Clinical implication of metabolic syndrome in nonobese patients with antineutrophil cytoplasmic antibody-associated vasculitis

Clinical implication of metabolic syndrome in nonobese patients with antineutrophil cytoplasmic antibody-associated vasculitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Clinical implication of metabolic syndrome in nonobese patients with antineutrophil cytoplasmic antibody-associated vasculitis Soo Bin [email protected] , Hyeok Chan Kwon, Jung Yoon Pyo, Mi Il Kang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-28821/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: We investigated the prevalence of metabolic syndrome (MetS) in all or nonobese patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) and compared it with age- and gender-matched controls. Also, we assessed the effect of variables at diagnosis on the risk of cardiovascular disease (CVD) in all or nonobese AAV patients. Methods: In this study, 173 AAV patients and 344 controls were included and MetS was defined by the National Cholesterol Education Program Adults Treatment Panel III criteria. The obesity based on BMI was defined as BMI ≥ 25 kg/m 2 . The follow-up duration was defined as the period from diagnosis to the last visit or to each poor outcome occurrence. Results: The median age of AAV patients was 58.7 years and 57 patients were men. The prevalence of MetS was 50.9% in all AAV patients and 46.5% in nonobese AAV patients, which were significantly higher than 37.8% in all controls and 28.2% in nonobese controls. In the Kaplan-Meier survival analysis, Mets at diagnosis significantly reduced the cumulative CVD-free survival rate in both all and nonobese AAV patients. In the multivariable Cox hazards model analysis, CVD during follow-up was significantly associated with both BVAS (HR 1.159) and MetS at diagnosis (HR 9.036) in nonobese AAV patients. Conclusions: The prevalence of MetS at diagnosis in all or nonobese AAV patients was significantly higher than those in all or nonobese controls. Furthermore, both BVAS and MetS at diagnosis increased the risk of CVD in nonobese AAV patients. Endocrinology & Metabolism Rheumatology Metabolic syndrome antineutrophil cytoplasmic antibody-associated vasculitis cardiovascular disease prevalence risk Figures Figure 1 Figure 2 Introduction The concept of metabolic syndrome (MetS) is defined by the constellation of metabolic abnormalities that confer to the increased risk of cardiovascular disease (CVD), type 2 diabetes mellitus and all-cause morbidity and mortality [ 1 , 2 ]. So far, several definitions of MetS, such as the World Health Organization (WHO) definition and the European Group for the Study of Insulin Resistance definition, have been proposed [ 3 , 4 ]. The National Cholesterol Education Program Adults Treatment Panel III criteria for MetS (the 2005 NCEP-ATP-III criteria) is currently used for the classification of MetS: insulin resistance, obesity (waist circumference), hyperlipidaemia, glucose intolerance, and hypertension have been recognized and accepted as the fundamental mechanisms [ 5 , 6 ]. Among these mechanisms, insulin resistance is the most important contributor and it is associated with vascular thrombosis- and inflammation-related factors, such as lipoprotein dysregulation, prothrombotic changes, low-grade inflammatory conditions and vascular dysfunction [ 1 ]. In particular, MetS has been proved to be associated with proinflammatory cytokines including tumour necrosis factor (TNF)-α, interleukin (IL)-1 and IL-6, which can often participate in and accelerate the process of atherosclerosis and thrombosis [ 7 ]. Therefore, the primary concern regarding the systemic complication of MetS is the risk of CVD [ 2 ]. Recently, the interlink between the metabolic and immune systems has been a global emerging interest. The role of the immune system in maintaining metabolic homeostasis has been implicated through many studies and is now acknowledged that the disturbance in the immune-metabolic interaction may result in abnormal metabolic states, culminating in metabolic diseases such as MetS [ 8 ]. So far, there have been several studies investigating the association of MetS with autoimmune rheumatic diseases and the increased prevalence of MetS in patients with autoimmune rheumatic diseases such as systemic lupus erythematosus, rheumatoid arthritis and vasculitis have been reported. Especially in patients with systemic vasculitis, the prevalence of MetS was high, and consequentially to CVD as well [ 9 ]. Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of systemic necrotizing vasculitis which primarily affects the small-sized vessels and occasionally the medium-sized ones and consists of three subtypes such as microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA) and eosinophilic granulomatosis with polyangiitis (EGPA) [ 10 , 11 ]. Since chronic low-grade inflammation of AAV may provoke insulin resistance and disturb metabolic homeostasis, leading to MetS, it can be theoretically assumed that the prevalence of Mets can be gradually increased in AAV patients. Based on this assumption, a previous study conducted in the UK, which compared the prevalence of Mets between AAV patients and controls, reported a higher proportion of MetS in AAV patients compared to controls [ 12 ]. MetS may have an influence on the poor outcomes of AAV: the high risk of CVD has been reported in AAV, which, in turn, has led to the revision of the European League Against Rheumatism (EULAR) guideline for periodic CVD risk evaluation in AAV patients [ 13 – 15 ]. In addition, the effect of MetS on the risk of CVD might be clearer in nonobese people [ 16 ]. However, to our knowledge, there was no study sufficiently investigating whether Mets at diagnosis could increase the risk of CVD during follow-up in AAV patients. Hence, in this study, we investigated and compared the prevalence of Mets at diagnosis between AAV patients and age- and gender-matched controls, and furthermore between nonobese AAV patients and controls who had body mass index (BMI) < 25 kg/m 2 [ 17 ]. Also, we investigated whether MetS at diagnosis could increase the risk of CVD and other poor outcomes of AAV during follow-up in both all AAV patients and nonobese AAV patients. Patients And Methods Patients We included 173 patients with AAV, who were reclassified as AAV based on the 2007 European Medicines Agency algorithm for AAV and polyarteritis nodosa and the 2012 revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides and we reviewed their medical records. All patients were initially diagnosed as AAV at the Division of Rheumatology, the Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, from October 2000 to March 2019. They all had well-documented medical records with which clinical and laboratory data that reviewed ANCA positivity and both Birmingham vasculitis activity score (BVAS) version 3 and five-factor score (FFS) were calculated at diagnosis [ 18 , 19 ]. AAV patients, who had serious medical conditions mimicking AAV or enabling ANCA false-positivity, such as chronic liver diseases, coexisting malignancies, serious infections, and drugs at the time of diagnosis, were excluded from this study. For controls, the medical records of general people, who had consecutively visited Severance Executive Healthcare Clinic in Severance Hospital, a university-affiliated tertiary care hospital, for a comprehensive medical health check-up, were also reviewed [ 20 ]. And, 344 age- and gender-matched people without any serious medical condition were included in this study as controls. The cross-sectional diseases at diagnosis or the poor outcomes of AAV during follow-up were identified by the 10th revised International Classification Diseases (ICD-10) and drugs being administered were confirmed using the Korean Drug Utilization Review (DUR) system. This study was approved by the Institutional Review Board (IRB) of Severance Hospital ( 4-2017-0673 for AAV patients and 4-2018-0856 for controls ), who waived the need for the written informed consent, as this was a retrospective study. The 2005 NCEP-ATP-III criteria The 2005 NCEP-ATP-III criteria consist of five components: i) central obesity based on waist circumference for Asian countries (men ≥ 90 Cm and women ≥ 80 Cm); ii) hypertension (blood pressure ≥ 130/85 mmHg); iii) hypertriglyceridemia (triglyceride ≥ 150 mg/dL); iv) low high-density lipoprotein (HDL)-cholesterol (men < 40 mg/dL and women < 50 mg/dL) and v) Impaired glucose tolerance (fasting glucose ≥ 100 mg/dL) or type 2 diabetes mellitus [ 6 , 12 , 21 ]. Variables at diagnosis and during follow-up In terms of the variables at diagnosis, age, male gender and BMI were obtained. In the general Korean population, obesity based on BMI was defined as BMI ≥ 25 kg/m 2 [ 17 ], and thus, in this study, nonobese patients and controls were defined as those having BMI < 25 kg/m 2 . AAV subtypes, ANCA positivity and both BVAS and FFS were reviewed. We reviewed the results for ANCA by both an indirect immunofluorescence assay (perinuclear (P)-ANCA and cytoplasmic (C)-ANCA) and antigen-specific assays for ANCA (myeloperoxidase (MPO)-ANCA and proteinase 3 (PR3)-ANCA). In patients who tested positive in the indirect fluorescence assay, but negative in antigen-specific assays, P-ANCA positivity was considered as MPO-ANCA positivity and C-ANCA positivity as PR3-ANCA positivity [ 22 , 23 ]. Comorbidities at diagnosis, such as chronic kidney disease, diabetes mellitus, hypertension, dyslipidaemia and interstitial lung disease, were collected. The results of routine laboratory tests at diagnosis were also evaluated. In terms of the variables during follow-up, the poor outcomes of AAV were defined as all-cause mortality, relapse, end-stage renal disease (ESRD), cerebrovascular accident (CVA) and CVD. The follow-up duration was defined as the period between the date of the diagnosis of AAV and the date of the last visit for survived patients. For deceased patients, the follow-up duration based on all-cause mortality was defined as the period between the initial diagnosis of AAV and the time of death. For patients who had any poor outcomes, the follow-up duration based on each poor outcome was defined as the period starting from the diagnosis of AAV until each poor outcome appeared. Also, administered medications were assessed. Statistical analyses All statistical analyses were conducted using the SPSS software (version 23 for Windows; IBM Corp., Armonk, NY, USA). Continuous variables were expressed as a median (interquartile range), and categorical variables were expressed as number and the percentage. Significant differences in categorical variables between the two groups were analysed using the Chi-square and Fisher’s exact tests. Significant differences in continuous variables between the two groups were compared using the Mann-Whitney test. The odds ratio (OR) was assessed using the multivariable logistic regression analysis of variables with p-values less than 0.05 in the comparative analysis. The optimal cut-off for BMI in predicting MetS at diagnosis was extrapolated by calculating the receiver operator characteristic (ROC) curve and selecting the maximised sum of the sensitivity and specificity. The relative risk (RR) was analysed using contingency tables and the chi-square test. Each cumulative poor outcome-free survival rate was analysed using the Kaplan-Meier survival analysis. The multivariable Cox hazard model using variables with p-values less than 0.05 in the univariable Cox hazard model was conducted to appropriately obtain the hazard ratios (HRs) during the considerable follow-up duration. P-values less than 