Bibliographic record
Abstract
PURPOSE OF REVIEW: Protein anabolism is abnormal in human type 2 diabetes (T2DM). We review studies of anabolic stimuli that identify potential causes. If uncorrected, and combined with aging effects, they will compromise muscle function and mass. Knowing causes can guide studies of preventive and treatment measures. RECENT FINDINGS: T2DM accelerates age-related decreases in muscle mass. This could be related to insulin resistance of whole-body protein anabolism demonstrated in hyperglycemic obese men. In contrast, their protein anabolic response to hyperaminoacidemia suggested that ample amino acid administration, especially branched chain amino acids might overcome such insulin resistance. One study of chronic leucine supplementation in elderly T2DM patients did not increase muscle mass. However, they lacked sarcopenia and had adequate dietary protein intake, so may be atypical. Exercise induced similar increases in muscle protein synthesis, mass and strength in healthy and T2DM patients suggesting that physical activity might also overcome insulin resistance of protein anabolism. SUMMARY: Muscle protein anabolism in T2DM is resistant to the action of insulin but perhaps not to amino acid supply or exercise. Whether leucine supplementation improves muscle mass and function in persons with T2DM (especially elderly) with reduced protein intake or muscle mass needs to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.004 | 0.004 |
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".