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Evaluating eight indicators for identifying metabolic syndrome in Chinese and American adolescents

Pediatric Research (2024)Cite this article

Abstract

Background

Early intervention and diagnosis of Metabolic Syndrome (MetS) are crucial for preventing adult cardiovascular disease. However, the optimal indicator for identifying MetS in adolescent remains controversial.

Methods

In total,1408 Chinese adolescents and 3550 American adolescents aged 12–17 years were included. MetS was defined according to the modified version for adolescents based on Adult Treatment Panel III (NCEP-ATP III) criteria. Areas under the curve (AUC) and corresponding 95% confidence interval (95% CI) of 8 anthropometric/metabolic indexes, such as waist circumference (WC), body mass index (BMI), a body shape index (ABSI), waist triglyceride index (WTI), were calculated to illustrate their ability to differentiate MetS. Sensitivity analysis using the other MetS criteria was performed.

Results

Under the modified NCEP-ATP III criteria, WTI had the best discriminating ability in overall adolescents, with AUC of 0.922 (95% CI: 0.900–0.945) in Chinese and 0.959 (95% CI: 0.949–0.969) in American. In contrast, ABSI had the lowest AUCs. Results of sensitivity analysis were generally consistent for the whole Chinese and American population, with the AUC for WC being the highest under some criteria, but it was not statistically different from that of WTI.

Conclusions

WTI had relatively high discriminatory power for MetS detection in Chinese and American adolescents, but the performance of ABSI was poor.

Impact

  • While many studies have compared the discriminatory power of some anthropometric indicators for MetS, there are few focused on pediatrics.

  • The current study is the first to compare the discriminating ability of anthropometric/metabolic indicators (WC, BMI, TMI, ABSI, WHtR, VAI, WTI, and TyG) for MetS in adolescents.

  • WTI remains the optimal indicator in screening for MetS in adolescents.

  • WC was also a simple and reliable indicator when screening for MetS in adolescents, but the performance of ABSI was poor.

  • This study provides a theoretical basis for the early identification of MetS in adolescents by adopting effective indicators.

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Fig. 1: Flow chart of the study population selection.
Fig. 2: The partial correlation coefficient between WC, BMI, TMI, ABSI, WHTR, VAI, WTI, and TyG in Chinese and American adolescents.
Fig. 3: The partial correlation coefficient between different anthropometric/metabolic indexes with MetS components in Chinese and American adolescents.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the CHNS and the NHANES repository, https://www.cpc.unc.edu/projects/china/data and https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

References

  1. Medina, G. et al. Metabolic syndrome, autoimmunity and rheumatic diseases. Pharm. Res. 133, 277–288 (2018).

    Article  CAS  Google Scholar 

  2. Xu, H., Li, X. P., Adams, H., Kubena, K. & Guo, S. D. Etiology of metabolic syndrome and dietary intervention. Int. J. Mol. Sci. 20, 128 (2019).

    Article  Google Scholar 

  3. Engin, A. The definition and prevalence of obesity and metabolic syndrome. Adv. Exp. Med. Biol. 960, 1–17 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Weihe, P. & Weihrauch-Bluher, S. Metabolic syndrome in children and adolescents: diagnostic criteria, therapeutic options and perspectives. Curr. Obes. Rep. 8, 472–479 (2019).

    Article  PubMed  Google Scholar 

  5. Rodríguez-Colón, S. M. et al. Metabolic syndrome burden in apparently healthy adolescents is adversely associated with cardiac autonomic modulation—Penn State Children Cohort. Metabolism 64, 626–632 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  7. Zimmet, P. et al. The metabolic syndrome in children and adolescents—an Idf Consensus Report. Pediatr. Diabetes 8, 299–306 (2007).

    Article  PubMed  Google Scholar 

  8. Yang, H. R., Yi, D. Y. & Choi, H. S. Comparison between a pediatric health promotion center and a pediatric obesity clinic in detecting metabolic syndrome and non-alcoholic fatty liver disease in children. J. Korean Med. Sci. 29, 1672–1677 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Noubiap, J. J. et al. Global, regional, and country estimates of metabolic syndrome burden in children and adolescents in 2020: a systematic review and modelling analysis. Lancet Child Adolesc. Health 6, 158–170 (2022).

