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Record W3134281565 · doi:10.1093/cvr/cvab068

Ketone therapy for heart failure: current evidence for clinical use

2021· article· en· W3134281565 on OpenAlexaff
Shingo Takahara, Shubham Soni, Zaid H. Maayah, Mourad Ferdaoussi, Jason R.B. Dyck

Bibliographic record

VenueCardiovascular Research · 2021
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKetone bodiesHeart failureKetoneHeart diseaseMedicineCardiac function curveCardiologyIntensive care medicineInternal medicineChemistryMetabolism

Abstract

fetched live from OpenAlex

During conditions that result in depleted circulating glucose levels, ketone bodies synthesized in the liver are necessary fuel substrates for the brain. In other organs, such as the heart, the reliance on ketones for generating energy in the absence of glucose is less important as the heart can utilize alternative fuel sources, such as fatty acids. However, during pathophysiological conditions, such as heart failure, cardiac defects in metabolic processes that normally allow for sufficient energy production from fatty acids and carbohydrates contribute to a decline in contractile function. As such, it has been proposed that the failing heart relies more on ketone bodies as an energy source than previously appreciated. Furthermore, it has been shown that ketone bodies function as signaling molecules that can suppress systemic and cardiac inflammation. Thus, it is possible that intentionally elevating circulating ketones may be beneficial as an adjunct treatment for heart failure. Although many approaches can be used for 'ketone therapy', each of these has their own advantages and disadvantages in the treatment of heart failure. Thus, we summarize current preclinical and clinical studies involving various types of ketone therapy in cardiac disease and discuss the advantages and disadvantages of each modality as possible treatments for heart failure.

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)0.007
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.461
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.583
GPT teacher head0.559
Teacher spread0.024 · 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

Citations52
Published2021
Admission routes1
Has abstractyes

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