Early Kidney Injury Markers in Patients with Seronegative Spondyloarthropathies: A Retrospective Cross-Sectional Comparative Study

Early Kidney Injury Markers in Patients with Seronegative Spondyloarthropathies: A Retrospective Cross-Sectional Comparative Study | 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 Article Early Kidney Injury Markers in Patients with Seronegative Spondyloarthropathies: A Retrospective Cross-Sectional Comparative Study Kinga Maria Tyczyńska, Piotr Krzysztof Krajewski, Hanna Augustyniak-Bartosik, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7907238/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Patients with seronegative spondyloarthropathies (SpA) are at increased risk of kidney involvement due to chronic inflammation, comorbidities and pharmacotherapy. There is a need for early kidney injury markers (EKIMs) to identify subclinical renal damage, potentially enabling timely intervention and preventing irreversible kidney disease progression. Objectives This study aimed to comprehensively evaluate the concentrations of various established and emerging early kidney injury markers, including Neutrophil Gelatinase-Associated Lipocalin (NGAL), Kidney Injury Molecule-1 (KIM-1), Retinol Binding Protein 4 (RBP4), Fibroblast Growth Factor 23 (FGF23), N-acetyl-β-D-glucosaminidase (NAG), and Interleukin-18 (IL-18), in serum and urine of SpA patients without clinically recognized kidney disease, comparing them to healthy controls (HC) and analyzing their associations with SpA subtypes and therapeutic regimens. Methods This was a retrospective cross-sectional comparative study involving 125 patients with SpA (50 with ankylosing spondylitis, 41 with psoriatic arthritis, 34 with non-radiographic spondyloarthritis) and 53 healthy individuals serving as controls. Demographic data, clinical characteristics, and treatment regimens were collected. Serum and urinary levels of EKIMs (NGAL, KIM-1, RBP4, FGF23, NAG, and IL-18) were measured. Statistical analyses, including the Mann-Whitney U test and Kruskal-Wallis test with post-hoc analysis, were performed to assess differences between groups and identify significant associations. Results Our findings indicate subclinical kidney involvement in SpA patients. Serum and urinary KIM-1 levels were significantly elevated in SpA patients compared to HC (serum: p = 0.039; urine: p = 0.024), suggesting early tubular damage. Urinary RBP4 was significantly higher in SpA patients (p = 0.005), while serum RBP4 was significantly lower (p < 0.001) compared to HC, pointing to altered RBP4 handling. Urinary FGF23 concentrations were significantly higher in SpA patients than in HC (p < 0.001), reflecting early tubular stress. Serum NAG levels also differed significantly between SpA and HC (p = 0.018). While no overall differences were observed for urinary NGAL or IL-18 between the SpA group and HC, subgroup analyses revealed specific differences. Notably, psoriatic arthritis patients showed distinct profiles, including lower serum FGF23 (p = 0.028 vs nr-axSpA and HC), lower serum NAG (p = 0.013 vs HC), and higher urinary IL-18 (p = 0.012 vs HC) compared to healthy individuals. Levels of most EKIMs did not significantly differ between patients on first-line versus second-line therapies, except for serum FGF23, which was lower in patients receiving bDMARDs/tsDMARDs (p = 0.018). Conclusions Patients with seronegative spondyloarthropathies without overt kidney disease demonstrate early, subclinical tubular injury and altered biomarker profiles, particularly involving KIM-1, RBP4, FGF23, and NAG. These findings underscore the potential utility of these novel markers in detecting kidney compromise at an early stage in SpA, which is crucial for timely management. Further longitudinal studies are needed to validate the predictive value of these EKIMs for long-term renal outcomes and to explore their role in monitoring disease progression and therapeutic response in SpA. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Health sciences/Nephrology Health sciences/Rheumatology INTRODUCTION Seronegative spondyloarthritis (SpA) is a group of rheumatic diseases with shared clinical, laboratory and imaging features. It is divided into forms with predominant axial involvement (axSpA) and those with mainly peripheral symptoms [1, 2]. The SpA spectrum includes radiographic axSpA (also known as ankylosing spondylitis), non-radiographic axSpA (nr-axSpA), psoriatic arthritis (PsA), reactive arthritis (ReA), SpA associated with inflammatory bowel disease, juvenile-onset SpA and undifferentiated SpA. [1, 2]. The term “seronegative” refers to the absence of IgM rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) [2]. Each type of SpA shows positive correlation with the human leukocyte antigen – HLAB27 [1, 2]. Depending on the diagnostic criteria and genetic background SpA affects between 0.20% and 1.61% of the population [3]. Common features across all types include axial joint inflammation (particularly of the sacroiliac joints), asymmetric peripheral arthritis, enthesitis or dactylitis, and extra-articular manifestations such as uveitis, psoriasis and inflammatory bowel disease [1, 2]. However, clinicians should always remember about higher risk of other extraarticular involvement like osteoporosis, cardio-vascular diseases, pulmonary and renal abnormalities [4]. Kidney damage in SpA is multifactorial and may result from drug nephrotoxicity, immune complex deposition, secondary amyloidosis and atherosclerosis driven by persistent inflammation. However, the exact mechanisms remain unclear [5]. Systematic review from 2025 [6] indicated that the prevalence of renal involvement in SpA varies widely (ranging from 0.2% to 77.5%) reflecting differences in study methods, diagnostic criteria, and patient populations [6]. In the largest cohort to date (21,473 AS patients) Haroon et al. reported a 1.7% prevalence of renal involvement, using International Classification of Diseases codes (ICD codes) and the Office for Health Improvement and Disparities codes (OHID codes) to identify kidney disease [7]. Moreover, recent population-based