Supplementary Material for: Causal Relationships between Gut Microbiotas, Blood Metabolites, and Neuroendocrine Tumors: A Mediated Mendelian Randomization Study
Bibliographic record
Abstract
Introduction Neuroendocrine tumors (NETs) are a heterogeneous group of epithelial tumors originating from different anatomical sites, identifying the gut microbiota and metabolic mechanisms involved in the onset of NETs may help to develop appropriate disease prevention and monitoring strategies. Methods We employed a mediated two-sample Mendelian Randomization (MR) approach, analyzing gut microbiota from German studies and NET datasets from the 10th round of the FinnGen project. Mediation analyses were conducted using the metabolites dataset from the Canadian Longitudinal Study of Aging (CLSA) and the TwinsUK study. Instrumental variables (IVs) chosen according to established MR criteria and analyzed using the Wald ratio, inverse-variance weighted (IVW), MR-Egger, and weighted median methods. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out (LOO) methods. Results Causal relationships were identified between the genetic determinants of 6, 5, 2, 1, 2, 3 gut microbiotas and the risk of colorectal, lung, pancreatic, rectum, small intestine and stomach NETs. Similarly, the genetic determinants of 4, 6, 1, 5, 10 and 7 metabolites were found to be causally related to the risk of colorectal, lung, pancreatic, rectum, small intestine and stomach NETs, respectively. Through Wald ratio and IVW methods, we preliminarily identified 957 microbiota-metabolite pairs with significant causal associations, and formed 13 mediated relationships between the impact of gut microbiotas on NETs. Conclusion Our study suggests that gut microbiotas and its derived metabolites may contribute to the onset of NET, offering a novel insight into the disease's pathogenesis.
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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 | 0.005 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.700 | 0.060 |
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".