• Mohsen Hosseini, Nizal Sarrafzadegan, Roya Kelishadi, Mehri Monajemi, Sedigheh Asgary, and Vardanjani Hossein Molavi HM Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran..
• Department of Biostatistics and Epidemiology, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran.
• J Res Med Sci. 2014 Dec 1; 19 (12): 1167-74.

BackgroundMetabolic syndrome (MetSy), an important predisposing factor for the most of noncommunicable diseases, has become a global pandemic. Given different definitions used for the MetSy, recently using a score termed "continuous MetSy risk score (CMetSyS)" is recommended. The aim of this study was to provide a CMetSyS in a population-based sample of Iranian adults and to assess its determinants.Materials And MethodsWe used the data of the baseline survey of a community trial entitled "the Isfahan health heart program." The MetSy was defined according to the Revised National Cholesterol Education Program Third Adult Treatment Panel. All probable predictive models and their predictive performance were provided using leave-one-out cross-validated logistic regression and the receiver operation characteristic curve methods. Multiple linear regression was performed to assess factors associated with the CMetSyS.ResultsThe study population consisted of 8313 persons (49.9% male, mean age 38.54 ± 15.86 years). The MetSy was documented in 1539 persons (21.86%). Triglycerides and waist circumference were the best predictive components, and fasting plasma glucose had the lowest area under curve (AUC). The AUC for our best model was 95.36 (94.83-95.83%). The best predictive cutoff for this risk score was -1.151 with 89% sensitivity and 87.93% specificity.ConclusionWe provided four population-based leave-one-out cross-validated risk score models, with moderate to perfect predictive performance to identify the MetSy in Iranian adults. The CMetSyS had significant associations with high sensitive C-reactive protein, body mass index, leisure time, and workplace physical activity as well as age and gender.

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