Immunogenetic susceptibility to diabetes mellitus in patients with liver disease
Bibliographic record
Abstract
BACKGROUND/AIM: Genetic, environmental, metabolic and infectious influences, such as hepatitis C virus (HCV) infection, are thought to impact on the development of diabetes in patients with liver disease. As specific human leucocyte antigen (HLA) alleles provide the major genetic risk factors for type 1 diabetes, our aim was to investigate whether HLA class I and II alleles constitute additional risk factors for diabetes in patients with liver disease. METHODS: We evaluated two independent databases of 193 and 728 adult patients with chronic liver disease for the diagnosis of diabetes and the presence of specific HLA subtypes. RESULTS: In each database, 24 and 19% of patients met criteria for diabetes. In the first database, specific class I and II alleles were observed more frequently in diabetics compared with non-diabetics: Cw7 (50 vs. 32%, P=0.04), DR51 (17 vs. 3%P=0.003) and DQ6 (37 vs. 18%, P=0.02). In the second database, DQ6 was observed in 16% of diabetics vs. 8% of non-diabetics (P=0.04). The DR2-DR51-DQ6 haplotype was higher in patients with diabetes in both databases (22 vs.7%, P=0.02 and 12 vs. 5%, P=0.02). In a subgroup analysis of patients with HCV infection, increased frequencies of Cw7, DR2/DR51, DQ6 and DR2-DR51-DQ6 were also observed to be higher in subjects with diabetes compared with those without diabetes. CONCLUSIONS: Patients with chronic liver disease, especially those with HCV infection, have an immunogenetic risk for diabetes characterized by the presence of Cw7, DR51, DQ6 and DR2-DR51-DQ6.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".