Proteomics and the Risk of Incident Embolic and Thrombotic Stroke
Bibliographic record
Abstract
OBJECTIVE: Personalized approaches to ischemic stroke diagnosis are needed. We determine differences in proteomic signatures of incident embolic (EIS) and thrombotic stroke (TIS) by age and resultant pathways using large-scale proteomics. METHODS: Participants in the Atherosclerosis Risk in Communities Study (ARIC) from visit 2 (V2, 1990-1992) until 2020 without prevalent stroke with available SomaScan data (4,955 protein targets) at V2 (mid-life, n = 10,929), and then again at visit 5 (V5, 2011-2013, n = 4,463) were included. Covariate adjusted Cox hazard models determined the association between proteins, and adjudicated incident EIS or TIS from V2 to V5 and from V5 to 2020. RESULTS: Among 10,929 participants (56% female, 23% Black, follow-up ~20 years), 20 proteins measured in mid-life were associated with either EIS (n = 168) or TIS (n = 459) in mid-life, and 4 measured in late-life were associated with late-life stroke (73 EIS and 124 TIS events) at the Bonferroni threshold p < 1E-5. In mid-life, N-terminal pro-B-type natriuretic peptide (NPPB) was significantly associated with EIS, but not TIS (p-difference = 9.14E-7). Nineteen mid-life proteins were strongly associated with TIS; 7 strongly associated with TIS and only nominally (p < 0.05) with EIS and the remaining 12 with TIS only. In late-life, NPPB, serine protease inhibitor Kazal-type 4, oligodendrocyte-myelin-glycoprotein, and neurocan-core protein were significantly associated with EIS, but not TIS. Ingenuity Pathway Analysis tools implicated cancer for EIS-associated proteins, whereas TIS pathways reflected cell-structure and atherogenesis. INTERPRETATION: We identified plasma proteins associated with risk of EIS versus TIS reflecting distinct stroke mechanisms: cardiac dysfunction protein in EIS (eg, NPPB) and inflammation dysregulation in TIS (eg, interleukins). ANN NEUROL 2025;98:1125-1135.
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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 | 0.000 |
| 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 | 0.000 |
| 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".