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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.
- DEMENTIA
- CLUSTER ANALYSIS
- COHORT STUDIES
- EPIDEMIOLOGY
Data availability statement
Data may be obtained from a third party and are not publicly available. UK Biobank data access agreement does not allow to share data publicly. However, data are accessible directly from the UK Biobank via appropriate project applications.
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Data availability statement
Data may be obtained from a third party and are not publicly available. UK Biobank data access agreement does not allow to share data publicly. However, data are accessible directly from the UK Biobank via appropriate project applications.
Footnotes
Twitter @MizanKhondoker, @N/A
Contributors MK conceived the idea, MK and LS designed the study with contributions from AM, MOB, MH and CF. AM guided on the inflammation aspects of the project. AM, MH and CF advised on selection of chronic conditions. MOB advised on pathophysiology-based grouping of multimorbidity. MH advised on the selection of dementia ICD 10 codes. MK analysed the data, written-up and revised the manuscript. All authors provided critical feedback on important intellectual contents, edited the manuscript and contributed to the interpretation of data and discussion of results. The corresponding author (MK) is responsible for the overall content.
Funding This study was funded by Norwich Medical School, University of East Anglia.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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