The effects of colchicine on lipoprotein(a)- and oxidized phospholipid-associated cardiovascular disease risk
Bibliographic record
Abstract
AIMS: Inflammatory lipoprotein(a) [Lp(a)] and oxidized phospholipids (OxPLs) on lipoproteins convey residual cardiovascular disease risk. The low-dose colchicine 2 (LoDoCo2) trial showed that colchicine reduced the risk of cardiovascular events occurring on standard therapies in patients with chronic coronary syndrome (CCS). We explored the effects of colchicine on Lp(a)- and oxidized lipoprotein-associated risk in a LoDoCo2 biomarker subpopulation. METHODS AND RESULTS: Lipoprotein(a) and OxPLs on apolipoprotein(a) [OxPL-apo(a)] and apolipoprotein B-100 (OxPL-apoB) levels were determined in the biomarker population of the LoDoCo2 trial (n = 1777). The Cox regression analysis was used to compare the risk of the primary endpoint, consisting of myocardial infarction, ischaemic stroke, or ischaemia-driven revascularization by biomarker levels. Interactions between treatment, Lp(a), and OxPL levels were evaluated. Lipoprotein(a), OxPL-apo(a), and OxPL-apoB levels were similar between the colchicine and placebo groups. Consistent risk reduction by colchicine was observed in those with Lp(a) < 125 nmol/L and ≥125 nmol/L and the highest OxPL-apo(a) tertile compared with the lowest (Pinteraction = 0.92 and 0.66). The absolute risk reduction for those with Lp(a) ≥ 125 nmol/L appeared higher compared with those with Lp(a) < 125 nmol/L (4.4% vs. 2.4%). A treatment interaction for colchicine was found in those with the highest OxPL-apoB tertile vs. the lowest (Pinteraction = 0.04). CONCLUSION: In patients with CCS, colchicine reduces cardiovascular disease risk in those with and without elevated Lp(a) but absolute benefits appeared higher in those with Lp(a) ≥ 125 nmol/L. Patients with higher levels of OxPL-apoB experienced greater benefit of colchicine, suggesting that colchicine may be more effective in subjects with heightened oxidation-driven inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".