Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis
Bibliographic record
Abstract
Abstract INTRODUCTION The relationship between cerebrovascular disease (CVD) and amyloid beta (Aβ) in Alzheimer's disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)–based CVD biomarkers—including cerebral microbleeds (CMBs), lacunar infarction, and white matter hyperintensities (WMHs)—would correlate with Aβ positivity on positron emission tomography (Aβ‐PET). METHODS We cross‐sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1352). Logistic regression was used to calculate odds ratios (ORs), with Aβ‐PET positivity as the standard‐of‐truth. RESULTS Following adjustment, WMHs (OR = 1.25) and superficial CMBs (OR = 1.45) remained positively associated with Aβ‐PET positivity (p < 0.001). Deep CMBs and lacunes exhibited a varied relationship with Aβ‐PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aβ‐PET (pseudo‐R2 = 0.41). DISCUSSION The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. ClinicalTrials.gov: ADNI‐2 (NCT01231971), ADNI‐3 (NCT02854033). Highlights Cerebrovascular biomarkers linked to amyloid beta (Aβ) in Alzheimer's disease (AD). White matter hyperintensities and cerebral microbleeds reliably predict Aβ‐PET positivity. Relationships with Aβ‐PET vary by cognitive stage. Novel accessible model predicts Aβ‐PET status. Study supports multimodal diagnostic approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".