Association Between Remnant Cholesterol and Risk of Incident Atrial Fibrillation: Population‐Based Evidence From a Large‐Scale Prospective Cohort Study
Bibliographic record
Abstract
Background Evidence for the relationship between remnant cholesterol (RC) and incident atrial fibrillation (AF) risk remains sparse and limited. Methods and Results Participants were enrolled between 2006 and 2010 and followed up to 2021. The multivariable Cox proportional hazards model was used to examine the relationship between RC quartiles and risk of incident AF. Subgroup analyses and sensitivity analyses were performed to explore the potential modification of the association and the robustness of the main findings. A total of 422 316 participants (mean age, 56 years; 54% women) were included for analyses. During a median follow‐up of 11.9 years (first quartile–third quartile, 11.6–13.2 years), there were 24 774 AF events documented with an incidence of 4.92 events per 1000 person‐years (95% CI, 4.86–4.98). Participants in higher RC quartiles had a lower risk of incident AF than those in the lowest quartile (first quartile): hazard ratio (HR)=0.96 (95% CI, 0.91–1.00) for second quartile; HR=0.92 (95% CI, 0.88–0.96) for third quartile; and HR=0.85 (95% CI, 0.81–0.89) for fourth quartile ( P for trend <0.001). The association between RC quartiles and risk of incident AF was stronger in participants aged ≥65 years, in men, and in participants without history of diabetes when compared with control groups ( P <0.001 for interaction). Conclusions On the basis of data from this large‐scale prospective cohort study, elevated RC was associated with a lower risk of incident AF.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".