Lifestyle Factors and Development and Natural Course of Gastroenteropancreatic Neuroendocrine Tumors: A Review of the Literature
Bibliographic record
Abstract
INTRODUCTION: The rarity of neuroendocrine tumors (NETs) and their heterogeneous presentation complicate the identification of risk factors for their development and natural course. Several tumor-specific prognostic factors have been identified, but less attention has been given to lifestyle factors as risk and prognostic factors. This review aimed to identify studies on smoking, alcohol use, physical activity, diet, body mass index (BMI), and diabetes and their association with the development and course of gastroenteropancreatic (GEP-) NETs. METHODS: The literature was systematically searched for articles on lifestyle factors and NETs available via PubMed and Embase. Study quality was assessed using the Newcastle-Ottawa scale. RESULTS: A total of 25 eligible studies out of 3,021 screened articles were included. Most studies reported on smoking and alcohol, reporting conflicting results. Diet seems to have an influence on NET development, but few studies were published. Articles reporting on BMI were not unanimous on the effect on GEP-NETs. Diabetes was reported as a risk factor for NETs, while a protective effect was observed with metformin use. CONCLUSION: Different tissues, i.e., the pancreas and small intestine, may respond differently to exposure to alcohol and smoking. Evidence for diet so far is too limited to draw conclusions. Diabetes seems to be an important risk factor for the development of pancreatic NETs with a protective role in disease progression, while BMI is not unequivocally associated with the development and prognosis of NETs. Hence, our findings suggest that lifestyle factors play an important role in NET development as a disease course. Future research should consider lifestyle as an influence on disease progression and treatment response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".