Paradoxical Association of Lipoprotein Measures With Incident Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Low-density lipoprotein (LDL) cholesterol is a strong risk factor for atherosclerosis but has an inverse association with atrial fibrillation (AF). We aimed to provide insight into the paradoxical association of LDL cholesterol with AF by evaluating the relationship of various lipoprotein measures and incident AF. METHODS AND RESULTS: We prospectively evaluated lipoprotein measures among 23 738 healthy middle-aged and older women (median follow-up 16.4 years; N=795 incident AF events). Baseline LDL cholesterol was directly measured, lipoprotein particle concentrations and size were measured by nuclear magnetic resonance spectroscopy, and apolipoproteins were measured by immunoassay. Cox regression models were adjusted for age, AF risk factors, inflammatory, and dysglycemic biomarkers. After multivariable adjustment, inverse associations with AF were observed (hazard ratio, 95% confidence interval for top versus bottom quintile, P value) for LDL cholesterol (0.72, 0.56-0.92, P=0.009), the total number of LDL particles (0.77, 0.60-0.99, P=0.045), and very-low-density lipoprotein particles (0.78, 0.61-0.99, P=0.04), which was driven by the number of cholesterol-poor small LDL (0.78, 0.61-1.00, P=0.05) and small very-low-density lipoprotein particles (0.78, 0.62-0.99, P=0.04). By contrast, the larger cholesterol-rich LDL particles and all high-density lipoprotein measures were not associated with AF in multivariable models. Adjustment for inflammatory and dysglycemic biomarkers had minimal impact on these associations. CONCLUSIONS: In this prospective study, the inverse association between LDL cholesterol and AF extended to several other atherogenic lipoproteins, and these associations are unlikely to be mediated by direct cholesterol effects. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; Unique Identifier: NCT00000479.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".