Abstract 17748: Lipoprotein(a) and Oxidized Phospholipids on Lipoproteins and Cardiovascular Outcomes in Patients With Chronic Coronary Syndrome Treated With Colchicine
Bibliographic record
Abstract
Introduction: Lipoproteins carrying pro-inflammatory oxidized phospholipids (OxPL), exemplified by lipoprotein(a) [Lp(a)], contribute to residual risk for major adverse cardiovascular events (MACE) in patients with chronic coronary syndrome (CCS) despite optimal medical treatment. Hypothesis: Anti-inflammatory colchicine therapy modifies MACE in patients with elevated Lp(a), OxPL on apo(a) [OxPL-apo(a)], and OxPL on apoB containing lipoproteins (OxPL-apoB). Aims To study the relationship of Lp(a), OxPL-apo(a) and OxPL-apoB with MACE in 1777 LoDoCo2 trial participants with CCS randomized to colchicine or placebo. Methods: End of study but not baseline samples were available. Because Lp(a), OxPL-apo(a) and OxPL-apoB levels are strongly genetically determined, we assumed that end of study values would reflect baseline values. The primary endpoint was a composite of MI, ischemic CVA or ischemia driven coronary revascularization. Cox regression was used to compare the incidence of the primary endpoint by Lp(a) dichotomized at 125 nmol/L and OxPL-apo(a) and OxPL-apoB in tertiles. Results: At study end, median (IQR) on-colchicine Lp(a), OxPL-apo(a), and OxPL-apoB levels in nmol/L were 22.4 (7.2-102.7), 7.8 (2.0-34.5) and 3.1 (1.3-6.3), similar to on-placebo levels of 23.9 (9.9-95.7), 8.8 (2.7-34.0), and 3.1 (1.4-5.9), respectively (p = 0.20, 0.98 and 0.18). MACE reduction with colchicine was independent of Lp(a) and OxPL-apo(a) (P interaction = 0.92 and 0.66 respectively). However, the largest MACE reduction was present in the highest vs lowest OxPL-apoB tertile ( Figure , P interaction < 0.05). Conclusions: The benefit of colchicine in reducing MACE was highest in subjects with elevated OxPL-apoB, suggesting colchicine may be most effective in subjects with heightened oxidation-driven inflammation. These findings may be hypothesis-generating and require validation in larger trials with baseline biomarker assessment including CRP and IL-6.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".