Sympathetic neural modulation of arterial stiffness in humans
Bibliographic record
Abstract
Elevated large-artery stiffness is recognized as an independent predictor of cardiovascular and all-cause mortality. The mechanisms responsible for such stiffening are incompletely understood. Several recent cross-sectional and acute experimental studies have examined whether sympathetic outflow, quantified by microneurographic measures of muscle sympathetic nerve activity (MSNA), can modulate large-artery stiffness in humans. A major methodological challenge of this research has been the capacity to evaluate the independent neural contribution without influencing the dynamic blood pressure dependence of arterial stiffness. The focus of this review is to summarize the evidence examining 1) the relationship between resting MSNA and large-artery stiffness, as determined by carotid-femoral pulse wave velocity or pulse wave reflection characteristics (i.e., augmentation index) in men and women; 2) the effects of acute sympathoexcitatory or sympathoinhibitory maneuvers on carotid-femoral pulse wave velocity and augmentation index; and 3) the influence of sustained increases or decreases in sympathetic neurotransmitter release or circulating catecholamines on large-artery stiffness. The present results highlight the growing evidence that the sympathetic nervous system is capable of modulating arterial stiffness independent of prevailing hemodynamics and vasomotor tone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".