Concomitant use of statins and sodium‐glucose co‐transporter 2 inhibitors and the risk of myotoxicity reporting: A disproportionality analysis
Bibliographic record
Abstract
AIMS: Recent case reports have suggested that sodium-glucose co-transporter 2 (SGLT2) inhibitors may interact with statins to increase their risk of myotoxicity. We assessed the risk of myotoxicity reporting associated with concomitant use of SGLT2 inhibitors and statins. METHODS: We queried the US Food and Drug Administration Adverse Event Reporting System (FAERS) from 2013 to 2021 for reports including SGLT2 inhibitors, statins or both. We estimated several measures of disproportionate reporting of myopathy and rhabdomyolysis associated with concomitant use of SGLT2 inhibitors and statins: reporting odds ratio (ROR) with 95% confidence interval (CI), Ω shrinkage measure (safety signal if >0) and an extension of the proportional reporting ratio (PRR) (two-criteria set, safety signal if both criteria are met), using the full FAERS dataset as the reference set. In sensitivity analyses, we focussed on specific SGLT2 inhibitor-statin pairs with higher interaction potential (canagliflozin-rosuvastatin, empagliflozin-rosuvastatin) and accounted for stimulated reporting. RESULTS: There were 456 myopathy and 77 rhabdomyolysis reports involving both an SGLT2 inhibitor and a statin. Concomitant use of SGLT2 inhibitors and statins was not associated with an increased risk of myopathy (ROR 0.79, 95% CI 0.70 to 0.89) or rhabdomyolysis (ROR 0.58, 95% CI 0.41 to 0.83) reporting. For both outcomes, the Ω shrinkage measure was negative and only one criterion of the PRR extension was met. SGLT2 inhibitor-statin pairs with higher interaction potential yielded potential signals for rhabdomyolysis; these signals disappeared after accounting for stimulated reporting. CONCLUSION: There was no increased risk of myotoxicity reporting associated with concomitant use of SGLT2 inhibitors and statins or for specific drug pairs.
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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 | 0.057 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.014 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".