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Journal of Diabetes Research
Volume 2015, Article ID 539835, 10 pages
http://dx.doi.org/10.1155/2015/539835
Research Article

A New Approach to Define and Diagnose Cardiometabolic Disorder in Children

1Center of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
2Department of Sports Medicine, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway
3Exercise and Health Laboratory, CIPER, Fac Motricidade Humana, Universidade de Lisboa, Estrada Dacosth, Cruz-Quebrada, 1499 Lisbon, Portugal
4Department of Exercise and Sport Science, University of North Carolina, 025 Fetzer Gym, CB No. 8700, Chapel Hill, NC 27599-8700, USA
5School of Physical Education, University of Pernambuco, Campus Universitario HUOC-ESEF, Arnobio Marques 310, Santo Amaro, 50.100-130 Recife, PE, Brazil
6Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Hirschengraben 84, 8001 Zürich, Switzerland
7The Centre of Inflammation and Metabolism and Trygfondens Center for Aktiv Sundhed, Department of Infectious Diseases and CMRC, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Tagensvej 20, 2100 Copenhagen, Denmark
8MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge, Hills Road, Cambridge CB2 0QQ, UK

Received 26 November 2014; Accepted 17 March 2015

Academic Editor: Francesco Chiarelli

Copyright © 2015 Lars Bo Andersen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The aim of the study was to test the performance of a new definition of metabolic syndrome (MetS), which better describes metabolic dysfunction in children. Methods. 15,794 youths aged 6–18 years participated. Mean z-score for CVD risk factors was calculated. Sensitivity analyses were performed to evaluate which parameters best described the metabolic dysfunction by analysing the score against independent variables not included in the score. Results. More youth had clustering of CVD risk factors (>6.2%) compared to the number selected by existing MetS definitions (International Diabetes Federation (IDF) < 1%). Waist circumference and BMI were interchangeable, but using insulin resistance homeostasis model assessment (HOMA) instead of fasting glucose increased the score. The continuous MetS score was increased when cardiorespiratory fitness (CRF) and leptin were included. A mean z-score of 0.40–0.85 indicated borderline and above 0.85 indicated clustering of risk factors. A noninvasive risk score based on adiposity and CRF showed sensitivity and specificity of 0.85 and an area under the curve of 0.92 against IDF definition of MetS. Conclusions. Diagnosis for MetS in youth can be improved by using continuous variables for risk factors and by including CRF and leptin.