Interleukin-6 modifies Lipoprotein(a) and oxidized phospholipids associated cardiovascular disease risk in a secondary prevention cohort
Bibliographic record
Abstract
Background and aims There is a need for effective tools to stratify and modify cardiovascular risk associated with elevated lipoprotein(a) [Lp(a)] and oxidized phospholipids (OxPL). The objective of this analysis was to explore the modifying effects of low-grade inflammation on Lp(a)- and OxPL-associated risk in a secondary prevention cohort. Methods Levels of Lp(a), OxPL associated with apolipoprotein(a) (OxPL-apo[a]) and apolipoprotein B (OxPL-apoB) were determined in the placebo-arm of the low-dose colchicine 2 trial. Patients were between 35 and 82 years, had established chronic coronary syndrome (CCS), and were clinically stable for at least six months prior to randomization. The outcome was the incidence of the composite endpoint of spontaneous myocardial infarction, ischemic stroke, or ischemia-driven coronary revascularization stratified by biomarker levels using a Cox regression model. Results There was a significant interaction between Lp(a) and IL-6 <3.2 ng/L (median) and IL-6 ≥3.2 ng/L for the composite endpoint (HR 0.90; 95 %CI 0.78–1.03 vs HR 1.18; 95 %CI 1.01–1.39, P interaction = 0.01). No interaction was found for Lp(a) levels in participants with hsCRP <2 mg/L (HR 1.00; 95 %CI 0.89–1.14) versus those with hsCRP ≥2 mg/L (HR 1.04; 95 %CI 0.86–1.25, P interaction = 0.79). In line with Lp(a) levels, significant interaction was observed between OxPL-apo(a) as well as OxPL-apoB levels for the composite endpoint with IL-6 (P interaction <0.01 and 0.03, respectively), but not for hsCRP. Conclusions In patients with CCS, Lp(a), OxPL-apo(a) and OxPL-apoB associated cardiovascular risk was only pertinent in those with elevated IL-6 but not hsCRP levels.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".