Lipid‐lowering efficacy and safety of alirocumab in patients with or without diabetes: <scp>A</scp> sub‐analysis of <scp>ODYSSEY COMBO II</scp>
Bibliographic record
Abstract
AIM: This sub-analysis of the ODYSSEY COMBO II study compared the effects of alirocumab, a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor, in high cardiovascular risk patients with or without diabetes mellitus (DM) receiving maximally tolerated statin therapy. METHODS: COMBO II was a 104-week, double-blind study (n = 720) enrolling patients with documented atherosclerotic cardiovascular disease (ASCVD) and baseline LDL-C ≥70 mg/dL (1.8 mmol/L), and patients without documented ASCVD at high cardiovascular risk with LDL-C ≥100 mg/dL (2.6 mmol/L). Patients receiving maximally tolerated statin therapy were randomized (2:1) to alirocumab 75 mg every 2 weeks (Q2W; 1 mL subcutaneous injection) or oral ezetimibe 10 mg daily. Alirocumab dose was increased to 150 mg Q2W (also 1 mL) at Week 12 if Week 8 LDL-C was ≥70 mg/dL. RESULTS: History of DM was reported in 31% (n = 148) of patients on alirocumab and 32% (n = 77) of patients on ezetimibe. At Week 24, alirocumab consistently reduced LDL-C from baseline in patients with (-49.1%) or without DM (-51.2%) to a significantly greater extent than ezetimibe (-18.4% and -21.8%, respectively). Occurrence of treatment-emergent adverse events was similar between groups. Efficacy results at 104 weeks were similar to those at 24 weeks. CONCLUSIONS: Over a 104-week double-blind study period, alirocumab provided consistently greater LDL-C reductions than ezetimibe, with similar LDL-C results in patients with or without DM. Safety of alirocumab was similar regardless of baseline DM status.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".