AJCN EB Program 2010
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Rizzo, N. S
Right arrow Articles by Sjöström, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Rizzo, N. S
Right arrow Articles by Sjöström, M.
Agricola
Right arrow Articles by Rizzo, N. S
Right arrow Articles by Sjöström, M.
American Journal of Clinical Nutrition, Vol. 87, No. 3, 586-592, March 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Associations between physical activity, body fat, and insulin resistance (homeostasis model assessment) in adolescents: the European Youth Heart Study1,2,3

Nico S Rizzo, Jonatan R Ruiz, Leila Oja, Tomas Veidebaum and Michael Sjöström

1 From the Unit for Preventive Nutrition, Department of Biosciences and Nutrition, NOVUM, Karolinska Institutet, Huddinge, Sweden (NSR, JRR, and MS); Department of Physiology, School of Medicine, University of Granada, Granada, Spain (JRR); and National Institute for Health Development and Estonian Centre of Behavioural and Health Sciences, Talinn, Estonia (LO and TV)

2 Supported by grants from the Stockholm County Council (MS); by the Estonian Center of Behavioral and Health Sciences and ESF grants 3277, 5209, and 6788; by grants 0942706s06 and 0192112s02 from the Ministry of Education and Science (TV); and by grant AP2003-2128 from the Ministerio de Educación y Ciencia de España (JRR).

3 Reprints not available. Address correspondence to NS Rizzo, Karolinska Institutet, Unit for Preventive Nutrition, Department of Biosciences and Nutrition at NOVUM, 14157 Huddinge, Sweden. E-mail: nico.rizzo{at}biosci.ki.se.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: More and better data are needed to understand the action of physical activity (PA) on insulin resistance and the concomitant relation with body fat in adolescence.

Objective: We examined the relation between total PA and intensity levels with insulin resistance under special consideration of waist circumference and skinfold thickness.

Design: This was a cross-sectional study of 613 adolescents (352 girls, 261 boys) with a mean (±SD) age of 15.5 ± 0.5 y from Sweden and Estonia. Total, low, moderate, and vigorous PA was measured by accelerometry. Body fat estimators included waist circumference and the sum of 5 skinfold thicknesses. Fasting insulin and glucose were measured, and insulin resistance was calculated according to the homeostasis model assessment (HOMA). Linear regression analysis and analysis of covariance were used to determine the association between PA and insulin resistance while considering body fat. All estimates were adjusted for sex, country, pubertal status, and indicators of body fat when applicable.

Results: Total, moderate, and vigorous PA were inversely correlated with HOMA. Body fat estimators were positively correlated with HOMA. Significant contrasts in HOMA concentrations were seen when comparing the lower 2 tertiles with the upper tertile of PA indicators. Repeating the analysis with body fat estimators showed significant contrasts in HOMA concentrations when comparing the lower tertiles with the upper tertile.

Conclusion: In view of an increase in obesity in young people, the results accentuate the role of PA in sustaining metabolic balance in adolescence and the potential benefit of an increase of time spent at higher PA levels for youth with relatively elevated amounts of body fat.

Key Words: Insulin resistance • metabolic syndrome • diabetes • physical activity • epidemiology


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The past decade has witnessed a significant rise in the prevalence of insulin resistance among adolescents in both developing and industrialized countries (1, 2). Obesity seems to be the single most important cause of peripheral insulin resistance in young people (3). Total obesity and more central obesity in particular (4-6) were reported to be associated with metabolic risk factors, such as elevated blood pressure, hypercholesterolemia, hypertriglycerinemia, and insulin resistance in both older and younger persons (7-9).

The development of type 2 diabetes is a continuous process in time, and longitudinal studies indicate that obesity during childhood and adolescence is closely correlated to markers of insulin resistance later in life (9). Therefore, a greater in-depth knowledge of the factors affecting insulin resistance in younger age groups is of necessity in the development of effective prevention programs.

