Carbohydrate nutrition and development of adiposity during adolescence

Carbohydrate Nutrition and Developmentof Adiposity During AdolescenceBamini Gopinath1, Victoria M. Flood2, Elena Rochtchina1, Louise A. Baur3,4, Jimmy Chun Yu Louie1,2,5,Wayne Smith6 and Paul Mitchell1

Objective: To examine the prospective association between glycemic index, glycemic load (GL) of diets

and intakes of carbohydrates, sugars, fiber, and the main carbohydrate containing food groups (e.g., soft

drinks) with changes in percent body fat, body mass index (BMI), and waist circumference among

adolescents.

Design and Methods: Students aged 12 at baseline (n ¼ 856) were examined both in 2004-2005 and

2009-2011. A semiquantitative food frequency questionnaire was administered. Anthropometric

parameters were measured and defined using standardized protocols.

Results: After multivariable adjustment, in girls, each 1-SD increase in dietary GL was associated with

concurrent 0.77 kg/m2 and 1.45 cm increase in BMI and waist circumference, respectively (both P ¼0.01). Conversely, each 1-SD increase in dietary fiber intake was associated with a concurrent 0.44 kg/

m2 decrease in mean BMI in girls (P ¼ 0.02) and 1.45 cm decrease in waist circumference in boys (P ¼0.002). Girls who consumed soft drinks once or more per day versus those who never/rarely consumed

soft drinks, had a 4.45% increase in percent body fat after 5 years (Ptrend ¼ 0.01).

Conclusions: High-GL foods might adversely influence development of body composition, particularly in

girls, whereas fiber-rich diets could limit excess weight during adolescence.

Obesity (2013) 21, 1884-1890. doi:10.1002/oby.20405

IntroductionThe tracking of adiposity in childhood and adolescence is quite

strong (1), and many behaviors adopted in childhood and adoles-

cence also track into adulthood. It is therefore important to assess

the roles of modifiable behaviors (diet, physical inactivity) on ado-

lescent adiposity (2). Carbohydrate nutrition, including dietary gly-

cemic index (GI) and glycemic load (GL) of foods consumed, has

been the recent focus of research to increase understanding of its

impact on health outcomes.

Only the epidemiological study by Cheng et al. (3) investigated the

association between dietary GI and GL intakes with change in per-

cent body fat (%BF) and BMI among adolescents. In this German

population-based study of 215 children aged nine at baseline who

were subsequently followed over four years, dietary GI, GL, and

fiber intakes were not associated with the development of %BF and

BMI during puberty (3). Moreover, only three prospective studies in

preadolescents and/or adolescents have examined the association of

fiber and wholegrain intakes with the development of body composi-

tion (2–4).

Consumption of soft drinks has also increased dramatically in the

recent decades, in parallel with increasing prevalence of overweight

and obesity (5). Prior studies have provided evidence of a longitudi-

nal relationship between intake of sugar-sweetened beverages and

adiposity among children and adolescents (6-9). Further, a study of

196 nonobese girls showed that consumption of sugar-sweetened

beverages was associated with an increase in BMI z-score, but was

not related to changes in %BF during adolescence (10). However, a

recent quantitative meta-analysis and qualitative review by Forshee

et al. (11), found that the association between sugar-sweetened bever-

ages and BMI was near zero, based on the current body of scientific

evidence. Additionally, analyses of 38,409 US adults aged 20-74

years showed that elevated BMI values or increased obesity rates

were not observed in those frequently compared to those infrequently

1 Centre for Vision Research, Department of Ophthalmology and Westmead Millennium Institute, University of Sydney, New South Wales, Australia.Correspondence: Paul Mitchell ([email protected]) 2 Faculty of Health and Behavioural Sciences, University of Wollongong, Sydney, New SouthWales, Australia 3 University of Sydney Clinical School, The Children’s Hospital at Westmead, Sydney, New South Wales, Australia 4 School of Public Health,University of Sydney, Sydney, New South Wales, Australia 5 Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, University of Sydney,Sydney, New South Wales, Australia 6 School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia

Funding agencies: The Sydney Myopia Study (Sydney Childhood Eye Study) was supported by the Australian National Health & Medical Research Council (grant no.

