Blood biomarkers of vascular dysfunction in small vessel disease progression: Insights from a longitudinal neuroimaging study
Bibliographic record
Abstract
INTRODUCTION: This study explored the relationship between blood biomarkers of cerebrovascular function and small vessel disease (SVD) neuroimaging markers and cognitive outcomes in highly-phenotyped participants. METHODS: We conducted cross-sectional and 1-year longitudinal analyses on 181 patients with mild ischemic stroke, enriched for SVD features. We examined relationships between a panel of 13 blood biomarkers and magnetic resonance imaging (MRI) markers of SVD (structural lesions, diffusion-weighted imaging [DWI]-positive lesions, blood-brain barrier (BBB) permeability, and cerebrovascular reactivity (CVR), and cognition. RESULTS: In linear mixed models, vascular endothelial growth factor was significantly associated with incident DWI-positive lesions over 1 year. Intercellular adhesion molecule-1 was linked with lower CVR while platelet-derived growth factor-subunit B and Endothelin-1 were associated with higher CVR. Platelet-Selectin levels were associated with mild cognitive impairment at 1 year. DISCUSSION: Our results support the role of endothelial and pericyte dysfunction in SVD burden and progression and suggest that specific biomarkers relate to distinct SVD manifestations. HIGHLIGHTS: Small vessel disease (SVD) lacks specific or predictive biomarker signatures. Vascular endothelial growth factor levels were linked to incident lesions detected over 1 year. Circulating intercellular adhesion molecule-1 related to lower cerebrovascular reactivity. Platelet-selectin levels were associated with mild cognitive impairment longitudinally. These findings could help stratify patients at high-risk of rapid-progression SVD.
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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 | 0.002 | 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".