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Record W4410482484 · doi:10.1093/eurjpc/zwaf236.345

Association between comorbidities and the effects of exercise training among patients with heart failure with preserved ejection fraction

2025· article· en· W4410482484 on OpenAlexaff
Stephan Mueller, Isabel Fegers‐Wustrow, Sophia Dinges, Andreas B. Gevaert, Ephraim B. Winzer, Mark J. Haykowsky, Ulrik Wisløff, Rolf Wachter, Volker Adams, Emeline M. Van Craenenbroeck, Frank Edelmann, Burkert Pieske, Martin Halle, Simon Wernhart

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineEjection fractionHeart failureHeart failure with preserved ejection fractionCardiologyInternal medicineAssociation (psychology)Physical therapy

Abstract

fetched live from OpenAlex

Abstract Background Patients with heart failure with preserved ejection fraction (HFpEF) often have multiple cardiac and/or non-cardiac comorbidities that may contribute to exercise intolerance, the hallmark symptom in HFpEF. Exercise training is one of the most effective treatments to improve exercise tolerance but it is unclear whether individual comorbidities or the comorbidity burden are associated with altered exercise training effects in HFpEF. Purpose To evaluate the association between baseline comorbidities and the change in peak oxygen consumption (VO2) in patients with HFpEF. Methods This is a pooled analysis of the two largest randomized controlled exercise training trials performed in HFpEF to date. In the OptimEx-Clin trial, 180 patients were randomized (1:1:1) to 12 months of high-intensity interval training (3× per week), moderate continuous training (5× per week) or usual care (UC). In the Ex-DHF trial, 322 patients were randomized (1:1) to 12 months of endurance and resistance training (3× per week) or UC. For the present analysis, all exercise training groups and both UC groups were combined into one exercise and one UC group. Peak V̇O2 was defined as the highest 30-sec average during symptom-limited incremental cardiopulmonary exercise testing. Comorbidity burden was evaluated by a simple score assigning 1 point to each assessed comorbidity (arterial hypertension, hyperlipidaemia, obesity, coronary heart disease, atrial fibrillation, diabetes mellitus, chronic kidney disease, cancer, anaemia, sleep apnoea, cerebrovascular disease, depression, chronic obstructive pulmonary disease, primary valve disease, peripheral vascular disease, congenital heart disease). The associations between baseline comorbidities and change in peak VO2 after 12 months were analysed using linear regression analyses adjusted for sex, age at inclusion and baseline peak VO2. All analyses were performed using R Statistical Software with significant levels of α=0.05. Results A total of 400 patients with available peak VO2 measurements at baseline and 12 months (60.7% women; mean age: 70 years; median no. of comorbidities: 4 [range: 0-10; IQR: 2-5]) were included in this analysis. Change in peak VO2 at 12 months was significantly higher following exercise training vs. UC (mean difference, 1.22 mL/kg/min [95% CI, 0.58-1.86], P<0.001). There was no significant interaction between any of the investigated comorbidities and treatment group for the change in peak VO2 (Figure 1). Comorbidity burden was also not significantly associated with the change in peak VO2 (P-interaction = 0.99) with mean between-group differences of 1.26 mL/kg/min (95%CI, -0.25-2.77) for ≤2 comorbidities, 1.37 mL/kg/min (95%CI, 0.44-2.31) for 3-4 comorbidities and 1.27 mL/kg/min (95%CI, 0.19-2.36) for ≥5 comorbidities. Conclusions In patients with HFpEF, exercise training significantly improved peak VO2 over 12 months, regardless of individual comorbidities and overall comorbidity burden.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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.

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
Published2025
Admission routes1
Has abstractyes

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