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Article |

Relationship Between Insulin Resistance–Associated Metabolic Parameters and Anthropometric Measurements With Sugar-Sweetened Beverage Intake and Physical Activity Levels in US Adolescents:  Findings From the 1999-2004 National Health and Nutrition Examination Survey FREE

Andrew A. Bremer, MD, PhD; Peggy Auinger, MS; Robert S. Byrd, MD, MPH
[+] Author Affiliations

Author Affiliations: Department of Pediatrics, University of California Davis School of Medicine, Sacramento, California (Drs Bremer and Byrd); and the Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, New York (Ms Auinger).


Arch Pediatr Adolesc Med. 2009;163(4):328-335. doi:10.1001/archpediatrics.2009.21.
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Objective  To evaluate the relationship between insulin resistance–associated metabolic parameters and anthropometric measurements with sugar-sweetened beverage intake and physical activity levels.

Design  A cross-sectional analysis of the National Health and Nutrition Examination Survey data collected by the National Center for Health Statistics.

Setting  Nationally representative samples of US adolescents participating in the National Health and Nutrition Examination Survey during the years 1999-2004.

Participants  A total of 6967 adolescents aged 12 to 19 years.

Main Exposure  Sugar-sweetened beverage consumption and physical activity levels.

Outcome Measures  Glucose and insulin concentrations, a homeostasis model assessment of insulin resistance (HOMA-IR), total, high-density lipoprotein, and low-density lipoprotein cholesterol concentrations, triglyceride concentrations, systolic and diastolic blood pressure, waist circumference, and body mass index (calculated as weight in kilograms divided by height in meters squared) percentile for age and sex.

Results  Multivariate linear regression analyses showed that increased sugar-sweetened beverage intake was independently associated with increased HOMA-IR, systolic blood pressure, waist circumference, and body mass index percentile for age and sex and decreased HDL cholesterol concentrations; alternatively, increased physical activity levels were independently associated with decreased HOMA-IR, low-density lipoprotein cholesterol concentrations, and triglyceride concentrations and increased high-density lipoprotein cholesterol concentrations. Furthermore, low sugar-sweetened beverage intake and high physical activity levels appear to modify each others' effects of decreasing HOMA-IR and triglyceride concentrations and increasing high-density lipoprotein cholesterol concentrations.

Conclusions  Sugar-sweetened beverage intake and physical activity levels are each independently associated with insulin resistance–associated metabolic parameters and anthropometric measurements in adolescents. Moreover, low sugar-sweetened beverage intake and high physical activity levels appear to modify each others' effects on several health-related outcome variables.

The increasing prevalence of obesity, insulin resistance, and metabolic syndrome (a group of conditions associated with insulin resistance including hypertension, dyslipidemia, central adiposity, and impaired glucose metabolism) in the pediatric population is a global health issue, especially given that these conditions in childhood may be antecedents to adult disease.16 As such, the long-term public health consequences of these disorders in children and adolescents with respect to premature morbidity and mortality are significant.

A considerable amount of research over the past decade has been devoted to studying the genetic aspects of these conditions79 and which individuals may be predisposed to disorders of weight management or insulin sensitivity. However, environmental factors are also undoubtedly contributors to these conditions' development. Specifically, 2 lifestyle behaviors associated with obesity, insulin resistance, and metabolic syndrome are (1) high levels of sugar-sweetened beverage (SSB) intake1014 and (2) low levels of physical activity (PA).1521 Dietary modifications and consistent exercise are thus 2 recommendations typically given by pediatricians to children and adolescents either at risk for or currently diagnosed with these disorders.

Experimental studies support the hypothesis that SSBs may increase energy intake and induce weight gain via their reduced satiety response, the promotion of a positive energy balance by liquid calories relative to isoenergetic solid calories, and their dysregulation of energy homeostasis.2226 Although not all studies support an association between SSB consumption and obesity,27,28 SSB intake has nonetheless been associated with increased body weight, increased fat mass, dyslipidemia, and blood pressure.29,30 Experimental studies also suggest that increased PA improves insulin sensitivity.20,21,3139 Exercise acutely improves insulin sensitivity for up to 48 hours because of an increase in insulin-stimulated glucose transport and glycogen synthesis,40,41 and regular exercise training induces long-term changes within the skeletal muscle that may improve whole-body insulin sensitivity.35 Moreover, several clinical studies have shown that exercise improves a subject's overall health.42,43

The increasing incidence and prevalence of obesity, insulin resistance, and metabolic syndrome in the pediatric population is well recognized4447; however, the relationship between insulin resistance–associated metabolic parameters and anthropometric measurements with SSB intake and PA levels in children and adolescents remains poorly understood. Furthermore, studies evaluating the association of SSB consumption and exercise with these conditions are limited,16,48 which is unfortunate given that a better understanding of how diet and PA are related to these conditions may provide clinical and public health information for prevention and treatment strategies.

