure 3-2a. Total fat mass
15
20
25
30
35
1 2 3
Visit
Total fat (g)
Never
Intermittent
Persistent
Figure 3-2b. Insulin IAUC
100
200
300
400
500
600
12 3
Visit
Insulin IAUC
(µU/min/ml)

Figure legend: Never (N) =negative for metabolic syndrome at all 3 annual visits;
Intermittent (I)=positive for metabolic syndrome at 1 or 2 visits; Persistent (P) =
positive for metabolic syndrome at all 3 visits. Repeated measures analysis of covariance
used to compare groups. Predicted values adjusted for gender and baseline age, Tanner
stage, and total lean tissue mass. Figures 1b-d also adjusted for total fat mass. Figure 3-
2a: Total fat mass (n=71). .Effect of time: p=0.066; Differences by group: omnibus
p=0.106, N vs P p=0.039; Interaction for time by group p=0.024. Figure 3-2b: Insulin
IAUC (n=69). Effect of time: p=0.025; Differences by group: omnibus p=0.025, N vs. P
p=0.012, I vs. P p=0.018; Interaction for time by group p=0.012. Figure 3-2c: Insulin
sensitivity (n=68).. Effect of time: p=0.001; Differences by group: omnibus p=0.019, N
vs. P p=0.006, N vs. I p=0.073; Interaction for time by group p=0.872. Figure 3-2d:
Disposition index (n=68) Effect of time: p=0.419; Differences by group: omnibus
p=0.044, N vs. P p=0.020, I vs P p=0.024; Interaction for time by group: p=0.482.
59

CHAPTER 4 THE EFFECTS OF A RANDOMIZED, CONTROLLED,
MODIFIED-CARBOHYDRATE NUTRITION INTERVENTION ON THE
METABOLIC SYNDROME IN OVERWEIGHT LATINO ADOLESCENTS

Introduction

In national data, Latino youth have the highest prevalence of the metabolic
syndrome, a clustering of clinical markers linked to insulin resistance that is associated
with risk for both cardiovascular disease and type 2 diabetes.
20
The five features of the
metabolic syndrome are high waist circumference, high triglycerides, high blood
pressure, high blood glucose, and low HDL cholesterol, and the syndrome is generally
defined as having three or more of the five features. In a convenience, cross-sectional
sample of overweight Latino children in Los Angeles, we have previously shown that
30% have the metabolic syndrome.
26
In addition, over multiple annual visits, we have
shown that 19% have persistent metabolic syndrome and that those with persistent
metabolic syndrome show increased risk for type 2 diabetes over time.
126

Diet is one of the main modifiable risk factors for the metabolic syndrome and
also for type 2 diabetes, and there is evidence to show that the quality of dietary
carbohydrate plays an important role in these conditions. In a previous cross-sectional
analysis in overweight Latino youth, we showed that dietary fiber consumption is
inversely associated with both waist circumference and the metabolic syndrome.
125
We
have also showed that intake of total sugar is associated with poor beta-cell function, an
indicator of risk for type 2 diabetes.
29
Additionally, we showed that in a 12-week,
modified-carbohydrate nutrition education pilot study, overweight Latina girls with
greater reductions in added sugar intake showed greater reductions in insulin secretion.
30

However, to date, no studies from our group or others have examined the effects of a
60

modified carbohydrate intervention on the metabolic syndrome in overweight youth, and
specifically not in Latino youth, a high-risk population.
The purpose of the current analysis is to examine the effects of a 16-week
randomized control trial with a control group, a nutrition only group, and a nutrition plus
strength training group, on the metabolic syndrome in overweight Latino adolescents.
Outcomes related to body composition as well as insulin/glucose regulation have been
previously described.
28
The main outcomes analysis showed no significant overall effects
of the intervention on body weight, body composition, or insulin/glucose indices, with
the exception of an improvement in oral glucose response (6% and 18% reductions in
nutrition and combined groups, respectively, compared to a 32% increase in the control
group). However, the effects of the intervention on the features of the metabolic
syndrome have not yet been assessed. Specific features of the metabolic syndrome, such
as blood pressure and triglycerides, have been shown to be sensitive to short term dietary
changes
57
and thus may be more affected during a 16 week intervention than direct
measurements of risk for type 2 diabetes, such as insulin sensitivity and beta cell
function.
In addition, despite the overall lack of significant main outcomes intervention
effects, there was significant individual variation in dietary changes and metabolic
outcomes within each of the randomized groups. In a previous, secondary analysis of the
intervention data, we found that those participants who made the suggested
improvements in added sugar intake and/or fiber intake over the 16 weeks had
improvements in both insulin response to oral glucose as well as visceral fat.
124

Therefore, a secondary aim of the current paper is to investigate whether participants who
61

made the suggested dietary modifications had an improvement in metabolic syndrome
profiles as compared to those who did not, regardless of randomization group.
Methods

Participants
Participants were recruited from Los Angeles County and met the following
inclusion criteria: BMI ≥ 85
th
percentile (CDC, 2000), Latino ethnicity, and grades 9
th

thru 12
th
. Participants were excluded if they 1) were using medication or were diagnosed
with any syndrome or disease that could influence dietary intake, exercise ability, body
composition and fat distribution, or insulin action and secretion, 2) were previously
diagnosed with any major illness since birth, 3) met diagnostic criteria for diabetes, or 4)
participated in structured exercise, nutrition, or weight loss program in the past 6 months.
Informed written consent from parents and assent from the children were obtained. This
study was approved by the Institutional Review Board of the University of Southern
California, Health Sciences Campus.
Description of Interventions
The Nutrition Only group (N) received one 90 minute nutrition class/week for 16
weeks. The dietary intervention targeted two goals: 1) a decrease in added sugar toward a
goal of ≤ 10% of total daily calorie intake from added sugar
132
and 2) an increase in
dietary fiber toward a goal of 14g/1000 kcal per day.
60
Given the low socioeconomic
status of the families, participants were given $25 grocery gift cards each week and
transportation to and from class was provided if needed. Participants in the Nutrition plus
Strength Training (N+ST) group received the same weekly nutrition classes along with
strength training 2X week for 16 weeks.
62

Participants randomized to the control group (C) received no intervention between
pre and post-intervention data collection. After post-testing, participants were offered a
delayed intervention for one month but no testing was done after the delayed
intervention.
Protocol and Outcome Measures
Outpatient visit: Participants arrived at the USC General Clinical Research Center
(GCRC) at ~7:30 am after an overnight fast. A licensed pediatric health care provider
conducted a medical history exam and determined Tanner staging using established
guidelines
87, 88
. Weight and height were measured to the nearest 0.1 kg and 0.1 cm,
respectively, using a beam medical scale and wall-mounted stadiometer. BMI and BMI
percentiles for age and gender were determined using EpiInfo 2000, Version 1.1 (CDC,
Atlanta, GA). Whole body fat and soft lean tissue was measured by dual energy x-ray
absorptiometry (DEXA) using a Hologic QDR 4500W (Hologic, Bedford, MA). Sitting
blood pressure was measured in triplicate.
2

In addition, a 3-hour oral glucose tolerance test (OGTT) was conducted. Two
hour glucose levels were used to determine normal glucose tolerance (NGT; 2-hour
glucose < 140 mg/dl) or impaired glucose tolerance (IGT; 2-hour glucose ≥140 and <200
mg/dl) as defined by the American Diabetes Association.
6

In-patient visit: Approximately 7-14 days following the out-patient visit, fasting
blood samples were drawn again as part of an in-patient visit and were measured for
triglycerides and total and HDL cholesterol using the Vitros chemistry DT slides
(Johnson and Johnson Clinical Diagnostics Inc., Rochester, NY).

63

Definition of the Metabolic Syndrome
The metabolic syndrome was categorized using a definition we have previously
proposed
26
which applies pediatric cutoffs to the Adult Treatment Panel III definition
3
.
The metabolic syndrome was defined as having ≥3 of the following: 1) abdominal
obesity (waist circumference ≥90th percentile for age, gender, and Hispanic ethnicity
from NHANES III data),
38
hypertriglyceridemia (triglycerides ≥90th percentile of age
and gender),
58
low HDL cholesterol (HDL cholesterol ≤ 10th percentile for age and
gender),
58
elevated blood pressure (systolic or diastolic blood pressure >90th percentile
adjusted for height, age, and gender),
2
and impaired glucose tolerance, as described
above.
Dietary Intake
Participants were given 3-day diet records to complete at home and bring to the
inpatient visit. Participants were given a short lesson (10 min) on how to estimate portion
sizes and complete the diet records and were given measuring cups and rulers to aid in
accurate reporting. Research staff, trained and supervised by a Registered Dietitian,
clarified all dietary records. Nutrition data were analyzed using the Nutrition Data
System for Research (NDS-R version 5.0_35), a software program developed by the
University of Minnesota.
Statistical Analysis
Data Cleaning and Normalization: Data was examined for normality and variables
that were not normally distributed were log transformed. Though 54 participants
completed the intervention, only 50 of the participants had complete data for the
metabolic syndrome coding. Waist circumference data was not collected on two
64

participants due to staffing limitations and lipid data was lost for 2 participants.
Therefore, 50 subjects were included in the present analysis (16 Control, 17 Nutrition
Only, and 17 Combo). Of these 50 subjects, 5 were missing DEXA data. Four of the 5
did not have DEXA because of the upper weight limit (300 lbs) for the machine and 1 did
not have DEXA due to logistical constraints. For these 5 subjects, Bod Pod data was
instead used and a correction factor was applied to the Bod Pod values to account for
differences in the measurements. In addition, 6 of the 50 participants did not complete
the 3 day diet records. When comparing the 44 participants with dietary data and the 6
participants without, there were no significant differences in age, gender, Tanner stage,
randomization group, or metabolic syndrome features at baseline (p>0.05).
Baseline Comparisons: Using independent t-tests, unadjusted descriptive
characteristics were compared between participants with and without the metabolic
syndrome at baseline. Chi-square tests were used to compare gender distributions,
randomization assignments, and Tanner stages between metabolic syndrome groups.
Intervention Effects on the Metabolic Syndrome: The intervention effects of the
metabolic syndrome were explored in three manners. First, Chi-square tests were used to
compare the percent of participants in each randomization group who changed metabolic
syndrome status from pre to post test. Four comparison groups were created: those who
coded negative for the metabolic syndrome at both time points, those who coded positive
at both time points, those who reversed metabolic syndrome status, and those who gained
the metabolic syndrome. Second, participants were categorized according to those who
reduced the number of features of the metabolic syndrome (post testing-pre testing) vs.
those who gained features, and these categories were compared by randomization group
65

