Cardiac Energy Metabolism in Heart Failure
Bibliographic record
Abstract
Alterations in cardiac energy metabolism contribute to the severity of heart failure. However, the energy metabolic changes that occur in heart failure are complex and are dependent not only on the severity and type of heart failure present but also on the co-existence of common comorbidities such as obesity and type 2 diabetes. The failing heart faces an energy deficit, primarily because of a decrease in mitochondrial oxidative capacity. This is partly compensated for by an increase in ATP production from glycolysis. The relative contribution of the different fuels for mitochondrial ATP production also changes, including a decrease in glucose and amino acid oxidation, and an increase in ketone oxidation. The oxidation of fatty acids by the heart increases or decreases, depending on the type of heart failure. For instance, in heart failure associated with diabetes and obesity, myocardial fatty acid oxidation increases, while in heart failure associated with hypertension or ischemia, myocardial fatty acid oxidation decreases. Combined, these energy metabolic changes result in the failing heart becoming less efficient (ie, a decrease in cardiac work/O 2 consumed). The alterations in both glycolysis and mitochondrial oxidative metabolism in the failing heart are due to both transcriptional changes in key enzymes involved in these metabolic pathways, as well as alterations in NAD redox state (NAD + and nicotinamide adenine dinucleotide levels) and metabolite signaling that contribute to posttranslational epigenetic changes in the control of expression of genes encoding energy metabolic enzymes. Alterations in the fate of glucose, beyond flux through glycolysis or glucose oxidation, also contribute to the pathology of heart failure. Of importance, pharmacological targeting of the energy metabolic pathways has emerged as a novel therapeutic approach to improving cardiac efficiency, decreasing the energy deficit and improving cardiac function in the failing heart.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".