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formerly Central European Journal of Medicine

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Predictive values of metabolic syndrome in children

Ivana Vorgučin
  • Institute for child and youth health care of Vojvodina, Novi Sad, Serbia
  • :
/ Nada Naumović
  • Department of physiology, Faculty of medicine Novi Sad, Novi Sad, Serbia
  • :
/ Jovan Vlaški
  • Institute for child and youth health care of Vojvodina, Novi Sad, Serbia
  • :
/ Dragan Katanić
  • Institute for child and youth health care of Vojvodina, Novi Sad, Serbia
  • :
/ Georgios Konstantinidis
  • Institute for child and youth health care of Vojvodina, Novi Sad, Serbia
  • :
Published Online: 2011-06-01 | DOI: https://doi.org/10.2478/s11536-011-0032-2


Metabolic syndrome is a clinical term encompassing risk factors (obesity, insulin resistance, dyslipidemia and hypertension), which yield an increased risk for the development of diabetes mellitus type 2 and cardiovascular disorders in adolescence. Two sets of criteria for diagnosing metabolic syndrome were applied, the criteria for adults, specifically adapted for children, and the criteria defined by the International Diabetes Federation (IDF). A reliability analysis was conducted; sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of applying certain criteria of both definitions of metabolic syndrome. Metabolic syndrome in adolescents was diagnosed much more frequently using the specific criteria (41%) in comparison to the IDF criteria (22%). Using the specific criteria for children and adolescents, it was established that the HDL cholesterol was the most specific and had the largest PPV. Using the IDF criteria for diagnosing metabolic syndrome, the reliability analysis established that the highest PPV was recorded with the elevated level of triglycerides. The specific criteria have been found to be more efficient in diagnosing metabolic syndrome in adolescents. The highest predictive value was displayed by dyslipidemic disorders, hypertriglyceridemia and hypo HDL cholesterolemia.

Keywords: Metabolic syndrome; Children; Predictive value

  • [1] Reaven G.M., Banting lecture 1988. Role of insulin resistance in human disease, Diabetes, 1988, 37. 1595–1607 http://dx.doi.org/10.2337/diabetes.37.12.1595 [Crossref]

  • [2] Alberti K.G.M.M., Zimmet P., Shaw J., for the IDF Epidemiology Task Force Consensus Group, The metabolic syndrome-a new worldwide definition, Lancet, 2005, 366(9491), 1059–1062 http://dx.doi.org/10.1016/S0140-6736(05)67402-8 [Crossref]

  • [3] Zimmet P., Alberti G., Kaufman F., Tajima N., The metabolic syndrome in children and adolescents, Lancet, 2007, 369(9579), 2059–2061 http://dx.doi.org/10.1016/S0140-6736(07)60958-1 [Web of Science] [Crossref]

  • [4] 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., 2003, 157, 821–827 http://dx.doi.org/10.1001/archpedi.157.8.821 [Crossref]

  • [5] De Ferranti S.D., Gauvreau K., Ludwing D.S., Newfwld E.J., Newburger J.W., Rifai N., Prevalence of the metabolic syndrome in American adolescents: findings from the third national health and nutrition examination survey, Circulation, 2004, 110, 2494–2497 http://dx.doi.org/10.1161/01.CIR.0000145117.40114.C7 [Crossref]

  • [6] Cruz M.L., Weigensberg M.J., Huang T.T., Ball G., Shaibi G.Q., Goran M.I., The metabolic syndrome in overweight Hispanic youth ant the role of insulin sensitivitiy, J. Clin. Endocrinol. Metab., 2004, 89, 108–113 http://dx.doi.org/10.1210/jc.2003-031188 [Crossref]

  • [7] Weiss R., Dziura J., Burgert T.S., Tamborlane W.V., Taksali S.E., Yeckel C.W., et al., Obesity and the metabolic syndrome in children and adolescents, N. Engl. J. Med., 2004, 350, 2362–2374 http://dx.doi.org/10.1056/NEJMoa031049 [Crossref]

  • [8] Ford E.S., Ajani U.A., Mokdad A.H., The metabolic syndrome and concentrations of C-reactive protein among U.S youth, Diabetes Care, 2005, 28, 878–881 http://dx.doi.org/10.2337/diacare.28.4.878 [Crossref]

  • [9] Goodman E., Daniels S., Morrison J., Huang B., Dolan L., Contrasting prevalence of and demographic disparities in the World Health Organization and National Cholesterol Education Program Adult Treatment Panel III definitions of metabolic syndrome among adolescents, J. Pediatr., 2004, 145, 445–451 http://dx.doi.org/10.1016/j.jpeds.2004.04.059 [Crossref]

  • [10] Raikkonen K., Matthews K.A., Salomon K., Hostility Predicts Metabolic Syndrome Risk Factors in Children and Adolescents, Health Psychology, 2003, 22(3), 279–286 http://dx.doi.org/10.1037/0278-6133.22.3.279 [Crossref]

  • [11] Huang T.T.K., Nansel T.R., Belsheim A.R., Morrison J.A., Sensitivity, Specificity, and Predictive Values of Pediatric Metabolic Syndrome Components in Relation to Adult Metabolic Syndrome: The Princeton LRC Follow-up Study, J. Pediatr., 2008, 152(2), 185–190 http://dx.doi.org/10.1016/j.jpeds.2007.08.007 [Crossref] [Web of Science]

