Application of Latent Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and Glucose study

• Published in: Scientific reports Vol. 9; no. 1; p. 1572
• Main Authors: Ahanchi, Noushin Sadat, Hadaegh, Farzad, Alipour, Abbas, Ghanbarian, Arash, Azizi, Fereidoun, Khalili, Davood
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.02.2019; Nature Publishing Group
• Subjects: 692/163/2743/137/773; 692/163/2743/2037; Diabetes; Diabetes mellitus; Dyslipidemia; Humanities and Social Sciences; Latent class analysis; Metabolic disorders; Metabolic syndrome; multidisciplinary; Population studies; Science; Science (multidisciplinary); Smoking
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-38095-0

In this study, using latent class analysis (LCA), we investigated whether there are any homogeneous subclasses of individuals exhibiting different profiles of metabolic syndrome (MetS) components. The current study was conducted within the framework of the Tehran Lipid and Glucose Study (TLGS), a population-based cohort including 6448 subjects, aged 20–50 years. We carried out a LCA on MetS components and assessed the association of some demographic and behavioral variables with membership of latent subclasses using multinomial logistic regression. Four latent classes were identified:(1) Low riskclass, with the lowest probabilities for all MetS components (its prevalence rate in men: 29%, women: 64.7%), (2) MetS with diabetes medication (men: 1%, women: 2.3%), (3) Mets without diabetes medication (men: 32%, women: 13.4%), (4) dyslipidemia (men: 38%, women: 19.6%). In men the forth subclass was more significantly associated with being smoker (odds ratio: 4.49; 95% CI: 1.89–9.97). Our study showed that subjects with MetS could be classified in sub-classes with different origins for their metabolic disorders including drug treated diabetes, those with central obesity and dyslipidemia associated with smoking.