Evidence for Causal Links Between Known Modifiable Risk Factors and Dementia: A Systematic Review of Mendelian
Roopal Desai1, Amber John1, Emma Anderson2
1ADAPT Lab, Research Department of Clinical Educational and Health Psychology, University College London, London, UK.
Background:
We aimed to systematically review the evidence for associations between the known modifiable risk factors and dementia based on Mendelian randomisation (MR) studies.
Method:
Five databases were searched from inception to April 2024 investigating the association between the 12 risk factors identified in the Lancet Commission and dementia. Evaluable analyses were categorized into one of four levels (robust, probable, suggestive, insufficient) based on estimate significance level and concordance of direction of effect between main and sensitivity analyses. Evidence from clinically diagnosed dementia outcomes was synthesized separately from proxy outcomes. A post hoc sensitivity analysis excluded estimates with concerns over construct validity.
Results:
A total of 47 studies were included, representing 240 MR associations (185 unique and evaluable). Over half (73.5%) of evaluable analyses were graded as providing insufficient evidence for a causal association. Among clinically diagnosed outcomes, the strongest evidence was for educational attainment (mainly probable evidence in a protective direction) and type 2 diabetes-related dysfunction (probable evidence in the risk direction). Smoking showed probable evidence of a protective association. Other risk factors, produced inconclusive or insufficient evidence. Proxy outcome analyses yielded weaker findings; in particular, the association between education and Alzheimer's disease reversed direction.
Conclusion:
MR evidence for most Lancet Commission risk factors remains insufficient or inconclusive. The most consistent support for causal associations was observed for lower educational attainment and type 2 diabetes. Null findings should be interpreted cautiously given limitations in GWAS phenotyping, sample composition, and MR methodology.
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