Cardiometabolic and Vascular Disease Factors and Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
INTRODUCTION: There is empirical evidence that cardiovascular risk factors and vascular pathology contribute to cognitive impairment and dementia. METHODS: We profiled cardiometabolic and vascular disease (CMVD) and CMVD burden in community-living older adults in the Singapore Longitudinal Ageing Study cohort and examined the association of CMVD risk markers with the prevalence and incidence of mild cognitive impairment (MCI) and dementia from a median 3.8 years of follow-up. RESULTS: Prevalent MCI and dementia, compared with normal cognition, was associated with higher proportions of persons with any CMVD, hypertension, diabetes, coronary heart disease, atrial fibrillation, or stroke. Diabetes, stroke, and the number of CMVD risk markers remained significantly associated with dementia or MCI after adjusting for age, sex, formal education level, APOE-ε4 genotype, and level of physical, social, or productive activities, with odds ratios ranging from 1.3 to 5.7. Among cognitively normal participants who were followed up, any CMVD risk factor, dyslipidemia, diabetes, or heart failure at baseline predicted incident MCI or its progression to dementia after adjusting for potential confounders. CONCLUSION: Older adults with higher burden of CMVD, driven especially by diabetes, are likely to increase the risk of prevalent and incident MCI and dementia.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".