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α <sub>2</sub> -adrenergic mechanisms influence sympathetic transduction to blood pressure in humans

2025· article· en· W4411875313 on OpenAlexaffabout
Stephen A. Klassen, Julia Spafford, Jacqueline K. Limberg, Ronée E. Harvey, Chad C. Wiggins, Nathaniel Iannarelli, Jonathon W. Senefeld, Wayne T. Nicholson, Timothy B. Curry, J. Kevin Shoemaker, Michael J. Joyner, Sarah E. Baker

Bibliographic record

VenuePhysiology · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsWestern UniversityBrock University
Fundersnot available
KeywordsAdrenergicBlood pressureInternal medicineEndocrinologySignal transductionSympathetic nervous systemAdrenergic receptorTransduction (biophysics)MedicineBiologyCell biologyReceptorBiochemistry

Abstract

fetched live from OpenAlex

Sympathetic control of human blood pressure is mechanistically regulated by α 2 -adrenergic receptors distributed throughout the nervous system and cardiovascular end-targets. Recently, we discovered the sympathoinhibitory effects of central α 2 -adrenergic mechanisms on human sympathetic neuronal emission patterns. However, our knowledge remains incomplete regarding the impact of α 2 -adrenergic mechanisms on beat-by-beat transduction of muscle sympathetic nerve activity (MSNA) bursts to changes in blood pressure and heart rate (HR). Based on evidence of α 2 -adrenergic receptors within central and peripheral sympathetic neural sites and on the vasculature, this study tested the hypothesis that activation of α 2 -adrenergic receptors with dexmedetomidine (DEX) would reduce sympathetic transduction to blood pressure. In eight healthy participants (5 females; 28 ± 7 years), diastolic blood pressure (DBP; brachial arterial catheter), HR (ECG), and MSNA (peroneal microneurography) were recorded during a 5-minute supine baseline (BSL) and an intravenous DEX infusion consisting of a loading dose (10 minutes at 0.225 μg/kg/hr) and a maintenance dose (~0.15 μg/kg/hr). Signal averaging quantified sympathetic transduction to DBP and HR for 12 cardiac cycles following integrated MSNA bursts. We also quantified sympathetic transduction to DBP using a transduction gain estimate derived from the slope of the linear relationship between the peak DBP response and the number of cardiac cycles until peak DBP response. Data (mean ± SD) are reported for the 5-minute BSL and the last 5-minute period during the DEX infusion. Mixed-effects modeling and t -tests were performed. Non-parametric tests were performed for non-normally distributed data. DEX reduced resting MAP (BSL: 92 ± 7, DEX: 82 ± 6 mmHg: p &lt; 0.001) but did not affect resting HR (BSL: 62 ± 14, DEX: 61 ± 14 bpm; p = 0.853). DEX reduced resting integrated MSNA burst frequency (BSL: 14 ± 6, DEX: 4 ± 3 bursts/min; p = 0.001) and total integrated MSNA (BSL: 663 ± 252, DEX: 188 ± 142 AU/min; p &lt; 0.001). DEX attenuated the DBP transduction response, particularly for the cardiac cycle associated with peak DBP during BSL (cardiac cycle 6: BSL: 4.3 ± 3.2, DEX: 3.3 ± 2.0 mmHg; p = 0.025) and prolonged the time to peak DBP transduction (BSL: 6 ±1, DEX: 10 ± 3 cardiac cycles; p = 0.014). Also, DEX reduced DBP transduction gain (BSL: 1.6 ± 2.4, DEX: 0.9 ± 1.3 mmHg/cardiac cycle; p = 0.029). By contrast, DEX did not affect the mean transduction of MSNA bursts to changes in HR (BSL: 3.2 ± 3.1, DEX: 3.9 ± 4.6 mmHg; p = 0.668). These data suggest that α 2 -adrenergic mechanisms distributed among central and peripheral sympathetic neural regions and the cardiovascular system influence moment-to-moment sympathetic regulation of blood pressure homeostasis, but not HR, in humans. This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Institutes of Health (NIH). This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.238
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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