Causal Relationships Between Gut Microbiota, Immune Cell and Pancreatic Cancer: A Two-Step, Two-Sample Mendelian Randomization Study
Bibliographic record
Abstract
Background: Gut microbiota (GM) is associated with both the occurrence and development of pancreatic cancer (PC), and immune cells potentially play a role in this process. This study sought to evaluate the causative effect of GM on PC and to ascertain possible immune cell mediators. Methods: The study primarily employed a two-step, two-sample Mendelian randomization (MR) analysis to explore the causal relationship between GM and PC within the European population, placing particular emphasis on the application of the inverse variance weighted (IVW) approach. Additionally, mediation analysis was conducted to explore the potential influence of immune cells as mediators. Results: The MR analysis revealed a significant association between Geminocystis and the risk of PC. Increased abundance of Geminocystis was positively associated with the risk of PC (odds ratio (OR): 2.580, 95% confidence interval (CI): 1.050 - 6.342). The validity of the outcomes was also verified by the sensitivity analysis. The mediation MR analysis showed that the B-cell absolute count served as a partial intermediary in the causal link between Geminocystis and the risk of PC, contributing to 15.321% of the mediating impact. Conclusion: This MR study demonstrated that Geminocystis has a causal relationship with PC and potentially mediates B-cell absolute count in the TBNK panel.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".