study shows that SpA confers a measurable excess risk of glomerulonephritis and chronic kidney disease (CKD), even after adjustment for age, sex and comorbidities [8]. Diagnosis of kidney diseases has been traditionally centered on glomerular filtration. Early, subclinical kidney involvement often escapes detection, precluding timely modification of nephrotoxic therapy or escalation of disease modifying anti rheumatic drugs (DMARDs) [9]. For this reason, it is relevant to investigate condition of nephron tubule as well. When tubular cells are injured, they trigger a cascade of responses that release and accumulate low-molecular-weight proteins into the urine and bloodstream. Recent advances in molecular analysis and proteomics now allow these proteins to be detected and measured, serving as biomarkers to assess renal disease. For sake of this study, several early kidney injury markers (EKIMs) were selected to represent injury across the most diverse possible mechanisms and assessed them in SpA population. To date very few researchers raised a subject of subclinical kidney involvement in SpA. Shukla et al. in 2017 [10] performed a prospective biomarker study in SpA patients on NSAIDs [10]. Authors enrolled 103 subjects: 37 healthy controls, 41 SpA patients on at least 3 months of regular NSAIDs administration, and 25 SpA patients with minimal NSAID exposure who were started on NSAIDs during the study. Blood and urine samples were collected at 0,1, 6, and 12 weeks. NGAL, KIM-1, cystatin-C were measured in both urine and serum samples. Results shows that regular NSAID use in SpA patients triggered rise in biomarkers. Levels of selected markers started rising as early as 7 days of regular NSAID use and were reversible on stopping the drug [10]. Moreover, several other studies have been performed on this subject and each of them will be mentiond in the “Discussion” section. However, most authors typically evaluate only a single selected marker in one specific type of SpA. To the best of our knowledge, our study assesses the widest range of EKIMs, with a division into three distinct types of SpA. METHODOLOGY Objective and Study Design: The study therefore aimed to: (1) quantify serum and urinary concentrations of six EKIMs - retinol-binding protein 4 (RBP4), neutrophil gelatinase-associated lipocalin (NGAL), fibroblast growth factor 23 (FGF23), kidney injury molecule 1 (KIM-1), N-acetyl-β-D-glucosaminidase (NAG), and interleukin 18 (IL-18) in patients’ serum and urine with AS, nr axSpA and PsA versus matched healthy controls; (2) compare biomarker profiles between first (NSAIDs) and second-line therapy (tsDMARDs/bDMRDs). The study was retrospective, cross-sectional, observational, and comparative in design. Participants and Sample Selection: The study population comprised randomly selected patients from the Clinic of Rheumatology and Internal Diseases at Wroclaw Medical University, enrolled between 2022 and 2024. Participants were included to the study based on the following criteria: age ≥ 18 years, Caucasian ethnicity, capacity to provide informed consent, and a prior diagnosis of ankylosing spondylitis (AS), non-radiographic axial spondyloarthritis (nr-axSpA), or psoriatic arthritis (PsA) according to specific criteria – the ASAS Classification Criteria for Spondyloarthropathies (2010), the Modified New York Criteria for Ankylosing Spondylitis (1984), or the CASPAR criteria for Psoriatic Arthritis (2006). Exclusion criteria were as follow : age < 18 years, active infection, any current or past kidney disease, or a history of chronic conditions that could significantly affect renal function (e.g., diabetes mellitus, systemic lupus erythematosus, systemic vasculitis, viral hepatitis, uncontrolled arterial hypertension, or other conditions deemed significant by the investigator), as well as any documented renal impairment observed in previous examinations that is currently under diagnostic evaluation. Both patients receiving first-line therapy (NSAIDs) and those treated with bDMARDs/tsDMARDs as part of a therapeutic program were recruited. No additional age limit was imposed, and patients were not excluded based on weight or well-controlled hypertension, as doing so would have adversely affected the recruitment of a sufficient number of participants for the study group. The control group consisted of healthy Caucasian volunteers, matched to the study group by age and sex. Research Instruments and Procedures: After obtaining informed consent, the investigator conducted an interview with each patient to collect demographic data (age, sex, weight, height), as well as information regarding diagnosis, comorbidities, and current treatment. Subsequently, participants were evaluated for signs of potential infections that might influence study outcomes. Blood and urine samples were collected once. A portion of the collected blood and urine was centrifuged and subsequently frozen at -30°C. The assessment of the early kidney injury markers in serum and urine commenced after the collection of biological samples from all participants in both the study and control groups. All assays were performed in the same laboratory using commercial ELISA kits from the same manufacturer. The reference values for the early kidney injury markers were established based on the manufacturer's information provided with the commercial kits and existing literature data. Statistical Analysis: Statistical analyses were carried out using IBM SPSS Statistics, version 26 (SPSS Inc., Chicago, IL, USA). The distribution of all datasets was first evaluated with the Shapiro–Wilk test. Continuous variables were expressed as mean ± standard deviation (SD), while categorical variables were reported as counts and percentages. For comparisons between two groups, the independent samples t-test was applied when data followed a normal distribution, whereas the Mann–Whitney U test was used in cases of non-normal distribution. Associations between variables were examined using Pearson’s correlation coefficient for parametric data and Spearman’s rank correlation coefficient for non-parametric data. For analyses involving