Hepatitis B virus infection status and risk of type 2 diabetes mellitus: a network meta-analysis
Bibliographic record
Abstract
Aim: Whether hepatitis B virus (HBV) infection increases the risk of type 2 diabetes mellitus (T2DM) is controversial. We carried out a meta-analysis to evaluate the association between HBV infection status and the risk of T2DM. Methods: The PubMed, Embase and Ovid databases were searched for relevant studies on an association between HBV infection and the risk of diabetes. Methodological quality was assessed using the adapted Newcastle–Ottawa Quality Assessment Scale. A fix- or random-effects model was used to summarize odd ratios (OR) and 95% confidence intervals (CI). Results: We included seven cohort studies, four case–control and four cross-sectional studies, involving 118530 participants. The prevalence of T2DM differs by HBV infection status. Pooled estimators indicated a nearly twofold excess T2DM risk with hepatitis B cirrhosis (HBC) status. The summary OR of the risk of T2DM for HBC patients was 1.99 (95% CI, 1.08–3.65) when compared with the non-HBV individuals, and 1.74 (95% CI, 1.43–2.13) when compared with non-cirrhotic chronic hepatitis B (NC-CHB) patients. In contrast, no significant correlation was found between asymptomatic HBV carriers or NC-CHB patients and the incidence ofdiabetes, comparedwithnon-HBV controls. Conclusion: Our findings suggest that while HBV itself may not be pro-diabetic, the HBV-derived cirrhosis is an independent risk factor for T2DM.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".