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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2019, Vol. 13 Issue (5) : 618-625    https://doi.org/10.1007/s11684-018-0620-5
RESEARCH ARTICLE
Comparative analysis of membranous and other nephropathy subtypes and establishment of a diagnostic model
Hanyu Zhu1, Bo Fu1, Yong Wang1(), Jing Gao2, Qiuxia Han3, Wenjia Geng1, Xiaoli Yang1, Guangyan Cai1, Xiangmei Chen1, Dong Zhang1()
1. Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing 100853, China
2. Department of Clinical Biochemistry, Chinese PLA General Hospital, Beijing 100853, China
3. Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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Abstract

This study aimed to compare clinical features between membranous nephropathy (MN) and nonmembranous nephropathy (non-MN), to explore the clinically differential diagnosis of these two types, and to establish a diagnostic model of MN. After renal biopsy was obtained, 798 patients were divided into two groups based on their examination results: primary MN group (n = 248) and non-MN group (n = 550). Their data were statistically analyzed. Logistic regression analysis indicated that anti-PLA2R antibodies, IgG, and Cr were independently correlated with MN, and these three parameters were then used to establish the MN diagnostic model. A receiver operating characteristic (ROC) curve confirmed that our diagnostic model could distinguish between patients with and without MN, and their corresponding sensitivity, specificity, and AUC were 79.9%, 89.4%, and 0.917, respectively. The cutoff value for this combination in MN diagnosis was 0.34. The established diagnostic model that combined multiple factors shows a potential for broad clinical applications in differentiating primary MN from other kidney diseases and provides reliable evidence supporting the feasibility of noninvasive diagnosis of kidney diseases.

Keywords multiparameter analysis      diagnosis      model      membranous nephropathy     
Corresponding Author(s): Yong Wang,Dong Zhang   
Just Accepted Date: 24 April 2018   Online First Date: 09 August 2018    Issue Date: 14 October 2019
 Cite this article:   
Hanyu Zhu,Bo Fu,Yong Wang, et al. Comparative analysis of membranous and other nephropathy subtypes and establishment of a diagnostic model[J]. Front. Med., 2019, 13(5): 618-625.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-018-0620-5
https://academic.hep.com.cn/fmd/EN/Y2019/V13/I5/618
Index full name Abbreviation Reference range
Anti-phospholipase A2 receptor antibodies Anti-PLA2R antibodies 0−20 RU/mL
Alanine aminotransferase ALT 0−40 U/L
Aspartate aminotransferase AST 0−40 U/L
Total protein TP 55−80 g/L
Albumin ALB 35−50 g/L
Total bilirubin TB 0−21 mmol/L
Direct bilirubin DB 0−8.6 mmol/L
Alkaline phosphatase ALP 0−130 U/L
g-Glutamyltransferase GGT 0−50 U/L
Glucose GLU 3.4−6.2 mmol/L
Urea nitrogen UN 1.8−7.5 mmol/L
Creatinine Cr 30−110 mmol/L
Uric acid Ua 104−444 mmol/L
Total cholesterol CH 3.1−5.7 mmol/L
Triglyceride TG 0.4−1.7 mmol/L