    Article  PubMed  Google Scholar 

  10. Zong, X., Bovet, P. & Xi, B. A proposal to unify the definition of the metabolic syndrome in children and adolescents. Front. Endocrinol. 13, 925976 (2022).

    Article  Google Scholar 

  11. GBD 2015 Obesity Collaborators, et al. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377, 13–27 (2017).

    Article  Google Scholar 

  12. Lo, K., Wong, M., Khalechelvam, P. & Tam, W. Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis. Obes. Rev. 17, 1258–1275 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Kobo, O., Karban, A. M. I. R., Leiba, R. & Avizohar, O. Normal body mass index (BMI) can rule out metabolic syndrome. Eur. Heart J. 40, 3843–3843 (2019).

    Article  Google Scholar 

  14. Peterson, C. M. et al. Tri-ponderal mass index vs body mass index in estimating body fat during adolescence. JAMA Pediatr. 171, 629–636 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Anoop, S., Krakauer, J., Krakauer, N. & Misra, A. A body shape index significantly predicts MRI-defined abdominal adipose tissue depots in non-obese Asian Indians with type 2 diabetes mellitus. BMJ Open Diab. Res. Care 8, e001324 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Vizzuso, S. et al. Visceral adiposity index (VAI) in children and adolescents with obesity: no association with daily energy intake but promising tool to identify metabolic syndrome (Mets). Nutrients 13, 413 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tao, L. C., Xu, J. N., Wang, T. T., Hua, F. & Li, J. J. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc. Diabetol. 21, 68 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yang, R. F., Liu, X. Y., Lin, Z. & Zhang, G. Correlation study on waist circumference-triglyceride (Wt) index and coronary artery scores in patients with coronary heart disease. Eur. Rev. Med. Pharm. 19, 113–118 (2015).

    CAS  Google Scholar 

  19. Sekgala, M. D., Opperman, M., Mpahleni, B. & McHiza, Z. J. Anthropometric indices and cut-off points for screening of metabolic syndrome among South African taxi drivers. Front. Nutr. 9, 974749 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Li, Y. et al. Association between four anthropometric indexes and metabolic syndrome in US adults. Front. Endocrinol. 13, 889785 (2022).

    Article  Google Scholar 

  21. Sun, J. H., Yang, R., Zhao, M., Bovet, P. & Xi, B. Tri-ponderal mass index as a screening tool for identifying body fat and cardiovascular risk factors in children and adolescents: a systematic review. Front. Endocrinol. 12, 694681 (2021).

    Article  Google Scholar 

  22. Zhang, B., Zhai, F. Y., Du, S. F. & Popkin, B. M. The China health and nutrition survey, 1989-2011. Obes. Rev. 15, 2–7 (2014).

    Article  PubMed  Google Scholar 

  23. Volpe, M. & Patrono, C. Decline in blood pressure control trends in the US: a real step back: comment on National Health and Nutrition Examination Survey (NHANES) data. Eur. Heart J. 41, 3986–3987 (2020).

    Article  PubMed  Google Scholar 

  24. Margolis-Gil, M., Yackobovitz-Gavan, M., Phillip, M. & Shalitin, S. Which predictors differentiate between obese children and adolescents with cardiometabolic complications and those with metabolically healthy obesity? Pediatr. Diabetes 19, 1147–1155 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Yan, S. et al. The expanding burden of cardiometabolic risk in China: the China Health and Nutrition Survey. Obes. Rev. 13, 810–821 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Myers, G. L., Cooper, G. R., Winn, C. L. & Smith, S. J. The Centers for Disease Control-National-Heart-Lung-and-Blood-Institute Lipid Standardization Program—an approach to accurate and precise lipid measurements. Clin. Lab. Med. 9, 105–135 (1989).