Although obesity is positively associated with insulin resistance, it was reported that physical activity (PA) is both inversely correlated with markers of insulin resistance (10, 11) and indicators of body fat (12, 13). Many studies investigating the association between PA and insulin resistance were done in overweight and obese subjects (14-17). Inasmuch, it remains of interest to elucidate the association between PA and insulin resistance in leaner persons.

Studies investigating the relation between PA and insulin resistance are often confined to questionnaire-based inquiries (18, 19). These questionnaires depend on the accuracy of recalling activities and give a less-refined account of PA than do more objective methods such as accelerometry, which were shown to provide adequate measures for PA in children and adolescents (20).

It is also of a public health interest to better understand the relative importance of total PA and the intensity levels of PA in respect to insulin resistance and body fat. The measurement of body fat and its distribution need to be, for practical purposes, both cost effective and reliable. As such, waist circumference is a preferred marker of central obesity in population studies because it measures visceral and subcutaneous fat in the abdominal region and hence total central fatness (5, 21), whereas skinfold thickness was shown to be a reliable indicator of body fat (21-23). Therefore, the aim of this study was to examine the relation between total PA and intensity levels with insulin resistance under special consideration of waist circumference and skinfold thickness. This becomes of special interest when considering the effect that amendable lifestyle factors may have on a person's health and well-being.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
In total, 613 adolescents (352 girls and 261 boys), mean (±SD) age of 15.5 ± 0.5 y, belonging to the Estonian and Swedish part of the European Youth Heart Study, were used in this analysis. Study design, selection criteria, and sample calculations were reported elsewhere (24, 25).

The local ethical committees approved the study protocols (Örebro City Council case no. 680/98, Huddinge University Hospital case no. 474/98, and Tartu University no. 49/30/1997). The subjects and their families received written information about the purpose and the content of the study. Written informed consent was obtained from all the participants.

Physical activity
PA was measured with an activity monitor (MTI model WAM 7164; Manufacturing Technology Inc, Shalimar, FL; formerly known as Computer Science and Applications Inc) during 4 consecutive days (2 weekdays and at least 1 weekend day) and can provide a valid estimation of PA (26). The small device (4.5 x 3.5 x 1.0 cm, 43 g) was initialized, and the data were downloaded through a computer interface having no external controls that could be manipulated. The device was attached with an elastic waistband on the right hip and was to be worn during the entire day except for activities that could damage it such as showering and swimming. At least 3 d of recording, with a minimum of 10 h registration/d, was set as an inclusion criterion. A measure of average PA intensity (henceforth called total PA) was expressed as the sum of recorded counts per epoch (1 min) divided by total daily registered time. Activity intensity levels were determined by applying the age-specific energy expenditure prediction formula by Freedson et al (27-29) with metabolic equivalent (MET) = 2.757 + (0.0015 x counts per minute) –[0.08957 x age (in y)] –[0.000038 x counts per minute x age (in y)]. MET cutoff values for moderate and vigorous PA were based on limits published by Trost et al (30). Cutoff points for PA intensity levels were set at MET rates corresponding to low PA of <1.5 MET, moderate PA of 3 ≤ 6 MET, moderate and vigorous PA of ≥3 MET, and vigorous PA of >6 MET. Each minute over the specific cutoff was summarized in the corresponding intensity level group. Time spent in low PA was defined as the sum of time per day in which counts per minute were <100 and would correspond to <1.5 MET (31). All accelerometers were calibrated by a manufacturer-supplied calibrator. Validation studies examining the accelerometer used in this study and the construction of summary variables for intensity of movement suggest that it is a valid and reliable measure of children's PA (32, 33).

Physical examination
Body mass index (BMI; in kg/m2) was calculated. Skinfold thicknesses and waist circumference were used as markers of body fat. Both have the advantage of being relatively simple to measure and being also well correlated with metabolic risk factors (5, 6, 21-23). Skinfold thickness was measured with a Harpenden caliper (Baty International, Burgess Hill, United Kingdom) at the biceps, triceps, subscapular, suprailiac, and triceps surae areas on the left side of the body according to the criteria described by Lohman et al (34). All measurements were taken twice and in rotation, and the mean was calculated. If the difference between the 2 measurements was >2 mm, a third measurement was taken and the 2 closest measurements were averaged. The sum of the 5 skinfold thicknesses was used as an indicator of comparative subcutaneous body fat (35, 36).

