P4‐284: Vascular risk factors confer domain‐specific deficits in cognitive performance within an elderly russian population
Bibliographic record
Abstract
Vascular risk factors have been shown to contribute to risk of cognitive impairment in later life. A large urban elderly population from Tomsk, Russia was studied with respect to age, education, and association between cardiovascular and cerebrovascular conditions and cognitive performance, using the Montreal Cognitive Assessment (MoCA). Volunteers were identified through the local centralized medical care system. All were invited for neurocognitive assessment as a part of a study of cognitive aging, regardless of health history. Detailed information was collected on 2073 individuals, including demographics, medical history, and family history. Arterial hypertension was reported by 82.4% of participants, 42.9% reported hypercholesterolemia, 38.4% were obese (defined as body mass index ≥30), 32.3% had one or more other cardiovascular conditions (defined as history of heart attack, pacemaker, or valve replacement), 28.5% had atrial fibrillation (afib), 19.3% reported type 2 diabetes, and 8.5% survived stroke. The cognitive battery, which included the MoCA, CERAD Word List Learning and Recall, and Trails B, was administrated by trained psychometrists (Hayden et al., 2014). Mean age was 72±5 years (from 56 to 90), 77.1% were female. Age (r=-0.338, p<0.001) and education (r=+0.422, p<0.001) significantly influenced MoCA total score, but male and female subjects performed similarly. A series of multiple regressions were conducted to determine whether vascular disease predicted MoCA scores after controlling for covariates of age and education. Health variables significantly predicted MoCA total scores, F(9, 1837) = 67.80, MoCA percent retention memory scores, F(9,1828) = 11.662, and MoCA executive function scores, F(9, 1837) = 35.33, all models were significant at p<0.001. However, only afib (β=-0.05, p<0.05) and stroke (β=-0.05, p<0.05) individually predicted MoCA total scores, diabetes (β=-0.05, p<0.05) predicted poorer memory performance (determined as percent retention), and afib (β=-0.08, p<0.001) predicted poor executive function. As expected, diabetes, afib and other cardiovascular and cerebrovascular conditions and risk factors were prevalent in this Russian urban elderly population. Vascular risk factors differentially predicted cognitive domains, suggesting differential effects of these risk factors within discrete brain systems. These results indicate that timely treatment and effective control of certain vascular risk factors may help maintain cognition in later life.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".