Arterial Pulse Wave Velocity as a Marker of Poor Cognitive Function in an Elderly Community-Dwelling Population
Bibliographic record
Abstract
BACKGROUND: Knowledge about potentially modifiable risk factors for cognitive decline is limited at this time. The aim of this study was to determine the cross-sectional relationship between a low level of cognitive function and brachial-ankle pulse wave velocity (baPWV) in a community-dwelling elderly population. METHODS: The study population included 352 community-dwelling Japanese persons ages 70 years and older who participated in a comprehensive health examination in April 2003. None had any history of cardiovascular disease. In addition to conventional medical examinations such as blood pressure and routine blood analyses, cognitive function was tested using the Mini-Mental State Examination (MMSE), and baPWV was determined using a recently developed noninvasive and automatic arterial waveform analyzer (AT-Form). This measure, with well-established validity and reproducibility, reflects both central and peripheral arterial flow. A multivariate logistic regression model tested the possible association between poor cognitive function (an MMSE score < 24) and baPWV. RESULTS: Poor cognitive function was independently associated with the middle tertile of baPWV (odds ratio [OR] = 9.66, 95% confidence interval [CI] = 1.15 to 80.93), age (1-year increment; OR = 1.12, 95% CI = 1.04 to 1.22), and the highest tertile of pulse pressure (OR = 4.70, 95% CI = 1.08 to 20.48) even after multivariate adjustment of data for the effects of age, educational level, and hemodynamic and metabolic antecedents of atherosclerosis. CONCLUSIONS: A high baPWV may be a potent risk factor for poor cognitive function in an elderly community-dwelling population, and this effect is independent of another marker of arterial stiffness: pulse pressure.
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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 | 0.003 |
| 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) | 0.001 | 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".