253732); the Westmead Millennium Institute, University of Sydney; the Vision Co-operative Research Centre, University of New South Wales, Sydney; and the National

Heart Foundation of Australia (grant no. G11S 6106), Melbourne, Australia.

Author’s contributions: Study concept and design: BG and PM, Acquisition of data: PM; Analysis and interpretation of data: BG, VF, JCYL, ER, PM; Drafting of the

manuscript: BG and PM; Critical revision of the manuscript: BG, VF, ER, LAB, JCYL, WS, PM.

Received: 18 April 2012 Accepted: 21 January 2012 Published online 21 March 2013. doi:10.1002/oby.20405

1884 Obesity | VOLUME 21 | NUMBER 9 | SEPTEMBER 2013 www.obesityjournal.org

Original ArticlePEDIATRIC OBESITY

Obesity

consuming soft drinks (12). Finally, a recent randomized control

study of 103 adolescents (13) showed that decreasing soft drink con-

sumption in the intervention group did not result in an appreciable

decrease in BMI.

The authors used a large cohort of schoolchildren who were followed

over 5 years to determine the following: 1) the association between

baseline dietary intakes of carbohydrates, sugars, fiber, and the GI and

GL of foods consumed, and their associations with temporal changes

in BMI, %BF, and waist circumference; 2) the relationship between

baseline consumption of soft drinks/ cordials and the principal carbo-

hydrate containing food groups (e.g., cereals, potatoes, energy-dense

nutrient poor sources of carbohydrates such as cookies) and develop-

ment of adiposity; and 3) the association between change in dietary

intakes of carbohydrate nutrition variables (e.g., carbohydrates, sugars,

and soft drinks), and the GI and GL of foods consumed with concur-

rent changes in anthropometric measures during adolescence.

MethodsStudy populationThe Sydney Childhood Eye Study is a population-based survey of

eye conditions in school children living within the Sydney Metropol-

itan Area, Australia. It was approved by the Human Research Ethics

Committee, University of Sydney, the Department of Education and

Training, and the Catholic Education Office, New South Wales,

Australia (14). We obtained informed written consent from at least

one parent of each child, as well as verbal assent from each child

before the examinations. The study methods have been previously

described (14). Briefly, students of mean age 12.7 years in a strati-

fied random cluster sample of 21 high schools across Sydney were

eligible to participate. Stratification was based on socioeconomic

status data and led to a proportional mix of public, private, or reli-

gious high-schools. Of the 3144 eligible 12-year-old children, 2367

were given parental permission to participate and 2353 underwent

examinations (74.9%). Data for the 12-year-old cohort were col-

lected during 2004-2005 and then after 5 years during 2009-2011,

when 1213 were re-examined (51.6% of baseline participants). Par-

ticipants versus nonparticipants were more likely to have tertiary

qualified parents and lower BMI, and have a higher consumption of

fruits, but less likely to be older and Caucasian

Dietary dataDietary data were collected using a 120-item self-administered food

frequency questionnaire (FFQ), designed for specific use in Austra-

lian children and adolescents (15). An allowance for seasonal varia-

tion of fruit and vegetables was made during analysis by weighting

seasonal fruits and vegetables. The average daily intake of seasonal

fruits and vegetables were calculated by adjusting the number of

months per year the fruit was available. The validity of the FFQ has

been previously reported in children (15). Briefly, Pearson correla-

tion coefficients were calculated on the data after statistical adjust-

ment (transformation, energy-adjustment, and de-attenuation). To

adjust for within-person variation in the food records, de-attenuated

correlation coefficients were calculated (16). Values were adjusted

for energy by using the residual method (17). The de-attenuated,

energy-adjusted Pearson correlation coefficient for carbohydrate and

total sugars was 0.47 and 0.41, respectively, with 56% of carbohy-

drates and 58% of total sugars results ranked within one quintile

when compared to weighed food records. Less than 5% of the

results were grossly misclassified (i.e., ranked highest by FFQ

method and lowest by weighed food records method, or vice-versa).

