Life-Course Multidisciplinary Psychosocial Predictors of Dementia Among Older Adults: Results From the Health and Retirement Study.

Abstract

Background and objectives: Identifying predictors of dementia may help improve risk assessments, increase awareness for risk reduction, and identify potential targets for interventions. We use a life-course psychosocial multidisciplinary modeling framework to examine leading predictors of dementia incidence.

Research design and methods: We use data from the Health and Retirement Study to measure 57 psychosocial factors across 7 different domains: (i) demographics, (ii) childhood experiences, (iii) socioeconomic conditions, (iv) health behaviors, (v) social connections, (vi) psychological characteristics, and (vii) adverse adulthood experiences. Our outcome is dementia incidence (over 8 years) operationalized using Langa-Weir classification for adults aged 65+ years who meet criteria for normal cognition at the baseline when all psychosocial factors are measured (N = 1 784 in training set and N = 1 611 in testing set). We compare the standard statistical method (Logistic regression) with machine learning (ML) method (Random Forest) in identifying predictors across the disciplines of interest.

Results: Standard and ML methods identified predictors that spanned multiple disciplines. The standard statistical methods identified lower education and childhood financial duress as among the leading predictors of dementia incidence. The ML method differed in their identification of predictors.

Discussion and implications: The findings emphasize the importance of upstream risk and protective factors and the long-reaching impact of childhood experiences on cognitive health. The ML approach highlights the importance of life-course multidisciplinary frameworks for improving evidence-based interventions for dementia. Further investigations are needed to identify how complex interactions of life-course factors can be addressed through interventions.