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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".