Waist circumference (in cm), being used as a marker of central body fat, was measured with a metal anthropometric tape midway between the lower rib margin and the iliac crest, at the end of gentle expiration. The measurements were taken twice, and the mean of the 2 values was used for further calculations. Waist-to-height ratio was also calculated. For the purpose of comparing the data with previous publications, the subjects were also categorized as nonoverweight, overweight, and obese, applying the cutoff points suggested by Cole et al (37).

Identification of pubertal development was assessed according to Tanner (38) by a researcher of the same sex as the subject. Breast development in girls and genital development in boys were used for pubertal classification.

Blood samples
Blood samples were taken by venipuncture after an overnight fast with the subject in the supine position; the blood was collected in vacuum tubes (Vacuette; Greiner International, Düsseldorf, Germany). The fasting state was verbally confirmed by the subject before blood sampling. Serum concentrations of insulin and glucose were measured by standardized methods. A detailed description of the blood analysis was reported by Wennlöf et al (39).

In this study homeostasis model assessment (HOMA) was calculated with the HOMA2 computer model (HOMA CALCULATOR, version 2.2; Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Medicine, Oxford, United Kingdom) (40, 41). HOMA models β cell function and insulin resistance from fasting insulin and glucose concentrations and was compared with a number of well-validated methods (41, 42).

Statistical analysis
To achieve normality in the residuals, HOMA, fasting insulin, and glucose were logarithmically transformed. Sex differences were assessed by analysis of variance and by the chi-square test for nominal data and corrected for mass significance according to Bonferroni Holm (43). Spearman's correlation coefficients were reported for markers of insulin resistance and body fat.

Linear regression analysis was used to measure the relation between markers of insulin resistance and PA intensity levels after controlling for sex, country, pubertal status, and markers of body fat (waist circumference and skinfold thickness). Waist circumference and skinfold thickness were centered by subtracting their respective mean for the regression analysis. Interaction effects between PA intensity levels and markers of body fat and between sex and PA intensity levels were tested by inserting product terms for the relevant variables. Regression analysis was repeated on all of the dependent variables (HOMA, insulin, and glucose) for each of the PA intensity levels (low, moderate, moderate and vigorous, vigorous, and total PA).

Analysis of covariance (ANCOVA) was used in testing differences of logarithmically transformed HOMA concentrations stratified by tertiles of PA intensity (total, moderate, and vigorous PA) and tertiles of body fat indicators by skinfold thickness and waist circumference. Nonoverweight and overweight or obesity according to the cutoffs published by Cole et al (37) were compared. Adjustments were made for sex, county, and pubertal status. Bonferroni's adjustments for multiple comparisons were used to examine the contrasts between the tertiles.

Further analysis was performed by adjusting also for age, height, or BMI in both the regression analyses and the ANCOVA. Analysis was performed with the statistical software packages SPSS 15.0 (SPSS Inc, Chicago, IL) with the level of significance set at 0.05.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The descriptive characteristics of the study sample, including a comparison between the sexes, are shown in Table 1Go. Bivariate correlation analysis showed strong correlations between waist circumference and skinfold thickness with BMI. All markers of insulin resistance were significantly correlated to each other. Waist circumference and BMI were significantly correlated to HOMA, insulin, and glucose. Skinfold thickness was significantly correlated with HOMA and insulin (Table 2Go).


