Assessing cerebrovascular autoregulation from critical closing pressure and resistance area product during upright posture in aging and hypertension
Bibliographic record
Abstract
Static cerebral autoregulation (sCA) is believed to be resistant to aging and hypertensive pathology. However, methods to characterize autoregulation commonly rely on beat-by-beat mean hemodynamic measures and do not consider within-beat pulse wave characteristics that are impacted by arterial stiffening. We examined the role of critical closing pressure (CrCP) and resistance area product (RAP), two measures derived from the pulse wave, across supine lying, sitting, and standing postures in young adults, normotensive older adults, and older adults with controlled and uncontrolled hypertension (N = 80). Traditional measures of sCA, using both intracranial and extracranial methods, indicated similar efficiency across all groups, but within-beat measures suggested different mechanisms of regulation. At rest, RAP was increased in hypertension compared with young adults (P < 0.001), but CrCP was similar. In contrast, the drop in CrCP was the primary regulator of change in cerebrovascular resistance upon adopting an upright posture. Both CrCP and RAP demonstrated group-by-posture interaction effects (P < 0.05), with older hypertensive adults exhibiting a rise in RAP that was absent in other groups. The posture-related swings in CrCP and RAP were related to changes in both the pulsatile and mean components of arterial pressure, independent of age, cardiac output, and carbon dioxide. Group-by-posture differences in pulse pressure were mediated in part by an attenuated heart rate response in older hypertensive adults (P = 0.002). Examination of pulsatile measures in young, elderly, and hypertensive adults identified unique differences in how cerebral blood flow is regulated in upright posture.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".