Cardiovascular health predicts working memory decline among cognitively healthy older African Americans
Bibliographic record
Abstract
Abstract Background African Americans are at increased risk for cardiovascular‐related health problems (e.g., hypertension, cardiovascular disease, stroke, obesity) and cognitive dysfunction, including Alzheimer’s disease (AD)1,2. However, few studies have looked at the longitudinal influence of cardiovascular health and cognitive function in older African Americans. This study investigated the relationship between cardiovascular health markers and cognition in older African Americans. Method 836 participants were drawn from Pathways to Healthy Aging in African Americans, a longitudinal cohort study at Rutgers University–Newark. Participants completed a battery of self‐reported health and demographic questionnaires, neuropsychological tests, and a short physical battery of both anthropometric and physical assessments. Linear mixed models were used to examine the relationship between baseline cardiovascular health markers and cognitive function up to 6 years of follow up. Result Higher diastolic blood pressure was significantly associated with worse Trail Making Test Part A (B = 0.035, p < .001), and Montreal Cognitive Assessment (B = 0.085, p = .0029) performance across time. Higher systolic blood pressure was significantly associated with poorer Trail Making Test Part A (B = 0.017, p < .001) and MoCA (B = 0.055, p < .001). Higher resting heart rate was significantly associated with worse Trail Making Test Part A (B = 0.018, p = .0031) and Part B (B = 0.085, p = .013), Controlled Oral Word Association Test (B = 0.036, p = .025), and Montreal Cognitive Assessment performance across time (B = 0.047, p = .046). Conclusion In cognitively unimpaired older African Americans, cardiovascular health markers are predictive biomarkers for early working memory decline in preclinical AD. Our findings may inform holistic clinical decision‐making in AD prevention and personalized AD monitoring in patients with cardiovascular disease.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".