Sex differences in drug-induced osteoporosis: a pharmacovigilance study based on the FAERS database
Bibliographic record
Abstract
Background: Osteoporosis is a prevalent condition globally, often linked to a significant risk of fractures. Drug-induced osteoporosis (DIOP) is an increasingly recognized adverse effect of various medications, but the sex-specific risks and time-to-onset patterns remain inadequately understood. Addressing these gaps in knowledge is critical to improving patient safety and pharmacovigilance. Objective: This study aimed to explore sex-related differences in DIOP, identify high-risk medications, and assess the onset patterns of osteoporosis-related adverse events by analyzing data from the FDA Adverse Event Reporting System (FAERS) and validating the findings using the Canada Vigilance Adverse Reaction Online Database (Canada Vigilance ADR). Methods: We analyzed adverse event reports from the FAERS database covering the period from Q1 2004 to Q4 2024. Drugs were standardized using the RxNorm drug terminology system, and adverse events were matched to MedDRA 27.1. Disproportionality analysis was conducted using Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN) methods. To validate our findings, we performed external validation using the Canada Vigilance ADR database. Stratified analyses by sex were performed to assess differences in drug-osteoporosis associations. Results: A total of 236,928 osteoporosis-related reports were identified, with 64.6% of the reports coming from females. We identified 68 drugs associated with DIOP, including 15 male-specific and 26 female-specific potential risk drugs. Notable drugs such as tenofovir disoproxil and esomeprazole were linked to both sexes. Drugs like upadacitinib exhibited early-onset failure patterns, while others like tenofovir demonstrated cumulative risk patterns over prolonged use. External validation with the Canada Vigilance ADR confirmed 32 drugs with potential osteoporosis risks. Conclusions: This study highlights important sex-specific differences in the risk of drug-induced osteoporosis and underscores the need for targeted pharmacovigilance strategies. The findings contribute to a more personalized approach to drug safety, promoting more informed decision-making regarding medication use in osteoporosis-prone populations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".