Inter-individual variability in muscle sympathetic nerve activity at rest and during exercise: Disconnection with blood pressure
Bibliographic record
Abstract
Microneurographic recordings of muscle sympathetic nerve activity (MSNA) have provided fundamental insight into sympathetic discharge patterns at rest and during exercise in health and disease. A key feature of MSNA recordings at rest is a large inter-individual variability, even among healthy adults. The physiological consequences of inter-individual variability in MSNA are commonly discussed as being associated with the regulation of resting blood pressure. However, available evidence from large cross-sectional analyses demonstrate a near absence of an association between resting MSNA and blood pressure. Less appreciated, MSNA also exhibits inter-individual variability in response to stress, such as exercise. Again, the consequences of variability in MSNA are unclear and can be dissociated from the blood pressure response, particularly at low-to-moderate intensity muscle contractions for short durations (≤2 min). In this brief review, we summarize several examples of how inter-individual variability in MSNA is unrelated to blood pressure control at rest and during exercise and discuss potential mechanisms responsible for this observation, and key methodological considerations for future study design and interpretation. Additionally, we highlight several unanswered questions that could pave the way for future investigations in the field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".