A cross-sectional analysis of racial differences in accelerated aging and cognitive function among patients with atrial fibrillation: The SAGE-AF study
Bibliographic record
Abstract
BACKGROUND: Non-Whites are more likely to suffer from cognitive impairment and complications of atrial fibrillation (AF) than Whites, though Whites are more likely to be diagnosed with AF. We examined whether non-Whites with AF are biologically older than Whites with AF and whether accelerated biological aging is associated with cognitive functioning. METHODS: We used baseline data from the ongoing Systematic Assessment of Geriatric Elements in Atrial Fibrillation prospective cohort study, collected 2016-2020 across ambulatory care practices in Massachusetts and Georgia. Of 1244 enrolled, 974 participants with full biological data were included in the present analysis. Accelerated aging (AccA) was calculated based on a combination of biomarkers associated with age and physiological "wear and tear." FINDINGS: The main outcome was score on Montreal Cognitive Assessment (MoCA). Non-Whites had 2.9 years more AccA than Whites and higher AccA was associated with a lower MoCA score among both Whites (-0.06, 95% CI: -0.10, -0.03) and non-Whites (-0.14, 95% CI: -0.27, 0.02). This association was significantly greater among non-whites (-0.11, 95% CI: -0.20, -0.01). INTERPRETATION: Non-White AF patients are functionally "older" than their White counterparts and experience a stronger deleterious association between AccA and cognition. These findings underscore the importance of taking functional age into account when treating patients with AF, particularly non-White patients, to enhance treatment and improve AF outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".