Association of cerebrovascular reactivity and Alzheimer pathologic markers with cognitive performance
Bibliographic record
Abstract
Objective To determine whether MRI-based cerebrovascular reactivity (CVR) can predict cognitive performance independently of Alzheimer pathologic markers, we studied the relationship between cognition, CVR, and CSF-derived β-amyloid42 (Aβ42) and tau in a group of elderly individuals with mixed Alzheimer and vascular cognitive impairment and dementia. Methods This was a cross-sectional study of 72 participants 69 ± 8 years of age consisting of individuals with normal cognition (n = 28) and cognitive impairment (n = 44) (including 36 with mild cognitive impairment [MCI] and 8 with mild dementia). CVR was measured with hypercapnia-MRI. Whole-brain CVR (percent blood oxygen level–dependent per 1 mm Hg Etco2) was used to estimate vasodilatory capacity. Montreal Cognitive Assessment (MoCA) scores, cognitive domains scores, and a global composite cognitive score were obtained. AD biomarkers included CSF assays of Aβ42 and tau. Results Whole-brain CVR was lower in the impaired (mean ± SE, 0.132 ± 0.006%/mm Hg) compared to the normal (0.151 ± 0.007%/mm Hg) group (β = −0.02%/mm Hg; 95% confidence interval [CI] −0.038 to −0.001). After adjustment for CSF Aβ42 and tau, higher whole-brain CVR was associated with better performance on the MoCA (β = 29.64, 95% CI 9.94–49.34) and with a global composite cognitive score (β = 4.32, 95% CI 0.05–8.58). When the CVR marker was compared with the Fazekas score based on white matter hyperintensities and vascular risk-score in a single regression model predicting the MoCA score, only CVR revealed a significant effect (β = 28.09, 95% CI 6.14–50.04), while the other 2 measures were not significant. Conclusions CVR was significantly associated with cognitive performance independently of AD pathology. Whole-brain CVR may be a useful biomarker for evaluating cognitive impairment related to vascular disease in older individuals. Classification of evidence This study provides Class II evidence that CVR was significantly associated with cognitive performance independent of AD pathology.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".