Patterns of risk factors for cardiovascular diseases in Kheramah PERSION cohort population using a latent class analysis

• Published in: Scientific reports Vol. 15; no. 1; pp. 24496 - 9
• Main Authors: Salimi, Hadis, Vali, Mohebat, Rezaianzadeh, Abbas, Johari, Masoumeh Ghoddusi, Ghaem, Haleh
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/499; 692/700; Abdomen; Adult; Aged; Alcohol; Blood pressure; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - epidemiology; Cardiovascular Diseases - etiology; Cholesterol; Cohort analysis; Cohort Studies; Demography; Diabetes; Disease prevention; Female; Gender; Health care; Heart attacks; Humanities and Social Sciences; Humans; Hypertension; Iran - epidemiology; Ischemia; Latent class; Latent Class Analysis; Life Style; Lifestyles; Male; Middle Aged; Morbidity; multidisciplinary; Obesity; Population; Probability; Risk factor; Risk Factors; Science; Science (multidisciplinary); Sleep; Socioeconomic factors; Socioeconomic status; Statistical analysis; Stroke; Variables; Iran; Latent class; Cardiovascular diseases; Risk factor
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-08334-2

Cardiovascular diseases (CVDs) are a leading cause of mortality and morbidity worldwide, with a significant burden in Iran. Limited research has investigated patterns of modifiable CVDs risk factors in Iran. This study aims to address this gap by identifying distinct patterns of modifiable CVDs risk factors among adult aged + 40 and explore the relationship between demographic characteristics and risk factor patterns. This study was conducted using data from the Kherameh cohort study. The participants consisted of 9,422 individuals aged 40–70 years without CVDs. Latent Class Analysis (LCA) was used to identify latent classes of modifiable CVD risk factors. Multinomial logistic regression assessed the relationship between latent classes (LCs) and demographic variables. Three latent classes were identified as follows: low-risk (42%), clinical-risk (52%) and lifestyle-risk (6%) classes. Female gender (Adjusted OR: 13.48, 95% CI: 11.81–15.39), older age (Adjusted OR: 1.16, 95% CI: 0.99-1.35) and rural residence (Adjusted OR: 0.76, 95% CI: 0.67–0.86) had a greater risk of being in clinical-risk class compared to the low-risk class. Moreover, individuals of Fars ethnicity exhibited a significantly elevated risk of being classified in the clinical risk class for CVD (Adjusted OR: 1.28, 95% CI: 1.14–1.43) and they demonstrated a markedly higher risk of belonging to the lifestyle risk class (Adjusted OR: 1.51, 95% CI: 1.11–2.07). The study identified distinct latent classes of modifiable CVD risk factors and provided insights into their associations with demographic characteristics. Understanding risk patterns is crucial for developing effective preventive strategies and providing appropriate health protocols.