Role of Dietary Proteins and Amino Acids in the Pathogenesis of Insulin Resistance
Bibliographic record
Abstract
Dietary proteins and amino acids are important modulators of glucose metabolism and insulin sensitivity. Although high intake of dietary proteins has positive effects on energy homeostasis by inducing satiety and possibly increasing energy expenditure, it has detrimental effects on glucose homeostasis by promoting insulin resistance and increasing gluconeogenesis. Varying the quality rather than the quantity of proteins has been shown to modulate insulin resistance induced by Western diets and has revealed that proteins derived from fish might have the most desirable effects on insulin sensitivity. In vitro and in vivo data also support an important role of amino acids in glucose homeostasis through modulation of insulin action on muscle glucose transport and hepatic glucose production, secretion of insulin and glucagon, as well as gene and protein expression in various tissues. Moreover, amino acid signaling is integrated by mammalian target of rapamycin, a nutrient sensor that operates a negative feedback loop toward insulin receptor substrate 1 signaling, promoting insulin resistance for glucose metabolism. This integration suggests that modulating dietary proteins and the flux of circulating amino acids generated by their consumption and digestion might underlie powerful new approaches to treat various metabolic diseases such as obesity and diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".