Creatine kinase CK 2−200 U/L
Lactate dehydrogenase LDH 40−250 U/L
High-density lipoprotein cholesterol HDL 1−1.6 mmol/L
Low-density lipoprotein cholesterol LDL 0−3.4 mmol/L
Thrombin time TT 16.0−18.0 s
Prothrombin time PT 11.0−15.0 s
Plasma fibrinogen FIB 200−400 mg/dL
D-dimer D2 0.0−0.5 mg/L
Immunoglobulin A IgA 70−180 mg/dL
Immunoglobulin G IgG 700−1600 mg/dL
Immunoglobulin M IgM 40−230 mg/dL
Immunoglobulin E IgE 0−100 IU/mL
Complement 3 C3 90−180 mg/dL
Complement 4 C4 10−40 mg/dL
Body mass index BMI 18.5−24.99
Activated partial thromboplastin time APTT 30−45 s
Prothrombin activity PTA 70%−150%
Tab.1  Biological parameters assessed in the present study
Clinicopathologic features Number of cases (n, %) MN group (n, %) Non-MN group (n, %) c2 P value
Total 798 (100) 248 (31.08) 550 (68.92)
Age (year) 49.115 0.000
<45 464 (58.15) 99 (39.91) 365 (66.36)
≥45 334 (41.85) 149 (60.09) 185 (33.64)
Hypertension 13.537 0.000
Yes 370 (46.37) 91 (36.69) 279 (50.73)
No 428 (53.63) 157 (63.31) 271 (49.27)
Gender 1.714 0.190
Male 487 (61.03) 143 (57.66) 344 (65.55)
Female 311 (38.97) 105 (42.34) 206 (37.45)
Diabetes 1.562 0.211
Yes 115 (14.41) 30 (12.10) 85 (15.45)
No 683 (85.59) 218 (87.90) 465 (84.55)
NS 154.875 0.000
Yes 311 (38.97) 176 (70.97) 135 (24.55)
No 487 (61.03) 72 (29.03) 415 (75.45)
IgAN 164.466 0.000
Yes 280 (35.09) 7 (2.82) 273 (49.64)
No 518 (64.91) 241 (97.18) 277 (50.36)
Tab.2  Differences in clinicopathological features between MN and non-MN groups
Parameter Mean±SD
MN group non-MN group
BMI 25.89±4.69 25.22±4.23
Anti-PLA2R antibodies 97.56±183.78** 2.73±8.75
ALT (U/L) 21.05±16.64 23.69±25.27
AST (U/L) 18.53±9.06 19.13±12.13
TP (g/L) 49.36±9.94* 61.07±11.70
ALB (g/L) 26.98±6.99* 36.10±8.90
TB (mmol/L) 7.77±3.57* 8.96±4.63
DB (mmol/L) 1.49±1.15* 2.16±1.33
ALP (U/L) 58.95±16.55* 69.50±36.20
GGT (U/L) 35.42±48.55 33.97±55.55
GLU (mmol/L) 4.91±1.21 4.98±1.90
UN (mmol/L) 5.21±2.41** 6.83±3.97
Cr (mmol/L) 75.86±25.93** 115.20±75.14
Ua (mmol/L) 345.78±95.38 374.43±104.02
CH (mmol/L) 7.08±2.33 5.38±2.23
TG (mmol/L) 2.54±1.86 2.09±1.38
CK (U/L) 101.31±135.22 93.31±76.94
LDH (U/L) 194.79±58.53 179.47±59.33
HDL (mmol/L) 1.45±0.52 1.22±0.95
LDL (mmol/L) 4.86±2.02* 3.55±1.86
TT (s) 17.24±1.62 16.43±1.52
APTT 36.27±5.99 37.45±4.61
PT (s) 12.96±2.10 13.21±1.16
PTA 107.92±15.88 100.74±13.56
Serum fibrinogen 4.88±1.49 4.03±1.49
D2 (mg/L) 1.28±2.32* 0.86±1.48
IgA (mg/dL) 215.11±84.46* 276.09±123.41
IgG (mg/dL) 622.33±280.82** 935.78±380.02
IgM (mg/dL) 115.39±77.72 112.30±139.87
IgE (IU/mL) 124.77±187.59** 262.64±1071.52
C3 (mg/dL) 113.95±24.64 105.47±26.91
C4 (mg/dL) 27.99±9.68 25.91±9.40
Tab.3  Differences in biological parameters between MN and non-MN groups
Fig.1  Diagnostic values of anti-PLA2R antibodies, UN, Cr, IgG, and IgE were analyzed in 798 patients through ROC analysis. (A) The diagnostic value of anti-PLA2R antibodies; (B) the diagnostic values of UN, Cr, IgG, and IgE. The AUCs of anti-PLA2R antibodies, UN, Cr, IgG, and IgE were 0.854, 0.640, 0.730, 0.755, and 0.481, respectively.
