Abstract P3009: Biomarkers and Risk of Cognitive Impairment in Atrial Fibrillation: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) Cohort
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) is highly prevalent, and its health effects outside of stroke are under intense study. AF increases dementia risk, but underlying mechanisms are unknown. Stroke or cerebrovascular pathophysiology, possibly attenuated with anticoagulation, may be contributory. Hypotheses: We assessed two hypotheses: (1) that higher levels of 9 circulating biomarkers (see Figure) would be associated with greater risk of incident cognitive impairment (ICI) in REGARDS participants with AF, and (2) that associations would be attenuated after adjusting for oral anticoagulant use. Methods: REGARDS enrolled 30,239 persons > 45 years old in 2003-07. AF was defined by self-report or ECG. ICI was defined during follow-up by robust cognitive norms based on the Montreal Cognitive Assessment and Six-Item Screener. Biomarkers were measured at baseline in participants with AF and no prior stroke. HRs of time to ICI by biomarkers were calculated by Cox proportional hazards models, with covariates shown in the Figure. Interaction testing by baseline oral anticoagulant use was also performed. Results: Among 2,261 participants with baseline AF, mean follow-up was 10.6 years (mean age 66.5 years, 51% women, 28.7% Black), and there were 149 ICI cases (7%). Higher NT-proBNP, FVIII, and GDF15 were each associated with ICI (Figure), with the largest association for FVIII (HR adj 2.41 per SD higher FVIII). Added adjustment for anticoagulant use did not attenuate associations, but the association of GDF15 was lower among those on anticoagulation (HR 1.0, 95% CI 0.4-2.6 compared to HR 1.8, 95% CI 1.1-3.0 without anticoagulation; p interaction 0.07). Conclusion: Higher NT-proBNP, FVIII, and GDF15 were associated with ICI in this biracial cohort with AF. Risk associated with GDF15, an inflammation marker, might be reduced with anticoagulation, but further study is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".