Aortic–Brachial Pulse Wave Velocity Ratio
Bibliographic record
Abstract
Aortic stiffness, a cardiovascular risk factor, depends on the operating mean arterial pressure (MAP). The impact of aortic stiffness on cardiovascular outcomes is proposed to be mediated by the attenuation or the reversal of the arterial stiffness gradient. We hypothesized that arterial stiffness gradient is less influenced by changes in MAP. We aimed to study the relationship between MAP and aortic stiffness, brachial stiffness, and arterial stiffness gradient. In a cross-sectional study of a dialysis cohort (group A, n=304) and a cohort of hypertensive or kidney transplant recipient with an estimated glomerular filtration rate of >45 mL/min/1.73 m 2 (group B, n=114), we assessed aortic and brachial stiffness by measuring carotid–femoral and carotid–radial pulse wave velocities (PWV). We used aortic–brachial PWV ratio as a measure of arterial stiffness gradient. Although there was a positive relationship between MAP and carotid–femoral PWV ( R 2 =0.10 and 0.08; P <0.001 and P =0.003) and MAP and carotid–radial PWV ( R 2 =0.22 and 0.12; P <0.001 and P <0.001), there was no statistically or clinically significant relationship between MAP and aortic–brachial PWV ratio ( R 2 =0.0002 and 0.0001; P =0.8 and P =0.9) in group A and B, respectively. Dialysis status and increasing age increased the slope of the relationship between MAP and cf-PWV. However, we found no modifying factor (age, sex, dialysis status, diabetes mellitus, cardiovascular disease, and class of antihypertensive drugs) that could affect the lack of relationship between MAP and aortic–brachial PWV ratio. In conclusion, these results suggest that aortic–brachial PWV ratio could be considered as a blood pressure–independent measure of vascular aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".