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

Abstract

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.

Improved Disease Course Associated With Early Initiation of Biologics in Polyarticular Juvenile Idiopathic Arthritis: Trajectory Analysis of a Childhood Arthritis and Rheumatology Research Alliance Consensus Treatment Plans Study

“…In these models, underlying trajectories are empirically inferred from data. LCTM methods have been used to characterize differing clinical phenotypes of rheumatoid arthritis (10), asthma (11), acute respiratory distress syndrome (12), vitiligo (13), metabolic syndrome (14), and depressive and anxiety disorders (15). We hypothesize that this approach will enable better understanding of treatment effects and offer strategies for identifying early‐diverging adverse trajectories for intervention.…”

Does the metabolic syndrome exist? The identification of cardio-metabolic profiles in a South Asian population study

“…Studies using statistical clustering methods to test the construct validity of MetS have produced inconsistent results (5). Whilst some have found support for a cohesive MetS construct (6)(7)(8), most find 3-4 clusters, and the clinical composition often varies substantially between ethnicities, countries, gender, and ages (5,(9)(10)(11)(12)(13)(14)(15)(16). This heterogeneous evidence base speaks against the "natural" clustering of components, united by a single underlying mechanism (1), and the universality of MetS.…”

“…For example, a two-class solution was found in men and women of Hispanic ethnicity in the US (7); a study of Japanese-American men found a distinct MetS class in a three-class solution (11); a study of an Iranian population sample observed a MetS class among four identified classes (13); and a US study identified a MetS class in an ethnically diverse population sample (14). However, the identified MetS classes did not score highly consistently across all components (13, 14), certain components were excluded for the purposes of achieving parsimony (7), and additional distinct cardiometabolic risk classes were typically found with high risk on some components, consistent with our findings (11, 13, 14). Furthermore, the MetS classes identified varied in size, and in terms of in the prominence of MetS components contributing to them, and differed by gender (14).…”

Subtypes of Premorbid Metabolic Syndrome and Associated Clinical Outcomes in Older Adults

“…The effects of metabolic syndrome components may vary among its subclasses. Latent class analysis (LCA) may be capable of identifying distinct profiles in individuals based on their presentation of metabolic syndrome components (17). The purpose of LCA is to classify similar individuals into groups.…”

“…The purpose of LCA is to classify similar individuals into groups. A latent cluster consists of homogeneous individuals in terms of the observed variables, where different latent clusters represent the unobserved heterogeneity among individuals with respect to these observed variables (17,18). The WHO describes metabolic syndrome as a pre-morbid condition, rather than a clinical diagnosis for primary care and preventive services (19), and suggests excluding individuals with diabetes mellitus (DM) or CVD from those included in the definition of metabolic syndrome.…”