Correlations between aortic stiffness and parasympathetic autonomic function in healthy volunteers
Bibliographic record
Abstract
Cardiovascular autonomic dysfunction and alterations in vascular elasticity are known complications of several disorders, including diabetes mellitus, hypertension, hypercholesterolemia, aging, and chronic kidney disease. The current study was designed to test whether a relationship existed between pulse wave velocity (PWV), augmentation index (AIx), aortic elastic properties, and cardiovascular autonomic function in healthy volunteers. The study comprised 25 healthy volunteers, whose aortic strain, distensibility, and stiffness index were measured by echocardiography, whereas PWV and AIx were evaluated by Arteriograph (TensioMed, Budapest, Hungary) in all cases. Autonomic function was assessed by means of 5 standard cardiovascular reflex tests. We found that heart rate response to deep breathing, as the most reproducible cardiovascular reflex test to characterize parasympathetic function, showed low to moderate correlations with PWV (r = -0.431, p = 0.032), aortic strain (r = 0.594, p = 0.002), distensibility (r = 0.407, p = 0.043), and stiffness index (r = -0.453, p = 0.023). Valsalva ratio and autonomic neuropathy score (ANS) correlated with PWV (r = -0.557, p = 0.004 and r = -0.421, p = 0.036, respectively) and AIx (r = -0.461, p = 0.020 and r = -0.385, p = 0.057, respectively), while ANS correlated with even aortic stiffness index (r = -0.457, p = 0.022). Cardiovascular reflex tests mainly characterizing sympathetic function had no correlation with aortic stiffness parameters (p = NS for all correlations). Correlations exist between parameters characterizing aortic elasticity and parasympathetic autonomic function, as shown by standard cardiovascular reflex tests in healthy volunteers.
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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 | 0.002 |
| 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) | 0.001 | 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".