Background
The antineutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV) comprise three distinct diseases: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA) [
1]. AAV are characterized by pauci-immune necrotizing inflammation of small to medium-sized vessels and affect multiple organs. ANCA have been shown to play a potential role in the pathogenesis of vasculitis [
2]. However, use of ANCA for monitoring disease activity is insufficiently sensitive or specific [
3‐
7]. Traditional acute-phase indicators, including C-reactive protein (CRP), also lack the sensitivity and specificity for monitoring of AAV disease activity. Therefore, additional markers are needed as a guide for management and for distinguishing active disease from remission. Moreover, biomarkers that reflect organ damage are also necessary because AAV affects multiple organs. Several circulating biomarkers allowing comparison of active disease with remission in AAV have been reported in large-cohort studies and systematic literature searches [
8‐
14]. However, these biomarkers are not yet recognized as clinically useful for monitoring of disease activity or predicting prognosis.
Targeted proteomics involves large-scale protein quantification using selected reaction monitoring (SRM; also known as multiple reaction monitoring), and its application for clinical biomarker discovery has been explored in recent years [
15‐
24]. SRM is a quantitative mass spectrometry (MS) technique for selective detection of targeted molecules in a complex analytical sample, and it has become a broadly acceptable approach for protein quantification without the use of antibodies [
15,
17,
18]. The multiple target selectivity of SRM is particularly useful for parallel monitoring of different marker proteins, enabling highly sensitive quantitation of proteins in a crude serum sample containing the proteins of interest at subfemtomolar levels. The targeted proteomics approach consists of three steps: (1) selection of novel biomarker candidates based on experimental and/or bioinformatics information, (2) quantitative evaluation of candidate biomarkers using SRM, and (3) verification of selected candidates using an antibody-based quantitative technology such as an enzyme-linked immunosorbent assay (ELISA). This facilitates high-throughput identification of reliable biomarker candidates from limited-availability biological samples.
To analyze the characteristics of Japanese patients with AAV, the Research Committee for Intractable Vasculitis Syndrome and the Research Committee for Intractable Renal Disease of the Ministry of Health, Labour and Welfare of Japan collaboratively implemented a nationwide prospective cohort study of remission induction therapy in Japanese patients with antineutrophil cytoplasmic antibody-associated vasculitis and rapidly progressive glomerulonephritis (RemIT-JAV-RPGN) [
25]. In contrast to studies [
8,
9,
13] related to the RAVE trial, which have indicated that GPA with positive proteinase-3 (PR3) is predominant in patients with AAV from the United States and The Netherlands, Japanese patients with AAV appear to have a higher incidence of MPA than of GPA, and they show a predominance of myeloperoxidase (MPO)-ANCA. In the present study, we employed targeted proteomics to identify novel circulating protein biomarkers of disease activity and severity, as well as organ damage, in AAV using serum samples obtained from a large-cohort Japanese study (RemIT-JAV-RPGN).
Methods
Healthy donors and patients
Serum samples from patients with AAV were obtained at our hospital (
n = 17) and from a cohort study (RemIT-JAV-RPGN) (
n = 152) [
25]. All cases of AAV fulfilled the criteria for primary systemic vasculitis proposed by the European Medicines Agency (EMEA) algorithm [
26]. From among 321 patients who had been enrolled initially in the RemIT-JAV-RPGN study, serum samples for use in the present study were collected from 152 patients with active disease (before treatment) and 64 patients at 6 months after the start of treatment. On the basis of the EMEA algorithm, 169 of these patients were diagnosed as follows: 105 with MPA, 36 with GPA, 25 with EGPA, and 3 with unclassifiable disease. Paired serum samples before and 6 months after the start of treatment were obtained from 79 patients: 42 with MPA, 20 with GPA, 14 with EGPA, and 3 with unclassifiable disease.
