Inflammation and Cognitive Decline: A Population‐Based Cohort Study Among Aging Adults With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Despite associations between atrial fibrillation (AF) and cognitive decline independent of stroke, pathways underlying this relationship remain unclear. Inflammatory markers, such as CRP (C-reactive protein), are associated with blood-brain barrier (BBB) permeability, potentially leading to neuroinflammation and neurodegeneration. We estimated associations of CRP with cognitive impairment and death in aging adults with AF. METHODS: Adults aged ≥45 years with prevalent AF and no cognitive impairment were identified from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) cohort (N=30 239). Plasma CRP was measured at baseline and cognitive status measured annually using the Six-Item Screener. Competing risks Cox proportional hazards regression was used to estimate cause-specific hazard for cognitive impairment (Six-Item Screener score ≤3). Cognitive trajectories were identified using latent class growth models and adjusted binomial logistic regression used to estimate associations between CRP and cognitive trajectories, with interactions by sex. RESULTS: Among 2109 participants, 285 developed cognitive impairment and 786 died over a median 9-year follow-up. A doubling of baseline CRP levels was associated with increased death (hazard ratio [HR], 1.13 [95% CI, 1.08-1.19]) but not incident cognitive impairment (HR, 0.98 [95% CI, 0.91-1.04]). Latent class analyses identified 2 unique cognitive trajectories: 91% had a stable trajectory, while 9% showed progressive decline. Sex-specific models showed a 9% increased odds of progressive cognitive decline in men (odds ratio, 1.09 [95% CI, 1.01-1.17]) but not women (odds ratio, 1.04 [95% CI, 0.97-1.12]). CONCLUSIONS: Higher CRP was associated with cognitive impairment in aging men with AF, highlighting inflammation-mediated blood-brain barrier dysfunction as a potential pathway linking AF to cognitive decline.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".