Pre‐diagnostic immunological markers of bacterial translocation and liver cancer risk: A nested case–control analysis of 12 prospective cohorts
Bibliographic record
Abstract
The gut-liver axis may play an important role in hepatocarcinogenesis. However, limited prospective research has explored associations with liver cancer risk. We conducted a nested case-control study based in 12 prospective cohort studies from across the United States, which included 867 cases of liver cancer and 867 matched controls. We measured bacterial translocation markers, specifically immunoglobulin (Ig) A, IgG, and IgM against lipopolysaccharide and flagellin; soluble CD14 (a co-receptor for lipopolysaccharide); and lipopolysaccharide-binding protein. Multivariable conditional logistic regression was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI) between bacterial translocation marker concentrations per doubling in concentrations and liver cancer risk. Lipopolysaccharide-binding protein concentrations were most strongly associated with higher liver cancer risk (OR per doubling in concentrations: 1.48, 95% CI: 1.23-1.79). Concentrations of anti-flagellin IgA (1.13, 1.01-1.28) and IgG (1.13, 1.01-1.28), anti-lipopolysaccharide IgG (1.20, 1.01-1.42), and soluble CD14 (1.12, 1.01-1.24) were also associated with liver cancer risk. When analyses were separated into hepatocellular carcinoma (HCC, N = 436 cases) and intrahepatic cholangiocarcinoma (ICC, N = 110 cases), no evidence of heterogeneity was observed except for lipopolysaccharide-binding protein concentrations, which were positively associated with HCC (1.77, 1.34-2.33) but not ICC (0.67, 0.37-1.22; p-heterogeneity = .003). Associations did not differ by time to liver cancer diagnosis or other subgroups. These findings support the role of gut barrier dysfunction in hepatocarcinogenesis, necessitating further research to understand the complex interplay among the mechanisms and risk factors disrupting the gut barrier, microbiota, and liver cancer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".