Is there a genetic cause of appetite loss?—an explorative study in 1,853 cancer patients
Bibliographic record
Abstract
BACKGROUND: Appetite loss has a major impact on cancer patients. It is exceedingly prevalent, is a prognostic indicator and is associated with inferior quality of life. Cachexia is a multi-factorial syndrome defined by a negative protein and energy balance, driven by a variable combination of reduced food intake and abnormal metabolism. Not all cancer patients that experience weight loss have appetite loss, and the pathophysiology between cachexia and appetite loss may thus be different. Knowledge of pathophysiology of appetite loss in cancer patients is still limited. The primary object of this study was to explore the association with 93 predefined candidate single-nucleotide polymorphisms (SNPs) and appetite loss in cancer patients to possibly generate new theories of the pathophysiology of the condition. METHODS: A total of 1,853 cancer patients were phenotyped according to appetite loss and then genotyped. RESULTS: After allowing for multiple testing, there was no statistically significant association between any of the SNPs analysed and appetite loss. The ten most significant SNPs in the co-dominant model had observed odds ratios varying from 0.72 to 1.28. CONCLUSIONS: This large exploratory study could not find any associations with loss of appetite and 93 SNPs with a potential to be involved in appetite loss in cancer patients. This does not however rule out genes putative role in the development of the symptom, but the observed odds ratios are close to one which makes it unlikely that any of the individual SNPs explored in the present study have great importance.
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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) | 0.000 | 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 | 0.000 |
| 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".