Neuroglial Biomarkers for Risk Assessment of Ischemic Stroke and Other Cardiovascular Events in Patients With Atrial Fibrillation Not Receiving Oral Anticoagulation
Bibliographic record
Abstract
BACKGROUND: Cardiac biomarkers improve risk prediction in patients with atrial fibrillation (AF). We recently demonstrated that the NFL (neuron-specific protein neurofilament light chain) was associated with ischemic stroke in patients with AF not receiving oral anticoagulation. The association of other neuroglial biomarkers reflecting brain injury (ie, GFAP [glial fibrillary acidic protein], total tau [tau], and UCHL1 [ubiquitin carboxy-terminal hydrolase L1]) with the risk of stroke and other cardiovascular outcomes in AF is unknown. METHODS AND RESULTS: Baseline plasma samples were available from 967 patients with AF not receiving oral anticoagulation treatment. Concentrations of NFL, GFAP, tau, and UCHL1 were determined with a Single Molecule Array kit (Simoa). Associations between baseline biomarker level, clinical characteristics, and outcomes (ischemic stroke, hospitalization for heart failure, and all-cause death) were analyzed with multivariable Cox regression adjusted for clinical characteristics and other biomarkers. Higher levels of all 4 neuroglial biomarkers were correlated with increasing age and female sex. During a median follow-up of 3.6 years, NFL was associated with increased risk of ischemic stroke (for a doubling in NFL, hazard ratio [HR], 1.27 [95% CI, 1.03-1.56]) and death (HR, 1.46 [95% CI, 1.25-1.70]). In adjusted analyses, GFAP, tau, and UCHL1were not associated with stroke or death. NFL, tau, and UCHL1 were significantly associated with hospitalization for heart failure. CONCLUSIONS: In patients with AF not receiving oral anticoagulation, NFL was the only neuroglial biomarker significantly and independently associated with the risk of ischemic stroke and death. Further studies evaluating NFL for stroke risk assessment in patients with AF and the impact of contemporary oral anticoagulation treatment are warranted.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".