Identifying drugs associated with Alzheimer's disease risk using machine learning-assisted signal detection

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Abstract
Background: Several Alzheimer's disease (AD) clinical trials currently focus on drug repurposing. Machine learning (ML)-based pharmacoepidemiological approaches may help identify candidate drugs for trials. Objective: We applied an ML-based signal detection method to identify drugs associated with AD risk. Methods: Using Finnish nationwide register-based data, we defined drug exposure at both subgroup and drug substance levels according to the Anatomical Therapeutic Chemical classification. Three binary embedded variables were calculated for each drug based on the frequency of dispensations over three years, starting 10 years before the index date. An algorithm combining subsampling and lasso-logistic regression was applied, with 500 replications for each drug exposure variable. Drug variables with a selection percentage (SP) ≥ 70% across replications were considered as signals. The average beta describes the direction and strength of the association. Results: The study population comprised 43,926 AD cases and 43,926 matched controls. The mean age was 80.3 years in both groups, and 65% of participants were women. Four drug subgroups and three individual drugs were associated with a lower risk of AD, whereas 14 subgroups and 11 individual drugs were associated with a higher risk. Drugs associated with decreased AD risk belonged to cardiovascular drugs: antihypertensives affecting the renin-angiotensin and diuretics. Conclusions: The identified associations are consistent with established risk factors of AD and support the importance of effective hypertension management in AD prevention. Further studies are needed to evaluate whether antihypertensive drugs may exert neuroprotective effects and represent viable candidates for drug repurposing in AD.

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