Nutrient-Induced Metabolic Stress, Adaptation, Detoxification, and Toxicity in the Pancreatic β-Cell
Bibliographic record
Abstract
Paraphrasing the Swiss physician and father of toxicology Paracelsus (1493-1541) on chemical agents used as therapeutics, "the dose makes the poison," it is now realized that this aptly applies to the calorigenic nutrients. The case here is the pancreatic islet β-cell presented with excessive levels of nutrients such as glucose, lipids, and amino acids. The short-term effects these nutrients exert on the β-cell are enhanced insulin biosynthesis and secretion and changes in glucose sensitivity. However, chronic fuel surfeit triggers additional compensatory and adaptive mechanisms by β-cells to cope with the increased insulin demand or to protect itself. When these mechanisms fail, toxicity due to the nutrient surplus ensues, leading to β-cell dysfunction, dedifferentiation, and apoptosis. The terms glucotoxicity, lipotoxicity, and glucolipotoxicity have been widely used, but there is some confusion as to what they mean precisely and which is most appropriate for a given situation. Here we address the gluco-, lipo-, and glucolipo-toxicities in β-cells by assessing the evidence both for and against each of them. We also discuss potential mechanisms and defend the view that many of the identified "toxic" effects of nutrient excess, which may also include amino acids, are in fact beneficial adaptive processes. In addition, candidate fuel-excess detoxification pathways are evaluated. Finally, we propose that a more general term should be used for the in vivo situation of overweight-associated type 2 diabetes reflecting both the adaptive and toxic processes to mixed calorigenic nutrients excess: "nutrient-induced metabolic stress" or, in brief, "nutri-stress."
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".