Factor Analysis of Cardiovascular Risk Clustering in Pediatric Metabolic Syndrome: CASPIAN StudyKelishadi R. · Ardalan G. · Adeli K. · Motaghian M. · Majdzadeh R. · Mahmood-Arabi M.S. · Delavari A. · Riazi M.M. · Namazi R. · Ramezani M.A.
aPreventive Pediatric Cardiology Department, Isfahan Cardiovascular Research Center, Isfahan University of Medical Sciences, Isfahan, and bSchool Health Office, Ministry of Health, Tehran, Iran; cClinical Biochemistry, Hospital for Sick Children, University of Toronto, Toronto, Ont., Canada; dBureau of Health, Iranian Ministry of Education, Tehran, eSchool of Public Health and Institute of Public Health Research, Tehran University of Medical Sciences, Tehran, fSchool Health Office, Iranian Ministry of Health, Tehran, gNoncommunicable Disease Department, Center for Disease Control, Ministry of Health, Tehran, and hIsfahan University of Medical Sciences, Isfahan, Iran
Aims: To assess the results of factor analysis of coronary artery disease risk factors in a large national representative sample of children, and to compare its results on the variables measured between those with or without metabolic syndrome (MetS). Methods: This cross-sectional multicenter population survey was conducted on 4,811 nationally representative school students aged 6–18 years. MetS was defined based on criteria analogous to the Adult Treatment Panel III. Factor analysis by principle components analysis and Varimax rotation was carried out to cluster risk factors. Results: MetS was present in 14.1% of subjects (n = 678).From the nine variables assessed, factor analysis of the z scores of variables show that in all age groups, three similar factors were loaded: lipids, adiposity, and blood pressure, that accounted for 87.4–90.8% of the variance. Three factors were loaded in those with MetS (cholesterol/TG, metabolic/adiposity, and blood pressure) (65.9% of variance); and four factors (cholesterol, metabolic, adiposity, and blood pressure) were loaded among those without the MetS (75.6% of variance). We did not find a central feature that underlies all three factors among children with the MetS; however, waist circumference was the only variable that was loaded for two factors. Conclusion: These findings support a change in the concept of MetS from that of a single entity to one that represents several distinct but intercorrelated entities. An approach to assessing risk clustering from early life, and longitudinal studies that would elucidate how these various risk domains interact over time are needed.
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