Weight loss enhances cardiac energy metabolism and function in heart failure associated with obesity
Bibliographic record
Abstract
Abstract Aims Obesity is associated with high rates of cardiac fatty acid oxidation, low rates of glucose oxidation, cardiac hypertrophy and heart failure. Whether weight loss can lessen the severity of heart failure associated with obesity is not known. We therefore determined the effect of weight loss on cardiac energy metabolism and the severity of heart failure in obese mice with heart failure. Materials and methods Obesity and heart failure were induced by feeding mice a high‐fat (HF) diet and subjecting them to transverse aortic constriction (TAC). Obese mice with heart failure were then switched for 8 weeks to either a low‐fat (LF) diet (HF TAC LF) or caloric restriction (CR) (40% caloric intake reduction, HF TAC CR) to induce weight loss. Results Weight loss improved cardiac function (%EF was 38 ± 6% and 36 ± 6% in HF TAC LF and HF TAC CR mice vs 25 ± 3% in HF TAC mice, P < 0.05) and it decreased cardiac hypertrophy post TAC (left ventricle mass was 168 ± 7 and 171 ± 10 mg in HF TAC LF and HF TAC CR mice, respectively, vs 210 ± 8 mg in HF TAC mice, P < 0.05). Weight loss enhanced cardiac insulin signalling, insulin‐stimulated glucose oxidation rates (1.5 ± 0.1 and 1.5 ± 0.1 μmol/g dry wt/min in HF TAC LF and HF TAC CR mice, respectively, vs 0.2 ± 0.1 μmol/g dry wt/min in HF TAC mice, P < 0.05) and it decreased pyruvate dehydrogenase phosphorylation. Cardiac fatty acid oxidation rates, AMPK Tyr172 /ACC Ser79 signalling and the acetylation of ß‐oxidation enzymes, were attenuated following weight loss. Conclusions Weight loss is an effective intervention to improve cardiac function and energy metabolism in heart failure associated with obesity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".