Carotid Intima-Media Thickness (cIMT) and Cognitive Performance
Bibliographic record
Abstract
INTRODUCTION: Atherosclerosis has been shown to impact cognitive impairment, with most of the evidence originating from European, African, or East Asian populations that have employed carotid intima-media thickness (cIMT) as a biomarker for atherosclerosis. Vascular disease is related to dementia/cognitive decline. There is no community-based study from India that has looked at the association of cIMT with cognitive performance. METHODS: In this cross-sectional study between December 2014 and 2019, we recruited 7505 persons [(mean age 64.6 (9.2) y) and 50.9% women] from a community-dwelling population in New Delhi. These persons underwent carotid ultrasound to quantify cIMT and a cognitive test battery that tapped into memory, processing speed, and executive function. We also computed the general cognitive factor (g-factor), which was identified as the first unrotated component of the principal component analysis and explained 37.4% of all variances in the cognitive tests. We constructed multivariate linear regression models adjusted for age, sex, education, and cardiovascular risk factors. Additional adjustment was made for depression, anxiety, and psychosocial support in the final model. RESULTS: We found a significant association of higher cIMT with worse performance in general cognition (β=-0. 01(95% CI: -0.01; -0.01); P<0.001), processing speed (β=-0.20; 95% CI: -0.34; -0.07); P=0.003), memory (β=-0.29; 95% CI: -0.53; -0.05); P=0.016), and executive function (β=-0.54; 95% CI: -0.75; -0.33); P=<0.001). There was no statistically significant association of cIMT with Mini-Mental Status Examination score (β=0.02; 95% CI: -0.34; 0.40; 0.89). CONCLUSION: The cross-sectional study found significant associations of increased cIMT with worse performance in global cognition, information processing, memory, and executive function.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".