Associations Between Atherosclerosis and Subsequent Cognitive Decline: A Prospective Cohort Study
Bibliographic record
Abstract
Background This study aimed to examine whether baseline atherosclerosis was associated with subsequent short‐term domain‐specific cognitive decline. Methods and Results This research was based on the BRAVE (Beijing Research on Aging and Vessel) study, a population‐based prospective cohort study of adults aged 40 to 80 years, free of dementia. At baseline (wave 1, 2019), cognitive assessments and atherosclerosis measures, including carotid intima‐media thickness, carotid plaques, coronary artery calcification, and brachial–ankle pulse wave velocity were conducted. Cognitive function was reassessed in wave 2 (2022–2023) using linear mixed models for analysis. A total of 932 participants (63.7% women; mean age, 60.0±6.9 years) were included. Compared with the lowest tertile of carotid intima‐media thickness, carotid plaques, and brachial–ankle pulse wave velocity, or a coronary artery calcification score=0, the highest tertile of carotid intima‐media thickness (β=−0.065 SD/y [95% CI, −0.112 to −0.017]; P =0.008), carotid plaques (β=−0.070 SD/y [95% CI, −0.130 to −0.011]; P =0.021), and brachial–ankle pulse wave velocity (β=−0.057 SD/y [95% CI, −0.105 to −0.010]; P =0.018), and a coronary artery calcification score≥400 (β=−0.081 SD/y [95% CI, −0.153 to −0.008]; P =0.029) were significantly associated with a faster decline in semantic fluency after multivariable adjustment. Moreover, greater carotid intima‐media thickness, coronary artery calcification, and brachial–ankle pulse wave velocity were significantly associated with a faster decline in global cognition. Conclusions More significant atherosclerosis was associated with faster semantic fluency and global cognition declines.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".