title: Clustering and combining pattern of metabolic syndrome components among Iranian population with latent class analysis.
abstract: Background:Metabolic syndrome (MetS), a combination of coronary heart disease and diabetes mellitus risk factor, refer to one of the most challenging public health issues in worldwide. The aim of this study was to identify the subgroups of participants in a study on the basis of MetS components.Methods:The cross-sectional study took place in the districts related to Tehran University of Medical Sciences. The randomly selected sample consists of 415 subjects. All participants provided written informed consent. Latent class analysis was performed to achieve the study's objectives. Analyses were conducted by using proc LCA in SAS 9.2 software.Results:Except systolic and diastolic blood pressure, the prevalence of all MetS components is common in female than male. Four latent classes were identified: (a) non MetS, (b) low risk, (c) high risk, and (d) MetS. Notably, 24.2% and 1.3% of the subjects were in the high risk and MetS classes respectively.Conclusion:Most of the study participants were identified as high risk and MetS. Design and implementation of preventive interventions for this segment of the population are necessary.
authors.affiliation: PhD Student in Epidemiology, School of Public Health, Department of Epidemiology, Iran University of Medical Sciences, Tehran, Iran, & Iran Virtual Research Core, Iran University of Medical Sciences, Tehran, Iran. abbasi.abbas49@yahoo.com.
authors.forename: Abbas
authors.initials: A
authors.lastname: Abbasi-Ghahramanloo
authors.affiliation: PhD student, Department of Nutritional Sciences, School of Public Health, Iran University of Medical Sciences, Tehran, Iran. s.soltani1979@yahoo.com.
authors.forename: Sepideh
authors.initials: S
authors.lastname: Soltani
authors.affiliation: PhD Student, Department of Public Health, Neyshabur University of Medical Sciences, Neyshabur, Iran, & School of Public Health, Department of Epidemiology, Iran University of Medical Sciences, Tehran, Iran, & Iran Virtual Research Core, Iran University of Medical Sciences, Tehran, Iran. aagholami80@yahoo.com.
authors.forename: Ali
authors.initials: A
authors.lastname: Gholami
authors.affiliation: PhD Student, Ewaz School of Health, Larestan School of Medical Sciences, Larestan, Iran. erfanimr@sums.ac.ir.
authors.forename: Mohammadreza
authors.initials: M
authors.lastname: Erfani
authors.affiliation: PhD Student, Department of Nutritional Sciences, School of Public Health, Iran University of Medical Sciences, Tehran, Iran, & Ewaz School of Health, Larestan School of Medical Sciences, Larestan, Iran. s_yousai2006@yahoo.com.
authors.forename: Somayeh
authors.initials: S
authors.lastname: Yosaee
keywords: Iran; Latent class analysis; MetS component subgrouping; Metabolic syndrome