Ketoisocaproic acid, a metabolite of leucine, suppresses insulin-stimulated glucose transport in skeletal muscle cells in a BCAT2-dependent manner
Bibliographic record
Abstract
Although leucine has many positive effects on metabolism in multiple tissues, elevated levels of this amino acid and the other branched-chain amino acids (BCAAs) and their metabolites are implicated in obesity and insulin resistance. While some controversies exist about the direct effect of leucine on insulin action in skeletal muscle, little is known about the direct effect of BCAA metabolites. Here, we first showed that the inhibitory effect of leucine on insulin-stimulated glucose transport in L6 myotubes was dampened when other amino acids were present, due in part to a 140% stimulation of basal glucose transport (P < 0.05). Importantly, we also showed that α-ketoisocaproic acid (KIC), an obligatory metabolite of leucine, stimulated mTORC1 signaling but suppressed insulin-stimulated glucose transport (-34%, P < 0.05) in an mTORC1-dependent manner. The effect of KIC on insulin-stimulated glucose transport was abrogated in cells depleted of branched-chain aminotransferase 2 (BCAT2), the enzyme that catalyzes the reversible transamination of KIC to leucine. We conclude that although KIC can modulate muscle glucose metabolism, this effect is likely a result of its transamination back to leucine. Therefore, limiting the availability of leucine, rather than those of its metabolites, to skeletal muscle may be more critical in the management of insulin resistance and its sequelae.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".