Protein anabolism is resistant to insulin action in lung cancer cachexia
Bibliographic record
Abstract
Cachexia is frequently observed in advanced non‐small cell lung cancer (NSCLC) and compromises functional status, response to treatment and survival. Inherent muscle loss could be due to a defective protein response to the main anabolic hormone, insulin. We assessed insulin resistance of whole body protein and glucose metabolism using the hyperinsulinemic, euglycemic, isoaminoacidemic clamp with 13 C‐leucine and 3 H‐glucose tracers, in 5 men with NSCLC (stage III‐IV) and 8 healthy weight‐stable men matched for age (67 ± 2 vs. 69 ± 1 yrs). In NSCLC, recent weight loss was 6.3 ± 0.9% and BMI (20.8 ± 1.3 vs. 25.1 ± 0.9 kg/m 2 ), body fat and fat‐free mass (FFM) were lower. Postabsorptive plasma glucose and insulin did not differ, but branched‐chain amino acid (BCAA) concentrations were lower. During hyperinsulinemia, glucose infusion rates did not differ between groups (4.4 ± 0.4 vs. 5.5 ± 0.7 mg/kg.min), indicating no further resistance beyond that conferred by aging. In contrast, AA infusion rates were markedly lower in NSCLC: 31.1 ± 2.5 vs. 39.9 ± 1.8 mg/min, adjusted for FFM and postabsorptive BCAAs (p=0.039). This could be due to impaired suppression of protein breakdown or stimulation of synthesis by insulin, or both, which will be determined from kinetic analyses. These preliminary results are consistent with a blunted protein anabolism and may help define optimal approaches to prevent muscle loss in cancer cachexia. (CIHR)
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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 | 0.000 |
| 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".