Serum samples were also obtained from 30 healthy donors, 30 patients with active rheumatoid arthritis (RA), 21 patients with active systemic lupus erythematosus (SLE), and 25 patients with bacterial infectious diseases treated at our hospital. RA was diagnosed on the basis of the 2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) criteria for RA [
27]. The criterion for the active phase of RA in this study was considered to be a Simplified Disease Activity Index ≥ 11 [
28]. Patients with SLE fulfilled the ACR revised criteria for SLE [
29]. The active phase of SLE was defined as a Systemic Lupus Erythematosus Disease Activity Index score ≥ 4, based on clinical findings in the 2 weeks prior to sample collection [
30]. The 25 patients with infectious diseases included 15 with bacterial pneumonia, 4 with urinary infection, 5 with acute cholecystitis, and 1 with enterocolitis. Information on the characteristics of the healthy donors and patients is given (Table
1, Additional file
1: Table S1). Serum samples were frozen at −80 °C until use.
Table 1
Baseline characteristics of the 169 patients with antineutrophil cytoplasmic antibody-associated vasculitis
Male/female, n/n
| 65/104 | 45/60 | 14/22 | 6/19 |
Age, years | 71 (61–78) | 73 (66–78) | 69 (60–79) | 60 (46–70) |
MPO-ANCA-positive | 137 (81.1%) | 103 (98.1%) | 22 (61.1%) | 9 (36%) |
PR3-ANCA-positive | 20 (12.2%) | 4 (4.0%) | 15 (41.7%) | 1 (4.2%) |
ANCA-negative | 18 (10.7%) | 1 (1.0%) | 1 (2.8%) | 16 (64%) |
WBC, count/μl | 9400 (7000–14,300) | 8500 (6200–12,300) | 9950 (7825–14,050) | 19,200 (15,090–27,700) |
Serum creatinine, mg/dl | 0.92 (0.66–2.43) | 1.41 (0.81–3.56) | 0.71 (0.61–1.24) | 0.59 (0.44–0.76) |
eGFR, ml/minute/1.73 m2
| 50 (17–74) | 34 (12–61) | 65 (41–75) | 81 (68–107) |
Disease activity and organ involvement |
BVAS score | 16 (12–21) | 15 (12–19) | 19 (12–24) | 17 (14–20) |
BVAS chest positive | 75 (44.4%) | 43 (41%) | 17 (47.2%) | 15 (60%) |
BVAS renal positive | 120 (71%) | 92 (87.6%) | 23 (63.9%) | 4 (16%) |
Disease severity |
EUVAS disease severity, L/ES/Ge/Se, n
| 5/37/96/31 | 2/27/52/24 | 3/7/23/3 | 0/1/20/14 |
RPGN clinical grading, I/II/III/IV, n
| 40/86/38/5 | 18/54/28/5 | 8/21/7/0 | 14/9/2/0 |
Five-Factor Score, ≤ 1/2/≥ 3, n
| 67/57/45 | 18/43/44 | 29/7/0 | 20/4/1 |
Outcomes at 6 months |
Clinical remission (BVAS score = 0) | 121/157 (77.1%) | 71/95 (74.7%) | 28/35 (80%) | 19/24 (79.2%) |
ESRD | 15/157 (9.6%) | 14/95 (14.7%) | 1/35 (2.9%) | 0/24 (0.0%) |
Outcome measures
Details of the RemIT-JAV-RPGN study protocol were reported previously [
25]. Disease activity was evaluated according to the Birmingham Vasculitis Activity Score version 3 (BVAS) system [
31]. Remission was defined as complete absence of disease activity attributable to active vasculitis. Absence of disease activity was determined systematically using a BVAS of 0 on two occasions at least 1 month apart according to the EULAR recommendations [
32]. Organ involvement was evaluated in accordance with the BVAS system. Renal damage was defined as a renal BVAS score ≥ 1. End-stage renal disease (ESRD) was defined as dependence on dialysis or an irreversible increase in the serum creatinine level to > 5.6 mg/dl. Lung involvement was defined as a chest BVAS score ≥ 1.
