Independent Causal Effect of Remnant Cholesterol on Atherosclerotic Cardiovascular Outcomes: A Mendelian Randomization Study
Bibliographic record
Abstract
Background: Observational studies suggested that residual risk of cardiovascular events after LDL (low-density lipoprotein) cholesterol lowering may be linked to remnant cholesterol (RC). We conducted a large-scale Mendelian randomization study to investigate the causal role of RC to predict coronary artery disease (CAD), myocardial infarction (MI), and stroke risk. Methods: We extracted single-nucleotide polymorphisms for RC and LDL from large-scale genome-wide association databases. We estimated the genetic association with outcomes from the CARDIoGRAMplusC4D consortium (Coronary Artery Disease Genome-Wide Replication and Meta-Analysis Plus the Coronary Artery Disease Genetics), the Metastroke consortium, as well as the GLGC (Global Lipids Genetics Consortium). Genetic variants were used as instruments, thereby minimizing residual confounding and reverse causation biases of observational studies. Results: By leveraging data from a combined sample of 958 434 participants, we found evidence for a significant causal effect of RC on the risk of CAD (odds ratio [OR], 1.51 per SD unit increase in RC [95% CI, 1.42–1.60]; P =5.3×10 -5 ), MI (OR, 1.57 [95% CI, 1.21–2.05]; P =9.5×10 -4 ), and stroke (OR, 1.23 [95% CI, 1.12–1.35]; P =3.72×10 -6 ). There was no evidence of pleiotropy. The effect of RC on CAD and MI remained consistent after accounting for the effects of RC-associated genetic variants on LDL cholesterol: OR, 1.49 (95% CI, 1.37–1.61) for CAD and OR, 1.80 (95% CI, 1.70–19.1) for MI without a meaningful indirect effect exerted on these outcomes via the LDL cholesterol mediator. Conclusions: This large-scale Mendelian randomization study showed a robust genetic causal association between RC and cardiovascular outcomes. The effect on CAD and MI is independent of LDL cholesterol. 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 | 0.046 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".