with Chi-square tests. Finally, changes in the individual metabolic syndrome features,
coded as continuous variables, were explored by comparing change scores across groups
using analysis of variance (ANOVA.) Subsequently, analysis of covariance (ANCOVA)
was conducted to compare change scores adjusted for the following variables: pre-test
values for the given metabolic syndrome feature tested, age, gender, and fat mass at both
pre and post test. Significant differences across groups were explored using post hoc
pairwise comparisons with Bonferroni adjustments.
Changes in Metabolic Syndrome Profiles by Sugar and Fiber Improvement
Categories: For these secondary analyses, all randomization groups were combined and
subjects were divided into categorizes based on whether or not they improved sugar
intake and/or fiber intake. Added sugar improvement was defined as a decrease in added
sugar of any magnitude (Post – Pre < 0), as a percentage of total caloric intake; and fiber
improvement was defined as an increase of any magnitude in fiber (g) per 1,000 calories
of total energy intake (Post – Pre > 0).
Changes in metabolic syndrome status, reductions vs. increases in number of
features, as well as changes in continuous metabolic syndrome features were compared
by sugar and fiber improvement categories in a similar method to the analysis for the
intervention effects, described above. In addition to the covariates used in the intent to
treat analyses, randomization group was used as an additional covariate. Data was
analyzed with SPSS version 13.0 (SPSS Inc, Chicago, IL), and type 1 error was set at
α<0.05. When there were significant findings in the sugar and/or fiber complier analyses,
food subgroup data from NDS was analyzed to investigate which food subgroups were
66

implicated. To accomplish this, associations between changes in food subgroups and
changes in individual metabolic syndrome features were explored.
Results
Baseline comparisons
Of the 50 participants, 14 (or 28%) had the metabolic syndrome at pre-testing.
Baseline characteristics of the participants by metabolic syndrome status (yes vs. no) are
presented in Table 4-1. There were no statistically significant differences in
randomization group assignment, gender distributions, age, or Tanner stage between
metabolic syndrome groups (p ≥0.38). Participants with the metabolic syndrome weighed
more (109.0 vs. 83.7, p<0.001) and had a higher BMI percentile (98.4 vs. 95.8; p=0.02)
and a higher total fat mass (44.0 kg vs. 29.8 kg, p<0.01) and total lean mass (60.1 vs.
51.5, p=0.01) than those without the metabolic syndrome. As expected by group
definitions, the participants with the metabolic syndrome had higher values for systolic
blood pressure, waist circumference, 2 hour glucose and triglycerides, as well as lower
levels of HDL cholesterol (p ≤0.04). The comparison of diastolic blood pressure was not
significant between groups (p=0.16). There were no significant baseline differences in
macronutrient, sugar, or fiber intake between metabolic syndrome groups (p ≥0.14).
Changes by intervention group
Changes in metabolic syndrome status by randomization group are shown in
Figure 4-1. In both the nutrition and the combo groups, 4 of 5 (80%) of the participants
with baseline metabolic syndrome reversed status and did not have the metabolic
syndrome at post-testing, as compared to only 1 of 4 (25%) in the control group.
However, in the combination group, 3 participants who did not have the metabolic
67

syndrome at baseline coded positive for the metabolic syndrome at post testing, as
compared to 1 participant in the nutrition group and no participants in the control group.
None of these differences in categorizations were significant as assessed by chi-square
(p=0.27). In addition, when the dichotomous categories of those who decreased the
number of features vs. those who increased the number of features were compared by
randomization group, there were no significant differences (p=0.98), with 41% of
participants in each of the intervention groups showing a decrease in the number of
features, as compared to 38% of control participants.
Changes in the individual metabolic syndrome features by randomization group
are shown in Table 4-2. In unadjusted ANOVA analyses, there was a significant overall
difference in change in diastolic blood pressure (p=0.02), with the nutrition group having
a reduction of –4.4 mm Hg as compared to an increase of 1.1 mm Hg in the control group
(p=0.02). There was also a significant overall difference in change in 2 hour glucose
(p=0.03), with the Nutrition group decreasing by 19.3 mg/dl compared to the control
group which had an increase of 0.25 mg/dl (0.07). There were no significant overall
comparisons with the other metabolic syndrome features and no significant post-hoc
comparisons involving the combination group. In ANCOVA analyses, after controlling
for gender, age, baseline value of the dependent metabolic syndrome feature in the
model, and total fat mass at pre and post test, the differences in change in diastolic blood
pressure and in change in 2 hour glucose were no longer significant (p ≥0.27). There were
also no adjusted differences in the other metabolic syndrome features.

68

Changes by sugar improvement categories
Participants who decreased their added sugar intake had a mean decrease of 45.8
± 43.2 g, as compared to an increase of 27.7 ± 31.5 g in those who increased their added
sugar intake. There were no significant differences in the change in metabolic syndrome
status when comparing those who decreased added sugar intake (n=24) and those who
increased added sugar intake over the course of the intervention (n=20). Of the added
sugar decreasers, 4 of 8 (50%) with baseline metabolic syndrome reversed their status
and 1 of 13 (7.7%) participants without the metabolic syndrome at baseline gained it by
16 weeks, as compared to 5 of 6 (83.3%) and 2 of 14 (14.3%), respectively in those who
increased added sugar intake (p=0.53). There were also no significant differences in the
percentage of participants who decreased vs. increased the number of features of the
metabolic syndrome by added sugar change categories (p=0.61); 42% of sugar decreasers
had a decrease in number of features as compared to 35% of sugar increasers. In ANOVA
analyses, there were no significant differences in the change in the individual features by
added sugar change categories, with the except of a marginally significant difference in
change in triglycerides (p=0.05), with the sugar decreasers showing a decrease of 12.5
mg/dl and the sugar increasers showing an increase of 2.8 mg/dl. However, when
covariates were added to the model, the association between sugar reduction and change
in triglycerides was no longer significant (p=0.24). Furthermore, there were no significant
associations between the change in added sugar intake, coded as a continuous variable,
with changes in the individual features. Similarly, there were no significant associations
with changes in food subgroups that contain added sugar, such as sweetened beverages,
and change in any of the individual features of the metabolic syndrome.
69

Changes by fiber improvement categories
Participants who increased their fiber intake had a mean increase of 5.8 ± 8.9g, as
compared to a decrease of 4.6 ± 6.7 grams in those who decreased their fiber intake.
There were also no significant differences in the change in metabolic syndrome status
when comparing those who increased fiber intake (n=24) and those who decreased fiber
intake over the course of the intervention (n=20), data not shown. Of the fiber increasers
5 of 8 (62.5%) reversed the metabolic syndrome and none gained it, as compared to 4 of
6 (66.7%) and 3 of 14 (21.4%), respectively, in those who decreased their intake
(p=0.27). However, there was a significant difference in the percentage of participants
who decreased vs. increased the number of features of the metabolic syndrome by fiber
change categories (p=0.04). Half of participants who increased fiber had a decrease in
number of features, as compared to 25% of participants who decreased fiber. Those who
increased their fiber intake relative to caloric intake had an average decrease of 1.4
features, as compared to an average increase of 1.3 features in those who did not increase
their fiber intake, data not shown. In ANOVA analyses, there were no significant
differences in the change in the individual features by fiber increase categories (p ≥0.29).
However, due to the significant result seen with the decrease in metabolic syndrome
features, we explored partial correlations between change in fiber intake as a continuous
variable and change in the individual features. These correlations revealed that a change
in fiber intake was most associated with a change in waist circumference. When
exploring food sources involved in the increase in fiber intake that was potentially
associated with the decrease in metabolic syndrome features, we found that the only
significant association between a change in a food subgroup and a change in a metabolic
70

syndrome feature was an inverse association between fruit intake and waist
circumference (p=0.03), after controlling for gender, age, randomization group, baseline
waist circumference, and change in energy intake. The adjusted difference in change in
waist circumference by those who increased fruit vs. those who did not is shown in
Figure 4-2. Those who increased fruit intake had an adjusted decrease of 0.3 centimeters
as compared to a 3.2 centimeter increase in those who did not increase fruit intake.
However, when pre and post test total fat mass were added to the ANCOVA model, the
association between an increase in fruit intake and a change in waist circumference lost
statistical significance (p=0.49).
Overlap between sugar and fiber categories
There was considerable overlap between participants who decreased added sugar
intake and those who increased fiber intake. Eighteen of the 24 (75%) participants who
decreased sugar intake also increased fiber intake. However, there were no significant
differences in metabolic syndrome status change or a decrease in metabolic syndrome
features across those that achieved none, one, or both of the dietary changes of interest
(p>0.05).
Discussion
In the current study, we report the effects of a randomized, controlled, 16 week
nutrition education program, with and without strength training, on metabolic syndrome
profiles in overweight Latino adolescents. By the end of the 16 weeks, 80% of
participants in each of the intervention groups who coded positive for the metabolic
syndrome at baseline reversed their status and coded negative at post-testing, as
compared to 25% of participants in the control group. However, the effect of the
71

intervention was not consistent, as 1 participant in the Nutrition group and 3 participants
in the combination actually gained the classification of the metabolic syndrome over the
course of the 16 weeks, as compared to zero participants in the Control group. The only
significant improvements in metabolic syndrome features occurred in the Nutrition only
group, as compared to the Control group, yet the absolute decreases in 2 hour glucose
levels and in diastolic blood pressure did not remain significant after controlling for the
selected a-priori covariates.
In addition to the intent to treat analysis, we also completed secondary analyses
according to changes in dietary intake. When analyzing the data by those who decreased
vs. increased added sugar intake, regardless of randomization group, we did not see
significant differences in changes in metabolic syndrome status. There was a marginally
significant decrease in triglycerides in those participants who decreased their added sugar
intake, but this effect was not significant after controlling for covariates. Finally, when
results were compared by those who increased vs. decreased fiber intake, we did see a
significant decrease in the number of features of the metabolic syndrome. Though there
were no significant differences in the change in the individual metabolic syndrome
features between those who increased vs. decreased fiber, upon further examination using
the food group data, there was a significant relationship between a change in fruit intake
and a change in waist circumference. This effect appears to be explained by the change in
total fat mass between those who increased their fruit intake and those who did not, and
not isolated to changes in waist circumference.
This study is the first to examine the effects of a randomized nutrition education
intervention focused on decreasing sugar intake and increasing fiber intake on metabolic
72

syndrome profiles in youth, let alone Latino youth. There have been several studies by
other investigators which have shown beneficial effects of a modified carbohydrate
intervention on metabolic health in overweight youth of other ethnicities, though they did
not measure the metabolic syndrome.
34, 35
A review of the literature yielded four studies
which examined dietary intervention effects on the metabolic syndrome in youth, though
they did not test a modified carbohydrate approach. All four interventions also included
physical activity components and only one study isolated the effects of dietary
modification as separate from physical activity.
The one study which isolated the effects of dietary change was conducted by
Kang et al, and consisted of an eight month intervention for black and white adolescents
with three arms: lifestyle education every two weeks (which consisted of general
instruction regarding diet as well as physical activity), lifestyle education plus moderate-
intensity physical activity, and lifestyle intervention plus high-intensity physical
activity.
66
Effects on the individual components of the metabolic syndrome are reported
but not the effects on the overall metabolic syndrome. The authors found that compared
to the lifestyle education only group, the lifestyle plus high intensity physical activity
group had significantly greater improvements in triglyceride levels and diastolic blood
pressure. When comparing the dietary changes between these two groups, the authors
found that the lifestyle education plus high-intensity physical activity group significantly
increased energy intake as compared to the lifestyle education only group, but there were
no differences by group in macronutrient composition changes. No information is
provided regarding the specific dietary advice given to participants during the lifestyle
education classes. Furthermore, because there was not a control group that did not receive
73

nutrition education, it is difficult to assess the effectiveness of the lifestyle education
component alone.
The other three studies which included a nutrition component in interventions to
reduce the prevalence of the metabolic syndrome but did not test its separate effects were
conducted by Park et al, Monzavi et al, and Chen et al. Park et al tested the effects of a 12
week intervention for obese Korean female adolescents comparing a lifestyle plus
exercise group and a control group which did not receive an intervention.
105
The nutrition
component is described very generally as providing principles related to “changes in
eating habits.” No differences in energy intake or macronutrient composition of the diet
were observed between intervention or control groups over the 12 weeks. The authors
found that the intervention group had significant improvements in unadjusted systolic
blood pressure, triglycerides, waist circumference, and fasting glucose as compared to the
control group, but no covariates were included in the analyses. Monzavi et al showed that
a 12 week, family-centered lifestyle intervention for overweight youth (78% Hispanic) at
Children’s Hospital Los Angeles resulted in a significant improvement in metabolic
syndrome status through a decrease in blood pressure, triglycerides, and glucose levels.
94

