Systematic review and meta-analysis of the sero-epidemiological association between Epstein-Barr virus and systemic lupus erythematosus
Bibliographic record
Abstract
INTRODUCTION: Infection with Epstein-Barr virus (EBV) has been suggested to contribute to the pathogenesis of systemic lupus erythematosus (SLE). We sought to determine whether prior infection with the virus occurs more frequently in patients with SLE compared to matched controls. METHODS: We performed a systematic review and meta-analyses of studies that reported the prevalence of anti-EBV antibodies in the sera from cases of SLE and controls by searching Medline and Embase databases from 1966 to 2012, with no language restriction. Mantel-Haenszel odds ratios (OR) for the detection of anti-EBV antibodies were calculated, and meta-analyses conducted. Quality assessments were performed using a modified version of the Newcastle-Ottawa scale. RESULTS: Twenty-five case-control studies were included. Quality assessment found most studies reported acceptable selection criteria but poor description of how cases and controls were recruited. There was a statistically significant higher seroprevalence of anti-viral capsid antigen (VCA) IgG (OR 2.08; 95% confidence interval (CI) 1.15 - 3.76, p = 0.007) but not anti-EBV-nuclear antigen1 (EBNA1) (OR 1.45; 95% CI 0.7 to 2.98, p = 0.32) in cases compared to controls. The meta-analyses for anti-early antigen (EA) /D IgG and anti-VCA IgA also showed significantly high ORs (4.5; 95% CI 3.00 to 11.06, p < 0.00001 and 5.05 (95% CI 1.95 - 13.13), p = 0.0009 respectively). However, funnel plot examination suggested publication bias. CONCLUSIONS: Overall, our findings support the hypothesis that infection with EBV predisposes to the development of SLE. However, publication bias cannot be excluded and the methodological conduct of studies could be improved, with regard to recruitment, matching and reporting of blinded laboratory analyses.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".