Abstract 14328: Relation of Lipoprotein(a) Levels to Incident Diabetes and Modification by Alirocumab Treatment: An Analysis of the Odyssey Outcomes Trial
Bibliographic record
Abstract
Background: Cohort studies and clinical trials have shown a greater prevalence of diabetes among subjects with lower levels of lipoprotein(a) [Lp(a)]. Some healthy cohort studies have shown a greater incidence of new onset diabetes (NOD) among those with lower Lp(a). It is unknown whether the risk of NOD associates with Lp(a) levels in patients (pts) with established cardiovascular disease or whether pharmacologic reduction of Lp(a) with PCSK9 inhibitors modulates this risk. Objective: Using data from the ODYSSEY OUTCOMES trial that compared the PCSK9 inhibitor alirocumab (ALI) with placebo (PBO) in pts with recent acute coronary syndrome, we examined whether NOD was related to baseline Lp(a) level and whether any such relationship was modified by ALI treatment. Methods and Results: Lp(a) was measured with a mass assay in 13,480 trial pts without diabetes at baseline; median (IQR) baseline Lp(a) was 21.9 mg/dL (6.9-61.1); median follow-up was 2.7 years. Intensive statin therapy was utilized in 89%. In the PBO group, NOD was greatest in Quartile 1 and least in Quartile 4 of baseline Lp(a) ( Figure , 4.6 vs 3.1 cases per 100 pt-years, P trend 0.0003). ALI lowered Lp(a) by a median of 23% from baseline. Absolute median reduction in Lp(a) with ALI ranged from nil in baseline Lp(a) Quartile 1 to 15 mg/dL in Quartile 4. Treatment HR (ALI/PBO) for NOD was neutral overall (0.95, 95% CI 0.85-1.05) but varied across baseline Lp(a) quartiles from 0.79 (0.64-0.96) in Quartile 1 to 1.09 (0.87-1.38) in Quartile 4 ( Figure , P trend =0.025). Conclusion: In pts with recent acute coronary syndrome, there is greater NOD among those with lower baseline Lp(a) levels. ALI has an overall neutral effect on NOD: In pts with low baseline Lp(a), ALI has minimal effect on Lp(a) levels and tends to reduce NOD. In pts with high baseline Lp(a), ALI reduces Lp(a) levels with a non-significant excess of NOD. The findings may have implications for emerging therapies that reduce Lp(a) more substantially than PCSK9 inhibitors.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".