Hepatitis B and C virus infection and risk of multiple myeloma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Multiple myeloma (MM) is a clonal proliferative disorder of plasma cells with limited curative options. Hepatitis B (HBV) and hepatitis C (HCV) viruses have been implicated in the development of various hematological malignancies, but their association with MM remains unclear. This systematic review and meta-analysis aimed to investigate the risk of MM in individuals with HBV and HCV infections. METHODS: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, Embase, and additional sources for cohort and case-control studies published between January 1990 and January 2025. The relative risk (RR) of developing MM in individuals with HBV and HCV infections was pooled using a random-effects model. Subgroup analyses were performed based on age, geographic region, and diagnostic method. The Newcastle-Ottawa Scale (NOS) was used to assess study quality. Statistical heterogeneity was evaluated using the I² statistic, and publication bias was assessed using Egger's test. RESULTS: Seventeen studies, comprising 1 cohort and 16 case-control studies, were included. Nine studies examined the association between HBV and MM, yielding a pooled RR of 1.25 (95% CI: 0.99-1.58) with moderate heterogeneity (I² = 56.52%). Fifteen studies evaluated the association between HCV and MM, with a pooled RR of 1.84 (95% CI: 1.27-2.67), indicating a higher risk in HCV-infected individuals. Subgroup analysis revealed a stronger association in European populations for both HBV (RR: 1.67, 95% CI: 1.05-2.66) and HCV (RR: 2.27, 95% CI: 1.21-4.25). No significant publication bias was detected for either HBV or HCV analyses. CONCLUSION: HBV and HCV infections are associated with an increased risk of developing multiple myeloma, with HCV demonstrating a stronger association. These findings highlight the importance of screening and monitoring patients with chronic hepatitis for potential hematological malignancies, especially in high-risk regions.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".