Thus, to evaluate the relationship between insulin resistance–associated metabolic parameters and anthropometric measurements with SSB intake and PA levels in the US adolescent population, we analyzed these associations using data from the National Health and Nutrition Examination Survey (NHANES) during the years 1999-2004.

STUDY DESIGN AND POPULATION

The NHANES is conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention and is designed to monitor the health and nutritional status of the US civilian noninstitutionalized population. Since 1999, NHANES has been planned and conducted as a continuous annual survey, and data are released in 2-year periods (eg, 1999-2000, 2001-2002). A nationally representative sample is selected annually using a stratified multistage probability cluster sample design49; oversampling Mexican American and black individuals, adolescents aged 12 to 19 years, persons 60 years and older, low-income white individuals, and pregnant women permits more precise estimates for these groups. This study is based on data obtained from NHANES 1999-2000, 2001-2002, and 2003-2004, as these were the most recently available NHANES that had released all of the data needed for the inclusion criteria, exclusion criteria, and outcome variables. The NHANES protocol was approved by the National Center for Health Statistics institutional review board, and written informed consent was obtained from all participants aged 18 years and older; for participants younger than 18 years, written informed assent was obtained in addition to parent or guardian consent. This study was approved by the institutional review board at the University of California, Davis.

DATA COLLECTION

The NHANES protocol consists of a home interview performed by a trained interviewer followed by a visit to an examination center where participants underwent physical examinations, provided blood and urine samples, and completed additional questionnaires. The details of the participant examinations and laboratory assessments are available on the NHANES Web site.49 For our study, only data from participants aged 12 to 19 years were analyzed; individuals were excluded from analyses if they were pregnant and/or used steroids, blood glucose regulators, insulin, other antidiabetic agents, growth hormones, or sex hormones.

MEASUREMENTS

Outcome variables included glucose levels, insulin levels, homeostasis model assessment of insulin resistance (HOMA-IR) measurements, total cholesterol levels, high-density lipoprotein cholesterol (HDL-C) levels, low-density lipoprotein cholesterol (LDL-C) levels, triglyceride (TG) levels, systolic blood pressure (SBP), diastolic blood pressure, waist circumference (WC), and body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) percentile for age and sex (per the National Center for Health Statistics references).50 The HOMA-IR ([fasting glucose in millimoles per liter × fasting insulin in milliunits per milliliter]/22.5),51 LDL-C, and TG results were limited to those who had completed at least an 8-hour fast. Mean WC is presented as the least squares mean, controlling for age and sex.

DEFINITIONS

Sugar-sweetened beverage information was obtained through a 24-hour dietary recall interview. (In NHANES 2003-2004, the 24-hour recall was assessed on 2 separate days; the first day was an in-person interview comparable with the previous NHANES study periods' primary interview mode, whereas the second day was a telephone interview 3-10 days later. For consistency in the methodology of data collection among the study periods, only the first day of the NHANES 2003-2004 24-hour recall was used in our analyses.) Sugar-sweetened beverages were defined as caloric soft drinks, colas, sugar-sweetened fruit drinks, or other SSBs; pure fruit juices and diet soft drinks were not included. Sugar-sweetened beverage intake in grams for each reported beverage was divided by 250 g (a serving equivalent; approximately 8 ounces or a cup of beverage) and summed for each adolescent. In each NHANES analyzed, low SSB intake was defined as the lowest quintile (≤20th percentile) of the sum of the number of SSB serving equivalents a subject consumed per day; medium was defined as the 2nd to 4th quintiles (>20th to <80th percentile); high was defined as the highest quintile (≥80th percentile). Units of SSB intake are defined as the number of SSB serving equivalents per day. Physical activity information was obtained during the interview questionnaire. Low PA was defined as the lowest quintile (≤20th percentile) of the sum of (the mean number of times a subject did activity per day) × (the average duration of each time in minutes) × (the metabolic equivalent score)52,53; medium was defined as the 2nd to 4th quintiles (>20th to <80th percentile); high was defined as the highest quintile (≥80th percentile).