There was no control group and the analyses consisted only of paired t-tests. The dietary
component was broad-based and focused on topics such as the food pyramid and portion
sizes. However, since dietary data was not collected, it is not possible to separate the
effects of dietary change from change in physical activity levels. Chen et al conducted a
one group, non-randomized, 2 week intensive diet and exercise program in overweight
youth ages 10-17 (n=16) where the participants lived at the educational center in
Florida.
18
Information is not provided regarding the participant’s ethnicities. The authors
74

reported a reversal in the classification of the metabolic syndrome in the 7 children who
had it at baseline, results which were driven by a decrease in both triglycerides and blood
pressure. Paired t-tests were used for the analyses. The dietary intervention, the Pritikin
diet, focused on high fiber and very low fat foods with the percent of calories from fat in
the diet around 10%. In this study, the authors were similarly not able to isolate the
effects of diet vs. physical activity as no dietary data was collected at baseline.
Therefore, given the few studies published on this topic, the current study is novel
in that it reports the effects of a randomized, controlled dietary modification on the
metabolic syndrome as separate from the effects of physical activity. In addition, it tests a
specific dietary approach that is focused exclusively on sugar and fiber intake. The study
also reports intervention effects in a group of high-risk, yet understudied youth. Finally,
the analysis is comprehensive in that in addition to reporting changes in unadjusted data,
it includes adjustment for a-priori covariates known to affect metabolic syndrome profiles
such as baseline values, gender, age, and fat mass.
Although there were modest intervention effects, the results are not as strong as
was hypothesized. It is interesting to explore why the effects were not more pronounced.
For the intent to treat analysis, one potential explanation for the lack of effects is the wide
variation in dietary changes seen within each of the intervention groups. As we have
previously documented, participants in the control group were equally likely to reduce
their added sugar intake and/or to increase their fiber intake as were participants in the
intervention groups.
124
The Control participants were not blind to the purpose and focus
of the nutrition education classes, and independently made similar modifications to their
eating habits over the 16 weeks. In a separate secondary analysis using the intervention
75

data, we found that the randomized intervention did not prompt increases in intrinsic
motivation. However, when the results were analyzed according to those who increased
motivation over the course of the intervention vs. those who did not, independent of
randomization group, participants who had increases in intrinsic motivation over the 16
weeks had increases in fiber intake.
89
Another factor to consider which may have
contributed to the lack of beneficial results, specifically in the Combination group, is the
fact that the program intervened on multiple behaviors within a short time frame, which
may have been overwhelming for participants. Participants in this group may have
compensated for the added exercise when eating. Finally, the current study is somewhat
limited by the small sample size, which may have prevented further findings, and by the
relatively short duration of the intervention.
In the secondary analyses looking at effects by sugar and or fiber improvement,
we also did not see as many favorable effects as were hypothesized. The association
between decreased added sugar intake and decreased triglyceride levels is consistent with
the literature, yet this effect was not robust enough to remain after controlling for
covariates. The decrease in features of the metabolic syndrome in those who increase
fiber intake is also consistent with the literature, and is similar to our previous findings
that fiber intake is associated with waist circumference and metabolic syndrome status
125

and that increasing fiber intake leads to a decrease in BMI and in visceral fat.
124
The
association between an increase in fruit intake and a decrease in waist circumference is
also consistent with our cross-sectional findings linking fiber intake and the metabolic
syndrome, as we found that fruit was one of the primary sources of soluble fiber in the
diets of our population of Latino youth.
125
In addition, the fruit finding is consistent with
76

the results from a larger cross-sectional study examining associations between dietary
intake and the metabolic syndrome in youth using NHANES data from 1999-2002.
27
In
this study, an analysis of the subcomponents of the Healthy Eating Index revealed that
the only component associated with the metabolic syndrome was the fruit score, with
those eating the most fruit having the lowest prevalence of the metabolic syndrome.
However, to our knowledge, there are no other intervention studies which have examined
the effects of increasing fruit intake on the metabolic syndrome in youth.
In summary, this paper shows that a 16 week nutrition education intervention
focusing on decreasing added sugar intake and increasing fiber intake produced modest
improvements in metabolic syndrome profiles in overweight Latino youth. Additional
research is needed to further evaluate the specific effects of dietary fiber and of particular
fiber-rich food sources, such as fruit, in this high-risk population. Finally, more research
is needed to understand how to successfully promote increased fiber intake in overweight
Latino youth. Future interventions designed for this population should consider
intervening for a longer period of time and should take more steps to prompt compliance,
such as by screening for baseline motivation of participants. In conclusion, though the
effects of the rigorous intervention were modest, they support the notion that increasing
fiber intake is beneficial for reducing adiposity and improving metabolic health in
overweight Latino youth.

77

Figure 4-1 Change in metabolic syndrome status by randomization group

Co Nutrition (n=17)
11 1
4
1
ntrol (n=16)
12
3
1
0
Negative at Both
Positive at Both
Reversed
Gained
Combination (n=17)
9
1
4
3
78

Figure 4-2 Changes in waist circumference by fruit increase vs. fruit decrease

Fruit increasers
Fruit
decreasers
-2
0
2
4
6
8
10
Waist circumference (cm)

79

Table 4-1 Baseline characteristics of study participants (n=50) by metabolic syndrome
status

Without
Metabolic
Syndrome
(n=36)
With Metabolic
Syndrome (n=14) P-value
Randomization group
(Control/Nutrition Only /Combo) 12/ 12/ 12 4/ 5/ 5 0.95
Male gender % 47.2 57.1 0.38
Age (years) 15.4 ± 1.2 15.4 ± 1.0 0.85
Tanner stage (Median (range)) 5 (4) 5 (2) 0.75
Adiposity
Weight (kg) 83.7 ± 17.1 109.0 ± 26.7 <0.001
Height (cm) 163.1 ± 7.3 167.3 ± 9.2 0.10
BMI percentile 95.8 ± 4.1 98.4 ± 3.0 0.02
Total fat mass (kg) 29.8 ± 9.7 44.0 ± 16.7 <0.01
Total lean mass (kg) 51.5 ± 9.5 60.1 ± 10.1 0.01
Metabolic Syndrome Features
Systolic blood pressure (mm Hg) 116.4 ± 10.5 131.9 ± 11.2 <0.001
Diastolic blood pressure (mm Hg) 66.1 ± 7.6 69.6 ± 8.5 0.16
Waist circumference (cm) 90.4 ± 11.1 107.4 ± 14.3 <0.001
2 hour glucose (mg/dl) 123.4 ± 26.0 142.1 ± 20.1 0.01
Triglycerides (mg/dl) 91.5 ± 36.4 115.0 ± 39.8 0.04
HDL cholesterol (mg/dl) 38.4 ± 10.3 32.3 ± 3.8 <0.01
Macronutrient, Sugar, and Fiber
Intake
Energy intake (calories) 1914.3 ± 616.5 1879.8 ± 700.3 0.87
% of calories from protein 15.8 ± 3.8 16.6 ± 2.6 0.48
% of calories from fat 31.7 ± 6.5 33.7 ± 5.4 0.32
% of calories from carbohydrate 53.8 ± 7.8 50.9 ± 7.2 0.25
% of calories from total sugar 23.9 ± 6.1 21.1 ± 5.6 0.14
% of calories from added sugar 15.2 ± 6.3 13.7 ± 6.8 0.48
Fiber (grams/1000 calories) 8.7 ± 3.2 8.5 ± 3.2 0.85
Soluble fiber (grams/1000 calories) 2.5 ± 0.9 2.4 ± 0.9 0.76
Insoluble fiber (grams/1000 calories 5.9 ± 2.4 5.7 ± 2.5 0.81
Unless otherwise noted, data are means ± standard deviations and data were compared using independent t-tests. Chi-square
tests were used for gender, randomiation group, and Tanner stage. Although untransformed data are presented, statistical tests
were run using transformed data for the following variables: weight, BMI percentile, total fat, total lean, waist, 2 hour
glucose, triglycerides, HDL cholesterol, % kcals from total sugar, and insoluble fiber per 1,000 kcals.
80

Table 4-2 Changes in metabolic syndrome features by randomization group

Control (n=16) Nutrition (n=17) Nutrition+Strength Training (n=17)
Pre Post
Adjusted
Change
Score Pre Post
Adjusted
Change
Score Pre Post
Adjusted
Change
Score
P-
value
Systolic blood
pressure (mm Hg) 117.9 (12.5) 113.4 (12.2) -5.8 (2.2) 120.3 (13.9) 117.9 (13.3) -1.8 (2.1) 123.8 (11.7) 121.5 (13.2) -1.7 (2.1) 0.32
Diastolic blood
pressure (mm Hg) 62.5 (6.5) 63.6 (6.2) 0.0 (1.4) 70.7 (7.3) 66.3 (7.4) -3.3 (1.3) 67.8 (8.2) 65.7 (8.3) -2.2 (1.3) 0.27
2 hour glucose
(mg/dl) 121.9 (25.4) 122.1 (32.0) -4.3 (5.3) 136.0 (19.8) 116.7 (26.5) -13.1 (5.2) 127.6 (30.3) 124.3 (17.4) -5.2 (5.2) 0.32
Waist
circumference
(cm) 94.4 (17.5) 96.3 (18.8) 1.5 (1.1) 91.9 (9.5) 93.2 (11.0) 1.4 (1.1) 99.2 (14.6) 99.7 (13.8) 0.9 (1.1) 0.97
Triglycerides
(mg/dl) 85.4 (25.9) 86.4 (28.9) -4.0 (5.6) 94.8 (38.6) 88.0 (28.0) -8.1 (5.3) 113.2 (44.8) 109.6 (32.9) 2.3 (5.4) 0.39
HDL cholesterol
(mg/dl) 35.8 (8.5) 37.3 (8.6) 1.0 (1.4) 38.8 (12.4) 34.8 (5.4) -3.0 (1.4) 36.7 (9.4) 35.8 (7.0) -0.5 (1.4) 0.13
Data are pre and most means (SD) and adjusted change scores (SE).
ANCOVA models included the following covariates: age, gender, pretest value of metabolic syndrome feature and total fat mass at pre and post test.
While untransformed data is presented, logged values of the following were used for statistical testing: 2 hour glucose, waist, triglycerides, and HDL cholesterol.

