Gray matter volume mediates the association of long‐term blood pressure variability with cognitive function in an adult population
Bibliographic record
Abstract
INTRODUCTION: We delineated the associations among long-term blood pressure variability (BPV), brain structure, and cognitive function. METHODS: We included 1254 adult participants from the Kailuan study. BPV was calculated from 2006 to 2020. Brain magnetic resonance imaging (MRI) and Montreal Cognitive Assessment (MoCA) were conducted in 2020. RESULTS: Higher systolic BPV (SBPV) and diastolic BPV (DBPV) were associated with lower total and frontal gray matter (GM) volume, and higher SBPV was associated with lower temporal GM volume. Elevated DBPV was associated with lower volume of total brain and parietal GM, and higher white matter hyperintensity (WMH) volume. Higher SBPV and DBPV were associated with lower MoCA scores. Decreased total and regional GM volume and increased WMH volume were associated with lower MoCA scores. The association between SBPV and cognitive function was mediated by total, frontal, and temporal GM volume. DISCUSSION: GM volume may play key roles in the association between SBPV and cognitive function. HIGHLIGHTS: SBPV and DBPV were negatively associated with total and regional brain volume. SBPV and DBPV were negatively associated with cognitive function. Decreased brain volume was associated with cognitive decline. GM volume mediated the negative association between SBPV and cognitive 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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".