Exercise Improves Cancer-free Survival and Health Span in a Model of Radiation-induced Cancer
Bibliographic record
Abstract
INTRODUCTION: Radiation therapy increases the risk of secondary malignancy and morbidity in cancer survivors. The role of obesity and exercise training in modulating this risk is not well understood. As such, we used a preclinical model of radiation-induced malignancy to investigate whether diet-induced obesity and/or endurance exercise training altered lifelong survival, cancer incidence, and morbidity. METHODS: Male CBA mice were randomly divided into control diet/sedentary group (CTRL/SED), high-fat diet (45% fat)/sedentary group (HFD/SED), control diet/exercise group (2-3 d·wk-1; CTRL/EX), or high-fat diet/exercise group (HFD/EX) groups then exposed to whole-body radiation (3 Gy). End point monitoring and pathology determined mortality and cancer incidence, respectively. Health span index, a measure of morbidity, was determined by a composite measure of 10 anthropometric, metabolic, performance, and behavioral measures. RESULTS: Overall survival was higher in HFD/SED compared with CTRL/SED (P < 0.05). The risk of cancer-related mortality by 18 months postradiation was 1.99 and 1.63 in HFD/SED compared with CTRL/EX (RR = 1.99, 95% confidence interval = 1.20-3.31, P = 0.0081) and CTRL/SED (RR = 1.63, 95% confidence interval = 1.06-2.49, P = 0.0250), respectively. The number of mice at end point with cancer was higher in HFD/SED compared with CTRL/EX and CTRL/SED (P < 0.05). Health span index was highest in CTRL/EX (score = +2.5), followed by HFD/EX (score = +1), and HFD/SED (score = -1) relative to CTRL/SED. CONCLUSION: This work provides the basis for future preclinical studies investigating the dose-response relationship between exercise training and late effects of radiation therapy as well as the mechanisms responsible for these effects.
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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) | 0.000 | 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) | 0.002 | 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".