Disease severity
The disease severity of the patients enrolled in the RemIT-JAV-RPGN study was classified as localized, early systemic, generalized, or severe in accordance with the definition of the European Vasculitis Study Group (EUVAS) [
32]. Patients with threatened vital organ function were classified as having generalized disease, and patients with organ failure were classified as having severe disease. The definitions of disease severity have been described in detail previously [
25].
The patients were also classified into four groups according to the Japanese rapidly progressive glomerulonephritis (RPGN) clinical grading [
33], which considers the following parameters: serum creatinine level (<3 mg/dl = 0 points, 3–6 mg/dl = 1 point, > 6 mg/dl = 2 points, and dialysis-dependent = 3 points), age (≤ 59 years = 0, 60–69 years = 1, and ≥ 70 years = 2), lung involvement (negative = 0 and positive = 2), and serum CRP level (< 2.6 mg/dl = 0, 2.6–10.0 mg/dl = 1, and > 10 mg/dl = 2). Lung involvement was defined as the presence of chest symptoms in BVAS or interstitial lung disease (ILD). In the present study, ILD was diagnosed by site investigators using chest radiography and/or thoracic computed tomography. The point totals for these four parameters were summed, and patients were divided into four groups as follows: grade I, 0–2 points; grade II, 3–5 points; grade III, 6–7 points; and grade IV, 8–9 points.
For calculation of the Five-Factor Score (FFS) 2009 [
34], renal insufficiency was defined as a serum creatinine level > 1.7 mg/dl; cardiac insufficiency as the presence of cardiac symptoms in BVAS; gastrointestinal involvement as the presence of abdominal symptoms in BVAS; and ear, nose, and throat (ENT) involvement as the presence of ENT symptoms in BVAS. Age > 65 years, cardiac insufficiency, gastrointestinal involvement, and renal insufficiency were each accorded +1 point, and absence of ENT manifestations was also accorded +1 point. The patients were divided into three groups according to the summed point totals for these five parameters: ≤ 1, 2, or ≥ 3.
Protein digestion
Protein digestion for SRM analysis was performed as described previously [
35]. Fourteen major serum proteins were depleted using a Multiple Affinity Removal Spin Cartridge Human-14 (Agilent Technologies, Santa Clara, CA, USA). For sample preparation prior to liquid chromatography-tandem mass spectrometry (LC-MS/MS), serum proteins (5 μg of total protein) separated on NuPAGE 4–12% gel (Life Technologies, Carlsbad, CA, USA) were digested with sequencing grade trypsin (Promega, Madison, WI, USA) in 100 mM ammonium bicarbonate (pH 8.8) overnight at 37 °C. Digested peptides were desalted using a self-made C18 STop And Go Extraction tip and eluted with 40 μl of 0.1% (vol/vol) trifluoroacetic acid (TFA)/80% (vol/vol) acetonitrile. Eluates were dried by vacuum centrifugation and reconstituted with 10 μl of 0.1% (vol/vol) TFA for MS analysis.
Mass spectrometry
MS/MS and SRM analyses were carried out on a QTRAP 5500 mass spectrometer equipped with a nanoelectrospray ionization source (SCIEX, Framingham, MA, USA) as described previously [
21,
24]. Chromatographic separation of the digested peptides was performed using an Eksigent NanoLC system (SCIEX). The mobile phases consisted of 0.1% (vol/vol) formic acid in H
2O as solvent A and 0.1% (vol/vol) formic acid/80% (vol/vol) acetonitrile as solvent B. For MS/MS analysis, peptide samples were injected onto a 200-μm inner diameter (i.d.) × 0.5-mm cHiPLC trap column (SCIEX). Concentrated peptides were then separated on a 75-μm i.d. × 15-cm C18 reversed-phase cHiPLC column (SCIEX) at a flow rate of 300 nl/minute using the following gradient schedule: 0–60 minutes, 2–18% B; 60–95 minutes, 18–40% B; 95–100 minutes, 40–90% B; holding at 90% B for 5 minutes, and re-equilibration at 2% B for 15 minutes. MS/MS spectra were searched against the UniProt human proteome database using ProteinPilot software version 4.1 (SCIEX) with the following parameters: cysteine alkylation, acrylamide; digestion, trypsin; processing parameters, biological modification; and search effort, through ID.