    Article  CAS  PubMed  Google Scholar 

  27. Ji, M., Zhang, S. & An, R. Effectiveness of A body shape index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis. Obes. Rev. 19, 737–759 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Wang, X. J. et al. Use of tri-ponderal mass index in predicting late adolescent overweight and obesity in children aged 7-18. Front. Nutr. 9, 785863 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Xi, B. et al. International waist circumference percentile cutoffs for central obesity in children and adolescents aged 6 to 18 years. J. Clin. Endocrinol. Metab. 105, e1569–e1583 (2020).

    Article  PubMed  Google Scholar 

  30. Xi, B. et al. Establishing international blood pressure references among nonoverweight children and adolescents aged 6 to 17 years. Circulation 133, 398–408 (2016).

    Article  PubMed  Google Scholar 

  31. de Winter, J. C., Gosling, S. D. & Potter, J. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychol. Methods 21, 273–290 (2016).

  32. Wang, L., Du, Z. H., Qiao, J. M. & Gao, S. Association between metabolic syndrome and endometrial cancer risk: a systematic review and meta-analysis of observational studies. Aging 12, 9825–9839 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Dipietro, L. et al. Physical activity and cardiometabolic risk factor clustering in young adults with obesity. Med. Sci. Sports Exerc. 52, 1050–1056 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Magge, S. N., Goodman, E. & Armstrong, S. C. The metabolic syndrome in children and adolescents: shifting the focus to cardiometabolic risk factor clustering. Pediatrics 140, e20171603 (2017).

    Article  PubMed  Google Scholar 

  35. Ma, C. M. et al. Three novel obese indicators perform better in monitoring management of metabolic syndrome in type 2 diabetes. Sci. Rep. 7, 9843 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Neeland, I. J., Poirier, P. & Després, J. P. Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation 137, 1391–1406 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wildman, R. P. et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch. Intern. Med. 168, 1617–1624 (2008).

    Article  PubMed  Google Scholar 

  38. Ross, R. et al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR Working Group on visceral obesity. Nat. Rev. Endocrinol. 16, 177–189 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Smith, U. Abdominal obesity: a marker of ectopic fat accumulation. J. Clin. Investig. 125, 1790–1792 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Al-Shami, I. et al. Assessing metabolic syndrome prediction quality using seven anthropometric indices among Jordanian adults: a cross-sectional study. Sci. Rep. 12, 21043 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Shin, K. A. & Kim, Y. J. Usefulness of surrogate markers of body fat distribution for predicting metabolic syndrome in middle-aged and older Korean populations. Diabetes Metab. Syndr. Obes. 12, 2251–2259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Author information

Author notes

  1. These authors contributed equally: Xuan Hu, Zhuoqiao Yang.

Authors and Affiliations

  1. Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, Jiangsu, China

    Xuan Hu, Zhuoqiao Yang, Yaling Ding, Yi Zhong, Jianing Long & Jieyun Yin

  2. Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China

    Wenxin Ge

  3. Suzhou Center for Disease Prevention and Control, Suzhou, Jiangsu, China

    Xiaoyan Zhu & Jia Hu

  4. Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, Suzhou, Jiangsu, China

    Jieyun Yin

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Contributions

Conceptualization: Jieyun Yin, Jia Hu, Xuan Hu, and Zhuoqiao Yang; Methodology: Xuan Hu and Zhuoqiao Yang; Formal analysis and investigation: Xuan Hu and Zhuoqiao Yang; Writing—original draft preparation: Xuan Hu and Zhuoqiao Yang; Writing—review and editing: Wenxin Ge, Yaling Ding, Yi Zhong, Jianing Long, Xiaoyan Zhu, Jia Hu, and Jieyun Yin; Supervision: Jieyun Yin and Jia Hu.

Corresponding authors

Correspondence to Jia Hu or Jieyun Yin.

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The authors declare no competing interests.

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Consent was obtained from all individual participants included in the study. Ethics approval was accepted by the institutional review board of the National Center for Health Statistics (NCHS), Chinese Center for Disease Control and Prevention (CCDC) and National Institute for Nutrition and Health (NINH).

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Hu, X., Yang, Z., Ge, W. et al. Evaluating eight indicators for identifying metabolic syndrome in Chinese and American adolescents. Pediatr Res (2024). https://doi.org/10.1038/s41390-024-03247-8

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