Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program

Abstract

Abstract Metabolic syndrome (MetS), comprising obesity, insulin resistance, hypertension, and dyslipidemia, increases the risk of type II diabetes mellitus and cardiovascular disease. This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumption among older adults in Iran. The current study was conducted in the second stage of the Bushehr Elderly Health (BEH) program in southern Iran—a population-based cohort including 2424 subjects aged ≥ 60 years. Latent class analysis (LCA) was used to identify MetS and tobacco consumption patterns. Multinomial logistic regression was conducted to investigate factors associated with each MetS class, including sociodemographic and behavioral variables. Out of 2424 individuals, the overall percentage of people with one or more components of MetS or current tobacco use was 57.8% and 20.8%, respectively. The mean (SD) age of all participants was 69.3(6.4) years. LCA ascertained the presence of four latent classes: class 1 (“low risk”; with a prevalence of 35.3%), class 2 (“MetS with medication-controlled diabetes”; 11.1%), class 3 (“high risk of MetS and associated medication use”; 27.1%), and class 4 (“central obesity and treated hypertension”; 26.4%). Compared to participants with a body mass index (BMI) < 30, participants with BMI ≥ 30 were more likely to belong to class 3 (OR 1.91, 95% CI 1.31–2.79) and class 4 (OR 1.49, 95% CI 1.06–2.08). Polypharmacy was associated with membership in class 2 (OR 2.07, 95% CI 1.12–3.81), class 3 (OR 9.77, 95% CI 6.12–15.59), and class 4 (OR 1.76, 95% CI 1.07–2.91). The elevated triglyceride-glucose index was associated with membership in class 2 (OR 12.33, 95% CI 7.75–19.61) and class 3 (OR 12.04, 95% CI 8.31–17.45). Individuals with poor self-related health were more likely to belong to class 3 (OR 1.43; 95% CI 1.08–1.93). Four classes were identified among older adults in Iran with distinct patterns of cardiometabolic risk factors. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population.

Keywords

• Latent class analysis
• Metabolic syndrome
• Cardiometabolic
• Older adults