81

CHAPTER 5 SUMMARY AND CONCLUSIONS

Summary of Findings
The overall goal of this dissertation was to examine the associations between
dietary intake, the metabolic syndrome, and risk for type 2 diabetes in overweight Latino
youth. The first objective was to explore the relationship between dietary intake and the
metabolic syndrome in this high-risk group with a specific focus on the quality of
carbohydrate intake. The second objective was to examine the longitudinal relationships
between the metabolic syndrome and risk for type 2 diabetes within
childhood/adolescence. The third objective was to assess the effects of a randomized,
controlled, 16 week, modified-carbohydrate nutrition education intervention on metabolic
syndrome profiles.
The results from paper 1 showed that soluble fiber intake was the only dietary
variable significantly associated with the metabolic syndrome, and that this relationship
was driven by an inverse association between soluble fiber intake and waist
circumference. The difference in soluble fiber intake between those with and those
without the metabolic syndrome was 1g daily (4.1g vs. 5.2g), or the equivalent of the
soluble fiber in one piece of fruit or one serving of beans.
The results from paper 2 show that persistent metabolic syndrome, as measured
over 2 years (at 3 annual visits) was associated with indicators of increased risk for type 2
diabetes. Specifically, as compared to participants who never had the metabolic
syndrome, the participants with persistent metabolic syndrome had increased rates of fat
mass gain (20% vs. 15% gain of baseline value) and insulin response to oral glucose
82

(70% increase as compared to a slight decrease) along with consistently lower levels of
insulin sensitivity (43% lower) and beta cell function (25% lower). Those with
intermittent metabolic syndrome also had elevated risk for type 2 diabetes as compared to
those who never had it, though they exhibited less risk than those with persistent
metabolic syndrome. However, these group differences were not entirely evident as
baseline, suggesting the utility of annual measurements of the metabolic syndrome for
assessing diabetes risk.
The results from paper 3 show that an intensive, 16 week, randomized, controlled
intervention had few effects on metabolic syndrome profiles when analyzed by
randomization group. However, when the results were analyzed according to those that
made the suggested changes in their dietary intake, regardless of randomization group,
the participants who increased their fiber intake (average increase of 5.8 grams) had a
decrease in the number of features of the metabolic syndrome (average decrease 1.4
features). When food subgroups were analyzed, the only relationship between changes in
food subgroups and changes in metabolic syndrome features was between fruit intake and
waist circumference. Those who increased fruit intake had an adjusted decrease of 0.3
centimeters as compared to a 3.2 centimeter increase in those who did not increase fruit
intake. Though the association between soluble fiber intake and waist circumference was
not significant, soluble fiber may contribute to this effect given that fruit is rich in soluble
fiber.
When the findings from these three studies are combined, a few overarching
conclusions as well as future research directions emerge. Although we hypothesized that
we would see differences in both fiber and sugar intake between metabolic syndrome
83

groups in paper 1, there was only a difference in soluble fiber and not in sugar
consumption. Similarly, in paper 3, changes in total fiber intake over the course of the
intervention were related to a change in the number of metabolic syndrome features,
whereas changes in sugar intake were not. Furthermore, the relationship between fiber
intake and the metabolic syndrome in both papers 1 and 3 were driven by a relationship
between fiber and waist circumference. These findings are also corroborated by the
results from the previous, secondary analysis with the SANO intervention data which
showed that improvements in fiber intake were related to decreases in BMI and visceral
fat, whereas improvements in sugar intake were related to improvements in insulin
secretion.
124
Furthermore, other studies conducted by our group have shown that sugar
intake is related to insulin secretion and beta cell function, whereas fiber was not
significantly associated with these outcomes or other variables related to insulin/glucose
kinetics.
29, 30
These results suggest that although increasing fiber and decreasing sugar
both constitute an improvement in the quality of carbohydrate intake, changes in these
nutrients produce different physiological outcomes related to reductions in risk for type 2
diabetes. Fiber intake appears to be more directly related to adiposity parameters whereas
sugar intake is more directly related to insulin/glucose kinetics. However, given that we
have shown, both cross-sectionally in the Cruz et al paper
26
as well as longitudinally in
paper 2, that the metabolic syndrome is associated with insulin/glucose kinetics,
independent of adiposity, we would expect to also see relationships between the
metabolic syndrome and sugar consumption.
These effects are difficult to tease apart given that adiposity and insulin/glucose
kinetics are related. Accordingly, in paper 2, we found that participants with persistent
84

metabolic syndrome had the fastest rate of fat mass increase as well as consistently,
though not significantly, higher levels of visceral fat. In additional, apart from these
differences in adiposity, the persistent group had faster increases in insulin response to
oral glucose as well as consistently lower SI and DI. Although the changes in insulin and
glucose kinetics were not accounted for by the changes in adiposity, these changes may
be related. For example, in the discussion of paper 2, we suggest that the increased
insulin response may be driven by increased hepatic insulin resistance, which is caused
by fatty liver. Though we did not measure liver fat in the SOLAR study, visceral fat is
correlated with liver fat,
104
and we have previously shown that in the SOLAR cohort,
persistent pre-diabetes is associated with an increase in visceral fat over time.
52
More
research is needed to understand the dynamic relationships between fiber intake, sugar
intake, and changes in adiposity, insulin/glucose kinetics, and the metabolic syndrome.
This future research and other future directions are explored below.
Future Research
The results from this dissertation identify the need for future research in two
particular arenas. The first need is to investigate the particular mechanisms which may
drive the observed relationship between fiber intake and metabolic health in overweight
Latino youth. Exploring these mechanisms should also involve further research on the
physiological effects of different types of fiber. The second need is to study which public
health strategies may be more effective in facilitating dietary change, specifically
improvements in the quality of carbohydrate consumption, considering that the 16 week
randomized, controlled study did not produce systematic changes in dietary intake as
hypothesized.
85

Mechanisms
A potential mechanism for the association between fiber intake and waist
circumference, which is shown in paper 1 as well as implicated in paper 3, is the
influence of dietary fiber on lipid metabolism. In adults it has been shown that inclusion
of soluble fiber, in particular, in a mixed meal can reduce post-prandial levels of lipids.
14,
79
Soluble fiber forms a viscous solution in the gut which can reduce the rate of lipid
emulsification and lower the extent of fat lipolysis.
106
It has also been shown that soluble
fiber can bind to bile acids and mixed micelle components such as monoacylglycerols
and free fatty acids, which can also lead to delayed intestinal uptake or increased fecal
excretion of lipids.
75, 79
These effects translate to a reduction in postprandial chylomicron
triglyceride circulation.
14, 79
A decrease in postprandial triglyceride levels can then lead to
decreased lipid deposition, specifically in visceral adipose tissue.
127
This decrease in
visceral fat may be reflected in a decrease in waist circumference. Waist circumference is
correlated with visceral fat, as measured by MRI or CT,
17, 108
and waist circumference has
been deemed a valuable, though crude, surrogate measure and has been recommended for
clinical use.
71, 98

A second mechanism which could explain the relationship between fiber intake
and central adiposity pertains to glucose and insulin regulation. Dietary fiber intake has
been shown to lower plasma glucose and insulin peaks.
85
There is evidence to show that
soluble fiber may improve glycemic response by increasing the viscosity of the stomach
and impeding carbohydrate digestion and absorption.
77
In a study of 2,909 healthy black
and white young adults, Ludwig et al found that dietary fiber intake was a significant
predictor of fasting insulin levels, body weight, waist-to-hip ratio, and 2 hour insulin
86

following a glucose challenge. Although the authors did not distinguish between types of
fiber, they found that associations between total dietary fiber consumption and lipid
levels were attenuated by adjusting for fasting insulin levels.
84
In discussing the potential
mechanism driving their findings, Ludwig et al conclude that dietary fiber lowers the
glycemic (and insulinemic) response to a meal. Insulin levels in turn influence appetite as
well as lipid metabolism. However, it has also been shown that increased FFAs reduces
insulin action, and therefore it is also possible that FFAs mediate the relationship between
fiber intake and insulin.
42
Future research is need to better understand the relationships
between fiber intake, insulin and glucose kinetics, and lipid metabolism.
75

In order to study these complex mechanistic relationships, additional analyses
could be conducted with existing data and/or new studies could be designed for more
targeted analysis. Though we do have measures of triglycerides in both the SOLAR and
SANO studies, the samples were collected when participants were fasting. Considering
that lipid levels only remain elevated for 5-8 hours after a fat-containing meal,
33
the
postprandial effects of fiber on triglyceride levels would not likely be captured with a
fasting measure. This is likely why we do not see associations between fiber consumption
and triglyceride levels in papers 1 or 3. In both the SOLAR and SANO studies, FFAs
were also collected at fasting and during the OGTT and VLDL levels were collected at
fasting. Although fasting FFAs (or FFA area under the curve from the OGTT) or fasting
VLDL levels are less likely to be associated with fiber intake than post-prandial levels, as
is the case with triglycerides, it may still be worthwhile to explore this data. Finally,
another marker which could be assessed which would reflect longer-term lipid
87

metabolism would be carotid intima-media thickness, which measures atherosclerosis,
and is collected in the SOLAR study.
In order to study the acute effects of fiber intake on insulin and glucose
parameters as well as on lipid metabolism and visceral fat, it would be useful to conduct a
feeding study with the same target study population. Through this study, different types
of fiber, including soluble, insoluble, and resistant starch, could be evaluated and post-
prandial levels of glucose, insulin, FFAs, triglycerides, and cholesterol could be
measured. In addition, if the feeding study was extended, visceral fat could be measured
by MRI in order to confirm the findings seen in our previous analyses within a more
controlled study design. These studies would allow us to account for not only the type of
fiber consumed but also the amount of fiber, which is important given that the range of
fiber consumed even in the context of the intervention (Paper 3) was limited.
Public Health Strategies to Increase Fiber Intake
The second direction for future research relates to the need to identify effective
public health strategies to increase fiber intake in youth, and particularly in youth at
increased risk for type 2 diabetes such as overweight Latino youth. As was discussed in
Paper 1, our participants consume well below the recommended daily goal for fiber. The
problem of carbohydrate quality is not limited to our study population, however, and is
instead an issue of national, if not global, concern. Refined grains constitute 85% of the
grains consumed in the United States and fiber-rich plant foods included fruits and
vegetables have been replaced by novel dietary staples such as processed foods.
22
Barry
Popkin and colleagues have coined the term “the nutrition transition” to describe the
global shifts in dietary patterns that come with development and modernization. They
88

explain that “modern societies seem to be converging on a diet high in saturated fats,
sugar, and refined foods but low in fiber – often termed the “Western diet”.
107