View this table:
[in this window]
[in a new window]

  		TABLE 1. Descriptive characteristics of the study sample1

 

View this table:
[in this window]
[in a new window]

  		TABLE 2. Spearman's correlation coefficient r between insulin resistance markers and markers of body fat1

 
The statistics of the regression models that used HOMA, insulin, and glucose as dependent variable are depicted in Table 3Go. Each model was controlled for sex, country, pubertal status, and waist circumference. All the PA intensity values (total, moderate, moderate and vigorous, and vigorous PA) with the exception of low PA were significantly and inversely correlated to the outcome variables HOMA, insulin, and glucose. Repeating the analysis with skinfold thickness instead of waist circumference as a predictor in the regression analysis did not change the outcome. The results also remained unaltered when adding BMI or body height as confounders in the regression equation (data not shown). Stratifying the analysis by sex did not alter the results for the outcome variables HOMA and insulin, whereas PA intensity values remained significantly associated with glucose only in boys. Including an interaction term in each model constituted by the product of waist circumference and the respective PA intensity value showed that there was a significant interaction between vigorous PA and waist circumference with HOMA and also with insulin as outcome variables. In the regression analysis with HOMA as an outcome variable, the standardized β coefficients were vigorous PA (β1 = –0.143, P < 0.001), waist circumference (β2 = 0.417, P < 0.001), vigorous PA x waist circumference (β3 = –0.138, P = 0.001). With insulin as outcome variable, the standardized β coefficients were vigorous PA (β1 = –0.142, P < 0.001), waist circumference (β2 = 0.420, P < 0.001), vigorous PA x waist circumference (β3 = –0.137, P = 0.006). Repeating the regression analysis with skinfold thickness and waist-to-height ratios as further indicators of body fat yielded similar results (data not shown).


View this table:
[in this window]
[in a new window]

  		TABLE 3. Associations between markers of insulin resistance and physical activity (PA) intensity1

 
When differentiating PA and body fat markers into groups of tertiles, ANCOVA showed significant main effects for PA and body fat markers as represented in Table 4Go. No significant interaction effect between tertiles of body fat markers and PA intensity tertiles was reached. Significant contrasts in HOMA concentrations were seen when comparing the lowest tertile with the upper tertiles of PA intensity levels and also when comparing the middle with the upper tertiles of total and vigorous PA (Figure 1Go).


View this table:
[in this window]
[in a new window]

  		TABLE 4. Main effects of physical activity (PA) and body fat marker tertiles on homeostasis model assessment with ANCOVA to adjust for sex, country, and pubertal status1

 

Figure 1
View larger version (14K):
[in this window]
[in a new window]

  		FIGURE 1.. ANCOVA was used in testing differences in logarithmically transformed homeostasis model assessment (HOMA) concentrations stratified by tertiles of physical activity (PA) intensity (total, moderate, and vigorous PA). Adjustments were made for sex, country, and pubertal status. Bonferroni's adjustments for multiple comparisons were used to examine the contrasts between the tertiles. Mean values are shown with error bars representing SE. P values represent significant contrasts between HOMA concentrations. n = 204 or 205 per tertile.

 
Repeating the analysis with tertiles of body fat estimators showed significant contrasts in HOMA concentrations when comparing the lowest and middle tertiles with the upper tertiles of body fat estimators (Figure 2Go). Analysis with tertiles of BMI showed similar results (data not shown). When differentiating nonoverweight from overweight or obesity by the cutoff points published by Cole et al (37), ANCOVA showed a significant main effect for weight on HOMA with mean square = 7.597, R2 = 0.162, and P < 0.001 (Figure 2Go).


Figure 2
View larger version (11K):
[in this window]
[in a new window]

  		FIGURE 2.. ANCOVA was used in testing differences in logarithmically transformed homeostasis model assessment (HOMA) concentrations stratified by tertiles of body fat estimators (n = 204 or 205 per tertile): skinfold thickness (SF) and waist circumference (WC). Nonoverweight BMI (in kg/m2; n = 551) and overweight or obesity BMI (n = 62) according to the cutoff points published by Cole et al (37) were compared with the use of ANCOVA. Adjustments were made for sex, country, and pubertal status in all analyses. Bonferroni's adjustments for multiple comparisons were used to examine the contrasts between the tertiles. Mean values are shown with error bars representing SE. P values represent significant contrasts between HOMA concentrations.

 
Additional adjustments for BMI when applicable or for body height did not alter the outcomes in any of the analyses (data not shown). All tertile ranges used in the analysis, including their mean and CI, are shown in Table 5Go.


