A PLA2R-IgG4 Antibody-Based Predictive Model for Assessing Risk Stratification of Idiopathic Membranous Nephropathy

Author:

Liu Xiaobin1,Xue Jing2,Guo Xiaoyi2,Ding Yijie3ORCID,Zhang Yi4,Zhang Xiran2,Huang Yiqing2,Huang Biao5,Hu Zhigang67ORCID,Lu Guoyuan1ORCID,Wang Liang2ORCID

Affiliation:

1. Department of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China

2. Department of Nephrology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi 214023, China

3. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China

4. NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, Wuxi 214063, China

5. College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China

6. Medical Laboratory, The Affiliated Wuxi Children’s Hospital of Nanjing Medical University, Wuxi 214023, China

7. Medical Laboratory, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi 214023, China

Abstract

Background. Known as an autoimmune glomerular disease, idiopathic membranous nephropathy (IMN) is considered to be associated with phospholipase A2 receptor (PLA2R) in terms of the main pathogenesis. The quantitative detection of serum PLA2R-IgG and PLA2R-IgG4 antibodies by time-resolved fluoroimmunoassay (TRFIA) was determined, and the value of them, both in the clinical prediction of risk stratification in IMN, was observed in this study. Methods. 95 patients with IMN proved by renal biopsy were enrolled, who had tested positive for serum PLA2R antibodies by ELISA, and the quantitative detection of serum PLA2R-IgG and PLA2R-IgG4 antibodies was achieved by TRFIA. All the patients were divided into low-, medium-, and high-risk groups, respectively, which were set as dependent variables, according to proteinuria and renal function. Random forest (RF) was used to estimate the value of serum PLA2R-IgG and PLA2R-IgG4 in predicting the risk stratification of progression in IMN. Results. Out-of-bag estimates of variable importance in RF were employed to evaluate the impact of each input variable on the final classification accuracy. The variable of albumin, PLA2R-IgG, and PLA2R-IgG4 had high values (>0.3) of 0.3156, 0.3981, and 0.7682, respectively, which meant that these three were more important for the risk stratification of progression in IMN. In order to further assess the contribution of PLA2R-IgG and PLA2R-IgG4 to the model, we built four different models and found that PLA2R-IgG4 played an important role in improving the predictive ability of the model. Conclusions. In this study, we established a random forest model to evaluate the value of serum PLA2R-IgG4 antibodies in predicting risk stratification of IMN. Compared with PLA2R-IgG, PLA2R-IgG4 is a more efficient biomarker in predicting the risk of progression in IMN.

Funder

Top Talent Support Program for Young and Middle-Aged People of Wuxi Health Committee

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

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