Hippocampal volume mediates the association of arterial stiffness with cognitive impairment in adult population
Bibliographic record
Abstract
OBJECTIVES: To investigate the association of arterial stiffness with brain perfusion, brain tissue volume and cognitive impairment in the general adult population. MATERIALS AND METHODS: This prospective study included 1488 adult participants (age range: 22.8-83.9 years) from the Kailuan study. All participants underwent brachial-ankle pulse wave velocity (PWV) measurement, brain MRI, and Montreal Cognitive Assessment (MoCA). The association of PWV with cerebral blood flow (CBF), brain tissue volume and MoCA score was investigated. Mediation analysis was used to determine whether CBF and brain tissue volume changes mediated the associations between PWV and MoCA score. RESULTS: A 1 standard deviation (SD) increase in PWV was associated with lower total brain CBF [ β (95% CI) -0.67 (-1.2 to -0.14)], total gray matter CBF [β (95% CI) -0.7 [-1.27 to -0.13)], frontal lobe CBF [ β (95% CI) -0.59 (-1.17 to -0.01)], parietal lobe CBF [ β (95% CI) -0.8 (-1.43 to -0.18)], and temporal lobe CBF [ β (95% CI) -0.68 (-1.24 to -0.12)]. Negative associations were found for PWV and total brain volume [ β (95% CI) -4.8 (-7.61 to -1.99)] and hippocampus volume [ β (95% CI) -0.08 (-0.13 to -0.04)]. A 1 SD increase PWV was significantly associated with elevated odds of developing cognitive impairment [odds ratio (95% CI) 1.21 (1.01-1.45)]. Mediation analysis showed that hippocampal volume partially mediated the negative association between PWV and MoCA scores (proportion: 14.173%). CONCLUSION: High arterial stiffness was associated with decreased total and regional CBF, brain tissue volume, and cognitive impairment. Hippocampal volume partially mediated the effects of arterial stiffness on cognitive impairment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".