View this table:
[in this window]
[in a new window]

  		TABLE 5. Tertile ranges, means, and CIs of physical activity (PA) intensity, waist circumference, and sum of 5 skinfold thicknesses

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Previous studies were ambivalent on the issue of whether the association between PA and insulin resistance is independent of adiposity (14-17). Some of the discrepancies could be caused by differing assessment methods in measuring PA and insulin resistance. The use of questionnaires to assess PA instead of accelerometers might lead to diverse results, which might be the cause of failing to see an independent association between PA and markers of insulin in previous studies (14, 15). Differences seen in the relation between PA and insulin resistance could also be a consequence of analyzing different amounts and distribution of body fat.

The results of the current study show that there is a significant inverse relation between PA and insulin resistance and a positive relation between markers of body fat and insulin resistance in adolescents. When comparing the contrasts between PA tertiles on HOMA concentrations, the results indicate that principally higher levels of PA intensity and longer times spent at moderate and vigorous PA may have a positive effect on insulin resistance in adolescents. At the same time the results show the particular relation between higher amounts of body fat and insulin resistance when considering the significant contrasts that were seen between the highest tertile of body fat markers with the lower 2 tertiles, whereas there was no significant difference seen between the lowest and the middle tertiles. This relation is even more marked when comparing nonoverweight and overweight or obese adolescents in which case the HOMA concentrations are almost tripled in the overweight or obese group.

Inasmuch, the results may indicate that the inverse association between PA and insulin resistance may not always be detectable when comparing leaner persons. Concurrently, the results may also suggest that an increase of time spent at vigorous PA might be of singular benefit for adolescents with higher amounts of body fat.

A question that has been raised in the past is whether insulin resistance response to PA is similar in girls and boys. A recent study that used questionnaire-derived PA data and quantitative insulin sensitivity check index found an inverse association between PA and insulin resistance only in adolescent boys after controlling for BMI (16). Because the differing results could have been caused by the use of diverse markers of insulin resistance, we repeated the analysis with the use of the quantitative insulin sensitivity check index as a replacement for HOMA, yet the outcomes remained unchanged. The differing results could be caused by the dissimilar methods used in assessing PA that could explain why studies that used accelerometry confirm our findings (10).

In our study sample, PA remained independently associated with HOMA in both sexes even after controlling for pubertal maturity, BMI, and waist circumference or skinfold thickness. This finding becomes even more relevant when considering that BMI, waist circumference, and skinfold thickness are strongly correlated and that all 3 measures are considered markers of body fat (21). As indicated in previous studies (10), our results confirm the finding that the association between PA and glucose concentrations is not always equivocal in the sexes, insofar as in our study the inverse relation between PA and glucose was only significant in girls.

It is known that regular PA influences insulin action on skeletal muscle glucose and fat metabolism (44). The mechanisms by which PA may decrease insulin resistance are not yet fully explored (45, 46). It was shown that vigorous PA results in physiologic adaptations of skeletal muscle cells in adults. Some of the physiologic adaptations include an increase in capillary supply to skeletal muscles (47), an increase in the activities of enzymes of the mitochondrial electron transport chain, and a concomitant increase in mitochondrial volume and density (48). In addition, an increased substrate use (49) with a decrease of carbohydrate oxidation and an increased muscle glucose transport might play a role (50). Indirectly, regular PA may act by increasing lean body mass and concomitantly reducing body fat (12).

In view of the increased importance that is given to the role of body fat and in particular central body fat in metabolic disease (51), our results indicate the relative importance of PA during adolescence when symptoms of insulin resistance might not yet be manifest. This becomes more pressing when considering the epidemic increase of obesity in young people (1).

A limitation of the study is the cross-sectional design, which implies that the direction of the causality cannot be determined. Furthermore, the standard for measuring insulin resistance or sensitivity is the euglycemic insulin glucose clamp method (52, 53). Yet its invasive nature, cost, and protocol make it difficult to implement in population studies. HOMA models β cell function and insulin resistance from fasting insulin and glucose concentrations and has as such been adapted as a validated global measure of changes in insulin resistance (41, 54, 55). At the same time the study was strengthened by the relatively large study population and accessibility to PA measurements by accelerometers in contrast to PA questionnaires.

