Association between cardiovascular disease risk, regional brain age gap, and cognition in healthy adults
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) and its associated risk factors accelerate neurodegeneration and cognitive decline. This study examined relationships between CVD risk, cognition, and Brain Age Gap (BAG)-the difference between MRI-predicted brain age and chronological age. While prior research has linked CVD risk factors to global (i.e., "whole-brain") BAG, we extend these findings by examining region-specific associations, offering more spatially precise insights into brain aging across the cortex. Methods: , an automated brain volumetrics pipeline, to calculate global and regional BAG. CVD risk was assessed using the QRISK3 calculator, which provides a 10-year CVD risk percentage and Heart Age value. The Heart Age Gap (HAG) was calculated as Heart Age minus chronological age. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Six data-driven brain aging factors were identified, and participant-level BAG scores for each factor were analyzed. Spearman correlations examined associations between CVD risk metrics, regional BAG factors, and cognition, controlling for age and sex. Results: < 0.001), even after adjusting for covariates. The BAGs of Factors 3-6 showed significant positive correlations with 10-year CVD risk and HAG, indicating region-specific vulnerability. Total MoCA was negatively associated with the BAGs of Factors 4-6. In addition, the Language Index was negatively correlated with the BAGs of Factors 1, 4, and 5, while the Executive Index was negatively associated with Factor 5's BAG. No CVD risk-cognition associations remained significant after adjusting for age. Conclusion: CVD risk is associated with global and regional brain aging, with specific cortical regions demonstrating greater vulnerability to CVD risk burden than others. These findings highlight the added value of regional BAG analyses, which reveal heterogeneity in aging patterns not captured by global estimates alone and may clarify vascular contributions to brain aging.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".