Model-based recursive partitioning to identify risk clusters for metabolic syndrome and its components: findings from the International Mobility in Aging Study

Abstract

ObjectiveConceptual models underpinning much epidemiological research on ageing acknowledge that environmental, social and biological systems interact to influence health outcomes. Recursive partitioning is a data-driven approach that allows for concurrent exploration of distinct mixtures, or clusters, of individuals that have a particular outcome. Our aim is to use recursive partitioning to examine risk clusters for metabolic syndrome (MetS) and its components, in order to identify vulnerable populations.Study designCross-sectional analysis of baseline data from a prospective longitudinal cohort called the International Mobility in Aging Study (IMIAS).SettingIMIAS includes sites from three middle-income countries—Tirana (Albania), Natal (Brazil) and Manizales (Colombia)—and two from Canada—Kingston (Ontario) and Saint-Hyacinthe (Quebec).ParticipantsCommunity-dwelling male and female adults, aged 64–75 years (n=2002).Primary and secondary outcome measuresWe apply recursive partitioning to investigate social and behavioural risk factors for MetS and its components. Model-based recursive partitioning (MOB) was used to cluster participants into age-adjusted risk groups based on variabilities in: study site, sex, education, living arrangements, childhood adversities, adult occupation, current employment status, income, perceived income sufficiency, smoking status and weekly minutes of physical activity.Results43% of participants had MetS. Using MOB, the primary partitioning variable was participant sex. Among women from middle-incomes sites, the predicted proportion with MetS ranged from 58% to 68%. Canadian women with limited physical activity had elevated predicted proportions of MetS (49%, 95% CI 39% to 58%). Among men, MetS ranged from 26% to 41% depending on childhood social adversity and education. Clustering for MetS components differed from the syndrome and across components. Study site was a primary partitioning variable for all components except HDL cholesterol. Sex was important for most components.ConclusionMOB is a promising technique for identifying disease risk clusters (eg, vulnerable populations) in modestly sized samples.