Corresponding food items from the nutrition composition database

(NUTTAB2006) (18) were linked to the FFQ items, and the reported

consumption frequencies were taken into account when translating the

FFQ responses into actual food and nutrients intake using a purpose

built query program in Microsoft Access 2007 based on the formula:

(nutrient per 100 g � weight specified on FFQ question � frequency)/

100. NUTTAB2006, however, does not provide the full range of

nutrients of interest, for which we used values from other nutrient data-

bases (19,20). GI values were assigned to individual food items in the

FFQ based on previous published methods (21,22). The GL of each

food item was calculated as the corresponding GI (as %) � amount (in

grams) of available carbohydrates in a serve of that food. The daily di-

etary GL of each subject was calculated as the sum of GL from various

food items, and the dietary GI was obtained by (dietary GL / total

available carbohydrate intake in the day) � 100%. Analytical data on

fructose were available for >90% of the foods used in the nutrient

database. Fructose values were subjectively assigned to the remaining

foods based on their compositions, where recipes were used to deter-

mine the ingredients of foods wherever available.

We also extracted data on the fiber contribution from vegetables,

fruit, and bread and cereals and on the consumption of main carbo-

hydrate-containing food groups: vegetables, potatoes, fruit, bread,

and cereals (comprising breakfast cereals, bread {white or other},

pasta, and rice), as well as foods high in refined sugars or refined

starches (soft drinks, cordials, sweet biscuits, cakes, buns, scones,

pastries, confectionary, sugar, honey, jams, and syrups), which we

term as energy-dense, nutrient poor sources of carbohydrates. Data

on the frequency of soft drink, cordial (a sweet flavored concen-

trated syrup that is mixed with water to taste), and fruit juice con-

sumption, were also obtained from the FFQ.

Assessment of anthropometric measuresData were collected during a pre-organized visit to each school. Each

child’s weight and body fat percentage (utilizing leg--leg bioimpe-

dance analysis) were measured using a Body Composition Analyser

(Model TBF-300, Tanita, Tokyo, Japan). The mid-point between the

lower rib border and iliac crest functioned as the standardized hori-

zontal plane and was measured using a measuring tape to determine

waist circumference in centimeters. Height was measured with shoes

off using a freestanding SECA height rod (Model 220, Hamburg,

Germany). Weight in kilograms was measured using a standard port-

able weighing machine, after removing any heavy clothing. BMI was

calculated as weight divided by height squared (kg/m2).

Covariate assessmentParents were asked to complete a comprehensive 193-item question-

naire. Socio-demographic information covering ethnicity, country of

birth, education, occupation, and parental age was collected. Parents

were also asked whether they or other people living in their home

smoked inside the house. This defined the child’s exposure to cur-

rent passive smoking.

The questions relating to physical activity comprised a list of nine

common activities in which school-aged children participate such as

athletics, swimming, soccer, etc. Children self-reported the usual

Original Article ObesityPEDIATRIC OBESITY

www.obesityjournal.org Obesity | VOLUME 21 | NUMBER 9 | SEPTEMBER 2013 1885

number of hours per week they spent in each of these activities and

whether the activity was performed outdoors or indoors (hall gym,

classroom). The time spent in each activity was summated and the

average hours per day spent were calculated separately for outdoor

activities, indoor activities, and total activity time (i.e., sum of out-

door and indoor activities).

Total screen time (hours/day) was calculated as the time reported that

was spent on the following activities: watching TV, playing video

games, and use of a computer for recreational and educational purposes.

