Analysis of the relationship between histamine H1 receptor antagonists and broad dementia events using the FAERS, JADER, and CVAR databases
Bibliographic record
Abstract
Background Recent studies suggest histamine H1 receptor antagonists (H1RAs) may elevate broad dementia events risk, though real-world data remain scarce.Methods This pharmacovigilance study analyzed FDA Adverse Event Reporting System (FAERS), Japanese Adverse Drug Event Report (JADER), and Canada Vigilance Adverse Reaction (CVAR) databases from January 2004 to June 2024. Using disproportionality analyses such as the reporting odds ratio (ROR) and proportion reporting ratio (PRR), time-to-onset analysis, propensity score matching, and multivariate regression, we compared broad dementia event signals among first-generation H1RAs (FG-H1RAs), second-generation H1RAs (SG-H1RAs), and benzodiazepines (BDs).Results According to the FAERS database, FG-H1RAs (ROR = 3.33, 95%CI 3.22–3.44; PRR = 3.11, χ2 = 5426.01), SG-H1RAs (ROR = 2.03, 95%CI 1.98–2.07; PRR = 1.97, χ2 = 3706.58), and BDs (ROR = 2.74, 95%CI 2.68–2.80; PRR = 2.6, χ2 = 8338.34) were significantly associated with broad dementia events, with FG-H1RAs having a stronger association with broad dementia events compared to SG-H1RAs (aROR = 0.60, 95%CI 0.54–0.67, p < 0.001). Analysis of the JADER and CVAR databases yielded similar results. FG-H1RAs exhibited immediate broad dementia events reporting (median = 0 days), while SG-H1RAs showed delayed onset (median = 1 day), both with early risk decay.Conclusion This study provides evidence for the association between H1RAs treatment and broad dementia events, highlighting signaling differences among H1RAs. However, large-scale, high-quality epidemiological studies are still needed for validation.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".