When is Muscle Sympathetic Nerve Activity ‘Abnormal’?
Bibliographic record
Abstract
The sympathetic nervous system participates in both short‐ and long‐term cardiovascular regulation. The microneurographic technique, enabling in humans direct recordings from the populations of post‐ganglionic sympathetic neurons innervating the skeletal muscle vasculature (i.e. muscle sympathetic nerve activity; MSNA), has yielded key insights into mechanisms of sympathetic regulation of the circulation in health, and, importantly, its contributions to cardiovascular disease development and progression. Due to its invasive nature, as well as the technical challenges involved in site acquisition and burst quantification, its application has been limited primarily to trained investigators studying small, selected cohorts, rather than populations. In healthy individuals, MSNA burst frequency or burst incidence, as measured under supine resting conditions, exhibit considerable inter‐individual variability, yet are highly reproducible within individuals over time. However, such variability, as well as the limited data, has impaired the establishment of age‐ and sex‐dependent normative values. Therefore, we aimed to develop resting MSNA reference charts, representing MSNA percentile curves, in a large cohort of young and older men and women. Specifically, we retrospectively assembled a dataset of 654 healthy, unmedicated normotensive (<140/90 mmHg) individuals from four participating Canadian laboratories, including 396 men (aged 18–71 yrs) and 258 women (aged 18–81 yrs), in whom baseline resting MSNA was evaluated over a minimum of 5 min. Quantile regression was used to estimate the 5 th , 20 th , 50 th (i.e. median), 80 th , and 95 th percentiles for resting MSNA burst frequency and burst incidence in men and women as a function of age. Quantiles were parameterized as functions of age by both a one‐ and two‐term fractional polynomial model with power selected from a ladder of values (i.e. −2, −1, −0.5, 0.5, 1, 2, 3). Next, an iterative fitting approach was applied to the MSNA versus age data to determine the fractional coefficients and the combination of values for the two powers from the ladder that provided the greatest goodness of fit (i.e. lowest χ 2 ). The 5 th , 20 th , 50 th , 80 th , and 95 th percentiles for MSNA burst frequency and burst incidence in men and women as a function of age are displayed in the figure below. In conclusion, these MSNA percentile curves, developed from a large sample of men and women, provide age‐ and sex‐related reference values for resting MSNA levels in healthy, normotensive individuals, and, furthermore, by establishing 95% confidence limits, may help inform comparative studies of disease states referenced to age‐ and sex‐matched controls. Figure 1
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".