Hyperglycaemic disorders associated with PCSK9 inhibitors: a real-world, pharmacovigilance study
Bibliographic record
Abstract
AIMS: While genetic and biological studies indicated a potential association between proprotein-convertase subtilisin/kexin type 9 inhibitors (PCSK9i) and hyperglycaemia, real-world data are limited. Therefore, we sought to investigate this association using the FDA adverse event reporting system (FAERS). METHODS AND RESULTS: The FAERS database (2015-2020) was retrospectively queried to characterize reporting of hyperglycaemic adverse events (AEs) with PCSK9i. Disproportionality analyses were performed using the adjusted reporting odds ratio (adj.ROR), and the lower bound of the information component (IC) 95% credibility interval (IC025 > 0 is deemed significant). Among 7 295 624 eligible patients, 71 748 reports of evolocumab and 15 976 of alirocumab were identified. Compared to the full database, PCSK9i treatment was associated with increased reporting of hyperglycaemic AEs [n = 1841, adj.ROR = 1.14 (1.07-1.22), IC025 = 0.13]. Hyperglycaemic AEs were primarily mild hyperglycaemia [n = 1469, adj. ROR = 1.48 (1.36-1.62), IC025 = 0.51] rather than diabetes [n = 372, adj. ROR = 0.67 (0.60-0.74), IC025 = -0.90]. Among PCSK9i agents, evolocumab, but not alirocumab, was associated with hyperglycaemic AEs [n = 1587, adj. ROR = 1.24 (1.15-1.32), IC025 = 0.20; n = 254, adj. ROR = 0.73 (0.60-0.88), IC025 = -0.38, respectively]. Hyperglycaemic AEs were reported more often with PCSK9i compared to ezetimibe [adj.ROR = 1.99 (1.35-2.94)], and less often compared to statins [adj.ROR = 0.26 (0.25-0.28)]. Notably, hyperglycaemic AEs were reported more frequently by diabetic than by non-diabetic patients (P < 0.001), mostly occurred within 6 months of treatment and were reversible upon drug discontinuation. CONCLUSION: In a real-world setting, PCSK9i treatment was associated with increased reporting of mild hyperglycaemia, but not diabetes. While initial monitoring is warranted, the favourable glycaemic safety profile compared to statins supports their essential role in the management of lipid disorders.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".