Impact of Resting Differences in Muscle Sympathetic Nerve Activity on Hemodynamic and Neural Responses to Exercise
Bibliographic record
Abstract
Resting muscle sympathetic nerve activity (MSNA) displays high inter‐individual variability, yet the neural characteristics of those with high vs. low MSNA and resultant cardiovascular consequences are largely unclear. The purpose of this study was to examine blood pressure (BP) variability at rest and during exercise in individuals with high vs. low resting MSNA, and to characterize differences in resting sympathetic and cardiac baroreflex sensitivity (cBRS), in order to understand the accompanying neural characteristics and cardiovascular consequences of different levels of vasoconstrictor outflow. We retrospectively analyzed MSNA (microneurography) and continuous BP (Finometer) obtained at rest and during 2 minutes of static handgrip exercise at 30% maximal voluntary contraction from 60 young healthy men (n=30) and women (n=30). Within each sex, data was split into tertiles based on resting MSNA burst frequency and a comparative analysis was performed on the highest vs. lowest MSNA groups. BP variability was calculated as the beat‐to‐beat standard deviation. Spontaneous sympathetic baroreflex sensitivity (sBRS) was calculated using a weighted linear regression between diastolic blood pressure (DBP) and MSNA burst incidence. Threshold values (T 50 ) were obtained by calculating the resting DBP associated with 50% of the MSNA bursts. cBRS was determined using the sequence technique. As expected, resting MSNA burst frequency was different between high and low groups (30±4 vs. 14±6 bursts/min [mean±SD], p<0.01), however, baseline anthropometrics, BP, heart rate, and cBRS sensitivity were not different (all, p>0.05). Resting DBP variability was lower in the high group (4±1 vs. 5±2 mmHg, p=0.05). sBRS was higher in the high group (−5.4±1.9 vs. −4.6±1.6 bursts/100 heartbeats/mmHg, p<0.01), and was correlated with resting MSNA (r=−0.46, p<0.01). T 50 did not differ between the groups (60 vs. 55 mmHg, p>0.05), however, the prevailing mean DBP was above the T 50 DBP to a lower extent in the high group (mean DBP – T 50 DBP: 10±3 vs. 18±7 mmHg, p<0.01), and this value was negatively correlated with resting MSNA burst incidence (r=−0.76, p<0.01). During static handgrip exercise, BP and heart rate increased similarly in both groups (all, p>0.05), yet the increase in MSNA burst frequency was smaller in the high group (Δ 4±7 vs 10±6 bursts/min, p<0.01); similar results were obtained using MSNA burst incidence and total MSNA (both p<0.01). The change in burst frequency was unrelated to resting sBRS (r=0.23, p>0.05). Systolic BP variability was lower in the high group during exercise (7±3 vs 10±5 mmHg, p<0.05), but DBP variability was not different. In conclusion, differences in resting MSNA are unrelated to resting and exercising BP, but are likely associated with inter‐individual differences in the degree of BP variability and arterial baroreflex‐mediated suppression of MSNA. Further, baseline MSNA was found to influence the magnitude of the change during static handgrip exercise, though the hemodynamic consequences of these differences, and mechanisms responsible warrant further research. Support or Funding Information Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant; Canada Foundation for Innovation This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.000 | 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".