Determinants of the aortic pulse wave velocity index in hypertensive and diabetic patients
Bibliographic record
Abstract
OBJECTIVE: Aortic stiffness may provide information to classical risk factors information regarding cardiovascular risk. Aortic pulse wave velocity (PWV) can be measured by applanation tonometry but also theoretical PWV was calculated according to age, blood pressure, heart rate and sex. We aim to highlight biological and hemodynamic determinants of the aortic PWV index, that is the individually calculated [(measured PWV - theoretical PWV)/theoretical PWV] difference, in hypertensive diabetic patients. METHODS: A cross-sectional study was conducted in 514 patients, involving normotensive and hypertensive patients and people with and without diabetes. Biological parameters were measured during day-hospital for cardiovascular screening. Hemodynamic parameters were determined by applanation tonometry. Multivariate regression analyses evaluated the PWV index determinants. RESULTS: Hypertensive and/or diabetic population presents higher PWV index in correlation with the presence of proteinuria (P = 0.0428) and previous cardiovascular events (P = 0.0227). Hypertensive diabetic patients present a higher PWV index than the other patients (P < 0.05). Presence of insulin therapy (P = 0.0101) and the type 1 diabetes (P = 0.0065) were positively and independently modulating PWV index in hypertensive diabetic patients. HDL cholesterol levels (P = 0.0245) and absence of carotid (P = 0.0468) plaques were independently modulating PWV index with a negative correlation in hypertensive without diabetes patients. C reactive protein levels were significantly associated with increased PWV index in hypertensive patients (P = 0.0074) and in hypertensive and/or diabetic population (P = 0.0184). CONCLUSION: PWV index was correlated with numerous cardiovascular risk factors, in addition of being a marker of age and hypertension. Therefore, this index appears as a cardiovascular risk integrator. Its use could be interesting in cardiovascular risk assessment and reduction strategies.
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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 | 0.001 |
| 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 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".