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This is to certify that the
thesis entitled

DIETARY PREDICTORS OF COMPONENTS OF THE INSULIN-
LIKE GROWTH FACTOR SYSTEM IN ADOLESCENT GIRLS,
AGED 14-18 YEARS: RESULTS FROM THE DIETARY
INTERVENTION STUDY IN CHILDREN

presented by

Jean M. Kerver

has been accepted towards fulfillment
of the requirements for the

Master of degree in Epidemiology

 

 

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5/08 K:/Prolecc&Pres/ClRC/DateDue.indd

DIETARY PREDICTORS OF COMPONENTS OF THE INSULIN-LIKE GROWTH
FACTOR SYSTEM IN ADOLESCENT GIRLS, AGED 14-18 YEARS: RESULTS
FROM THE DIETARY INTERVENTION STUDY IN CHILDREN
By

Jean M. Kerver

A THESIS

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

MASTER OF SCIENCE
Epidemiology

2010

ABSTRACT
DIETARY PREDICTORS OF COMPONENTS OF THE IN SULIN-LIKE GROWTH
FACTOR SYSTEM IN ADOLESCENT GIRLS, AGED 14-18 YEARS: RESULTS
FROM THE DIETARY INTERVENTION STUDY IN CHILDREN
By
Jean M. Kerver

Evidence suggests that diet may affect risk for chronic diseases through its effect on the
insulin-like grth factor (IGF) system. While dietary factors have been associated with
the IGF system in adults, there is limited information available to describe dietary
correlates of IGF-related biomarkers in healthy children. The objective of this thesis was
to explore associations between dietary intake and IGF-1, IGF binding protein (BP)-l ,
lGFBP-3, and the IGF-I/IGFBP-3 molar ratio in post-pubertal girls (aged 14-18 y) who
participated in the Dietary Intervention Study in Children, a multicenter randomized
controlled trial (n=159). Cross-sectional associations between dietary intake (total fat,
protein, carbohydrate, animal and vegetable protein, lactose, fiber, calcium, zinc, and
sodium) and the IGF-related biomarkers were assessed using correlation and multivariate
linear regression analyses. It was hypothesized that animal protein, lactose, and calcium
intake would be associated with increased levels of IGF-I, IGFBP-3, and the IGF-
I/IGFBP-3 molar ratio. In multivariable analyses, significant predictors of IGF-I were
energy and calcium; of IGFBP-3 was calcium; and of IGFBP-1 were vegetable protein
and BMI for age percentile. This study provides evidence that dietary intake affects IGF-
related biomarkers — particularly increased calcium with IGF-I and IGFBP-3 and

increased vegetable protein with lGFBP-l — and is novel in reporting these associations

in adolescent girls.

TABLE OF CONTENTS

LIST OF TABLES ................................................................................... iv
CHAPTER 1: INTRODUCTION
1.1 Overview ................................................................................. l
1.2 Study Aim ................................................................................ 3
1.3 Organization of thesis .................................................................. 5
CHAPTER 2: BACKGROUND
2.1 Biologic organization of the IGF system ............................................. 6
2.2 Major determinants of the components of the IGF system in children. . . . . ......7
2.2.1 Age, sex, and pubertal maturation .............................................. 7
2.2.2 Diet/nutritional status ........................................................... 10
2.2.2.1 Energy and macronutrients ........................................... 10
2.2.2.2 Micronutrients .......................................................... 13
2.2.2.3 Dairy/milk ............................................................... 13
2.2.3 Physical activity ................................................................. 15
2.2.4 Body Size ......................................................................... 16
2.2.4.1 Height/height velocity ................................................. 16
2.2.4.2 BMl/body composition ................................................ 17
2.2.5 Race/ethnicity ................................................................... 19
CHAPTER 3: METHODS AND RESULTS
3.1 Manuscript for publication ........................................................... 21
3.1.1 Abstract .......................................................................... 21
3.1.2 Introduction ..................................................................... 22
3.1.3 Subjects and methods .......................................................... 24
3.1.3.1 Subjects .................................................................. 24
3.1.3.2 Insulin-Related Biomarkers Study (IRBS) ........................ 25
3.1.3.3 Data collection ............................................................................ 26
3.1.3.4 Diet assessment ......................................................... 27
3.1.3.5 Biochemical analyses .................................................. 27
3.1.3.6 Statistical analyses ..................................................... 28
3.1.4 Results ............................................................................ 30
3.1.5 Discussion ........................................................................ 33
CHAPTER 4: CONCLUSION
4.1 Summary of findings .................................................................. 38
4.2 Public health implications ............................................................ 39
4.3 Plans for related research ............................................................ 39
APPENDIX .......................................................................................... 41
REFERENCES ...................................................................................... 49

iii

LIST OF TABLES

Appendix:

Table 1: Cross-sectional studies on the association between diet and IGF-1, IGFBP-3, or
lGF—I/IGFBP-3 in healthy children by publication year ....................................... 41
Table 2. Sociodemographic characteristics of girls in the DISC/IRBS (n=1 59). . . . . . .43
Table 3. Characteristics of girls in the DISC/IRBS at last visit (N=159) .................... 44
Table 4. Pearson correlations between dietary intakes and IGF-related biomarkers among
girls in the DISC/IRES at last visit ............................................................... 45
Table 5. Adjusted geometric means (95% CI) of IGF -re1ated biomarkers by quartile of
dietary intake among girls in the DISC/IRBS ................................................... 46
Table 6. Major factors predicting IGF-related biomarkers ..................................... 47

iv

CHAPTER 1: INTRODUCTION
1.1 Overview

Abnormal levels of insulin-like growth factor (IGF)-I and its binding proteins are
used in pediatric medicine as indicators of growth hormone deficiency and other specific
disorders (1). However, there is now growing evidence to suggest that IGF-I levels that
fall within normal limits are associated with chronic diseases in adults (2). While
variation within normal limits of IGF-I levels have not been associated with childhood
diseases, IGF-I levels in childhood may track into adulthood. Furthermore, it has been
suggested that the childhood environment may influence levels of components of the IGF
system (3, 4), suggesting that potentially modifiable lifestyle factors during phases of
growth and development could affect lifetime disease risk.

Both high normal and low normal IGF-I levels in adults have been associated
with different chronic diseases (2, 5). High normal plasma IGF-I and IGF binding protein
(IGFBP)-3 concentrations have been suggested as potential markers for increased risk of
breast cancer, prostate, colorectal, and lung cancers in adults (6). It has been postulated
that components of the IGF system may advance cell cycle progression and/or inhibit
apoptosis either directly or indirectly through interactions with other hormones such as
insulin (7). Diet and other lifestyle factors may affect cancer risk through their effects on
serum insulin concentrations and/or levels of components of the IGF system (8).

Furthermore, in contrast to the possible increased risk of certain cancers with high
levels of IGF-I, some evidence implies that low normal serum IGF-I concentrations may
be associated with increased risk of osteoporosis (5), impaired glucose tolerance (9), and

coronary artery disease (10). The IGF system is critically important in normal growth

and development and accordingly, components of the IGF system change considerably
during puberty when children undergo a period of rapid growth (1 1). IGF—I and IGFBP-3
reach a peak during puberty and then begin a slow decline throughout adulthood whereas
IGFBP-l reaches a nadir during puberty. Taken together, the evidence of associations of
adult levels of IGF-I with varied chronic diseases and the fact that the pubertal time
period may set the stage for lifetime levels of components of the IGF system, it is
important to pursue an understanding of the correlates of components of the IGF system
during adolescence.

In adults, a large body of evidence has accumulated tO suggest that specific
dietary components may affect IGF-related biomarkers. The most consistent associations
have been intakes of animal protein, dairy products, and some minerals with IGF-I and
IGFBP-3, although it is difficult to disentangle effects of single minerals from other
nutrients or food groups (12). However, even as it is well-established that severe energy
restriction lowers IGF-I levels and nutrient repletion increases IGF-I levels in grth
retarded children (13), there is very little information on dietary correlates of components
of the IGF system in healthy children. In the few studies conducted in healthy children,
evidence indicates that early childhood and pre-pubertal dietary intakes, especially high
intakes of animal protein and/or dairy intakes, may affect the serum concentration of
IGF-I and IGFBP-3 during childhood (14-17). We are aware of no studies exploring
possible dietary determinants of IGF-related biomarkers during late adolescence in
healthy children, but because the IGF axis regulates growth and development and the
highest rates of IGF-I production occur during the pubertal growth spurt (13), it may be a

critical window of time in affecting lifetime levels of IGF—related biomarkers.

