Targeting ceramide metabolism in obesity
Bibliographic record
Abstract
Obesity is a major health concern that increases the risk for insulin resistance, type 2 diabetes (T2D), and cardiovascular disease. Thus, an enormous research effort has been invested into understanding how obesity-associated dyslipidemia and obesity-induced alterations in lipid metabolism increase the risk for these diseases. Accordingly, it has been proposed that the accumulation of lipid metabolites in organs such as the liver, skeletal muscle, and heart is critical to these obesity-induced pathologies. Ceramide is one such lipid metabolite that accumulates in tissues in response to obesity, and both pharmacological and genetic strategies that reduce tissue ceramide levels yield salutary actions on overall metabolic health. We will review herein why ceramide accumulates in tissues during obesity and how an increase in intracellular ceramide impacts cellular signaling and function as well as potential mechanisms by which reducing intracellular ceramide levels improves insulin resistance, T2D, atherosclerosis, and heart failure. Because a reduction in skeletal muscle ceramide levels is frequently associated with improvements in insulin sensitivity in humans, the beneficial findings reported for reducing ceramides in preclinical studies may have clinical application in humans. Therefore, modulating ceramide metabolism may be a novel, exciting target for preventing and/or treating obesity-related diseases.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".