Potential protective role of GLP-1 receptor agonists for lithium-induced nephrotoxicity: a population-based observational study
Bibliographic record
Abstract
Abstract Objectives We hypothesized that semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1 RA) and treatment currently U.S. Food and Drug Administration (FDA) approved to reduce worsening of kidney disease and kidney failure may protect against lithium-induced nephrotoxicity. Methods Renal adverse events (AEs) reported to the FDA Adverse Event Reporting System (FAERS) between December 2003 and December 2024 were analyzed using the validated OpenVigil 2.1 platform. Reporting odds ratios (RORs) were computed for the MedDRA terms renal impairment, renal failure, chronic kidney disease (CKD), end-stage renal disease (ESRD), and acute kidney injury (AKI) in association with lithium, semaglutide, and their co-reporting. A disproportionality signal was considered statistically significant when p < 0.05, and the lower bound of the information component (IC 025 ) was greater than 0. Results Lithium was associated with significantly elevated reporting odds across all renal AEs, with each outcome exceeding statistical signal thresholds ( p < 0.0001, IC 025 > 0). In contrast, semaglutide demonstrated inverse associations for renal impairment, renal failure, CKD, and ESRD (RORs = 0.25–0.54), and a neutral association for AKI (ROR = 0.96); however, none met the criteria for a disproportionality signal (IC 025 < 0). Co-reported use of lithium and semaglutide did not yield a significant signal for any renal outcome, including AKI (ROR = 5.81, p = 0.015, IC 025 < 0). Conclusions This observation, provides impetus to conduct adequate, thorough, mechanistic, clinical, and observational studies to determine whether semaglutide (and/or other incretin receptor agonists) could possibly mitigate the risk for, and/or modify the trajectory of, lithium-induced nephrotoxicity.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".