STATISTICAL ANALYSIS

Statistical analyses were performed with SUDAAN version 9.0 (Research Triangle Institute, Research Triangle Park, North Carolina) using techniques appropriate for the complex NHANES survey design. All of the analyses used the NHANES-provided sampling weights that were calculated to take into account unequal probabilities of selection resulting from the sample design, nonresponse, and planned oversampling of selected subgroups so the results are representative of the US community-dwelling population. Dietary and activity variables were analyzed both as continuous variables and in quintiles to minimize the chance that a small number of extreme observations would have undue influence on the results. Descriptive statistics summarize the data and are expressed as the mean (standard error [SE]). Multivariate linear regression analyses were performed to determine independent associations between each outcome variable and the number of serving equivalents of SSBs consumed and/or the levels of PA after adjusting for age, sex, race, and energy intake (in kilocalories). Analyses involving female subjects were adjusted for the occurrence or nonoccurrence of menarche. All P values are 2-sided and statistical significance was established a priori at α = .05.

CHARACTERISTICS

The baseline characteristics of the study participants are shown in Table 1. A total of 6967 adolescents were studied; the mean age of the participants was 15.5 years, 51.1% of the participants were male, and 48.9% of the participants were female. The ethnic distribution of the participants was 62.1% white, 14.8% black, 10.9% Mexican American, 5.3% other (including multiracial), and 6.9% other Hispanic. For the metabolic evaluations, 442 individuals (approximately 6% of the study population) were excluded from analyses because of pregnancy and/or the use of steroids, blood glucose regulators, insulin, other antidiabetic agents, growth hormones, or sex hormones.

Table Graphic Jump LocationTable 1. Characteristics of US Adolescents Aged 12 to 19 Years: NHANES 1999-2004 Cohorts
METABOLIC PARAMETERS AND ANTHROPOMETRIC MEASUREMENTS ASSOCIATED WITH SSB INTAKE

The characteristics of the low-, medium-, and high-SSB intake groups as well as the results of the multivariate linear regression analyses evaluating the relationship between SSB intake and insulin resistance–associated metabolic parameters and anthropometric measurements are shown in Table 2. The low-SSB intake group consumed a mean of 0.01 SSB serving equivalents (approximately 0.1 oz) per day (SE, 0.003; range, 0-0.4 [approximately 0-3 oz]); the medium–SSB intake group consumed a mean of 2.5 SSB serving equivalents (approximately 20 oz) per day (SE, 0.03; range, 0.5-4.8 [approximately 4-38 oz]), and the high–SSB intake group consumed a mean of 7.4 SSB serving equivalents (approximately 60 oz) per day (SE, 0.2; range, 4.9-33.4 [approximately 40-266 oz]). Each additional SSB serving equivalent consumed per day was associated with a 5% increase in HOMA-IR, a 0.16-mm Hg increase in SBP, a 0.47-cm increase in WC, a 0.90-percentile increase in BMI for age, and a 0.48-mg/dL decrease in HDL-C concentrations.

Table Graphic Jump LocationTable 2. Characteristics of SSB Intake Groups and the Multivariate Linear Regression Coefficientsa
METABOLIC PARAMETERS AND ANTHROPOMETRIC MEASUREMENTS ASSOCIATED WITH PA LEVELS

The characteristics of the low-, medium-, and high-PA level groups as well as the results of the multivariate linear regression analyses evaluating the relationship between PA levels and insulin resistance-associated metabolic parameters and anthropometric measurements are shown in Table 3. In the low-PA group, 14.4% reported having engaged in at least 1 moderate activity during the past 30 days, whereas 7.4% reported having engaged in at least 1 vigorous activity during the past 30 days. In the medium-PA group, 75.4% reported having engaged in at least 1 moderate activity during the past 30 days, whereas 79.8% reported having engaged in at least 1 vigorous activity during the past 30 days. In the high-PA group, 74.6% reported having engaged in at least 1 moderate activity during the past 30 days, whereas 96.9% reported having engaged in at least 1 vigorous activity during the past 30 days. Each incremental increase in PA level per day was associated with a 0.03% decrease in HOMA-IR, a 0.004-mg/dL decrease in LDL-C concentrations, a 0.01-mg/dL decrease in TG concentrations, and a 0.001-mg/dL increase in HDL-C concentrations.

