Efficacy and safety of alirocumab in people with prediabetes vs those with normoglycaemia at baseline: a pooled analysis of 10 phase <scp>III ODYSSEY</scp> clinical trials
Bibliographic record
Abstract
Abstract Aim To assess the lipid‐lowering efficacy and safety of alirocumab, a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor, in people with hypercholesterolaemia and prediabetes at baseline vs people with normoglycaemia at baseline in a pooled analysis of 10 ODYSSEY phase III trials. Methods People classified as having prediabetes had baseline HbA1c ≥39 mmol/mol (5.7%) and <48 mmol/mol (6.5%), or two baseline fasting plasma glucose values ≥5.6 mmol/l (100 mg/dl) but no more than one fasting plasma glucose value ≥7.0 mmol/l (126 mg/dl), or had specific terms reported in their medical history; people diagnosed with diabetes at baseline were excluded, and the remainder were classified as having normoglycaemia. Participants received alirocumab or control (placebo/ezetimibe) for 24–104 weeks, with maximally tolerated statin in most cases. The primary efficacy endpoint was LDL cholesterol reductions from baseline to week 24 in the intention‐to‐treat population using the mixed‐effect model with a repeated measures approach. Results Reductions in LDL cholesterol from baseline to week 24 with alirocumab were 44.0–61.8% (prediabetes group) and 45.8–59.5% (normoglycaemia group). In both subgroups, LDL cholesterol reductions were generally similar in those with and without baseline triglycerides ≥1.7 mmol/l (150 mg/dl). Alirocumab was not associated with changes in HbA1c or fasting plasma glucose over time in either subgroup (up to 24 months' follow‐up). Adverse event rates were generally similar in those with and without prediabetes. Conclusions Over a mean follow‐up of 24–104 weeks, alirocumab treatment resulted in significant LDL cholesterol reductions from baseline that were similar in participants with prediabetes and those with normoglycaemia at baseline, with no effect on glycaemia and a safety profile similar to that of the 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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".