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Record W4409212085 · doi:10.1152/ajpheart.00615.2024

Cardiac energy metabolism in diabetes: emerging therapeutic targets and clinical implications

2025· review· en· W4409212085 on OpenAlexafffund
Anjali Panwar, M Adib, Gary D. Lopaschuk

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2025
Typereview
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsLipotoxicityDiabetes mellitusBeta oxidationDiabetic cardiomyopathyMedicineInternal medicineCarbohydrate metabolismHeart failureEndocrinologyFatty acidMetabolismDiseasePharmacologyCardiomyopathyChemistryInsulin resistanceBiochemistry

Abstract

fetched live from OpenAlex

Patients with diabetes are at an increased risk for developing diabetic cardiomyopathy and other cardiovascular complications. Alterations in cardiac energy metabolism in patients with diabetes, including an increase in mitochondrial fatty acid oxidation and a decrease in glucose oxidation, are important contributing factors to this increase in cardiovascular disease. A switch from glucose oxidation to fatty acid oxidation not only decreases cardiac efficiency due to increased oxygen consumption but it can also increase reactive oxygen species production, increase lipotoxicity, and redirect glucose into other metabolic pathways that, combined, can lead to heart dysfunction. Currently, there is a lack of therapeutics available to treat diabetes-induced heart failure that specifically target cardiac energy metabolism. However, it is becoming apparent that part of the benefit of existing agents such as GLP-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors may be related to their effects on cardiac energy metabolism. In addition, direct approaches aimed at inhibiting cardiac fatty acid oxidation or increasing glucose oxidation hold future promise as potential therapeutic approaches to treat diabetes-induced cardiovascular disease.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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 designOther design
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

Citations16
Published2025
Admission routes2
Has abstractyes

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