The Effect of Age and Sex on Chemotherapy-Induced Cachexia and Branched-Chain Amino Acid Metabolism
Bibliographic record
Abstract
Cachexia remains one of the most complex and challenging aspects in cancer care. The impact of cachexia on body wasting, particularly on the depletion of muscle mass, significantly impairs patient’s energy, strength and quality of life. Tumour-related factors and poor nutritional status are associated with cachexia development. Additionally, while effective in combatting cancer, chemotherapy causes appetite loss, fatigue, altered metabolism and decreased physical activity, all contributing to muscle tissue breakdown and cachexia. Decades of research into investigating interventions to mitigate the effects of cachexia have been unsuccessful. Even the use of nutritional supplements like the branched-chain amino acids (BCAA) in reversing cachexia remains limited and inconsistent. However, researchers typically prioritize tumour BCAA metabolism and overlook these amino acids in the skeletal muscle. In addition, studies investigating chemotherapy-induced cachexia frequently utilize young male animals and the effects of chemotherapy on the abundance and activity of enzymes involved in BCAA metabolism have not extensively been compared across the sexes, nor studied in aged animal tissues. Understanding the alterations in skeletal muscle BCAA metabolism and availability following chemotherapy, especially across different sex and age groups, may help provide a better understanding of why certain interventions are ineffective in treating cachexia and may present findings that more accurately represent clinical diversity. Therefore, the objective of this dissertation is to examine the effect of age and sex on the mechanisms and severity of chemotherapy-induced cachexia, with a focus on BCAA availability, metabolism and transporter expression in young and aged animals. \n\nBriefly, I showed that preventing the degradation of LAT1, a transporter protein crucial for BCAA uptake into skeletal muscle, counteracted the effects of chemotherapy-induced damage on myotubes. Further, in-vivo, I showed sex-dependent differences in the susceptibility of young and aged animals to chemotherapy-induced cachexia. I also show that altered BCAA availability and metabolism following chemotherapy may contribute to muscle wasting in a sex and age-dependent manner. Findings from this dissertation suggest that interventions regulating muscle amino acid transporters may represent a promising strategy to treat cachexia. These findings underscore the need for age- and sex-specific responses when developing interventions that can manage cachexia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".