Assessing vascular health by measuring arterial stiffness in response to hemodynamic load
Bibliographic record
Abstract
OBJECTIVE: Arterial stiffness, a well established cardiovascular risk factor, is accelerated in metabolic conditions such as diabetes and chronic kidney disease. It is typically assessed by measuring pulse transit time along an arterial path in the supine position. We hypothesized that introducing a hydrostatic pressure gradient by changing body position could reveal additional vascular biomechanical properties. This study aimed to quantify the increase in finger-to-toe pulse wave velocity (Δft-PWV) from supine to sitting and identify its determinants across varying cardiovascular risk profiles. METHODS: In this cross-sectional study, 248 adults were recruited, and 210 had reliable ft-PWV measurements in both positions. Ft-PWV was determined from pulse transit time between the finger and toe using two photoplethysmographic sensors. RESULTS: The mean age of participants was 55 ± 19 years; 112 (53%) were male, 104 (50%) had hypertension, 76 (36%) had diabetes, and 75 (36%) were on hemodialysis. Mean SBP and DBPs were 127 ± 17 and 77 ± 12 mmHg (mean ± standard deviation). Ft-PWV increased significantly from 8.5 ± 3.3 m/s (supine) to 14.3 ± 9.1 m/s (sitting; P < 0.001). In univariable analyses, Δft-PWV was significantly associated with supine ft-PWV ( r = 0.405, P < 0.001), age ( r = 0.337, P < 0.001), diabetes ( r = 0.219, P < 0.001), and cardiovascular disease ( r = 0.188, P = 0.006). Sex, dialysis status, weight, height, and mean BP changes were not significantly associated with Δft-PWV. In stepwise multivariable regression, Δft-PWV was independently associated with supine ft-PWV (β = 0.379, P < 0.001) and diabetes (β = 0.154, P = 0.016). CONCLUSION: Ft-PWV increased significantly from supine to sitting. The magnitude of change was independently associated with supine ft-PWV and diabetes, highlighting biomechanical insights from postural change.
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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) | 0.001 | 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 | 0.001 |
| 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".