Association of Plasma Biomarkers With Longitudinal Atrophy and Microvascular Burden on MRI Across Neurodegenerative and Cerebrovascular Diseases
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Plasma biomarkers of Alzheimer disease (AD), neuroinflammation, and neurodegeneration are increasingly being used in clinical trials for diagnosis and monitoring of dementia. However, their association with longitudinal structural brain MRI changes, an important outcome measure across neurodegenerative and cerebrovascular diseases, is less known. We investigated how baseline plasma biomarkers reflect MRI markers of progression over time in patients with neurodegenerative and cerebrovascular diseases. METHODS: ) diplotypes, waist-hip circumference ratio, and disease duration. RESULTS: = 0.049 to <0.001) in the pooled disease-agnostic group. Within disease-specific cohorts, GFAP and NfL were associated with cerebral atrophy and/or small vessel disease copathology in AD/MCI, PD, FTD, or CVD. P-tau181 and p-tau217 were associated with cerebral atrophy and/or small vessel disease copathology in AD/MCI, CVD, PD-MCI, or PD-dementia. DISCUSSION: Selected plasma biomarkers seem useful as prognosis and monitoring tools of longitudinal imaging changes within real-world populations of neurodegenerative and/or cerebrovascular diseases, and provide insight into overlap across diseases in shared pathologic 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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".