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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".