B S.E. Wald df Sig. Exp (B) 95% CI for EXP(B)
Lower Upper
Anti-PLA2R antibodies 0.080 0.014 32.146 1 0.000 1.083 1.054 1.113
Cr -0.030 0.005 38.813 1 0.000 0.971 0.962 0.980
IgG -0.002 0.000 30.439 1 0.000 0.998 0.998 0.999
Constant 2.290 0.477 23.043 1 0.000 9.872
Tab.4  Parameters used in multivariate logistic regression analysis for diagnostic model establishment
Fig.2  Diagnostic value of the “anti-PLA2R antibodies+ IgG+ Cr” combination in MN was examined through ROC analysis. The ROC curve revealed that the “anti-PLA2R antibodies+ IgG+ Cr” combination could be used to distinguish between patients with MN and patients without MN, with an AUC value of 0.917, a cutoff value of 0.34, a sensitivity of 79.9%, and a specificity of 89.4%.
1 P Ronco, H Debiec. Pathophysiological advances in membranous nephropathy: time for a shift in patient’s care. Lancet 2015; 385(9981): 1983–1992
https://doi.org/10.1016/S0140-6736(15)60731-0 pmid: 26090644
2 LH Beck Jr, DJ Salant. Membranous nephropathy: recent travels and new roads ahead. Kidney Int 2010; 77(9): 765–770
https://doi.org/10.1038/ki.2010.34 pmid: 20182413
3 X Pan, J Xu, H Ren, W Zhang, Y Xu, P Shen, X Li, W Wang, X Chen, P Wu, X Feng, C Hao, N Chen. Changing spectrum of biopsy-proven primary glomerular diseases over the past 15 years: a single-center study in China. Contrib Nephrol 2013; 181(24): 22–30
https://doi.org/10.1159/000348638 pmid: 23689564
4 J Xie, N Chen. Primary glomerulonephritis in mainland China: an overview. Contrib Nephrol 2013; 181(43): 1–11
pmid: 23689562
5 C Ponticelli. Membranous nephropathy. J Nephrol 2007; 20(3): 268–287
pmid: 17557260
6 P Ronco, H Debiec. First identification of an antigen in autoimmune idiopathic membranous nephropathy: toward targeted therapy? Am J Kidney Dis 2010; 55(5): 820–823
https://doi.org/10.1053/j.ajkd.2009.09.017 pmid: 19926373
7 C Bazzi, R Wight. Urinary N-acetyl-β-glucosaminidase and eGFR may identify patients to be treated with immuno-suppression at diagnosis in idiopathic membranous nephropathy. Nephrology (Carlton) 2018; 23(2):175–182
https://doi.org/10.1111/nep.12952 pmid: 27764902
8 X Wu, S Wen, X Zhu, S Yuan, X Xu, D Yang, L Sun, H Liu, F Liu. Diagnostic value of renal phospholipase A2 receptor and serum anti-phospholipase A2 receptor antibody in membranous nephropathy. Med J Cent South Univ (Zhong Nan Da Xue Xue Bao Yi Xue Ban) 2017; 42(4): 395–399 (in Chinese)
pmid: 28490696
9 LH Beck Jr, RG Bonegio, G Lambeau, DM Beck, DW Powell, TD Cummins, JB Klein, DJ Salant. M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. N Engl J Med 2009; 361(1): 11–21
https://doi.org/10.1056/NEJMoa0810457 pmid: 19571279
10 LM Ortega, DR Schultz, O Lenz, V Pardo, GN Contreras. Lupus nephritis: pathologic features, epidemiology and a guide to therapeutic decisions. Lupus 2010; 19(5): 557–574
https://doi.org/10.1177/0961203309358187 pmid: 20089610
11 T Iwakura, Y Fujigaki, N Katahashi, T Sato, S Ishigaki, N Tsuji, Y Naito, S Isobe, M Ono, Y Sakao, T Tsuji, N Ohashi, A Kato, H Miyajima, H Yasuda. Membranous nephropathy with an enhanced granular expression of thrombospondin type-1 domain-containing 7A in a pregnant woman. Intern Med 2016; 55(18): 2663–2668