Table Graphic Jump LocationTable 3. Characteristics of PA Level Groups and the Multivariate Linear Regression Coefficientsa
METABOLIC PARAMETERS AND ANTHROPOMETRIC MEASUREMENTS ASSOCIATED WITH SSB INTAKE AND PA LEVELS

Because the combination of dietary modification (with decreased SSB intake) and exercise (with increased PA levels) are typically recommended to children and adolescents either at risk for or currently diagnosed with obesity, insulin resistance, or metabolic syndrome, we evaluated the factors associated with the combination of each extreme (ie, low SSB intake and high PA vs high SSB intake and low PA level); the characteristics of the 2 groups as well as the results of the multivariate linear regression analyses evaluating the relationship between low SSB intake and high PA levels with insulin resistance–associated metabolic parameters and anthropometric measurements are shown in Table 4. The combination of low SSB intake and high PA levels was significantly associated with increased HDL-C concentrations, a lower HOMA-IR, and lower TG concentrations. Furthermore, the finding that the β coefficients for these 3 outcome variables (HDL-C concentrations, HOMA-IR, and TG concentrations) are greater with the combination of low SSB intake and high PA levels than with either alone suggests an effect modification.

Table Graphic Jump LocationTable 4. Characteristics of SSB and PA Level Group Combinations and the Multivariate Linear Regression Coefficients Evaluating Their Relationship With Insulin Resistance–Associated Metabolic Parameters and Anthropometric Measurements
SEX-SPECIFIC SUBGROUP ANALYSES

Because differences between the sexes may influence the m etabolic parameters and anthropometric measurements associated with SSB intake, PA levels, and their combination, sex-specific subgroup analyses were also performed; these results are shown in Table 5. (Although the NHANES 1999-2004 data did not include any information on pubertal status, it did include the age of first menses in female subjects. Thus, we were able to control for menarche in our subgroup analyses of female adolescents; subgroup analyses of male adolescents are included for comparison.)

Table Graphic Jump LocationTable 5. Sex-Specific Multivariate Linear Regression Analyses Evaluating the Relationship Between SSB Intake, PA Levels, and Their Combination With Insulin Resistance–Associated Metabolic Parameters and Anthropometric Measurements

In female adolescents, each additional SSB serving equivalent consumed per day was associated with a 7% increase in HOMA-IR, a 2.25-mg/dL increase in TG concentrations, a 0.38-mm Hg increase in SBP, a 0.75-cm increase in WC, a 0.84-percentile increase in BMI for age, and a 0.73-mg/dL decrease in HDL-C concentration. However, in male adolescents, each additional SSB serving equivalent consumed per day was associated with a 0.29-cm increase in WC, a 0.78-percentile increase in BMI for age, and a 0.35-mg/dL decrease in HDL-C concentration. Alternatively, each incremental increase in PA level in female adolescents was only associated with a 0.01-mg/dL decrease in TG concentrations, whereas each incremental increase in PA level in male adolescents was associated with a 0.04% decrease in HOMA-IR, a 0.005-mg/dL decrease in LDL-C concentrations, a 0.01-mg/dL decrease in TG concentrations, and a 0.001-cm decrease in WC. Furthermore, although the combination of low SSB intake and high PA levels in female adolescents revealed no significant associations with any insulin resistance–associated metabolic parameters or anthropometric measurements, the combination of low SSB intake and high PA levels in male adolescents was significantly associated with increased HDL-C concentrations, a lower HOMA-IR, and lower TG concentrations.

In these nationally representative samples of US adolescents, we found significant differences between the degrees of SSB intake and PA levels with insulin resistance–associated metabolic parameters and anthropometric measurements.

Multivariate linear regression analyses showed that increased SSB consumption was independently associated with increased HOMA-IR, SBP, WC, and BMI percentile for age and sex and decreased HDL-C concentrations; alternatively, increased PA levels were independently associated with decreased HOMA-IR, LDL-C concentrations, and TG concentrations and increased HDL-C concentrations. Furthermore, low SSB intake and high PA levels appear to modify each others' effects on decreasing HOMA-IR and TG concentrations and increasing HDL-C concentrations. These findings thus support the promotion of each lifestyle parameter (ie, decreased SSB consumption and increased PA levels) in children or adolescents either at risk for or currently diagnosed with obesity, insulin resistance, or metabolic syndrome.

Subgroup analyses based on sex also showed that SSB intake and PA levels were independently associated with insulin resistance–associated metabolic parameters and anthropometric differences; however, the associations of each were sex-specific. In female adolescents, increased SSB consumption was associated with an increase in HOMA-IR, TG concentrations, SBP, WC, and BMI percentile and a decrease in HDL-C concentrations. However, in male adolescents, SSB consumption was only associated with an increase in WC and BMI percentile and a decrease in HDL-C concentrations. Alternatively, increased PA levels in female adolescents were only associated with a decrease in TG concentrations, whereas increased PA levels in male adolescents were associated with a decrease in HOMA-IR, LDL-C concentrations, TG concentrations, and WC. Furthermore, although the combination of low-SSB intake and high PA levels revealed no significant associations with any insulin resistance–associated metabolic parameters or anthropometric measurements in female adolescents, the combination of low SSB intake and high PA levels was significantly associated with a lower HOMA-IR, lower TG concentrations, and increased HDL-C concentrations, just as it was when the cohort was studied in its entirety.

