Urine PLA2R Antibody Detection in Hazard Stratification of PLA2R-Associated Membranous Nephropathy.

Tianyu Zheng, Yuan Qin, Xuanli Tang, Peng Bi, Xuxiang Hui, Zixuan Zhou, Yulin Fu, Huiming Sheng, Xiumei Zhou, Xueqin Zhao, Yuanyuan Du, Qiang He, Biao Huang
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Abstract

Context.—: M-type phospholipase A2 receptor (PLA2R) is the major autoantigen of membranous nephropathy (MN). As the specific antibodies of MN, the correlation between serum PLA2R antibody (sPLA2R-Ab) levels and PLA2R-associated MN (PMN) risk stratification is still controversial.

Objective.—: To apply the time-resolved fluorescence immunoassay (TRFIA) method on urine PLA2R-Ab (uPLA2R-Ab), detect, and then establish a more sensitive method of combined serum and urine PLA2R-Ab detection for PMN hazard stratification.

Design.—: A highly sensitive TRFIA method was used to detect the initial serum and urine samples of patients with PMN. Patients were grouped into remission and nonremission groups according to the outcomes after 12 months of treatment and the data were analyzed.

Results.—: The cutoff values of sPLA2R-IgG (sPLA2R-immunoglobulin G), uPLA2R-IgG, sPLA2R-IgG4, and uPLA2R-IgG4 for distinguishing between remission and nonremission groups were 50 relative units (RU)/mL, 3.51 RU/mL, 6835 ng/mL, and 143.4 ng/mL, respectively. The average value in the remission group for sPLA2R-IgG, uPLA2R-IgG, sPLA2R-IgG4, and uPLA2R-IgG4 was 37.39 RU/mL, 1.10 RU/mL, 3498.99 ng/mL, and 33.83 ng/mL, respectively. The average value in the nonremission group for sPLA2R-IgG, uPLA2R-IgG, sPLA2R-IgG4, and uPLA2R-IgG4 was 279.96 RU/mL, 45.36 RU/mL, 25762.47 ng/mL, and 1383.89 ng/mL, respectively. For sPLA2R-Ab as the primary factor, in combination with uPLA2R-Ab, the high-risk predictive value of combined detection of serum and urine PLA2R-IgG and of serum and urine PLA2R-IgG4 was upgraded from 54.55% to 100% and from 75% to 100%, respectively.

Conclusions.—: A highly sensitive TRFIA method was applied in this study; the combined detection of serum and urine PLA2R-Ab improves the efficiency of PMN risk stratification, and can provide a better assessment of PMN monitoring.

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