Risk of Incident Diabetes Related to Lipoprotein(a), LDL Cholesterol, and Their Changes With Alirocumab: Post Hoc Analyses of the ODYSSEY OUTCOMES Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: Previous genetic and clinical analyses have associated lower lipoprotein(a) and LDL cholesterol (LDL-C) with greater risk of new-onset type 2 diabetes (NOD). However, PCSK9 inhibitors such as alirocumab lower both lipoprotein(a) and LDL-C without effect on NOD. RESEARCH DESIGN AND METHODS: In a post hoc analysis of the ODYSSEY OUTCOMES trial (NCT01663402), we examined the joint prediction of NOD by baseline lipoprotein(a), LDL-C, and insulin (or HOMA-insulin resistance [HOMA-IR]) and their changes with alirocumab treatment. Analyses included 8,107 patients with recent acute coronary syndrome on optimized statin therapy, without diabetes at baseline, assigned to alirocumab or placebo with median follow-up 2.4 years. Splines were estimated from logistic regression models. RESULTS: Lower baseline lipoprotein(a) and higher baseline insulin or HOMA-IR independently predicted 782 cases of NOD; baseline LDL-C did not predict NOD. Alirocumab reduced lipoprotein(a) and LDL-C without affecting insulin or NOD risk (odds ratio [OR] vs. placebo 0.998; 95% CI 0.860-1.158). However, in logistic regression, decreased lipoprotein(a) and LDL-C on alirocumab were independent, opposite predictors of NOD. OR for NOD for 25% and 50% lipoprotein(a) reductions on alirocumab were 1.12 (95% CI 1.01-1.23) and 1.24 (1.02-1.52). OR for NOD for 25% and 50% LDL-C reductions on alirocumab were 0.88 (95% CI 0.80-0.97) and 0.77 (0.64-0.94). CONCLUSIONS: Baseline lipoprotein(a) was inversely associated with risk of NOD. Alirocumab-induced reductions of lipoprotein(a) and LDL-C were associated with increased and decreased risk of NOD, respectively, without net effect on NOD. Ongoing trials will determine the impact of larger and longer lipoprotein(a) reductions on NOD.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".