Hepatitis B virus infection status and risk of type 2 diabetes mellitus: A 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 118 530 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 of diabetes, compared with non-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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".