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Record W4410141942 · doi:10.1139/cjpp-2024-0270

Epigenetic regulation by ketone bodies in cardiac diseases and repair

2025· review· en· W4410141942 on OpenAlexvenueno aff
Narasimman Gurusamy, J. A. H. Murray, Robert C. Speth, Lisa S. Robison

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2025
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Neurological Disorders and StrokeAmerican Heart Association
KeywordsEpigeneticsHistone deacetylaseHistoneDNA methylationBiologyEpigenomicsAcetylationRegulation of gene expressionKetone bodiesGene expressionCell biologyGeneBiochemistryMetabolism

Abstract

fetched live from OpenAlex

Ketone bodies, particularly β-hydroxybutyrate (BHB), play an important role in the epigenetic regulation of gene expression in cardiac tissues, impacting both cardiac health and disease. This review explores the multifaceted influence of ketone bodies on epigenetic mechanisms, including histone acetylation, DNA methylation, ubiquitination, sirtuins activation, and RNA modulation. By acting as endogenous histone deacetylase inhibitors, ketone bodies enhance histone acetylation, thereby promoting the expression of genes involved in antioxidant defenses, anti-inflammatory responses, and metabolic regulation. Furthermore, BHB affects DNA methylation patterns by altering the availability of key metabolites such as S-adenosylmethionine. Ketogenic diet, which elevates BHB levels, has been shown to modulate gene expression, such as increasing FOXO3a and metallothionein 2, and improve cardiac function. This review highlights the therapeutic potential of ketone bodies in managing cardiac diseases through their epigenetic effects, underscoring the need for further research to elucidate the detailed molecular pathways and long-term impacts of these metabolic interventions.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.328
Teacher spread0.309 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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