Multimorbidity pattern and risk of dementia in later life: an 11-year follow-up study using a large community cohort and linked electronic health records

Abstract

Background: Several long-term chronic illnesses are known to be associated with an increased risk of dementia independently, but little is known how combinations or clusters of potentially interacting chronic conditions may influence the risk of developing dementia.

Methods: 447 888 dementia-free participants of the UK Biobank cohort at baseline (2006-2010) were followed-up until 31 May 2020 with a median follow-up duration of 11.3 years to identify incident cases of dementia. Latent class analysis (LCA) was used to identify multimorbidity patterns at baseline and covariate adjusted Cox regression was used to investigate their predictive effects on the risk of developing dementia. Potential effect moderations by C reactive protein (CRP) and Apolipoprotein E (APOE) genotype were assessed via statistical interaction.

Results: LCA identified four multimorbidity clusters representing Mental health, Cardiometabolic, Inflammatory/autoimmune and Cancer-related pathophysiology, respectively. Estimated HRs suggest that multimorbidity clusters dominated by Mental health (HR=2.12, p<0.001, 95% CI 1.88 to 2.39) and Cardiometabolic conditions (2.02, p<0.001, 1.87 to 2.19) have the highest risk of developing dementia. Risk level for the Inflammatory/autoimmune cluster was intermediate (1.56, p<0.001, 1.37 to 1.78) and that for the Cancer cluster was least pronounced (1.36, p<0.001, 1.17 to 1.57). Contrary to expectation, neither CRP nor APOE genotype was found to moderate the effects of multimorbidity clusters on the risk of dementia.

Conclusions: Early identification of older adults at higher risk of accumulating multimorbidity of specific pathophysiology and tailored interventions to prevent or delay the onset of such multimorbidity may help prevention of dementia.

Keywords: CLUSTER ANALYSIS; COHORT STUDIES; DEMENTIA; EPIDEMIOLOGY.