Systematic review and meta-analysis of the sero-epidemiological association between Epstein-Barr virus and rheumatoid arthritis
Bibliographic record
Abstract
INTRODUCTION: Infection with Epstein-Barr virus (EBV) has been suggested to contribute to the pathogenesis of autoimmune diseases, including rheumatoid arthritis (RA). We sought to determine whether prior infection with the virus occurs more frequently in patients with RA compared to controls. METHODS: We performed a systematic review and meta-analyses of studies that reported the prevalence of anti-EBV antibodies in the sera of cases with RA and controls by searching Medline and Embase databases from 1946 to 2014, with no language restriction. Mantel-Haenszel odds ratios 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-three studies were included. Quality assessment found most studies reported acceptable selection criteria but poor descriptions of how cases and controls were recruited. When all studies were included, there was a statistically significant higher seroprevalence of anti-VCA IgG in patients with RA compared to controls with an odds ratio (OR) of 1.61 (95 % confidence interval (CI) 1.05-2.46, p = 0.03), which is a similar-sized summary OR to that reported for systemic lupus erythematosus (SLE). However, when studies were restricted to those reporting more plausible levels of exposure to EBV in the control groups, no significant association was apparent, OR 1.47 (95 % CI 0.88-2.46, p = 0.14). Using anti-EBNA 1 or anti-EA IgG as markers of previous infection also did not yield significant associations (OR 1.05, 95 % CI 0.68-1.61, p = 0.82; OR 2.2, 95 % CI 0.86-5.65, p = 0.10 respectively). CONCLUSIONS: Overall, these findings do not demonstrate an association between EBV seroprevalence and RA and therefore do not support the hypothesis that prior infection with EBV predisposes to the development of RA. This contrasts with meta-analyses that indicate EBV infection is associated with multiple sclerosis and SLE.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".