Efficacy and safety of alirocumab among individuals with diabetes mellitus and atherosclerotic cardiovascular disease in the ODYSSEY phase 3 trials
Bibliographic record
Abstract
AIMS: Individuals with both diabetes mellitus (DM) and atherosclerotic cardiovascular disease (ASCVD) are at very high risk of cardiovascular events. This post-hoc analysis evaluated efficacy and safety of the PCSK9 inhibitor alirocumab among 984 individuals with DM and ASCVD pooled from 9 ODYSSEY Phase 3 trials. MATERIALS AND METHODS: Changes in low-density lipoprotein cholesterol (LDL-C) and other lipids from baseline to Week 24 were analysed (intention-to-treat) in four pools by alirocumab dosage (150 mg every 2 weeks [150] or 75 mg with possible increase to 150 mg every 2 weeks [75/150]), control (placebo/ezetimibe) and background statin usage (yes/no). RESULTS: At Week 24, LDL-C changes from baseline in pools with background statins were -61.5% with alirocumab 150 (vs -1.0% with placebo), -46.4% with alirocumab 75/150 (vs +6.3% with placebo) and -48.7% with alirocumab 75/150 (vs -20.6% with ezetimibe), and -54.9% with alirocumab 75/150 (vs +4.0% with ezetimibe) without background statins. A greater proportion of alirocumab recipients achieved LDL-C < 70 and < 55 mg/dL at Week 24 vs controls. Alirocumab also resulted in significant reductions in non-high-density lipoprotein cholesterol, apolipoprotein B and lipoprotein(a) vs controls. Alirocumab did not appear to affect glycaemia over 78-104 weeks. Overall safety was similar between treatment groups, with a higher injection-site reaction frequency (mostly mild) with alirocumab. CONCLUSION: Alirocumab significantly reduced LDL-C and other atherogenic lipid parameters, and was generally well tolerated in individuals with DM and ASCVD.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".