more than two groups, ANOVA was employed for normally distributed variables, while the Kruskal–Wallis test was used for non-normally distributed ones. When relevant, post-hoc testing with Bonferroni correction was performed to account for multiple comparisons. Relationships between categorical variables were assessed with the Chi-square test. A p-value of ≤ 0.05 was considered the threshold for statistical significance in all tests. Ethical Considerations: The study was conducted in accordance with the World Medical Association Declaration of Helsinki. The study received approval on 07.11.2022 from the Ethics Committee of Wroclaw Medical University (KB-826/2022). All participants provided informed consent prior to inclusion in the study, and patient data were anonymized prior to statistical analysis. RESULTS Descriptive statistics of the study population and control group: The study group comprised 125 patients with spondyloarthropathies, including 50 with ankylosing spondylitis, 41 with psoriatic arthritis, and 34 with non-radiographic spondyloarthritis, with a mean BMI of 27.15 ± 4.97 and 43 (34.4%) of the participants being female, 82 (65.6%) being male. Regarding treatment, 64 patients were receiving NSAIDs (with or without csDMARDs), while 61 of patients were receiving second line therapy – tsDMARDs/ bDMARDs at the sample collections (Table 1 .) 14 of all patients reported temporary use of GCs at the time of sample collections. Kidney function assessment in the study group revealed a median serum creatinine of 0.81 mg/dL and an estimated glomerular filtration rate (eGFR) of 98.58 ± 17.56 mL/min/1.73 m². The control group comprised 53 healthy individuals, 15 (28,3%) women and 38 (71.7%) men, with a mean BMI of 2456 ± 3,50. The results of the concentration measurements of individual EKIMs in both the study and control groups are presented in the Tables 2 . and 3. below. Table 1 Treatment – Study group Treatment Categories n % tsDMARDs/bDMARDs TNF-alfa inhibitor 29 23,2 IL-17 inhibitor 24 19,2 IL-23 inhibitor 3 2,4 JAK inhibitor 5 4,0 csDMARDs Yes 50 40,0 No 75 60,0 NSAIDs Yes 64 51,2 No 61 48,8 Tabela 1. Treatment – Study group. Legend: tsDMARDs - Targeted synthetic disease-modifying anti-rheumatic drugs; bDMARDs - Biological disease-modifying anti-rheumatic drugs; csDMARDs – classic synthetic disease-modifying anti-rheumatic drugs; NSAIDs - Non-steroidal anti-inflammatory drugs; GCs – Glucocorticoids; TNF-alpha - Tumor necrosis factor alpha; IL -Interleukin. Table 2 Early kidney injury markers in serum and urine – Study group Marker [unit] n Miss Mean SD Median Min Max P25 P75 IL-18-S [pg/mL] 120 5 281.08 207.33 247.5 0.0 906.48 108.26 399.84 RBP4-S [mg/L] 120 5 0.2361 0.2282 0.205 0.0 1.455 0.057 0.34 FGF23-S [pg/mL] 120 5 56.24 144.89 0.0 0.0 1050.0 0.0 53.14 NAG-S [U/L] 120 5 61.2 33.26 60.77 0.098 105.0 32.71 98.67 KIM1-S [ng/mL] 120 5 0.0942 0.4933 0.0 0.0 4.718 0.0 0.0 NGAL-U [ng/mL] 118 7 109.75 202.79 30.39 0.79 1025.9 14.48 80.54 IL-18-U [pg/mL] 124 1 27.08 45.78 16.19 0.0 444.94 8.25 30.38 RBP4-U [mg/L] 124 1 1.6025 0.6888 1.6015 0.129 3.169 1.2943 2.0503 FGF23-U [pg/mL] 124 1 25.38 70.06 0.0 0.0 421.4 0.0 1.5205 NAG-U [U/L] 124 1 71.34 36.47 86.78 0.502 105.0 41.73 105.0 KIM1-U [ng/mL] 124 1 1.8802 0.7812 0.7525 0.0 4.094 0.291 1.2565 Table 2 . Legend: IL-18 – Interleukin-18, RBP4 – Retinol-binding protein 4, FGF23 – Fibroblast growth factor 23, NAG – N-acetyl-β-D-glucosaminidase, KIM1 – Kidney injury molecule 1, NGAL – Neutrophil gelatinase-associated lipocalin, S – serum, U – urine, SD – standard deviation, P25–25ᵗʰ percentile, P75–75ᵗʰ percentile Table 3 Early kidney injury markers in serum and urine – Healthy control Marker [unit] n Miss Mean SD Median Min Max P25 P75 IL-18-S [pg/mL] 53 0 262.4 226.62 196.11 6.5 887.44 99.67 333.95 RBP4-S [mg/L] 53 0 0.373 0.371 0.2519 0.0 0.889 0.203 0.5285 FGF23-S [pg/mL] 53 0 67.43 0.0 99.79 0.0 454.65 0.0 149.37 NAG-S [U/L] 53 0 74.55 81.61 29.03 22.33 105.0 49.94 105.0 KIM1-S [ng/mL] 53 0 0.008 0.0 0.0325 0.0 0.204 0.0 0.0 NGAL-U [ng/mL] 52 1 110.92 28.78 189.79 1.01 851.9 19.26 79.63 IL-18-U [pg/mL] 53 0 22.69 17.72 18.79 0.0 71.38 8.98 29.12 RBP4-U [mg/L] 53 0 1.3632 1.377 0.4122 0.464 2.357 1.105 1.6875 FGF23-U [pg/mL] 53 0 2.2413 0.0 16.32 0.0 118.79 0.0 0.0 NAG-U [U/L] 53 0 67.02 67.9 32.44 4.65 105.0 37.81 105.0 KIM1-U [ng/mL] 53 0 0.5777 0.47 0.4945 0.0 2.006 0.2025 0.9005 Table 2 . Legend: IL-18 – Interleukin-18, RBP4 – Retinol-binding protein 4, FGF23 – Fibroblast growth factor 23, NAG – N-acetyl-β-D-glucosaminidase, KIM1 – Kidney injury molecule 1, NGAL – Neutrophil gelatinase-associated lipocalin, S – serum, U – urine, SD – standard deviation, P25–25ᵗʰ percentile, P75–75ᵗʰ percentile Groups comparisons and key findings: There were no statistically significant differences between patients with seronegative spondyloarthropathies and healthy controls in terms of age (p = 0.097) or sex distribution (p = 0.110) 1. SpA vs Healthy Control Table 3 Differences in mean concentrations of early kidney injury markers – SpA vs healthy control (U Mann-Whitney) Marker [unit] SpA (mean ± SD) HC (mean ± SD) p-value IL-18-S [pg/mL] 281.08 ± 207.33 262.40 ± 196.11 0.591 RBP4-S [mg/L] 0.24 ± 0.23 0.37 ± 0.25 0.000 FGF23-S [pg/mL] 51.30 ± 134.99 67.43 ± 99.79 0.101 NAG-S [U/L] 61.20 ± 33.26 74.55 ± 29.03 0.018 KIM1-S [ng/mL] 0.09 ± 0.49 0.01 ± 0.03 0.039 NGAL-U [ng/mL] 109.75 ± 202.79 110.92 ± 189.79 0.829 IL-18-U [pg/mL] 27.08 ± 45.78 22.69 ± 18.79 0.655 RBP4-U [ng/mL] 1.60 ± 0.69 1.36 ± 0.41 0.005 FGF23-U [pg/mL] 25.38 ± 70.06 2.24 ± 16.32 0.000 NAG-U [U/L] 71.34 ± 36.47 67.02 ± 32.44 0.398 KIM1-U [ng/mL] 0.88 ± 0.78 0.58 ± 0.49 0.024 Table 3 . Legend: IL-18 – Interleukin-18, RBP4 – Retinol-binding protein 4, FGF23 – Fibroblast growth factor 23, NAG – N-acetyl-β-D-glucosaminidase, KIM1 – Kidney injury molecule 1, NGAL – Neutrophil gelatinase-associated lipocalin, S – serum, U – urine, HC – healthy controls, SpA – spondyloarthritis 2. AS vs PsA vs nr-axSpA vs Healthy Control Table 4 Differences in mean concentrations of selected markers – SpA, PsA, nr-axSpA, healthy control – only statistically significant results. Marker [unit] AS (mean ± SD) PsA (mean ± SD) nr-axSpA (mean ± SD) HC (mean ± SD) p-value* Post- Hoc RBP4- S [ng/mL] 0,22 (0,26) 0,21 (0,21) 0,68 (0,28) 0,37 (0,25) 0,001 PsA vs AS – NS PsA vs nr-axSpA – NS PsA vs HC 0,004 AS vs Nr-axSpA NS AS vs HC 0,002 nr-axSpA vs HC NS FGF23- S [pg/mL] 15,7 (36,04) 10,87 (32,93) 72,92 (109,30) 67,43 (99,79) 0,013 PsA vs AS – NS PsA vs nr-axSpA – 0,028 PsA vs HC 0,028 AS vs Nr-axSpA - NS AS vs HC - NS nr-axSpA vs HC -NS FGF23- U [pg/mL] 29,41 (74,64) 31,63 (76,85) 12,09 (52,78) 2,24 (16,32) 0,002 PsA vs AS – NS PsA vs nr-axSpA – 0,023 PsA vs HC − 0,002 AS vs Nr-axSpA -NS AS vs HC - NS nr-axSpA vs HC - NS NAG- S [U/L] 53,97 (33,38) 65,88 (32,93) 66,18 (32,55) 74,55 (29,03) 0,023 PsA vs AS – NS PsA vs nr-axSpA – NS PsA vs HC − 0,013 AS vs Nr-axSpA - NS AS vs HC - NS nr-axSpA vs HC - NS IL-18- U [pg/mL] 38,87 (65,94) 21,84 (24,65) 15,89 (16,29) 2,24 (16,32) 0,019 PsA vs AS – NS PsA vs nr-axSpA – NS PsA vs HC 0,012 AS vs Nr-axSpA - NS AS vs HC - NS nr-axSpA vs HC -NS Table 4 . Legend: IL-18 – Interleukin-18, RBP4 – Retinol-binding protein 4, FGF23 – Fibroblast growth factor 23, NAG – N-acetyl-β-D-glucosaminidase, S – serum, U – urine, AS – ancylosing spondylitis, PsA- psoriatic arthritis, nr-axSpA – non-radiographic spondyloarthritis, HC – healthy controls, NS - not significant. *Kruskal-Wallis Test for Independent Samples 3. First line therapy vs second line therapy Table 5 Differences in mean concentrations of selected markers – the first line therapy vs bDMARDs/tsDMARDs for at least 6 months Marker [unit] NSAIDs ± csDMARDs (mean ± SD) bDMARDs/tsDMARDs (mean ± SD) p-value* IL-18-S [pg/mL] 246.82 ± 208.28 307.34 ± 215.26 0.111 RBP4-S [ng/mL] 0.27 ± 0.29 0.16 ± 0.13 0.119 FGF23-S [pg/mL] 64.99 ± 126.54 19.00 ± 51.68 0.018 NAG-S [U/L] 55.18 ± 33.67 60.76 ± 32.49 0.447 KIM1-S [ng/mL] 0.06 ± 0.17 0.01 ± 0.04 0.957 NGAL-U [ng/mL] 81.80 ± 158.21 89.91 ± 168.17 0.650 IL-18-U [pg/mL] 32.99 ± 66.09 22.15 ± 25.56 0.997 RBP4-U [ng/mL] 1.48 ± 0.61 1.63 ± 0.75 0.097 FGF23-U [pg/mL] 18.43 ± 58.60 27.15 ± 75.28 0.529 NAG-U [U/L] 71.45 ± 36.40 64.68 ± 38.80 0.414 KIM1-U [ng/mL] 0.80 ± 0.83 0.92 ± 0.81 0.436 Table 5 . Legend: IL-18 – Interleukin-18, RBP4 – Retinol-binding protein 4, FGF23 – Fibroblast growth factor 23, NAG – N-acetyl-β-D-glucosaminidase, KIM1 – Kidney injury molecule 1, NGAL – Neutrophil gelatinase-associated lipocalin, S – serum, U – urine, NSAIDs – nonsteroidal anti-inflammatory drugs, csDMARDs – conventional synthetic disease-modifying antirheumatic drugs, bDMARDs – biologic DMARDs, tsDMARDs – targeted synthetic DMARDs. *U Mann-Whitney DISCUSSION The present study represents so far one of the most comprehensive analyses of early kidney injury markers in patients with seronegative spondyloarthropathies (SpA), assessing both established and emerging biomarkers of renal dysfunction in a real-world, cross-sectional cohort. Given the increased risk of kidney involvement in SpA due to chronic inflammation, comorbidity, and pharmacotherapy, early detection remains of utmost importance. Commonly used kidney function indices (e.g., serum creatinine) often detect renal impairment only after substantial nephron loss [9, 11]. Researchers are therefore actively investigating biomarkers of subclinical kidney injury that may help predict future irreversible renal damage [9, 11, 12]. In this context, our study evaluated the performance of NGAL, KIM-1, RBP4, FGF23, NAG, and IL-18 in SpA patients compared to healthy control. Below, we discuss the existing literature and our findings for each marker individually. NGAL (Neutrophil Gelatinase-Associated Lipocalin) Neutrophil gelatinase-associated lipocalin (NGAL) is a 25-kDa protein belonging to the lipocalin superfamily, which is involved in transport of hydrophilic substances through membranes to maintain cellular homeostasis [13]. NGAL is also recognized as one of the most well-established biomarkers of both AKI and CKD [9]. It is produced in several tissues, including the lungs, gastrointestinal tract, liver, and kidneys. Its expression increases significantly in damaged epithelial cells due to injury, inflammation, or neoplastic transformation. While both plasma and urine NGAL have been studied as markers of kidney injury, urinary NGAL is more specific to kidney damage. Animal studies have shown that NGAL is one of the earliest EKIMs activated after tubular injury, especially in the distal nephron. Urinary NGAL levels rise as early as 2 hours after kidney injury in mouse models [14] and in children developing AKI after cardiac surgery [15]. In chronic kidney disease, urinary NGAL is negatively correlated with eGFR and positively correlated with interstitial fibrosis and tubular atrophy [9]. Shukla et al. in 2017 performed a prospective biomarker study in SpA patients on NSAIDs [10]. Serum and urine NGAL levels were measured at baseline and follow-ups. Importantly, urinary and serum NGAL were elevated ~ 2–3 fold in SpA patients on chronic NSAIDs compared to both controls and NSAID-naive SpA patients, despite normal serum creatinine and eGFR [10]. After initiating NSAIDs, NGAL levels rose as early as 1 week and became significantly elevated by 6 weeks, then declined after NSAID cessation, indicating the changes were reversible [10]. This suggests NGAL can detect subclinical tubular injury due to NSAIDs in SpA before creatinine rises [10]. In psoriatic disease, NGAL seems to be promising marker of early kidney injury. Kucukyangoz et al., in 2024 [16], studied 44 psoriasis patients without clinical kidney disease and 44 controls, measuring urine NGAL levels [16]. Psoriasis patients had significantly higher proteinuria than controls and urinary NGAL (normalized to creatinine) correlated positively with proteinuria (r ≈ 0.36, p < 0.02) [16]. About one in four psoriasis patients have PsA [17]. Notably, Kucukyangoz et al. study didn’t raise a subject of arthritis in study sample, however elevation in NGAL likely reflects occult tubular injury from chronic inflammation or nephrotoxic therapy, used in both psoriasis and PsA [16]. In contrast, our study did not detect significant differences in urinary NGAL