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The Mechanisms Underlying the Muscle Metaboreflex Modulation of Sweating and Cutaneous Vascular Conductance in Passively Heated Humans

2016· article· en· W4389024314 on OpenAlexafffundabout
Baies Haqani, Naoto Fujii, Gabrielle Paull, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsometric exerciseMicrodialysisForearmThermoregulationMedicinePerfusionNitric oxideInternal medicineChemistryNitric oxide synthaseCyclooxygenaseEndocrinologyCardiologyAnesthesiaSurgeryEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Previous reports demonstrate that the nonthermal muscle metaboreflex increases sweating and reduces cutaneous perfusion during heat stress. However the mechanisms mediating these responses remain unknown. Given that nitric oxide synthase (NOS) and cyclooxygenase (COX) contribute to the sweating response during heat stress, these enzymes may also play a role in the metaboreflex‐induced increases in sweating during heat stress. Additionally, NOS, COX, and adenosine receptors contribute to cutaneous vascular responses during heat stress. Thus these factors may be involved in reducing cutaneous perfusion in response to metaboreceptor activation during heat stress. Eleven healthy males (31 ± 13 years) donned a water‐perfused suit whereby mean skin temperature was clamped to ~35 °C (pre‐heating; to activate sweating without changes in core temperature) which was followed by a period of whole‐body heating to induce and maintain a 1.0 °C increase in core temperature (post‐heating) above pre‐heating levels. Participants performed the metaboreceptor activation protocol [1 min bout of isometric handgrip (IHG) exercise at 60% of their maximal voluntary contraction followed by 3 min of forearm occlusion (OCC; to stimulate metaboreceptors)] twice at each heating phase (pre‐heating: IHG+OCC‐1 and ‐2; and post‐heating: IHG+OCC‐3 and ‐4), each separated by 10 min. Prior to the start of the trial, participants were instrumented with four intradermal microdialysis fibres in the forearm skin that were continuously perfused with (1) lactated Ringer solution (Control), (2) 10 mM N G ‐nitro‐L‐arginine methylester (L‐NAME; a non‐selective NOS inhibitor), (3) 10 mM ketorolac (KETO; a non‐selective COX inhibitor), or (4) 4 mM theophylline (THEO; a non‐selective adenosine receptor antagonist). Forearm sweat rate (ventilated capsule; n=9) and cutaneous vascular conductance (CVC; perfusion units divided by mean arterial pressure, n=9) were measured at each of the four skin sites. For all sites, sweating increased and remained elevated during IHG and OCC respectively, regardless of the level of hyperthermia (all P < 0.05). The increase in sweat rate from pre‐IHG levels during IHG+OCC‐2 and IHG+OCC‐3 measured at the Control (0.26 ± 0.14 and 0.03 ± 0.01 mg· min −1 · cm −2 , respectively) was partially attenuated by NOS inhibition (23 ± 23 and 22 ± 26%, respectively, P <0.05). During the pre‐heating phase, no influence of IHG+OCC‐1 or ‐2 was observed on CVC (all P >0.05). Moreover, relative to pre‐IHG levels, CVC was reduced during IHG+OCC‐3 at Control (IHG; −9.4 ± 8.2 and OCC; −3.2 ± 3.2 %CVC max , P <0.05). Additionally, at all sites compared to Control, there were no differences in CVC during IHG+OCC at each heating phase (all P >0.05). We show that during heat stress NOS, but not COX and adenosine receptors, contributes to the sweating response to muscle metaboreceptor activation, whereas CVC is not modulated by NOS, COX, and adenosine receptors. Support or Funding Information This study was supported by the Natural Sciences and Engineering Research Council of Canada (Discover grant, RGPIN‐06313‐2014; Discovery Grants Program ‐ Accelerator Supplement, RGPAS‐462252‐2014; funds held by Dr. Glen P. Kenny).

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.307
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2016
Admission routes3
Has abstractyes

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