The sex-related differences that we observed with respect to the association of SSB intake and PA levels with insulin resistance–associated metabolic parameters and anthropometric measurements are intriguing; however, given the many physiological differences that exist between male and female adolescents during the adolescent years and our limited ability in female adolescents and inability in male adolescents to assess and control for pubertal status, our results are not necessarily surprising. Furthermore, many other variables that we could not account for during our analyses such as a subject's dietary behavior and precise body composition measurements may have influenced our results.

Sugar-sweetened beverages may negatively affect hepatic metabolism and energy homeostasis,24 and the consumption of SSBs has been implicated in many10,11,24,54 but not all27,28 studies as a contributing factor to the increased incidence and prevalence of overweight and obesity. The odds of a pediatric patient becoming obese—and therefore at risk for developing metabolic syndrome—has been reported to increase by approximately 60% for each additional SSB serving per day,12 and individuals who consume a large portion of their daily energy in the form of SSBs are reported to have increased body weight, increased fat mass, dyslipidemia, and high blood pressure.29,30 Although discrepancy exists in the literature regarding the association of SSB intake with a subject's metabolic status (which may in large part be because of the many types of studies that have been performed [ie, prospective, cross-sectional, and retrospective—all with different inclusion/exclusion criteria and means of data analysis]), our study shows an independent relationship between increased SSB intake and increased HOMA-IR, SBP, WC, and BMI percentile for age and sex and decreased HDL-C concentrations, thus supporting the beneficial relationship of limited SSB intake with insulin resistance– associated parameters.

Alternatively, PA causes more of its metabolic-changing effects by its action on skeletal muscle, and regular exercise induces long-term changes within the skeletal muscle that improve whole-body insulin sensitivity.35,40,41 Although there have been fewer epidemiological studies regarding the association of PA levels with overweight and obesity in children, data from large adult studies42,43 have convincingly shown the metabolic benefits of increased exercise. Moreover, a recent study in adolescents showed that moderate PA was positively related to improved glucose metabolism and resting energy expenditure.55 Our study further supports the metabolic and health benefits of exercise and shows an independent relationship between increased PA levels and decreased HOMA-IR, LDL-C concentrations, and TG concentrations and increased HDL-C concentrations.

Importantly, the finding that the absolute values of the β coefficients from our multivariate linear regression analyses were consistently smaller for the PA analyses than for the SSB analyses does not diminish the significance of exercise and its relationship with insulin resistance–associated parameters; rather, it is a reflection of the methodology used. Sugar-sweetened beverage intake was defined as the number of SSB serving equivalents a subject consumed per day, whereas PA levels were defined as (the mean number of times a subject did activity per day) × (the average duration of each time in minutes) × (the metabolic equivalent score). As would be expected from its determination from multiple variables, a much wider range of PA levels were calculated in the study population than SSB intake levels, influencing the effect of a single unit of incremental change on the outcome. However, based on our model, even a small increase in daily exercise by an individual (eg, increasing the mean number of times a subject engaged in PA from 2 to 3 per day and increasing the average duration of physical activity from 10 to 15 minutes) would have a profound effect on their calculated PA level (increasing it by 225%), leading to larger changes in the outcome variables than would be expected by the small value of the β coefficient.

Given that the biological effects of SSB consumption and exercise are different, we were not surprised to find that the combination of low SSB intake and high PA levels appeared to modify each others' effects on several insulin resistance–associated parameters. However, the fact that we only observed this effect for HOMA-IR, HDL-C concentrations, and TG concentrations, but not the other outcome variables, is interesting and requires further investigation with well-designed prospective studies. Nevertheless, HOMA-IR, HDL-C concentrations, and TG concentrations are commonly used in clinical practice as markers of a subject's metabolic status, and thus our data suggest that promoting decreased SSB consumption and increased levels of exercise in adolescents is important for overall health.

