Beat-to-beat blood pressure variability, hippocampal atrophy, and memory impairment in older adults
Bibliographic record
Abstract
Visit-to-visit blood pressure variability (BPV) predicts age-related hippocampal atrophy, neurodegeneration, and memory decline in older adults. Beat-to-beat BPV may represent a more reliable and efficient tool for prospective risk assessment, but it is unknown whether beat-to-beat BPV is similarly associated with hippocampal neurodegeneration, or with plasma markers of neuroaxonal/neuroglial injury. Independently living older adults without a history of dementia, stroke, or other major neurological disorders were recruited from the community (N = 104; age = 69.5 ± 6.7 (range 55-89); 63% female). Participants underwent continuous blood pressure monitoring, brain MRI, venipuncture, and cognitive testing over two visits. Hippocampal volumes, plasma neurofilament light, and glial fibrillary acidic protein levels were assessed. Beat-to-beat BPV was quantified as systolic blood pressure average real variability during 7-min of supine continuous blood pressure monitoring. The cross-sectional relationship between beat-to-beat BPV and hippocampal volumes, cognitive domain measures, and plasma biomarkers was assessed using multiple linear regression with adjustment for demographic covariates, vascular risk factors, and average systolic blood pressure. Elevated beat-to-beat BPV was associated with decreased left hippocampal volume (P = .008), increased plasma concentration of glial fibrillary acidic protein (P = .006), and decreased memory composite score (P = .02), independent of age, sex, average systolic blood pressure, total intracranial volume, and vascular risk factor burden. In summary, beat-to-beat BPV is independently associated with decreased left hippocampal volume, increased neuroglial injury, and worse memory ability. Findings are consistent with prior studies examining visit-to-visit BPV and suggest beat-to-beat BPV may be a useful marker of hemodynamic brain injury in older adults.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".