Treatment effect of alirocumab according to age group, smoking status, and hypertension: Pooled analysis from 10 randomized ODYSSEY studies
Bibliographic record
Abstract
BACKGROUND: Age, smoking, hypercholesterolemia, and hypertension are major risk factors for atherosclerotic cardiovascular disease. OBJECTIVE: We examined whether the effects of alirocumab on low-density lipoprotein cholesterol (LDL-C) differed according to age, hypertension, or smoking status. METHODS: Data were pooled from 10 Phase 3 ODYSSEY randomized trials (24-104 weeks' duration) in 4983 people with heterozygous familial hypercholesterolemia (FH) or non-familial hypercholesterolemia (3188 on alirocumab, 1795 on control [620 on ezetimibe and 1175 on placebo]). Most participants received concomitant maximum tolerated statin therapy. In 8 trials, the alirocumab dose was increased from 75 mg every 2 weeks (Q2W) to 150 mg Q2W at Week 12 if predefined risk-based LDL-C goals were not achieved at Week 8 (≥70 mg/dL in very high cardiovascular risk; ≥100 mg/dL in moderate or high cardiovascular risk). Two trials compared alirocumab 150 mg Q2W vs placebo. The efficacy and safety of alirocumab were assessed post hoc in subgroups stratified by age (<65, ≥65 to <75, ≥75 years) and baseline hypertension or smoking status. RESULTS: Alirocumab reduced LDL-C by 23.7% (75/150 mg vs ezetimibe + statin) to 65.4% (150 mg vs placebo + statin) from baseline to Week 24 vs control. Subgroup analyses confirmed no significant interactions in response to alirocumab between age group, hypertension, or smoking status. Overall rates of treatment-emergent adverse events were similar between alirocumab and control groups. CONCLUSIONS: In this pooled analysis from 10 trials, alirocumab led to substantial LDL-C reductions vs control in every age group and regardless of hypertension or smoking status. Alirocumab was well tolerated in all subgroups.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| 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 | 0.002 | 0.002 |
| 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".