Our study has several significant limitations. First, because our study is cross-sectional, all we are able to report are associations as opposed to causality. Second, because the pubertal status of our subjects was not documented in the NHANES periods that we studied, we are unable to adjust our analyses for the subjects' degree of sexual maturation. Third, studies such as ours that use questionnaire data have inherent limitations: (1) the recall method is subject to inaccuracy and bias, especially with behaviors such as dietary habits56 and levels of exercise52; and (2) an individual's dietary habits and levels of exercise can vary greatly from one day to the next, limiting the reliability of short-term recall on long-term patterns. However, given that overweight subjects often underreport their levels of energy intake56 and less active adolescents often overestimate their degree of physical fitness,52 we can have confidence in our results because these biases would be expected to diminish rather than enhance our ability to find significant associations between SSB consumption and PA levels with insulin resistance-associated measures.

In summary, we report that SSB intake and PA levels are each independently associated with insulin resistance–associated metabolic parameters and anthropometric measurements in adolescents; moreover, low SSB intake and high PA levels appear to modify each others' effect on several health-related outcome variables. Thus, although prospective studies are needed to directly test the effects of dietary modification and consistent exercise on the development of obesity, insulin resistance, and metabolic syndrome in the pediatric population, pediatricians should continue promoting these lifestyle modifications in efforts to improve overall health.

Correspondence: Andrew A. Bremer, MD, PhD, Department of Pediatrics, Division of Endocrinology, 2516 Stockton Blvd, Ste 384, Sacramento, CA 95817-2208 (andrew.bremer@ucdmc.ucdavis.edu).

Accepted for Publication: December 17, 2008.

Author Contributions: Dr Bremer had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Bremer and Byrd. Acquisition of data: Auinger. Analysis and interpretation of data: Bremer, Auinger, and Byrd. Drafting of the manuscript: Bremer. Critical revision of the manuscript for important intellectual content: Bremer, Auinger, and Byrd. Statistical analysis: Bremer, Auinger, and Byrd. Administrative, technical, and material support: Bremer. Study supervision: Byrd.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grants KL2 RR024144 and UL1 RR024146 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research.

Disclaimer: The contents of this article are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or the NIH. Information on NCRR is available at http://www.ncrr.nih.gov. Information on Re-engineering the Clinical Research Enterprise is available at http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp.

Additional Contributions: We thank Daphne Carlson Bremer, DVM, MPVM, and Daniel Tancredi, PhD, for their assistance in the preparation of this manuscript.