LC-SRM assays were developed using [
13C
6,
15N
2]lysine-labeled or [
13C
6,
15N
4]arginine-labeled standard peptides (Sigma-Aldrich, St. Louis, MO, USA) as described previously [
21,
24]. For SRM analysis using the LC-MS system, targeted peptides were separated at a flow rate of 300 nl/minute employing the following gradient schedule: 0–60 minutes, 2–18% B; 60–95 minutes, 18–40% B; 95–100 minutes, 40–90% B; holding at 90% B for 5 minutes, and re-equilibration at 2% B for 15 minutes.
Quantitative analysis of the obtained SRM data was performed using Skyline software [
36]. Quantification of relative protein abundance across different serum samples is based on the peak area ratios of the light (endogenous) and heavy (
13C/
15N-labeled internal standard) forms of each peptide.
ELISA
Analysis of samples was performed using commercially available ELISA kits in accordance with the manufacturer’s instructions. We measured heterodimer S100A8/A9 because S100A8 and S100A9 form a heterodimer. The following ELISA kits were used: CRP, CD93, matrix metalloproteinase 9 (MMP9), S100A8/A9, and tissue inhibitor of metalloproteinase 1 (TIMP1) (R&D Systems, Minneapolis, MN, USA); leucine-rich alpha-2-glycoprotein 1 (LRG1) and tenascin C (TNC) (Immuno-Biological Laboratories, Gunma, Japan); transketolase (TKT) (LifeSpan BioSciences, Seattle, WA, USA); and MPO-ANCA (MBL, Nagoya, Japan).
Statistical analysis
Values are expressed as medians and IQRs or as numbers and percentages. Continuous nonparametric variables were compared using the Mann-Whitney U test. Distinction of active AAV from remission in the same patients with AAV was compared using the Wilcoxon signed-rank test. Analysis of covariance was employed for comparison analysis when adjustment was necessary for age, sex, and four distinct AAV groups (MPA, GPA, EGPA, and unclassifiable disease) to calculate the adjusted means for each biomarker level. When using analysis of covariance, each biomarker level was logarithmically transformed to attain a normal distribution and then compared using Student’s t test and Dunnett’s test. For easier reading, biomarker levels presented in the tables and figures were not transformed. ROC curves were constructed for each marker using logistic regression to further assess the ability of the biomarkers and to define the optimal cutoff point. The AUC was calculated, and positive likelihood ratios (LRs) (sensitivity/[1 − specificity]) were determined at the optimal cutoff points. Correlations between paired data were analyzed using Spearman’s rank correlation. Differences at p < 0.05 were considered to be statistically significant. When comparing 44 biomarker candidates in protocol 1 or 15 in protocol 2 identified by SRM assay between active AAV and remission, statistical significance was determined by < 0.05/44, or < 0.05/15 by Bonferroni correction to adjust for multiple testing. Statistical analyses were performed using JMP version 9 software (SAS Institute, Cary, NC, USA).
Discussion
Targeted protein quantification using SRM has emerged as a promising new methodology for clinical biomarker discovery [
15‐
24]. Although reliable SRM assay information on the human serum proteome still has limited public availability, multiple-target SRM quantification without the need for time-consuming antibody development is particularly useful for marker verification. In the present study, we selected 52 proteins as candidate markers for targeted proteomics on the basis of an experimental dataset derived from serum proteomic analysis. Moreover, to maximize the chance of discovering novel marker proteins, 87 vascular endothelium-related proteins were selected as targets on the basis of database search results. A total of 135 proteins were selected as potential marker candidates and subjected to targeted proteomic analysis using SRM. Of these proteins, 74 were successfully quantified. Ultimately, nine proteins—TNC, CRP, TIMP1, LRG1, CD93, S100A8, S100A9, MMP9, and TKT—were identified as candidate biomarkers of disease activity in the SRM assay.
