Increased Levels of Circulating Angiogenic Cells and Signaling Proteins in Older Adults With Cerebral Small Vessel Disease
Bibliographic record
Abstract
Background: Cerebral small vessel disease (SVD) is associated with increased risk of stroke and dementia. Progressive damage to the cerebral microvasculature may also trigger angiogenic processes to promote vessel repair. Elevated levels of circulating endothelial progenitor cells (EPCs) and pro-angiogenic signaling proteins are observed in response to vascular injury. We aimed to examine circulating levels of EPCs and proangiogenic proteins in older adults with evidence of SVD. Methods: Older adults (ages 55–90) free of dementia or stroke underwent venipuncture and brain magnetic resonance imaging (MRI). Flow cytometry quantified circulating EPCs as the number of cells in the lymphocyte gate positively expressing EPC surface markers (CD34+CD133+CD309+). Plasma was assayed for proangiogenic factors (VEGF-A, VEGF-C, VEGF-D, Tie-2, and Flt-1). Total SVD burden score was determined based on MRI markers, including white matter hyperintensities, cerebral microbleeds and lacunes. Results: Sixty-four older adults were included. Linear regression revealed that older adults with higher circulating EPC levels exhibited greater total SVD burden [β = 1.0 × 105, 95% CI (0.2, 1.9), p = 0.019], after accounting for age and sex. Similarly, a positive relationship between circulating VEGF-D and total SVD score was observed, controlling for age and sex [β = 0.001, 95% CI (0.000, 0.001), p = 0.048]. Conclusion: These findings suggest that elevated levels of circulating EPCs and VEGF-D correspond with greater cerebral SVD burden in older adults. Additional studies are warranted to determine whether activation of systemic angiogenic growth factors and EPCs represents an early attempt to rescue the vascular endothelium and repair damage in 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".