Systolic blood pressure variability in late-life predicts cognitive trajectory and risk of Alzheimer’s disease
Bibliographic record
Abstract
Background The relationship of systolic blood pressure variability (SBPV) with Alzheimer’s disease (AD) remains controversial. We aimed to explore the roles of SBPV in predicting AD incidence and to test the pathways that mediated the relationship of SBPV with cognitive functions. Methods Longitudinal data across 96 months (T 0 to T 4 ) were derived from the Alzheimer’s disease Neuroimaging Initiative cohort. SBPV for each participant was calculated based on the four measurements of SBP across 24 months (T 0 to T 3 ). At T 3 , logistic regression models were used to test the SBPV difference between 86 new-onset AD and 743 controls. Linear regression models were used to test the associations of SBPV with cognition and AD imaging endophenotypes for 743 non-demented participants (median age = 77.0, female = 42%). Causal mediation analyses were conducted to explore the effects of imaging endophenotypes in mediating the relationships of SBPV with cognitive function. Finally, Cox proportional hazard model was utilized to explore the association of SBPV with incident risk of AD (T 3 to T 4 , mean follow-up = 3.5 years). Results Participants with new-onset AD at T 3 had significantly higher SBPV compared to their controls ( p = 0.018). Higher SBPV was associated with lower scores of cognitive function ( p = 0.005 for general cognition, p = 0.029 for memory, and p = 0.016 for executive function), higher cerebral burden of amyloid deposition by AV45 PET ( p = 0.044), lower brain metabolism by FDG PET ( p = 0.052), and higher burden of white matter hyperintensities (WMH) ( p = 0.012). Amyloid pathology, brain metabolism, and WMH partially (ranging from 17.44% to 36.10%) mediated the associations of SBPV with cognition. Higher SBPV was significantly associated with elevated risk of developing AD (hazard ratio = 1.29, 95% confidence interval = 1.07 to 1.57, p = 0.008). Conclusion These findings supported that maintaining stable SBP in late life helped lower the risk of AD, partially by modulating amyloid pathology, cerebral metabolism, and cerebrovascular health.
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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 | 0.001 |
| 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".