Assessment of 352 plasma biomarkers for predicting ischaemic stroke risk in atrial fibrillation patients without anticoagulation - A proteomic profiling study
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) is a major risk factor for ischaemic stroke. Recent advancements in proteomics enable high-throughput screening of proteins using small plasma samples. However, studies on biomarkers in AF patients without oral anticoagulation are lacking. Purpose This proteomics screening study aims to identify plasma biomarkers associated with ischaemic stroke risk in AF patients without oral anticoagulation, thereby improving the mechanistic understanding of this complication. Methods This case-cohort design study included 166 cases with ischaemic stroke and a random sample of 1,437 controls from patients in the ACTIVE-A and AVERROES trials randomized to aspirin, with baseline plasma samples available. All ischaemic stroke outcomes were independently adjudicated. A total of 352 unique biomarkers were measured with OLINK Target 96 panels using proximity extension assay (CVDII, CVDIII, inflammation, and oncology), along with conventional and prototype immunoassays (Roche Diagnostics). The association between biomarkers and outcomes was evaluated by Random Survival Forest analysis, including Boruta and Cox models adjusted for clinical characteristics, comorbidities, and kidney function. Results Out of the 352 biomarkers, the top 15 risk predictors for ischaemic stroke in AF according to Random Survival Forest are shown in the Figure. The corresponding Boruta and Cox regression model p-values and hazard ratios (95% CI) for an interquartile difference validated 6 of these biomarkers (Table). Conclusions In AF patients not receiving oral anticoagulation, out of 352 plasma biomarkers, the six biomarkers most strongly associated with subsequent ischaemic stroke represent cardiac dysfunction/injury (NT-proBNP and hs-troponin T), atrial stress (BMP10), angiogenesis (CYR61), and myocardial remodelling/fibrosis (PRELP and WISP-1). The latter three proteins emerged as novel markers associated with ischaemic stroke in AF.Figure Table
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".