Does high dietary protein intake contribute to the increased risk of developing prediabetes and type 2 diabetes?
Bibliographic record
Abstract
Insulin resistance is a complex metabolic disorder implicated in the development of many chronic diseases. While it is generally accepted that body mass loss should be the primary approach for the management of insulin resistance-related disorders in overweight and obese individuals, there is no consensus among researchers regarding optimal protein intake during dietary restriction. Recently, it has been suggested that increased plasma branched-chain amino acids concentrations are associated with the development of insulin resistance and type 2 diabetes. The exact mechanism by which excessive amino acid availability may contribute to insulin resistance has not been fully investigated. However, it has been hypothesised that mammalian target of rapamycin (mTOR) complex 1 hyperactivation in the presence of amino acid overload contributes to reduced insulin-stimulated glucose uptake because of insulin receptor substrate (IRS) degradation and reduced Akt-AS160 activity. In addition, the long-term effects of high-protein diets on insulin sensitivity during both weight-stable and weight-loss conditions require more research. This review focusses on the effects of high-protein diets on insulin sensitivity and discusses the potential mechanisms by which dietary amino acids can affect insulin signalling. Novelty: Excess amino acids may over-activate mTOR, resulting in desensitisation of IRS-1 and reduced insulin-mediated glucose uptake.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".