Research has shown that as Latinos become more acculturated in the United States, their
diets change and exhibit characteristics which are associated with metabolic risk. The
National Longitudinal Study of Adolescent Health data revealed that first generation
Mexicans in the United States reported lower intake of cheese and fast foods, and greater
intake of rice, beans, fruits, and vegetables as compared to second generation Mexican-
Americans.
53
Not only have the diets of first generation Latinos been shown to be
healthier than second generation Latinos, but also have been found to be favorable to the
diets of their Caucasian counterparts. For example, first generation Latinos ages 12-17
from the 2001 California Health Interview Survey had higher fruit and vegetable
consumption and lower soda consumption than Caucasians, but by the third generation,
the Latinos’ nutrition was poorer in comparison.
4
Considering that acculturation was
measured in both the SANO and SOLAR studies, it would be interesting to explore the
hypothesis that the more acculturated participants eat less dietary fiber.
It is difficult to isolate which elements of life in the United States contribute to the
shift in dietary patterns. Following the logic of the nutrition transition theory, the United
States is more industrialized than most countries in Latin America, which contributes to
dietary norms. In addition, socio-economic status, age, and employment status are
arguably major contributors. For example, in a study which examined food preferences
among Latina women, researchers found that there were a number of factors that were
associated with a preference for fast food. Those who were younger, employed, living in
higher income households, and living in the U.S. for a greater number of years tended to
89

prefer fast food more than those who did not fall into these categories.
8
Although this
particular study found that higher income was positively associated with fast food
preference, there is other evidence which suggests that low income is a risk factor for
poor diet.
41
In order to account for potentially low socio-economic status of our study
participants, in the SANO intervention, we provided each participant with $25 per week
in grocery gift cards in order to assist the families with purchasing high fiber foods.
However, $25 per week is not much, especially for families with multiple children. It
would be interesting to explore relationships between dietary change and socio-economic
status and/or food access, both of which were measured in the SANO study.
Interventions or policy changes which focus on access to fresh, fiber-rich food for
families with low socio-economic status may be the appropriate next step to facilitate
dietary change.
One potential means to make large-scale changes in dietary intake in our study
population is through changes in the school food program. Most of our study participants
attend schools in the Los Angeles Unified School District (LAUSD), the nation’s second
largest school district, where 72% of the students are Latino
82
and 70% of the students are
eligible for free or reduced breakfast and lunch programs.
39
There are currently no
guidelines in place related to the fiber content of the meals. For example, a typical meal
consists of around 650 calories but only approximately 2-4g of fiber. Considering that the
Institute of Medicine recommends the consumption of 14g of total fiber per 1,000
calories, a meal with 655 calories should have about 9g of total fiber.
60
Given these facts,
the school lunch program currently creates a major obstacle for youth who participate in
90

that they are eating the majority of their calories at school but are not receiving quality,
fiber-rich food.
Changing the school food system is challenging considering that it is
fundamentally restricted in terms of the source and variety. Schools depend on funding
from the United States Department of Agriculture (USDA) through the Child Nutrition
and National School Lunch Acts for their nutrition programs. The National School
Program was established under the National School Lunch Act, which was signed by
President Harry Truman in 1946 with the purpose of providing free or inexpensive
lunches to children in need by making use of agricultural surpluses. Despite these
limitations, there have been some positive changes to the guidelines for the school lunch
program with the LAUSD. For example, in 2003 the LAUSD passed an Obesity
Prevention Motion. In this motion, it was resolved that foods sold at school could have no
more than 35% of total calories from fat, no more than 10% of total calories from
saturated fat, no more than 35% added sugar by weight, and no more than 600mg of
sodium per serving.
13
Though this motion was useful in removing soft drinks from the
schools, it did not include guidelines for fiber and also did not stress the importance of
fresh as opposed to processed foods. For example, now baked chips are sold instead of
regular chips, but these new lower fat snack options are still highly processed and low in
fiber. Many advocates for reform in school food service, such as Alice Waters, head of
the Chez Panisse Foundation and a leader in the Slow Food movement, suggest that a
major overhaul of the system is necessary in order to liberate the program from reliance
on subsidies.
128
In addition to recommending a major change in the source of food, many
experts have suggested structural changes which would incorporate experiential
91

education programs, such as school gardens, into school food programs. However,
although school gardens and cooking programs have become popular, even model
programs such as Alice Waters’ Edible Schoolyard in Berkeley, CA has not been
scientifically tested to explore the effects on dietary habits or obesity or obesity-related
metabolic parameters.
Before large-scale changes are implemented in the public school system to
improve school food programs, small pilot programs need to be conducted and rigorously
tested. As an example, in the light of the findings from this dissertation, a study could be
conducted in the LAUSD to test the effects of a high-fiber breakfast program on dietary
intake and associated metabolic health. The study would provide the first theoretically-
based test of a school breakfast intervention in this high risk, yet understudied group. The
program would be novel in two dimensions. First, it would provide a targeted dietary
approach to improve metabolic health, which draws on our former work. Secondly, the
program would incorporate a novel, theoretically based educational component which
uses cooking classes as a form of experiential learning. Though experiential learning has
been established as a valuable tool in other disciplines
73, 112
and though it has been
shown that young adults who cook have healthier diets,
76
no studies have been conducted
to improve diet quality through cooking classes. The results of this study will serve to
inform future prevention programs for Latino youth. The specific aims of the study would
be as follows: 1) To develop a culturally appropriate, high fiber pilot breakfast program
with a culinary education component for middle school or high school students; 2) To
conduct a randomized, controlled feeding study to test the effects of the high fiber
breakfast compared to the standard USDA breakfast program on simple measures of
92

metabolic health such as waist circumference, fasting glucose, and blood pressure; 3) To
evaluate the effects of the experiential culinary education component on food selection
and fiber consumption in a second phase where students have free choice between the
high-fiber breakfast or the USDA breakfast. With novel pilot studies such as this, it
would be possible to test the hypothesis that access to healthier food in a community
setting, combined with novel education approaches, would impact dietary change on a
larger scale.
Strengths and Limitations

Despite the need for future research, there are a number of unique features of this
dissertation. First, the focus on the high-risk, yet understudied population of overweight
Latino youth is a valuable attribute. Second, the use of precise measures of adiposity as
well as insulin and glucose kinetics in paper 2 provides depth to the analyses. Finally, the
overall focus on the metabolic syndrome is useful and practical in a public health context,
as complex tests such as frequently sampled intravenous glucose tolerance tests are not
practical in community settings.
Limitations inherent in this work should be noted as well. The sample sizes used
in the analyses are relatively small and may preclude additional findings. Also the
population is homogenous as all participants are Latinos from Los Angeles County.
Therefore, although Latinos are the largest minority group in the United States, these
findings may not be generalizable to Latinos living in other geographic locations in the
country.

93

Contribution to the Literature
This dissertation makes an important contribution to the literature for the
prevention of type 2 diabetes in overweight Latino youth by 1) identifying soluble fiber
as a potential protective dietary factor for metabolic health; 2) showing that measuring
the metabolic syndrome over 3 annual visits is useful in identifying overweight Latino
youth who are at particularly elevated risk for developing type 2 diabetes; and 3)
suggesting that youth who are able to increase fiber intake over a period of 16 weeks
exhibit improvements in metabolic syndrome profiles.
94

ALPHABETIZED BIBLIOGRAPHY

1. Diagnosis and classification of diabetes mellitus. Diabetes Care. 28 Suppl 1:S37-
42, 2005.

2. The fourth report on the diagnosis, evaluation, and treatment of high blood
pressure in children and adolescents. Pediatrics. 114:555-576, 2004.

3. Adult Treatment Panel III. Executive Summary of The Third Report of The
National Cholesterol Education Program (NCEP) Expert Panel on Detection,
Evaluation, And Treatment of High Blood Cholesterol In Adults. Jama.
285:2486-2497, 2001.

4. Allen, M. L., M. N. Elliott, L. S. Morales, A. L. Diamant, K. Hambarsoomian,
and M. A. Schuster. Adolescent participation in preventive health behaviors,
physical activity, and nutrition: differences across immigrant generations for
Asians and Latinos compared with Whites. Am J Public Health. 97:337-343,
2007.

5. American Diabetes Association. Clinical practice recommendations 2002.
Diabetes Care. 25 Suppl 1:S1-147, 2002.

6. American Diabetes Association. Type 2 diabetes in children and adolescents.
Pediatrics. 105:671-680, 2000.

7. Anderson, J. W. and T. J. Hanna. Impact of nondigestible carbohydrates on serum
lipoproteins and risk for cardiovascular disease. J Nutr. 129:1457S-1466S, 1999.

8. Ayala, G. X., K. Mueller, E. Lopez-Madurga, N. R. Campbell, and J. P. Elder.
Restaurant and food shopping selections among Latino women in Southern
California. J Am Diet Assoc. 105:38-45, 2005.

9. Azadbakht, L., P. Mirmiran, A. Esmaillzadeh, and F. Azizi. Dairy consumption is
inversely associated with the prevalence of the metabolic syndrome in Tehranian
adults. Am J Clin Nutr. 82:523-530, 2005.

10. Berg, C. M., G. Lappas, E. Strandhagen, A. Wolk, K. Toren, A. Rosengren, N.
Aires, D. S. Thelle, and L. Lissner. Food patterns and cardiovascular disease risk
factors: the Swedish INTERGENE research program. Am J Clin Nutr. 88:289-
297, 2008.

11. Buchanan, T. A. (How) can we prevent type 2 diabetes? Diabetes. 56:1502-1507,
2007.

95

12. California Department of Finance. Race/Ethnic Population with Age and Sex
Detail, 1970-2040. Available at:
http://www.californiateenhealth.org/download/lfs_data_tables.doc Accessed
3/19/09.

13. Canter, M. and J. Korenstein. LAUSD Obesity Prevention Motion. Available at:
http://departments.oxy.edu/uepi/cfj/publications/obesity_prevention_motion.pdf
Accessed February 12, 2007, 2003.

14. Cara, L., C. Dubois, P. Borel, M. Armand, M. Senft, H. Portugal, A. M. Pauli, P.
M. Bernard, and D. Lairon. Effects of oat bran, rice bran, wheat fiber, and wheat
germ on postprandial lipemia in healthy adults. Am J Clin Nutr. 55:81-88, 1992.

