Mechanism of insulin's anabolic effect on muscle: measurements of muscle protein synthesis and breakdown using aminoacyl-tRNA and other surrogate measures
Bibliographic record
Abstract
Despite being an anabolic hormone in skeletal muscle, insulin's anticatabolic mechanism in humans remains controversial, with contradictory reports showing either stimulation of protein synthesis (PS) or inhibition of protein breakdown (PB) by insulin. Earlier measurements of muscle PS and PB in humans have relied on different surrogate measures of aminoacyl-tRNA and intracellular pools. We report that insulin's effect on muscle protein turnover using aminoacyl-tRNA as the precursor of PS and PB is calculated by mass balance of tracee amino acid (AA). We compared the results calculated from various surrogate measures. To determine the physiological role of insulin on muscle protein metabolism, we infused tracers of leucine and phenylalanine into 18 healthy subjects, and after 3 h, 10 subjects received a 4-h femoral arterial infusion of insulin (0.125 mUxkg(-1)xmin(-1)), while eight subjects continued with saline. Tracer-to-tracee ratios of leucine, phenylalanine, and ketoisocaproate were measured in the arterial and venous plasma, muscle tissue fluid, and AA-tRNA to calculate muscle PB and PS. Insulin infusion, unlike saline, significantly reduced the efflux of leucine and phenylalanine from muscle bed, based on various surrogate measures which agreed with those based on leucyl-tRNA (-28%), indicating a reduction in muscle PB (P < 0.02) without any significant effect on muscle PS. In conclusion, using AA-tRNA as the precursor pool, it is demonstrated that, in healthy humans in the postabsorptive state, insulin does not stimulate muscle protein synthesis and confirmed that insulin achieves muscle protein anabolism by inhibition of muscle protein breakdown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".