Statistical analysisStatistical analyses were performed using SAS (v9.1, SAS Institute,

NC). We used mixed models and generalized estimating equations to

appropriately model predictors of change in adiposity while accounting

for variability attributable to the stratified randomized school-cluster

design. The analysis focused on separate models for predicting three adi-

posity assessments: 1) change in BMI, 2) change in %BF, and 3) change

in waist circumference. In each model the primary predictors of change

were: carbohydrate nutrition assessments and the main carbohydrate-

containing food groups. Secondary predictors were the covariates: age,

sex, ethnicity, parental education, exposure to passive smoking, change

in height, screen viewing time, and time spent in physical activity. Die-

tary GI, GL, carbohydrates, sugar, and fiber variables were adjusted for

total energy intake using the residual method (17). Intakes of principal

carbohydrate containing food groups (e.g., cereals) were energy adjusted

by using the energy partition model, that is, consumption was adjusted

for the energy intake from all other foods (17).

Linear regression models were used to estimate the slopes (magni-

tudes) of possible linear relationships between the dietary variables

and anthropometric measures. These analyses generated two sets of

regression coefficients representing the following: 1) prospective

estimate – coefficients in models for predicting dietary variables at

baseline on the change in BMI, %BF, and waist circumference at

the 5-year follow-up; and 2) concurrent estimate – coefficients in

models for predicting change in the dietary variables over the 5

years on the concurrent change in anthropometric measures. The

change in dietary variables during the study period was calculated

by subtracting baseline intake from the intake recorded at the 5-year

follow-up examination (17).

After multivariable adjustment, linear regression analyses indicated

interactions between sex and the associations of soft drinks with

%BF (Pinteraction ¼ 0.02) and waist circumference (Pinteraction ¼0.01), and potato consumption with BMI (Pinteraction ¼ 0.04). Inter-

actions were also observed between sex and the associations of fruc-

tose with BMI (Pinteraction ¼ 0.02), waist circumference (Pinteraction

¼ 0.02), and %BF (Pinteraction ¼ 0.04). Finally, interactions were

observed between sex and change in dietary GI and GL with concur-

rent change in waist circumference (Pinteraction ¼ 0.01) and %BF

(Pinteraction ¼ 0.02), respectively. Subsequent analyses of all carbo-

hydrate nutrition variables and the principal carbohydrate containing

food groups were therefore stratified by sex.

ResultsBaseline characteristics of study participants are shown in Table 1.

Girls were more likely than boys to have smaller waist circumference

and lower soft drink consumption but had higher consumption of

vegetables, and spent less time in screen viewing and physical activ-

ity. Study characteristics of participants with (n ¼ 856) and without

(n ¼ 360) complete data were also compared. Participants with, ver-

sus those without complete data were more likely to be Caucasian,

older (significant difference in mean age at baseline) and have lower

BMI.

Association between carbohydrate nutritionwith adiposity among girls onlyNonsignificant associations were observed between baseline con-

sumption of total carbohydrates, total sugars, fiber, and dietary GI

and GL with temporal change in the three anthropometric measures

(Table 2). Energy-dense nutrient poor sources of carbohydrates (e.g.,

cookies and cakes) and starch intake at baseline were also not asso-

ciated with 5-year change in BMI among girls, b¼0.06 (P ¼ 0.63)

and b¼-0.03 (P ¼ 0.77), respectively.

After controlling for all covariates, soft drink consumption remained

significantly and positively associated with %BF among girls, that

is, girls who consumed one or more soft drinks per day compared to

those who never/ rarely drank soft drinks had significantly higher

%BF, 3.79 versus �0.66% (Ptrend ¼ 0.01). Although the trend was

nonsignificant, girls who consumed one or more soft drinks per day

compared to those who never/rarely consumed soft drinks at base-

line had a significantly greater increase in BMI (3.20 versus 1.96

kg/m2, P ¼ 0.01) and waist circumference (10.00 versus 6.46 cm, P¼ 0.004) after 5 years.