  • [12] Hu G., Qiao Q., Tuomilehto J., Balkau B., Borch-Johnsen K., Pyorala K., Prevalence of the Metabolic Syndrome and Its Relation to All-Cause and Cardiovascular Mortality in Nondiabetic European Men and Women, Arch. Intern. Med., 2004, 164, 1066–1076 http://dx.doi.org/10.1001/archinte.164.10.1066 [Crossref]

  • [13] Schubert C.M., Sun S.S., Burns T.L., Morrison J.A., Huang T.T., Predictive ability of childhood metabolic components for adult metabolic syndrome and type 2 diabetes, J. Pediatr., 2009, 55(3), 6–7

  • [14] Dhanaraj E., Bhansali A., Jaggi S., Dutta P., Jain S., Tiwari P., Ramarao P., Predictors of metabolic syndrome in Asian north Indians with newly detected type 2 diabetes, Indian. J. Med. Res., 2009, 129, 506–514

  • [15] Ehrmann D.A., Liljenquist D.R., Kasza K., Azziz R., Legro R.S., Ghazzi M.N., Prevalence and Predictors of the Metabolic Syndrome in Women with Polycystic Ovary Syndrome, J. Clin. Endocrinol. Metab., 2006, 91, 48–53 http://dx.doi.org/10.1210/jc.2005-1329 [Crossref]

  • [16] Bernard C.M.Y., Nelson W.M.S., Sidney T., Neil T.G., Gabriel L.M., Ho C.C., et al., Components of the metabolic syndrome predictive of its development: a 6-year longitudinal study in Hong Kong Chinese, Clin. Endocrinol., 2008, 68(5), 730–737 http://dx.doi.org/10.1111/j.1365-2265.2007.03110.x [Crossref]

  • [17] Lawlor D.A., Smith D.G., Ebrahim S., Does the new International Diabetes Federation definition of the metabolic syndrome predict CHD any more strongly than older definitions? Findings from the British Womens Heart and Health Study, Diabetologia, 2006, 49, 41–48 http://dx.doi.org/10.1007/s00125-005-0040-3 [Crossref]

  • [18] Khunti K., Metabolic syndrome:time to weight or waist? Pract. Diab. Int., 2006, 23(7), 302–308 http://dx.doi.org/10.1002/pdi.990 [Crossref]

  • [19] Invitti C.M.C., Gilardini L., Pontiggia B., Mazzilli G., Morabito F., Viberti C., Prevalence of metabolic syndrome in obese children, Diabetes, 2003, 52, A70

  • [20] Singh R., Shaw J., Zimmet P., Epidemiology of childhood type 2 diabetes in the developing world, Pediatric Diabetes, 2004, 5, 154–168 http://dx.doi.org/10.1111/j.1399-543X.2004.00060.x [Crossref]

  • [21] The Forth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents, Pediatrics, 2004, 114, 555–576

  • [22] American Diabetes Association. Diagnosis and classification of diabetes mellitus, Diabetes Care, 2004, 27, 5–10 http://dx.doi.org/10.2337/diacare.27.2007.S5 [Web of Science] [Crossref]

  • [23] Heinze E., Holl R.W., Test of β-Cell function in childhood and adolescence, In: Ranke MB., editor, Diagnostics of Endocrine Function in Children and Adolescents, Karger, 2003

  • [24] CDC., National Center For Health Statistics (NCHS) Growth Chart in collaboration with the National Center for Chronic Disease Prevention and Health Promotion, 2002, http://www.cdc.gov/growthcharts

  • [25] Fernandez J.R., Redden D., Pietrobelli A., Allison D.B., Waist circumference percentiles in nationally representative samples of African-American, European-American, and Mexican-American children and adolescents, J. Pediatr., 2004, 145, 439–444 http://dx.doi.org/10.1016/j.jpeds.2004.06.044 [Crossref]

  • [26] Nicholson J.F., Pesce M.A., Reference ranges for laboratory tests and procedures, In: Behrman R.E., Kliegman R.M., Jenson H.B., (Eds.), Nelson Textbook of Pediatric 17th ed., Philadelphia, Saunders, 2003

  • [27] Hirschler V., Aranda C., de Luján Calcagno M., Maccalini G., Jadzinsky M., Can Waist Circumference Identify Children With the Metabolic Syndrome? Arch. Pediatr. Adolesc. Med., 2005, 159, 740–744 http://dx.doi.org/10.1001/archpedi.159.8.740 [Crossref]

  • [28] Reaven G.M., Is diagnosing metabolic syndrome a uniquely simple way to predict incident type 2 diabetes mellitus? Can. Med. Assoc. J., 2009, 180, 601–602 http://dx.doi.org/10.1503/cmaj.090092 [Crossref]

Published Online: 2011-06-01

Published in Print: 2011-08-01

Citation Information: Open Medicine. Volume 6, Issue 4, Pages 379–385, ISSN (Online) 2391-5463, DOI: https://doi.org/10.2478/s11536-011-0032-2, June 2011

© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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