Cancer cachexia and diabetes: similarities in metabolic alterations and possible treatment<sup>,</sup>
Bibliographic record
Abstract
Cancer cachexia is a metabolic syndrome featuring many alterations typical of type 2 diabetes (T2D). While muscle wasting is a hallmark of cachexia, epidemiological evidence also supports an accelerated age-related muscle loss in T2D. Insulin resistance manifests in both conditions and impairs glucose disposal and protein anabolism by tissues. A greater contribution of gluconeogenesis to glucose production may limit amino acid availability for muscle protein synthesis, further aggravating muscle loss. In the context of inter-dependence between glucose and protein metabolism, the present review summarizes the current state of knowledge on alterations that may lead to muscle wasting in human cancer. By highlighting the similarities with T2D, a disease that has been more extensively studied, the objective of this review is to provide a better understanding of the pathophysiology of cancer cachexia and to consider potential treatments usually targeted for T2D. Nutritional approaches aimed at stimulating protein anabolism might include specially formulated food with optimal protein and amino acid composition. Because the gradual muscle loss in T2D may be attenuated by diabetes treatment, anti-diabetic drugs might be considered in cachexia treatment. Metformin emerges as a choice candidate as it acts both on reducing gluconeogenesis and improving insulin sensitivity, and has demonstrated tumour suppressor properties in multiple cancer types. Such a multimodal approach to slow or reverse muscle wasting in cachexia warrants further investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".