We analyzed the relationship between intake changes in dietary vari-

ables with concurrent change in BMI, %BF, and waist circumfer-

ence. For these particular analyses we had 513 (279 girls and 234

boys) participants with complete data on all covariates included in

the multivariable model, as well as anthropometric and FFQ infor-

mation at both the baseline and 5-year follow-up. Each 1-SD

increase in dietary GL was concurrently associated with an increase

in both mean BMI (b ¼ 0.77, P ¼ 0.01) and waist circumference (b¼ 1.45, P ¼ 0.01) in girls over the study period (Table 3). Simi-

larly, each SD increase in carbohydrates was associated with a 0.77

kg/m2 (P ¼ 0.03) and 1.90 cm (P ¼ 0.02) increase in BMI and

waist circumference, respectively. In contrast, each 1-SD increase in

fiber intake over the 5 years was associated with a concurrent

decrease of 0.44 kg/m2 in mean BMI (P ¼ 0.02; Table 3).

Association between carbohydrate nutritionwith adiposity among boys onlyThere were no significant associations observed between most car-

bohydrate nutrition variables at baseline and anthropometric meas-

ures in boys. However, boys in the highest compared to those in the

lowest tertile of baseline dietary intake of total sugars, had a lower

decline in %BF over the 5 years (Ptrend ¼ 0.02; Table 4). In boys, a

marginally significant association was observed with fructose con-

sumption, that is, each 1-SD (1-SD ¼ 10.7) increase in fructose con-

sumption at baseline was associated with 0.52% increase in %BF (P¼ 0.05) after 5 years. With respect to concurrent change, after mul-

tivariable adjustment, each 1-SD (1-SD ¼ 11.8) increase in dietary

fiber intake was associated with a concurrent 1.45-cm decrease in

waist circumference in boys (P ¼ 0.002). Also, a marginally signifi-

cant association was observed with energy-dense, nutrient poor

Obesity Carbohydrate Nutrition and Adiposity Gopinath et al.


sources of carbohydrates, i.e., each 1-SD (1-SD ¼ 215.1) increase in

energy-dense, nutrient poor sources of carbohydrates was concur-

rently associated with a 0.54-cm increase in waist circumference

(P¼ 0.05).

DiscussionTo our best knowledge, no prospective observational study has

shown that both carbohydrate quantity and quality could be relevant

to the development of adiposity among adolescents. In our study,

increased dietary GL and carbohydrate intake was associated with

concurrent development of adiposity in girls. Also, girls who con-

sumed more servings of soft drinks per day at an age of 12 years

had significantly higher %BF 5 years later during adolescence. Con-

versely, an increase in total dietary fiber consumption was associ-

ated with a concurrent decrease in mean BMI in girls and in waist

circumference in boys during adolescence.

In contrast with some other prospective adolescent studies (3), we

show that dietary GL and carbohydrate intake appear to be relevant

to the development of body composition in girls. The inconsistent

TABLE 1 Baseline characteristics of 12-year old children (n 5 856), stratified by gender

Characteristics Girls (n ¼ 421) Boys (n ¼ 435) P-value

Age, yr 12.7 (0.4) 12.8 (0.5) 0.06

Ethnicity, %

Caucasian 257 (61.1) 290 (66.7) 0.37

East Asian 82 (19.5) 70 (16.1)

Middle Eastern 17 (4.0) 14 (3.2)

Other 65 (15.4) 61 (14.0)