15. Census Scope. US Population by Race. Available at:
http://www.censusscope.org/us/chart_race.html Accessed September 29, 2000.

16. Centers for Disease Control and Prevention Department of Health and Human
Services National Center for Health Statistics. CDC growth Charts. 2000.

17. Chan, D. C., G. F. Watts, P. H. Barrett, and V. Burke. Waist circumference,
waist-to-hip ratio and body mass index as predictors of adipose tissue
compartments in men. QJM. 96:441-447, 2003.

18. Chen, A. K., C. K. Roberts, and R. J. Barnard. Effect of a short-term diet and
exercise intervention on metabolic syndrome in overweight children. Metabolism.
55:871-878, 2006.

19. Cohen, J. Statistical power analysis for the behavioral sciences. Revised ed. New
York: Academic Press, 1977

20. Cook, S., P. Auinger, C. Li, and E. S. Ford. Metabolic syndrome rates in United
States adolescents, from the National Health and Nutrition Examination Survey,
1999-2002. J Pediatr. 152:165-170, 2008.

21. Cook, S., M. Weitzman, P. Auinger, M. Nguyen, and W. H. Dietz. Prevalence of
a metabolic syndrome phenotype in adolescents: findings from the third National
Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med.
157:821-827, 2003.

22. Cordain, L., S. B. Eaton, A. Sebastian, N. Mann, S. Lindeberg, B. A. Watkins, J.
H. O'Keefe, and J. Brand-Miller. Origins and evolution of the Western diet: health
implications for the 21st century. Am J Clin Nutr. 81:341-354, 2005.

23. Crespo, P. S., J. A. Prieto Perera, F. A. Lodeiro, and L. A. Azuara. Metabolic
syndrome in childhood. Public Health Nutr. 10:1121-1125, 2007.

96

24. Cruz, M. L., R. N. Bergman, and M. I. Goran. Unique effect of visceral fat on
insulin sensitivity in obese Hispanic children with a family history of type 2
diabetes. Diabetes Care. 25:1631-1636, 2002.

25. Cruz, M. L., G. Q. Shaibi, M. J. Weigensberg, D. Spruijt-Metz, G. D. Ball, and
M. I. Goran. Pediatric obesity and insulin resistance: chronic disease risk and
implications for treatment and prevention beyond body weight modification. Annu
Rev Nutr. 25:435-468, 2005.

26. Cruz, M. L., M. J. Weigensberg, T. T. Huang, G. Ball, G. Q. Shaibi, and M. I.
Goran. The metabolic syndrome in overweight Hispanic youth and the role of
insulin sensitivity. J Clin Endocrinol Metab. 89:108-113, 2004.

27. D'Adamo, E., M. Impicciatore, R. Capanna, M. Loredana Marcovecchio, F. G.
Masuccio, F. Chiarelli, and A. A. Mohn. Liver steatosis in obese prepubertal
children: a possible role of insulin resistance. Obesity (Silver Spring). 16:677-683,
2008.

28. Davis, J. N., L. E. Kelly, C. J. Lane, E. E. Ventura, C. E. Byrd-Williams, K. A.
Alexandar, S. Azen, C.-P. Chou, D. Spruijt-Metz, M. J. Weigensberg, and M. I.
Goran. Randomized control trial of a Nutrition Education and Strength Training
Program to Prevent Obesity Related Diseases in Overweight Latino Adolescents.
In press at Obesity, 2008.

29. Davis, J. N., E. E. Ventura, M. J. Weigensberg, G. D. Ball, M. L. Cruz, G. Q.
Shaibi, and M. I. Goran. The relation of sugar intake to beta cell function in
overweight Latino children. Am J Clin Nutr. 82:1004-1010, 2005.

30. Davis, J. N., Ventura, E.E., Shaibi, G.Q., Spruijt-Metz, D., Watanabe, R.M.,
Weigensberg, M.J., Goran, M.I. Reductions in added sugar intake and
improvement in insulin secretion in overweight Latina Adolescents. Met Syn Rel
Dis. . 5:183-193, 2007.

31. Dhingra, R., L. Sullivan, P. F. Jacques, T. J. Wang, C. S. Fox, J. B. Meigs, R. B.
D'Agostino, J. M. Gaziano, and R. S. Vasan. Soft drink consumption and risk of
developing cardiometabolic risk factors and the metabolic syndrome in middle-
aged adults in the community. Circulation. 116:480-488, 2007.

32. Draznin, B. Molecular mechanisms of insulin resistance: serine phosphorylation
of insulin receptor substrate-1 and increased expression of p85alpha: the two sides
of a coin. Diabetes. 55:2392-2397, 2006.

33. Dubois, C., G. Beaumier, C. Juhel, M. Armand, H. Portugal, A. M. Pauli, P.
Borel, C. Latge, and D. Lairon. Effects of graded amounts (0-50 g) of dietary fat
on postprandial lipemia and lipoproteins in normolipidemic adults. Am J Clin
Nutr. 67:31-38, 1998.
97

34. Ebbeling, C. B., M. M. Leidig, K. B. Sinclair, J. P. Hangen, and D. S. Ludwig. A
reduced-glycemic load diet in the treatment of adolescent obesity. Arch Pediatr
Adolesc Med. 157:773-779, 2003.

35. Ebbeling, C. B., M. M. Leidig, K. B. Sinclair, L. G. Seger-Shippee, H. A.
Feldman, and D. S. Ludwig. Effects of an ad libitum low-glycemic load diet on
cardiovascular disease risk factors in obese young adults. Am J Clin Nutr. 81:976-
982, 2005.

36. Eisenmann, J. C., G. J. Welk, E. E. Wickel, and S. N. Blair. Stability of variables
associated with the metabolic syndrome from adolescence to adulthood: the
Aerobics Center Longitudinal Study. Am J Hum Biol. 16:690-696, 2004.

37. Esmaillzadeh, A., P. Mirmiran, and F. Azizi. Whole-grain consumption and the
metabolic syndrome: a favorable association in Tehranian adults. Eur J Clin Nutr.
59:353-362, 2005.

38. Fernandez, J. R., D. T. Redden, A. Pietrobelli, and D. B. Allison. Waist
circumference percentiles in nationally representative samples of African-
American, European-American, and Mexican-American children and adolescents.
J Pediatr. 145:439-444, 2004.

39. Food Research and Action Center. School Breakfast in America's Big Cities.
Available at: http://www.frac.org/pdf/urbanbreakfast07.pdf Accessed 3/20/2009,
2007.

40. Ford, E. S., W. H. Giles, and W. H. Dietz. Prevalence of the metabolic syndrome
among US adults: findings from the third National Health and Nutrition
Examination Survey. Jama. 287:356-359, 2002.

41. Forshee, R. A. and M. L. Storey. Demographics, not beverage consumption, is
associated with diet quality. Int J Food Sci Nutr. 57:494-511, 2006.

42. Frangioudakis, G. and G. J. Cooney. Acute elevation of circulating fatty acids
impairs downstream insulin signalling in rat skeletal muscle in vivo independent
of effects on stress signalling. J Endocrinol. 197:277-285, 2008.

43. Franks, P. W., R. L. Hanson, W. C. Knowler, C. Moffett, G. Enos, A. M. Infante,
J. Krakoff, and H. C. Looker. Childhood predictors of young-onset type 2
diabetes. Diabetes. 56:2964-2972, 2007.

44. Gastaldelli, A., K. Cusi, M. Pettiti, J. Hardies, Y. Miyazaki, R. Berria, E.
Buzzigoli, A. M. Sironi, E. Cersosimo, E. Ferrannini, and R. A. Defronzo.
Relationship between hepatic/visceral fat and hepatic insulin resistance in
nondiabetic and type 2 diabetic subjects. Gastroenterology. 133:496-506, 2007.
98

45. Goodman, E., S. R. Daniels, J. B. Meigs, and L. M. Dolan. Instability in the
diagnosis of metabolic syndrome in adolescents. Circulation. 115:2316-2322,
2007.

46. Goran, M. I., G. D. Ball, and M. L. Cruz. Obesity and risk of type 2 diabetes and
cardiovascular disease in children and adolescents. J Clin Endocrinol Metab.
88:1417-1427, 2003.

47. Goran, M. I., R. N. Bergman, Q. Avila, M. Watkins, G. D. Ball, G. Q. Shaibi, M.
J. Weigensberg, and M. L. Cruz. Impaired glucose tolerance and reduced beta-cell
function in overweight Latino children with a positive family history for type 2
diabetes. J Clin Endocrinol Metab. 89:207-212, 2004.

48. Goran, M. I., R. N. Bergman, M. L. Cruz, and R. Watanabe. Insulin resistance and
associated compensatory responses in African-American and Hispanic children.
Diabetes Care. 25:2184-2190, 2002.

49. Goran, M. I., R. N. Bergman, and B. A. Gower. Influence of total vs. visceral fat
on insulin action and secretion in African American and white children. Obes Res.
9:423-431, 2001.

50. Goran, M. I., K. Coronges, R. N. Bergman, M. L. Cruz, and B. A. Gower.
Influence of family history of type 2 diabetes on insulin sensitivity in prepubertal
children. J Clin Endocrinol Metab. 88:192-195, 2003.

51. Goran, M. I. and B. A. Gower. Longitudinal study on pubertal insulin resistance.
Diabetes. 50:2444-2450, 2001.

52. Goran, M. I., C. Lane, C. Toledo-Corral, and M. J. Weigensberg. Persistence of
pre-diabetes in overweight and obese Hispanic children: association with
progressive insulin resistance, poor beta-cell function, and increasing visceral fat.
Diabetes. 57:3007-3012, 2008.

53. Gordon-Larsen, P., K. M. Harris, D. S. Ward, and B. M. Popkin. Acculturation
and overweight-related behaviors among Hispanic immigrants to the US: the
National Longitudinal Study of Adolescent Health. Soc Sci Med. 57:2023-2034,
2003.

54. Grundy, S. M. A constellation of complications: the metabolic syndrome. Clin
Cornerstone. 7:36-45, 2005.

55. Gungor, N., R. Saad, J. Janosky, and S. Arslanian. Validation of surrogate
estimates of insulin sensitivity and insulin secretion in children and adolescents. J
Pediatr. 144:47-55, 2004.

99

56. Haffner, S. M., H. Miettinen, G. S.P., and S. M.P. Decreased insulin secretion and
increased insulin resistance are independently related tot he 7-year risk of
NIDDM in Mexican-Americans. . Diabetes. 44:1386-1391, 1995.

57. Hays, N. P., R. D. Starling, X. Liu, D. H. Sullivan, T. A. Trappe, J. D. Fluckey,
and W. J. Evans. Effects of an ad libitum low-fat, high-carbohydrate diet on body
weight, body composition, and fat distribution in older men and women. Archives
of Internal Medicine. 164:210-217, 2004.

