Active Epstein–Barr virus infection and its association with multiple myeloma: evidence from a meta-analytical perspective
Bibliographic record
Abstract
OBJECTIVES: To clarify the association between active Epstein-Barr virus (EBV) infection and the risk of multiple myeloma (MM), given longstanding uncertainty regarding EBV's etiologic contribution to plasma cell malignancies. METHODS: A meta-analysis was conducted using eight case-control studies comprising 795 MM patients and 367 controls. Active EBV infection was defined as EBV DNA positivity or EBER detection by in situ hybridization (EBER-ISH). Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using fixed-effects and random-effects models (DerSimonian and Laird method). Subgroup analyses were performed by geographic region, detection method, and study quality. Heterogeneity, sensitivity analyses, and trial sequential analysis (TSA) were conducted to assess robustness. RESULTS: = 0%). Analyses restricted to high-quality studies (Newcastle-Ottawa Scale ≥7) yielded consistent results (OR = 2.90; 95% CI: 2.00-4.20). Sensitivity analyses and TSA supported the stability and sufficiency of the evidence. DISCUSSION: The findings provide quantitative support for a potential role of EBV in MM pathogenesis, particularly in specific populations and when assessed using sensitive histopathologic methods. Although causality cannot be inferred from case-control designs, the consistent effect sizes across subgroups and robustness analyses strengthen the plausibility of a biological link between EBV reactivation and clonal plasma cell expansion. Variations in viral detection approaches, population background, and study quality may partially explain interstudy differences. CONCLUSION: This meta-analysis demonstrates a significant association between active EBV infection and increased MM risk. These results highlight the clinical relevance of monitoring EBV activity in patients with plasma cell dyscrasias and support further mechanistic and translational research to evaluate EBV-targeted preventive or therapeutic strategies in the context of MM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".