Re‐Examining the Measurement of Sympathetic Baroreflex Sensitivity at Rest and During Stress
Bibliographic record
Abstract
The sympathetic arterial baroreflex regulates blood pressure through negative feedback changes in vasoconstrictor muscle sympathetic nerve activity (MSNA). Spontaneous sympathetic baroreflex sensitivity (BRS) is quantified using a weighted linear regression relating absolute diastolic blood pressure (DBP) with occurrence (burst incidence [BI]) or strength (burst amplitude [BA]) of a multi‐unit MSNA burst. Such as assessment generally produces strong negative linear relationships between DBP and MSNA at rest, but are often weakened or absent during sympathoexcitatory stimuli, such as exercise or mental stress. We hypothesized that sympathetic BRS would be better represented during stress by using the change in DBP between two subsequent cardiac cycles (DBP change) rather than the absolute DBP value (absolute DBP). We retrospectively examined 64 participants whom underwent continuous recordings of heart (ECG), blood pressure (Finometer), and MSNA (microneurography). Each participant completed a 2–5 min resting baseline followed by one or more of the following protocols: rhythmic handgrip (RHG; n=24; 3 min at 40% maximal voluntary contraction [MVC]), static handgrip (SHG; n=16; 2 min at 30% MVC), mental stress (MS; n=28; 2 min serial subtraction). At rest, we observed a similar number of strong negative linear relationships (r>0.6) with BI (52/64 vs. 59/64, p=0.12) and BA (41/64 vs. 49/64, p=0.18) using the absolute and change methods. In participants possessing strong relationships across both methods (BI: n=49; BA: n=32), we observed differences in sympathetic BRS of BI (−4.7±1.8 bursts/100 heartbeats/mmHg vs. −5.4±2.0 bursts/100 heartbeats/ΔmmHg, p=0.03) and BA (−2.9±1.0 AU/mmHg vs. −2.5±1.3 AU/ΔmmHg, p=0.06), and modest correlations between methods for BRS of BI (r=0.32) and for BRS of BA (r=0.42). During stress, the absolute and change methods yielded a similar proportion of strong negative linear relationships with BI during RHG (6/24 vs. 10/24, p=0.36), but a higher number using the DBP change method during SHG (7/16 vs. 15/16, p=0.006) and MS (16/28 vs. 24/28, p=0.04). Similarly, the change method identified a greater proportion of strong negative linear relationships with BA during RHG (0/24 vs. 12/24, p<0.001), SHG (0/16 vs. 14/16, p<0.001), and MS (9/28 vs. 20/28, p=0.007). The DBP change method more consistently identified strong negative linear relationships with MSNA BI and BA. The modest relationships between methods at rest may highlight the importance of beat‐to‐beat DBP changes in arterial baroreflex regulation of muscle sympathetic outflow. Support or Funding Information Natural Sciences and Engineering Research Council of Canada (NSERC); 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 | 0.001 | 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".