The Influence of Vascular Risk Factors and Stroke on Cognition in Late Life
Bibliographic record
Abstract
OBJECTIVE: Vascular risk factors in mid-life predict late life cognitive decline in previously normal populations. We sought to investigate the contribution of vascular risk factors in late life to cognitive decline in a cohort of normal elderly individuals. METHODS: Cognitively normal subjects were identified from the longitudinal cohort of participants in the National Alzheimer Coordinating Center (NACC) database (n=2975). The association between a composite score of vascular risk factors (based on the Framingham Stroke Risk Profile) and cognitive function was tested at baseline visit and estimated in longitudinal analyses using linear mixed-effects models. RESULTS: Total vascular risk factor burden was associated with worse cognitive performance at baseline and faster decline longitudinally in univariate analyses but only with worse WAIS digit symbol performance in cross-sectional (estimate=-0.266 units/1 unit of Framingham Stroke Risk Profile Score; 95% confidence interval, -0.380 to -0.153; P<0.001) and longitudinal (estimate=-0.034 units/1 unit of Framingham Stroke Risk Profile Score/year; 95% confidence interval, -0.055 to -0.012; P=0.002) analyses after adjusting for age, education, and APOE genotype. Individuals with history of stroke performed significantly worse on the trails B, category fluency, and Boston naming tests in cross-sectional analyses and in delayed logical memory and digit span backwards in longitudinal analyses. CONCLUSIONS: Although the modified Framingham Stroke Risk Profile in late-life predicts rate of decline on selective neurocognitive measures in previously normal elderly individuals, age appears to be the strongest risk factor for cognitive impairment in this population. History of stroke independently influences rate of cognitive decline in these individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".