Associations between endothelial inflammatory markers and cerebral small vessel disease in a community-based population
Bibliographic record
Abstract
Background: Endothelial inflammation is involved in cerebral small vessel disease (CSVD) pathogenesis. Vascular cell adhesion molecule 1 (VCAM-1) and intercellular adhesion molecule 1 (ICAM-1) are biomarkers of endothelial inflammation. Aims: This study investigated the association of VCAM-1 and ICAM-1 with the presence of CSVD and CSVD burden. Methods: This cross-sectional study included community residents from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events (PRECISE) study. Fasting venous blood was drawn to assay VCAM-1 and ICAM-1. Cognition was assessed by the Montreal Cognitive Assessment (MoCA). Cognitive impairment was defined as MoCA scores < 26. White matter hyperintensity, lacunes, cerebral microbleeds, and enlarged perivascular spaces were evaluated in a 3.0T MRI scanner. CSVD burden was rated according to the criteria of Wardlaw’s (score 0–4) and Rothwell’s (score 0–6), and classified into four grades. Presence of CSVD was defined as CSVD burden score ⩾ 1. Results: This study included 2596 participants with a mean age of 61.2 ± 6.7 years and 50.9% of males. Elevated VCAM-1 was associated with increased odds of presence of CSVD (Rothwell: odds ratio (OR) = 1.16, 95% confidence interval (CI): 1.06–1.26, P = 0.001), higher CSVD burden (Wardlaw: common OR (cOR) = 1.11, 95% CI: 1.02–1.21, P = 0.02; Rothwell: cOR = 1.16, 95% CI: 1.07–1.25, P < 0.001), and presence of cognition-impaired CSVD (Rothwell: OR = 1.15, 95% CI: 1.05–1.25, P = 0.003). VCAM-1 improved net reclassification index and integrated discrimination improvement for the presence of CSVD (Rothwell) and cognition-impaired CSVD (Rothwell). However, ICAM-1 was not associated with CSVD and did not improve prediction of CSVD. Conclusion: Endothelial inflammation, especially VCAM-1, was associated with the presence of CSVD and higher CSVD burden.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".