Abstracts of the Cancer Cachexia Conference, Boston, USA, 21–23 September 2012
Bibliographic record
Abstract
Obesity and high-fat diet (HFD) are risk factors for multiple types of cancer.Compelling evidence indicates that obesity and diet also play a role after the diagnosis of cancer, influencing treatment, tumor progression, overall well-being and survival.Previously we reported that obesity is associated with increased overall survival in lung cancer patients.Others have shown that HFD was protective in murine MAC16 cachexia.Based on those data and similar obesity risk paradox observations in other diseases, we posited that obesity or HFD might provide increased physiological reserve and slow cachexia in cancer.Here we sought to determine whether diet induced obesity (DIO) or HFD were protective in the Lewis Lung carcinoma mouse model of cancer cachexia.DIO obese and C57Bl/6J lean mice were fed a HFD (60 %kcal from fat), while another lean group was fed normal chow (LFD) (10 %kcal from fat).Mice were inoculated with tumor cells and euthanized 17 and 23 days later.Both obese and lean tumor-bearing mice fed HFD showed increased loss of total body mass, skeletal muscle and fat mass compared with tumor-bearing mice fed LFD.This increased muscle wasting in DIO and HFD mice was associated with greatly reduced plasma insulin and adiponectin levels and increased levels of proinflammatory cytokines IL-6 and LIF versus LFD tumor-bearing controls.In DIO mice, plasma growth factor/cytokine changes corresponded to decreased muscle pAKT and pFOXO3a, and increased pSTAT3 levels, while pSMAD2 and NF-kB levels were unchanged.Taken together, these changes would inhibit anabolism, promote catabolism and drive the inflammatory phenotype, thus contributing to enhanced muscle wasting.In conclusion, our data suggest that both obesity, as a pre-existing condition, and high-fat diet consumption result in worsening of muscle wasting induced by tumor.Furthermore, neither increased body mass nor HFD were protective in experimental cancer cachexia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.062 |
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".