58. Hickman, T. B., R. R. Briefel, M. D. Carroll, B. M. Rifkind, J. I. Cleeman, K. R.
Maurer, and C. L. Johnson. Distributions and trends of serum lipid levels among
United States children and adolescents ages 4-19 years: data from the Third
National Health and Nutrition Examination Survey. Prev Med. 27:879-890, 1998.

59. Huang, T. T., N. C. Howarth, B. H. Lin, S. B. Roberts, and M. A. McCrory.
Energy intake and meal portions: associations with BMI percentile in U.S.
children. Obes Res. 12:1875-1885, 2004.

60. Institute of Medicine of the National Academies. Dietary reference intakes for
energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids.
2003.

61. Isomaa, B., P. Almgren, T. Tuomi, B. Forsen, K. Lahti, M. Nissen, M. R.
Taskinen, and L. Groop. Cardiovascular morbidity and mortality associated with
the metabolic syndrome. Diabetes Care. 24:683-689, 2001.

62. Johnson, R. K., P. Driscoll, and M. I. Goran. Comparison of multiple-pass 24-
hour recall estimates of energy intake with total energy expenditure determined
by the doubly labeled water method in young children. J Am Diet Assoc. 96:1140-
1144, 1996.

63. Jones, K. L. The dilemma of the metabolic syndrome in children and adolescents:
disease or distraction? Pediatr Diabetes. 7:311-321, 2006.

64. Kahn, R., J. Buse, E. Ferrannini, and M. Stern. The metabolic syndrome: time for
a critical appraisal: joint statement from the American Diabetes Association and
the European Association for the Study of Diabetes. Diabetes Care. 28:2289-
2304, 2005.

65. Kahn, S. E., R. L. Hull, and K. M. Utzschneider. Mechanisms linking obesity to
insulin resistance and type 2 diabetes. Nature. 444:840-846, 2006.

66. Kang, H. S., B. Gutin, P. Barbeau, S. Owens, C. R. Lemmon, J. Allison, M. S.
Litaker, and N. A. Le. Physical training improves insulin resistance syndrome
markers in obese adolescents. Med Sci Sports Exerc. 34:1920-1927, 2002.

100

67. Katzmarzyk, P. T., L. Perusse, R. M. Malina, J. Bergeron, J. P. Despres, and C.
Bouchard. Stability of indicators of the metabolic syndrome from childhood and
adolescence to young adulthood: the Quebec Family Study. J Clin Epidemiol.
54:190-195, 2001.

68. Kelishadi, R., M. M. Gouya, K. Adeli, G. Ardalan, R. Gheiratmand, R.
Majdzadeh, M. S. Mahmoud-Arabi, A. Delavari, M. M. Riazi, H. Barekati, M.
Motaghian, K. Shariatinejad, and R. Heshmat. Factors associated with the
metabolic syndrome in a national sample of youths: CASPIAN Study. Nutr Metab
Cardiovasc Dis. 18:461-470, 2008.

69. Kim, J. A., S. M. Kim, J. S. Lee, H. J. Oh, J. H. Han, Y. Song, H. Joung, and H.
S. Park. Dietary patterns and the metabolic syndrome in Korean adolescents: 2001
Korean National Health and Nutrition Survey. Diabetes Care. 30:1904-1905,
2007.

70. Kimm, S. Y., B. A. Barton, E. Obarzanek, R. P. McMahon, Z. I. Sabry, M. A.
Waclawiw, G. B. Schreiber, J. A. Morrison, S. Similo, and S. R. Daniels. Racial
divergence in adiposity during adolescence: The NHLBI Growth and Health
Study. Pediatrics. 107:E34, 2001.

71. Klein, S., D. B. Allison, S. B. Heymsfield, D. E. Kelley, R. L. Leibel, C. Nonas,
and R. Kahn. Waist Circumference and Cardiometabolic Risk: a Consensus
Statement from Shaping America's Health: Association for Weight Management
and Obesity Prevention; NAASO, the Obesity Society; the American Society for
Nutrition; and the American Diabetes Association. Obesity (Silver Spring).
15:1061-1067, 2007.

72. Kohler, I. V. and B. J. Soldo. Childhood predictors of late-life diabetes: the case
of Mexico. Soc Biol. 52:112-131, 2005.

73. Kolb, D. A. and R. Fry. Toward an applied theory of experiential learning. In:
Theories of Group Process. C. Cooper (Ed.) London: John Wiley, 1975.

74. Kotronen, A., S. Vehkavaara, A. Seppala-Lindroos, R. Bergholm, and H. Yki-
Jarvinen. Effect of liver fat on insulin clearance. Am J Physiol Endocrinol Metab.
293:E1709-1715, 2007.

75. Lairon, D., B. Play, and D. Jourdheuil-Rahmani. Digestible and indigestible
carbohydrates: interactions with postprandial lipid metabolism. J Nutr Biochem.
18:217-227, 2007.

76. Larson, N. I., C. L. Perry, M. Story, and D. Neumark-Sztainer. Food preparation
by young adults is associated with better diet quality. J Am Diet Assoc. 106:2001-
2007, 2006.

101

77. Leclere, C. J., M. Champ, J. Boillot, G. Guille, G. Lecannu, C. Molis, F. Bornet,
M. Krempf, J. Delort-Laval, and J. P. Galmiche. Role of viscous guar gums in
lowering the glycemic response after a solid meal. Am J Clin Nutr. 59:914-921,
1994.

78. Lee, J. M., M. J. Okumura, M. M. Davis, W. H. Herman, and J. G. Gurney.
Prevalence and determinants of insulin resistance among U.S. adolescents: a
population-based study. Diabetes Care. 29:2427-2432, 2006.

79. Lia, A., H. Andersson, N. Mekki, C. Juhel, M. Senft, and D. Lairon. Postprandial
lipemia in relation to sterol and fat excretion in ileostomy subjects given oat-bran
and wheat test meals. Am J Clin Nutr. 66:357-365, 1997.

80. Liese, A. D., R. B. D'Agostino, Jr., R. F. Hamman, P. D. Kilgo, J. M. Lawrence,
L. L. Liu, B. Loots, B. Linder, S. Marcovina, B. Rodriguez, D. Standiford, and D.
E. Williams. The burden of diabetes mellitus among US youth: prevalence
estimates from the SEARCH for Diabetes in Youth Study. Pediatrics. 118:1510-
1518, 2006.

81. Lorenzo, C., M. Okoloise, K. Williams, M. P. Stern, and S. M. Haffner. The
metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study.
Diabetes Care. 26:3153-3159, 2003.

82. Los Angeles Unified School District. District Profiles. Available at:
http://search.lausd.k12.ca.us/cgi-bin/fccgi.exe?w3exec=PROFILE0 Accessed
12/07/2008.

83. Love-Osborne, K. A., K. J. Nadeau, J. Sheeder, L. Z. Fenton, and P. Zeitler.
Presence of the metabolic syndrome in obese adolescents predicts impaired
glucose tolerance and nonalcoholic fatty liver disease. J Adolesc Health. 42:543-
548, 2008.

84. Ludwig, D., M. Pereira, C. Kroenke, J. Hilner, L. Van Horn, M. Slattery, and D.
Jacobs. Dietary fiber, weight gain, and cardiovascular disease risk factors in
young adults. JAMA. 282:1539-1546, 1999.

85. Lundin, E. A., J. X. Zhang, D. Lairon, P. Tidehag, P. Aman, H. Adlercreutz, and
G. Hallmans. Effects of meal frequency and high-fibre rye-bread diet on glucose
and lipid metabolism and ileal excretion of energy and sterols in ileostomy
subjects. Eur J Clin Nutr. 58:1410-1419, 2004.

86. Lutsey, P. L., L. M. Steffen, and J. Stevens. Dietary intake and the development
of the metabolic syndrome: the Atherosclerosis Risk in Communities study.
Circulation. 117:754-761, 2008.

102

87. Marshall, W. A. and J. M. Tanner. Variations in pattern of pubertal changes in
girls. Arch Dis Child. 44:291-303, 1969.

88. Marshall, W. A. and J. M. Tanner. Variations in the pattern of pubertal changes in
boys. Arch Dis Child. 45:13-23, 1970.

89. McClain, A., J. Davis, S. Nguyen-Rodriguez, Y. Hsu, B. Belcher, E. Ventura, C.
Lane, M. Weigensberg, M. Goran, and D. Spruijt-Metz. Intrinsic Motivation
Predicts Fiber and Fat Intake in Latina Youth. In preparation, 2009.

90. McKeown, N. M., J. B. Meigs, S. Lui, E. Saltzman, P. W. F. Wilson, and P. F.
Jacques. Carbohydrate nutrition, insulin resistance, and the prevalence of the
metabolic syndrome in the Framingham Offspring Cohort. Diabetes Care.
27:538-546, 2004.

91. Merrick, J., L. Birnbaum, I. Kandel, and M. Morad. Obesity and adolescence. A
public health concern. Int J Adolesc Med Health. 16:387-388, 2004.

92. Millen, B. E., M. J. Pencina, R. W. Kimokoti, L. Zhu, J. B. Meigs, J. M. Ordovas,
and R. B. D'Agostino. Nutritional risk and the metabolic syndrome in women:
opportunities for preventive intervention from the Framingham Nutrition Study.
Am J Clin Nutr. 84:434-441, 2006.

93. Misra, A., L. Khurana, S. Isharwal, and S. Bhardwaj. South Asian diets and
insulin resistance. Br J Nutr:1-9, 2008.

94. Monzavi, R., D. Dreimane, M. E. Geffner, S. Braun, B. Conrad, M. Klier, and F.
R. Kaufman. Improvement in risk factors for metabolic syndrome and insulin
resistance in overweight youth who are treated with lifestyle intervention.
Pediatrics. 117:e1111-1118, 2006.

95. Morrison, J. A., L. A. Friedman, P. Wang, and C. J. Glueck. Metabolic syndrome
in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to
30 years later. J Pediatr. 152:201-206, 2008.

96. National Heart Lung and Blood Institute. Epidemiologic Research in Hispanic
Populations Opportunities, Barriers and Solutions. Bethesda, MD, 2003.

97. National Institutes of Health. Do you know the health risks of being overweight?
Available at: http://www.win.niddk.nih.gov/publications/health_risks.htm
Accessed September 28, 2004.

98. Ness-Abramof, R. and C. M. Apovian. Waist circumference measurement in
clinical practice. Nutr Clin Pract. 23:397-404, 2008.

103

99. Ogden, C. L., M. D. Carroll, L. R. Curtin, M. A. McDowell, C. J. Tabak, and K.
M. Flegal. Prevalence of overweight and obesity in the United States, 1999-2004.
Jama. 295:1549-1555, 2006.

100. Ogden, C. L., M. D. Carroll, and K. M. Flegal. High body mass index for age
among US children and adolescents, 2003-2006. Jama. 299:2401-2405, 2008.

101. Ostman, J., P. Arner, P. Engfeldt, and L. Kager. Regional differences in the
control of lipolysis in human adipose tissue. Metabolism. 28:1198-1205, 1979.

