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Training Heart Failure Patients with Reduced Ejection Fraction Attenuates their Muscle Metaboreflex and Lowers Muscle Sympathetic Nerve Activity at Rest and During Mild Dynamic Exercise

2018· article· en· W3176173727 on OpenAlexaffabout
Catherine F. Notarius, Philip J. Millar, Daniel A. Keir, Hisa Murai, Nobuhiko Haruki, Emma O’Donnell, Susan Marzolini, Paul Oh, John S. Floras

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of GuelphToronto Rehabilitation InstituteUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineMicroneurographyCardiologyInternal medicineEjection fractionHeart failureVO2 maxAerobic exerciseVasodilationPhysical exerciseHeart rateBlood pressureBaroreflex

Abstract

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We have shown that muscle sympathetic nerve activity (MSNA) is elevated at rest in patients with heart failure due to reduced ejection fraction (HFrEF) and, in contrast to healthy subjects, increases during moderate dynamic 1‐leg exercise. We hypothesized that exercise training in HFrEF may reduce MSNA during both mild and moderate intensity 1‐leg dynamic exercise and static handgrip; attenuate the muscle metaboreflex; and improve skin blood flow. We studied 27 stable, medicated HFrEF patients (6 women; mean age 65 ± 2 SE [range 42 to 80] years; mean left ventricular ejection fraction 30 ± 1% [range 12–40]); of either ischemic (n=18) or non‐ischemic (n=9) etiology, both before and after 6 months of cardiac rehabilitation (aerobic exercise for 45 minutes, 5 times per week at 60–70 % of VO 2peak ); plus 18 healthy, age and sex‐matched, medication‐free volunteers (6 women; mean age 57 ± 2 [range 48 to 72]). Before and after training, we assessed VO 2peak (open‐circuit spirometry); the skin blood flow (SBF) response to reactive hyperemia (laser flowtometry); and fibular MSNA (microneurography) at rest (n=18), during 1‐leg cycling (2 min each unloaded and at 50% of VO 2peak , n=16) and, to assess the muscle metaboreflex, during post‐handgrip ischemia (PHGI) after 2 min of 30% static handgrip (HG). In HFrEF, pre‐training VO 2peak and skin vasodilatation were lower (P<0.001) and resting MSNA higher compared to controls (P=0.01). Exercise training improved VO 2peak by 3.0±1.0 ml·kg −1 ·min −1 (P<0.001) and increased the SBF vasdilatory response in HFrEF to a mean value similar to controls (P=0.78). Relative to pre‐training, resting MSNA decreased by 6.0±2.0 bursts/min (P=0.01, N=18) and 7.3±3.4 burst/100 heartbeats (P=0.04). During 1‐leg cycling, MSNA decreased in control (P=0.01) but increased in HFrEF (P<0.001). Exercise training attenuated MSNA burst frequency and incidence during unloaded (P=0.04) but not loaded cycling (P=0.34). The rise in MSNA burst frequency during HG in HFrEF was unaffected by training, but its elevation during PHGI was abolished (P<0.05). We conclude that the exaggerated metaboreflex of HFrEF is attenuated by exercise training. This may contribute to a lower MSNA during mild but not moderate intensity cycling. Support or Funding Information Supported by Heart and Stroke Foundation of Ontario and Canadian Institutes for Health Research This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
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 head0.014
GPT teacher head0.243
Teacher spread0.228 · 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
Published2018
Admission routes2
Has abstractyes

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