The Effect of Body Position on Measures of Arterial Stiffness in Humans
Bibliographic record
Abstract
BACKGROUND: The dynamics ofpulsatile waveforms travelling the central aorta are governed by pressures and arterial compliance. Arterial stiffness, the inverse of compliance, is an independent risk factor for cardiovascular disease and has been suggested as a superior risk index compared to brachial blood pressure (BP). Arterial stiffness is typically measured via carotid-femoral pulse wave velocity (cfPWV) in the supine position; however, different body positions alter orthostatic column height, impacting heart rate and BP. The purpose of this investigation was to examine different body positions and associated measures of cfPWV. METHODS: Measures of resting cfPWV were acquired simultaneously with BP during supine, head-up tilt (HUT), head-down tilt (HDT), and Fowler's position, all at 45 degrees from vertical. RESULTS: Relative to supine, cfPWV was increased 1.1 ± 1.0 and 1.5 ± 1.1 m/s (both p ≤ 0.001) in HUT and Fowler's positions, respectively. Supine to HDT cfPWV was unaltered (p = 0.1), despite an increase in mean arterial pressure (MAP) (10 ± 9 mm Hg). When cfPWV was normalized to MAP, the same effects persisted (p ≤ 0.001). CONCLUSION: Increasing orthostatic column height by changing posture independently increases resting cfPWV, concurrent with increases in BP. This data demonstrates the impact of body position on measures of central artery stiffness, which may have clinical relevance.
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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) | 0.002 | 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".