Meta-analysis of anti-muscarinic receptor type 3 antibodies for the diagnosis of Sjögren syndrome

Chuiwen Deng; Chaojun Hu; Si Chen; Jing Li; Xiaoting Wen; Ziyan Wu; Yuan Li; Fengchun Zhang; Yongzhe Li

doi:10.1371/journal.pone.0116744

Meta-analysis of anti-muscarinic receptor type 3 antibodies for the diagnosis of Sjögren syndrome

PLoS One. 2015 Jan 28;10(1):e0116744. doi: 10.1371/journal.pone.0116744. eCollection 2015.

Authors

Chuiwen Deng¹, Chaojun Hu¹, Si Chen¹, Jing Li¹, Xiaoting Wen¹, Ziyan Wu¹, Yuan Li¹, Fengchun Zhang¹, Yongzhe Li¹

Affiliation

¹ Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China.

Abstract

Purpose: To conduct a meta-analysis to evaluate the diagnostic value of anti-muscarinic receptor type 3 (M3R) antibodies in Sjögren syndrome (SS).

Methods: Two databases, PUBMED and the Cochrane Library, were systematically searched. Approximately 2,000 participants from several studies were included in this research. STATA 11.2 software and Meta-DiSc 1.4 was used to conduct the meta-analysis.

Results: Eleven studies were included in the meta-analysis. The pooled DOR was 13.00 (95% CI, 6.00-26.00). The sensitivity was 0.43 (95% CI, 0.28-0.58) and the specificity was 0.95 (95%CI, 0.91-0.97). The LR+ and LR- were 7.90 (95% CI, 4.70-13.40), 0.61 (95% CI, 0.46-0.79), respectively. The AUC was 0.89 (95% CI, 0.86-0.92).

Conclusion: The anti-M3R antibody had high specificity but relatively low sensitivity for the diagnosis of SS.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't

MeSH terms

Autoantibodies / blood*
Humans
Receptor, Muscarinic M3 / immunology*
Sensitivity and Specificity
Sjogren's Syndrome / diagnosis*
Sjogren's Syndrome / immunology

Substances

Autoantibodies
Receptor, Muscarinic M3

Grants and funding

This work was supported by funding from the Research Special Fund for Public Welfare Industry of Health (201202004), and the National Natural Science Foundation of China Grants (81302591, 81172857, 81373188), the Chinese National High Technology Research and Development Program, Ministry of Science and Technology Grants (2011AA02A113), and the National Science Technology Pillar Program in the 12th Five-year Plan (2014BAI07B00). This funding played an important role in the study design, data collection and analysis, and decision to publish.