Use of Continuous Metabolic Syndrome Score in Overweight and Obese Children

  • Sangeeta P. SawantEmail author
  • Alpa S. Amin
Original Article



To assess the utility of continuous metabolic syndrome score (cMetS) for predicting metabolic syndrome (MS) and determine the cut-off values in overweight and obese children.


This study was conducted among 104 children (7–14 y) attending obesity clinics of a tertiary care hospital in Mumbai, India. The cMetS was computed by standardizing the residuals of waist circumference (WC), mean arterial blood pressure (MAP), high density lipoprotein cholesterol (HDL-C), triglycerides (TG), and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) by regressing them according to age and sex and aggregating them. The optimal cut-off of cMetS for predicting MS was determined by the receiver operation characteristic (ROC) curve analysis.


The cMetS increased significantly with increase in the number of MS risk factors. It was significantly high in children with MS than those without it (boys: 1.070 + 1.834 vs. -1.478 + 2.262; girls: 2.092 + 1.963 vs. -2.253 + 2.140; combined children group: 1.572 + 1.950 vs. -1.907+ 2.374; p < 0.001). The score predicted MS with high accuracy in girls; (AUC of 0.95, 95% CI: 0.90–1.00), moderate accuracy in boys (AUC of 0.79, 95% CI: 0.65–0.92) and in the combined group (AUC of 0.87, 95% CI 0.80–0.94) respectively. The cut-off of cMetS yielding maximal sensitivity and specificity for predicting the MS was −1.009 in boys (sensitivity 93% and specificity 62%); −0.652 in girls (sensitivity 96.4% and specificity 77%) and − 0.6881 in the combined group (sensitivity 91.2% and specificity 70.2%).


cMetS predicted MS with moderate to high accuracy. It had high sensitivity and specificity in predicting MS in overweight and obese children.


Continuous metabolic syndrome score Children Overweight Obesity Metabolic syndrome 



The authors thank all children and their parents who agreed for the participation in this study. They acknowledge help of Dr. Susan Cherian, Head Pathology, BARC Hospital and Dr. A. R. Kulkarni, MO-In-charge, Medical Section, BARC Hospital.

Authors’ Contribution

SPS: Data collection, statistical analysis, manuscript preparation and editing the manuscript. ASA: Data collection and editing the manuscript. ASA is the guarantor for this paper.

