Systemic evaluation of inclisiran on the risk of new-onset diabetes and hyperglycemia compared to evolocumab and atorvastatin
Bibliographic record
Abstract
Background: Inclisiran is an siRNA-based cholesterol-lowering drug with N-acetylgalactosamine carbohydrate (GalNAc) and is used for the treatment of hypercholesterolemia or dyslipidemia. It reduces LDL-C by 50%, with a convenient dosing schedule and fewer adverse events. Unlike statins, inclisiran has not been associated with an increased risk of muscle or liver adverse events in clinical studies. This favorable safety profile makes inclisiran a valuable alternative for patients who are intolerant of statins due to muscle or hepatic side effects. However, its impact on glycemic control and diabetes risk is unclear and understudied. Methods and results: The US Food and Drug Administration Adverse Event Reporting System (FAERS) study analyzed hyperglycemia and diabetes risks for inclisiran, atorvastatin, and evolocumab. Data from 2021 to 2024 were assessed for Medical Dictionary (MedDRA) terms, and SAS 9.4 with a reporting advantage ratio (ROR) and Bayesian credible interval progressive neural network (BCPNN) was used for analysis. Systematic review and meta-analysis were conducted using PubMed, Embase, and specific search terms. Two research workers extracted data independently, and the study quality was assessed with the Cochrane and Newcastle-Ottawa scales. RevMan 5.4 and Stata 18.0 were used for analyses. Ethical approval was waived due to the use of public, anonymous data. From 2015 Q1 to 2024 Q1, 12,821,285 adverse events were reported in FEARS, with 3,375 inclisiran, 126,620 evolocumab, and 42,228 atorvastatin cases. Atorvastatin had a higher ROR for type 2 diabetes (195.03) than inclisiran (0.95) and evolocumab, but it was not statistically significant. Glucose intolerance and blood glucose issues showed weak signals for inclisiran and atorvastatin. A literature search yielded 16 relevant articles, including six cohort studies and 10 RCTs, totaling 297,863 patients. The incidence of new-onset diabetes was higher with atorvastatin than with inclisiran, placebo, and evolocumab. The SUCRA rankings were atorvastatin > inclisiran > placebo > evolocumab for new diabetes incidence. Conclusion: The FAERS study and meta-analysis indicate that inclisiran may carry a lower risk of new-onset diabetes than atorvastatin, warranting further investigation into inclisiran's impact on glycemic control.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".