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Bell  LMWatts  KSiafarikas  A  et al.  Exercise alone reduces insulin resistance in obese children independently of changes in body composition. J Clin Endocrinol Metab 2007;92 (11) 4230- 4235
PubMed
Winnick  JJSherman  WMHabash  DL  et al.  Short-term aerobic exercise training in obese humans with type 2 diabetes mellitus improves whole-body insulin sensitivity through gains in peripheral, not hepatic insulin sensitivity. J Clin Endocrinol Metab 2008;93 (3) 771- 778
PubMed
Szamosi  ACzinner  ASzamosi  T  et al.  Effect of diet and physical exercise treatment on insulin resistance syndrome of schoolchildren. J Am Coll Nutr 2008;27 (1) 177- 183
PubMed
Venables  MCJeukendrup  AE Endurance training and obesity: effect on substrate metabolism and insulin sensitivity. Med Sci Sports Exerc 2008;40 (3) 495- 502
PubMed
Johnson  JLSlentz  CAHoumard  JA  et al.  Exercise training amount and intensity effects on metabolic syndrome (from studies of a targeted risk reduction intervention through defined exercise). Am J Cardiol 2007;100 (12) 1759- 1766
PubMed
LaMonte  MJBarlow  CEJurca  RKampert  JBChurch  TSBlair  SN Cardiorespiratory fitness is inversely associated with the incidence of metabolic syndrome: a prospective study of men and women. Circulation 2005;112 (4) 505- 512
PubMed
Rennie  KL McCarthy  NYazdgerdi  SMarmot  MBrunner  E Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003;32 (4) 600- 606
PubMed
Ford  ESKohl  HW  IIIMokdad  AHAjani  UA Sedentary behavior, physical activity, and the metabolic syndrome among US adults. Obes Res 2005;13 (3) 608- 614
PubMed
Ren  JMSemenkovich  CFGulve  EAGao  JHolloszy  JO Exercise induces rapid increases in GLUT4 expression, glucose transport capacity, and insulin-stimulated glycogen storage in muscle. J Biol Chem 1994;269 (20) 14396- 14401
PubMed
Perseghin  GPrice  TBPetersen  KF  et al.  Increased glucose transport-phosphorylation and muscle glycogen synthesis after exercise training in insulin-resistant subjects. N Engl J Med 1996;335 (18) 1357- 1362
PubMed
Tuomilehto  JLindstrom  JEriksson  JG  et al. Finnish Diabetes Prevention Study Group, Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344 (18) 1343- 1350
PubMed
Knowler  WCBarrett-Connor  EFowler  SE  et al. Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346 (6) 393- 403
PubMed
Cook  SWeitzman  MAuinger  PNguyen  MDietz  WH Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med 2003;157 (8) 821- 827
PubMed
Duncan  GELi  SMZhou  XH Prevalence and trends of a metabolic syndrome phenotype among US adolescents, 1999-2000. Diabetes Care 2004;27 (10) 2438- 2443
PubMed
Weiss  RDziura  JBurgert  TS  et al.  Obesity and the metabolic syndrome in children and adolescents. N Engl J Med 2004;350 (23) 2362- 2374
PubMed
Srinivasan  SRMyers  LBerenson  GS Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study. Diabetes 2002;51 (1) 204- 209
PubMed
Mundt  CABaxter-Jones  ADWhiting  SJBailey  DAFaulkner  RAMirwald  RL Relationships of activity and sugar drink intake on fat mass development in youths. Med Sci Sports Exerc 2006;38 (7) 1245- 1254
PubMed
National Center for Health Statistics, National health and nutrition examination survey. Centers for Disease Control and Prevention Web site. www.cdc.gov/nchs/nhanes.htm. Accessed August 1, 2007
National Center for Health Statistics, National health and nutrition examination survey clinical growth charts. Centers for Disease Control and Prevention Web site. www.cdc.gov/nchs/about/major/nhanes/growthcharts/clinical_charts.htm. Accessed August 1, 2007
Matthews  DRHosker  JPRudenski  ASNaylor  BATreacher  DFTurner  RC Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28 (7) 412- 419
PubMed
Pate  RRWang  CYDowda  MFarrell  SWO'Neill  JR Cardiorespiratory fitness levels among US youth 12 to 19 years of age: findings from the 1999-2002 National Health and Nutrition Examination Survey. Arch Pediatr Adolesc Med 2006;160 (10) 1005- 1012
PubMed
Haskell  WLLee  IMPate  RR  et al. American College of Sports Medicine; American Heart Association, Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 2007;116 (9) 1081- 1093
PubMed
Schulze  MBManson  JELudwig  DS  et al.  Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA 2004;292 (8) 927- 934
PubMed
Thomas  ASGreene  LFArd  JDOster  RADarnell  BEGower  BA Physical activity may facilitate diabetes prevention in adolescents [published online ahead of print October 7, 2008]. Diabetes Care 2009;32 (1) 9- 13
PubMed
Briefel  RRSempos  CT McDowell  MAChien  SAlaimo  K Dietary methods research in the third National Health and Nutrition Examination Survey: underreporting of energy intake. Am J Clin Nutr 1997;65 (4) ((suppl)) 1203S- 1209S
PubMed

Figures

Tables

Table Graphic Jump LocationTable 1. Characteristics of US Adolescents Aged 12 to 19 Years: NHANES 1999-2004 Cohorts
Table Graphic Jump LocationTable 2. Characteristics of SSB Intake Groups and the Multivariate Linear Regression Coefficientsa
Table Graphic Jump LocationTable 3. Characteristics of PA Level Groups and the Multivariate Linear Regression Coefficientsa
Table Graphic Jump LocationTable 4. Characteristics of SSB and PA Level Group Combinations and the Multivariate Linear Regression Coefficients Evaluating Their Relationship With Insulin Resistance–Associated Metabolic Parameters and Anthropometric Measurements
Table Graphic Jump LocationTable 5. Sex-Specific Multivariate Linear Regression Analyses Evaluating the Relationship Between SSB Intake, PA Levels, and Their Combination With Insulin Resistance–Associated Metabolic Parameters and Anthropometric Measurements

