Reduced levels of angiogenesis biomarkers predict increased symptom severity in Chinese Americans with Alzheimer’s disease with demographic-specific effect
Bibliographic record
Abstract
Alzheimer’s disease (AD) symptomatology, while classically studied through the lens of amyloid-β and tau burden, is likely also influenced by multiple-interacting co-pathologies like vascular disease and dysmetabolism. These co-pathologies, especially vascular disease, occur disparately in the Chinese-American population and are often treatable via therapeutics and lifestyle modifications. Given this, we explored whether plasma biomarkers, including an array of vascular-related proteins, associate with cognition in a cohort of 34 Chinese Americans clinically diagnosed as cognitively normal, with mild cognitive impairment, or with AD. We found that a composite score of plasma angiogenesis biomarkers (MMP-1, bFGF, VEGF, and VEGF-C) were positively associated with total Mini Mental State Examination scores (p = 0.045) as well as memory performance (p = 0.006), and that this relationship was most pronounced in AD (biomarker composite score within AD vs MMSE & memory, both p < 0.001). To explore whether these findings were specific to the Chinese-American population, we repeated the above analyses in 73 demographically matched non-Hispanic White American participants and found no significant associations between angiogenesis biomarkers and MMSE or memory, highlighting the potential relevance of vascular dysregulation in Chinese Americans at risk for AD.
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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 | 0.000 |
| 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".