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Record W4381125443 · doi:10.1093/ehjci/jead119.319

Relationships between biventricular myocardial function and oxygen uptake during exercise in healthy adolescent male athletes

2023· article· en· W4381125443 on OpenAlexaff
Dan M. Dorobantu, Curtis A. Wadey, Diane Ryding, Stephen McNally, D. Perry, Mark K. Friedberg, Graham Stuart, G E Pieles, Craig A. Williams

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersMedical Research CouncilUniversity of BristolUK Research and Innovation
KeywordsMedicineCardiologyInternal medicineVO2 maxAthletesHeart ratePhysical therapySpeckle tracking echocardiographyCardiorespiratory fitnessHeart failureEjection fractionBlood pressure

Abstract

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Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Research and Innovation Medical Research Council Doctoral Training Programme Grant Work supported as part of a research partnership between the University of Bristol and Canon Medical Systems UK that determines the independence of the research from either parties. Background Biventricular cardiac function improves with increasing work-rate during exercise in healthy elite athletes, but how it relates to concomitantly measured oxygen uptake (VO2) is not well known. Assessing cardiac determinants of cardiorespiratory fitness through multimodal evaluations may lead to improvements in clinical stress testing protocols and better understanding of exercise physiology. Purpose The aim of this study was to investigate whether left and right ventricular (LV and RV) systolic function measured by speckle tracking echocardiography (STE) at submaximal exercise (moderate and high intensity domains) is associated with concomitantly measured VO2 in healthy adolescent male athletes. Methods Elite male football players <16 years-old underwent cardiopulmonary exercise testing with concomitant STE at 50 W steps. Exercise intensity domains were defined based on the gas exchange threshold (GET). LV and RV free wall (FW) peak systolic longitudinal strain (Sl), strain rate (SRl) and LV average (12 mid and basal segments) circumferential strain (Sc) and strain rate (SRc) were measured. Cardiac function relation to peak VO2 was evaluated using linear regression, and its relationships to VO2 within each exercise intensity domain using repeated measures mixed linear models, before and after adjusting for heart rate (HR). Results A total of n=94 athletes were included (mean age 14.7 ± 1 years). Mean peak VO2 and work-rate were 43 ± 7 mL·min-1·kg-1 and 215 ± 40 W, respectively. Relationships between cardiac function and VO2, by exercise intensity domain are shown in Figure 1 with all regression coefficients shown in Table 1. Only LV Sl at high intensity was correlated to peak VO2 (p = 0.04). Within exercise intensity domains higher LV Sl (Figure 1A) and SRl (Figure 1B) were associated with higher concomitantly measured VO2 at both moderate and high intensity. Higher SRc (Figure 1C) and RV FW SRl (Figure 1F) were associated with higher VO2 only at moderate intensity. LV Sc (Figure 1C) and RV FW Sl (Figure 1E) were not associated with concomitant VO2 during exercise at moderate or high intensity. Only LV Sl and SRl at moderate and high intensity remained significantly associated to concomitantly measured VO2 after further adjusting by HR (Table 1). Conclusions In healthy adolescent athletes, only LV longitudinal strain and strain rate measured at moderate and high intensity were associated with VO2 independent of HR response. There were differences in myocardial response to exercise both between the two ventricles, and between the longitudinal and circumferential components of LV function. These require further research, both in healthy and disease groups, especially in RV pathology, where components of myocardial function could play different roles in limiting exercise capacity.

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 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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.032
GPT teacher head0.259
Teacher spread0.227 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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