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Record W4407399876 · doi:10.1016/j.autneu.2025.103250

Inter-individual variability in muscle sympathetic nerve activity at rest and during exercise: Disconnection with blood pressure

2025· review· en· W4407399876 on OpenAlexafffund
André L. Teixeira, Philip J. Millar

Bibliographic record

VenueAutonomic Neuroscience · 2025
Typereview
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Economic Development, Job Creation and TradeMinistero dello Sviluppo EconomicoOntario Ministry of Research, Innovation and ScienceCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaAmerican Physiological Society
KeywordsRest (music)Blood pressureSympathetic activitySympathetic nerveMedicineSympathetic nervous systemRelation (database)Physical medicine and rehabilitationInternal medicineCardiologyHeart rateComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.281
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

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