Biomarker and cognitive decline in atrial fibrillation: a prospective cohort study
Bibliographic record
Abstract
We investigated associations of a broad biomarker panel with cognitive decline in atrial fibrillation (AF) patients to characterize possible mechanisms. We enrolled 1440 AF patients with available baseline biomarkers and cognitive testing by the Montreal Cognitive assessment (MoCA) score at inclusion and at ≥ 2 yearly follow-ups. We investigated the associations of biomarkers with cognitive decline in univariate logistic regression models, LASSO regression analysis and built a combined model. Mean age was 72 years, 75% male, 47% paroxysmal AF. Over 4 years, 93 patients (6.5%) had cognitive decline. These patients had more often permanent AF (32.3 vs 21.5%, p = 0.007) and more often a history stroke (23.7 vs 11.2%, p < 0.001), but similar baseline MoCA scores (24.9 vs 25.3 points, p = 0.22) and anticoagulation rates (93.5 vs 89.5%, p = 0.29). The three biomarkers with the highest univariate AUC for cognitive decline were GDF-15 (0.67 [0.62-0.72]), Cystatin C (0.67 [0.61-0.72]) and high-sensitivity Troponin T (hs-TnT) (0.65 [0.60-0.70]). In LASSO regression analysis, the best cross validation included GDF-15, GFAP, ESM-1, NfL and ALAT. The combined prediction model with the highest AUC of 0.73 (0.68-0.78) included IGFBP-7, GDF-15, Cystatin C, hsCRP, ALAT, GFAP, ESM-1 and FGF23. Over 4 years, 6.5% of AF patients had cognitive decline despite a high rate of anticoagulation. Inflammation, neuronal damage, and increased amyloid-beta might be important non-ischemic mechanisms of cognitive decline in AF patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".