This study utilizes existing data from the Dietary Intervention Study in Children
(DISC) to explore associations between specific dietary constituents and components of
the IGF system in adolescent girls. The DISC was a longitudinal randomized controlled
trial designed to test the safety and efficacy of a cholesterol-lowering diet in children and
is described in detail in Chapter 4 of this thesis. A DISC ancillary study, the Insulin-
Related Biomarkers Study (IRBS) was conducted with sera fiom girls (n=274) making
serum IGF-I, IGFBP-1 and IGFBP-3 data available for analysis.

The goals of this study are to explore the associations between the more subtle
effects of diet on the IGF system in healthy adolescent girls and determine if those
associations are still present after consideration of potential confounding variables.
Toward that end, the purpose of this exploratory study is to examine the effect of
specified dietary exposures on post-pubertal measures of the IGF system in girls who
participated in the DISC/IRBS regardless of study arm assignment. This study will focus
on the dietary correlates of total IGF-I, IGFBP-l , IGFBP-3, and the IGF-I/IGFBP-3
molar ratio.

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The specific aim of this study is to cross-sectionally explore the crude association
between major dietary constituents and serum concentrations of IGF-I, IGFBP-l, IGFBP-
3, and the IGF-I/IGFBP-3 molar ratio in post-pubertal girls (last DISC visit, ages 14-18
y) among girls in the DISC/IRES. Major dietary constituents to be examined include the
macronutrients (i.e. total fat, protein, and carbohydrate as percentages of energy), and
animal and vegetable protein, lactose, fiber, calcium, zinc, and sodium in g or mg/ 1000

kcal.

Hypothesis: Animal protein, lactose, and calcium intake will be associated with
increased levels of IGF -I, IGFBP-3, and the IGF-I/IGFBP-3 molar ratio.

Cross-sectional analyses will be conducted to explore associations between diet
and the system at the last DISC visit (ages 14-17 y). In order to ensure the girls are post-
pubertal at last visit, we will assess the relationship between age and IGF-I at last DISC
visit, as well as the relationship between time since menarche and IGF-I at last DISC
visit. If there is significant variation in IGF-I by age or time since menarche at the last
DISC visit, we will consider limiting the analyses to girls who are at least 2 years post-
menarche at the time of last DISC visit. Crude correlations between each dietary
exposure and each biomarker will be assessed using Pearson correlation analysis. Where
necessary, dietary exposures and biomarker outcomes will be transformed (e. g. to the
natural logarithm) in order to reduce skewness in the distribution and the effect of
extreme outliers will be evaluated using the studentized residuals. Median levels Of each
biomarker within quartiles of each dietary exposure will also be assessed. After
evaluating results from the crude analyses, multivariable linear regression analyses will
be conducted to test the strength of the relationships between dietary exposures and
biomarker outcomes while including potential confounding variables in the model.
Covariates to be considered will include age, race, household income, maternal education
level, physical activity, treatment group, and age at menarche. Sample size permitting,

we will stratify on vitamin/mineral supplement use.

1.3 Organization of thesis

This thesis includes a detailed review of the literature in chapter two. The third
chapter includes the methods and results of the study and is presented as a stand-alone
manuscript to be submitted for publication. The fourth and final chapter includes
conclusions and public health implications of the results. Because of this organizational
format, there will be some redundancy between the third chapter and the rest of the

thesis.

CHAPTER 2: BACKGROUND
2.1 Biologic organization of the IGF system

IGF-I is part of a complex system that is under control of the larger hypothalamic-
pituitary axis. The IGF system is comprised of three ligands (IGF-I, IGF-II, and insulin)
that share peptide homology, three receptors (IGF-I receptor, IGF-II receptor, and insulin
receptor), and six binding proteins (IGFBP-l-6) (18). The synthesis and bioavailability of
IGF-I is primarily regulated by growth hormone (GH) and insulin. IGF-I has autocrine
and paracrine functions as well as the ability to regulate cellular proliferation via
mitogenic actions. These myriad functions may explain how IGF-I could be involved in
multiple and distinct disease processes. IGF-I action is determined by the availability of
IGF-I to interact with the IGF-I receptor, which is dependent on IGF-I concentrations and
also on the relative concentrations of the IGFBPs (13). Total serum IGF-I includes both
free IGF-I and that which is bound to the binding proteins (IGFBP-1 -6) and an acid labile
subunit (ALS) and has been shown to change throughout life in a pattern similar to free
serum IGF-I (19).

Each IGFBP has a different structure and is thought to have different roles in
regulating the action of IGF-I (20). IGFBP-1 only binds a relatively small percentage of
IGF-I but it is regulated by the portal supply of insulin and is thought to regulate IGF-l
bioavailability in relation to energy intake. IGFBP-1 has been shown to inhibit IGF-I
action by limiting the availability of fi'ee IGF-I (21 , 22). IGFBP-1 concentrations vary
rapidly in response to food intake/insulin levels; decreasing with eating and increasing
dramatically under even short conditions of fasting (i.e. during the night) (20).

IGFBP-3 is the binding protein that binds the majority of circulating IGF-I (over

90% in combination with the ALS) and similarly to IGFBP-1, it is thought to inhibit IGF-
I action The IGF-IzlGFBP-3 molar ratio is often estimated in order to get a better
understanding of the relative concentrations of IGF-I and IGFBP-3 levels and has been
proposed as an index of bioavailable IGF-I (23, 24).
2.2 Major determinants of the components of the IGF system in children
2. 2.1 Age, sex, and pubertal maturation

There is an abundance of research on the effects of age, sex, and pubertal
maturation on the IGF system. Most of the published research utilizes cross-sectional
data, with several studies including over 1,000 subjects throughout the lifecycle (25-27).
Although results have been reported from diverse study populations including large
samples from Germany (n=1584) (25), Japan (n=1,075; n=354) (27, 28), Denmark
(n=1,030; n=907) (26, 29), Turkey (n=807) (30), Spain (n=600; n=354; n=121) (31-33),
India (n=434) (34), Thailand (n=260; n=244) (35, 36), Israel (n=217) (37), England
(n=333; n=102) (38, 39), and the United States (n=110) (40), results of the patterns of
components of the IGF system with age, sex, and pubertal maturation have been
remarkably similar among populations even as differences exist in the absolute numbers.

Cross-sectional studies show that total IGF-I levels increase steadily throughout
childhood, with a more pronounced increase during puberty and a peak level between 1 1-
15 years of age, after which time there is a slow decline (23, 43-60). The magnitude of
the increase is on the order of 8-9 times from infancy to midpuberty in those studies that
report data throughout the entire spectrum of childhood (25-28, 37). The peak in IGF-I
generally occurs about 1-2 years earlier in girls than in boys in most (23, 44-52, 54, 55,

57, 59), but not all (25), of the studies in which it was assessed. In the one study where

concentrations of serum IGF-I peaked in both boys and girls at age 15 y, IGF-I levels
were measured by a newer assay technique (automated chemiluminescent) (25).

The earlier peak in IGF-I levels generally seen in girls is consistent with the
earlier pubertal maturation of girls vs. boys (13). Accordingly, IGF-I increases with
increased pubertal maturation and peak levels have been shown in Tanner stages III-V in
girls and Tanner stages IV-V in boys (26, 30, 32, 35, 37, 38, 40, 41). Although overall
there is considerable consistency among all of the many published studies regarding the
increase in IGF-I levels with age, gender, and pubertal maturation, there is also a
considerable amount of variation in serum IGF-I concentrations between children and
with age within the Tanner stages (13). In the early stages of puberty, IGF-I has been
shown to increase with age, while in the later stages of puberty it decreases with age (26).
Additionally, because these attributes change at different rates in different individuals,
the diagnostic cut-offs for grth disorders for an individual must concurrently take into
account age, gender, and pubertal maturation. To this end, reference models (regression)
have been developed to make comparisons of IGF-I, IGFBP-3, and the IGF-I/IGFBP-3
molar ratio possible between children and thus improve the diagnostic utility of IGF-I in
suspected growth disorders (42, 43) but there are no published reports assessing the
reliability and validity of this technique as of yet.

Given the abundance of data from cross-sectional studies of IGF-I, there are
surprisingly few studies with longitudinal data on measures of the IGF-I system in
children. There are only two published studies with longitudinal measures of IGF-I
throughout the pubertal growth spurt and both were focused on growth and height/height

velocity (44, 45). Briefly, both studies reported results on age, sex, and pubertal

maturation similar to the body of cross-sectional literature. An increase in IGF-I
(formerly termed somatomedin-C) with age and pubertal maturation was reported, with
the peak in IGF-I occurring earlier in girls than boys, prior to decreasing to adult levels
(44, 45).

