Heterogeneity of diabetes as a risk factor for major adverse cardiovascular events in anticoagulated patients with atrial fibrillation: an analysis of the ARISTOTLE trial
Bibliographic record
Abstract
AIMS: Whether diabetes without insulin therapy is an independent cardiovascular (CV) risk factor in atrial fibrillation (AF) has recently been questioned. We investigated the prognostic relevance of diabetes with or without insulin treatment in patients in the ARISTOTLE trial. METHODS AND RESULTS: Patients with AF and increased stroke risk randomized to apixaban vs. warfarin were classified according to diabetes status: no diabetes; diabetes on no diabetes medications; diabetes on non-insulin antidiabetic drugs only; or insulin-treated. The associations between such patient subgroups and stroke/systemic embolism (SE), myocardial infarction (MI), and CV death were examined by Cox proportional hazard regression, both unadjusted and adjusted for other prognostic variables. Patients with diabetes were younger and had a higher body mass index. Median CHA2DS2VASc score was 4.0 in patients with diabetes and 3.0 in patients without diabetes. We found no significant difference in stroke/SE incidence across patient subgroups. Compared with no diabetes, only insulin-treated diabetes was significantly associated with higher risk. When adjusted for clinical variables, compared with no diabetes, the hazard ratios (HRs) for MI (95% confidence intervals) were for diabetes on no medication: 1.15 (0.62-2.14); for diabetes on non-insulin antidiabetic drugs: 1.32 (0.90-1.94); for insulin-treated diabetes: 2.34 (1.43-3.82); interaction P = 0.008. HRs for CV death were for diabetes on no medication: 1.19 (0.86-166); for diabetes on non-insulin antidiabetic drugs: 1.12 (0.88-1.42); for insulin-treated diabetes 1.85 (1.36-2.53), interaction P = 0.001. CONCLUSION: In anticoagulated patients with AF, a higher risk of MI and CV death is largely confined to diabetes treated with insulin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".