Recombinant human growth hormone and brain neoplasm association: a pharmacovigilance and Mendelian randomization analysis based on US FAERS, Japanese JADER, and Canadian CVARD
Bibliographic record
Abstract
Objective This study aimed to analyze the statistical association between recombinant human growth hormone (rhGH) and brain neoplasm adverse events (AEs) by mining data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, the Japanese Adverse Drug Event Report (JADER) database, and the Canada Vigilance Adverse Reaction Online Database (CVARD). Furthermore, Mendelian randomization (MR) was utilized to evaluate the potential causal link between rhGH and brain neoplasm, thereby providing a reference for safe clinical medication use. Methods Reports of brain neoplasm associated with rhGH originated from the FAERS database (Q1 2004 - Q4 2024), the JADER database (April 2004 - December 2024), and the CVARD database (January 1991 - December 2024). Disproportionality analysis methods, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Information Component (IC), were used to detect pharmacovigilance signals. Subsequently, a two-sample Mendelian randomization analysis was conducted, treating rhGH as the exposure and brain neoplasm as the outcome, to evaluate the causal relationship. Results A total of 323 reports of rhGH-associated brain neoplasms AEs were identified (FAERS: n = 282; JADER: n = 14; CVARD: n = 27). Brain neoplasm was detected as a positive signal in all three databases by all three signal detection methods, consistently classified as medium clinical priority. Multivariable logistic regression analysis demonstrated an independent association between age and the increased risk of rhGH-induced brain tumors. Specifically, age groups 18–45 years and 46–65 years showed significantly elevated risks [OR (95% CI): 3.71 (1.09–13.34), P = 0.048; and 3.78 (1.02–14.62), P = 0.049, respectively]. The two-sample MR analysis, using the Inverse Variance Weighted (IVW) method, yielded an OR of 1.415 (95% CI: 1.005, 1.991, P = 0.046), suggestive of causal relationship between rhGH and brain neoplasms. Conclusion Pharmacovigilance analysis revealed a significant statistical association between rhGH and brain neoplasms. Mendelian randomization analysis further suggested a causal relationship between them. These findings highlight the need to consider the potential risk of brain neoplasm adverse events during treatment with rhGH.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".