In the largest (n=907) published study to provide reference values of IGFBP-1
levels in children, IGFBP-l decreased with age in prepubertal and pubertal children, with
lowest values during puberty (29). No significant differences in IGFBP-l were found
between males and females (29). In a study designed to assess the correlation between
IGFBP-1 and fasting insulin (n=1 l6; ages 5-48 y), Holly et al. show a decrease in
IGFBP-1 from childhood throughout puberty with multiple regression analyses
attributing these changes more to pubertal status than age (46). Results from Holly et al.
also show no significant difference between males and females (P=0.06), although there
was a tendency for prepubertal boys to have higher peak levels of IGFBP-1 than
prebubertal girls (46).

IGFBP-3 concentrations also increase with age, achieving maximal levels in
puberty (28-30, 32, 34—36, 43). In studies that report IGFBP-3 throughout the entire
spectrum of childhood, IGFBP-3 levels increased almost linearly from infancy to 2-3
times as high in puberty (25, 28, 29, 43). The peak level of IGFBP-3 in adolescence is
similar in boys and girls and occurs between 11-13 y (25, 28, 29).

IGF-l has been shown to increase to relatively higher levels than IGFBP-3 during
puberty in the studies that have measured both, and therefore there is an increased IGF-
I/IGFBP-3 molar ratio (29, 43, 47). In a Japanese population (n=354), the concomitant

finding that free IGF-I increased in parallel to the increase in the IGF-I/IGFBP-3 molar

ratio during puberty supports the hypothesis that the increased IGF-I/IGFBP-3 molar
ratio in puberty reflects an increase in free, biologically active IGF-I. This suggests that
the IGF-I/IGFBP-3 molar ratio may be indicative of active IGF-I (28).
2. 2. 2 Diet/nutritional status

Nutrition is considered to be one of the main regulators of circulating IGF-I (48).
Severe energy, protein, and zinc deficits have all been Shown to decrease serum IGF-I
concentrations, while energy and/or nutrient repletion is able to restore normal serum
IGF-I (49, 50). Until recently, however, the relationship between nutrition and the IGF
system has primarily been focused on disorders of nutrition (e. g. malnutrition, anorexia,
Obesity), rather than normal nutritional states. More recently, however, evidence suggests
that variation in nutrient intakes even within the normal range is associated with variation
in IGF-I levels. The most consistent associations reported are positive associations of
IGF-I with dietary intakes of animal protein, conSumption of milk or dairy products, and
with mineral intakes, including zinc, potassium, calcium, phosphorus, and magnesium
(12). Nearly all of the studies of dietary determinants of components of the IGF system
are in adults with relatively few published studies of normal dietary intakes in healthy
children in relation to components of the IGF system. Table 1 summarizes the cross-
sectional literature on dietary associations with IGF-I, IGFBP-3, and the IGF-I/IGFBP-3
molar ratio in children. To our knowledge, there are no published reports assessing the
relationship between dietary intake and IGFBP-1 in children.

2.2.2.1 Energy and Macronutrients

There have been four cross-sectional reports from three studies of the association

between energy and macronutrient intakes of healthy prepubertal children and IGF-1 (I6,

10

51, 52) and IGFBP-3 (16, 53) with conflicting results. The earliest results were from two
reports of healthy girls (mean age=12.3 i 0.7 y; n=324) by Wilson et al., where diet was
assessed by a semiquantitative food frequency questionnaire (FFQ) (52, 53). These
reports showed no associations between dietary intake (energy, protein, fat, or
carbohydrate) and IGF-1 (52) or IGFBP-3 (53). In agreement with these results is a
recent study of European American (mean age=7.9 :I: 2.0 y; n=29) and Afiican American
(AA) children (mean age=8.3 :t 1.4 y; n=26) in which none of the dietary intake variables
(energy, protein, fat, or carbohydrate; assessed by an average of three 24-hour dietary
recalls) were significantly correlated with IGF-I (51). Because dietary protein intake was
higher in AA children, it was included as an independent predictor of IGF-I in a multiple
regression model along with genetic admixture, fat mass, socioeconomic status, adjusted
energy expenditure, and IGFBP-3, however, the dietary protein intake was not found to
independently predict IGF-I levels.

In contrast to these three reports, cross-sectional data derived from the Avon
Longitudinal Study of Parents and Children (ALSPAC) (ages 7-8 y; n=521) (16), showed
positive associations between IGF-I and dietary intake (assessed by 3-day unweighed
dietary records) of energy-adjusted total protein (r=0.19; P<0.001) and animal protein
(r=0. l 6; P<0.001), (but not vegetable protein), and negative associations with energy-
adjusted total fat (r= —0.12; P<0.01), monounsaturated fat (r= -0.11; P<0.01), and
polyunsaturated fat (r= -0.09; P<0.05). Additionally, IGFBP-3 was positively associated
with energy intake (r= 0.13; P<0.01), and negatively associated with energy-adjusted
total fat (r= -0.09; P<0.05) and monounsaturated fat (r= -0.11; P<0.05). Lastly, the IGF-

I/IGFBP-3 molar ratio was positively associated with energy-adjusted intakes of total

11

protein (r= 0.14; P<0.01) and animal protein (r= 0.13; P<0.01), and negatively associated
with polyunsaturated fat (r= -O.10; P<0.05). Most of these associations remained
consistent whether adjusting for energy, age, and sex only or the additional confounders
of maternal education and housing tenure, BMI and birthweight-for-gestational age (1 6).
While all of these cross-sectional studies were in prepubertal children, the
ALSPAC study differed fiom the other studies in two important respects. First, the
subjects in the studies that found no association between diet and the IGF system (51-53)
included a more heterogeneous subject population in terms of race/ethnicity, which has
Shown to be associated with variation in IGF-I levels (54). A wider variation in study-
specific IGF-I levels may lead to a lower ability to detect subtle associations between
IGF-I concentrations and measures of dietary intake. Secondly, the ALSPAC study is the
only study of the three reported that adjusted for total energy intake. Because total
energy intake reflects to a large extent body size, physical activity, and metabolic
efficiency, the effect of not controlling for energy intake weakens evidence of any
association between nutrient intake and the outcome of interest (i.e. IGF system
components) if the outcome is independent of the factors influencing total energy intake
(55). Furthermore, many of the associations observed in the ALSPAC data are similar to
findings from the literature on adults. Positive associations were found between measures
of the IGF system and energy, protein, and mineral intakes (reviewed below) and
negative associations with fat intakes (16). The inverse association between IGF-I and
fat intakes is consistent with associations seen in adults between both high fat intakes and
low IGF-I concentrations and the risk of coronary heart disease. Taken together, these

cross—sectional data suggest that variation in dietary intakes, even within normal ranges,

12

may be associated with variation in concentrations of components of the IGF system in
childhood, which may confer risk for chronic diseases in adulthood.

2.2.2.2 Micronutrients

 

The only report of associations between micronutrient intakes and components of
the IGF system in healthy children comes from the cross-sectional analysis of the
ALSPAC study (described previously) (16). In that study, IGF-I was positively
associated with energy-adjusted intakes of zinc (r=0.14; P<0.01), magnesium (r= 0.15;
P<0.01), calcium (r= 0.12; P<0.01), potassium (r= 0.14; P<0.01), phosphorus (r= 0.16;
P<0.001), and the vitamin, folate (r= 0.11; P<0.01). IGFBP-3 had no linear relations
with any of the minerals (or vitamins) studied. The IGF-I/IGFBP-3 molar ratio was
positively associated with energy-adjusted zinc (r= 0.09; P<0.05), and phosphorus (r=
0.10; P<0.05) intakes (16). Thus variation in IGF-I within a range of normal dietary
intakes in healthy children was shown to be associated with mineral intakes, including
zinc, which is consistent with observations in adults (12).

2.2.2.3 Dairy/Milk

Cow’s milk is a source of animal protein that is associated with a higher
childhood linear growth velocity in developing countries and to a lesser extent in well-
nourished populations (56). Because IGF-I is a growth regulator, and decreased IGF-I
concentrations are associated with deficits in protein intakes, it has been hypothesized
that IGF-I may mediate the positive association between protein and grth (15). There
have been only three cross-sectional studies and two clinical trials studying the effects of
milk or dairy intake on the IGF system in healthy children and all five reflect higher IGF-

I levels with higher milk consumption (14, 15, 57, 58) (l 7).

13

The first cross-sectional study to report effects of milk intake on the IGF system
was in 2.5 y old Danish children (n=90) (15). Serum IGF—I concentrations were
significantly associated with animal protein and milk intakes (P<0.05 for both), but not
vegetable protein or meat intakes even after adjusting for sex and body weight (body
weight was included in the model to control for the influence of body size on dietary
intake, instead of adjusting for energy intake) (15). An increase in milk intake from 200
to 600 mL/d (240 mL=1 Cup) was associated with a 30% higher serum IGF-I
concentration. The same research group recently reported somewhat similar results in a
cross-sectional study of 56 healthy, prepubertal boys (mean age=8.l i 0.1 y) (17). In
multiple linear regression models adjusted for age and BMI, free serum IGF-I was
positively associated with energy-adjusted total protein (P=0.01) calcium (P<0.01) and
milk (P=0.03) intakes, but not with meat, dairy, or plant protein.

