Alirocumab vs usual lipid‐lowering care as add‐on to statin therapy in individuals with type 2 diabetes and mixed dyslipidaemia: The ODYSSEY DM‐DYSLIPIDEMIA randomized trial
Bibliographic record
Abstract
AIM: To compare alirocumab, a proprotein convertase subtilisin-kexin type 9 inhibitor, with usual care (UC) in individuals with type 2 diabetes (T2DM) and mixed dyslipidaemia not optimally managed by maximally tolerated statins in the ODYSSEY DM-DYSLIPIDEMIA trial (NCT02642159). MATERIALS AND METHODS: The UC options (no additional lipid-lowering therapy; fenofibrate; ezetimibe; omega-3 fatty acid; nicotinic acid) were selected prior to stratified randomization to open-label alirocumab 75 mg every 2 weeks (with increase to 150 mg every 2 weeks at week 12 if week 8 non-HDL cholesterol concentration was ≥2.59 mmol/L [100 mg/dL]) or UC for 24 weeks. The primary efficacy endpoint was percentage change in non-HDL cholesterol from baseline to week 24. RESULTS: The randomized population comprised 413 individuals (intention-to-treat population, n = 409; safety population, n = 412). At week 24, the mean non-HDL cholesterol reductions were superior with alirocumab (-32.5% difference vs UC, 97.5% confidence interval -38.1 to -27.0; P < .0001). Overall, 63.6% of alirocumab-treated individuals were maintained on 75 mg every 2 weeks. Alirocumab also reduced LDL cholesterol (-43.0%), apolipoprotein B (-32.3%), total cholesterol (-24.6%) and LDL particle number (-37.8%) at week 24 vs UC (all P < .0001). Consistent with the overall trial comparison, alirocumab reduced non-HDL cholesterol to a greater degree within each UC stratum at week 24. The incidence of treatment-emergent adverse events was 68.4% (alirocumab) and 66.4% (UC). No clinically meaningful effect on glycated haemoglobin, or change in number of glucose-lowering agents, was seen. CONCLUSIONS: In individuals with T2DM and mixed dyslipidaemia on maximally tolerated statin, alirocumab showed superiority to UC in non-HDL cholesterol reduction and was generally well tolerated.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".