Insulin supplementation attenuates cancer-induced cardiomyopathy and slows tumor disease progression
Bibliographic record
Abstract
Advanced cancer induces fundamental changes in metabolism and promotes cardiac atrophy and heart failure. We discovered systemic insulin deficiency in cachectic cancer patients. Similarly, mice with advanced B16F10 melanoma (B16F10-TM) or colon 26 carcinoma (C26-TM) displayed decreased systemic insulin associated with marked cardiac atrophy, metabolic impairment, and function. B16F10 and C26 tumors decrease systemic insulin via high glucose consumption, lowering pancreatic insulin production and producing insulin-degrading enzyme. As tumor cells consume glucose in an insulin-independent manner, they shift glucose away from cardiomyocytes. Since cardiomyocytes in both tumor models remained insulin responsive, low-dose insulin supplementation by subcutaneous implantation of insulin-releasing pellets improved cardiac glucose uptake, atrophy, and function, with no adverse side effects. In addition, by redirecting glucose to the heart in addition to other organs, the systemic insulin treatment lowered glucose usage by the tumor and thereby decreased tumor growth and volume. Insulin corrected the cancer-induced reduction in cardiac Akt activation and the subsequent overactivation of the proteasome and autophagy. Thus, cancer-induced systemic insulin depletion contributes to cardiac wasting and failure and may promote tumor growth. Low-dose insulin supplementation attenuates these processes and may be supportive in cardio-oncologic treatment concepts.
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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".