TIMP1 is an endogenous inhibitor of MMPs and an important regulator of extracellular matrix turnover, tissue remodeling, and cellular behavior [
41,
42]. TIMP1 is expressed by a variety of cell types in most human tissues. The level of circulating TIMP1 was reportedly elevated in patients with myocardial infarction, sepsis, and various cancers (reviewed in [
41‐
44]). TIMP1 has been also described as elevated in patients with active AAV relative to those in remission or healthy control subjects [
9,
45]. Moreover, Monach et al. reported that TIMP1 allows discrimination between mild disease and remission at 6 months, although the AUC was limited at 0.68 [
9]. In this study, we showed that TIMP1 was able to distinguish between patients with mildly active AAV and those in remission, whereas CRP and MPO-ANCA were unable to do so. Serum levels of TIMP1 in patients with AAV were significantly higher than those in patients with infectious diseases. In contrast, serum levels of CRP were lower in the former than in the latter. The serum levels of LRG1, MMP9, S100A8/A9, CD93, and TNC were less sensitive than that of TIMP1 for evaluation of disease activity. Our present findings and those of Monach et al. [
9] suggest that TIMP1 would be superior to CRP and MPO-ANCA as a biomarker for monitoring the disease activity of AAV. Moreover, because the TIMP1 cutoff point for remission was < 144 ng/ml with a specificity of 94%, this may represent the target level for achieving complete remission.
In the present study, we found two novel markers that reflected renal damage in AAV: TKT and CD93. For prediction of ESRD at 6 months, the cutoff level of TKT was > 229 ng/ml with a sensitivity of 87% and a specificity of 93%, whereas that of CD93 was > 356 ng/ml with a sensitivity of 73% and a specificity of 86%. Moreover, the levels of TKT and CD93 were associated with disease severity as defined by EUVAS, FFS 2009, and RPGN clinical grading. Therefore, high serum levels of TKT and CD93 are able to predict poor overall and ESRD-free survival in patients with AAV. In addition, TKT seems to be a better biomarker than CD93 for reflecting renal involvement because the level of TKT was less elevated than that of CD93 in patients with infectious diseases. TKT is a thiamine diphosphate-dependent enzyme that catalyzes several key reactions in the nonoxidative branch of the pentose phosphate pathway in the cytoplasm [
46‐
48]. TKT is found in all mammalian tissues, and it is highly expressed in cornea, erythrocytes, kidney, and liver [
47,
49]. Because TKT is not secreted from cells, its release is a result of cell damage. Therefore, the serum level of TKT in active AAV may be associated with the degree of tissue damage. In addition, the elevated level of circulating TKT in patients with AAV with renal involvement may be due to its high expression in kidney.
CD93 is a type I transmembrane glycoprotein that is upregulated on activated neutrophils, monocytes, and vascular endothelial cells in response to inflammatory mediators such as lipopolysaccharide and tumor necrosis factor-α, and it is shed in soluble form [
50,
51]. CD93 is reportedly associated with the risk of coronary artery disease [
52,
53]. In the kidney, CD93 is expressed predominantly on infiltrating neutrophils and monocytes, as well as on interstitial and glomerular capillary endothelium [
54]. The expression pattern of CD93 can explain the elevation of serum CD93 levels in our study and suggests its involvement in renal inflammation in active AAV.
It has been reported that S100A8/A9 is highly expressed within crescents and in areas of endocapillary proliferation in the kidneys of patients with AAV [
55]. However, in the present study, there was no significant difference in the level of circulating S100A8/A9 in patients with or without renal involvement. S100A8/A9 is highly expressed by neutrophils, monocytes, activated macrophages, and microvascular endothelial cells, and it acts as a critical alarmin modulating the inflammatory response after its release from these cells [
56]. Therefore, it is suggested that the serum S100A8/A9 level reflects the degree of inflammation more strongly than the degree of renal damage.
