The Impact of Coronary Artery Disease on Brain Vascular and Metabolic Health: Links to Cognitive Function
Bibliographic record
Abstract
Coronary artery disease (CAD), the leading cause of mortality worldwide, is increasingly recognized for its impact on brain health and cognition, yet the mechanisms linking CAD to vascular and metabolic alterations in the brain remain poorly understood. Prior work has identified regional deficits in cerebral blood flow (CBF) and cerebrovascular reactivity (CVR), a measure of vascular reserve, in patients with CAD, but the consequences of these impairments for cognition and cerebral metabolism have not been established. This study investigated how CAD influences cerebral vascular and metabolic health, and how these alterations relate to cognitive function across multiple domains. Using quantitative magnetic resonance imaging (MRI), we measured CBF, CVR, cerebral metabolic rate of oxygen (CMRO₂), and oxygen extraction fraction (OEF), alongside a validated neuropsychological battery yielding composite scores of executive functions, working memory, processing speed, and verbal episodic memory. The final sample included 35 CAD patients (CAD; n = 35, 66 ± 9 years, 6 females) and 37 healthy controls (n = 37, 65 ± 8 years, 10 females). Compared with controls, CAD patients demonstrated widespread vascular and metabolic impairments, including lower CBF, CVR, and CMRO₂, and elevated OEF, consistent with insufficient oxygen delivery. CBF deficits were more pronounced in patients with prior myocardial infarction. Importantly, lower CVR was associated with poorer performance in the cognitive domain of executive function, while higher OEF related to poorer working memory, underscoring the role of vascular reserve and oxygen consumption in cognition. These findings demonstrate that CAD impairs cerebral vascular and metabolic health, highlighting CVR and OEF as sensitive biomarkers linking brain health to cognitive outcomes and as promising targets for interventions to preserve cognition in CAD.
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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 | 0.000 | 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".