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Record W4406555388 · doi:10.1093/cvr/cvaf004

Therapeutic potential of ketone bodies on exercise intolerance in heart failure: looking beyond the heart

2025· review· en· W4406555388 on OpenAlexafffund
Shubham Soni, Rachel J. Skow, Stephen Foulkes, Mark J. Haykowsky, Jason R.B. Dyck

Bibliographic record

VenueCardiovascular Research · 2025
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesKillam TrustsDiabetes CanadaHeart and Stroke Foundation of Canada
KeywordsHeart failureExercise intoleranceMedicineCardiologyKetone bodiesInternal medicine

Abstract

fetched live from OpenAlex

Recent evidence suggests that ketone bodies have therapeutic potential in many cardiovascular diseases including heart failure (HF). Accordingly, this has led to multiple clinical trials that use ketone esters (KEs) to treat HF patients highlighting the importance of this ketone therapy. KEs, specifically ketone monoesters, are synthetic compounds which, when consumed, are de-esterified into two β-hydroxybutyrate (βOHB) molecules and increase the circulating βOHB concentration. While many studies have primarily focused on the cardiac benefits of ketone therapy in HF, ketones can have numerous favourable effects in other organs such as the vasculature and skeletal muscle. Importantly, vascular and skeletal muscle dysfunction are also heavily implicated in the reduced exercise tolerance, the hallmark feature in HF with reduced ejection fraction and preserved ejection fraction, suggesting that some of the benefits observed in HF in response to ketone therapy may involve these non-cardiac pathways. Thus, we review the evidence suggesting how ketone therapy may be beneficial in improving cardiovascular and skeletal muscle function in HF and identify various potential mechanisms that may be important in the beneficial non-cardiac effects of ketones in HF.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.063
GPT teacher head0.387
Teacher spread0.323 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2025
Admission routes2
Has abstractyes

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