Altered cancer cell metabolism and cachexia: Calculating the energetic cost of cancer
Bibliographic record
Abstract
Background Cachexia affects most patients with incurable cancer impacting quality of life and prognosis especially in the late stage of disease. While much research has investigated the causes of cancer cachexia, the precise mechanisms causing cachexia are still poorly understood. Concurrently, it is increasingly documented that tumors function with elevated glycolysis. Methods and Results We model an anaerobic component of tumor energy metabolism to assess its impact and contribution to cachexia. In this model, with a high level of anaerobic energy production, the energetic cost to sustain the tumor may reach or exceed 394 kcal/ day per kg of tumor. In addition, the tumor’s high level of glucose and glutamine consumption causes muscle breakdown to fuel the tumor, especially in the fasting state. We calculate an estimate of the tumor’ se nergetic cost on the body in terms of aerobic and anaerobic components, as well as the Cori cycling cost of recycling lactate generated by the tumor back into glucose, at varying levels of tumor mass and of anaerobic energy metabolism. Conclusions Our model suggests the energetic drain caused by a tumor is substantial when anaerobic energy metabolism is taken into account, and that elevated anaerobic energy metabolism in cancer may be a key contributor to cancer cachexia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".