Role of malonyl-CoA in heart disease and the hypothalamic control of obesity
Bibliographic record
Abstract
Obesity is an important contributor to the risk of developing insulin resistance, diabetes, and heart disease. Alterations in tissue levels of malonyl-CoA have the potential to impact on the severity of a number of these disorders. This review will focus on the emerging role of malonyl-CoA as a key "metabolic effector" of both obesity and cardiac fatty acid oxidation. In addition to being a substrate for fatty acid biosynthesis, malonyl-CoA is a potent inhibitor of mitochondrial carnitine palmitoyltransferase (CPT) 1, a key enzyme involved in mitochondrial fatty acid uptake. A decrease in myocardial malonyl-CoA levels and an increase in CPT1 activity contribute to an increase in cardiac fatty acid oxidation. An increase in malonyl-CoA degradation due to increased malonyl-CoA decarboxylase (MCD) activity may be one mechanism responsible for this decrease in malonyl-CoA. Another mechanism involves the inhibition of acetyl-CoA carboxylase (ACC) synthesis of malonyl-CoA, due to AMP-activated protein kinase (AMPK) phosphorylation of ACC. Recent studies have demonstrated a role of malonyl-CoA in the hypothalamus as a regulator of food intake. Increases in hypothalamic malonyl-CoA and inhibition of CPT1 are associated with a decrease in food intake in mice and rats, while a decrease in hypothalamic malonyl-CoA increases food intake and weight gain. The exact mechanism(s) responsible for these effects of malonyl-CoA are not clear, but have been proposed to be due to an increase in the levels of long chain acyl CoA, which occurs as a result of malonyl-CoA inhibition of CPT1. Both hypothalamic and cardiac studies have demonstrated that control of malonyl-CoA levels has an important impact on obesity and heart disease. Targeting enzymes that control malonyl-CoA levels may be an important therapeutic approach to treating heart disease and 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".