Milder Alzheimer's disease pathology in heart failure and atrial fibrillation
Bibliographic record
Abstract
INTRODUCTION: Heart failure (HF) and atrial fibrillation (AF) have been associated with a higher risk of Alzheimer's disease (AD). Whether HF and AF are related to AD by enhancing AD neuropathological changes is unknown. METHODS: We applied network analyses and multiple logistic regression models to assess the association between HF and AF with severity of AD neuropathology in patients from the National Alzheimer's Coordinating Center database with primary neuropathological diagnosis of AD. RESULTS: We included 1593 patients, of whom 129 had HF and 250 had AF. HF and AF patients were older and had milder AD pathology. In the network analyses, HF and AF were associated with milder AD neuropathology. In the regression analyses, age (odds ratio [OR] 0.94, 95% confidence interval [CI] 0.93-0.95 per 1-year increase in age, P < .001) and the interaction term HF × AF (OR 0.61, 95% CI 0.40-0.91, P = .014) were inversely related to severe AD pathology, whereas APOE ε4 genotype showed a direct association (OR 1.68, 95% CI 1.31-2.16). Vascular neuropathology was more frequent in patient with HF and AF patients than in those without. DISCUSSION: HF and AF had milder AD neuropathology. Patients with milder AD lived longer and had more exposure to vascular risk factors. HF and AF patients showed a higher frequency of vascular neuropathology, which could have contributed to lower the threshold for clinically evident dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".