https://doi.org/10.2169/internalmedicine.55.6726 pmid: 27629964
12 Q Li, X Lin, Z Wu, L He, W Wang, Q Cao, J Zhang. Immuno-histochemistry analysis of Helicobacter pylori antigen in renal biopsy specimens from patients with glomerulonephritis. Saudi J Kidney Dis Transpl 2013; 24(4): 751–758
https://doi.org/10.4103/1319-2442.113871 pmid: 23816725
13 LS Li, ZH Liu. Epidemiologic data of renal diseases from a single unit in China: analysis based on 13,519 renal biopsies. Kidney Int 2004; 66(3): 920–923
https://doi.org/10.1111/j.1523-1755.2004.00837.x pmid: 15327382
14 M Knehtl, H Debiec, P Kamgang, P Callard, J Cadranel, P Ronco, JJ Boffa. A case of phospholipase A2 receptor-positive membranous nephropathy preceding sarcoid-associated granulomatous tubulointerstitial nephritis. Am J Kidney Dis 2011; 57(1): 140–143
https://doi.org/10.1053/j.ajkd.2010.09.015 pmid: 21087816
15 C Ponticelli, P Passerini. Can prognostic factors assist therapeutic decisions in idiopathic membranous nephropathy? J Nephrol 2010; 23(2): 156–163
pmid: 20213607
16 B Svobodova, E Honsova, P Ronco, V Tesar, H Debiec. Kidney biopsy is a sensitive tool for retrospective diagnosis of PLA2R-related membranous nephropathy. Nephrol Dial Transplant 2013; 28(7): 1839–1844
https://doi.org/10.1093/ndt/gfs439 pmid: 23223223
17 JM Hofstra, JF Wetzels. Phospholipase A2 receptor antibodies in membranous nephropathy: unresolved issues. J Am Soc Nephrol 2014; 25(6): 1137–1139
https://doi.org/10.1681/ASN.2014010091 pmid: 24610931
18 C Dähnrich, L Komorowski, C Probst, B Seitz-Polski, V Esnault, JF Wetzels, JM Hofstra, E Hoxha, RA Stahl, G Lambeau, W Stöcker, W Schlumberger. Development of a standardized ELISA for the determination of autoantibodies against human M-type phospholipase A2 receptor in primary membranous nephropathy. Clin Chim Acta 2013; 421(11): 213–218
https://doi.org/10.1016/j.cca.2013.03.015 pmid: 23541686
19 LF Quintana, M Blasco, M Seras, NS Pérez, M López-Hoyos, P Villarroel, E Rodrigo, O Viñas, G Ercilla, F Diekmann, JJ Gómez-Roman, G Fernandez-Fresnedo, F Oppenheimer, M Arias, JM Campistol. Antiphospholipase A2 receptor antibody levels predict the risk of posttransplantation recurrence of membranous nephropathy. Transplantation 2015; 99(8): 1709–1714
https://doi.org/10.1097/TP.0000000000000630 pmid: 25675198
20 W Na, K Yi, YS Song, MH Park. Dissecting the relationships of IgG subclasses and complements in membranous lupus nephritis and idiopathic membranous nephropathy. PLoS One 2017; 12(3): e0174501
https://doi.org/10.1371/journal.pone.0174501 pmid: 28334051
21 XL Li, TK Yan, HF Li, PC Xu, JY Jia, L Wei, WY Shang, S Lin. IgG4-related membranous nephropathy with high blood and low urine IgG4/IgG ratio: a case report and review of the literature. Clin Rheumatol 2014; 33(1): 145–148
https://doi.org/10.1007/s10067-013-2406-0 pmid: 24105363