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Bell  LMWatts  KSiafarikas  A  et al.  Exercise alone reduces insulin resistance in obese children independently of changes in body composition. J Clin Endocrinol Metab 2007;92 (11) 4230- 4235
PubMed
Winnick  JJSherman  WMHabash  DL  et al.  Short-term aerobic exercise training in obese humans with type 2 diabetes mellitus improves whole-body insulin sensitivity through gains in peripheral, not hepatic insulin sensitivity. J Clin Endocrinol Metab 2008;93 (3) 771- 778
PubMed
Szamosi  ACzinner  ASzamosi  T  et al.  Effect of diet and physical exercise treatment on insulin resistance syndrome of schoolchildren. J Am Coll Nutr 2008;27 (1) 177- 183
PubMed
Venables  MCJeukendrup  AE Endurance training and obesity: effect on substrate metabolism and insulin sensitivity. Med Sci Sports Exerc 2008;40 (3) 495- 502
PubMed
Johnson  JLSlentz  CAHoumard  JA  et al.  Exercise training amount and intensity effects on metabolic syndrome (from studies of a targeted risk reduction intervention through defined exercise). Am J Cardiol 2007;100 (12) 1759- 1766
PubMed
LaMonte  MJBarlow  CEJurca  RKampert  JBChurch  TSBlair  SN Cardiorespiratory fitness is inversely associated with the incidence of metabolic syndrome: a prospective study of men and women. Circulation 2005;112 (4) 505- 512
PubMed
Rennie  KL McCarthy  NYazdgerdi  SMarmot  MBrunner  E Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003;32 (4) 600- 606
PubMed
Ford  ESKohl  HW  IIIMokdad  AHAjani  UA Sedentary behavior, physical activity, and the metabolic syndrome among US adults. Obes Res 2005;13 (3) 608- 614
PubMed
Ren  JMSemenkovich  CFGulve  EAGao  JHolloszy  JO Exercise induces rapid increases in GLUT4 expression, glucose transport capacity, and insulin-stimulated glycogen storage in muscle. J Biol Chem 1994;269 (20) 14396- 14401
PubMed
Perseghin  GPrice  TBPetersen  KF  et al.  Increased glucose transport-phosphorylation and muscle glycogen synthesis after exercise training in insulin-resistant subjects. N Engl J Med 1996;335 (18) 1357- 1362
PubMed
Tuomilehto  JLindstrom  JEriksson  JG  et al. Finnish Diabetes Prevention Study Group, Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344 (18) 1343- 1350
PubMed
Knowler  WCBarrett-Connor  EFowler  SE  et al. Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346 (6) 393- 403
PubMed
Cook  SWeitzman  MAuinger  PNguyen  MDietz  WH Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med 2003;157 (8) 821- 827
PubMed
Duncan  GELi  SMZhou  XH Prevalence and trends of a metabolic syndrome phenotype among US adolescents, 1999-2000. Diabetes Care 2004;27 (10) 2438- 2443
PubMed
Weiss  RDziura  JBurgert  TS  et al.  Obesity and the metabolic syndrome in children and adolescents. N Engl J Med 2004;350 (23) 2362- 2374
PubMed
Srinivasan  SRMyers  LBerenson  GS Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study. Diabetes 2002;51 (1) 204- 209
PubMed
Mundt  CABaxter-Jones  ADWhiting  SJBailey  DAFaulkner  RAMirwald  RL Relationships of activity and sugar drink intake on fat mass development in youths. Med Sci Sports Exerc 2006;38 (7) 1245- 1254
PubMed
National Center for Health Statistics, National health and nutrition examination survey. Centers for Disease Control and Prevention Web site. www.cdc.gov/nchs/nhanes.htm. Accessed August 1, 2007
National Center for Health Statistics, National health and nutrition examination survey clinical growth charts. Centers for Disease Control and Prevention Web site. www.cdc.gov/nchs/about/major/nhanes/growthcharts/clinical_charts.htm. Accessed August 1, 2007
Matthews  DRHosker  JPRudenski  ASNaylor  BATreacher  DFTurner  RC Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28 (7) 412- 419
PubMed
Pate  RRWang  CYDowda  MFarrell  SWO'Neill  JR Cardiorespiratory fitness levels among US youth 12 to 19 years of age: findings from the 1999-2002 National Health and Nutrition Examination Survey. Arch Pediatr Adolesc Med 2006;160 (10) 1005- 1012
PubMed
Haskell  WLLee  IMPate  RR  et al. American College of Sports Medicine; American Heart Association, Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 2007;116 (9) 1081- 1093
PubMed
Schulze  MBManson  JELudwig  DS  et al.  Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA 2004;292 (8) 927- 934
PubMed
Thomas  ASGreene  LFArd  JDOster  RADarnell  BEGower  BA Physical activity may facilitate diabetes prevention in adolescents [published online ahead of print October 7, 2008]. Diabetes Care 2009;32 (1) 9- 13
PubMed
Briefel  RRSempos  CT McDowell  MAChien  SAlaimo  K Dietary methods research in the third National Health and Nutrition Examination Survey: underreporting of energy intake. Am J Clin Nutr 1997;65 (4) ((suppl)) 1203S- 1209S
PubMed

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