Perspectively, our results are in agreement with previous findings from accelerometry data that show that total PA is inversely associated with markers of insulin resistance (10). Nevertheless, there is a need to differentiate. In subjects with relatively low body fat, one might fail to elucidate a significant association between PA and markers of insulin resistance. At the same time the results indicate that adolescents with higher amounts of body fat might profit most from increased time spent at vigorous PA levels and an overall increase of total PA.


   ACKNOWLEDGMENTS
 
We thank the late Maarika Harro, principal investigator of the Estonian section of the European Youth Heart Study, for her enthusiasm and foresight in her scientific work and data collection, the staff and teachers at the recruiting sites, all the volunteers, and the children without whom this investigation would not have been possible. We thank Manfred Hollenhorst, Hochschulrechenzentrum, Justus Liebig University, Giessen and Olle Carlsson, Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, for statistical advice.

The author's responsibilities were as follows—NSR: conceived the hypothesis, conducted the statistical analyses, and drafted the manuscript; JRR, LO, TV, and MS: contributed to the interpretation and discussion of the results; MS: selected the data and contributed to the concept and design of the European Youth Heart Study; all the authors critically revised the drafted manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Weiss R, Taksali SE, Caprio S. Development of type 2 diabetes in children and adolescents. Curr Diab Rep 2006;6:182–7.[Medline]
  2. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 2004;291:2847–50.[Abstract/Free Full Text]
  3. Caprio S. Insulin resistance in childhood obesity. J Pediatr Endocrinol Metab 2002;15(suppl 1):487–92.[Medline]
  4. Kopelman PG. Obesity as a medical problem. Nature 2000;404:635–43.[Medline]
  5. McCarthy HD. Body fat measurements in children as predictors for the metabolic syndrome: focus on waist circumference. Proc Nutr Soc 2006;65:385–92.[Medline]
  6. Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol 2006;35:83–92.[Free Full Text]
  7. Mesa JL, Ortega FB, Ruiz JR, et al. Anthropometric determinants of a clustering of lipid-related metabolic risk factors in overweight and non-overweight adolescents–influence of cardiorespiratory fitness. The Avena study. Ann Nutr Metab 2006;50:519–27.[Medline]
  8. Ruiz JR, Rizzo NS, Ortega FB, Loit HM, Veidebaum T, Sjostrom M. Markers of insulin resistance are associated with fatness and fitness in school-aged children: the European Youth Heart Study. Diabetologia 2007;50:1401–8.[Medline]
  9. Srinivasan SR, Myers L, Berenson GS. Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study. Diabetes 2002;51:204–9.[Abstract/Free Full Text]
  10. Brage S, Wedderkopp N, Ekelund U, et al. Objectively measured physical activity correlates with indices of insulin resistance in Danish children. Int J Obes Relat Metab Disord 2004;28:1503–8.[Medline]
  11. Rizzo NS, Ruiz JR, Hurtig-Wennlof A, Ortega FB, Sjostrom M. Relationship of physical activity, fitness, and fatness with clustered metabolic risk in children and adolescents: the European Youth Heart Study. J Pediatr 2007;150:388–94.[Medline]
  12. Ruiz JR, Rizzo NS, Hurtig-Wennlöf A, Ortega FB, Warnberg J, Sjöström M. Relations of total physical activity and intensity to fitness and fatness in children: the European Youth Heart Study. Am J Clin Nutr 2006;84:299–303.[Abstract/Free Full Text]
  13. Gutin B, Yin Z, Humphries MC, Barbeau P. Relations of moderate and vigorous physical activity to fitness and fatness in adolescents. Am J Clin Nutr 2005;81:746–50.[Abstract/Free Full Text]
