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Effects of Prior Heavy Exercise in Heart Failure with Preserved Ejection Fraction on VO <sub>2</sub> Kinetics

2018· article· en· W3173813985 on OpenAlexafffundabout
Natasha G. Boyes, Janine Eckstein, Stephen Pylypchuk, Scotty Butcher, Darcy D. Marciniuk, Dalisizwe M.K. Dewa, C. Robert E. Wells, Mark J. Haykowsky, Corey R. Tomczak

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsCardiologyInternal medicineHeart failureKineticsHeart rateCTL*Heart failure with preserved ejection fractionEjection fractionIntensity (physics)ChemistryMedicineBlood pressureBiochemistryPhysics

Abstract

fetched live from OpenAlex

Exercise intolerance and muscle dysfunction characterize heart failure with preserved ejection fraction (HFpEF). Prior heavy exercise (“priming”) speeds pulmonary oxygen uptake (V̇O 2p ) kinetics in older adults. The mechanism behind speeding of V̇O 2p kinetics following priming is either through increased oxygen delivery to the exercising muscles or increased intracellular metabolic activity; the former remains dominant in older and diseased populations. PURPOSE We tested the hypothesis that priming would not speed V̇O 2p on‐kinetics in patients with HFpEF due to muscle dysfunction commonly found in patients with HFpEF. METHODS Eight HFpEF patients, 4 high‐fit (CTL‐HF), and 5 low‐fit age‐matched controls (CTL‐LF) performed 3 repetitions of 2 exercise transitions: MOD1, rest to 4‐min moderate‐intensity cycling; and MOD2, MOD1 preceded by 2‐min heavy‐intensity cycling and 5‐min rest. V̇O 2p (oxidative metabolism; pulmonary gas exchange), heart rate (HR, cardiac function; ECG), total peripheral resistance (TPR, vascular function; ModelFlow), and tissue oxygenation of the vastus lateralis (TOI, muscle oxygen delivery; near‐infrared spectroscopy) on‐kinetics were measured, linearly interpolated, time‐aligned, and averaged. V̇ O 2p and HR were curve‐fitted using a monoexponential equation to yield a time constant (tau). ΔTPR and ΔTOI were calculated from baseline at 15s, 30s, and end‐exercise. Significance was P <0.05. RESULTS HFpEF V̇O 2p on‐kinetics (tau 45 ± 15 s) were slower than CTL‐HF (25 ± 6 s) but not CTL‐LF (50 ± 19 s; P =0.008). MOD2 V̇O 2p on‐kinetics (tau 37 ± 14 s) were faster than MOD1 (47 ± 19 s), pooled across groups ( P =0.039). Although not statistically significant, the priming effect was notably smaller in HFpEF compared to control groups (9% in HFpEF, 33% in low‐fit controls, and 30% faster in high‐fit controls). HR on‐kinetics were slower following priming in all groups (MOD1 tau 34 ± 21 vs. MOD2 44 ± 27 s; P =0.001), suggesting cardiac output as an unlikely assistant to the observed speeding in V̇O 2p on‐kinetics across groups. TPR was lower in CTL‐HF throughout exercise compared to HFpEF (all P <0.038) and at 30s and end‐exercise compared to CTL‐LF (all P <0.032), pooled across conditions with no effect of priming. TOI in CTL‐HF increased at 15–30s (all P ≤0.024) while HFpEF patients and CTL‐LF had decreased TOI at 15–30s in MOD1 (all P ≤0.024). The latter suggests lower oxygen delivery at early exercise onset compared to CTL‐HF. Priming reduced TOI in HFpEF at 30s ( P =0.033) and CTL‐HF throughout exercise (all P <0.05), suggesting either lower muscle oxygen delivery or greater mitochondrial activity following priming. CONCLUSION Slow HFpEF V̇O 2p on‐kinetics may not be speeded by priming exercise. Heart rate and vascular responses to priming may not account for speeding of V̇O 2p on‐kinetics in older adults. Intracellular mechanisms may limit HFpEF exercise tolerance. Support or Funding Information Canadian Institutes of 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 .

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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)0.001
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: Observational
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 head0.008
GPT teacher head0.231
Teacher spread0.224 · 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 routes3
Has abstractyes

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