The other cross-sectional study to report efi‘ects of milk intake on the IGF system
in healthy children is fiom the ALSPAC cohort (described previously; n=53 8) (58).
Cows’ milk and total dairy products (which included milk) were both positively
associated with IGF-l (P<0.05 for both) and IGFBP-3 (P=0.08, and P=0.07, respectively)
in boys in fully adjusted models (including maternal education, housing tenure, birth
weight and the child’s BMI). There were no significant associations between cows’ milk
or dairy products and the molar ratio IGF-I/IGFBP-3, or between cows’ milk or dairy
products and any measure of the IGF system in girls.

Two clinical trials of milk consumption in children verified the positive
associations seen between milk consumption and serum IGF-I concentrations in cross-

sectional data. A one week intervention study was conducted in 8 yr old Danish boys

14

(n=12 with increased milk consumption; n=12 with increased low-fat meat consumption)
(14). The milk intervention was significantly associated with a 19% increase in IGF-I
(P=0.001) and a 13% increase in the IGF-I/IGFBP-3 ratio (P>0.0001), whereas the meat
group showed no significant increases in either IGF-I or IGFBP-3. These results suggest
that milk consumption and not simply high protein consumption, affects the IGF system,
at least in the Short-term. In a longer randomized controlled trial with English adolescent
girls (n=82), where the experimental group received one pint (568 mL or ~16 oz) of milk
(fat content chosen by subject) daily for 18 months and the control group was asked to
continue their usual milk intake, IGF-I increased significantly more in the experimental
group compared with the control group (35% vs. 25%; P=0.02) (57).

Altogether, the few data available regarding associations between measures of
dietary intake and measures Of the IGF system in healthy children indicate that variation
in normal dietary intakes may be associated with levels of IGF-I and possibly IGFBP-3 in
healthy children.

2. 2.3 Physical Activity

Most of the studies conducted on the effects of physical activity on the IGF
system during childhood have been small clinical trials conducted to answer the
following questions: 1) is physical fitness associated with the IGF system? And; 2) does
strenuous exercise, such as that experienced by child athletes, have an adverse effect on
pubertal development and growth?

Addressing the first question, two studies conducted in late pubertal adolescent
males (59, 60) report no association between measures of fitness and IGF-1, whereas

three studies in pre- or early pubertal males (61), or girls (both pubertal and pre-pubertal)

15

(62, 63) report positive associations between measures of fitness and IGF-1. More studies
relating measures of fitness to measures Of the IGF system in healthy children will be
needed to address the question Of whether or not the IGF system differs by level of fitness
and these studies may need to include longitudinal measures of the GH-IGF system in
order to fully elucidate the adaptations that may occur in response to physical activity.

Regarding strenuous activity, results are also conflicting. Because the GH-IGF
axis is comprised of growth hormones and other mediators of growth, its role in grth
responses to exercise (e. g. muscle hypertrophy) is widely accepted (64). However, as
noted by Nemet and Cooper (64), results from studies of exercise training in children
have shown that exercise training (5 weeks duration) leads to reductions in IGF-I, instead
of the expected increases (61 -63, 65). Eliakim was the first to hypothesize that initiation
Of an endurance training program may first lead to hormonal adaptations such as
decreased IGF-I that are suggestive of a catabolic state, but at some point an anabolic
rebound will likely occur as suggested by the higher levels of IGF-I seen in fitter
individuals (62). The effect of regular physical activity on measures of the IGF system in
adolescents, if any, is largely unstudied.
2. 2.4 Body size

2.2.4.1 Height/Height Velocity

IGF-I is strongly associated with both height and height velocity. A 2006 study
assessed the longitudinal relation between IGF-I and height velocity in girls (n=309) and
boys (n=359) between 5 and 10 years of age who were participating in the ALSPAC
cohort in southwest England (66). IGF-I was associated with subsequent height velocity

in both girls and boys (P<0.001) and all the associations were only slightly attenuated

l6

after adjusting for maternal education, housing tenure, BMI, and birth weight as potential
confounders. This recent report on prepubertal children is in agreement with earlier
longitudinal studies and a large body of cross-sectional literature (3, 31, 37, 44, 45, 66-
74), although two studies reported no association between IGF-I and height or height
velocity (52, 75).

2.2.4.2 BMI/Body Composition

 

Most of the studies that have assessed the association between BMI or other
measures of body fatness and IGF-I in normal weight children have found a positive
relationship (3, 31, 67, 69, 70, 72, 74, 76). However, IGF-I was unassociated with BMI
in normal weight prepubertal (26, 52) and pubertal children (75) in three cross-sectional
studies. Two of the studies that did find a positive relationship between IGF-I and BMI or
weight for height and age only found it in prepubertal subjects, and not in pubertal
subjects (31, 67), suggesting that discrepancies in the literature may result from various
degrees of statistical control over pubertal stage.

It appears clear that the IGF system is altered by states Of ovemutrition. Of 22
studies exploring associations of obesity with IGF-I or other components of the IGF
system (77-98), 17 were cross-sectional (79-81, 83-95, 97) and 4 were trials that involved
a weight loss component and thus IGF-I was reported before and after an intervention
(78, 82, 96, 98). Of the 14 descriptive studies that assessed IGF-I in relation to BMI (79-
81, 83, 84, 86-92, 94, 97), 9 report higher IGF-I in obese children (79, 81, 83, 86-89, 92,
97), and 5 report no difference in IGF-I by obesity status (80, 84, 90, 91, 94). Of the 9
studies that report higher IGF-l in obese children, two found higher IGFBP-3 in obese

children vs. normal weight controls (81, 88), one found lower IGFBP-3 (79), and the rest

17

did not measure IGFBP-3. All three of the nine studies reporting higher IGF—I in Obese
children that measured IGFBP-1 found lower IGFBP-l with obesity (79, 86, 95).

Among the five studies that found no difference in IGF-I by obesity status, three
found higher IGFBP-3 concentrations among obese subjects (84, 90, 91), while two
found no difference (80, 94). Additionally, four of the five aforementioned studies
measured IGFBP-1 (84, 90, 91 , 94) and all but one reported lower IGFBP-1
concentrations in obese children (90). Two additional studies measured IGFBP-l, but no
other components of the IGF system and found lower IGFBP-1 concentrations in obese
children (85, 93). Thus, the preponderance of the evidence implies that components of the
IGF system in children differ by obesity status.

The results of an altered IGF system in obese children in relation to normal
weight control subjects are consistent in the literature despite enormous variation in
important participant characteristics such as the degree of overweight (definitions for
obesity status vary across studies), boys and girls analyzed together, and varying pubertal
status analyzed together in the same study. Of note is that while age was controlled in
most Of the studies referenced above, most did not control for pubertal stage, which is an
important determinant of components of the IGF system.

The hyperinsulinemia of obesity has long been hypothesized to be the cause of
higher IGF-I levels in obese children (81). Recently, hyperinsulinemia in Obese
prepubertal children has been shown to be positively related to IGF-I and the 173-
oestradiOl/sex hormone-binding globulin (SHBG) ratio and inversely related to SHBG
(83). (95). Furthermore, in response to the apparent suppression of IGFBP-1 in obese

children, Travers, et al. conclude that perhaps insulin-mediated suppression of IGFBP-1

18

in obese prepubertal children increases free IGF-I levels and contributes to somatic
grth early, and in pubertal children, where insulin resistance and grth acceleration
occur simultaneously (95 ). Accordingly, it is likely that the association between obesity
and IGF-1 or other components of the IGF system is confounded by pubertal status.
Clearly, the effects of obesity on the complex hormonal milieu of puberty are not yet
fully elucidated, but with the high prevalence of childhood obesity, this is an area that
deserves further research attention.

An area of particular relevance that has scarcely been researched involves
assessing the effects of weight loss on components of the IGF system. Of the four trials
that involved a weight loss component and thus IGF-I was reported before and after an
intervention (78, 82, 96, 98), the two shortest trials (3 and 6 wks, respectively) (96, 98)
showed significant reductions in IGF-I after weight loss, although the 3 week trial only
showed significant results in boys, but not girls (98). The other two trials differed
considerably in design yet neither showed reductions in IGF-I afier weight loss (78, 82).
Most notably, the former included a 12-week intervention, while the latter reported
results after a 6 and 12 month intervention. These results may imply that the short-term
changes in the IGF system may result from weight loss, but these changes may be
transient, or follow-up of longer than one year is required to see permanent changes.
Clearly, this is an area where more research is needed.