between SpA patients and controls (p = 0.82), nor between subtypes. The high variability in urinary NGAL values within both groups may have masked subtle differences. Several factors may account for the discrepancy with previous literature: (1) our cohort excluded patients with overt renal disease, potentially lowering baseline biomarker expression; (2) cross-sectional sampling may have missed transient NGAL elevations linked to short-term drug exposure or inflammatory flares. Notably, according to Canki et al. [18], NGAL is characterized by high intra-individual variability, which may affect the accuracy of its measurements and effectively mask subtle differences within the studied cohort. Moreover, the analysis by Seibert et al. [19] demonstrated that although urinary NGAL levels are significantly higher in CKD compared with healthy controls, it had no diagnostic value in differentiating stable chronic kidney diseases of inflammatory versus non-inflammatory origin [19]. Due to technical issue, we were unable to measure serum NGAL levels, which represents a major limitation in the interpretation of our findings. KIM-1 (Kidney Injury Molecule-1) KIM-1 is a membrane protein highly upregulated in proximal tubules following toxic, ischemic or inflammatory injury [20, 21]. Its urinary excretion reflects ongoing tubular repair or chronic low-grade injury rather than filtration impairment [12]. KIM-1 has been shown to be a highly sensitive and specific marker of kidney injury in several animal models of kidney disease, including models of injury due to ischemia [20, 21] and various nephrotoxins [9, 12]. Canki et al. describe KIM-1 as the most promising marker for the diagnosis of CKD [18]. Studies in rheumatoid arthritis and SLE further reinforce KIM-1’s sensitivity for inflammatory kidney injury, and reviews recommend its use for both monitoring and research in patients exposed to nephrotoxic medications [22]. In psoriasis, KIM-1 has shown similar utility. Kucukyangoz et al. [16] found urinary KIM-1 and creatinine was elevated in psoriatic patients and correlated with proteinuria (r ≈ 0.37, p = 0.013) [16]. Patients had no clinical CKD, so higher KIM-1 signals early tubular damage [16]. In the 2017 study by Shukla et al. assessed both urinary and serum KIM-1 levels in SpA patients on NSAIDs [10]. Regular NSAID users had ~ 2–3× higher KIM-1 (both serum and urine) than controls or NSAID-naive patients ( p < 0.001) despite normal renal function tests [10]. Initiating NSAIDs led to a rise in KIM-1 within one week, significant by 6 weeks, and levels dropped after NSAID withdrawal, indicating subclinical AKI induced by NSAIDs [10]. Mentioned above studies showed that in chronic inflammatory diseases, including SpA, KIM-1 is elevated even in patients without clear eGFR reduction, emphasizing its value in identifying hidden tubular dysfunction before clinical CKD is apparent [10, 16]. Our findings align with this perspective, demonstrating a significantly higher serum KIM-1 in SpA compared with HC (p = 0.03) and higher urinary KIM-1 in SpA compared with HC (p = 0.02), despite preserved eGFR. Interestingly, KIM-1 levels did not differ significantly between patients on first-line (NSAIDs ± csDMARDs) and second-line (bDMARDs/tsDMARDs) therapy. This may indicate that subclinical tubular injury is not limited to NSAID use but could also arise from biologic therapy or disease-related factors such as persistent low-grade inflammation. It is also noteworthy that the study by Seibert et al. [19] did not demonstrate KIM-1 to differentiate between stable CKD of inflammatory and non-inflammatory origin [19]. These observations may support the idea that proximal tubular injury in this context may occur independently of the specific inflammatory etiology or treatment strategy. Clinically, present findings support the inclusion of KIM-1 in biomarker panels for SpA, particularly for long-term renal safety surveillance across treatment modalities. RBP4 (Retinol Binding Protein 4) RBP4 is a low-molecular-weight protein primarily secreted by the liver and adipose tissue. [23] It functions as the principal carrier of retinol in the bloodstream. Once retinol is delivered to target cells, the remaining apo-RBP4 (retinol-free form) is freely filtered by the glomeruli, reabsorbed by proximal tubular cells, and subsequently degraded [23]. The presence of RBP4 in urine, or alterations in its serum concentration, are indicative of proximal tubular dysfunction. Notably, the homeostasis of RBP4 is influenced by both hepatic and renal function [23]. Literature supports its utility as part of a biomarker panel for early detection and risk monitoring in CKD patients [22–24]. Some studies have noted RBP4’s sensitivity in reflecting tubular injury across a range of etiologies, including drug toxicity and systemic inflammation [23, 24]. In a large study conducted in 2018 by Wu et al. [25], involving 926 patients with AS, the authors aimed to identify risk factors for renal involvement, defined as hematuria, proteinuria, and/or estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² [25]. Among various laboratory parameters assessed, serum RBP4 was included. Interestingly, RBP4 did not emerge as a strong indicator of kidney involvement. In univariate analysis, mean serum RBP4 levels were slightly lower in AS patients with renal abnormalities compared to those without (p < 0.05) [25]. In multivariate logistic regression RBP4 was not independently predictive for renal involvement [25].This is in line with earlier observations that urinary RBP excretion is typically normal in AS patients without overt renal disease [26]. However, RBP4 remains of interest in SpA as an adipokine and metabolic marker. For example, a proteomic study suggested that higher baseline serum RBP4 levels predicted better response to TNF inhibitor therapy in patients with axial SpA [24]. According to our study, urinary RBP4 was significantly higher in SpA compared with HC (p = 0.00), while serum RBP4 was significantly lower in SpA patients (p < 0.001). Although the absolute difference in urinary levels is moderate (~ 18%), it may still indicate