Malonyl-CoA Signaling, Lipid Partitioning, and Glucolipotoxicity
Bibliographic record
Abstract
Beta-cells possess inherent mechanisms to adapt to overnutrition and the prevailing concentrations of glucose, fatty acids, and other fuels to maintain glucose homeostasis. However, this is balanced by potentially harmful actions of the same nutrients. Both glucose and fatty acids may cause good/adaptive or evil/toxic actions on the beta-cell, depending on their concentrations and the time during which they are elevated. Chronic high glucose dramatically influences beta-cell lipid metabolism via substrate availability, changes in the activity and expression of enzymes of glucose and lipid metabolism, and modifications in the expression level of key transcription factors. We discuss here the emerging view that beta-cell "glucotoxicity" is in part indirectly caused by "lipotoxicity," and that beta-cell abnormalities will become particularly apparent when both glucose and circulating fatty acids are high. We support the concept that elevated glucose and fatty acids synergize in causing toxicity in islets and other organs, a process that may be instrumental in the pleiotropic defects associated with the metabolic syndrome and type 1 and type 2 diabetes. The mechanisms by which hyperglycemia and hyperlipidemia alter insulin secretion are discussed and a model of beta-cell "glucolipotoxicity" that implicates alterations in beta-cell malonyl-CoA concentrations; peroxisome proliferator-activated receptor-alpha and -gamma and sterol regulatory element binding protein-1c expression; and lipid partitioning is proposed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".