Latent Class Analysis for Clustering Iranian Individuals Based on Cardiovascular Risk Factors.

Abstract

Background: Cardiovascular disease (CVD) remains a major public health concern, and conventional risk-stratification tools, such as WHO/ISH (IraPEN), may not adequately capture the heterogeneity of risk profiles within the population. This study aimed to identify the underlying patterns of CVD risk factors among employees undergoing routine occupational health assessments in Iran.

Methods: In this cross-sectional secondary analysis, data from 1,281 adults aged 40-79 years attending a governmental occupational health clinic in 2024 were analyzed. Core cardiovascular risk variables-including sex, diabetes, smoking, systolic blood pressure, and total cholesterol-were used. Latent class analysis (LCA) employing a five-class model was conducted.

Results: Five latent classes of cardiovascular risk were identified, reflecting heterogeneous conditions. These classes included high-risk males without DM (population share: 0.1824), hyperlipidemic non-smoking females (population share: 0.1487), low-risk individuals (population share: 0.3011), high-risk males without hypertension (population share: 0.36), and high-risk males without hyperlipidemia (population share: 0.0079).

Conclusion: The LCA-based classification identified five distinct and internally homogeneous cardiovascular risk profiles that captured sex-specific and metabolically clustered patterns of risk. This approach reveals heterogeneity not reflected in the conventional IraPEN five-tier risk groups.