Medication Exposure and Neurodegenerative Disease Risk Across National Biobanks
Bibliographic record
Abstract
Abstract Advanced age, genetics, and environmental exposures are leading contributors to the development of neurodegenerative disorders (NDD). In this study, we used data from the UK Biobank (UKB) and the All of Us (AoU) initiative to determine if exposure to specific medications are associated with an increased or decreased risk of NDD, including Alzheimer’s (AD), Parkinson’s disease (PD), and all-cause dementia (DEM). We investigated the associations between these diseases and prescription drug exposures through an unbiased analysis, assessing both lifetime risk and risk from exposures occurring more than ten years before diagnosis, while also accounting for comorbid conditions. Methods Cox proportional hazard models were used to evaluate both lifetime and ten-year lag-associated risks of developing a NDD following exposure to specific prescription medications. This analysis followed a two-stage design, incorporating separate discovery and replication cohorts sourced from national-scale biobanks. Findings We pulled data from over 700,000 health records from individuals of European ancestry to survey a total of 480 prescription medication exposures. After multiple test corrections, we found 241 significant associations between medication exposure and risk of NDDs in our discovery cohort, with 157 of these replicated in an independent dataset. After adjusting for potential comorbidities, 15 medication-NDD associations remained significant, some of which were attenuated after accounting for APOE -ε4 status. Most of these significant pairings were associated with increased risk, however, two antibiotics, one proton pump inhibitor, and one statin had replicated effects inversely associated with disease risk. Interpretation While correlation does not imply causation and some associations may be driven by medications used to treat prodromal stages of disease, we have utilized large, unbiased datasets to identify and replicate associations between commonly prescribed medications and NDD risk. Additional longitudinal and mechanistic investigations are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".