Parental educationa 212 (50.4) 237 (54.5) 0.23

Exposure to passive smoking 75 (17.8) 59 (13.6) 0.09

Height, cm 156.7 (7.1) 156.5 (8.1) 0.69

Weight, kg 49.4 (12.6) 48.8 (11.4) 0.48

Body mass index, kg/m2 20.0 (4.1) 19.8 (3.6) 0.50

Waist circumference, cm 65.4 (8.3) 67.0 (7.8) 0.005

Percent body fat, % 23.9 (9.0) 15.8 (7.4) <0.0001

Time spent in screen viewing, h/d 2.50 (1.3) 2.82 (1.5) 0.001

Time spent in physical activity, h/d 0.70 (0.6) 0.99 (0.7) <0.0001

Dietary intake of

Carbohydrate, g/d 254.3 (97.7) 267.3 (99.3) 0.05

Total sugars, g/d 129.2 (55.1) 136.6 (56.9) 0.06

Total fiber, g/d 28.6 (12.9) 28.1 (12.2) 0.61

Glycemic index 54.3 (3.4) 54.3 (3.2) 0.86

Glycemic load 138.0 (53.3) 145.1 (54.2) 0.05

Consumption of

Vegetables, g/d 177.0 (128.5) 149.8 (111.0) 0.001

% contribution to GL (SD) 2.2 (2.1)

Fruits, g/d 249.8 (182.3) 228.9 (178.7) 0.09


Cereals, g/d 229.6 (121.4) 233.9 (125.3) 0.61


Energy dense, nutrient poor sources

of carbohydrates, g/db

287.6 (245.7) 313.5 (221.0) 0.11


Soft drinks

Never or rarely 87 (20.7) 56 (12.9) 0.01

Up to once a week 201 (47.9) 212 (48.7)

2-6 times a week 97 (23.1) 126 (29.0)

Once or more a day 35 (8.3) 41 (9.4)


Data are mean (SD) or proportions unless otherwise stated.aTertiary qualified mother and/or father.bIncludes soft drinks, cordials, cookies, cakes, buns, scones, pastries, confectionary, sugar, honey, jams, and syrups.



findings between our study and previously published reports could

be at least partly explained by differences in study characteristics;

dietary habits and lifestyles of the participants examined; the dietary

assessment methods used; measures of adiposity applied; and poten-

tial confounding factors considered (23).

The significant linear association between carbohydrate intake and

change in body composition among girls could explain the relation-

ship with GL, since dietary GL is primarily determined by the intake

of carbohydrates (24). High-GL diets have previously been shown to

increase postprandial hyperinsulinemia, which favors fatty acid

uptake, inhibits lipolysis and increases energy storage resulting in

weight gain (25). Higher dietary GL intake could also result in other

postprandial metabolic changes, including an increase in counter-

regulatory hormones that could explain increased hunger and

increased energy intake in the postabsorptive phase, possibly leading

to weight gain over time (26,27). Indeed, some intervention studies

in children have found an increase in satiety and a reduction in food

consumption after lowering the GL of meals (28,29). It is unclear as

to why only dietary GL, and not GI, was associated with concurrent

changes in BMI and waist circumference, however, it suggests that

both the quality and quantity of dietary carbohydrate rather than

quality alone may be important predictors of development of body

composition in adolescence. Additionally, the observed strengthen-

ing of the association with multiplying GI by the amount of avail-

able carbohydrates (i.e., GL), suggests that postprandial hyperglyce-

mia and hyperinsulinemia are likely mediators of this weight gain

during adolescence.

Our study presents some potentially important implications for clini-

cal practice and public health guidelines. Specifically, the finding

that girls may be more sensitive to high GL foods is an important

message given the prevalence of obesity in adolescents and the role

of foods contributing to high GL in current dietary behavior in

younger generations (30). Decrements of 1-SD in dietary GL and

carbohydrates were previously shown to be achievable in practice in

young adults (31). Therefore, reducing dietary GL and carbohydrate

intake could be a feasible component of the nutritional advice for a

healthy lifestyle in adolescents (30).