22 AJ Branten, PW du Buf-Vereijken, IS Klasen, FH Bosch, GW Feith, DA Hollander, JF Wetzels. Urinary excretion of β2-microglobulin and IgG predict prognosis in idiopathic membranous nephropathy: a validation study. J Am Soc Nephrol 2005; 16(1): 169–174
https://doi.org/10.1681/ASN.2004040287 pmid: 15563570
23 H Suzuki, Z Moldoveanu, S Hall, R Brown, BA Julian, RJ Wyatt, M Tomana, Y Tomino, J Novak, J Mestecky. IgA nephropathy: characterization of IgG antibodies specific for galactose-deficient IgA1. Contrib Nephrol 2007; 157: 129–133
pmid: 17495450
24 QH Gu, Z Cui, J Huang, YM Zhang, Z Qu, F Wang, X Wang, SX Wang, G Liu, MH Zhao. Patients with combined membranous nephropathy and focal segmental glomerulosclerosis have comparable clinical and autoantibody profiles with primary membranous nephropathy: a retrospective observational study. Medicine (Baltimore) 2016; 95(21): e3786
https://doi.org/10.1097/MD.0000000000003786 pmid: 27227951
25 KV Lemley, SM Bagnasco, CC Nast, L Barisoni, CM Conway, SM Hewitt, PX Song. Morphometry predicts early GFR change in primary proteinuric glomerulopathies: a longitudinal cohort study using generalized estimating equations. PLoS One 2016; 11(6): e0157148
https://doi.org/10.1371/journal.pone.0157148 pmid: 27285824
26 E Wong, M Lasica, SZ He, A Bajel, AW Roberts, KD Mason, DS Ritchie, J Szer. Nephrotic syndrome as a complication of chronic graft-versus-host disease after allogeneic haemopoietic stem cell transplantation. Intern Med J 2016; 46(6): 737–741
https://doi.org/10.1111/imj.13098 pmid: 27257151
27 B Wang, H Yang, L Shen, J Wang, W Pu, Z Chen, X Shen, J Fu, Z Zhuang. Rs56288038 (C/G) in 3'UTR of IRF-1 regulated by miR-502-5p promotes gastric cancer development. Cell Physiol Biochem 2016; 40(1-2): 391–399
https://doi.org/10.1159/000452554 pmid: 27866197
28 K Togo, T Ueo, H Yonemasu, H Honda, T Ishida, H Tanabe, K Yao, A Iwashita, K Murakami. Two cases of adenocarcinoma occurring in sporadic fundic gland polyps observed by magnifying endoscopy with narrow band imaging. World J Gastroenterol 2016; 22(40): 9028–9034
https://doi.org/10.3748/wjg.v22.i40.9028 pmid: 27833394
29 N Tabata, D Sueta, T Akasaka, Y Arima, K Sakamoto, E Yamamoto, Y Izumiya, M Yamamuro, K Tsujita, S Kojima, K Kaikita, K Morita, K Oniki, J Saruwatari, K Nakagawa, S Hokimoto. Helicobacter pylori seropositivity in patients with interleukin-1 polymorphisms is significantly associated with ST-segment elevation myocardial infarction. PLoS One 2016; 11(11): e0166240
https://doi.org/10.1371/journal.pone.0166240 pmid: 27832202
30 HR Dong, YY Wang, XH Cheng, GQ Wang, LJ Sun, H Cheng, YP Chen. Retrospective study of phospholipase A2 receptor and IgG subclasses in glomerular deposits in Chinese patients with membranous nephropathy. PLoS One 2016; 11(5): e0156263
https://doi.org/10.1371/journal.pone.0156263 pmid: 27223897
31 JP Bastard, S Fellahi, FX Lescure, J Capeau, P Ronco, E Plaisier. Interest of the combined measurement of selected urinary proteins in the diagnosis approach in nephrology. Ann Biol Clin (Paris) 2017; 75(3): 327–333
pmid: 28540855
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