2. 2.5 Race/ethnicity

Circulating IGF-I concentrations have consistently been shown to be higher in

Afiican-American (AA) vs. white children (51, 54, 99-103), while no race/ethnic

differences were seen in the one study that compared IGF-I among Caucasian, Asian and

19

Hispanic girls (52). Interest in determining differences in components of the IGF system
by race/ethnicity is motivated by the desire to explain other physiological differences
seen between African-American and white children. For instance, it is possible that the
differences that have been shown in African American (AA) vs. white children in insulin
sensitivity (lower in AA) (99, 104-106), growth rates and pubertal maturation in girls
(higher and earlier, respectively, in AA) (107-109), and bone mineral density (higher in
AA) (110-112) all may be mediated by the GH-IGF axis.

The relationships between IGF-related biomarkers and race/ethnicity and their
interactions with other correlates of IGF-related biomarkers, such as body size, is an area
that warrants further research attention. A significant interaction has been shown
between race/ethnicity and Obesity status in predicting IGF-I levels in adults (113), but to

our knowledge, this has not yet been studied in children.

20

CHAPTER 3: METHODS AND RESULTS
3.1 Manuscript for publication
3.1.] Abstract
Background: The insulin-like growth factor (IGF) system is associated with adult diet
and chronic disease. Childhood diet may influence chronic disease through its effect on
the IGF system; limited information, however, describes dietary predictors of the IGF
system in adolescents.
Objective: We aimed to examine associations between dietary food intake (fat, protein
[animal and vegetable], carbohydrate, lactose, dietary fiber, calcium, zinc, sodium) and
serum IGF-l, IGFBP-1, IGFBP-3, and the IGF-I/IGFBP-3 molar ratio in adolescent girls
(n=159).
Design: Girls in the Dietary Intervention Study in Children (ages 14-18 y; 0.2-6.3 y post-
menarche) were included. Dietary intake was assessed via three 24-hour dietary recalls.
IGF-related biomarkers were determined with radioimmunoassays. Associations between
dietary intakes and biomarkers were assessed with Pearson correlations and multivariable
linear regression. Dietary intakes and biomarkers were all logarithmically transformed;
thus fl-coefficients represent percentages.
Results: In analyses adjusted for energy, age, and time since menarche, significant
correlations (at P<0.05) were: IGF-I with total protein, lactose, calcium, and sodium;
IGFBP-3 with total fat (inverse), lactose, fiber, and calcium; IGF-I/IGFBP-3 with lactose
and calcium; and IGFBP-1 with vegetable protein. In multivariable analyses, significant

predictors of IGF-I were energy (/3= 0.14, P<0.05) and calcium (6: 0.14, P<0.01); of

21

IGFBP-3 was calcium (6: 0.07, P<0.05); and of IGFBP-1 were vegetable protein (B:
0.49, P<0.05) and BMI for age percentile (6: -0.01, P<0.001).

Conclusions: This study provides evidence that dietary intake affects IGF-related
biomarkers — particularly increased calcium with IGF-I and IGFBP-3 and increased
vegetable protein with IGFBP-1 — and is novel in reporting these associations in
adolescent girls.

3.1.2 Introduction

Increased insulin-like growth factor-I (IGF-I) and IGF binding protein-3 (IGFBP-
3) concentrations have been associated with increased risk of breast, prostate, colorectal,
and lung cancers in adults (6), possibly through advancement of cell cycle progression
and/or inhibition of apoptosis (7). In contrast, some evidence suggests low levels of IGF-I
may be associated with increased risk of osteoporosis (5), impaired glucose tolerance (9),
and coronary artery disease (10). Because the IGF axis regulates growth and development
and the highest rates of IGF-I production occur during the pubertal growth spurt (l3),
adolescence may be a critical window of time in affecting lifetime levels of IGF-related
biomarkers.

IGF-I iS part of a complex system that is under control Of the larger hypothalamic-
pituitary axis. IGF-I action is determined by the availability of IGF-I to interact with the
IGF-I receptor, which is dependent on IGF-I concentrations and also on the relative
concentrations of the IGFBPs (13). Potential mechanisms by which dietary intake may
affect circulating levels of IGF-I include inhibition of hepatic synthesis or indirectly

through effects on IGF BPS (12).

22

IGFBP-3 is the binding protein that binds the majority of circulating IGF-I (over
90%) and it is thought to inhibit IGF-I action. The IGF-I/IGFBP-3 molar ratio is often
estimated to get a better understanding of the relative concentrations of IGF-I and
IGFBP-3 and has been proposed as an index Of bioavailable IGF-I (23, 24). IGFBP-l
only binds a relatively small percentage of IGF-I, but it is regulated by the portal supply
of insulin and is thought to regulate IGF-I bioavailability in relation to energy intake (23,
24).

In adults, positive associations have been identified between intakes of animal
protein, dairy products, and minerals (total or zinc) with IGF-I and IGFBP-3
concentrations, although it is difficult to disentangle effects of these highly correlated
dietary constituents (12). In children, it is well-established that severe energy restriction
lowers IGF-I concentrations and nutrient repletion increases IGF-I concentrations in
growth retarded children (13). Few studies, however, have examined dietary correlates
of components of the IGF system in healthy children. In the four studies we identified,
high intakes of animal protein and/or dairy affected serum concentrations of IGF-I and
IGFBP-3 (14-17). All were cOnducted in early childhood and pre-p‘uberty, prior to peak
IGF-I levels at menarche.

This study utilizes data from the last visit of the Insulin-Related Biomarkers Study
(IRBS), an ancillary study of the Dietary Intervention Study in Children (DISC). The
DISC was a longitudinal, randomized controlled trial designed to test the safety and
efficacy of a reduced fat diet (i.e. reduced total fat, saturated fat, and cholesterol) in
children who were 8-10 years Old at baseline (114). The purpose of the present study was

tO test the hypothesis that the positive associations between intakes of animal protein,

23

calcium, and zinc with the IGF system that have been Observed in adults (12) and
younger children (14-17) are also present in adolescent girls.
3.1.3 Subjects and methods

3.1.3.1 Subjects

Participants are girls who were in the DISC, a dietary intervention study initiated
in 1988 in 8-10 year old children (girls and boys) followed through childhood for a
median duration in the study of 7.0 years. The National Heart, Lung, and Blood Institute
(N HLB 1) sponsored the study to test the efficacy and safety of a diet intervention
designed to reduce serum LDL cholesterol in children. Specific details of the design and
results of the DISC are described elsewhere (114-116). In brief, the DISC was a
multicenter, randomized, controlled clinical trial, wherein 663 children were enrolled in
one of six clinical centers between 1988 and 1990 (Children’s Hospital, New Orleans,
LA; Johns Hopkins University Hospital, Baltimore, MD; Kaiser Perrnanente Center for
Health Research, Portland, OR; University of Medicine and Dentistry of New Jersey,
Newark, NJ; Northwestern University Medical School, Chicago, IL; and University of
Iowa Hospital and Clinics, Iowa City, IA). The study was approved by the institutional
review boards of all participating centers. The protocol was reviewed by an independent
data and safety monitoring committee appointed by the NHLBI. The DISC is registered
in the NIH ClinicalTrials. gov registry (identifier: NCT00000459).

Children were recruited through schools, a health maintenance organization, and
pediatric practices and were eligible if they had serum LDL cholesterol in the 80th-98th
age- and sex-specific percentiles of the Lipid Research Clinics population (117), were at

least in the 5th percentile for height and were in the 5m—90th percentiles for weight-for-

24

height (118). The LDL cholesterol eligibility criteria were established to exclude
children with severe hypercholesterolemia for whom medication might be clinically
indicated. Exclusion criteria included children with medical conditions or medications
that could affect growth or serum cholesterol, behavioral problems, or onset of pubertal
maturation. Children were randomly assigned to receive a dietary intervention or usual
care based on designation from the study coordinating center (Maryland Medical
Research Institute, Baltimore, MD).

3.1.3.2 Insulin-Related Biomarkers Study (IRBS)

A DISC ancillary study, the Insulin-Related Biomarkers Study (IRBS), was
conducted to determine the effects of the DISC dietary intervention, as well as specific
dietary constituents, on serum concentrations of insulin-related biomarkers measured
biennially in adolescent girls. The IRBS included 274 girls with stored serum available
for laboratory determination of IGF-I, IGFBP-l, IGFBP-3, glucose, insulin, and C-
peptide. The present analyses were designed to assess the associations between diet and
IGF-related biomarkers among post-menarcheal girls at the last DISC visit.