The extracellular matrix molecule TNC is highly expressed during embryonic development and tissue repair [
57]. TNC expression has been particularly well documented in inflammatory lung conditions such as ILD, bronchial asthma, and tuberculosis [
58,
59]. In this study, we found that the serum TNC level reflected the lung infiltration in AAV and was associated with disease severity in terms of the RPGN clinical grading.
Several serum biomarkers that can distinguish between active and inactive AAV and reflect the degree of renal involvement have been reported in large-cohort studies [
8‐
13]. Monach et al. tested 28 markers of inflammation, angiogenesis, and tissue damage and repair in patients enrolled in the RAVE trial, before and 6 months after the start of treatment to distinguish active disease from remission [
9]. They identified three promising biomarkers—MMP3, TIMP1, and CXCL13—that best discriminated these two conditions. Pepper et al. showed that the level of S100A8/A9 predicted relapse in patients with PR3-ANCA treated with rituximab in the same trial [
13]. Gou et al. reported that plasma levels of C3a, C5a, soluble C5b-9, and Bb were increased in patients with active AAV relative to those in remission [
10]. Among serum biomarkers of renal involvement in AAV, the levels of MMP3 and thrombomodulin have been reported to be higher in patients with active renal disease than in those without [
8]. Villacorta et al. showed that the baseline serum C3 level had prognostic value for predicting long-term renal and global survival in patients with active renal disease [
12]. Brix et al. reported that CCL18 could serve as a biomarker of disease activity and renal relapse in ANCA-associated crescentic glomerulonephritis [
11]. Although complement components and several low-content proteins such as cytokines and chemokines (present at the picograms per milliliter level in serum) have been selected as potential candidates in other studies, we eliminated them in the present study through sample pretreatment and the sensitivity limit of the SRM assay. Therefore, it may be possible to evaluate more accurately disease activity and severity, as well as organ damage, in patients with AAV by comparison among the biomarkers identified in the present and previous studies or by creating panels with such biomarkers.
Conclusions
Targeted proteomics has emerged as a promising new methodology for clinical biomarker discovery. In the present study, we identified promising biomarkers of disease activity and severity, as well as organ involvement, in AAV with a targeted proteomics approach using serum samples collected in a large-cohort Japanese study (RemIT-JAV-RPGN study). Nine proteins—CRP, TIMP1, LRG1, TNC, S100A8/A9, MMP9, CD93, TKT, and MPO-ANCA—were identified as biomarkers of disease activity. Of these, TIMP1 was the best-performing biomarker of disease activity. Moreover, we identified TKT and CD93 as biomarkers for the evaluation of renal involvement and kidney outcome, as well as TNC as a biomarker reflecting lung infiltration in AAV. AAV severity was associated with the levels of TKT, CD93, and TNC (markers reflecting organ involvement) rather than with inflammatory markers. It is expected that these biomarkers will be clinically useful for therapeutic decision-making, monitoring of disease activity, and predicting prognosis.
Acknowledgements
We appreciate Dr. Keiko Tanaka (Department of Epidemiology and Preventive Medicine, Ehime University Graduate School of Medicine) for help with statistical analysis and Dr. Seiichi Matsuo (Department of Nephrology, Nagoya University Hospital) for support with the experimental design.