  14. Ball GDC, Shaibi GQ, Cruz ML, Watkins MP, Weigensberg MJ, Goran MI. Insulin sensitivity, cardiorespiratory fitness, and physical activity in overweight Hispanic youth. Obes Res 2004;12:77–85.[Medline]
  15. Bunt JC, Salbe AD, Harper IT, Hanson RL, Tataranni PA. Weight, adiposity, and physical activity as determinants of an insulin sensitivity index in Pima Indian children. Diabetes Care 2003;26:2524–30.[Abstract/Free Full Text]
  16. Imperatore G, Cheng YJ, Williams DE, Fulton J, Gregg EW. Physical activity, cardiovascular fitness, and insulin sensitivity among U.S. adolescents: the National Health and Nutrition Examination Survey, 1999–2002. Diabetes Care 2006;29:1567–72.[Abstract/Free Full Text]
  17. Ku CY, Gower BA, Hunter GR, Goran MI. Racial differences in insulin secretion and sensitivity in prepubertal children: role of physical fitness and physical activity. Obes Res 2000;8:506–15.[Medline]
  18. Platat C, Wagner A, Klumpp T, Schweitzer B, Simon C. Relationships of physical activity with metabolic syndrome features and low-grade inflammation in adolescents. Diabetologia 2006;49:2078–85.[Medline]
  19. Romon M, Lafay L, Bresson JL, et al. Relationships between physical activity and plasma leptin levels in healthy children: the Fleurbaix-Laventie Ville Sante II Study. Int J Obes Relat Metab Disord 2004;28:1227–32.[Medline]
  20. de Vries SI, Bakker I, Hopman-Rock M, Hirasing RA, van Mechelen W. Clinimetric review of motion sensors in children and adolescents. J Clin Epidemiol 2006;59:670–80.[Medline]
  21. Brambilla P, Bedogni G, Moreno LA, et al. Crossvalidation of anthropometry against magnetic resonance imaging for the assessment of visceral and subcutaneous adipose tissue in children. Int J Obes (Lond) 2006;30:23–30.[Medline]
  22. Savva SC, Tornaritis M, Savva ME, et al. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord 2000;24:1453–8.[Medline]
  23. Schneider HJ, Glaesmer H, Klotsche J, et al. Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab 2007;92:589–94.[Abstract/Free Full Text]
  24. Riddoch C, Edwards D, Page A, et al. The European Youth Heart Study - cardiovascular disease risk factors in children: rationale, aims, study design, and validation of methods. J Phys Activity Health 2005;2:115–29.
  25. Wennlof AH, Yngve A, Sjostrom M. Sampling procedure, participation rates and representativeness in the Swedish part of the European Youth Heart Study (EYHS). Public Health Nutr 2003;6:291–9.[Medline]
  26. Trost SG. Objective measurement of physical activity in youth: current issues, future directions. Exerc Sport Sci Rev 2001;29:32–6.[Medline]
  27. Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc 2005;37(suppl):S523–30.
  28. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998;30:777–81.
  29. Freedson PSF, Sirard J, Debold E, et al. Calibration of the computer science and applications, inc. (CSA) Accelerometer 256. Med Sci Sports Exerc 1997;29:45.
  30. Trost SG, Pate RR, Sallis JF, et al. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc 2002;34:350–5.
  31. Ekelund U, Aman J, Yngve A, Renman C, Westerterp K, Sjostrom M. Physical activity but not energy expenditure is reduced in obese adolescents: a case-control study. Am J Clin Nutr 2002;76:935–41.[Abstract/Free Full Text]
  32. Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF. Prediction of activity energy expenditure using accelerometers in children. Med Sci Sports Exerc 2004;36:1625–31.
  33. Trost SG, Ward DS, Moorehead SM, Watson PD, Riner W, Burke JR. Validity of the computer science and applications (CSA) activity monitor in children. Med Sci Sports Exerc 1998;30:629–33.
  34. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign, IL: Human Kinetics Pub, 1991:55–70.
  35. Wang J, Thornton JC, Kolesnik S, Pierson RN Jr. Anthropometry in body composition. An overview. Ann N Y Acad Sci 2000;904:317–26.[Medline]
