No Association Between Proton Pump Inhibitor Use and Risk of Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVES: The objective of the study was to investigate whether proton pump inhibitor (PPI) use is associated with an increased risk of clinically verified Alzheimer's disease (AD). METHODS: A Finnish nationwide nested case-control study MEDALZ includes all community-dwelling individuals with newly diagnosed AD during 2005-2011 (N=70,718), and up to four age-, sex-, and region of residence-matched comparison individuals for each case (N=282,858). Data were extracted from Finnish nationwide health-care registers. PPI use was derived from purchases recorded in the Prescription register data since 1995 and modeled to drug use periods with PRE2DUP method. AD was the outcome measure. RESULTS: PPI use was not associated with risk of AD with 3-year lag window applied between exposure and outcome (adjusted odds ratio (OR) 1.03, 95% confidence interval (CI) 1.00-1.05). Similarly, longer duration of use was not associated with risk of AD (1-3 years of use, adjusted OR 1.01 (95% CI 0.97-1.06); ≥3 years of use adjusted OR 0.99 (95% CI 0.94-1.04)). Higher dose use was not associated with an increased risk (≥1.5 defined daily doses per day, adjusted OR 1.03 (95% CI 0.92-1.14)). CONCLUSIONS: In conclusion, we found no clinically meaningful association between PPI use and risk of AD. The results for longer duration of cumulative use or use with higher doses did not indicate dose-response relationship.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".