Members
of the Research Committee of Intractable Vasculitis Syndrome and the Research Committee of Intractable Renal Disease of the Ministry of Health, Labour and Welfare of Japan
: In addition to the authors, the following investigators and institutions participated in this study: Department of Rheumatology, Shimane University Faculty of Medicine (Yohko Murakawa); Center for Nephrology and Urology, Division of Nephrology and Dialysis, Kitano Hospital, Tazuke Kofukai Medical Research Institute (Eri Muso); Department of Hematology, Oncology, Nephrology, and Rheumatology, Akita University School of Medicine (Atsushi Komatsuda); Niigata Rheumatic Center (Satoshi Ito); Department of the Control for Rheumatic Diseases, Graduate School of Medicine, Kyoto University (Takao Fujii); Department of Immunology and Rheumatology, Clinical Neuroscience and Neurology, Endocrinology and Metabolism, Nagasaki University Graduate School of Biomedical Sciences (Atsushi Kawakami); Department of Nephrology, Iwate Prefectural Central Hospital (Izaya Nakaya); Division of Nephrology and Rheumatology, Department of Internal Medicine, Fukuoka University School of Medicine (Takao Saito); Shimane University, Faculty of Medicine, Division of Nephrology (Takafumi Ito); Department of Hemodialysis and Apheresis, Yokohama City University Medical Center (Nobuhito Hirawa); Center for Rheumatology, Okayama Saiseikai General Hospital (Masahiro Yamamura); Department of Medical Technology, School of Health Sciences, Faculty of Medicine, Niigata University (Masaaki Nakano); Department of Medicine, Kidney Center, Tokyo Women’s Medical University (Kosaku Nitta); Division of Nephrology and Hypertension, Kashiwa Hospital, Jikei University (Makoto Ogura); Department of Nephrology, Nagoya City University Graduate School of Medical Sciences (Taio Naniwa); Division of Rheumatology and Allergology, Department of Internal Medicine, St. Marianna University School of Medicine (Shoichi Ozaki); Department of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo (Junichi Hirahashi); Third Department of Internal Medicine, Division of Immunology and Rheumatology, Hamamatsu University School of Medicine, Hamamatsu (Noriyoshi Ogawa); Division of Kidney and Hypertension, Department of Internal Medicine, Jikei University School of Medicine (Tatsuo Hosoya); Division of Nephrology, Department of Laboratory Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Faculty of Medicine, Kanazawa University (Takashi Wada); Division of Nephrology, Department of Internal Medicine, Juntendo University Faculty of Medicine (Satoshi Horikoshi); Institute of Rheumatology, Tokyo Women’s Medical University (Yasushi Kawaguchi); Division of Clinical Immunology, Graduate School of Comprehensive Human Sciences, University of Tsukuba (Taichi Hayashi); Department of Nephrology, Tokyo Medical University Hachioji Medical Center (Masaharu Yoshida); Department of Nephrology, Hypertension, Diabetology, Endocrinology and Metabolic, Fukushima Medical University (Tsuyoshi Watanabe); Department of Nephrology, Japanese Red Cross Nagoya Daini Hospital (Daijo Inaguma); Department of Integrated Therapy for Chronic Kidney Disease, Kyushu University (Kazuhiko Tsuruya); Niigata Prefectural Shibata Hospital (Noriyuki Homma); Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine (Tsutomu Takeuchi); Cardiovascular Respiratory and Neurology Division, Department of Internal Medicine, Asahikawa Medical University (Naoki Nakagawa); Kurobe City Hospital (Shinichi Takeda); Fukuoka Higashi Medical Center (Ritsuko Katabuchi); Division of Nephrology, Department of Medicine, Faculty of Medical Sciences, University of Fukui (Masayuki Iwano); Division of Rheumatology, Endocrinology and Nephrology at the Graduate School of Medicine, Hokkaido University (Tatsuya Atsumi); Department of Hemovascular Medicine and Artificial Organs, Faculty of Medicine, Miyazaki University (Shoichi Fujimoto); Division of Rheumatology and Nephrology, Department of Internal Medicine, Aichi Medical School of Medicine (Shogo Banno); Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology (Takahiko Sugihara); Department of Nephrology, Tokyo Medical University Ibaraki Medical Center (Masaki Kobayashi); Department of Nephrology, Faculty of Medicine, University of Tsukuba (Kunihiro Yamagata); Department of Respiratory Medicine, Toho University Omori Medical Center (Sakae Homma); Division of Endocrinology and Metabolism, Hematology, Rheumatology and Respiratory Medicine, Department of Internal Medicine, Faculty of Medicine, Kagawa University (Hiroaki Dobashi); Department of Nephrology, Nagoya University Hospital (Naotake Tsuboi); Faculty of Health Sciences, Hokkaido University (Akihiro Ishizu); Department of Chronic Kidney Disease and Peritoneal Dialysis, Okayama University Graduate School of Medicine (Hitoshi Sugiyama).