Compliance with Ethical Standards

Conflict of Interest



  1. 1.
    National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. 2002;106:3143–421.CrossRefGoogle Scholar
  2. 2.
    Ford ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care. 2005;28:1769–78.CrossRefGoogle Scholar
  3. 3.
    Thivel D, Malina RM, Isacco L, Aucouturier J, Meyer M, Duche' P. Metabolic syndrome in obese children and adolescents: dichotomous or continuous? Metab Syndr Relat Disord. 2009;7:549–55.CrossRefGoogle Scholar
  4. 4.
    Heshmat R, Heidari M, Ejtahed HS, et al. Validity of a continuous metabolic syndrome score as an index for modeling metabolic syndrome in children and adolescents: the CASPIAN-V study. Diabetol Metab Syndr. 2017;9:89.CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Guseman EH, Eisenmann JC, Laurson KR, Cook SR, Stratbucker W. Calculating a continuous metabolic syndrome score using nationally representative reference values. Acad Pediatr. 2018;18:589–92.CrossRefGoogle Scholar
  6. 6.
    Minakshi B, Chithambaram NS. Early identification of risk factors and diagnosis of metabolic syndrome in overweight and obese children above 6 years of age. Int J Contemp Pediatrics. 2017;4:1439–44.CrossRefGoogle Scholar
  7. 7.
    Kahn R, Ferrannini E, Buse J, Stern M. The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of diabetes. Diabetes Care. 2005;28:2289–304.CrossRefGoogle Scholar
  8. 8.
    Villa JKD, e Silva AR, Santos TSS, Ribeiro AQ, Sant'Ana LFdaR. Metabolic syndrome risk assessment in children: use of a single score. Rev Paul Pediatr [Internet]. 2015;33:187–93. Available at: Accessed 15 April 2019.
  9. 9.
    Eisenmann JC. On the use of a continuous metabolic syndrome score in pediatric research. Cardiovasc Diabetol. 2008;7:17.CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Kelly AS, Steinberger J, Jacobs DR, Hong CP, Moran A, Sinaiko AR. Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood. Int J Pediatr Obes. 2011;6:e283–9.CrossRefGoogle Scholar
  11. 11.
    Khadilkar A, Ekbote V, Chiplonkar S, et al. Waist circumference percentiles in 2-18 year old indian children. J Pediatr. 2014;164:1358–62.CrossRefGoogle Scholar
  12. 12.
    Khadilkar V, Yadav S, Agrawal KK, et al. Revised IAP growth charts for height, weight and body mass index for 5- to 18-year-old Indian children. Indian Pediatr. 2015;52:47–55.CrossRefGoogle Scholar
  13. 13.
    Report of the Second Task Force on Blood Pressure Control in Children—1987. Task Force on Blood Pressure Control in Children. National Heart, Lung, and Blood Institute, Bethesda, Maryland. Pediatrics 1987;379:1–25.Google Scholar
  14. 14.
    Majid H, Masood Q, Khan AH. Homeostatic model assessment for insulin resistance (HOMA-IR): a better marker for evaluating insulin resistance than fasting insulin in women with polycystic ovarian syndrome. J Coll Physicians Surg Pak. 2017;27:123–6.Google Scholar
  15. 15.
    de Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the metabolic syndrome in American adolescents: findings from the third National Health and Nutrition Examination Survey. Circulation. 2004;110:2494–7.Google Scholar
  16. 16.
    Wittcopp C, Conroy R. Metabolic syndrome in children and adolescents. Pediatr Rev. 2016;37:193–202.CrossRefGoogle Scholar
  17. 17.
    Pandit D, Chiplonkar S, Khadilkar A, Kinare A, Khadilkar V. Efficacy of a continuous metabolic syndrome score in Indian children for detecting subclinical atherosclerotic risk. Int J Obes [Internet]. 2011;35:1318–24.CrossRefGoogle Scholar
  18. 18.
    Okosun IS, Boltri JM, Lyn R, Davis-Smith M. Continuous metabolic syndrome risk score, body mass index percentile, and leisure time physical activity in American children. J Clin Hypertens. 2010;12:636–44.CrossRefGoogle Scholar
  19. 19.
    Andrabi SMS, Bhat MH, Andrabi SR, et al. Prevalence of metabolic syndrome in 8-18-year-old school-going children of Srinagar city of Kashmir India. Indian J Endocr Metab. 2013;17:95–100.CrossRefGoogle Scholar
  20. 20.
    Tandon N, Garg MK, Singh Y, Marwaha RK. Prevalence of metabolic syndrome among urban Indian adolescents and its relation with insulin resistance (HOMA-IR). J Pediatr Endocrinol Metab. 2013;26:1123–30.CrossRefGoogle Scholar
  21. 21.
    Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS. The relation of childhood BMI to adult adiposity: the Bogalusa heart study. Pediatrics. 2005;115:22–7.CrossRefGoogle Scholar
  22. 22.
    Prince RL, Kuk JL, Ambler KA, Dhaliwal J, Ball GDC. Predictors of metabolically healthy obesity in children. Diabetes Care. 2014;37:1462–8.CrossRefGoogle Scholar
  23. 23.
    Magnussen CG, Cheriyan S, Sabin MA, et al. Continuous and dichotomous metabolic syndrome definitions in youth predict adult type 2 diabetes and carotid artery intima media thickness: the cardiovascular risk in young Finns study. J Pediatr. 2016;171:97–103.e3.CrossRefGoogle Scholar
  24. 24.
    Stavnsbo M, Resaland GK, Anderssen SA, et al. Reference values for cardiometabolic risk scores in children and adolescents: suggesting a common standard. Atherosclerosis. 2018;278:299–306.CrossRefGoogle Scholar

Copyright information

© Dr. K C Chaudhuri Foundation 2019

Authors and Affiliations

  1. 1.Department of PediatricsBhabha Atomic Research Centre HospitalMumbaiIndia

Personalised recommendations