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Mechanisms Determining VO <sub>2peak</sub> During Single Leg Knee‐Extension Exercise in Heart Failure with Preserved Ejection Fraction Patients: Peripheral vs. Central Phenotypes

2022· article· en· W4225412865 on OpenAlexaff
Rachel J. Skow, Zachary T. Martin, Damsara Nandadeva, Christopher M. Hearon, Mitchel Samels, James P. MacNamara, Mark J. Haykowsky, Benjamin D. Levine, Paul J. Fadel, Satyam Sarma

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Alberta
FundersNational Institutes of Health
KeywordsPeripheralCardiologyEjection fractionHeart failureMedicineInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Background Heart failure with preserved ejection fraction (HFpEF) is characterized by exercise intolerance, including a reduced maximal aerobic capacity that affects all aspects of daily living. The mechanisms responsible for the decreased exercise tolerance in HFpEF are incompletely understood. Our research group has characterized two unique HFpEF phenotypes based on cardiac output reserve relative to exercise metabolic work: those with either a “central” (Type A) or “peripheral” (Type B) limitation to exercise. We sought to investigate the peripheral limitations to exercise in these 2 phenotypes using single‐leg knee extension (SLKE) exercise, a modality that reduces the central limitations to exercise. Specifically, we tested the hypothesis that patients with the Type B phenotype would have lower leg VO 2peak secondary to insufficient oxygen extraction (i.e., lower ∆a‐vO 2diff ) when compared to patients with the Type A phenotype. Methods We studied 20 HFpEF patients with either the Type A (n = 8; 68 ± 2yr; 5F) or Type B (n = 12; 70 ± 2yr; 7F) phenotype. Participants performed an incremental SLKE peak exercise test on a custom ergometer. Leg blood flow (LBF) was measured at the common femoral artery using duplex Doppler ultrasound. Average LBF (ml/min) at each workload was determined from measured blood velocity and vessel diameter. a‐vO 2diff was calculated from hemoglobin and venous oxygen saturation directly from a femoral venous catheter, and arterial oxygen saturation via pulse oximetry. Leg VO 2 (ml/min) was determined as the product of LBF and a‐vO 2diff . All data are presented as mean ± SEM. Results Participants with the Type A “central” phenotype reached a peak exercise workload of 20 ± 4 W and those with the Type B “peripheral” phenotype reached 15 ± 2 W (p = 0.18). Leg VO 2peak was not different between the two phenotypes (Type A: 215 ± 23; Type B: 253 ± 34 ml/min; p = 0.42). The peak increase in LBF was ~ 50% greater in Type B (+1940 ± 243 ml/min) than Type A (+1309 ± 161 ml/min) patients, though this response was more variable (p = 0.07). Additionally, the ∆a‐vO 2diff from rest to peak exercise was significantly lower in those with the Type B phenotype (Type A: +7.17 ± 0.87; Type B: +4.63 ± 0.46 ml O 2 /dl blood; p = 0.01). Finally, the ∆LBF/∆VO 2 slope was lower in patients with the Type A phenotype (Type A: 6.78 ± 0.55; Type B: 8.72 ± 0.43; p = 0.01). Conclusion Contrary to our hypothesis, there was no difference in leg VO 2peak between phenotypes. Patients with Type B HFpEF had greater limitations in skeletal muscle oxygen extraction during exercise as evidenced by their diminished ability to increase a‐vO 2diff during incremental SLKE, similar to the observed a‐vO 2diff extraction impairments during whole body exercise. Importantly, by phenotyping HFpEF patients based on their limitations to exercise, we may be able to better individualize treatment strategies to improve exercise tolerance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
Teacher spread0.194 · 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
Published2022
Admission routes1
Has abstractyes

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