Modeling the importance of life exposure factors on memory performance in diverse older adults: A machine learning approach.

Abstract

IntroductionMany health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.MethodsKaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and ethnic group.ResultsAmong 2245 adults (mean age: 74 years; range 54-90), age, sex, education, volunteering, income, vision, hearing, sleep, and exercise contributed to memory performance regardless of group stratification.DiscussionThis innovative methodology can help identify risk factors important for memory performance and guide future dementia risk reduction interventions among older adults.HighlightsThis work uses a regression tree machine learning model (XGBoost) with highly interpretable Shapley Additive exPlanation values to analyze impacts of 12 life exposure factors plus age, sex and ethnoracial identity on episodic memory outcome. This approach has valuable properties, including the ability to implicitly account for variable interactions, non-linear relations with outcome, and missing values. Age, sex, education, income, volunteering, exercise, hearing and vision, and sleep (quality and duration) have important impacts on memory outcome in a combined model and in stratified models regardless of ethnoracial identity. We also demonstrate individualized models for subgroups of participants, showing how life exposure factors vary in importance between divergent populations and suggesting an approach to personalized interventions. This approach can be valuable for both policy decisions and individualized interventions to support healthy cognitive aging.