Of the 301 girls in the DISC study, 269 attended the last visit. IGF-related
biomarker data were available for 191 Of these girls, 172 of whom also had complete
information on diet. After excluding girls who had missing data on time since menarche
at the last visit (n=8), were pregnant (n=2) or had a serious illness (n=2), there were 160
girls with complete data on diet, menarcheal status, and IGF-1 at the last DISC visit. After
reviewing the sample distributions of all dietary intake variables, and reviewing residual
diagnostics to check for influential observations in regression models, one additional girl

Was excluded due to implausible dietary intake (three day average zinc intake of 84

25

mg/ 1000 kcal) resulting in a final sample of 159 girls. The final sample (n=159) was
compared to the full baseline sample (n=301) in terms of sociodemographic
characteristics and no significant differences (tested by chi-square) were found in race,
income, maternal education, or treatment group.

Because the only dietary intake differences between treatment groups in our study
Of adolescent girls were for saturated fat intake (data not shown), and saturated fat intake
has not been associated with the IGF-system in adults or been hypothesized to be
associated in healthy children, we combined treatment groups in analyses. This study was
approved by the DISC steering committee and the institutional review board of Michigan
State University, East Lansing, MI. All data, except for the IGF-related biomarkers, were
collected by each participating clinical center, prepared for analyses by the DISC
Coordinating Center at the Maryland Medical Research Institute, and provided to
investigators at Michigan State University for analyses.

3.1.3.3 Data collection

Sociodemographic, physical, biochemical, and lifestyle data were collected at
baseline, post-randomization years 1, 3, and 5, and at the last visit. Data were collected
via questionnaire and medical examination by project personnel trained specifically for
the DISC who were blinded to participants’ treatment assignments. Height and weight
were measured at baseline and annually thereafter. Body mass index was calculated as
weight (in kg) divided by height2 (in m). All girls were at Tanner stage 1 at baseline and
Tanner staging was performed to assess sexual maturation annually (119) until Tanner

stage 5 was reached. Date of onset of menses was ascertained annually until menarche.

26

3.1.3.4 Diet a_ssessment

Detailed description Of dietary assessment methodology is provided elsewhere
(15, 20). Because the DISC was a dietary intervention study, particular attention was paid
to the quality of dietary assessment. Dietary intakes were assessed by trained, certified
nutritionists, blinded to study group assignment using 3 nonconsecutive 24-hour dietary
recalls. Nutrient analyses were performed by the Nutrition Coordinating Center, (version
20; University of Minnesota, Minneapolis, MN) (120). Data from the three recalls at each
visit were averaged to estimate mean nutrient intake. Because information about added
salt was not ascertained, reported sodium values reflect intake from food and not total
sodium intake.

3.1.3.5 Biochemical wses

Blood samples were collected by venipuncture after an overnight fast. Blood
samples were kept at room temperature for at least 45 minutes to allow complete clotting
and then serum was separated by centrifugation. Serum was then aliquoted and stored in
glass vials at -80°C until it was analyzed for hormone, lipid, and micronutrient levels for
use in DISC analyses (114, 116). Serum samples for measurement of IGF related
biomarkers reported here were stored for a total of 14-16 years during which time they
were thawed twice under controlled conditions to allow re-aliquoting and each time were
re-frozen immediately at -80°C. IGF related biomarkers have been shown to remain
stable after long-term storage and repeated freeze-thaw cycles (121, 122). Laboratory
measurements for analytes assayed for this study were conducted in the laboratory of Dr.
Cliff J. Rosen at the Maine Center for Osteoporosis Research and Education. IGF-I levels

were determined using the IGF-I (IGFBP-blocked) radioimmunoassay (ALPCO,

27

Windham, NH). Serum IGFBP-1 levels were determined using the Total IGFBP-l
IRMA kit (DSL, Webster, TX). Serum IGFBP-3 levels were determined using the
“Active” IGFBP-3 IRMA kit (DSL, Webster, TX). Samples were assayed with three
randomly inserted laboratory-masked quality control samples included per batch. The
external interassay coefficients of variation for IGF-I, IGFBP-3, and IGFBP-1 were 4%,
3%, and 10%, respectively. The IGF-I/IGFBP-3 molar ratio was estimated as a possible
index Of bioavailable IGF-I.

3 . 1 .3 .6 Statistical analysis

All statistical analyses were performed using SAS software (version 9.1.3; SAS
Institute, Inc, Cary, NC). Descriptive statistics were calculated for age at study entry and
other sociodemographic variables to describe the sample at baseline, and for
anthropometric, physical activity, dietary and biochemical measures at the last DISC visit
to describe the exposures and outcomes Of interest. Associations between dietary intakes
and IGF-1, IGFBP-1, IGFBP-3, and the IGF-I/IGFBP-3 molar ratio were assessed first
with Pearson correlations in exploratory analyses and subsequently with multivariable
linear regression.

Dietary variables examined were energy, total fat, total protein, total
carbohydrate, animal protein, vegetable protein, lactose, dietary fiber, calcium, zinc, and
sodium. These nutrients were selected based on those shown to be associated with IGF-
related biomarkers in the literature (12, 16) and those available in our dataset. All dietary
intakes and IGF-related biomarkers were logarithmically transformed to stabilize
variances and mitigate skewness in distributions. Intake of dietary variables were

adjusted for energy using the multivariate nutrient density approach (55, 123). Because of

28

the known associations between energy and nutrient intake (123) and also between age
and time since menarche and IGF-related biomarkers (29), it was determined a priori that
correlation analyses should be assessed before and after controlling for energy intake, age
and time since menarche. Therefore, crude correlations were assessed, as well as partial
correlations, after controlling for the effect of energy intake, age (in years), and time
since menarche (in years). Additionally, all correlation results were assessed before and
after stratification on vitamin/mineral supplement usage (yes/no) and results were not
appreciably different (data not shown); therefore, further analyses did not consider
vitamin/mineral supplement usage. Geometric mean concentrations of each biomarker
(adjusted for energy, age, and time since menarche) within quartiles of each dietary
exposure were also assessed in order to fully describe the outcome (IGF-related
biomarkers) in relation to the exposure of interest (dietary intake).

Multivariable linear regression analyses were conducted to examine associations
between dietary exposures and biomarker outcomes, while controlling for potential
confounders. To avoid multicollinearity, dietary exposures were entered into the
regression model together only if univariate correlations were less than r= 0.60. Thus,
four dietary variables were not included in the analysis: total protein (% kcal) was
dropped in order to retain animal protein (g/1000 kcal) (r= 0.89); total carbohydrate (%
kcal) was dropped in order to retain total fat (% kcal) (r= - 0.81); fiber (g/1000 kcal) was
dropped in order to retain vegetable protein (g 1000 kcal) (r= 0.72); and lactose (g/1000
kcal) was dropped in order to retain calcium (mg/1000 kcal) (r= 0.71). The decision as to
which dietary variable to drop/retain was subjectively based on the nutrient of greater

interest in the IGF-related literature. Additional regression analyses, including the

29

dropped nutrients, were also conducted to test the significance of these nutrients in the
presence of identified confounders; none were significant and therefore retained in the
final model building process described below.

Initial univariate models included the following dietary variables: energy (kcals),
total fat (% energy), animal protein (g/1000 kcals), vegetable protein (g/1000 kcals),
calcium (mg/1000 kcals), zinc (mg/1000 kcals), and sodium (mg/1000 kcals); and the
following potential confounders: age, time Since menarche, BMI percentile for age,
physical activity (hours of moderate and intense activity/week), maternal education level
(high school or less; some college; college or graduate degree), and treatment group.
Covariates were entered in a multivariable model using a forward regression approach
with P < 0.30 as the initial significance criterion for entry. A parsimonious model was
derived from the forward multivariate model by retaining variables that were then
significant in the presence of other covariates at P < 0.10. An F test was used to assess
significance.

The sample was not large enough to justify stratification by race/ethnicity,
however, because of reported differences in associations between covariates and IGF
related biomarkers by race/ethnicity in girls (54), analyses restricted tO only White girls
(92%) were also conducted. Results were similar for models including all girls vs. only
White girls (data not shown) and therefore analyses including all race/ethnicities
combined are presented.

3.1.4 Results
Sociodemographic characteristics Of participants are presented in Table 2.

Approximately 94% of girls in our sample were between ages 15 and 17 y at last visit

30

with a mean age at menarche of 12.9 :I: 1.1 y. Participants were predominantly White
spanning a broad socioeconomic background based on household income and mother’s
education. Girls in our study sample were equally balanced among treatment and control
groups in the DISC.

Summary statistics for covariates and exposures Of interest for girls in the
DISC/IRBS at the last study visit are shown in Table 3. Mean (:I: SD) statistics are
reported to describe sample distributions. Reported nutrient intakes include a mean
energy intake of 1633 i 492 kcal and total fat of 28.0 i: 6.7 % energy, reflecting estimates
similar to those seen in a nationally representative sample of US female adolescents (age
12-19) (124). Geometric means (95% CI) are Shown for each IGF-related biomarker in
Table 3 and are within published references ranges for adolescent girls (26).