0.05 were considered as statistically significant. Results Characteristics of AAV patients In regard to variable at diagnosis, the median age of AAV patients was 58.7 years and 57 patients were men. Ninety-seven patients were classified as MPA, 42 patients were classified as GPA and 34 patients were classified as EGPA. MPO-ANCA (or P-ANCA) was detected in 115 patients and ANCA was negative in 35 patients. The most common comorbidity was hypertension (46.2%), followed by chronic kidney disease (stage 3–5) (29.5%) and dyslipidaemia (28.3%). In regard to variables during the follow-up period, 14 patients died of any cause. The most frequently occurred poor outcome of AAV was relapse (32.4%), followed by ESRD (19.1%). Twelve of 173 patients (6.9%) had experienced CVD after the diagnosis of AAV. One hundred sixty-two patients had received glucocorticoid (93.6%) during follow-up. The most common immunosuppressive drug administered was cyclophosphamide (49.7%), followed by azathioprine (48.0%) (Table 1 ). Table 1 Characteristics of AAV patients with at diagnosis and during follow-up (N = 173) AAV patients Values At the time of diagnosis Demographic data Age 58.7 (20.3) Male gender 57 (32.9) AAV Subtypes (N, (%)) MPA 97 (56.1) GPA 42 (24.3) EGPA 34 (19.7) ANCA positivity (N, (%)) MPO-ANCA (or P-ANCA) positivity 115 (66.5) PR3-ANCA (or C-ANCA) positivity 28 (16.2) Both ANCA positivity 6 (3.5) ANCA negativity 35 (20.2) AAV-specific indices BVAS 12.0 (12.0) FFS 1.0 (2.0) Comorbidities at diagnosis (N, (%)) Chronic kidney disease (stage 3–5) 51 (29.5) Diabetes mellitus 47 (27.2) Hypertension 80 (46.2) Dyslipidemia 49 (28.3) Interstitial lung disease 36 (20.8) Diffuse alveolar hemorrhage 7 (4.0) During the follow-up period Poor outcomes during follow-up (N, (%)) All-cause mortality 14 (8.1) Relapse 56 (32.4) ESRD 33 (19.1) CVA 12 (6.9) CVD 12 (6.9) Medications administered during follow-up (N, (%)) Glucocorticoid 162 (93.6) Cyclophosphamide 86 (49.7) Rituximab 26 (15.0) Azathioprine 83 (48.0) Mycophenolate mofetil 22 (12.7) Tacrolimus 10 (5.8) Methotrexate 17 (9.8) Values are expressed as a median (interquartile range, IQR) or N (%). AAV: antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis; MPA: microscopic polyangiitis; GPA: granulomatosis with polyangiitis; EGPA: eosinophilic granulomatosis with polyangiitis; MPO: myeloperoxidase; P: perinuclear; PR3: proteinase 3; C: cytoplasmic; BVAS: Birmingham vasculitis activity score; FFS: five-factor score; ESRD: end-stage renal disease; CVA: cerebrovascular accident; CVD: cardiovascular disease. Comparison of MetS-related variables between 173 AAV patients and 344 controls There were no significant differences in the demographic data, in particular, the number of ex-smokers between AAV patients and controls; however, AAV patients exhibited the lower median BMI than controls (22.2 vs. 23.4 kg/m 2 ). Inversely, the number of AAV patients, who fulfilled the 2005 NCEP-ATP-III criteria, was significantly higher than that of controls who satisfied the same criteria (50.9% vs. 37.8%). Among the five components of the 2005 NCEP-ATP-III criteria, AAV patients exhibited central obesity (53.2% vs. 71.2%) less frequently than controls. Meanwhile, they showed hypertriglyceridemia (49.7% vs. 23.5%) and impaired glucose tolerance (60.1% vs. 24.4%) more often than controls. In regard to MetS-related laboratory results, AAV patients exhibited the higher median harmful cholesterols than controls (Table 2 ). Table 2 Comparison of MetS-related variables between AAV patients and controls Variables Total (N = 517) AAV patients (N = 173) Controls (N = 344) P-value Demographic data Age (year old) 58.0 (21.1) 58.7 (20.3) 58.0 (21.8) 0.720 Male gender (N, (%)) 166 (32.1) 57 (32.9) 109 (31.7) 0.772 BMI (kg/m 2 ) 23.2 (3.5) 22.2 (4.4) 23.4 (3.5) < 0.001 Ex-smoker (N, (%)) 40 (7.7) 14 (8.1) 26 (7.6) 0.830 Fulfillment of NCEP-ATP III 2005 criteria for MetS (N, (%)) 218 (42.2) 88 (50.9) 130 (37.8) 0.004 2005 NCEP-ATP-III criteria for MetS (N, (%)) Waist circumference (male) 91.6 (9.0) 90.3 (10.6) 92.0 (8.0) 0.039 Waist circumference (female) 82.3 (9.5) 81.0 (10.9) 83.1 (8.4) < 0.001 Central obesity based on waist circumference 337 (65.2) 92 (53.2) 245 (71.2) < 0.001 Hypertension 215 (41.6) 80 (46.2) 135 (39.2) 0.208 Hypertriglyceridemia 167 (32.3) 86 (49.7) 81 (23.5) < 0.001 Low HDL cholesterol 211 (40.8) 62 (35.8) 149 (43.3) 0.103 Impaired fasting glucose or type 2 diabetes mellitus 188 (36.4) 104 (60.1) 84 (24.4) < 0.001 Laboratory data Total cholesterol (mg/dL) 183 (57) 174 (63) 187 (52) 0.017 Triglyceride (mg/dL) 103 (75) 117 (73) 100 (73) 0.002 HDL cholesterol (mg/dL) 49 (22) 48 (25) 49 (20) 0.257 LDL cholesterol (mg/dL) 100 (50) 92 (47) 104 (48) < 0.001 Fasting glucose (mg/dL) 96 (17) 102 (36) 94 (14) < 0.001 Creatinine (mg/dL) 0.8 (0.3) 0.9 (1.2) 0.7 (0.3) < 0.001 Values are expressed as a median (interquartile range, IQR) or N (%). MetS: metabolic syndrome; AAV: antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis; BMI: body mass index; NCEP-ATP-III: national cholesterol education program-adult treatment panel III; HDL: high-density lipoprotein; LDL: low-density lipoprotein; BUN: blood urea nitrogen; eGFR: estimated glomerular filtration rate. Comparison of MetS-related variables between 144 nonobese AAV patients and 255 nonobese controls To minimise the effect of obesity based on BMI on the prevalence of MetS, we compared the prevalence of MetS in nonobese AAV patients and controls who had BMI < 25 kg/m 2 . Sixty-seven of 144 AAV patients (46.5%) exhibited the cross-sectional MetS, whereas only 72 of 255 controls (28.2%) had it (RR 2.212, P < 0.001). Whereas, among obese AAV patients and controls who had BMI ≥ 25 kg/m 2 , the two groups showed the similar prevalence of MetS (Fig. 1 ). Comparison of variables at diagnosis between 88 AAV patients with Mets and 85 AAV patients without Mets AAV patients were divided into the two groups based on MetS at diagnosis: 88 patients were assigned to the groups of AAV patients with MetS and they showed higher frequency in all five components of the 2005 NCEP-ATP-III criteria than those without MetS. At the time of diagnosis, AAV patients with MetS were older and more obese than those without MetS: however, no difference in gender and ex-smoker between the two groups was found. In regard to ANCA positivity and AAV-specific inflammatory indices, AAV patients with Mets showed a significantly higher frequency of MPO-ANCA (or P-ANCA) positivity and the higher cross-sectional BVAS and FFS than those without MetS. Among comorbidities, the proportions of chronic kidney disease, diabetes mellitus, hypertension and dyslipidaemia were significantly increased in AAV patients with MetS compared to those without MetS. Among laboratory results, AAV patients with MetS exhibited higher levels in all the variables than those without MetS except for haemoglobin and serum albumin which showed an opposite tendency (Table 3 ). Table 3 Comparison of variables at diagnosis and during follow-up between AAV patients with MetS and those without AAV patients AAV patients with MetS (N = 88) AAV patients without MetS (N = 85) P-value At the time of diagnosis 2005 NCEP-ATP-III criteria for MetS Waist circumference (male) 91.8 (10.6) 87.5 (10.7) 0.026 Waist circumference (female) 84.0 (11.7) 76.9 (8.5) < 0.001 Waist circumference (male ≥ 90 cm, female ≥ 80 cm) 61 (69.3) 31 (36.5) 130/85 mmHg or medication) 60 (68.2) 20 (23.5) 150 mg/dL) 67 (76.1) 19 (22.4) < 0.001 HDL cholesterol (male < 40 mg/dL, female < 50 mg/dL) 44 (50.0) 18 (21.1) 100 mg/dL or medication) 70 (79.5) 34 (40.0) < 0.001 Demographic data Age (year old) 61.4 (14.0) 53.3 (26.3) 0.002 Male gender (N, (%)) 30 (34.1) 27 (31.8) 0.745 BMI (kg/m 2 ) 23.3 (3.3) 21.1 (3.6) < 0.001 Ex-smoker 9 (10.2) 5 (8.6) 0.405 AAV subtypes (N, (%)) MPA 47 (53.4) 50 (58.8) 0.473 GPA 23 (26.1) 19 (22.3) 0.562 EGPA 18 (20.5) 16 (18.8) 0.787 ANCA positivity (N, (%)) MPO-ANCA (or P-ANCA) positivity 66 (75.0) 49 (57.6) 0.016 PR3-ANCA (or C-ANCA) positivity 12 (13.6) 16 (18.8) 0.354 Both ANCA positivity 1 (1.1) 5 (5.9) 0.113 ANCA negativity 11 (12.5) 24 (28.2) 0.252 AAV-specific indices BVAS 14.0 (12.0) 11.0 (10.0) 0.018 FFS 1.0 (1.0) 1.0 (2.0) 0.010 Comorbidities at diagnosis (N, (%)) Chronic kidney disease (stage 3–5) 34 (38.6) 17 (20.0) 0.007 Diabetes mellitus 39 (44.3) 8 (9.4) < 0.001 Hypertension 60 (68.2) 20 (23.5) < 0.001 Dyslipidemia 42 (47.7) 7 (8.2) < 0.001 Interstitial lung disease 20 (22.7) 16 (18.8) 0.527 Laboratory results White blood cell count (/mm 3 ) 9,230.0 (6,570.0) 7,280.0 (5,345.0) 0.080 Hemoglobin (g/dL) 10.7 (3.9) 12.1 (2.9) 0.004 Platelet count (x1,000/mm 3 ) 317.5 (197.0) 247. (133.0) 0.015 Fasting glucose (mg/dL) 110 (46) 95 (25) < 0.001 Creatinine (mg/dL) 1.1 (2.2) 0.8 (0.5) 0.006 Serum albumin (g/dL) 3.5 (1.1) 3.8 (0.9) 0.005 ESR (mm/hr) 64.0 (70.0) 44.5 (62.0) 0.001 CRP (mg/L) 15.9 (92.2) 2.3 (13.8) < 0.001 During the follow-up period Follow-up duration (months) 32.0 (59.5) 36.2 (64.8) 0.347 Poor outcomes All-cause mortality (N, (%)) 8 (9.1) 6 (7.1) 0.624 Follow-up duration for death (months) 32.0 (59.5) 36.2 (64.8) 0.359 Relapse (N, (%)) 31 (35.2) 25 (29.4) 0.414 Follow-up duration for relapse (months) 20.1 (41.9) 17.8 (37.8) 0.524 ESRD (N, (%)) 22 (25.0) 11 (12.9) 0.044 Follow-up duration for ESRD (months) 20.2 (55.5) 23.6 (62.9) 0.616 CVA (N, (%)) 8 (9.1) 4 (4.7) 0.371 Follow-up duration for CVA (months) 28.0 (62.9) 31.5 (59.5) 0.522 CVD (N, (%)) 11 (12.5) 1 (1.2) 0.003 Follow-up duration for CVD (months) 31.1 (60.4) 36.2 (64.8) 0.935 Medication administered (N, (%)) Glucocorticoid 83 (94.3) 79 (92.9) 0.711 Cyclophosphamide 45 (51.1) 41 (48.2) 0.703 Rituximab 15 (17.0) 11 (12.9) 0.450 Azathioprine 45 (51.1) 38 (44.7) 0.397 Mycophenolate mofetil 11 (12.5) 11 (12.9) 0.931 Tacrolimus 3 (3.4) 7 (8.2) 0.206 Methotrexate 5 (5.7) 12 (14.1) 0.062 Values are expressed as a median (interquartile range, IQR) or N (%). AAV: antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis; MetS: metabolic syndrome; NCEP-ATP-III: national cholesterol education program-adult treatment panel III; HDL: high-density lipoprotein; BMI: body mass index; MPA: microscopic polyangiitis; GPA: granulomatosis with polyangiitis; EGPA: eosinophilic granulomatosis with polyangiitis; MPO: myeloperoxidase; P: perinuclear; PR3: proteinase 3; C: cytoplasmic; BVAS: Birmingham vasculitis activity score; FFS: five-factor score; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; ESRD: end-stage renal disease; CVA: cerebrovascular accident; CVD: cardiovascular disease. Multivariable logistic regression analysis of variables at diagnosis for the cross-sectional MetS in AAV patients We categorised variables at diagnosis with statistical significance in the comparison analysis into the two groups: the conventional risk factors for MetS and AAV-specific inflammatory indices as shown in Supplementary Table 1 . To determine the independent predictor of the cross-sectional MetS at diagnosis, we conducted the multivariable logistic regression analysis and found that BMI (OR 1.481), diabetes mellitus (OR 7.629), hypertension (OR 16.054) and dyslipidaemia (OR 8.800) were significantly associated with the cross-sectional MetS. Whereas, none of the AAV-specific inflammatory indices was associated with the cross-sectional MetS. Comparison of the effect of MetS at diagnosis on the risk of the outcomes of AAV during follow-up We simply compared the frequencies of the poor outcomes of AAV between the two groups and found that ESRD and CVD occurred in AAV patients with MetS more frequently than those without MetS during follow-up (Table 3 ). We also compared the cumulative risk of each poor-outcome during the follow-up period based on each poor outcome occurrence between the two groups using the Kaplan-Meier survival analysis. AAV patients with MetS exhibited the lower cumulative CVD-free survival rate than those without MetS during the follow-up period based on CVD occurrence (Fig. 2 ). Furthermore, we assessed the effect of MetS at diagnosis on the risk of CVD by dividing AAV patients into two categories based on BMI of 25 kg/m 2 . Among nonobese AAV patients, MetS at diagnosis significantly reduced the cumulative CVD-free survival rate. However, among obese AAV patients, there was no significant difference in the cumulative CVD-free survival rate between AAV patients with and without MetS ( F IG . 2 ). Hazard ratio of variables at diagnosis for the risk of CVD during follow-up in 173 AAV patients Firstly, in cases of all AAV patients, in the univariable Cox hazards model analysis, male gender, BVAS, FFS, diabetes mellitus, dyslipidaemia, fasting glucose level and MetS at diagnosis were significantly associated with CVD occurrence during follow-up. In the multivariable Cox hazards model analysis of variables with significance in the univariable analysis, only male gender (HR 6.006, 95% confidence interval (CI) 1.486, 24.283) was significantly associated with CVD occurrence during follow-up. With the same assumption above, among male gender, BVAS, FFS and MetS at diagnosis, both male gender (HR 4.625, 95% CI 1.258, 16.996) and MetS at diagnosis (HR 9.864, 95% CI 1.136, 85.679) were significantly associated with CVD during follow-up ( Supplementary Table 2 ). Hazard ratio of variables at diagnosis for the risk of CVD during follow-up in 144 nonobese AAV patients In cases of nonobese AAV patients, in the univariable Cox hazards model analysis, BVAS, dyslipidaemia, haemoglobin, fasting glucose and MetS at diagnosis were significantly associated with CVD during follow-up. In the multivariable analysis, only BVAS at diagnosis (HR 1.157, 95CI 1.038, 1.289) was significantly associated with CVD during follow-up. Given that diabetes, dyslipidaemia and fasting glucose are closely related to components of the 2005 NCEP-ATP-III criteria, they could be deleted in the multivariable analysis in order to clarify the effect of variables at diagnosis on the risk of CVD. Thus, only BVAS, haemoglobin, and MetS at diagnosis were included in the multivariable analysis, in which, both BVAS (HR 1.159, 95% CI 1.039, 1.293) and MetS at diagnosis (HR 9.036, 95% CI 1.011, 80.786) were significantly associated with CVD during follow-up (Table 4 ). Table 4 Univariable and multivariable Cox hazards model analyses of variables at diagnosis for CVD occurrence during follow-up in AAV patients with normal BMI (BMI < 25 kg/m2) (N = 145) Variables Univariable Multivariable * Multivariable ** HR 95% CI P value HR 95% CI P value HR 95% CI P value Demographic data Age 1.007 0.962, 1.055 0.757 Male gender 3.543 0.864, 14.537 0.079 BMI 1.263 0.908, 1.758 0.165 ANCA positivity MPO-ANCA (or P-ANCA) positivity 1.680 0.387, 7.292 0.489 PR3-ANCA (or C-ANCA) positivity 0.565 0.069, 4.600 0.565 ANCA positivity 1.781 0.306, 10.362 0.521 AAV-specific indices BVAS 1.167 1.063, 1.281 0.001 1.157 1.038, 1.289 0.008 1.159 1.039, 1.293 0.008 FFS 1.697 0.977, 2.947 0.061 Comorbidities (N, (%)) Chronic kidney disease (stage 3–5) 0.719 0.148, 3.485 0.682 Diabetes mellitus 3.314 0.828, 13.271 0.091 Hypertension 3.432 0.70, 16.834 0.129 Dyslipidaemia 5.869 1.456, 23.659 0.013 2.045 0.415, 10.069 0.379 Interstitial lung disease 0.489 0.061, 3.927 0.501 Laboratory results White blood cell count (/mm 3 ) 1.000 1.000, 1.000 0.191 Haemoglobin (g/dL) 0.697 0.487, 0.997 0.048 0.881 0.635, 1.223 0.449 0.922 0.657, 1.295 0.640 Platelet count (× 1,000/mm 3 ) 1.001 0.997, 1.005 0.717 Fasting glucose (mg/dL) 1.014 1.003, 1.026 0.011 1.013 1.000, 1.027 0.057 Creatinine (mg/dL) 1.234 0.957, 1.593 0.106 Serum albumin (g/dL) 0.453 0.178, 1.150 0.096 ESR (mm/hr) 1.010 0.993, 1.026 0.244 CRP (mg/L) 1.007 0.998, 1.015 0.114 Total cholesterol (mg/dL) 1.001 0.986, 1.016 0.935 Presence of MetS 10.029 1.249, 80.541 0.030 5.107 0.428, 60.916 0.197 9.036 1.011, 80.786 0.049 * : Dyslipidaemia and fasting glucose, which exhibited statistical significance in the univariable analysis, were included in the multivariable analysis. ** : Dyslipidaemia and fasting glucose, which exhibited statistical significance in the univariable analysis, were excluded from the multivariable analysis because they are related to five components of the 2005 NCEP-ATP-III criteria for MetS. CVD: cardiovascular disease; AAV: antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis; HR: hazard ratio, CI: confidence interval; BMI: body mass index; MPO: myeloperoxidase; P: perinuclear; PR3: proteinase 3; C: cytoplasmic; BVAS: Birmingham vasculitis activity score; FFS: five-factor score; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; MetS: metabolic syndrome; NCEP-ATP-III: national cholesterol education program-adult treatment panel III. Discussion In this study, we assessed the effect of variables at diagnosis on the risk of CVD during follow-up in AAV patients and found several interesting findings. Firstly, the prevalence of MetS based on the 2005 NCEP-ATP-III criteria was 50.9% in all AAV patients, which was significantly higher than 37.8% in age- and gender-matched controls. The 2013 annual report regarding the prevalence of MetS in approximately 10 million Korean individuals with an average age of 50.8 years and BMI of 23.9 kg/m 2 analysed the overall prevalence of MetS as 30.5% [ 24 ]. The next version of the report, Metabolic Syndrome Fact Sheet in Korea 2018, reported the increased prevalence of Mets of Korean people of an average age of 50 s up to 37.9% [ 25 ], which supports that controls in this study were representative of the general Korean population of an average age of 50 s. Moreover, the prevalence of MetS in AAV patients in Korea was slightly higher than that in the UK [ 12 ]. Despite insufficient studies investigating the prevalence of MetS in AAV patients, this discordance might be considered to appear due to the different ethnic or geographical backgrounds [ 26 ]. Secondly, the prevalence of MetS was significantly higher in nonobese AAV patients than that in nonobese controls (46.5% vs. 28.2%). This result may suggest the contribution of the inflammatory burden of AAV to the presence of MetS in AAV patients beyond obesity and its related complications. Interestingly, in the UK study, no difference in BMI between AAV patients and controls was observed [ 12 ]. Whereas, in our study, BMI of AAV patients was significantly lower than that of controls, which exhibited an opposite tendency of the prevalence of MetS. Although BMI is not one of the components of the 2005 NCEP-ATP-III criteria, BMI is another independent index for determining obesity and considered one of the risks for MetS [ 6 , 27 ]. This inverse tendency suggests that another unique risk factor exists in AAV patients other than the conventional risk factors for MetS in normal people and it was assumed as the inflammatory burden of AAV. To prove this assumption, we tried to compare the cross-sectional BVAS or FFS between the two studies but unfortunately, we could not due to no information on BVAS in the UK study. Thirdly, unlike the comparison analysis between AAV patients and controls, BMI was strongly associated with the cross-sectional MetS as shown in the comparison analysis between AAV patients with MetS and those without MetS. Based on this result, it might be assumed that the general association between obesity and MetS became apparent when compared only in AAV patients, resulting from minimizing the influence of the inflammatory burden of AAV. However, the burden of inflammation was not thoroughly removed, because BVAS was assessed significantly elevated in AAV patients with MetS, compared to those without MetS. Thus, this result may suggest the cooperative contribution of AAV activity to the presence of MetS in AAV patients along with obesity and its related complications. Supposed that variables directly related to the 2005 NCEP-ATP-III criteria were excluded, two categories of the risk factors at diagnosis for the cross-section MetS could be organized: one is the conventional risk factors such as age, BMI, diabetes mellitus, hypertension and dyslipidaemia; and the other is the AAV-specific inflammatory variables such as BVAS, FFS, haemoglobin, platelet count, creatinine, serum albumin, ESR and CRP at diagnosis. Using these variables, we conducted the multivariable logistic regression analysis and found that BMI, diabetes mellitus, hypertension and dyslipidaemia were independently and significantly associated with the cross-sectional MetS at diagnosis. By contrast, none of the AAV-specific inflammatory variables were significantly associated with the cross-sectional MetS. This analysis gave two conclusions: one is that BVAS itself might not be independently associated with the cross-sectional MetS in AAV patients, and the other is that BMI might independently contribute to the cross-sectional MetS in AAV patients and AAV activity might consolidate the association between BMI and MetS at diagnosis. Since BMI was an independent variable that could predict the cross-sectional MetS in AAV patients, we calculated the optimal cut-off of BMI at diagnosis for the cross-sectional Mets using the ROC curve analysis. We determined the BMI of 22.9 kg/m 2 as the cut-off for a strong predictor of the cross-sectional MetS (area 0.686, 95% CI 0.606, 0.766, P < 0.001, sensitivity 62.5%, specificity 75.3%) ( Supplementary Fig. 1A ). When we classified AAV patients into the two groups based on the cut-off of BMI and assessed its relative risk for the occurrence of the cross-sectional MetS using the chi-square test, 76 AAV patients were partitioned into the group of BMI ≥ 22.9 kg/m 2 . The cross-sectional MetS was identified more frequently in AAV patients with BMI ≥ 22.9 kg/m 2 than those without (72.4% vs. 34.0%, P < 0.001). Furthermore, patients with BMI ≥ 22.9 kg/m 2 had the significantly higher relative risk of having the cross-sectional MetS than those without (RR 5.079, 95% CI 2.638, 9.780) ( Supplementary Fig. 1B ). Prior to the investigation, it should be noted that except for relapse of AAV, Mets could increase the risks of all-cause mortality [ 28 ], chronic kidney disease or ESRD [ 29 ], CVA [ 30 ] and CVD [ 28 , 31 , 32 ] in both AAV patients and the general population with MetS. In addition, AAV itself without MetS also could increase the risk for CVD compared to healthy people [ 14 , 33 ]. Therefore, it should not be ignored that both AAV entity and the cross-sectional MetS at diagnosis may be simultaneously engaged in CVD occurrence in AAV patients: MetS might significantly initiate CVD occurrence and AAV might accelerate it. On the other hand, unlike, the UK study [ 12 ], we could not find any link between MetS at diagnosis and relapse during follow-up in this study. Fourthly, Mets at diagnosis significantly reduced the cumulative CVD-free survival rate and both BVAS and Mets at diagnosis significantly associated with CVD in nonobese AAV patients. In the survival analysis, MetS at diagnosis significantly reduced the cumulative CVD-free survival rate only in nonobese AAV patients. Whereas, obese AAV patients showed no association between MetS at diagnosis and CVD occurrence during follow-up. This result may suggest that the independent contribution of MetS to the development of CVD would have been offset because MetS is closely related to obesity itself and obesity-related complications in obese AAV patients. For this reason, the effect of MetS on the risk of CVD might be clearer in nonobese AAV patients. In addition, to discover the independent predictors of and contributors to CVD occurrence during follow-up in nonobese AAV patients, we conducted the multivariable Cox hazards model analysis using variables with P value less than 0.05 in the univariable analysis. In the multivariable analysis excluding variables related to the 2005 NCEP-ATP-III criteria, BVAS and MetS at diagnosis had influence on CVD in nonobese AAV patients. This result might support our assumption that both AAV entity and metabolic abnormalities could enhance the risk of CVD in nonobese AAV patients. Our study has several limitations. Controls, who visited Severance Executive Healthcare Clinic in Severance Hospital, and the two-thirds of AAV patients, who belong to the prospective cohort of AAV in our hospital, had information on smoking history, alcohol consumption, and family history of MetS and CVD. However, we could not gather them from all AAV patients and controls due to the nature of a retrospective study. In addition, the number of AAV patients of this study, particularly patients with CVD occurrence, was not large enough to represent all Korean patients with AAV, due to a limitation of a monocentric study. Nevertheless, we believe that this study has significant clinical implications as a pilot study in that we clarified the effect of BVAS and MetS at diagnosis on the risk of CVD in all or nonobese AAV patients, for the first time. In the near future, a prospective and multicentre study with a larger number of AAV patients will compensate for the limitations of this study. Conclusions The prevalence of MetS at diagnosis was 50.9% in all AAV patients and 46.5% in nonobese AAV patients, both of which were significantly higher than those in all controls and nonobese controls. Furthermore, both BVAS and MetS at diagnosis increased the risk of CVD in nonobese AAV patients. Declarations Ethics Approval and consent to participate This study was approved by the Institutional Review Board (IRB) of Severance Hospital (4-2017-0673 for AAV patients and 4-2018-0856 for controls), who waived the need for the written informed consent, as this was a retrospective study. Consent for publication Not applicable Availability of data and material Not applicable Competing interests The authors declare no competing interests Funding This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2017R1D1A1B03029050 ) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea ( HI14C1324 ). Author’s contributions All authors contributed to the study concept, design, acquisition and interpretation of data. SBL, HCK, JYP and SWL performed the statistical analysis. SBL, HCK and SWL drafted the manuscript. All authors revised and approved the manuscript for publication. Abbreviations AAV: ANCA-associated vasculitis; ANCA: antineutrophil cytoplasmic antibody; BMI: body mass index; BVAS: Birmingham vasculitis activity score; C: cytoplasmic; CI: confidence interval; CVA: cerebrovascular accident; CVD: cardiovascular disease; DUR: Drug Utilization Review; EGPA: eosinophilic granulomatosis with polyangiitis; ESRD: end-stage renal disease; EULAR: European League Against Rheumatism; FFS: five-factor score; GPA: granulomatosis with polyangiitis; HDL: high-density lipoprotein; HR: hazard ratios; ICD: International Classification Diseases; IL: interleukin; IRB: Institutional Review Board; MetS: metabolic syndrome; MPA: microscopic polyangiitis; MPO: myeloperoxidase; NCEP-ATP-III: National Cholesterol Education Program Adults Treatment Panel III; OR: odds ratio; P: perinuclear; PR3: proteinase 3; ROC: receiver operator characteristic; RR: relative risk; TNF: tumour necrosis factor; WHO: World Health Organization. References Eckel RH, Grundy SM, Zimmet PZ. 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Supplementary Files SUPPLEMENTARYTABLE2MetSAAV.docx SUPPLEMENTARYTABLE1MetSAAV.docx SUPPLEMENTARYFIGURE1MetSAAV.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-28821","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":575543,"identity":"3c4cfab6-33a5-4e35-acd1-7f625cce5866","order_by":1,"name":"Soo Bin [email protected]","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Soo","middleName":"Bin","lastName":"[email protected]","suffix":""},{"id":575544,"identity":"3034c075-cbee-48d3-a69e-91bd8bf52444","order_by":2,"name":"Hyeok Chan Kwon","email":"","orcid":"","institution":"Dankook University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hyeok","middleName":"Chan","lastName":"Kwon","suffix":""},{"id":575545,"identity":"e4f336fe-7045-4f31-a2f4-fabac450afae","order_by":3,"name":"Jung Yoon Pyo","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Yoon","lastName":"Pyo","suffix":""},{"id":575546,"identity":"425f8c04-2469-487d-bbd7-a2b6c12d77d5","order_by":4,"name":"Mi Il Kang","email":"","orcid":"","institution":"Dankook University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mi","middleName":"Il","lastName":"Kang","suffix":""},{"id":575547,"identity":"c081c6fb-7882-4dbd-8dfa-95f65499d9fb","order_by":5,"name":"Jason Jungsik Song","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jason","middleName":"Jungsik","lastName":"Song","suffix":""},{"id":575548,"identity":"2f88da81-56ba-4196-869d-b64a572a0bbb","order_by":6,"name":"Yong-Beom Park","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong-Beom","middleName":"","lastName":"Park","suffix":""},{"id":575549,"identity":"7b34ef5f-7bc6-44b5-9681-0eb3c1790400","order_by":7,"name":"Jun Yong Park","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"Yong","lastName":"Park","suffix":""},{"id":575550,"identity":"688f64e9-7363-44e5-96e9-dbfe5e51e6c0","order_by":8,"name":"Sang-Won Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBADGQYG5gMMjA0kaOFhYGBLIFkLjwFxWuTbzx5+zVNxh4efveebNO+OOgb+9gP4tRicyUuz5jnzjEey5+w2ad4zhxkkziQQ0MKQY2ac23aYx+BGLlBLG9CGG4Qc1v8GosX+/ptnQC11DPKEtDDcyDF+DLZFgocNqIWZwYCQFoMbb8yY/5w5zCNxJs3Yci5QryEhv8j35xh/nFFxWI6//fDDG2/b6uTkjh8g5DIGNgkogwXE4CGoHgiYP6AzRsEoGAWjYBSgAADxTUIXXjihXwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8038-3341","institution":"Yonsei University College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sang-Won","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2020-05-14 08:23:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-28821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-28821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1131432,"identity":"ada27a6b-4773-4d57-86f4-be66d344d813","added_by":"auto","created_at":"2020-05-19 17:27:19","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168950,"visible":true,"origin":"","legend":"Relative risk of MetS in obese and nonobese subjects.\nAmong nonobese subjects, the prevalence of MetS in nonobese AAV patients was significantly higher than that in nonobese controls (RR 2.212). Whereas, no difference in the prevalence of MetS was found between obese AAV patients and controls. MetS: metabolic syndrome, AAV: antineutrophil cytoplasmic antibody-associated vasculitis; RR: relative risk.","description":"","filename":"FIGURE1MetSAAV.tif","url":"https://assets-eu.researchsquare.com/files/rs-28821/v1/FIGURE1MetSAAV.tif"},{"id":1131433,"identity":"0b87ff23-2c19-43bb-9c58-288a941207e7","added_by":"auto","created_at":"2020-05-19 17:27:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5110356,"visible":true,"origin":"","legend":"Cumulative survival rates based on MetS. \n\nAmong the five poor outcomes of AAV, AAV patients with MetS exhibited the lower cumulative CVD-free survival rate than those without MetS during the follow-up period based on CVD. In addition, MetS at diagnosis significantly increased CVD occurrence only in nonobese AAV patients, but not in obese AAV patients. MetS: metabolic syndrome, AAV: antineutrophil cytoplasmic antibody-associated vasculitis; CVD: cardiovascular disease.","description":"","filename":"FIGURE2MetSAAV.png","url":"https://assets-eu.researchsquare.com/files/rs-28821/v1/FIGURE2MetSAAV.png"},{"id":13504771,"identity":"3ce50b68-2400-460f-a6f2-945bf2bcac46","added_by":"auto","created_at":"2021-09-16 23:24:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287573,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-28821/v1/0041ec27-78c0-424f-b201-1fc6309a4ba3.pdf"},{"id":1131435,"identity":"81d1c4c4-037d-4321-b49b-de2b6ef715de","added_by":"auto","created_at":"2020-05-19 17:27:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25282,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLE2MetSAAV.docx","url":"https://assets-eu.researchsquare.com/files/rs-28821/v1/SUPPLEMENTARYTABLE2MetSAAV.docx"},{"id":1131434,"identity":"7d42a56c-37cc-4121-ad17-2810ee72a056","added_by":"auto","created_at":"2020-05-19 17:27:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19606,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLE1MetSAAV.docx","url":"https://assets-eu.researchsquare.com/files/rs-28821/v1/SUPPLEMENTARYTABLE1MetSAAV.docx"},{"id":1131431,"identity":"dd4cc30c-07e2-43cd-9369-2ad372e1136f","added_by":"auto","created_at":"2020-05-19 17:27:19","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":623414,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFIGURE1MetSAAV.tif","url":"https://assets-eu.researchsquare.com/files/rs-28821/v1/SUPPLEMENTARYFIGURE1MetSAAV.tif"}],"financialInterests":"","formattedTitle":"Clinical implication of metabolic syndrome in nonobese patients with antineutrophil cytoplasmic antibody-associated vasculitis","fulltext":[{"header":"Introduction","content":" \u003cp\u003eThe concept of metabolic syndrome (MetS) is defined by the constellation of metabolic abnormalities that confer to the increased risk of cardiovascular disease (CVD), type 2 diabetes mellitus and all-cause morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. So far, several definitions of MetS, such as the World Health Organization (WHO) definition and the European Group for the Study of Insulin Resistance definition, have been proposed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The National Cholesterol Education Program Adults Treatment Panel III criteria for MetS (the 2005 NCEP-ATP-III criteria) is currently used for the classification of MetS: insulin resistance, obesity (waist circumference), hyperlipidaemia, glucose intolerance, and hypertension have been recognized and accepted as the fundamental mechanisms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among these mechanisms, insulin resistance is the most important contributor and it is associated with vascular thrombosis- and inflammation-related factors, such as lipoprotein dysregulation, prothrombotic changes, low-grade inflammatory conditions and vascular dysfunction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In particular, MetS has been proved to be associated with proinflammatory cytokines including tumour necrosis factor (TNF)-α, interleukin (IL)-1 and IL-6, which can often participate in and accelerate the process of atherosclerosis and thrombosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the primary concern regarding the systemic complication of MetS is the risk of CVD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, the interlink between the metabolic and immune systems has been a global emerging interest. The role of the immune system in maintaining metabolic homeostasis has been implicated through many studies and is now acknowledged that the disturbance in the immune-metabolic interaction may result in abnormal metabolic states, culminating in metabolic diseases such as MetS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. So far, there have been several studies investigating the association of MetS with autoimmune rheumatic diseases and the increased prevalence of MetS in patients with autoimmune rheumatic diseases such as systemic lupus erythematosus, rheumatoid arthritis and vasculitis have been reported. Especially in patients with systemic vasculitis, the prevalence of MetS was high, and consequentially to CVD as well [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of systemic necrotizing vasculitis which primarily affects the small-sized vessels and occasionally the medium-sized ones and consists of three subtypes such as microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA) and eosinophilic granulomatosis with polyangiitis (EGPA) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Since chronic low-grade inflammation of AAV may provoke insulin resistance and disturb metabolic homeostasis, leading to MetS, it can be theoretically assumed that the prevalence of Mets can be gradually increased in AAV patients. Based on this assumption, a previous study conducted in the UK, which compared the prevalence of Mets between AAV patients and controls, reported a higher proportion of MetS in AAV patients compared to controls [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. MetS may have an influence on the poor outcomes of AAV: the high risk of CVD has been reported in AAV, which, in turn, has led to the revision of the European League Against Rheumatism (EULAR) guideline for periodic CVD risk evaluation in AAV patients [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, the effect of MetS on the risk of CVD might be clearer in nonobese people [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, to our knowledge, there was no study sufficiently investigating whether Mets at diagnosis could increase the risk of CVD during follow-up in AAV patients. Hence, in this study, we investigated and compared the prevalence of Mets at diagnosis between AAV patients and age- and gender-matched controls, and furthermore between nonobese AAV patients and controls who had body mass index (BMI)\u0026thinsp;\u0026lt;\u0026thinsp;25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Also, we investigated whether MetS at diagnosis could increase the risk of CVD and other poor outcomes of AAV during follow-up in both all AAV patients and nonobese AAV patients.\u003c/p\u003e "},{"header":"Patients And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe included 173 patients with AAV, who were reclassified as AAV based on the 2007 European Medicines Agency algorithm for AAV and polyarteritis nodosa and the 2012 revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides and we reviewed their medical records. All patients were initially diagnosed as AAV at the Division of Rheumatology, the Department of Internal Medicine, Yonsei University College of Medicine, Severance Hospital, from October 2000 to March 2019. They all had well-documented medical records with which clinical and laboratory data that reviewed ANCA positivity and both Birmingham vasculitis activity score (BVAS) version 3 and five-factor score (FFS) were calculated at diagnosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. AAV patients, who had serious medical conditions mimicking AAV or enabling ANCA false-positivity, such as chronic liver diseases, coexisting malignancies, serious infections, and drugs at the time of diagnosis, were excluded from this study. For controls, the medical records of general people, who had consecutively visited Severance Executive Healthcare Clinic in Severance Hospital, a university-affiliated tertiary care hospital, for a comprehensive medical health check-up, were also reviewed [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. And, 344 age- and gender-matched people without any serious medical condition were included in this study as controls. The cross-sectional diseases at diagnosis or the poor outcomes of AAV during follow-up were identified by the 10th revised International Classification Diseases (ICD-10) and drugs being administered were confirmed using the Korean Drug Utilization Review (DUR) system. This study was approved by the Institutional Review Board (IRB) of Severance Hospital (\u003cb\u003e4-2017-0673 for AAV patients and 4-2018-0856 for controls\u003c/b\u003e), who waived the need for the written informed consent, as this was a retrospective study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe 2005 NCEP-ATP-III criteria\u003c/h2\u003e \u003cp\u003eThe 2005 NCEP-ATP-III criteria consist of five components: i) central obesity based on waist circumference for Asian countries (men\u0026thinsp;\u0026ge;\u0026thinsp;90 Cm and women\u0026thinsp;\u0026ge;\u0026thinsp;80 Cm); ii) hypertension (blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85\u0026nbsp;mmHg); iii) hypertriglyceridemia (triglyceride\u0026thinsp;\u0026ge;\u0026thinsp;150\u0026nbsp;mg/dL); iv) low high-density lipoprotein (HDL)-cholesterol (men\u0026thinsp;\u0026lt;\u0026thinsp;40\u0026nbsp;mg/dL and women\u0026thinsp;\u0026lt;\u0026thinsp;50\u0026nbsp;mg/dL) and v) Impaired glucose tolerance (fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;100\u0026nbsp;mg/dL) or type 2 diabetes mellitus [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariables at diagnosis and during follow-up\u003c/h2\u003e \u003cp\u003eIn terms of the variables at diagnosis, age, male gender and BMI were obtained. In the general Korean population, obesity based on BMI was defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and thus, in this study, nonobese patients and controls were defined as those having BMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e. AAV subtypes, ANCA positivity and both BVAS and FFS were reviewed. We reviewed the results for ANCA by both an indirect immunofluorescence assay (perinuclear (P)-ANCA and cytoplasmic (C)-ANCA) and antigen-specific assays for ANCA (myeloperoxidase (MPO)-ANCA and proteinase 3 (PR3)-ANCA). In patients who tested positive in the indirect fluorescence assay, but negative in antigen-specific assays, P-ANCA positivity was considered as MPO-ANCA positivity and C-ANCA positivity as PR3-ANCA positivity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Comorbidities at diagnosis, such as chronic kidney disease, diabetes mellitus, hypertension, dyslipidaemia and interstitial lung disease, were collected. The results of routine laboratory tests at diagnosis were also evaluated. In terms of the variables during follow-up, the poor outcomes of AAV were defined as all-cause mortality, relapse, end-stage renal disease (ESRD), cerebrovascular accident (CVA) and CVD. The follow-up duration was defined as the period between the date of the diagnosis of AAV and the date of the last visit for survived patients. For deceased patients, the follow-up duration based on all-cause mortality was defined as the period between the initial diagnosis of AAV and the time of death. For patients who had any poor outcomes, the follow-up duration based on each poor outcome was defined as the period starting from the diagnosis of AAV until each poor outcome appeared. Also, administered medications were assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using the SPSS software (version 23 for Windows; IBM Corp., Armonk, NY, USA). Continuous variables were expressed as a median (interquartile range), and categorical variables were expressed as number and the percentage. Significant differences in categorical variables between the two groups were analysed using the Chi-square and Fisher\u0026rsquo;s exact tests. Significant differences in continuous variables between the two groups were compared using the Mann-Whitney test. The odds ratio (OR) was assessed using the multivariable logistic regression analysis of variables with p-values less than 0.05 in the comparative analysis. The optimal cut-off for BMI in predicting MetS at diagnosis was extrapolated by calculating the receiver operator characteristic (ROC) curve and selecting the maximised sum of the sensitivity and specificity. The relative risk (RR) was analysed using contingency tables and the chi-square test. Each cumulative poor outcome-free survival rate was analysed using the Kaplan-Meier survival analysis. The multivariable Cox hazard model using variables with p-values less than 0.05 in the univariable Cox hazard model was conducted to appropriately obtain the hazard ratios (HRs) during the considerable follow-up duration. P-values less than 0.05 were considered as statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of AAV patients\u003c/h2\u003e \u003cp\u003eIn regard to variable at diagnosis, the median age of AAV patients was 58.7\u0026nbsp;years and 57 patients were men. Ninety-seven patients were classified as MPA, 42 patients were classified as GPA and 34 patients were classified as EGPA. MPO-ANCA (or P-ANCA) was detected in 115 patients and ANCA was negative in 35 patients. The most common comorbidity was hypertension (46.2%), followed by chronic kidney disease (stage 3\u0026ndash;5) (29.5%) and dyslipidaemia (28.3%). In regard to variables during the follow-up period, 14 patients died of any cause. The most frequently occurred poor outcome of AAV was relapse (32.4%), followed by ESRD (19.1%). Twelve of 173 patients (6.9%) had experienced CVD after the diagnosis of AAV. One hundred sixty-two patients had received glucocorticoid (93.6%) during follow-up. The most common immunosuppressive drug administered was cyclophosphamide (49.7%), followed by azathioprine (48.0%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of AAV patients with at diagnosis and during follow-up (N\u0026thinsp;=\u0026thinsp;173)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAV patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt the time of diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.7 (20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAAV Subtypes (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (56.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANCA positivity (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPO-ANCA (or P-ANCA) positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (66.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR3-ANCA (or C-ANCA) positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth ANCA positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANCA negativity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (20.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAAV-specific indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.0 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities at diagnosis (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (stage 3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (29.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (27.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (28.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterstitial lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (20.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse alveolar hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDuring the follow-up period\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoor outcomes during follow-up (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (32.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications administered during follow-up (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucocorticoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162 (93.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclophosphamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (49.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRituximab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzathioprine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (48.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycophenolate mofetil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTacrolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eValues are expressed as a median (interquartile range, IQR) or N (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAAV: antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis; MPA: microscopic polyangiitis; GPA: granulomatosis with polyangiitis; EGPA: eosinophilic granulomatosis with polyangiitis; MPO: myeloperoxidase; P: perinuclear; PR3: proteinase 3; C: cytoplasmic; BVAS: Birmingham vasculitis activity score; FFS: five-factor score; ESRD: end-stage renal disease; CVA: cerebrovascular accident; CVD: cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparison of MetS-related variables between 173 AAV patients and 344 controls\u003c/h2\u003e \u003cp\u003eThere were no significant differences in the demographic data, in particular, the number of ex-smokers between AAV patients and controls; however, AAV patients exhibited the lower median BMI than controls (22.2 vs. 23.4\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e). Inversely, the number of AAV patients, who fulfilled the 2005 NCEP-ATP-III criteria, was significantly higher than that of controls who satisfied the same criteria (50.9% vs. 37.8%). Among the five components of the 2005 NCEP-ATP-III criteria, AAV patients exhibited central obesity (53.2% vs. 71.2%) less frequently than controls. Meanwhile, they showed hypertriglyceridemia (49.7% vs. 23.5%) and impaired glucose tolerance (60.1% vs. 24.4%) more often than controls. In regard to MetS-related laboratory results, AAV patients exhibited the higher median harmful cholesterols than controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of MetS-related variables between AAV patients and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;517)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAV patients\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;173)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.0 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.0 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.2 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFulfillment of NCEP-ATP III 2005 criteria for MetS (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2005 NCEP-ATP-III criteria for MetS (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.6 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.3 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.0 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.3 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.0 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.1 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral obesity based on waist circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245 (71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertriglyceridemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HDL cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpaired fasting glucose or type 2 diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are expressed as a median (interquartile range, IQR) or N (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMetS: metabolic syndrome; AAV: antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis; BMI: body mass index; NCEP-ATP-III: national cholesterol education program-adult treatment panel III; HDL: high-density lipoprotein; LDL: low-density lipoprotein; BUN: blood urea nitrogen; eGFR: estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eComparison of MetS-related variables between 144 nonobese AAV patients and 255 nonobese controls\u003c/h2\u003e \u003cp\u003eTo minimise the effect of obesity based on BMI on the prevalence of MetS, we compared the prevalence of MetS in nonobese AAV patients and controls who had BMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e. Sixty-seven of 144 AAV patients (46.5%) exhibited the cross-sectional MetS, whereas only 72 of 255 controls (28.2%) had it (RR 2.212, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Whereas, among obese AAV patients and controls who had BMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, the two groups showed the similar prevalence of MetS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of variables at diagnosis between 88 AAV patients with Mets and 85 AAV patients without Mets\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAAV patients were divided into the two groups based on MetS at diagnosis: 88 patients were assigned to the groups of AAV patients with MetS and they showed higher frequency in all five components of the 2005 NCEP-ATP-III criteria than those without MetS. At the time of diagnosis, AAV patients with MetS were older and more obese than those without MetS: however, no difference in gender and ex-smoker between the two groups was found. In regard to ANCA positivity and AAV-specific inflammatory indices, AAV patients with Mets showed a significantly higher frequency of MPO-ANCA (or P-ANCA) positivity and the higher cross-sectional BVAS and FFS than those without MetS. Among comorbidities, the proportions of chronic kidney disease, diabetes mellitus, hypertension and dyslipidaemia were significantly increased in AAV patients with MetS compared to those without MetS. Among laboratory results, AAV patients with MetS exhibited higher levels in all the variables than those without MetS except for haemoglobin and serum albumin which showed an opposite tendency (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of variables at diagnosis and during follow-up between AAV patients with MetS and those without\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAV patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAV patients\u003c/p\u003e \u003cp\u003ewith MetS (N\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAV patients\u003c/p\u003e \u003cp\u003ewithout MetS (N\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAt the time of diagnosis\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2005 NCEP-ATP-III criteria for MetS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.8 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.5 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.0 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.9 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (male\u0026thinsp;\u0026ge;\u0026thinsp;90\u0026nbsp;cm, female\u0026thinsp;\u0026ge;\u0026thinsp;80\u0026nbsp;cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (\u0026gt;\u0026thinsp;130/85\u0026nbsp;mmHg or medication)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (\u0026gt;\u0026thinsp;150\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (male\u0026thinsp;\u0026lt;\u0026thinsp;40\u0026nbsp;mg/dL,\u003c/p\u003e \u003cp\u003efemale\u0026thinsp;\u0026lt;\u0026thinsp;50\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpaired fasting glucose (\u0026gt;\u0026thinsp;100\u0026nbsp;mg/dL or medication)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.4 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAAV subtypes (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANCA positivity (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPO-ANCA (or P-ANCA) positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR3-ANCA (or C-ANCA) positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth ANCA positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANCA negativity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAAV-specific indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities at diagnosis (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (stage 3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterstitial lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,230.0 (6,570.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,280.0 (5,345.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (x1,000/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317.5 (197.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247. (133.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.0 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.5 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.9 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDuring the follow-up period\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up duration (months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.0 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.2 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoor outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up duration for death (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.0 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.2 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up duration for relapse (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.1 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESRD (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up duration for ESRD (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVA (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up duration for CVA (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD (N, (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up duration for CVD (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.1 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.2 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication administered (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucocorticoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclophosphamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRituximab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzathioprine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycophenolate mofetil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTacrolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are expressed as a median (interquartile range, IQR) or N (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAAV: antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis; MetS: metabolic syndrome; NCEP-ATP-III: national cholesterol education program-adult treatment panel III; HDL: high-density lipoprotein; BMI: body mass index; MPA: microscopic polyangiitis; GPA: granulomatosis with polyangiitis; EGPA: eosinophilic granulomatosis with polyangiitis; MPO: myeloperoxidase; P: perinuclear; PR3: proteinase 3; C: cytoplasmic; BVAS: Birmingham vasculitis activity score; FFS: five-factor score; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; ESRD: end-stage renal disease; CVA: cerebrovascular accident; CVD: cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable logistic regression analysis of variables at diagnosis for the cross-sectional MetS in AAV patients\u003c/h2\u003e \u003cp\u003eWe categorised variables at diagnosis with statistical significance in the comparison analysis into the two groups: the conventional risk factors for MetS and AAV-specific inflammatory indices as shown in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. To determine the independent predictor of the cross-sectional MetS at diagnosis, we conducted the multivariable logistic regression analysis and found that BMI (OR 1.481), diabetes mellitus (OR 7.629), hypertension (OR 16.054) and dyslipidaemia (OR 8.800) were significantly associated with the cross-sectional MetS. Whereas, none of the AAV-specific inflammatory indices was associated with the cross-sectional MetS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of the effect of MetS at diagnosis on the risk of the outcomes of AAV during follow-up\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe simply compared the frequencies of the poor outcomes of AAV between the two groups and found that ESRD and CVD occurred in AAV patients with MetS more frequently than those without MetS during follow-up (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We also compared the cumulative risk of each poor-outcome during the follow-up period based on each poor outcome occurrence between the two groups using the Kaplan-Meier survival analysis. AAV patients with MetS exhibited the lower cumulative CVD-free survival rate than those without MetS during the follow-up period based on CVD occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, we assessed the effect of MetS at diagnosis on the risk of CVD by dividing AAV patients into two categories based on BMI of 25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e. Among nonobese AAV patients, MetS at diagnosis significantly reduced the cumulative CVD-free survival rate. However, among obese AAV patients, there was no significant difference in the cumulative CVD-free survival rate between AAV patients with and without MetS (\u003cb\u003eF\u003c/b\u003e\u003csub\u003e\u003cb\u003eIG\u003c/b\u003e\u003c/sub\u003e. \u003cb\u003e2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHazard ratio of variables at diagnosis for the risk of CVD during follow-up in 173 AAV patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirstly, in cases of all AAV patients, in the univariable Cox hazards model analysis, male gender, BVAS, FFS, diabetes mellitus, dyslipidaemia, fasting glucose level and MetS at diagnosis were significantly associated with CVD occurrence during follow-up. In the multivariable Cox hazards model analysis of variables with significance in the univariable analysis, only male gender (HR 6.006, 95% confidence interval (CI) 1.486, 24.283) was significantly associated with CVD occurrence during follow-up. With the same assumption above, among male gender, BVAS, FFS and MetS at diagnosis, both male gender (HR 4.625, 95% CI 1.258, 16.996) and MetS at diagnosis (HR 9.864, 95% CI 1.136, 85.679) were significantly associated with CVD during follow-up (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHazard ratio of variables at diagnosis for the risk of CVD during follow-up in 144 nonobese AAV patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn cases of nonobese AAV patients, in the univariable Cox hazards model analysis, BVAS, dyslipidaemia, haemoglobin, fasting glucose and MetS at diagnosis were significantly associated with CVD during follow-up. In the multivariable analysis, only BVAS at diagnosis (HR 1.157, 95CI 1.038, 1.289) was significantly associated with CVD during follow-up. Given that diabetes, dyslipidaemia and fasting glucose are closely related to components of the 2005 NCEP-ATP-III criteria, they could be deleted in the multivariable analysis in order to clarify the effect of variables at diagnosis on the risk of CVD. Thus, only BVAS, haemoglobin, and MetS at diagnosis were included in the multivariable analysis, in which, both BVAS (HR 1.159, 95% CI 1.039, 1.293) and MetS at diagnosis (HR 9.036, 95% CI 1.011, 80.786) were significantly associated with CVD during follow-up (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable Cox hazards model analyses of variables at diagnosis for CVD occurrence during follow-up in AAV patients with normal BMI (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u0026nbsp;kg/m2) (N\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eMultivariable\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962, 1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.864, 14.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908, 1.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANCA positivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPO-ANCA (or P-ANCA) positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.387, 7.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR3-ANCA (or C-ANCA) positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069, 4.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANCA positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.306, 10.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAAV-specific indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.063, 1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.038, 1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.039, 1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.977, 2.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities (N, (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (stage 3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148, 3.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.828, 13.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70, 16.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.456, 23.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.415, 10.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterstitial lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061, 3.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000, 1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.487, 0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.635, 1.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.657, 1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (\u0026times;\u0026thinsp;1,000/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.997, 1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.003, 1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000, 1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.957, 1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.178, 1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.993, 1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998, 1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.986, 1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of MetS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.249, 80.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.428, 60.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.011, 80.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003e*\u003c/sup\u003e: Dyslipidaemia and fasting glucose, which exhibited statistical significance in the univariable analysis, were included in the multivariable analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003e**\u003c/sup\u003e: Dyslipidaemia and fasting glucose, which exhibited statistical significance in the univariable analysis, were excluded from the multivariable analysis because they are related to five components of the 2005 NCEP-ATP-III criteria for MetS.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eCVD: cardiovascular disease; AAV: antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis; HR: hazard ratio, CI: confidence interval; BMI: body mass index; MPO: myeloperoxidase; P: perinuclear; PR3: proteinase 3; C: cytoplasmic; BVAS: Birmingham vasculitis activity score; FFS: five-factor score; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; MetS: metabolic syndrome; NCEP-ATP-III: national cholesterol education program-adult treatment panel III.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn this study, we assessed the effect of variables at diagnosis on the risk of CVD during follow-up in AAV patients and found several interesting findings. Firstly, the prevalence of MetS based on the 2005 NCEP-ATP-III criteria was 50.9% in all AAV patients, which was significantly higher than 37.8% in age- and gender-matched controls. The 2013 annual report regarding the prevalence of MetS in approximately 10\u0026nbsp;million Korean individuals with an average age of 50.8\u0026nbsp;years and BMI of 23.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e analysed the overall prevalence of MetS as 30.5% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The next version of the report, Metabolic Syndrome Fact Sheet in Korea 2018, reported the increased prevalence of Mets of Korean people of an average age of 50\u0026nbsp;s up to 37.9% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which supports that controls in this study were representative of the general Korean population of an average age of 50\u0026nbsp;s. Moreover, the prevalence of MetS in AAV patients in Korea was slightly higher than that in the UK [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite insufficient studies investigating the prevalence of MetS in AAV patients, this discordance might be considered to appear due to the different ethnic or geographical backgrounds [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecondly, the prevalence of MetS was significantly higher in nonobese AAV patients than that in nonobese controls (46.5% vs. 28.2%). This result may suggest the contribution of the inflammatory burden of AAV to the presence of MetS in AAV patients beyond obesity and its related complications. Interestingly, in the UK study, no difference in BMI between AAV patients and controls was observed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whereas, in our study, BMI of AAV patients was significantly lower than that of controls, which exhibited an opposite tendency of the prevalence of MetS. Although BMI is not one of the components of the 2005 NCEP-ATP-III criteria, BMI is another independent index for determining obesity and considered one of the risks for MetS [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This inverse tendency suggests that another unique risk factor exists in AAV patients other than the conventional risk factors for MetS in normal people and it was assumed as the inflammatory burden of AAV. To prove this assumption, we tried to compare the cross-sectional BVAS or FFS between the two studies but unfortunately, we could not due to no information on BVAS in the UK study.\u003c/p\u003e \u003cp\u003eThirdly, unlike the comparison analysis between AAV patients and controls, BMI was strongly associated with the cross-sectional MetS as shown in the comparison analysis between AAV patients with MetS and those without MetS. Based on this result, it might be assumed that the general association between obesity and MetS became apparent when compared only in AAV patients, resulting from minimizing the influence of the inflammatory burden of AAV. However, the burden of inflammation was not thoroughly removed, because BVAS was assessed significantly elevated in AAV patients with MetS, compared to those without MetS. Thus, this result may suggest the cooperative contribution of AAV activity to the presence of MetS in AAV patients along with obesity and its related complications.\u003c/p\u003e \u003cp\u003eSupposed that variables directly related to the 2005 NCEP-ATP-III criteria were excluded, two categories of the risk factors at diagnosis for the cross-section MetS could be organized: one is the conventional risk factors such as age, BMI, diabetes mellitus, hypertension and dyslipidaemia; and the other is the AAV-specific inflammatory variables such as BVAS, FFS, haemoglobin, platelet count, creatinine, serum albumin, ESR and CRP at diagnosis. Using these variables, we conducted the multivariable logistic regression analysis and found that BMI, diabetes mellitus, hypertension and dyslipidaemia were independently and significantly associated with the cross-sectional MetS at diagnosis. By contrast, none of the AAV-specific inflammatory variables were significantly associated with the cross-sectional MetS. This analysis gave two conclusions: one is that BVAS itself might not be independently associated with the cross-sectional MetS in AAV patients, and the other is that BMI might independently contribute to the cross-sectional MetS in AAV patients and AAV activity might consolidate the association between BMI and MetS at diagnosis.\u003c/p\u003e \u003cp\u003eSince BMI was an independent variable that could predict the cross-sectional MetS in AAV patients, we calculated the optimal cut-off of BMI at diagnosis for the cross-sectional Mets using the ROC curve analysis. We determined the BMI of 22.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e as the cut-off for a strong predictor of the cross-sectional MetS (area 0.686, 95% CI 0.606, 0.766, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, sensitivity 62.5%, specificity 75.3%) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). When we classified AAV patients into the two groups based on the cut-off of BMI and assessed its relative risk for the occurrence of the cross-sectional MetS using the chi-square test, 76 AAV patients were partitioned into the group of BMI\u0026thinsp;\u0026ge;\u0026thinsp;22.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e. The cross-sectional MetS was identified more frequently in AAV patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;22.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e than those without (72.4% vs. 34.0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;22.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e had the significantly higher relative risk of having the cross-sectional MetS than those without (RR 5.079, 95% CI 2.638, 9.780) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003ePrior to the investigation, it should be noted that except for relapse of AAV, Mets could increase the risks of all-cause mortality [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], chronic kidney disease or ESRD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], CVA [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and CVD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] in both AAV patients and the general population with MetS. In addition, AAV itself without MetS also could increase the risk for CVD compared to healthy people [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, it should not be ignored that both AAV entity and the cross-sectional MetS at diagnosis may be simultaneously engaged in CVD occurrence in AAV patients: MetS might significantly initiate CVD occurrence and AAV might accelerate it. On the other hand, unlike, the UK study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], we could not find any link between MetS at diagnosis and relapse during follow-up in this study.\u003c/p\u003e \u003cp\u003eFourthly, Mets at diagnosis significantly reduced the cumulative CVD-free survival rate and both BVAS and Mets at diagnosis significantly associated with CVD in nonobese AAV patients. In the survival analysis, MetS at diagnosis significantly reduced the cumulative CVD-free survival rate only in nonobese AAV patients. Whereas, obese AAV patients showed no association between MetS at diagnosis and CVD occurrence during follow-up. This result may suggest that the independent contribution of MetS to the development of CVD would have been offset because MetS is closely related to obesity itself and obesity-related complications in obese AAV patients. For this reason, the effect of MetS on the risk of CVD might be clearer in nonobese AAV patients. In addition, to discover the independent predictors of and contributors to CVD occurrence during follow-up in nonobese AAV patients, we conducted the multivariable Cox hazards model analysis using variables with P value less than 0.05 in the univariable analysis. In the multivariable analysis excluding variables related to the 2005 NCEP-ATP-III criteria, BVAS and MetS at diagnosis had influence on CVD in nonobese AAV patients. This result might support our assumption that both AAV entity and metabolic abnormalities could enhance the risk of CVD in nonobese AAV patients.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Controls, who visited Severance Executive Healthcare Clinic in Severance Hospital, and the two-thirds of AAV patients, who belong to the prospective cohort of AAV in our hospital, had information on smoking history, alcohol consumption, and family history of MetS and CVD. However, we could not gather them from all AAV patients and controls due to the nature of a retrospective study. In addition, the number of AAV patients of this study, particularly patients with CVD occurrence, was not large enough to represent all Korean patients with AAV, due to a limitation of a monocentric study. Nevertheless, we believe that this study has significant clinical implications as a pilot study in that we clarified the effect of BVAS and MetS at diagnosis on the risk of CVD in all or nonobese AAV patients, for the first time. In the near future, a prospective and multicentre study with a larger number of AAV patients will compensate for the limitations of this study.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThe prevalence of MetS at diagnosis was 50.9% in all AAV patients and 46.5% in nonobese AAV patients, both of which were significantly higher than those in all controls and nonobese controls. Furthermore, both BVAS and MetS at diagnosis increased the risk of CVD in nonobese AAV patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) of Severance Hospital (4-2017-0673 for AAV patients and 4-2018-0856 for controls), who waived the need for the written informed consent, as this was a retrospective study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (\u003cstrong\u003e2017R1D1A1B03029050\u003c/strong\u003e) and a grant from the Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (\u003cstrong\u003eHI14C1324\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study concept, design, acquisition and interpretation of data. SBL, HCK, JYP and SWL performed the statistical analysis. SBL, HCK and SWL drafted the manuscript. All authors revised and approved the manuscript for publication.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAV: ANCA-associated vasculitis; ANCA: antineutrophil cytoplasmic antibody; BMI: body mass index; BVAS: Birmingham vasculitis activity score; C: cytoplasmic; CI: confidence interval; CVA: cerebrovascular accident; CVD: cardiovascular disease; DUR: Drug Utilization Review; EGPA: eosinophilic granulomatosis with polyangiitis; ESRD: end-stage renal disease; EULAR: European League Against Rheumatism; FFS: five-factor score; GPA: granulomatosis with polyangiitis; HDL: high-density lipoprotein; HR: hazard ratios; ICD: International Classification Diseases; IL: interleukin; IRB: Institutional Review Board; MetS: metabolic syndrome; MPA: microscopic polyangiitis; MPO: myeloperoxidase; NCEP-ATP-III: National Cholesterol Education Program Adults Treatment Panel III; OR: odds ratio; P: perinuclear; PR3: proteinase 3; ROC: receiver operator characteristic; RR: relative risk; TNF: tumour necrosis factor; WHO: World Health Organization.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eEckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365:1415\u0026ndash;28.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMiranda PJ, DeFronzo RA, Califf RM, Guyton JR. Metabolic syndrome: definition, pathophysiology, and mechanisms. Am Heart J. 2005;149:33\u0026ndash;45.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAlberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15:539\u0026ndash;53.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBalkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 1999;16:442\u0026ndash;3.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eExpert Panel on Detection. Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285:2486\u0026ndash;97.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cdiv class=\"InstitutionalAuthorName\"\u003eNational Heart, Lung, and Blood Institute; American Heart Association\u003c/div\u003e \u003cspan\u003eGrundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C. National Heart, Lung, and Blood Institute; American Heart Association: Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. \u003cem\u003eArterioscler Thromb Vasc Biol\u003c/em\u003e 2004, 24: e13-e18.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSrikanthan K, Feyh A, Visweshwar H, Shapiro JI, Sodhi K. Systematic Review of Metabolic Syndrome Biomarkers: A Panel for Early Detection, Management, and Risk Stratification in the West Virginian Population. Int J Med Sci. 2016;13:25\u0026ndash;38.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZmora N, Bashiardes S, Levy M, Elinav E. The Role of the Immune System in Metabolic Health and Disease. Cell Metab. 2017;25:506\u0026ndash;21.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMedina G, Vera-Lastra O, Peralta-Amaro AL, Jim\u0026eacute;nez-Arellano MP, Saavedra MA, Cruz-Dom\u0026iacute;nguez MP, Jara LJ. Metabolic syndrome, autoimmunity and rheumatic diseases. 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Mayo Clin Proc. 2018;93:597\u0026ndash;606.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, antineutrophil cytoplasmic antibody-associated vasculitis, cardiovascular disease, prevalence, risk ","lastPublishedDoi":"10.21203/rs.3.rs-28821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-28821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: We investigated the prevalence of metabolic syndrome (MetS) in all or nonobese patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) and compared it with age- and gender-matched controls. Also, we assessed the effect of variables at diagnosis on the risk of cardiovascular disease (CVD) in all or nonobese AAV patients. \u003c/p\u003e\u003cp\u003eMethods: In this study, 173 AAV patients and 344 controls were included and MetS was defined by the National Cholesterol Education Program Adults Treatment Panel III criteria. The obesity based on BMI was defined as BMI ≥ 25 kg/m 2 . The follow-up duration was defined as the period from diagnosis to the last visit or to each poor outcome occurrence. \u003c/p\u003e\u003cp\u003eResults: The median age of AAV patients was 58.7 years and 57 patients were men. The prevalence of MetS was 50.9% in all AAV patients and 46.5% in nonobese AAV patients, which were significantly higher than 37.8% in all controls and 28.2% in nonobese controls. In the Kaplan-Meier survival analysis, Mets at diagnosis significantly reduced the cumulative CVD-free survival rate in both all and nonobese AAV patients. In the multivariable Cox hazards model analysis, CVD during follow-up was significantly associated with both BVAS (HR 1.159) and MetS at diagnosis (HR 9.036) in nonobese AAV patients. \u003c/p\u003e\u003cp\u003eConclusions: The prevalence of MetS at diagnosis in all or nonobese AAV patients was significantly higher than those in all or nonobese controls. Furthermore, both BVAS and MetS at diagnosis increased the risk of CVD in nonobese AAV patients.\u003c/p\u003e","manuscriptTitle":"Clinical implication of metabolic syndrome in nonobese patients with antineutrophil cytoplasmic antibody-associated vasculitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-05-19 17:27:16","doi":"10.21203/rs.3.rs-28821/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1396987a-7e36-4894-9db4-2275aa39f544","owner":[],"postedDate":"May 19th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":103031,"name":"Endocrinology \u0026 Metabolism"},{"id":103032,"name":"Rheumatology"}],"tags":[],"updatedAt":"2021-02-19T16:40:32+00:00","versionOfRecord":[],"versionCreatedAt":"2020-05-19 17:27:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-28821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-28821","identity":"rs-28821","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}