The positive association found between soft drink consumption and

%BF in girls confirms data from other adolescent populations dem-

onstrating a link between sweetened beverage consumption and BMI

(7,9,10,32), and with waist circumference and %BF (6). Although,

TABLE 2 Association between baseline dietary intakes of carbohydrate nutrition variables with change in mean BMI, percentbody fat (%BF) and waist circumference from age 12 to 17 years old among girls (n 5 420)

Nutritional intake (per SD increase)

Change in mean

BMIaChange in mean

%BFa

Change in mean waist

circumferencea

b (SE) P b (SE) P b (SE) P

Glycemic index (1-SD ¼ 3.3) �0.06 (0.06) 0.38 �0.25 (0.18) 0.17 0.22 (0.29) 0.45

Glycemic load (1-SD ¼ 21.8) �0.11 (0.06) 0.08 �0.44 (0.24) 0.07 �0.006 (0.21) 0.98

Carbohydrates (1-SD ¼ 31.7), g/day �0.08 (0.06) 0.23 �0.40 (0.25) 0.11 �0.04 (0.18) 0.83

Total sugars (1-SD ¼ 29.5), g/day �0.06 (0.10) 0.57 �0.01 (0.20) 0.96 �0.17 (0.26) 0.51

Total fiber (1-SD ¼ 7.1), g/day 0.09 (0.11) 0.38 �0.003 (0.28) 0.99 0.01 (0.24) 0.96

b-Coefficients refer to each SD increase in nutritional intake at baseline with a decrease/ increase in mean BMI, %BF, and waist circumference.aAdjusted for age, ethnicity, parental education, exposure to passive smoking, change in energy intake (residual method), change in height, screen viewing time, and timespent in physical activity

TABLE 3 Changes in dietary intake of carbohydrate nutrition variables with concurrent change in mean BMI, percent body fat(%BF), and waist circumference from age 12 to 17 years old among girls (n 5 279)

Nutritional intake

(per SD increase)

Concurrent change

in mean BMIaConcurrent changein

mean %BFa

Concurrent change

mean waist

circumferencea

b (SE) P b (SE) P b (SE) P

Glycemic index (1-SD ¼ 3.6) 0.28 (0.16) 0.09 0.61 (0.42) 0.15 0.40 (0.23) 0.08

Glycemic load (1-SD ¼ 50.9) 0.77 (0.31) 0.01 1.10 (0.82) 0.18 1.45 (0.58) 0.01

Carbohydrates (1-SD ¼ 92.7), g/day 0.77 (0.35) 0.03 1.07 (0.88) 0.23 1.90 (0.83) 0.02

Fructose (1-SD ¼ 14.2), g/day 0.29 (0.16) 0.07 0.46 (0.40) 0.25 1.18 (0.66) 0.08

Total fiber (1-SD ¼ 11.8), g/day �0.44 (0.19) 0.02 �1.07 (0.62) 0.09 �0.19 (0.72) 0.80

b-Coefficients refer to each SD increase in nutritional intake with a concurrent decrease/ increase in mean BMI, %BF and waist circumference.aAdjusted for age, ethnicity, parental education, exposure to passive smoking, change in energy intake (residual method), change in height, screen viewing time, and timespent in physical activity.



we caution that not all studies have confirmed an association

between soft drink consumption and adolescent weight gain (13,33).

It has been speculated that soft drinks increase hunger, decrease sati-

ety, or simply calibrate individuals to a high level of sweetness that

generalizes to preferences in other foods (34), which could lead to

an increase in %BF during adolescence. We also caution that fre-

quent soft drink consumption could be a marker of other unhealthy

dietary and lifestyle patterns, which could be driving the observed

associations with %BF in girls.

Interpreting the sex-specific differences observed in the current study

is complex. Vartanian et al. (34) in their meta-analysis of soft drink

consumption and body weight observed significantly larger effect

sizes among women, which concurs with our finding. Also, Sieri

et al. (35) demonstrated that dietary GL intake increased the overall

risk of coronary heart disease in women but not men. Given that girls

are intrinsically more insulin resistant than boys during puberty

(36,37), increased consumption of high-GL foods among girls could

lead to magnification of postprandial hyperglycemia, which in turn

causes the observed weight gain in girls but not boys. Thus, we spec-

ulate that differing actions of estrogen and testosterone could have

contributed to the sex-specific associations observed in our study. Fur-

ther prospective studies are required to both confirm and clarify the

existence of this sex-specific difference during adolescence.