subtle impairment of tubular reabsorption. This divergence between serum and urinary values may reflect altered tubular handling and systemic metabolism in the context of chronic inflammation. Notably, significant differences in serum RBP4 were observed between PsA and HC (p = 0.00) and between AS and HC (p = 0.00), with lower values in both SpA subtypes compared to HC, whereas urinary RBP4 did not differ significantly among SpA subtypes. These findings highlight the need for dual-compartment analysis when interpreting RBP4 in inflammatory diseases. FGF23 (Fibroblast growth factor 23) FGF23 is a bone-derived phosphaturic hormone that regulates phosphate and vitamin D metabolism. It is a well-established marker and key driver of CKD–mineral and bone disorder (CKD-MBD) and has been associated with the progression of CKD and increased cardiovascular morbidity and mortality in adults with renal impairment [27–29]. Elevated FGF23 levels are also observed in various chronic inflammatory conditions, including rheumatoid arthritis, where subclinical renal disturbances are common, even in the absence of CKD [30]. Chronic inflammation and persistent changes in vitamin D metabolism in SpA may potentially drive altered FGF23 secretion, although the exact pathophysiologic mechanisms in this patient population remain unclear [29]. To date, there are no published studies directly examining FGF23 as a biomarker of early kidney injury specifically in seronegative spondyloarthropathies. Most available data come from general CKD cohorts or other rheumatic diseases, and data extrapolation must be done with caution [28–30]. Some data suggest that in systemic inflammatory states, FGF23 may increase even before overt declines in eGFR, possibly reflecting a dynamic response to subclinical pertubations in mineral metabolism, inflammation, or early tubular stress. Present study represents the first comprehensive evaluation of FGF23 in patients with SpA and provides novel insights into its role as an early kidney injury marker in this cohort. We observed a statistically significant elevation in urinary FGF23 concentrations in SpA patients compared to healthy controls (p < 0.001). This finding suggests early tubular dysfunction or altered renal handling of FGF23 in SpA, even in the absence of clinically recognized kidney disease. Conversely, serum FGF23 levels did not significantly differ between the overall SpA group and healthy controls (p = 0.101). Further stratification of the cohort revealed specific differences among SpA subtypes. While overall serum FGF23-S levels showed a statistically significant difference across the four groups (AS, PsA, nr-axSpA, and HC; p = 0.013p = 0.013), post-hoc analysis indicated that was primarily driven by lower FGF23-S in PsA compared to both nr-axSpA (p = 0.028) and HC (p = 0.028). Similarly, urinary FGF23 levels were also significantly different across the groups (p = 0.002), with PsA showing lower levels compared to nr-axSpA (p = 0.023) and HC (p = 0.002). These results suggest potential disease-specific variations in FGF23 metabolism within different SpA subtypes. Additionally, present analysis showed that FGF23-S concentrations were significantly lower in patients receiving bDMARDs/tsDMARDs for at least 6 months compared to those on first-line therapy (NSAIDs ± csDMARDs) (p = 0.018). Both Schnedl et al. [31] and Zhang and Qin [9] provide extensive descriptions of the role of FGF23 as a marker in AKI/CKD and its association with inflammation, indicating that it is induced by pro-inflammatory cytokines [9, 31]. In the context of lower serum FGF23 levels observed in patients receiving bDMARDs/tsDMARDs, our findings are consistent with the hypothesis that inflammation may be a key regulatory factor of FGF23 in SpA. Furthermore, Mattinzoli et al. [32] discuss the potential role of FGF23 as a “risk biomarker ” in AKI, as well as the interactions between mineral bone disorders and cardiac injury, which opens new perspectives for future research on the bone–kidney–heart axis in SpA [32]. Further research targeting longitudinal measurement of FGF23 in well-defined SpA populations, especially with integrated assessment of CKD-MBD and inflammatory status, is necessary to clarify its clinical value. NAG (N-acetyl-β-D-glucosaminidase) NAG is a lysosomal enzyme present in high concentrations in renal tubular cells, and its urinary excretion increases rapidly with tubular cell damage due to toxins, inflammation or ischemia [11]. Several studies affirm that NAG is a sensitive marker for the earliest forms of tubular injury [22]. In chronic inflammatory and immune-mediated diseases such as lupus nephritis, heightened urinary NAG excretion has been observed and is frequently linked to long-term disease activity and renal prognosis [11]. However, data specifically in SpA are scant, representing a clear area for future research. Our results showed significantly lower serum NAG in SpA compared with HC (p = 0.01), with no significant urinary differences. The absence of urinary differences and the unexpected reduction in serum NAG may be due to large interindividual variability and the influence of extra-renal sources on serum NAG concentrations. This non-intuitive observation also gains context in the study by Canki et al. [18], who indicate that NAG activity may be altered by nephrotoxins, making it a less reliable marker of CKD. This suggests that extra-renal factors or interactions with pharmacotherapy in chronic inflammatory diseases such as SpA may have influenced its serum concentration, thereby masking potential changes related to tubular injury. Additionally, significant differences in serum NAG were found between PsA and HC (p = 0.01), with lower values in PsA. These results underscore the need for disease-specific validation before adopting NAG as a biomarker in SpA. IL-18 (Interleukin-18) IL-18 is a pro-inflammatory cytokine belonging to the IL-1 family, known for its role in the pathogenesis of immune-mediated kidney injury [33, 34]. Studies show that both serum and urinary IL-18 rise in the very early phase of ischemic AKI, prior to conventional lab changes