Both the unadjusted and adjusted correlations (Pearson’s r) between nutrients and
IGF-related biomarkers at last visit are shown in Table 4. Adjustment for energy intake,
age, and time since menarche did not substantially change the significance or the strength
of the associations between dietary variables and IGF-1, IGFBP-1, or IGFBP-3. In
unadjusted and adjusted correlation analyses, total protein, lactose, calcium, and sodium
were all positively associated with IGF-I; lactose, fiber and calcium were positively
associated with IGFBP-3 whereas total fat was negatively associated with IGFBP-3
(P<0.05 for all); and vegetable protein was positively associated with IGFBP-1. In
unadjusted correlation analyses of dietary variables and the IGF-I/IGFBP-3 molar ratio,
lactose and calcium were positively associated with the IGF-I/IGFBP-3 molar ratio
(P<0.05 for both), while after adjustment only the association between calcium and the

IGF-I/IGFBP-3 molar ratio remained significant (P<0.05).

31

Table 5 shows geometric mean levels of IGF-related biomarkers by quartiles of
nutrient intake after adjustment for energy intake, age, and time since menarche.
Biomarker values are only shown for those nutrients where the crude or adjusted
association with a measure of the IGF system was significant at P<0.05 in correlation
analysis (i.e. those shown in bold in table 4). Significant linear trends are present in IGF-
1 concentrations across increasing quartiles of total protein, calcium, and sodium intakes.
Similarly, there are significant linear trends in IGFBP-3 concentrations across increasing
quartiles of fiber and calcium intake and across decreasing quartiles of total fat intake. In
parallel analyses, a significant linear trend is seen in the IGF-I/IGFBP-3 molar ratio
across increasing quartiles Of calcium intake.

Results from regression analyses including mutually adjusted dietary factors as
predictors of the four IGF-related biomarkers are presented in Table 6. With IGF-I as the
dependent variable, two dietary variables remained significant in the most parsimonious
model: energy (6:0.14 :h 0.06, P=0.029) and calcium (6:0.14 i 0.05, P=0.007), while for
IGFBP-3 calcium was also Significant (B=0.07 :t 0.03, P=0.011),’but for the IGF-
I/IGFBP-3 molar ratio as the dependent variable, there were no significant dietary
predictors in the final model. Lastly, with IGFBP-1 as the dependent variable, vegetable
protein (B=0.49 i 0.24, P=0.041) and BMI for age percentile (B: -0.01 :i: 0.00, P<0.001)
remained significant in the final model. Because the IGF-related biomarkers and dietary
variables were log-transformed, the B -coefficients are interpretable as percentages. For
example, in the model where IGF-I is the dependent variable and energy and sodium are

held constant, a one percent increase in calcium yields a 0.14% increase in IGF-I (ng/ml).

32

3.1.5 Discussion

This study examined the associations between dietary intakes of specific nutrients
and serum IGF-I, IGFBP-3, the IGF-I/IGFBP-3 molar ratio, and IGFBP-1 in post-
menarcheal girls (aged 14-18 y) who participated in the DISC/IRBS. We found that IGF
related biomarker concentrations were associated with several nutrient intakes in this
group of adolescent girls. Calcium was a significant predictor of both IGF-I and IGFBP-
3 concentrations, whereas vegetable protein and BMI for age percentile were significant
predictors of IGFBP-1 concentrations.

The positive correlations in the present analyses between IGF-I and total protein
and calcium intakes and the negative correlation between IGFBP-3 and total fat are
consistent with results from the ALSPAC analyses in 7-8 year old children (16), as well
as multiple reports in adults (12). Unlike the ALSPAC study (1 6), in our analyses, both
IGFBP-3 and the IGF-I/IGFBP-3 molar ratio were also significantly correlated with
calcium. In other ALSPAC analyses energy-adjusted milk and dairy intake were
significantly and positively associated with IGF-I and positively, though not significantly,
associated with IGFBP-3 concentrations before but not after controlling for protein intake
(58).

Our study differs from the ALSPAC analyses in several ways: our analyses
included post-menarcheal girls, while the ALSPAC data included pre-pubertal (age 7-8
years) girls and boys (analyses adjusted for gender). Perhaps a more salient difference,
however, is in the diet composition between the two cohorts. Although median energy
intakes, total protein, vegetable protein and calcium intakes in girls in the ALSPAC

analyses (1638 kcal, 52 g (13% energy), 22 g, and 738 mg, respectively) were very

33

similar to the DISC girls in these analyses (1603 kcal, 58 g (14% energy), 19 g, and 752
mg, respectively), the diet composition differed markedly in median total fat and animal
protein intakes with 68 g total fat (37% energy) and 31 g animal protein in the ALSPAC
analyses and 50 g total fat (28% energy) and 38 g animal protein in these DISC analyses.
These differences in dietary composition likely reflect different sources of nutrients (e. g.
different sources of protein), which may underlie the differences seen in the relationships
between calcium and IGFBP-3 and the IGF-I/IGFBP-3 molar ratios between the two
cohorts.

In final parsimonious multivariable linear regression models, which account for
all nutrients and potential confounders simultaneously, calcium was a significant dietary
predictor Of both IGF-I and IGFBP-3, although it was not a significant predictor of the
IGF-I/IGFBP-3 molar ratio. These findings for the independent effects of IGF-1 and
IGFBP-3 are consistent with the literature. There have been three cross-sectional studies
and three clinical trials studying the effects of milk or dairy intake on the IGF system in
healthy children (ranging in age from 2-12 y) and all six reflect higher IGF-I levels with
even short term higher milk consumption (14, 15, 17, 57, 58, 125). Although we did not
look at food level data in the present analyses, milk is a major source of both calcium and
protein in the diets of US children. At the time of the DISC study, nationally
representative estimates indicated that milk contributed 43% of the total calcium intake
among females aged 12-18 y and 15% Of the total protein intake (CSFII 1988—1991)
(126). Milk is associated with a higher childhood linear growth velocity in developing
countries and to a lesser extent in well-nourished populations (56). Because IGF-I is a

growth regulator, and decreased IGF-I concentrations are associated with deficits in

34

protein intakes, it has been hypothesized that IGF-I may mediate the positive association
between protein intake and growth (15). Since milk is a significant source of both
calcium and protein, it is difficult to disentangle the effects of one from the other in
observational studies.

Sodium was also a borderline significant predictor of IGF-I (P=0.06), which may
indicate the importance of sodium per se, or may point to the need to consider the
potential effects of dietary patterns on IGF-related biomarkers given salty snacks are a
primary contributor to sodium intake among adolescents (127). As previously noted,
information about added salt was not ascertained, thus reported sodium values reflect
intake from food and not total sodium intake. However, the majority of sodium consumed
by Americans is added during commercial processing and preparation (128). In a
nationally representative sample of US children, 53% of 12-19 year old females never or
rarely added table salt to their foods (128), thus having only sodium from food may bias
estimates slightly, but this is not likely to be a major factor.

In both correlation and multivariable analyses, a significant positive association
was seen between IGFBP-1 and vegetable protein. In addition, BMI for age percentile
was a weak but highly significant negative predictor of IGFBP-1 in regression analyses.
We believe the present study is the first to assess the relationship between IGFBP-1 and
dietary predictors in adolescents. In adults, IGFBP-l has been shown to be positively
associated with energy and carbohydrate intake in adult men (129) and plant lignans in
postmenopausal women (130, 131), and negatively correlated with regular soda intake in
adult men and women (132). Although we did not include carbohydrate in our regression

models because it was highly correlated with fat intake (r= - 0.81), IGFBP-l was not

35

correlated with carbohydrate intake in adjusted correlation analyses (r=0.02, P=0.82).
Our finding of a negative association between BMI for age percentile and IGFBP-1, is
consistent with previous studies in adults (133), but we again did not identify any studies
for comparison in adolescents.

In this study, we make the assumption that three nonconsecutive 24-hour dietary
recalls represents the usual intake of the girls. Three nonconsecutive 24-hour dietary
recalls have been Shown to be a valid estimator of usual intake for nutrients that do not
have a high day to day variation (55). As two of our primary nutrients of interest are
protein and calcium intake, this is likely to be a valid assumption - particularly in
children given milk intake is more consistent from day to day (134). Furthermore, results
of the Observing Protein and Energy Nutrition (OPEN) Study Show that multiple 24-hour
recalls provide better estimates Of energy and protein intakes than a semiquantitative food
frequency questionnaire (135). An additional limitation Of this study is the cross-
sectional design, which prevents any assumption of dietary intake as a causal factor in
observed levels of IGF-related biomarkers. Dietary intake was assessed via multiple 24-
hour dietary recalls around the time of the blood draw [the first recall was obtained
concurrent to the blood draw and the 2 subsequent recalls were obtained within 2 weeks
after the blood draw(l 14)] and thus our assumption is that the dietary assessment
represents the usual intake Of the girls at the time the biomarkers are measured.

