Obesity paradox on outcome in atrial fibrillation maintained even considering the prognostic influence of biomarkers: insights from the ARISTOTLE trial
Bibliographic record
Abstract
Objective We investigated the association between obesity and biomarkers indicating cardiac or renal dysfunction or inflammation and their interaction with obesity and outcomes. Methods A total of 14 753 patients in the Apixaban for Reduction In STroke and Other ThromboemboLic Events in Atrial Fibrillation (ARISTOTLE) trial provided plasma samples at randomisation to apixaban or warfarin. Median follow-up was 1.9 years. Body Mass Index (BMI) was measured at baseline and categorised as normal, 18.5–25 kg/m2; overweight, >25 to <30 kg/m2; and obese, ≥30 kg/m2. We analysed the biomarkers high-sensitivity C reactive protein (hs-CRP), interleukin 6 (IL-6), growth differentiation factor-15 (GDF-15), troponin T and N-terminal B-type natriuretic peptide (NT-pro-BNP). Outcomes included stroke/systemic embolism (SE), myocardial infarction (MI), composite (stroke/SE, MI, or all-cause mortality), all-cause and cardiac mortality, and major bleeding. Results Compared with normal BMI, obese patients had significantly higher levels of hs-CRP and IL-6 and lower levels of GDF-15, troponin T and NT-pro-BNP. In multivariable analyses, higher compared with normal BMI was associated with a lower risk of all-cause mortality (overweight: HR 0.73 (95% CI 0.63 to 0.86); obese: 0.67 (0.56 to 0.80), p<0.0001), cardiac death (overweight: HR 0.74 (95% CI 0.60 to 0.93); obese: 0.71 (0.56 to 0.92), p=0.01) and composite endpoint (overweight: 0.80 (0.70 to 0.92); obese: 0.72 (0.62 to 0.84), p<0.0001). Conclusions Regardless of biomarkers indicating inflammation or cardiac or renal dysfunction, obesity was independently associated with an improved survival in anticoagulated patients with AF. Trial registration number NCT00412984 .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".