and often before NGAL or KIM-1 [34]. In systemic autoimmune and inflammatory conditions, increased IL-18 is associated with disease activity and renal risk [22]. Several studies have demonstrated elevated serum IL-18 levels in patients with PsA, correlating both with higher disease activity and an unfavorable metabolic - atherogenic profile [35[Waszczykowski, 2020 #56]]. In our study, no significant differences in serum IL-18 were observed between SpA and HC or among subtypes. Urinary IL-18 did not differ significantly between SpA and HC overall. However, urinary IL-18 was significantly higher in PsA compared to HC (p = 0.01). This may suggest that in PsA, urinary IL-18 appears earlier than it is reflected in the systemic circulation. This observation is supported by the literature, where urinary IL-18 is considered a very early marker of inflammatory stress and tubulointerstitial injury. Studies by Mattinzoli et al. [32] and Canki et al. [18] emphasize its role as a pro-inflammatory cytokine and a sensitive indicator of AKI, while the work of Hirooki [36] highlights its utility in identifying early tubular changes. In the context of PsA, elevated urinary IL-18 may therefore reflect a specific, localized inflammatory process in the kidneys that is not yet detected by serum levels, giving it potential as a biomarker of early PsA-specific inflammation-related renal stress. STRENGTHS AND LIMITATIONS A key strength of this study is its comprehensive evaluation of multiple early kidney injury markers in a well-characterized cohort of patients with seronegative spondyloarthropathies, stratified by disease subtype and treatment regimen. The inclusion of both serum and urinary measurements for most biomarkers allowed for nuanced interpretation of renal tubular and systemic alterations. Another strength is the rigorous selection of patients without clinically overt kidney disease, enabling detection of subclinical abnormalities potentially relevant for early intervention. The use of healthy controls matched in age and sex distribution adds to the robustness of the comparisons. However, several limitations must be acknowledged. First, the cross-sectional design precludes assessment of temporal changes in biomarker levels and their predictive value for future renal outcomes. Longitudinal follow-up would be essential to establish causality and clinical relevance. Second, the inability to measure serum NGAL limited the completeness of comparisons for this key biomarker. Third, potential confounders such as cumulative drug exposure, disease activity scores, and detailed comorbidity profiles were not controlled for in multivariate models, which may influence biomarker variability. Finally, the sample size within certain subgroups, particularly in nr-axSpA and in specific biologic therapy categories, may have limited the power to detect subtle between-group differences. CONCLUSION This study provides a comprehensive evaluation of early kidney injury markers in patients with seronegative spondyloarthropathies, a population at increased risk for renal involvement. Our findings reveal that despite preserved glomerular filtration rate, patients with SpA exhibit subclinical tubular dysfunction, as evidenced by significantly elevated serum and urinary KIM-1 levels compared to healthy controls (p = 0.039 and p = 0.024, respectively). Moreover, altered RBP4 metabolism, with significantly higher urinary (p = 0.005 p = 0.005) and lower serum (p < 0.001) levels in SpA patients, suggests disrupted tubular reabsorption and systemic changes. Notably, we observed significantly elevated urinary FGF23 in the overall SpA cohort compared to healthy controls (p < 0.001), indicating early tubular stress or altered mineral metabolism. While overall urinary NGAL and IL-18 did not differ significantly between SpA and healthy controls, subgroup analysis pointed to distinct biomarker profiles in specific SpA subtypes; for instance, psoriatic arthritis patients showed lower serum FGF23 (p = 0.028 vs HC) and NAG (p = 0.013 vs HC), and higher urinary IL-18 (p = 0.012 vs HC) compared to healthy individuals. These results underscore the potential of these novel biomarkers to detect early, subclinical kidney injury in SpA, offering a crucial window for intervention before overt renal damage occurs. Longitudinal studies are warranted to further elucidate the predictive value of these markers for long-term renal outcomes and to assess their utility in guiding therapeutic strategies in patients with spondyloarthropathies. Declarations Ethics approval and consent to participate The study was conducted in accordance with the World Medical Association Declaration of Helsinki. The study received approval on 07.11.2022 from the Ethics Committee of Wroclaw Medical University (KB-826/2022). All participants provided informed consent prior to inclusion in the study, and patient data were anonymized prior to statistical analysis. Consent for publication Not applicable. Competing interests I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding This research was funded by Wroclaw Medical University (subsidy number: SUBK.A270.23.049). The funding body had no involvement in the study design, data collection, analysis, interpretation of data, the decision to publish, or the preparation of the manuscript. Author Contribution KMT conceived and designed the study, obtained ethical approval, recruited study participants and controls, secured funding, and drafted the manuscript. PKK performed the statistical analyses. HA-B provided nephrological expertise and supervision. MŻ, DB, and KK-K conducted the laboratory analyses. JŚ provided rheumatological expertise and supervision. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Acknowledgements The authors would like to express their sincere gratitude to all patients who agreed to participate in this study, as well as to all volunteers who formed the control group. The authors also wish to thank the medical and technical staff for their valuable assistance and organizational support during the conduct of the research. Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. 