Remnant cholesterol and risk of major adverse cardiovascular events: a systematic review and dose–response meta-analysis of cohort studies
Bibliographic record
Abstract
Emerging evidence indicates a significant role of remnant cholesterol in contributing to the residual risk associated with major adverse cardiovascular events (MACE). This study aims to evaluate the dose-response relationship between remnant cholesterol and the risk of MACE. PubMed, Embase and Cochrane databases were reviewed to identify cohort studies published in English up to 1 August 2023. Twenty-eight articles were selected. Pooled hazard ratios (HR) and their 95% confidence intervals (CIs) were calculated using fixed or random-effects models to evaluate the association between remnant cholesterol and the risk of MACE. The dose-response relationship between remnant cholesterol levels and the risk of MACE was analyzed using the linear model and restricted cubic spline regression models. For calculated remnant cholesterol levels, the pooled HR (95% CI) of MACE for per 1-SD increase was 1.13 (1.08, 1.17); HR (95% CI) for the second quartile (Q2), the third quartile (Q3) and the highest quartile (Q4) of remnant cholesterol levels were 1.14 (1.03, 1.25), 1.43 (1.23, 1.68) and 1.68 (1.44, 1.97), respectively, compared with the lowest quartile (Q1). For measured remnant cholesterol levels, the pooled HR (95% CI) of MACE per 1-SD increase was 1.67 (1.39, 2.01). The dose-response meta-analysis showed a dose-response relationship between remnant cholesterol levels and the risk of MACE, both on a linear trend (P < 0.0001) and a nonlinear trend (P < 0.0001). The risk of MACE is associated with increased levels of remnant cholesterol, and the dose-response relationship between remnant cholesterol levels and the risk of MACE showed both linear and nonlinear trends.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".