VCAM is associated with impairment in individuals with amyloid and tau pathology
Bibliographic record
Abstract
Abstract Background Alzheimer's disease (AD) has been known for more than a century, but its complex pathophysiology remains unclear. Previous studies have been suggesting a potential role of neuroinflammation and cerebral vascular changes in AD progression. Part of the immune response relies on the role of Vascular Cell Adhesion Molecule (VCAM) in cell transit through the endothelium. However, there is little information about the impact of VCAM in AD‐related impairment. Here, we investigated the association of plasma VCAM levels with biological and clinical AD markers. Method We assessed 357 individuals from the Alzheimer's Disease Neuroimaging Initiative cohort with plasma VCAM and medical data available. Statistical analyses were performed using R Studio. Association with diagnosis was evaluated by a linear regression between VCAM and clinical diagnosis, with adjustments for age, sex, APOEε4 status and years of education. Regressions analysis was also used to assess the association of VCAM with Clinical Dementia Rating Scale ‐ Sum of Boxes (CDR‐SB), adjusting for age, sex, APOEε4 status and years of education, as well as cerebrospinal fluid Aβ42 and p‐Tau181. Result The group of individuals with dementia had higher blood VCAM levels (p=0.005) than both CN and MCI (p=0.003) groups, but there was no statistical significance between CN and MCI (Figure 1). VCAM showed a positive association (p = 0.018, Figure 2) with greater impairment as measured by CDR‐SB when adjustments for Aβ42 and p‐Tau181 were included. Conclusion Our results suggest differential effects of vascular factors in biological and clinical AD. Further studies are needed to assess whether these relations include causality and whether targeting VCAM can lead to improvements in neuroinflammatory or vascular‐related changes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".