A major strength Of this study is that it is the first step in understanding the associations
Of dietary intake with IGF-related biomarkers in healthy post-pubertal adolescent girls.
Because the DISC study collected detailed information on age at menarche—a factor

highly associated with the lifetime peaks and nadir Of IGF-related biomarkers (26)——we

36

can be sure that all girls in these analyses were post-menarcheal. The results presented
here are in agreement with the larger body of literature in adults (12) and the smaller
body of literature in pre-pubertal children (14-16, 136) that suggest IGF-related
biomarkers are associated with several aspects of diet, most notably protein and calcium
intakes. Overall, this study provides support to the speculation that variation in dietary
intakes, even within normal ranges in healthy adolescents, is associated with variation in
concentrations of components of the IGF system. Thus, childhood dietary intake, through
mediation of the IGF system, may affect risk for cancer and other chronic diseases in
adulthood, but longitudinal studies are needed to address the multiple complexities

inherent in lifetime dietary intake and growth.

37

CHAPTER 4: CONCLUSIONS
4.1 Summary of findings

This thesis provides a summary of the published literature of the major
determinants of the components of the IGF system in children and a report of original
analyses of diet and IGF-related biomarkers in adolescent girls. Age, sex, and pubertal
maturation clearly are the major predictors IGF-related biomarkers at any given time
during childhood and therefore studies relating to those characteristics were reviewed
first to put the entire system into some biological context. Secondly, because lifestyle
factors have been shown to be associated with adult levels of IGF-related biomarkers, the
literature pertaining to lifestyle factors in children as those factors relate to the IGF
system was reviewed.

Lifestyle factors reviewed here include dietary intake, physical activity, and the
related measures of body size because body weight and BMI are influenced by dietary
and physical activity behaviors. The literature in children regarding diet is sparse but is
emerging in agreement with results from studies in adults. Namely, the dietary
components most associated with IGF-related biomarkers appear to be protein/animal
protein, dairy intakes and the intakes of some minerals such as calcium, magnesium, and
zinc, which are highly correlated with animal protein and dairy intakes. However, this is
a very young body of literature and there are many hypotheses that have yet to be
explored. The results from the original analyses reported here exploring associations
between diet and IGF-related biomarkers in adolescent girls appears to support the
concept that diet, specifically intakes of nutrients found in meat and/or dairy products

may contribute to the variation in IGF-related biomarkers. The literature on both

38

physical activity and body size in relation to IGF-related biomarkers is also growing and
overall appears to support a role for lifestyle factors in influencing the IGF system.
4.2 Public health implications

Especially because the IGF system has been shown to be associated with different
chronic diseases in adults in different directions [i.e. high IGF-I has been associated with
cancer (6), while low IGF-I has been associated with coronary heart (10) disease and
osteoporosis (5)], it is imperative to carefully think through the public health implications
of this body of work. If the literature were to strongly support effects of lifestyle on IGF-
related biomarkers, for example, would it be prudent to suggest lifestyle modifications
that would likely increase or decrease IGF-I? We must pursue an understanding of
correlates of the IGF system firstly to help understand the etiology of chronic diseases
that manifest themselves in adulthood but may be initiated in adolescence or even earlier
in life. Secondly, once we gain a more complete understanding of the etiology, we may
find intervention strategies that are supportive of good health in general as opposed to
targeting prevention of specific diseases.
4.3 Plans for Related Research

Related research to be completed in the near future will include longitudinal
modeling of the effect of diet on IGF-related biomarkers while stratifying on body size.
Toward that end, I will be developing a system to categorize usual dietary intake of key
nutrients over time to predict IGF-related biomarkers among girls who participated in the
DISC/IRBS. Our first step will be to assess the strength of the relationship between
habitual longitudinal nutrient intake of a few key nutrients on IGF-related biomarkers at

the last DISC visit. Secondly, we will model the entire curve of specific dietary

39

components on IGF-related biomarkers at all available time points from baseline to last
visit. As mentioned, more emphasis will be placed on stratification by body size.

Finally, I have a particular interest in the effects of alcohol use on IGF-related biomarkers
in adolescence, and will pursue an exploratory analysis assessing that relationship among
girls who participated in the DISC/IRBS.

This thesis represents one part of a series of analyses regarding lifecycle impacts
on lifetime health and disease in general, and specifically relating dietary intake to
insulin-related biomarkers in adolescent girls. This work is being carried out under the
direction of Dr. Ellen Velie and will continue with further analyses to more
comprehensively explore dietary intake and body size as predictors of insulin-related
biomarkers (IGF-l, IGFBP-1, IGFBP-3, lGF-I/IGFBP-3, insulin, c-peptide, and glucose).
In addition to the data available to our research team at this time, we will continue to
pursue opportunities to collect and analyze longitudinal data in children, such as that

which may be available from the National Children’s Study.

40

APPENDIX

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Table 2. Sociodemographic characteristics of girls in the DISC/IRBS (n=159)l

 

 

n %2
Race/ethnicity
White 146 92
Black 8
Other 5
Total household income
<$20,000 15 9
$20,000 to <$30,000 25 16
$30,000 to <$50,000 S8 36
$50,000+ 60 38
Mother's education
High school or less 38 24
Some college 33 21
College or graduate degree 74 47
Treatment group
Intervention 76 48
Usual care 83 52

 

1Data presented for girls with complete data on diet, menarche, and IGF-I at last visit

2 . . , .
Percentages may not add up to 100 because of rounding or missmg values

Table 3. Characteristics of girls in the DISC/IRBS at last visit (N =159)l

 

 

 

 

 

 

Mean (SD)
Age (y) 16.6 ( 0.9 )
Time since menarche (y) 3.7 ( 1.2 )
Height (cm) 164.1 ( 6.2 )
Weight (kg) 60.9 ( 11.3 )
BMI 22.6 ( 3.7 )
BMI for age (percentile) 59.8 ( 27.8 )
Physical Activity (hr/wk)2 17.4 ( 12.2 )
Egg/nutrient
Energy (kcal) 1633 ( 492 )
Total fat (% kcal) 28.0 ( 6.7 )
Total protein (% kcal) 14.9 ( 3.2 )
Total carbohydrate (% kcal) 57.8 ( 7.5 )
Animal protein (g) 39.8 ( 17.0 )
Vegetable protein (g) 20.1 ( 7.6 )
Lactose (g) 17.5 ( 12.9 )
Fiber (g) 10.8 ( 4.6 )
Calcium (mg) 852 ( 433 )
Zinc (mg) 7.9 ( 3.5 )
Sodium (mg) 2718 ( 861 )
IGF-related biomarker Geometric mean (95% CI)
IGF-I (ng/mL) 345.5 ( 332.8 , 358.7 )
IGFBP-3 (ng/mL) 5009 ( 4912 , 5109 )
IGF-I/IGFBP-3 molar ratio 0.38 ( 0.37 , 0.39 )
IGFBP-1(ng/mL)3 31.2 ( 27.5 , 35.4)

 

1Data presented for girls with complete data on diet, menarche, and IGF-I at last visit
2Hours of moderate and intense physical activity per week; data missing on 15 girls

3Data missing on 7 girls

45

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47

Table 6. Major factors predicting IGF-related biomarkers
among girls in the DISC/IRBS at last visit assessed by
multiple linear regression analysis

Final Models (n==159)1’2

 

 

 

 

 

 

IGF-I gng/mL) [3 SEE P
Energy intake (kcal) 0.14 ( 0.06 ) 0.029
Calcium (mg/1000 kcal) 0.14 ( 0.05 ) 0.007
Sodium (mg/1000 kcal) 0.17 ( 0.09 ) 0.055
IGFBP-3 (ng/mL) E SE P
Calcium (mg/1000 kcal) 0.07 ( 0.03 ) 0.011
IGFBP-1 (ng/mL’3 5 SEE P
Vegetable protein (g/ 1000 kcal) 0.49 ( 0.24 ) 0.041
Bl\'fl for age %tile —0.01 ( 0.00 ) <0.001

 

lBiomarker & nutrient values log transformed (1n)

2Initial model contained: energy (kcal); fat (% kcal); animal
& vegetable protein (g/ 1000 kcal); calcium, zinc, sodium
(mg/1000 kcal); Maternal education: high school or less;
some college; college or graduate degree; Physical activity:
hrs of moderate & intense activity/wk; and treatment group

3IGFBP-l data is missing on 7 girls (n=152)

48

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