Effect of a high-protein diet on development of heart failure in response to pressure overload
Bibliographic record
Abstract
Heart failure treatment guidelines provide no recommendations regarding the intake of protein, though it has been proposed that increasing protein intake may result in clinical improvement. High-protein intake might improve protein synthesis and cell function, and prevent deterioration in mitochondrial and left ventricular function. We assessed the effects of a high-protein diet on the development of heart failure characterized by cardiac hypertrophy, impaired mitochondrial oxidative metabolism and contractile dysfunction induced by transverse aortic constriction in rats. A standard diet with 18% of energy intake from protein was compared with a high-protein diet (30% of energy intake). First, we evaluated the effects of protein intake on the development of heart failure during 14 weeks of aortic constriction, and found similar cardiac hypertrophy, contractile dysfunction, ventricular dilation, and decreased cardiac mitochondrial oxidative capacity with both 18% and 30% protein. We then assessed more advanced heart failure, with 22 weeks of aortic constriction. We again saw no difference in cardiac mass, left ventricular volume, mitochondrial oxidative capacity or resistance to permeability transition between the 18% and 30% protein diets. There was a modest but significant decrease in survival with heart failure with the 30% protein diet compared with 18% protein (p < 0.003). In conclusion, consumption of a high-protein diet did not affect cardiac mass, left ventricular volumes or ejection fraction, or myocardial mitochondrial oxidative capacity in rats with pressure overload induced heart failure, but significantly decreased survival.
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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".