GLP-1 Receptor Agonists and Serious Adverse Events of Hyperkalemia/Hypokalemia: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Hyperkalemia is a risk factor for cardiovascular disease and death and is a common side effect of kidney and cardiovascular protective medications. Small physiological studies and observational studies suggest that glucagon like peptide-1 receptor agonists (GLP1RAs) may have kaliuretic properties. The objective of this study to determine the effect of GLP1RAs on the risk of serious adverse events (SAEs) of hyperkalemia and hypokalemia. Methods: We completed a systematic review and meta-analysis via a PubMed Search Strategy. We selected for randomized controlled trials (RCT) enrolling at least 500 participants with 52 weeks of follow-up following adults with and without diabetes participating in RCTs of GLP1RAs versus placebo, irrespective of the primary outcome of the study. If available, we extracted the serious adverse event (SAE) data of hyperkalemia and/or hypokalemia available on clinicaltrials.gov. Results: We included 11 RCTs. The median incidence of SAEs of hyperkalemia in the placebo group was 0.13%. GLP1RAs reduced the incidence of SAEs of hyperkalemia by 31% (RR 0.69, 95% CI 0.49-0.99) with no evidence of heterogeneity (I2=0%). GLP1RAs did not increase in the incidence of SAEs of hypokalemia (HR: 1.08, 95% CI: 0.71-1.65) with no evidence of heterogeneity (I2=0%). Conclusion: GLP1RAs reduce the incidence of SAEs of hyperkalemia without increasing the risk of SAEs of hypokalemia. GLP1RAs may therefore prevent severe hyperkalemia when combined with other kidney and cardioprotective medications.GLP1RA is glucagon like peptide-1 receptor agonist. Displayed is a forest plot of individual relative risks and 95% confidence intervals. The size of the box represents the weight of the individual study. The diamond is the weighted summary estimate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".