We document an independent, inverse association between consump-

tion of total dietary fiber with subsequent development of body com-

position in both boys and girls. Contradictory results have been

reported by other adolescent population-based studies (2,3). However,

prior experimental studies conducted in adults have demonstrated a

modest long-term beneficial effect of higher consumption of fiber-rich

foods on body composition (38,39). Dietary fiber could facilitate

body-weight control through several mechanisms including: 1) high-

fiber diets are more satiating and can lead to lower energy intake; 2)

fiber can limit the access of other nutrients to digestive enzymes; and

3) increasing fiber intake can improve insulin sensitivity and stimulate

fat oxidation (38). All these aspects benefit weight control (39). We

caution, however, that the finding related to fiber intake could reflect

a dietary pattern rather than fiber consumption per se, so that the pos-

sibility of confounding by unmeasured or imprecisely measured fac-

tors contributing to this association cannot be excluded (40).

The key strengths of our study are its longitudinal design, random clus-

ter sample of a relatively large number of school children, and use of

standardized protocols to measure adiposity. Additionally, dietary infor-

mation was collected twice, which afforded us the opportunity to inves-

tigate the concurrent associations between changes in carbohydrate

nutrition variables and changes in anthropometric measures. Limitations

of our study include that we did not have information available on

pubertal stage and thus cannot discount that our findings could be con-

founded by the different pubertal stages of our participants. Also, while

the validity of the long FFQ tool has been previously reported (15), and

performs moderately well for carbohydrate, its validity for GI has not

been investigated. It is, however, encouraging that we see consistent

results between energy dense, nutrient poor foods with carbohydrate and

the GI data from the long FFQ. Finally, we cannot discount the possibil-

ity of residual confounding from unmeasured confounders (e.g., parental

BMI), which could have influenced findings.

In summary, we show that increased dietary intake of carbohydrates

and GL are independent risk factors for the development of body

composition among girls. We demonstrate a potential salutary role

of fiber-rich diets in the prevention of adiposity development during

puberty. Future research should include randomized controlled trials

aimed at reducing the intakes of carbohydrates and high-GL foods,

and increasing the consumption of dietary fiber, these studies could

contribute to the development of potentially effective strategies to

limit excess weight gain in adolescents.O

VC 2013 The Obesity Society

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TABLE 4 Association between baseline consumption of carbohydrate nutrition variables with change in BMI, percent body fat(%BF), and waist circumference from age 12 to 17 years among boys (n 5 435), presented as adjusted means(95% confidence interval, CI)

Carbohydrate nutrition

intake at baseline (range) n

Change in mean

BMI (95% CI)aChange in mean

%BF (95% CI)aChange in mean waist

circumference (95% CI)a

Total sugars, g/day

First tertile (�120.91) 141 2.81 (2.32-3.30) �3.60 (�4.61-2.58) 11.73 (10.35-13.10)

Second tertile (121.1–143.7) 142 2.84 (2.45-3.24) �3.61 (�4.80-2.41) 11.45 (9.93-13.0)

Third tertile (�143.8) 152 3.06 (2.67-3.45) �2.21 (�3.19-1.23) 12.08 (10.85-13.30)

P for trend 0.09 0.02 0.49

Fructose, g/day

First tertile (�26.10) 161 2.86 (2.31-3.40) �3.96 (�5.28-2.65) 11.60 (10.15-13.04)

Second tertile (26.2–34.6) 141 2.86 (2.49-3.22) �2.85 (�4.22-1.47) 11.57 (10.55-12.59)

Third tertile (�34.7) 133 3.03 (2.64-3.41) �2.62 (�3.46-1.79) 12.16 (10.25-14.07)

P for trend 0.45 0.05 0.32

aAdjusted for age, ethnicity, parental education, exposure to passive smoking, energy intake (residual method), change in height, screen viewing time, and time spent inphysical activity.



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Carbohydrate nutrition and development of adiposity during adolescence

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