102. Pan, Y. and C. A. Pratt. Metabolic syndrome and its association with diet and
physical activity in US adolescents. J Am Diet Assoc. 108:276-286; discussion
286, 2008.

103. Panagiotakos, D. B., C. Pitsavos, Y. Skoumas, and C. Stefanadis. The association
between food patterns and the metabolic syndrome using principal components
analysis: The ATTICA Study. J Am Diet Assoc. 107:979-987; quiz 997, 2007.

104. Park, B. J., Y. J. Kim, D. H. Kim, W. Kim, Y. J. Jung, J. H. Yoon, C. Y. Kim, Y.
M. Cho, S. H. Kim, K. B. Lee, J. J. Jang, and H. S. Lee. Visceral adipose tissue
area is an independent risk factor for hepatic steatosis. J Gastroenterol Hepatol.
23:900-907, 2008.

105. Park, T. G., H. R. Hong, J. Lee, and H. S. Kang. Lifestyle plus exercise
intervention improves metabolic syndrome markers without change in adiponectin
in obese girls. Ann Nutr Metab. 51:197-203, 2007.

106. Pasquier, B., M. Armand, C. Castelain, F. Guillon, P. Borel, H. Lafont, and D.
Lairon. Emulsification and lipolysis of triacylglycerols are altered by viscous
soluble dietary fibres in acidic gastric medium in vitro. Biochem J. 314 ( Pt
1):269-275, 1996.

107. Popkin, B. M. and P. Gordon-Larsen. The nutrition transition: worldwide obesity
dynamics and their determinants. Int J Obes Relat Metab Disord. 28 Suppl 3:S2-
9, 2004.

108. Pouliot, M. C., J. P. Despres, S. Lemieux, S. Moorjani, C. Bouchard, A.
Tremblay, A. Nadeau, and P. J. Lupien. Waist circumference and abdominal
sagittal diameter: best simple anthropometric indexes of abdominal visceral
adipose tissue accumulation and related cardiovascular risk in men and women.
Am J Cardiol. 73:460-468, 1994.

109. Reaven, G. M. Banting lecture 1988. Role of insulin resistance in human disease.
Diabetes. 37:1595-1607, 1988.

104

110. Reaven, G. M. Role of insulin resistance in human disease (syndrome X): an
expanded definition. Annu Rev Med. 44:121-131, 1993.

111. Riley, M. R., N. M. Bass, P. Rosenthal, and R. B. Merriman. Underdiagnosis of
pediatric obesity and underscreening for fatty liver disease and metabolic
syndrome by pediatricians and pediatric subspecialists. J Pediatr. 147:839-842,
2005.

112. Rogers C.R. and H. J. Freiberg. Freedom to Learn. 3 ed. Columbus, OH:
Merrill/Macmillan, 1994

113. Schwimmer, J. B., P. E. Pardee, J. E. Lavine, A. K. Blumkin, and S. Cook.
Cardiovascular risk factors and the metabolic syndrome in pediatric nonalcoholic
fatty liver disease. Circulation. 118:277-283, 2008.

114. Shaibi, G. Q., M. L. Cruz, G. D. Ball, M. J. Weigensberg, H. A. Kobaissi, G. J.
Salem, and M. I. Goran. Cardiovascular fitness and the metabolic syndrome in
overweight latino youths. Med Sci Sports Exerc. 37:922-928, 2005.

115. Shaibi, G. Q., M. L. Cruz, M. J. Weigensberg, C. M. Toledo-Corral, C. J. Lane, L.
A. Kelly, J. N. Davis, C. Koebnick, E. E. Ventura, C. K. Roberts, and M. I.
Goran. Adiponectin Independently Predicts Metabolic Syndrome in Overweight
Latino Youth. J Clin Endocrinol Metab, 2007.

116. Shaibi, G. Q. and M. I. Goran. Examining metabolic syndrome definitions in
overweight Hispanic youth: a focus on insulin resistance. J Pediatr. 152:171-176,
2008.

117. Slavin, J. L. Dietary fiber and body weight. Nutrition. 21:411-418, 2005.

118. Sonnenberg, L., M. Pencina, R. Kimokoti, P. Quatromoni, B. H. Nam, R.
D'Agostino, J. B. Meigs, J. Ordovas, M. Cobain, and B. Millen. Dietary patterns
and the metabolic syndrome in obese and non-obese Framingham women. Obes
Res. 13:153-162, 2005.

119. Sparks, J. D. and C. E. Sparks. Insulin regulation of triacylglycerol-rich
lipoprotein synthesis and secretion. Biochim Biophys Acta. 1215:9-32, 1994.

120. Steffen, L., D. Jacobs, J. Stevens, E. Shahar, T. Carithers, and A. Folsom.
Associations of whole-grain, refined grain, and fruit and vegetable consumption
with risks of all-cause mortality and incident coronary artery disease and ischemic
stroke: the Atherosclerosis Risk in Communities (ARIC) Study. American
Journal of Clinical Nutrition. 78:383-390, 2003.

121. Tanner, J. M. Growth and maturation during adolescence. Nutr Rev. 39:43-55,
1981.
105

122. University of Rochester Medical Center. Science Daily. Available at:
http://www.sciencedaily.com/releases/2008/03/080313124430.htm Accessed
6/29/2008, 2008.

123. Ventura, A. K., E. Loken, and L. L. Birch. Risk profiles for metabolic syndrome
in a nonclinical sample of adolescent girls. Pediatrics. 118:2434-2442, 2006.

124. Ventura, E., J. Davis, C. Byrd-Williams, K. Alexandar, A. McClain, C. Lane, D.
Spruijt-Metz, M. Weigensberg, and M. Goran. Reduction in risk factors for type 2
diabetes in response to a low-sugar, high-fiber dietary intervention in overweight
Latino adolescents. Arch Pediatr Adolesc Med. In press, 2008.

125. Ventura, E. E., J. N. Davis, K. E. Alexander, G. Q. Shaibi, W. Lee, C. E. Byrd-
Williams, C. M. Toledo-Corral, C. J. Lane, L. A. Kelly, M. J. Weigensberg, and
M. I. Goran. Dietary intake and the metabolic syndrome in overweight Latino
children. J Am Diet Assoc. 108:1355-1359, 2008.

126. Ventura, E. E., C. J. Lane, M. J. Weigensberg, C. M. Toledo-Corral, J. N. Davis,
and M. I. Goran. Persistence of the metabolic syndrome over 3 annual visits in
overweight Latino children: Association with progressive risk for type 2 diabetes.
In review, 2008.

127. Votruba, S. B. and M. D. Jensen. Regional fat deposition as a factor in FFA
metabolism. Annu Rev Nutr. 27:149-163, 2007.

128. Waters, A. and K. Heron. No Lunch Left Behind. New York Times. 2/19/2009,
2009.

129. Weigensberg, M. J., G. D. Ball, G. Q. Shaibi, M. L. Cruz, and M. I. Goran.
Decreased beta-cell function in overweight Latino children with impaired fasting
glucose. Diabetes Care. 28:2519-2524, 2005.

130. Weiss, R., J. Dziura, T. S. Burgert, W. V. Tamborlane, S. E. Taksali, C. W.
Yeckel, K. Allen, M. Lopes, M. Savoye, J. Morrison, R. S. Sherwin, and S.
Caprio. Obesity and the metabolic syndrome in children and adolescents. N Engl J
Med. 350:2362-2374, 2004.

131. Weyer, C., P. A. Tataranni, C. Bogardus, and R. E. Pratley. Insulin resistance and
insulin secretory dysfunction are independent predictors of worsening of glucose
tolerance during each stage of type 2 diabetes development. Diabetes Care.
24:89-94, 2001.

132. WHO. Diet, nutrition and the prevention of chronic diseases. Geneva, World
Health Organization. 2003.

106

107

133. Williams, D. E., A. T. Prevost, M. J. Whichelow, B. D. Cox, N. E. Day, and N. J.
Wareham. A cross-sectional study of dietary patterns with glucose intolerance and
other features of the metabolic syndrome. Br J Nutr. 83:257-266, 2000.

134. Wirfalt, E., B. Hedblad, B. Gullberg, I. Mattisson, C. Andren, U. Rosander, L.
Janzon, and G. Berglund. Food patterns and components of the metabolic
syndrome in men and women: a cross-sectional study within the Malmo Diet and
Cancer cohort. Am J Epidemiol. 154:1150-1159, 2001.

Abstract (if available)

Abstract One third of overweight Latino youth have the metabolic syndrome, a clustering of risk factors for diabetes and cardiovascular disease. The objectives of this dissertation were:1) to examine the association between dietary intake and the metabolic syndrome with a focus on the quality of carbohydrate intake

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Subclinical atherosclerosis in overweight Latino youth: influence of cardiometabolic risk factors

PDF

Motivation and the meanings of health behavior as factors associated with eating behavior in Latino youth

PDF

Molecular mechanisms of young-onset type 2 diabetes: integration of diet and multi-omics biomarkers

PDF

The vicious cycle of inactivity, obesity, and metabolic health consequences in at-risk pediatric populations

PDF

Quantity versus quality: how adipose tissue accumulation and immune cell profile associate with risk for type 2 diabetes in minority children and adults

PDF

Elevated fasting free fatty acids in overweight Latino children and adolescents

PDF

The physiologic and pathophysiologic role of sympathetic nervous system induced pulsatile lipolysis in metabolism

PDF

Dietary carcinogens and genetic variation in their metabolism: epidemiological studies on the risk of selected cancers

PDF

The longitudinal risk factors of diabetic retinopathy: the Los Angeles Latino Eye Study

PDF

An examination of the association between spousal support and type 2 diabetes self-management

PDF

Air pollution and childhood obesity

PDF

The associations between ultra-processed food consumption and type 2 diabetes and obesity among young adults

PDF

Renin-angiotensin system modulation for the prevention and treatment of metabolic dysfunction

PDF

Investigating a physiological pathway for the effect of guided imagery on insulin resistance

PDF

Diet quality and pancreatic cancer incidence in the multiethnic cohort

Asset Metadata

Creator Ventura, Emily Elizabeth (author)

Core Title The metabolic syndrome in overweight Latino youth: influence of dietary intake and associated risk for Type 2 diabetes

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Publication Date 05/04/2009

Defense Date 03/03/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Diabetes,diet,metabolic syndrome,Nutrition,OAI-PMH Harvest,overweight,Youth

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Goran, Michael I. (committee chair), Azen, Stanley Paul (committee member), Davis, Jaimie (committee member), Khoo, Michael C.K. (committee member), Weigensberg, Marc J. (committee member)

Creator Email emilyventura@gmail.com,eventura@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2168

Unique identifier UC1437231

Identifier etd-Ventura-2708 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-223903 (legacy record id),usctheses-m2168 (legacy record id)

Legacy Identifier etd-Ventura-2708.pdf

Dmrecord 223903

Document Type Dissertation

Rights Ventura, Emily Elizabeth

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu