Clinical significance of cystatin C based renal function assessment, proteinuria and albuminuria in patients with rheumatoid arthritis

Clinical significance of cystatin C based renal function assessment, proteinuria and albuminuria in patients with rheumatoid arthritis | 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 significance of cystatin C based renal function assessment, proteinuria and albuminuria in patients with rheumatoid arthritis Masafumi Sugiyama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8495512/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Aim : This study aimed to determine the relationship between renal function markers and clinical background factors in patients with rheumatoid arthritis (RA) as well as propose effective strategies for accurate renal assessment and management of RA, especially in the context of modern treatment options. Methods : A retrospective, observational, cross-sectional study was conducted in Hashima municipal hospital and Kindai University Nara Hospital involving 140patients with RA who met the 1987 American college of rheumatology (ACR) or 2010 ACR/European league against rheumatism criteria. Clinical background, comorbidities, disease activity, and serum C reactive protein (CRP). creatinine, cystatin C, and urinary protein levels, and albuminuria were assessed. eGFR was obtained from both creatinine and cystatin C levels. Data of 38 treatment-naïve patients were also analyzed longitudinally for changes in C reactive protein (CRP) and cystatin C levels. Statistical analyses included multivariate regression and correlation analysis. Results : Renal function and proteinuria: 20.7% of patients had proteinuria (urinary protein–creatinine ratio ≥0.15), and 26.4% had albuminuria (urinary albumin–creatinine ratio ≥30). An eGFR of <60 mL/min was noted in approximately 23% of cases based on both creatinine and cystatin C levels. Associations: CRP levels were significantly associated with proteinuria, cystatin C, and discordance between eGFR based on creatinine and cystatin C levels. Hypertension was related to albuminuria. Longitudinal analysis: In treatment-naïve patients, cystatin C levels decreased in parallel with the subsequent reduction in CRP following treatment. Conclusion : In patients with RA, elevated CRP levels are associated with increased cystatin C levels, proteinuria, and eGFR discordance even without glomerular disease. These markers may reflect inflammatory activity rather than intrinsic kidney damage and may improve with treatment. Due to implications for drug selection and risk management particularly with methotrexate and biologics, accurate and multifactorial assessment of renal function remains critical in RA. Figures Figure 1 Introduction Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints that ultimately results in joint dysfunction and destruction [ 1 ]. Advances in treatment, including the treat-to-target approach and biologic agents, have remarkably improved the prognosis of patients with RA [ 2 ]. RA and kidney disease are intertwined; RA is frequently complicated by drug-induced renal impairment and amyloidosis [ 3 ]. However, the incidence of these complications has been decreasing. Current challenges include an aging patient population and the increased incidence of lifestyle-related diseases [ 4 ], which negatively affect renal function of patients with RA. Additionally, although the selection of antirheumatic drugs has increased, renal impairment limits the use of some drugs. Methotrexate (MTX) is the first-line treatment for RA [ 5 ]; however, careful monitoring in patients with decreased renal function is necessary due to the heightened risk of adverse events [ 6 ]. Similarly, Janus kinase inhibitors (JAKi) also require dose reduction or discontinuation depending on renal function [ 7 ]. Therefore, an accurate evaluation of patients’ renal function is important. Furthermore, in cases complicated by chronic kidney disease (CKD), treatment strategies must also consider the increased risk of future cardiovascular events as well as the prevention of progression to end-stage renal disease (ESRD) [ 8 ]. Therefore, evaluating proteinuria has become essential. This study aimed to clarify the relationship between renal function markers, such as serum creatinine, cystatin C, and proteinuria, and clinical background factors, including disease activity, of patients with RA in real-world clinical settings as well as to propose strategies for addressing these issues. Materials and methods Study design and participants This retrospective, observational, cross-sectional study involved consecutive patients with RA from the Department of Nephrology and Rheumatology at Hashima Municipal Hospital and Department of Rheumatology at Kindai University Nara Hospital who were seen between April 2021 and March 2022. All participants met the 1987 American College of Rheumatology (ACR) classification criteria or the 2010 ACR/European League against Rheumatism criteria. Past medical history, clinical background (disease duration, type 2 diabetes mellitus, hypertension), disease activity, current treatment, body mass index (BMI), and blood and urine test results were assessed. Type 2 diabetes mellitus, hypertension, nonsteroidal anti-inflammatory drugs (NSAIDs) use was defined regular medical treatment. Measurements To evaluate systemic inflammatory findings, serum CRP was used, and the disease activity score (DAS)-28 CRP was employed to determine the disease activity index. Total urinary protein (U-TP, g/dL) and albumin (U-Alb, mg/dL) were measured separately by pyrogallol red and immunonephelometric assays, respectively. All U-TP and U-Alb levels were divided by the simultaneously measured urinary creatinine levels to eliminate the effects of urine concentration. During renal function serum testing, serum creatinine levels were measured using the enzyme assay method. The estimated glomerular filtration rate (eGFR, mL/min/1.73m 2 )from creatinine (eGFR creatinine) was obtained using the revised three-variable Japanese equation [ 9 ]. Cystatin C levels were measured using the latex coagulation nephelometry method standardized with ERM-DA471/IFCC, and eGFR from cystatin C (eGFR cystatin C) was obtained using the developed GFR-estimating equation in Japan [ 10 ]. Subgroups by proteinuria, albuminuria, and eGFR creatinine-cystatin C discordance All patients were divided according to the following criteria : UPCR < 0.15 or ≥ 0.15, UACR < 30 or ≥ 30, and eGFR creatinine-cystatin C discordance was defined as difference of ≥ 10mL/min/1.73m2 < 10 or ≥ 10, univariate analyses were performed to compare their clinical characteristics, disease activity, and comorbidities across each category. Subsequently, multivariate analyses adjusted for potential confounding factors were conducted to identify variables independently associated with these renal functions. Exploratory longitudinal analysis of cystatin C between pre and after treatment CRP and cystatin C levels in 38 treatment-naïve patients with RA were measured before initiating treatment and after achieving a clinical response; their results were then compared. For each case, the respective changes in CRP and cystatin C levels were calculated, and their correlation was evaluated. Statistical analysis All analyses were performed using EZR (version 1.60, Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R software (The R foundation for Statistical Computing, Vienna, Austria), GraphPad Prism 9 (GraphPad Software Inc., Boston, USA), and IBM SPSS Statistics Version 28.0 (IBM Corp., Armonk, NY, USA).Continuous variables are presented as median (interquartile range), categorical variables are presented as numbers and percentages. In univariate analysis, the Mann–Whitney U test was used for continuous variables, and Chi-squared test or Fisher’s exact test was used for categorical variables as appropriate. A multivariate linear regression model was conducted to identify the factors independently associated with proteinuria, albuminuria, and eGFR discordance in the adjusted model using confounders such as age, gender, hypertension, and CRP values > 0.15mg/dL. In the exploratory longitudinal analysis, the correlation between the corresponding CRP and cystatin C levels before and after treatment was evaluated using Spearman’s rank correlation coefficient. All statistical analysis tests were two-sided, and a p value < 0.05 was considered statistically significant. Results Patient characteristics Overall, 154 patients with RA were assessed; 11 and 3 were excluded due to incomplete laboratory results and lack of consent, respectively. Table 1 shows the profiles of the 140 enrolled patients. Disease activity was well controlled, with DAS 28-CRP remission in 112/140 patients. More than 60% of the patients were seropositive. The proportion of men was 24.3%, and the median disease duration was 6 years. Among the 140 patients, 83 (59.7%) received MTX, and 60 (42.9%) received biological agents. Additionally, 28 patients (20%) received nonsteroidal anti-inflammatory drugs (NSAIDs), while 3 patients received JAKi. Hypertension and type 2 diabetes mellitus were noted in 27 (19.3%) and 8 (5.7%) patients, respectively. Overall, the median serum CRP, creatinine, and cystatin C levels were 0.07 mg/dL, 0.61 mg/dL, and 0.87mg/dL, respectively. The median eGFR according to the creatinine and cystatin C levels were 81mL/min and 80 mL/min, respectively. The number of patients with an eGFR < 60 mL/min based on creatinine and cystatin C levels were 32 and 31, respectively. Meanwhile, urinary protein-to-creatinine ratio (UPCR) ≥ 0.15(g/Cr) and urinary albumin-to-creatinine ratio (UACR) ≥ 30(mg/Cr) was observed in 29 (20.7%) and 37 (26.4%) patients, respectively. There were no patients with ESRD requiring dialysis. Table 1 Patient characteristics Clinical backgrounds, treatment, clinical manifestations, and renal function and proteinuria of patients with rheumatoid arthritis Variables Overall n = 140 Age (years) (median [IQR]) 66 [55–72.25] Gender (male), n (%) 34 (24.3%) ACPA-positive, n (%) 92 (65.7%) ACPA titer IU/mL (median [IQR]) 21.35 [0.6–128.22] RF positive, n (%) 91 (65.0%) RF titer IU/mL (median [IQR]) 46 [7–121.5] Disease duration (years) (median [IQR]) 6 [3.0–15.0] MTX use, n (%) 83 (59.7%) MTX dose mg/week (median [IQR]) 4 [0–8.0] NSAIDS use, n (%) 28 (20.0%) b/tsDMARDs use, n (%) 60 (42.9%) CRP (mg/dL) (median [IQR]) 0.07 [0.03–0.30] DAS 28-CRP (median [IQR]) 1.6 [1.22–2.07] DAS-28-CRP remission, n (%) 112 (80.0%) HAQ (median [IQR]) 0.25 [0–1.0] HTN, n (%) 27 (19.3%) Type 2 diabetes mellitus, n (%) 8 (5.7%) Serum creatinine mg/dL (median [IQR]) 0.61 [0.51–0.77] eGFR creatinine mL/min/1.73 m 2 (median [IQR]) 81.0 [66.0–99.5] eGFR Creatinine < 60mL/min/1.73m 2 <, n (%) 32 (22.7%) Serum cystatin C mg/dL (median [IQR]) 0.87 [0.75–1.11] eGFR cystatin C mL/min/1.73 m 2 (median [IQR]) 80.0 [65.15–94.35] eGFR cystatin C 25, n (%) 27 (19.3%) Urinary albumin mg/g creatinine ratio (median [IQR]) 18.0 [12.5–32.18] Urinary albumin > 30mg/g creatinine ratio, n (%) 37 (26.4%) Urinary total protein g/g creatinine ratio (median [IQR]) 0.11 [0.07–0.11] Urinary total protein > 0.15g/g creatinine ratio, n (%) 29 (20.7%) ACPA: anti-cyclic citrullinated peptide antibodies; b/tsDMARDs use: biological targeted synthesized disease modifying antirheumatic drugs; BMI: body mass index; CRP: C-reactive protein; DAS28-CRP: disease activity score 28-joint count using CRP; eGFR: estimated glomerular filtration rate; HAQ: health assessment questionnaire; HTN: hypertension; IQR: interquartile range; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; RF: rheumatoid IgM factor Association Between Proteinuria, Estimated Renal Function, and Clinical Background Using a UPCR cutoff of 0.15, patients were divided into the < 0.15 and ≥ 0.15 groups, and their clinical backgrounds were compared (Table 2 ). There were no significant differences in gender, seropositivity, and use of biological agents between the two groups. Additionally, there were no differences in the prevalence of hypertension or type 2 diabetes mellitus. However, age, MTX non-use, and CRP levels were significantly higher in the UPCR ≥ 0.15 group. Moreover, serum cystatin C levels were also significantly elevated, and eGFR cystatin C was significantly lower in the UPCR ≥ 0.15 group. Table 2 Comparison of the clinical background of patients with and without proteinuria Variables U-TP/Cr ratio < 0.15 (n = 111) U-TP/Cr ratio ≥ 0.15 ( n = 29) p- Value Age (years) (median [IQR]) 64 (50–71) 70 (64–76) 0.005 Gender (male), n (%) 27 (24.3%) 7 (24.1%) 1.000 ACPA-positive, n (%) 74 (66.7%) 18 (62.1%) 0.665 ACPA titer (IU/mL) (median [IQR]) 22.5 (0.6–142.05) 15.9 (0.6–75.0) 0.530 RF-positive, n (%) 76 (68.5%) 15(51.7%) 0.125 RF titer (IU/mL) (median [IQR]) 50.0(8.0–127.0) 30.0 (6.0–83.0) 0.221 CRP (mg/dL) (median [IQR]) 0.06 (0.02–0.2) 0.29 (0.05–1.09) 0.006 MTX use, n (%) 72 (64.9%) 11 (37.9%) 0.011 MTX dose mg/week (median [IQR]) 6 (0–8) 0 (0–4) 0.032 NSAIDS use, n (%) 20 (18.0%) 8 (27.6%) 0.298 b/tsDMARDs use, n (%) 51 (45.9%) 9 (31.0%) 0.206 Disease duration (years) (median [IQR]) 6.0 (3.0–13.0) 9 (3.0–18.0) 0.463 Serum creatinine (mg/dL) (median [IQR]) 0.61 (0.51–0.75) 0.64 (0.54–0.8) 0.281 Serum cystatin C (mg/dL) (median [IQR]) 0.85 (0.72–0.98) 1.01(0.88–1.19) < 0.001 eGFR creatinine (mL/min/1.73 m 2 ) (median [IQR]) 90.0 (68.2–99.8) 74.0 (63.0–87.0) 0.067 eGFR cystatin C (mL/min/1.73 m 2) (median [IQR]) 82.2 (69.8–98.45) 68.6 (52.9–76.9) < 0.001 DAS 28-CRP (median [IQR]) 1.57 (1.21–1.98) 1.85 (1.23–2.85) 0.114 HAQ (median [IQR]) 0.19 (0.00–0.84) 0.25 (0.00–1.75) 0.191 HTN, n (%) 19 (17.1) 8 (27.6) 0.289 Type 2 diabetes mellitus, n (%) 6 (5.4) 2 (6.9) 0.67 ACPA: anti-cyclic citrullinated peptide antibodies; b/tsDMARDs: biological targeted synthesized disease modifying antirheumatic drugs; CRP: C-reactive protein; DAS28-ESR: disease activity score 28-joint count using CRP; eGFR: estimated glomerular filtration rate; HAQ: health assessment questionnaire; HTN: hypertension; IQR: interquartile range; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; RF: rheumatoid IgM factor; U-TP/Crn: urinary total protein/creatinine; p-Values were calculated to compare patients with U-TP/Crn less than 0.15 and those with U-TP/Crn equal to or greater than 0.15 using Mann–Whitney test or Fisher’s exact test Using a UACR cutoff of 30, patients were divided into the < 30 and ≥ 30 groups, and their clinical backgrounds were compared (Table 3 ). Similarly to the UPCR results, there were no significant differences in gender and use of biological agents between the two groups. However, seropositivity rate was significantly higher in the UACR ≥ 30 group, both of eGFR creatinine and cystatin are significantly lower in the UACR ≥ 30 group. Furthermore, unlike the UPCR results, no difference in CRP values was observed between the two groups. Although the prevalence of hypertension was significantly higher in the albuminuria-positive group. Table 3 Comparison of the clinical background of patients with and without albuminuria Variables U-Alb/Cr ratio < 30 (n = 103) U-Alb/Cr ratio ≥ 30 ( n = 37) p- Value Age (years) (median [IQR]) 63 (49–71) 70 (65–74) 0.003 Gender (male), n (%) 25 (24.3%) 9 (24.3%) 1.00 ACPA-positive, n (%) 73 (70.9%) 19(51.4%) 0.043 ACPA titer (IU/mL) (median [IQR]) 31.3 (0.6–171.5) 12.2 (0.6–43.3) 0.07 RF-positive, n (%) 72 (69.9%) 19 (51.4%) 0.047 RF titer (IU/mL) (median [IQR]) 54.0 (8.0–138.0) 15 (6.0–70.0) 0.050 CRP (mg/dL) (median [IQR]) 0.06 (0.02–0.23) 0.09 (0.04–0.54) 0.066 MTX use, n (%) 66 (64.1%) 17 (45.9%) 0.078 MTX dose mg/week (median [IQR]) 6 (0–8) 0 (0–6) 0.050 NSAIDS use, n (%) 17 (16.5%) 11 (29.7%) 0.097 b/tsDMARDs use, n (%) 48(46.6%) 12 (32.4%) 0.176 Disease duration (years) (median [IQR]) 6.5 (4.0–15.0) 6.0 (2.0–14.0) 0.532 Serum creatinine (mg/dL) (median [IQR]) 0.61 (0.51–0.75) 0.63 (0.58–0.8) 0.279 Serum cystatin C (mg/dL) (median [IQR]) 0.85 (0.73–0.98) 0.95(0.81–1.16) 0.014 eGFR creatinine (mL/min/1.73 m 2 ) (median [IQR]) 92.1(68.2–100.55) 74.0(64.1–81.0) 0.019 eGFR cystatin C (mL/min/1.73 m 2) (median [IQR]) 82.2 (69.8–99.7) 69.4 (54.0–81.7) 0.002 DAS 28-CRP (median [IQR]) 1.59 (1.19–2.0) 1.6 (1.36–2.14) 0.302 HAQ (median [IQR]) 0.19 (0.00–0.84) 0.25 (0.00–1.75) 0.274 HTN, n (%) 15 (14.6) 12 (32.4) 0.028 Type 2 diabetes mellitus, n (%) 4 (3.9) 4 (10.8) 0.208 ACPA: anti-cyclic citrullinated peptide antibodies; b/tsDMARDs: biological targeted synthesized disease modifying antirheumatic drugs; CRP: C-reactive protein; DAS28-ESR: disease activity score 28-joint count using CRP; eGFR: estimated glomerular filtration rate; HAQ: health assessment questionnaire; HTN: hypertension; IQR: interquartile range; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; RF: rheumatoid IgM factor; U-Alb/Crn: urinary albumin/creatinine; P -values were calculated to compare patients with U-Alb/Crn < 30 and those with ≥ 30 using the Mann–Whitney test or Fisher’s exact test Using a discordance between eGFR creatinine and cystatin C cutoff of 10, patients were divided into the < 10and ≥ 10 groups, and their clinical backgrounds were compared (Table 4 ). There were no significant differences in age, gender, and use of biological agents between the two groups. However, RF positivity, CRP levels, DAS-28 CRP, HAQ score, and disease duration were significantly higher in the discordance ≥ 10 group. Table 4 Comparison of the clinical background of patients with and without eGFR Cr-Cystatin C discordance Variables eGFR Cr-Cys <10 (n = 94) eGFR Cr-Cys ≧ 10 ( n = 46) p- Value Age (years) (median [IQR]) 66 (56.5-71.25) 66 (49.5–72.75) 0.814 Gender (male), n (%) 21 (22.3%) 13(28.3%) 0.530 ACPA-positive, n (%) 57 (60.6%) 35(76.1%) 0.088 ACPA titer (IU/mL) (median [IQR]) 15.45 (0.6–127.2) 32.05 (4.7–129.07) 0.293 RF-positive, n (%) 55(58.5%) 36 (78.3%) 0.024 RF titer (IU/mL) (median [IQR]) 36 (7–93) 88 (25–235.25) 0.004 CRP (mg/dL) (median [IQR]) 0.05(0.02–0.17) 0.21 (0.04–1.1) < 0.001 MTX use, n (%) 62 (66.0%) 21 (45.7%) 0.028 MTX dose mg/week (median [IQR]) 5(0–8) 0 (0–6) 0.056 NSAIDS use, n (%) 14 (14.9%) 14 (30.4%) 0.042 b/tsDMARDs use, n (%) 39(41.5%) 21 (45.7%) 0.717 Disease duration (years) (median [IQR]) 5 (0–8) 11 (0–6) 0.017 Serum creatinine (mg/dL) (median [IQR]) 0.64 (0.58–0.79) 0.51(0.46–0.64) < 0.001 Serum cystatin C (mg/dL) (median [IQR]) 0.85(0.73–0.99) 0.92(0.82–1.16) 0.012 eGFR creatinine (mL/min/1.73 m 2 )(median [IQR]) 75(57.25–92.78) 99.35(88.28–108.3) < 0.001 eGFR cystatin C (mL/min/1.73 m 2) (median [IQR]) 81.95(68.7-98.68) 76.6(56.15–85.2) 0.014 DAS 28-CRP (median [IQR]) 1.47 (1.16–1.95) 1.75 (1.41–2.81) 0.001 HAQ (median [IQR]) 0.12 (0.0–0.5) 0.5 (0.12–1.59) < 0.001 HTN, n (%) 20 (21.3) 7 (15.2) 0.496 Type 2 diabetes mellitus, n (%) 5 (5.3) 3 (6.5) 0.718 Logistic Regression Analysis of factors Associated with Proteinuria, Albuminuria, and eGFR Using logistic regression analysis, the relationships among proteinuria, albuminuria, eGFR, and clinical characteristics were evaluated (Table 5 ). Adjusting for age, gender, hypertension, and CRP as confounding factors, CRP positivity (> 0.15mg/dL) was revealed as an independent factor associated with UPCR > 0.15 g/g creatinine (odds ratio: 2.81; 95% confidence interval: 1.1–7.18), and hypertension was associated with UACR > 150 mg/g creatinine (odds ratio: 2.74; 95% confidence interval: 1.01–7.55). Table 5 Effect of clinical factors on proteinuria, albuminuria, and cystatin C in the multivariate models Proteinuria ≧ 0.15g/gCr Clinical factors Odds Raio 95% Confidence Interval P value Hypertension 1.59 0.54–4.66 0.40 Male gender 1.75 0.62–4.94 0.29 CRP > 0.15 mg/dL 2.53 1.02–6.28 0.045 Albuminuria ≧ 30 mg/gCr Clinical factors Odds Raio 95% Confidence Interval P value Hypertension 2.80 1.03–7.56 0.043 Male gender 1.88 0.71–4.97 0.246 CRP > 0.15 mg/dL 1.3 0.56–3.04 0.542 eGFR creatinine–eGFR cystatin C ≧ 10 Clinical factors Odds Raio 95% Confidence Interval P value Hypertension 0.533 0.191–1.49 0.23 Male gender 0.754 0.307–1.85 0.539 CRP > 0.15 mg/dL 3.65 1.51–7.41 0.0028 Results were obtained from multivariate logistic regression models after adjusting for age, gender, hypertension, and CRP value > 0.15mg/dL We also evaluated factors associated with the discordance between eGFR creatinine and cystatin C revealing that CRP positivity (> 0.15mg/dL) was significantly associated with this discordance (odds ratio: 3.42; 95% confidence interval: 1.51–7.73). There was no significant association with age, gender, and hypertension. CRP and Cystatin C Levels Before and After RA Treatment In a separate analysis of 38 treatment-naïve patients with RA, the correlation coefficient between CRP and cystatin C levels pre- and after treatment was 0.479 (95% confidence interval: 0.178–0.697), showing a significant positive correlation (Spearman’s rank, P < 0.005 ) (Fig. 1 ). Details of medical treatment and period were described in Fig. 1 legend. Discussion In this study, most of the patients were in their 60s and 70s, and approximately 35% were seronegative. While the MTX usage rate was < 60%, the rate of biologic agent use was relatively high at 40%. Disease activity was well-controlled, with a median DAS28-CRP score of 1.60, and most patients were not using glucocorticoids. This is different from previous patient profiles and treatment patterns and closely resembled those of recent large cohorts in Japan [ 11 ]. In these cohorts, patients with RA had an advanced age [ 12 ], with the prevalence of complications, such as renal and pulmonary disorders, increasing and the use of MTX declining. Although MTX is the first-line drug in the management of RA, its use is limited in patients with renal impairment [ 13 ]. In our study, proteinuria that was equivalent to CKD stage A2 or better was noted in 25 % of patients,and approximately 25% had decreased renal function corresponding to CKD stage G3a or worse, explaining the lower rate of MTX use. Renal impairment in RA was often attributed to drugs such as penicillamine, bucillamine, and NSAIDs [ 14 ]; however, they were not used in our study. Additionally, conditions such as chronic glomerulonephritis and AA amyloidosis that are directly caused by RA [ 15 ] but were not noted in our patients. Disease activity was well controlled as evidenced by 112 patients achieving DAS28-CRP remission and 90 patients having negative CRP levels. Meanwhile, aging and comorbidities, such as type 2 diabetes mellitus and hypertension, are thought to contribute to renal impairment [ 16 ]. In our cohort, these conditions were present in approximately 20% and 6 % of patients respectively. Although both eGFR creatinine and eGFR cystatin C were associated with age (data not shown), their associations with diabetes were not statistically significant, which may be attributed to the small sample size or lack of detailed diabetes control data. As well as our study, even without glomerulonephritis or amyloidosis, high disease activity in RA is correlated with the rate of eGFR decline[ 17 ], and aggressive treatment with biologics may further ameliorate renal impairment [ 18 ], which is thought to be dependent on CRP levels [ 19 ]. eGFR is widely used as an estimate of renal function and can be obtained based on serum creatinine, gender, and age; it is considered more useful than the serum creatinine level [ 20 ]. Although the estimation of eGFR varies by race and country [ 21 ]. Japan uses a specific formula recommended by the Japanese Society of Nephrology. eGFR creatinine can overestimate renal function in small-framed women with little muscle mass, sarcopenia [ 22 ], or bedridden individuals and underestimate it in well-built men with a large frame. Various medications also influence urinary creatinine excretion, and recent RA drugs, such as JAKi, may have similar effects [ 23 ]. Meanwhile, serum cystatin C is less affected by muscle mass and is attracting attention as a more accurate representation of renal function [ 24 ]. However, measuring cystatin C levels is costly and complex, limiting its routine use in real-world settings. Additionally, serum cystatin C levels can be influenced by thyroid function [ 25 ], cyclosporine use, steroid use [ 26 ], and disease activity [ 27 ]. Discordance between eGFR creatinine and cystatin C was also observed, complicating renal assessment. In such cases, decisions on MTX dosing and renal function monitoring are significantly affected. Several reports have addressed this issue in RA, suggesting that eGFR cystatin C may underestimate renal function in patients with high disease activity, and that discordance is associated with anemia or creatine kinase levels [ 28 ]. In our study, the difference between eGFR creatinine and cystatin C correlated with CRP levels, indicating that elevated CRP levels may increase cystatin C levels.. Along with findings relating to anemia [ 28 ], the results suggest that inflammatory cytokines may affect cystatin C levels. In refractory or flaring RA, reassessment of renal function using cystatin C may affect treatment decisions and should be interpreted cautiously. Otherwise, non-MTX therapy or reduced MTX dosages may be chosen due to perceived renal impairment. Meanwhile, in patients with sarcopenia, eGFR creatinine may overestimate renal function, and discordance is associated with increased MTX-related adverse events [ 29 ]. Furthermore, high serum cystatin C levels relative to creatinine are associated with an increased risk of cardiovascular events and heart failure [ 30 , 31 ]. potentially reflecting chronic inflammation. These results warrant the need for stricter RA management in such patients, although treatment decisions in discordant cases remain complex. Proteinuria is also a critical component of renal assessment, serving as both a predictor of future renal decline [ 32 ] and cardiovascular events [ 33 ] as well as an indicator of glomerular disease [ 34 ]. Various methods exist for protein quantification, including UACR, which is adopted by the KDIGO CKD classification system [ 35 ], although measuring albumin is expensive and not always feasible. In Japan, insurance typically limits UACR testing to patients with diabetic nephropathy. Albuminuria is more strongly correlated with cardiovascular risk than proteinuria [ 36 ], and discordance exists among dipstick, qualitative protein tests, and quantitative albumin tests [ 37 ]. In RA, low level of albuminuria is associated with poor prognosis, and urinary albumin evaluation is very important [ 38 ]. Few reports have assessed the relationship between proteinuria, albuminuria, patients’ clinical background, and disease activity in RA [ 39 ]. In this study, CRP was best correlated with total urinary protein, whereas hypertension was closely associated with albuminuria. Hypertension is prevalent in patients with RA, and comprehensive assessment and consideration of this condition is important [ 40 ]. Hypertension-related vascular stress, namely the strain vessel hypothesis, may explain the relationship between albuminuria and cardiovascular events [ 41 ], which was also noted in our patients. Although CRP levels are positively correlated with reduced renal function in RA [ 19 ], few studies have evaluated its correlation with proteinuria [ 42 ]. The mechanisms remain unclear, but tubular damage and impaired protein reabsorption as well as subclinical glomerular involvement may be present [ 43 ]. As proteinuria severity predicts CKD progression and CRP suppression prevents eGFR decline in RA, a strong association between renal decline and proteinuria is suggested even in the era of biologics. Beyond cardiovascular and ESRD risk prediction, urinalysis is employed in the initial RA assessment to aid in the differential diagnosis. Urinalysis also aids in decision making for renal biopsy particularly when glomerulonephritis or systemic vasculitis is suspected [ 44 ] and may influence the inappropriate use of glucocorticoids, delayed treatment, or unnecessary biopsies. Proteinuria may also preclude the use of specific DMARDs due to concerns regarding future renal impairment. Therefore, when evaluating renal function in patients with active RA, clinicians should consider the potential impact of elevated CRP levels on biomarkers such as cystatin C and urinalysis results. Our findings suggest that without accounting for inflammation, there is a risk of misinterpreting renal function, which may influence clinical decisions. For example, when renal impairment is assessed, decisions to avoid prescribing methotrexate should be made with cautious approach. In our exploratory findings CRP and Cystatin C Levels Before and After RA Treatment suggest that cystatin C may reflect inflammatory activity and could be reversible with effective treatment. However, because of small sample size, statistical evaluation was not satisfactory, considered exploratory hypothesis. This study has several limitations. This was a two-center study, and patient demographics were not evenly distributed. Only 3 patients using JAKi were included, and the relatively low seropositivity rate may limit the generalizability to seropositive RA. Additionally, MTX and biologic usage patterns may differ at other institutions. Because of the number of patients were limited, covariates are limited to minimizing the risk of overfitting. Based on clinical evidence, age, gender, hypertension, and CRP levels were selected as covariates for all multivariate models.Furthermore, detailed data on hypertension control, HbA1c trends, and other lifestyle-related factors such as smoking or dyslipidemia were lacking. Glucocorticoid use was extremely limited in this study, and the sample size was too small to include it as a confounding factor in the analysis. In addition, thyroid function can affect cystatin C levels, it was not measured in all cases and therefore not included in the present analysis. Although correlations between CRP and cystatin C levels were examined, confounding factors such as medication differences, thyroid function, or seropositivity were not adjusted. Conclusions In conclusion, high disease activity, indicated by elevated CRP levels, may increase serum cystatin C levels and lead to greater discordance between eGFR cystatin C and creatinine as well as proteinuria even in the absence of glomerular disease. With appropriate treatment and management, these values may be reversed. In the current era of b/tsDMARDs, accurate assessment of renal function and consideration of latent renal disease are important to guide appropriate treatment selection in RA. Declarations Data availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Conflicts of interest The author declares that there is no conflict of interest regarding the publication of this article. Funding statement No funding was received for this research. Ethics approval and informed consent This study was conducted in accordance with the Declaration of Helsinki, and the research protocol was approved by the ethics committees of the research institute (Hashima municipal hospital, Approval no. 2019-3). As this was a retrospective cohort study, informed consent from individual patients was waived. Author contributions The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. References Di Matteo A, Bathon JM, Emery P. Rheumatoid arthritis. The Lancet 2023;402:2019–33. https://doi.org/10.1016/S0140-6736(23)01525-8 Konzett V, Aletaha D. Management strategies in rheumatoid arthritis. Nat Rev Rheumatol 2024;20:760-769. https://doi.org/10.1038/s41584-024-01169-7 Boers M, Croonen AM, Dijkmans BAC, Breedveld FC, Eulderink F, Cats A et al. Renal findings in rheumatoid arthritis: Clinical aspects of 132 necropsies. Ann Rheum Dis 1987;46:658–63. Serhal L, Lwin MN, Holroyd C, Edwards CJ. Rheumatoid arthritis in the elderly: Characteristics and treatment considerations. Autoimmune Rev 2020;19. https://doi.org/10.1016/j.autrev.2020.102528 Smolen JS, Landewé RBM, Bijlsma JWJ, et al. 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PLoS One 2016;11. doi:10.1371/journal.pone.0160225 Sumida K, Molnar MZ, Potukuchi PK, Hassan F, Thomas F, Yamagata K et al. Treatment of rheumatoid arthritis with biologic agents lowers the risk of incident chronic kidney disease. Kidney Int 2018;93:1207–16. https://doi.org/10.1016/j.kint.2017.11.025 Hanaoka H, Kikuchi J, Hiramoto K, Saito S, Kondo Y, Kaneko Y. Decreased chronic kidney disease in rheumatoid arthritis in the era of biologic disease-modifying antirheumatic drugs. Clin Kidney J 2022;15:1373–8. file:///doi.org/10.1093/ckj/sfac036 Wei L, Macdonald TM, Jennings C, Sheng X, Flynn RW, Murphy MJ. Estimated GFR reporting is associated with decreased nonsteroidal anti-inflammatory drug prescription and increased renal function. Kidney Int 2013;84:174–8. http://dx.doi.org/10.1038/ki.2013.76 Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro III AF, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate [Internet]. Ann Intern Med. 2009;150:604-612 Okamura M, Konishi M, Butler J, Kalantar-Zadeh K, von Haehling S, Anker SD. Kidney function in cachexia and sarcopenia: Facts and numbers. J Cachexia Sarcopenia Muscle2023;14:1589–95. DOI: 10.1002/jcsm.13260 Nash P, Kerschbaumer A, Dörner T, Dougados M, Fleischmann RM, Geissler K et al. Points to consider for the treatment of immune-mediated inflammatory diseases with Janus kinase inhibitors: A consensus statement. Ann Rheum Dis2021;80:71–87. doi:10.1136/annrheumdis-2020-218398 Shlipak MG, Matsushita K, Ärnlöv J, Inker LA, Katz R, Polkinghorne KR, et al. Cystatin C versus Creatinine in Determining Risk Based on Kidney Function. New England Journal of Medicine 2013;369:932–43. DOI: 10.1056/NEJMoa1214234 Fricker M, Wiesli P, Bra¨ndle M, Bra¨ndle B, Schmid C. Impact of thyroid dysfunction on serum cystatin C. 2003.;63:1944-1947 Cimerman N, Mesko Brguljan P, Krasovec M, Suskovic S, Kos J. Serum cystatin C, a potent inhibitor of cysteine proteinases, is elevated in asthmatic patients 2000;300:83-95 Targońska-Stȩpniak B, Majdan M. Cystatin C concentration is correlated with disease activity in patients with rheumatoid arthritis. Scand J Rheumatol 2011;40:341–6. DOI: 10.3109/03009742.2011.571219 Nakashima A, Horita S, Matsunaga T, Inoue R, Zoshima T, Mizushima I et al. Factors contributing to the discrepant estimated glomerular filtration values measured by creatinine and cystatin C in patients with rheumatoid arthritis. Sci Rep 2021;11. https://doi.org/10.1038/s41598-021-89303-3 Kwon HC, Kang M Il, Kim SM. Cystatin C as a Predictor of Renal Function and Methotrexate-Associated Toxicities in Patients with Rheumatoid Arthritis. Journal of Rheumatology 2024;51:25–30. doi:10.3899/jrheum.2023-0218 Kim H, Park JT, Lee J, Jung JY, Lee KB, Kim YH, et al. The difference between cystatin C- and creatinine-based eGFR is associated with adverse cardiovascular outcomes in patients with chronic kidney disease. Atherosclerosis 2021;335:53–61. https://doi.org/10.1016/j.atherosclerosis.2021.08.036 Chen DC, Shlipak MG, Scherzer R, Bansal N, Potok OA, Rifkin DE, et al. Association of Intra-individual Differences in the Estimated GFR by Creatinine Versus Cystatin C With Incident Heart Failure. American Journal of Kidney Diseases 2022;80:762-772.e1. doi:10.1053/j.ajkd.2022.05.011 Iseki K, Ikemiya Y, Iseki C, Takishita S. Proteinuria and the risk of developing end-stage renal disease. Kidney Int 2003;63:1468–74. Nagata M, Ninomiya T, Kiyohara Y, Murakami Y, Irie F, Sairenchi T et al. Prediction of cardiovascular disease mortality by proteinuria and reduced kidney function: Pooled analysis of 39,000 individuals from 7 cohort studies in Japan. Am J Epidemiol2013;178:1–11. DOI: 10.1093/aje/kws447 Karstila K, Korpela M, Sihvonen S, Mustonen J. Prognosis of clinical renal disease and incidence of new renal findings in patients with rheumatoid arthritis: Follow-up of a population-based study. Clin Rheumatol 2007;26:2089–95. DOI 10.1007/s10067-007-0625-y Maruyama S, Tanaka T, Akiyama H, Hoshino M, Inokuchi S, Kaneko S et al. Cardiovascular, renal and mortality risk by the KDIGO heatmap in Japan. Clin Kidney J 2024;17. https:///doi.org/10.1093/ckj/sfae228 Sato H, Konta T, Ichikawa K, Suzuki N, Kabasawa A, Suzuki K et al. Comparison of the predictive ability of albuminuria and dipstick proteinuria for mortality in the Japanese population: the Yamagata (Takahata) study. Clin Exp Nephrol 2016;20:611–7. DOI 10.1007/s10157-015-1193-0 Nagai K, Yamagata K. Quantitative evaluation of proteinuria for health checkups is more efficient than the dipstick method. Clin Exp Nephrol 2015;19:152–3. DOI 10.1007/s10157-014-1034-6 Tang M, Du L, Peng J. Urinary albumin-to-creatinine ratio for predicting the risk of all-cause mortality and specific-cause mortality in patients with rheumatoid arthritis: evidence from NHANES 1999–2018. Clin Rheumatol 2024; https://doi.org/10.1007/s10067-024-07272-0 Daoussis D, Panoulas VF, John H, Toms TE, Antonopoulos I, Treharne G et al. Microalbuminuria in rheumatoid arthritis in the post-penicillamine/gold era: Association with hypertension but not therapy or inflammation. Clin Rheumatol 2011;30:477–84. DOI 10.1007/s10067-010-1446-y Panoulas VF, Douglas KMJ, Milionis HJ, Stavropoulos-Kalinglou A, Nightingale P, Kita MD, et al. Prevalence and associations of hypertension and its control in patients with rheumatoid arthritis. Rheumatology 2007;46:1477–82. doi:10.1093/rheumatology/kem169 Ito S, Nagasawa T, Abe M, Mori T. Strain vessel hypothesis: A viewpoint for the linkage of albuminuria and Cerebro-cardiovascular risk. Hypertensive Research2009;32:115–21. doi:10.1038/hr.2008.27 Mori S, Yoshitama T, Hirakata N, Ueki Y. Prevalence of and factors associated with renal dysfunction in patients with rheumatoid arthritis: a cross-sectional study in community hospitals. Clin Rheumatol 2017;36:2673–82. DOI 10.1007/s10067-017-3804-5 Niederstadt C, Happ T, Tatsis E, Schnabel A, Steinhoff J. Glomerular and tubular proteinuria as markers of nephropathy in rheumatoid arthritis. Rheumatology 1999;38:28–33. Mittal T, Rathi M. Rheumatological diseases and kidneys: A nephrologist’s perspective. Int J Rheum Dis2014;17:834–44. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Feb, 2026 Reviews received at journal 15 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 07 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 01 Jan, 2026 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-8495512","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588778689,"identity":"d46ac249-dd98-4805-9c7b-7502d82c8194","order_by":0,"name":"Masafumi Sugiyama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYNACAxsgkcDMkMDAzMAGFuEhqCWNZC0MhyFaGBiYCTtJt4H9meSXgvPR/OwJzAYPd1jL8zEwP/zAIHMHpxazAzxm0jIGt3Nn9jxgTkg8k27YxsBmLMHA8wy3lvtv2KQlgFo23EhgPpDYdpixDSgI9MthPLawPwNqOZe7H6rFvo2B/RsBLQxmkh8MDuRukEgAOqztcGIbAw8hW3iMrRkMknNnnHnYbJDYlp7cxsxTLJGAzy8H2B/e/PHHLre/Pfmw5M82a9v57e0bP3zswR1iIMAMiTfGBigXiBN7DuDVwvgDU+wHfi2jYBSMglEwogAAr9lQ/b8wGuAAAAAASUVORK5CYII=","orcid":"","institution":"Kindai University Nara Hospital","correspondingAuthor":true,"prefix":"","firstName":"Masafumi","middleName":"","lastName":"Sugiyama","suffix":""}],"badges":[],"createdAt":"2026-01-01 13:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8495512/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8495512/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102379700,"identity":"09740e4a-2d36-4a9e-a7ff-2a816b694dc1","added_by":"auto","created_at":"2026-02-11 06:26:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61429,"visible":true,"origin":"","legend":"\u003cp\u003eTransition of cystatin C and CRP levels in 38 treatment-naïve patients with rheumatoid arthritis. The correlation coefficient between pretreatment and after treatment was 0.479 (95% confidence interval: 0.178-0.697), showing a significant positive correlation (Spearman’s rank, \u003cem\u003eP\u0026lt;0.005\u003c/em\u003e). Their age median was 66 (54.5-72.5), median treatment period was 15 weeks (12.25-20.25), 35 received MTX, 10 received GC, 8 received Salazosulfapyridine.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8495512/v1/8f7359166a1c524647f00a0f.png"},{"id":102379760,"identity":"ae4a5f7c-bb90-47b2-b5e3-513931006ced","added_by":"auto","created_at":"2026-02-11 06:26:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1230174,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8495512/v1/9a1b582f-750e-4fb6-a112-a38946992b74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical significance of cystatin C based renal function assessment, proteinuria and albuminuria in patients with rheumatoid arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints that ultimately results in joint dysfunction and destruction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Advances in treatment, including the treat-to-target approach and biologic agents, have remarkably improved the prognosis of patients with RA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. RA and kidney disease are intertwined; RA is frequently complicated by drug-induced renal impairment and amyloidosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the incidence of these complications has been decreasing.\u003c/p\u003e \u003cp\u003eCurrent challenges include an aging patient population and the increased incidence of lifestyle-related diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which negatively affect renal function of patients with RA. Additionally, although the selection of antirheumatic drugs has increased, renal impairment limits the use of some drugs.\u003c/p\u003e \u003cp\u003eMethotrexate (MTX) is the first-line treatment for RA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; however, careful monitoring in patients with decreased renal function is necessary due to the heightened risk of adverse events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, Janus kinase inhibitors (JAKi) also require dose reduction or discontinuation depending on renal function [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, an accurate evaluation of patients\u0026rsquo; renal function is important. Furthermore, in cases complicated by chronic kidney disease (CKD), treatment strategies must also consider the increased risk of future cardiovascular events as well as the prevention of progression to end-stage renal disease (ESRD) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, evaluating proteinuria has become essential.\u003c/p\u003e \u003cp\u003eThis study aimed to clarify the relationship between renal function markers, such as serum creatinine, cystatin C, and proteinuria, and clinical background factors, including disease activity, of patients with RA in real-world clinical settings as well as to propose strategies for addressing these issues.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis retrospective, observational, cross-sectional study involved consecutive patients with RA from the Department of Nephrology and Rheumatology at Hashima Municipal Hospital and Department of Rheumatology at Kindai University Nara Hospital who were seen between April 2021 and March 2022. All participants met the 1987 American College of Rheumatology (ACR) classification criteria or the 2010 ACR/European League against Rheumatism criteria. Past medical history, clinical background (disease duration, type 2 diabetes mellitus, hypertension), disease activity, current treatment, body mass index (BMI), and blood and urine test results were assessed. Type 2 diabetes mellitus, hypertension, nonsteroidal anti-inflammatory drugs (NSAIDs) use was defined regular medical treatment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eTo evaluate systemic inflammatory findings, serum CRP was used, and the disease activity score (DAS)-28 CRP was employed to determine the disease activity index.\u003c/p\u003e \u003cp\u003eTotal urinary protein (U-TP, g/dL) and albumin (U-Alb, mg/dL) were measured separately by pyrogallol red and immunonephelometric assays, respectively. All U-TP and U-Alb levels were divided by the simultaneously measured urinary creatinine levels to eliminate the effects of urine concentration. During renal function serum testing, serum creatinine levels were measured using the enzyme assay method. The estimated glomerular filtration rate (eGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)from creatinine (eGFR creatinine) was obtained using the revised three-variable Japanese equation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Cystatin C levels were measured using the latex coagulation nephelometry method standardized with ERM-DA471/IFCC, and eGFR from cystatin C (eGFR cystatin C) was obtained using the developed GFR-estimating equation in Japan [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSubgroups by proteinuria, albuminuria, and eGFR creatinine-cystatin C discordance\u003c/h3\u003e\n\u003cp\u003eAll patients were divided according to the following criteria : UPCR\u0026thinsp;\u0026lt;\u0026thinsp;0.15 or \u0026ge;\u0026thinsp;0.15, UACR\u0026thinsp;\u0026lt;\u0026thinsp;30 or \u0026ge;\u0026thinsp;30, and eGFR creatinine-cystatin C discordance was defined as difference of \u0026ge;\u0026thinsp;10mL/min/1.73m2\u0026thinsp;\u0026lt;\u0026thinsp;10 or \u0026ge;\u0026thinsp;10, univariate analyses were performed to compare their clinical characteristics, disease activity, and comorbidities across each category. Subsequently, multivariate analyses adjusted for potential confounding factors were conducted to identify variables independently associated with these renal functions.\u003c/p\u003e\n\u003ch3\u003eExploratory longitudinal analysis of cystatin C between pre and after treatment\u003c/h3\u003e\n\u003cp\u003eCRP and cystatin C levels in 38 treatment-na\u0026iuml;ve patients with RA were measured before initiating treatment and after achieving a clinical response; their results were then compared. For each case, the respective changes in CRP and cystatin C levels were calculated, and their correlation was evaluated.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using EZR (version 1.60, Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R software (The R foundation for Statistical Computing, Vienna, Austria), GraphPad Prism 9 (GraphPad Software Inc., Boston, USA), and IBM SPSS Statistics Version 28.0 (IBM Corp., Armonk, NY, USA).Continuous variables are presented as median (interquartile range), categorical variables are presented as numbers and percentages. In univariate analysis, the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test was used for continuous variables, and Chi-squared test or Fisher\u0026rsquo;s exact test was used for categorical variables as appropriate. A multivariate linear regression model was conducted to identify the factors independently associated with proteinuria, albuminuria, and eGFR discordance in the adjusted model using confounders such as age, gender, hypertension, and CRP values\u0026thinsp;\u0026gt;\u0026thinsp;0.15mg/dL. In the exploratory longitudinal analysis, the correlation between the corresponding CRP and cystatin C levels before and after treatment was evaluated using Spearman\u0026rsquo;s rank correlation coefficient. All statistical analysis tests were two-sided, and a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eOverall, 154 patients with RA were assessed; 11 and 3 were excluded due to incomplete laboratory results and lack of consent, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the profiles of the 140 enrolled patients. Disease activity was well controlled, with DAS 28-CRP remission in 112/140 patients. More than 60% of the patients were seropositive. The proportion of men was 24.3%, and the median disease duration was 6 years. Among the 140 patients, 83 (59.7%) received MTX, and 60 (42.9%) received biological agents. Additionally, 28 patients (20%) received nonsteroidal anti-inflammatory drugs (NSAIDs), while 3 patients received JAKi. Hypertension and type 2 diabetes mellitus were noted in 27 (19.3%) and 8 (5.7%) patients, respectively. Overall, the median serum CRP, creatinine, and cystatin C levels were 0.07 mg/dL, 0.61 mg/dL, and 0.87mg/dL, respectively. The median eGFR according to the creatinine and cystatin C levels were 81mL/min and 80 mL/min, respectively. The number of patients with an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min based on creatinine and cystatin C levels were 32 and 31, respectively. Meanwhile, urinary protein-to-creatinine ratio (UPCR)\u0026thinsp;\u0026ge;\u0026thinsp;0.15(g/Cr) and urinary albumin-to-creatinine ratio (UACR)\u0026thinsp;\u0026ge;\u0026thinsp;30(mg/Cr) was observed in 29 (20.7%) and 37 (26.4%) patients, respectively. There were no patients with ESRD requiring dialysis.\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\u003ePatient characteristics Clinical backgrounds, treatment, clinical manifestations, and renal function and proteinuria of patients with rheumatoid arthritis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\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\u003eOverall\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;140\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 [55\u0026ndash;72.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA titer IU/mL (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.35 [0.6\u0026ndash;128.22]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF titer IU/mL (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 [7\u0026ndash;121.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 [3.0\u0026ndash;15.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX dose mg/week (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 [0\u0026ndash;8.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDS use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb/tsDMARDs use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07 [0.03\u0026ndash;0.30]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS 28-CRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6 [1.22\u0026ndash;2.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS-28-CRP remission, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25 [0\u0026ndash;1.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine mg/dL (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61 [0.51\u0026ndash;0.77]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR creatinine mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.0 [66.0\u0026ndash;99.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR Creatinine\u0026thinsp;\u0026lt;\u0026thinsp;60mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e \u0026lt;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum cystatin C mg/dL (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 [0.75\u0026ndash;1.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR cystatin C mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.0 [65.15\u0026ndash;94.35]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR cystatin C\u0026thinsp;\u0026lt;\u0026thinsp;60mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(22.9%)\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 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.57 [19.19\u0026ndash;23.83]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;25, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary albumin mg/g creatinine ratio (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.0 [12.5\u0026ndash;32.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary albumin\u0026thinsp;\u0026gt;\u0026thinsp;30mg/g creatinine ratio, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary total protein g/g creatinine ratio (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 [0.07\u0026ndash;0.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary total protein\u0026thinsp;\u0026gt;\u0026thinsp;0.15g/g creatinine ratio, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eACPA: anti-cyclic citrullinated peptide antibodies; b/tsDMARDs use: biological targeted synthesized disease modifying antirheumatic drugs; BMI: body mass index; CRP: C-reactive protein; DAS28-CRP: disease activity score 28-joint count using CRP; eGFR: estimated glomerular filtration rate; HAQ: health assessment questionnaire; HTN: hypertension; IQR: interquartile range; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; RF: rheumatoid IgM factor\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation Between Proteinuria, Estimated Renal Function, and Clinical Background\u003c/h3\u003e\n\u003cp\u003eUsing a UPCR cutoff of 0.15, patients were divided into the \u0026lt;\u0026thinsp;0.15 and \u0026ge;\u0026thinsp;0.15 groups, and their clinical backgrounds were compared (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were no significant differences in gender, seropositivity, and use of biological agents between the two groups. Additionally, there were no differences in the prevalence of hypertension or type 2 diabetes mellitus. However, age, MTX non-use, and CRP levels were significantly higher in the UPCR\u0026thinsp;\u0026ge;\u0026thinsp;0.15 group. Moreover, serum cystatin C levels were also significantly elevated, and eGFR cystatin C was significantly lower in the UPCR\u0026thinsp;\u0026ge;\u0026thinsp;0.15 group.\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 the clinical background of patients with and without proteinuria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eU-TP/Cr\u003c/p\u003e \u003cp\u003eratio\u0026thinsp;\u0026lt;\u0026thinsp;0.15 \u003cem\u003e(n\u003c/em\u003e\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eU-TP/Cr\u003c/p\u003e \u003cp\u003eratio\u0026thinsp;\u0026ge;\u0026thinsp;0.15 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (50\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (64\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA titer (IU/mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5 (0.6\u0026ndash;142.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9 (0.6\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF titer (IU/mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0(8.0\u0026ndash;127.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0 (6.0\u0026ndash;83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.02\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29 (0.05\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX dose mg/week (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDS use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb/tsDMARDs use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (31.0%)\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\u003eDisease duration (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (3.0\u0026ndash;18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.51\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.54\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum cystatin C (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.72\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01(0.88\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR creatinine (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.0 (68.2\u0026ndash;99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.0 (63.0\u0026ndash;87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR cystatin C (mL/min/1.73 m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.2 (69.8\u0026ndash;98.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.6 (52.9\u0026ndash;76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS 28-CRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57 (1.21\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85 (1.23\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.00\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.00\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eACPA: anti-cyclic citrullinated peptide antibodies; b/tsDMARDs: biological targeted synthesized disease modifying antirheumatic drugs; CRP: C-reactive protein; DAS28-ESR: disease activity score 28-joint count using CRP; eGFR: estimated glomerular filtration rate; HAQ: health assessment questionnaire; HTN: hypertension; IQR: interquartile range; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; RF: rheumatoid IgM factor; U-TP/Crn: urinary total protein/creatinine; p-Values were calculated to compare patients with U-TP/Crn less than 0.15 and those with U-TP/Crn equal to or greater than 0.15 using Mann\u0026ndash;Whitney test or Fisher\u0026rsquo;s exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing a UACR cutoff of 30, patients were divided into the \u0026lt;\u0026thinsp;30 and \u0026ge;\u0026thinsp;30 groups, and their clinical backgrounds were compared (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly to the UPCR results, there were no significant differences in gender and use of biological agents between the two groups. However, seropositivity rate was significantly higher in the UACR\u0026thinsp;\u0026ge;\u0026thinsp;30 group, both of eGFR creatinine and cystatin are significantly lower in the UACR\u0026thinsp;\u0026ge;\u0026thinsp;30 group. Furthermore, unlike the UPCR results, no difference in CRP values was observed between the two groups. Although the prevalence of hypertension was significantly higher in the albuminuria-positive group.\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 the clinical background of patients with and without albuminuria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eU-Alb/Cr ratio\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 \u003cem\u003e(n\u003c/em\u003e\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eU-Alb/Cr ratio\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (49\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (65\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA titer (IU/mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.3 (0.6\u0026ndash;171.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2 (0.6\u0026ndash;43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (69.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF titer (IU/mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.0 (8.0\u0026ndash;138.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (6.0\u0026ndash;70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.02\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.04\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX dose mg/week (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDS use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb/tsDMARDs use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (4.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (2.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.51\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.58\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum cystatin C (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.73\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95(0.81\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR creatinine (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.1(68.2\u0026ndash;100.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.0(64.1\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR cystatin C (mL/min/1.73 m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.2 (69.8\u0026ndash;99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.4 (54.0\u0026ndash;81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS 28-CRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.19\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.36\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.00\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.00\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eACPA: anti-cyclic citrullinated peptide antibodies; b/tsDMARDs: biological targeted synthesized disease modifying antirheumatic drugs; CRP: C-reactive protein; DAS28-ESR: disease activity score 28-joint count using CRP; eGFR: estimated glomerular filtration rate; HAQ: health assessment questionnaire; HTN: hypertension; IQR: interquartile range; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; RF: rheumatoid IgM factor; U-Alb/Crn: urinary albumin/creatinine; \u003cem\u003eP\u003c/em\u003e-values were calculated to compare patients with U-Alb/Crn\u0026thinsp;\u0026lt;\u0026thinsp;30 and those with \u0026ge;\u0026thinsp;30 using the Mann\u0026ndash;Whitney test or Fisher\u0026rsquo;s exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing a discordance between eGFR creatinine and cystatin C cutoff of 10, patients were divided into the \u0026lt;\u0026thinsp;10and\u0026thinsp;\u0026ge;\u0026thinsp;10 groups, and their clinical backgrounds were compared (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were no significant differences in age, gender, and use of biological agents between the two groups. However, RF positivity, CRP levels, DAS-28 CRP, HAQ score, and disease duration were significantly higher in the discordance\u0026thinsp;\u0026ge;\u0026thinsp;10 group.\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\u003eComparison of the clinical background of patients with and without eGFR Cr-Cystatin C discordance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eeGFR Cr-Cys\u003c/p\u003e \u003cp\u003e\u0026lt;10 \u003cem\u003e(n\u003c/em\u003e\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeGFR Cr-Cys\u003c/p\u003e \u003cp\u003e≧\u0026thinsp;10 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (56.5-71.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (49.5\u0026ndash;72.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACPA titer (IU/mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.45 (0.6\u0026ndash;127.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.05 (4.7\u0026ndash;129.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF-positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF titer (IU/mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (7\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (25\u0026ndash;235.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05(0.02\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (0.04\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX dose mg/week (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDS use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb/tsDMARDs use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.58\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51(0.46\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum cystatin C (mg/dL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0.73\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92(0.82\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR creatinine (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(57.25\u0026ndash;92.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.35(88.28\u0026ndash;108.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR cystatin C (mL/min/1.73 m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.95(68.7-98.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.6(56.15\u0026ndash;85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS 28-CRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47 (1.16\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75 (1.41\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.0\u0026ndash;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.12\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLogistic Regression Analysis of factors Associated with Proteinuria, Albuminuria, and eGFR\u003c/h2\u003e \u003cp\u003eUsing logistic regression analysis, the relationships among proteinuria, albuminuria, eGFR, and clinical characteristics were evaluated (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Adjusting for age, gender, hypertension, and CRP as confounding factors, CRP positivity (\u0026gt;\u0026thinsp;0.15mg/dL) was revealed as an independent factor associated with UPCR\u0026thinsp;\u0026gt;\u0026thinsp;0.15 g/g creatinine (odds ratio: 2.81; 95% confidence interval: 1.1\u0026ndash;7.18), and hypertension was associated with UACR\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/g creatinine (odds ratio: 2.74; 95% confidence interval: 1.01\u0026ndash;7.55).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of clinical factors on proteinuria, albuminuria, and cystatin C in the multivariate models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eProteinuria\u0026thinsp;≧\u0026thinsp;0.15g/gCr\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\u003eClinical factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOdds Raio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% Confidence Interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\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\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026ndash;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u0026thinsp;\u0026gt;\u0026thinsp;0.15 mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026ndash;6.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAlbuminuria\u0026thinsp;≧\u0026thinsp;30 mg/gCr\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOdds Raio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% Confidence Interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.03\u0026ndash;7.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026gt;\u0026thinsp;0.15 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eeGFR creatinine\u0026ndash;eGFR cystatin C\u0026thinsp;≧\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOdds Raio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% Confidence Interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\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\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.191\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.307\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u0026thinsp;\u0026gt;\u0026thinsp;0.15 mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.51\u0026ndash;7.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eResults were obtained from multivariate logistic regression models after adjusting for age, gender, hypertension, and CRP value\u0026thinsp;\u0026gt;\u0026thinsp;0.15mg/dL\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also evaluated factors associated with the discordance between eGFR creatinine and cystatin C revealing that CRP positivity (\u0026gt;\u0026thinsp;0.15mg/dL) was significantly associated with this discordance (odds ratio: 3.42; 95% confidence interval: 1.51\u0026ndash;7.73). There was no significant association with age, gender, and hypertension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCRP and Cystatin C Levels Before and After RA Treatment\u003c/h2\u003e \u003cp\u003eIn a separate analysis of 38 treatment-na\u0026iuml;ve patients with RA, the correlation coefficient between CRP and cystatin C levels pre- and after treatment was 0.479 (95% confidence interval: 0.178\u0026ndash;0.697), showing a significant positive correlation (Spearman\u0026rsquo;s rank, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.005\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Details of medical treatment and period were described in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e legend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, most of the patients were in their 60s and 70s, and approximately 35% were seronegative. While the MTX usage rate was \u0026lt;\u0026thinsp;60%, the rate of biologic agent use was relatively high at 40%. Disease activity was well-controlled, with a median DAS28-CRP score of 1.60, and most patients were not using glucocorticoids. This is different from previous patient profiles and treatment patterns and closely resembled those of recent large cohorts in Japan [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In these cohorts, patients with RA had an advanced age [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with the prevalence of complications, such as renal and pulmonary disorders, increasing and the use of MTX declining. Although MTX is the first-line drug in the management of RA, its use is limited in patients with renal impairment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In our study, proteinuria that was equivalent to CKD stage A2 or better was noted in 25 % of patients,and approximately 25% had decreased renal function corresponding to CKD stage G3a or worse, explaining the lower rate of MTX use.\u003c/p\u003e \u003cp\u003eRenal impairment in RA was often attributed to drugs such as penicillamine, bucillamine, and NSAIDs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; however, they were not used in our study. Additionally, conditions such as chronic glomerulonephritis and AA amyloidosis that are directly caused by RA [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] but were not noted in our patients. Disease activity was well controlled as evidenced by 112 patients achieving DAS28-CRP remission and 90 patients having negative CRP levels. Meanwhile, aging and comorbidities, such as type 2 diabetes mellitus and hypertension, are thought to contribute to renal impairment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our cohort, these conditions were present in approximately 20% and 6 % of patients respectively. Although both eGFR creatinine and eGFR cystatin C were associated with age (data not shown), their associations with diabetes were not statistically significant, which may be attributed to the small sample size or lack of detailed diabetes control data.\u003c/p\u003e \u003cp\u003eAs well as our study, even without glomerulonephritis or amyloidosis, high disease activity in RA is correlated with the rate of eGFR decline[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and aggressive treatment with biologics may further ameliorate renal impairment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which is thought to be dependent on CRP levels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eeGFR is widely used as an estimate of renal function and can be obtained based on serum creatinine, gender, and age; it is considered more useful than the serum creatinine level [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the estimation of eGFR varies by race and country [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Japan uses a specific formula recommended by the Japanese Society of Nephrology. eGFR creatinine can overestimate renal function in small-framed women with little muscle mass, sarcopenia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], or bedridden individuals and underestimate it in well-built men with a large frame. Various medications also influence urinary creatinine excretion, and recent RA drugs, such as JAKi, may have similar effects [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Meanwhile, serum cystatin C is less affected by muscle mass and is attracting attention as a more accurate representation of renal function [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, measuring cystatin C levels is costly and complex, limiting its routine use in real-world settings. Additionally, serum cystatin C levels can be influenced by thyroid function [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], cyclosporine use, steroid use [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and disease activity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiscordance between eGFR creatinine and cystatin C was also observed, complicating renal assessment. In such cases, decisions on MTX dosing and renal function monitoring are significantly affected. Several reports have addressed this issue in RA, suggesting that eGFR cystatin C may underestimate renal function in patients with high disease activity, and that discordance is associated with anemia or creatine kinase levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In our study, the difference between eGFR creatinine and cystatin C correlated with CRP levels, indicating that elevated CRP levels may increase cystatin C levels.. Along with findings relating to anemia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the results suggest that inflammatory cytokines may affect cystatin C levels.\u003c/p\u003e \u003cp\u003eIn refractory or flaring RA, reassessment of renal function using cystatin C may affect treatment decisions and should be interpreted cautiously. Otherwise, non-MTX therapy or reduced MTX dosages may be chosen due to perceived renal impairment. Meanwhile, in patients with sarcopenia, eGFR creatinine may overestimate renal function, and discordance is associated with increased MTX-related adverse events [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, high serum cystatin C levels relative to creatinine are associated with an increased risk of cardiovascular events and heart failure [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. potentially reflecting chronic inflammation. These results warrant the need for stricter RA management in such patients, although treatment decisions in discordant cases remain complex.\u003c/p\u003e \u003cp\u003eProteinuria is also a critical component of renal assessment, serving as both a predictor of future renal decline [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and cardiovascular events [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] as well as an indicator of glomerular disease [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Various methods exist for protein quantification, including UACR, which is adopted by the KDIGO CKD classification system [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], although measuring albumin is expensive and not always feasible. In Japan, insurance typically limits UACR testing to patients with diabetic nephropathy. Albuminuria is more strongly correlated with cardiovascular risk than proteinuria [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and discordance exists among dipstick, qualitative protein tests, and quantitative albumin tests [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In RA, low level of albuminuria is associated with poor prognosis, and urinary albumin evaluation is very important [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFew reports have assessed the relationship between proteinuria, albuminuria, patients\u0026rsquo; clinical background, and disease activity in RA [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, CRP was best correlated with total urinary protein, whereas hypertension was closely associated with albuminuria. Hypertension is prevalent in patients with RA, and comprehensive assessment and consideration of this condition is important [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Hypertension-related vascular stress, namely the strain vessel hypothesis, may explain the relationship between albuminuria and cardiovascular events [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which was also noted in our patients. Although CRP levels are positively correlated with reduced renal function in RA [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], few studies have evaluated its correlation with proteinuria [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The mechanisms remain unclear, but tubular damage and impaired protein reabsorption as well as subclinical glomerular involvement may be present [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As proteinuria severity predicts CKD progression and CRP suppression prevents eGFR decline in RA, a strong association between renal decline and proteinuria is suggested even in the era of biologics.\u003c/p\u003e \u003cp\u003eBeyond cardiovascular and ESRD risk prediction, urinalysis is employed in the initial RA assessment to aid in the differential diagnosis. Urinalysis also aids in decision making for renal biopsy particularly when glomerulonephritis or systemic vasculitis is suspected [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and may influence the inappropriate use of glucocorticoids, delayed treatment, or unnecessary biopsies. Proteinuria may also preclude the use of specific DMARDs due to concerns regarding future renal impairment. Therefore, when evaluating renal function in patients with active RA, clinicians should consider the potential impact of elevated CRP levels on biomarkers such as cystatin C and urinalysis results. Our findings suggest that without accounting for inflammation, there is a risk of misinterpreting renal function, which may influence clinical decisions. For example, when renal impairment is assessed, decisions to avoid prescribing methotrexate should be made with cautious approach.\u003c/p\u003e \u003cp\u003eIn our exploratory findings CRP and Cystatin C Levels Before and After RA Treatment suggest that cystatin C may reflect inflammatory activity and could be reversible with effective treatment. However, because of small sample size, statistical evaluation was not satisfactory, considered exploratory hypothesis.\u003c/p\u003e \u003cp\u003eThis study has several limitations. This was a two-center study, and patient demographics were not evenly distributed. Only 3 patients using JAKi were included, and the relatively low seropositivity rate may limit the generalizability to seropositive RA. Additionally, MTX and biologic usage patterns may differ at other institutions. Because of the number of patients were limited, covariates are limited to minimizing the risk of overfitting. Based on clinical evidence, age, gender, hypertension, and CRP levels were selected as covariates for all multivariate models.Furthermore, detailed data on hypertension control, HbA1c trends, and other lifestyle-related factors such as smoking or dyslipidemia were lacking. Glucocorticoid use was extremely limited in this study, and the sample size was too small to include it as a confounding factor in the analysis. In addition, thyroid function can affect cystatin C levels, it was not measured in all cases and therefore not included in the present analysis. Although correlations between CRP and cystatin C levels were examined, confounding factors such as medication differences, thyroid function, or seropositivity were not adjusted.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, high disease activity, indicated by elevated CRP levels, may increase serum cystatin C levels and lead to greater discordance between eGFR cystatin C and creatinine as well as proteinuria even in the absence of glomerular disease. With appropriate treatment and management, these values may be reversed. In the current era of b/tsDMARDs, accurate assessment of renal function and consideration of latent renal disease are important to guide appropriate treatment selection in RA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe author declares that there is no conflict of interest regarding the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki, and the research protocol was approved by the ethics committees of the research institute (Hashima municipal hospital, Approval no. 2019-3). As this was a retrospective cohort study, informed consent from individual patients was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDi Matteo A, Bathon JM, Emery P. Rheumatoid arthritis. The Lancet 2023;402:2019\u0026ndash;33. https://doi.org/10.1016/S0140-6736(23)01525-8\u003c/li\u003e\n \u003cli\u003eKonzett V, Aletaha D. Management strategies in rheumatoid arthritis. Nat Rev Rheumatol 2024;20:760-769. https://doi.org/10.1038/s41584-024-01169-7\u003c/li\u003e\n \u003cli\u003eBoers M, Croonen AM, Dijkmans BAC, Breedveld FC, Eulderink F, Cats A et al. Renal findings in rheumatoid arthritis: Clinical aspects of 132 necropsies. 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Kidney Int 2013;84:174\u0026ndash;8. http://dx.doi.org/10.1038/ki.2013.76\u003c/li\u003e\n \u003cli\u003eLevey AS, Stevens LA, Schmid CH, Zhang Y, Castro III AF, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate [Internet]. Ann Intern Med. 2009;150:604-612\u003c/li\u003e\n \u003cli\u003eOkamura M, Konishi M, Butler J, Kalantar-Zadeh K, von Haehling S, Anker SD. Kidney function in cachexia and sarcopenia: Facts and numbers. J Cachexia Sarcopenia Muscle2023;14:1589\u0026ndash;95. DOI: 10.1002/jcsm.13260\u003c/li\u003e\n \u003cli\u003eNash P, Kerschbaumer A, D\u0026ouml;rner T, Dougados M, Fleischmann RM, Geissler K et al. Points to consider for the treatment of immune-mediated inflammatory diseases with Janus kinase inhibitors: A consensus statement. Ann Rheum Dis2021;80:71\u0026ndash;87. doi:10.1136/annrheumdis-2020-218398\u003c/li\u003e\n \u003cli\u003eShlipak MG, Matsushita K, \u0026Auml;rnl\u0026ouml;v J, Inker LA, Katz R, Polkinghorne KR, et al. 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Factors contributing to the discrepant estimated glomerular filtration values measured by creatinine and cystatin C in patients with rheumatoid arthritis. Sci Rep 2021;11. https://doi.org/10.1038/s41598-021-89303-3\u003c/li\u003e\n \u003cli\u003eKwon HC, Kang M Il, Kim SM. Cystatin C as a Predictor of Renal Function and Methotrexate-Associated Toxicities in Patients with Rheumatoid Arthritis. Journal of Rheumatology 2024;51:25\u0026ndash;30. doi:10.3899/jrheum.2023-0218\u003c/li\u003e\n \u003cli\u003eKim H, Park JT, Lee J, Jung JY, Lee KB, Kim YH, et al. The difference between cystatin C- and creatinine-based eGFR is associated with adverse cardiovascular outcomes in patients with chronic kidney disease. Atherosclerosis 2021;335:53\u0026ndash;61. https://doi.org/10.1016/j.atherosclerosis.2021.08.036\u003c/li\u003e\n \u003cli\u003eChen DC, Shlipak MG, Scherzer R, Bansal N, Potok OA, Rifkin DE, et al. Association of Intra-individual Differences in the Estimated GFR by Creatinine Versus Cystatin C With Incident Heart Failure. American Journal of Kidney Diseases 2022;80:762-772.e1. doi:10.1053/j.ajkd.2022.05.011\u003c/li\u003e\n \u003cli\u003eIseki K, Ikemiya Y, Iseki C, Takishita S. Proteinuria and the risk of developing end-stage renal disease. Kidney Int 2003;63:1468\u0026ndash;74.\u003c/li\u003e\n \u003cli\u003eNagata M, Ninomiya T, Kiyohara Y, Murakami Y, Irie F, Sairenchi T et al. Prediction of cardiovascular disease mortality by proteinuria and reduced kidney function: Pooled analysis of 39,000 individuals from 7 cohort studies in Japan. Am J Epidemiol2013;178:1\u0026ndash;11. DOI: 10.1093/aje/kws447\u003c/li\u003e\n \u003cli\u003eKarstila K, Korpela M, Sihvonen S, Mustonen J. Prognosis of clinical renal disease and incidence of new renal findings in patients with rheumatoid arthritis: Follow-up of a population-based study. Clin Rheumatol 2007;26:2089\u0026ndash;95. DOI 10.1007/s10067-007-0625-y\u003c/li\u003e\n \u003cli\u003eMaruyama S, Tanaka T, Akiyama H, Hoshino M, Inokuchi S, Kaneko S et al. Cardiovascular, renal and mortality risk by the KDIGO heatmap in Japan. Clin Kidney J 2024;17. https:///doi.org/10.1093/ckj/sfae228\u003c/li\u003e\n \u003cli\u003eSato H, Konta T, Ichikawa K, Suzuki N, Kabasawa A, Suzuki K et al. Comparison of the predictive ability of albuminuria and dipstick proteinuria for mortality in the Japanese population: the Yamagata (Takahata) study. Clin Exp Nephrol 2016;20:611\u0026ndash;7. DOI 10.1007/s10157-015-1193-0\u003c/li\u003e\n \u003cli\u003eNagai K, Yamagata K. Quantitative evaluation of proteinuria for health checkups is more efficient than the dipstick method. Clin Exp Nephrol 2015;19:152\u0026ndash;3. DOI 10.1007/s10157-014-1034-6\u003c/li\u003e\n \u003cli\u003eTang M, Du L, Peng J. Urinary albumin-to-creatinine ratio for predicting the risk of all-cause mortality and specific-cause mortality in patients with rheumatoid arthritis: evidence from NHANES 1999\u0026ndash;2018. Clin Rheumatol 2024; https://doi.org/10.1007/s10067-024-07272-0\u003c/li\u003e\n \u003cli\u003eDaoussis D, Panoulas VF, John H, Toms TE, Antonopoulos I, Treharne G et al. Microalbuminuria in rheumatoid arthritis in the post-penicillamine/gold era: Association with hypertension but not therapy or inflammation. Clin Rheumatol 2011;30:477\u0026ndash;84. DOI 10.1007/s10067-010-1446-y\u003c/li\u003e\n \u003cli\u003ePanoulas VF, Douglas KMJ, Milionis HJ, Stavropoulos-Kalinglou A, Nightingale P, Kita MD, et al. Prevalence and associations of hypertension and its control in patients with rheumatoid arthritis. Rheumatology 2007;46:1477\u0026ndash;82. doi:10.1093/rheumatology/kem169\u003c/li\u003e\n \u003cli\u003eIto S, Nagasawa T, Abe M, Mori T. Strain vessel hypothesis: A viewpoint for the linkage of albuminuria and Cerebro-cardiovascular risk. Hypertensive Research2009;32:115\u0026ndash;21. doi:10.1038/hr.2008.27\u003c/li\u003e\n \u003cli\u003eMori S, Yoshitama T, Hirakata N, Ueki Y. Prevalence of and factors associated with renal dysfunction in patients with rheumatoid arthritis: a cross-sectional study in community hospitals. Clin Rheumatol 2017;36:2673\u0026ndash;82. DOI 10.1007/s10067-017-3804-5\u003c/li\u003e\n \u003cli\u003eNiederstadt C, Happ T, Tatsis E, Schnabel A, Steinhoff J. Glomerular and tubular proteinuria as markers of nephropathy in rheumatoid arthritis. Rheumatology 1999;38:28\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eMittal T, Rathi M. Rheumatological diseases and kidneys: A nephrologist\u0026rsquo;s perspective. Int J Rheum Dis2014;17:834\u0026ndash;44.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8495512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8495512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e:\u003cem\u003e \u003c/em\u003eThis study aimed to determine the relationship between renal function markers and clinical background factors in patients with rheumatoid arthritis (RA) as well as propose effective strategies for accurate renal assessment and management of RA, especially in the context of modern treatment options.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003cem\u003e \u003c/em\u003eA retrospective, observational, cross-sectional study was conducted in Hashima municipal hospital and Kindai University Nara Hospital involving 140patients with RA who met the 1987 American college of rheumatology (ACR) or 2010 ACR/European league against rheumatism criteria. Clinical background, comorbidities, disease activity, and serum C reactive protein (CRP). creatinine, cystatin C, and urinary protein levels, and albuminuria were assessed. eGFR was obtained from both creatinine and cystatin C levels. Data of 38 treatment-naïve patients were also analyzed longitudinally for changes in C reactive protein (CRP) and cystatin C levels. Statistical analyses included multivariate regression and correlation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Renal function and proteinuria: 20.7% of patients had proteinuria (urinary protein–creatinine ratio ≥0.15), and 26.4% had albuminuria (urinary albumin–creatinine ratio ≥30). An eGFR of \u0026lt;60 mL/min was noted in approximately 23% of cases based on both creatinine and cystatin C levels.\u003c/p\u003e\n\u003cp\u003eAssociations: CRP levels were significantly associated with proteinuria, cystatin C, and discordance between eGFR based on creatinine and cystatin C levels. Hypertension was related to albuminuria.\u003c/p\u003e\n\u003cp\u003eLongitudinal analysis: In treatment-naïve patients, cystatin C levels decreased in parallel with the subsequent reduction in CRP following treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: In patients with RA, elevated CRP levels are associated with increased cystatin C levels, proteinuria, and eGFR discordance even without glomerular disease. These markers may reflect inflammatory activity rather than intrinsic kidney damage and may improve with treatment. Due to implications for drug selection and risk management particularly with methotrexate and biologics, accurate and multifactorial assessment of renal function remains critical in RA.\u003c/p\u003e","manuscriptTitle":"Clinical significance of cystatin C based renal function assessment, proteinuria and albuminuria in patients with rheumatoid arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 06:24:48","doi":"10.21203/rs.3.rs-8495512/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"141417007636054658816867351554830529009","date":"2026-02-18T11:54:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T09:46:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268821462394542223407817802420886027769","date":"2026-02-09T06:07:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T10:52:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-07T18:44:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T10:12:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T10:07:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Rheumatology","date":"2026-01-01T13:37:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9d0b106-34aa-4ec6-b0cf-97172ce8fa72","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T06:24:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 06:24:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8495512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8495512","identity":"rs-8495512","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}