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Vascular compliance and viscoelasticity regulate human skeletal muscle blood flow at exercise onset

2025· article· en· W4411543800 on OpenAlexaffabout
Felicia Bouaban, Shamae Quinquito, Cameron Lynn, Deborah D. O’Leary, Stephen A. Klassen

Bibliographic record

VenuePhysiology · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsBrock University
Fundersnot available
KeywordsCompliance (psychology)Blood flowSkeletal muscleMedicineInternal medicineCardiologyEndocrinologyAnatomyPhysiologyBiologyPsychology

Abstract

fetched live from OpenAlex

At exercise onset, large and rapid rises in skeletal muscle blood flow are facilitated by reductions in vascular resistance. However, a focus on steady-state vascular resistance overlooks the contribution of vascular mechanical properties that regulate pulsatile blood flow. Therefore, this study tested the hypothesis that compliance and viscoelasticity of the skeletal muscle vascular bed regulate blood flow responses at exercise onset. We measured beat-by-beat brachial artery blood flow (Doppler ultrasound), brachial arterial blood pressure (Finometer), and heart rate (ECG) during a 2-minute supine baseline condition and for 2-minutes after a 2-second isometric handgrip exercise (IHG, handgrip dynamometer) in 16 healthy individuals (8 females, 23 ± 2 years). This protocol was repeated for IHGs performed at 20%, 40%, 60%, and 80% of maximal voluntary contraction (MVC). Vascular compliance, viscoelasticity, and resistance were calculated using a four-element modified Windkessel model. The mean ± standard deviation of maximal percent (%) change relative to baseline are reported. Two-tailed paired t-tests were performed. IHG elicited rises in blood flow (20%: Δ 166 ± 135%, 40%: Δ 272 ± 139%, 60%: Δ 341 ± 158%, 80%: Δ 397 ± 206%, all P < 0.01) and reductions in vascular resistance (20%: Δ -59 ± 12%, 40%: Δ -71 ± 11%, 60%: Δ -77 ± 8%, 80%: Δ -80 ± 7%, all P < 0.001). IHG-mediated rises in blood flow were associated with increases in vascular compliance (20%: Δ 55 ± 44%, 40%: Δ 64 ± 60%, 60%: Δ 48 ± 33%, 80%: Δ 85 ± 69%, all P < 0.01) and increases in viscoelasticity (20%: Δ 76 ± 34%, 40%: Δ 95 ± 40%, 60%: Δ 127 ± 68%, 80%: Δ 148 ± 83%, all P < 0.0001). IHG also reduced mean arterial pressure (20%: Δ -8 ± 4%, 40%: Δ -10 ± 8%, 60%: Δ -8 ± 3%, 80%: Δ -10 ± 5%, all P < 0.001). No changes were observed for heart rate (all P > 0.2). These data suggest that increases in vascular compliance and viscoelasticity regulate blood flow responses at the onset of physical exercise in humans. This work was supported by the Match of Minds Research Award by Brock University, and Natural Sciences and Engineering Research Council of Canada (NSERC). 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.

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 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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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