  36. Clarys JP, Provyn S, Marfell-Jones MJ. Cadaver studies and their impact on the understanding of human adiposity. Ergonomics 2005;48:1445–61.[Medline]
  37. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320:1240–3.[Abstract/Free Full Text]
  38. Tanner JM. Growth at adolescence. Oxford, United Kingdom: Blackwell, 1962.
  39. Wennlof AH, Yngve A, Nilsson TK, Sjostrom M. Serum lipids, glucose and insulin levels in healthy schoolchildren aged 9 and 15 years from Central Sweden: reference values in relation to biological, social and lifestyle factors. Scand J Clin Lab Invest 2005;65:65–76.[Medline]
  40. Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 1998;21:2191–2.[Medline]
  41. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care 2004;27:1487–95.[Abstract/Free Full Text]
  42. Pacini G, Mari A. Methods for clinical assessment of insulin sensitivity and beta-cell function. Best Pract Res Clin Endocrinol Metab 2003;17:305–22.[Medline]
  43. Holm S. A simple sequentially rejective multiple test procedure. Scand J Statist 1979;65–70.
  44. Christ-Roberts CY, Mandarino LJ. Glycogen synthase: key effect of exercise on insulin action. Exerc Sport Sci Rev 2004;32:90–4.[Medline]
  45. Gill JM, Malkova D. Physical activity, fitness and cardiovascular disease risk in adults: interactions with insulin resistance and obesity. Clin Sci (Lond) 2006;110:409–25.[Medline]
  46. Fluck M. Functional, structural and molecular plasticity of mammalian skeletal muscle in response to exercise stimuli. J Exp Biol 2006;209:2239–48.[Abstract/Free Full Text]
  47. Hawley JA. Adaptations of skeletal muscle to prolonged, intense endurance training. Clin Exp Pharmacol Physiol 2002;29:218–22.[Medline]
  48. Hoppeler H, Fluck M. Plasticity of skeletal muscle mitochondria: structure and function. Med Sci Sports Exerc 2003;35:95–104.
  49. Holloszy JO, Coyle EF. Adaptations of skeletal muscle to endurance exercise and their metabolic consequences. J Appl Physiol 1984;56:831–8.[Abstract/Free Full Text]
  50. Holloszy JO. Exercise-induced increase in muscle insulin sensitivity. J Appl Physiol 2005;99:338–43.[Abstract/Free Full Text]
  51. Despres J-P. Is visceral obesity the cause of the metabolic syndrome? Ann Med 2006;38:52–63.[Medline]
  52. Ferrannini E, Mari A. How to measure insulin sensitivity. J Hypertens 1998;16:895–906.[Medline]
  53. Gutt M, Davis CL, Spitzer SB, et al. Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures. Diabetes Res Clin Pract 2000;47:177–84.[Medline]
  54. Gungor N, Saad R, Janosky J, Arslanian S. Validation of surrogate estimates of insulin sensitivity and insulin secretion in children and adolescents. J Pediatr 2004;144:47–55.[Medline]
  55. Sarafidis PA, Lasaridis AN, Nilsson PM, et al. Validity and reproducibility of HOMA-IR, HOMA-IR, QUICKI and McAuley's indices in patients with hypertension and type II diabetes. J Hum Hypertens 2007;21:709–16.[Medline]
Received for publication May 24, 2007. Accepted for publication October 4, 2007.




This article has been cited by other articles:


Home page
 Am. J. Physiol. Regul. Integr. Comp. Physiol.Home page
L. L. Y. Chan, S. P. Sebert, M. A. Hyatt, T. Stephenson, H. Budge, M. E. Symonds, and D. S. Gardner
Effect of maternal nutrient restriction from early to midgestation on cardiac function and metabolism after adolescent-onset obesity
Am J Physiol Regulatory Integrative Comp Physiol, May 1, 2009; 296(5): R1455 - R1463.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Rizzo, N. S
Right arrow Articles by Sjöström, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Rizzo, N. S
Right arrow Articles by Sjöström, M.
Agricola
Right arrow Articles by Rizzo, N. S
Right arrow Articles by Sjöström, M.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS