Independent causal effect of remnant cholesterol on atherosclerotic cardiovascular outcomes: a Mendelian randomization study
Bibliographic record
Abstract
Abstract Background Observational studies suggested that residual risk of cardiovascular events following LDL-cholesterol (LDL-C) lowering may be linked to remnant cholesterol (RC). Purpose We conducted a large-scale Mendelian Randomization (MR) study to investigate the causal role of RC to predict coronary artery disease (CAD), myocardial infarction (MI) and stroke risk. Methods Outcome sources included 19,273 subjects of European ancestry from 14 cohorts, as well as data in 115,078 individuals from the OpenGWAS public data infrastructure. Over 12,000,000 SNPs were screened. We estimated the genetic association with outcomes from the Coronary Artery Disease (C4D) Genetics (CARDIoGRAMplusC4D) consortium, the Metastroke consortium as well as the Global Lipids Genetics Consortium. Our approach was to use genetic variants as instruments, thereby minimizing residual confounding, and reverse causation biases of observational studies. Results By leveraging data from a combined sample of ∼917,000 participants, we found evidence for a significant causal effect of RC on the risk of CAD (OR 1.51 per each mg/dL unit increase in RC, 95% CI 1.42–1.60), MI (OR 1.57, 1.21–2.05) and stroke (OR 1.23,1.12–1.35). There was no evidence of pleiotropy. Mediation analysis showed that the total effect of RC on CAD and MI was mainly direct: ORDirect 1.49 (1.37-1.61) for CAD and ORDirect 1.80 (1.70-19.1) for MI without a meaningful indirect effect exerted on these outcomes via the LDL-C mediator. Lifelong genetic inhibition of RC led to a 40% MI risk reduction which is superior to any intensity of pharmacological reduction from clinical trials with an average 3.5 years follow-up. Conclusions This large-scale MR study showed a robust genetic causal association between RC and cardiovascular outcomes. The effect on CAD and MI is independent of LDL-C. Early screening for RC along with long-term inhibition of RC should be the focus of future therapeutic interventions.
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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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".