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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-7907238","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601927463,"identity":"a105f00d-51de-4a1d-9dd1-e0eb6a0f9e9a","order_by":0,"name":"Kinga Maria Tyczyńska","email":"data:image/png;base64,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","orcid":"","institution":"Wroclaw Medical University","correspondingAuthor":true,"prefix":"","firstName":"Kinga","middleName":"Maria","lastName":"Tyczyńska","suffix":""},{"id":601927464,"identity":"fdb809b8-70a2-43d2-bb8a-52104c75d50e","order_by":1,"name":"Piotr Krzysztof Krajewski","email":"","orcid":"","institution":"Wrocław University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Piotr","middleName":"Krzysztof","lastName":"Krajewski","suffix":""},{"id":601927465,"identity":"db8fd6ed-b96d-4542-a9d1-80d8e8284307","order_by":2,"name":"Hanna Augustyniak-Bartosik","email":"","orcid":"","institution":"Wrocław University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Augustyniak-Bartosik","suffix":""},{"id":601927466,"identity":"044cdc83-1190-4cc9-aaac-285c200e9aaf","order_by":3,"name":"Marcelina Żabińska","email":"","orcid":"","institution":"Wrocław University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Marcelina","middleName":"","lastName":"Żabińska","suffix":""},{"id":601927467,"identity":"700c75a4-0ac3-4a7c-af56-3ea8d32d8534","order_by":4,"name":"Dorota Bartoszek","email":"","orcid":"","institution":"Wrocław University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dorota","middleName":"","lastName":"Bartoszek","suffix":""},{"id":601927468,"identity":"8ac534d0-60ee-41e2-94f8-270a9be852bf","order_by":5,"name":"Katarzyna Kościelska-Kasprzak","email":"","orcid":"","institution":"Wrocław University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Katarzyna","middleName":"","lastName":"Kościelska-Kasprzak","suffix":""},{"id":601927469,"identity":"0df7d6bc-b09c-4c8c-9158-17b68024d7a1","order_by":6,"name":"Jerzy Świerkot","email":"","orcid":"","institution":"Wroclaw Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jerzy","middleName":"","lastName":"Świerkot","suffix":""}],"badges":[],"createdAt":"2025-10-20 15:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7907238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7907238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104434729,"identity":"cce41622-a281-4d49-bbce-d4d26d0d2dcb","added_by":"auto","created_at":"2026-03-11 16:26:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7907238/v1/6bd7ec2b-e8fb-4ef3-9918-fa99a3da2d30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Kidney Injury Markers in Patients with Seronegative Spondyloarthropathies: A Retrospective Cross-Sectional Comparative Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSeronegative spondyloarthritis (SpA) is a group of rheumatic diseases with shared clinical, laboratory and imaging features. It is divided into forms with predominant axial involvement (axSpA) and those with mainly peripheral symptoms [1, 2]. The SpA spectrum includes radiographic axSpA (also known as ankylosing spondylitis), non-radiographic axSpA (nr-axSpA), psoriatic arthritis (PsA), reactive arthritis (ReA), SpA associated with inflammatory bowel disease, juvenile-onset SpA and undifferentiated SpA. [1, 2]. The term \u0026ldquo;seronegative\u0026rdquo; refers to the absence of IgM rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) [2]. Each type of SpA shows positive correlation with the human leukocyte antigen \u0026ndash; HLAB27 [1, 2]. Depending on the diagnostic criteria and genetic background SpA affects between 0.20% and 1.61% of the population [3]. Common features across all types include axial joint inflammation (particularly of the sacroiliac joints), asymmetric peripheral arthritis, enthesitis or dactylitis, and extra-articular manifestations such as uveitis, psoriasis and inflammatory bowel disease [1, 2]. However, clinicians should always remember about higher risk of other extraarticular involvement like osteoporosis, cardio-vascular diseases, pulmonary and renal abnormalities [4]. Kidney damage in SpA is multifactorial and may result from drug nephrotoxicity, immune complex deposition, secondary amyloidosis and atherosclerosis driven by persistent inflammation. However, the exact mechanisms remain unclear [5]. Systematic review from 2025 [6] indicated that the prevalence of renal involvement in SpA varies widely (ranging from 0.2% to 77.5%) reflecting differences in study methods, diagnostic criteria, and patient populations [6]. In the largest cohort to date (21,473 AS patients) Haroon et al. reported a 1.7% prevalence of renal involvement, using International Classification of Diseases codes (ICD codes) and the Office for Health Improvement and Disparities codes (OHID codes) to identify kidney disease [7]. Moreover, recent population-based study shows that SpA confers a measurable excess risk of glomerulonephritis and chronic kidney disease (CKD), even after adjustment for age, sex and comorbidities [8].\u003c/p\u003e \u003cp\u003eDiagnosis of kidney diseases has been traditionally centered on glomerular filtration. Early, subclinical kidney involvement often escapes detection, precluding timely modification of nephrotoxic therapy or escalation of disease modifying anti rheumatic drugs (DMARDs) [9]. For this reason, it is relevant to investigate condition of nephron tubule as well. When tubular cells are injured, they trigger a cascade of responses that release and accumulate low-molecular-weight proteins into the urine and bloodstream. Recent advances in molecular analysis and proteomics now allow these proteins to be detected and measured, serving as biomarkers to assess renal disease. For sake of this study, several early kidney injury markers (EKIMs) were selected to represent injury across the most diverse possible mechanisms and assessed them in SpA population.\u003c/p\u003e \u003cp\u003eTo date very few researchers raised a subject of subclinical kidney involvement in SpA. Shukla et al. in 2017 [10] performed a prospective biomarker study in SpA patients on NSAIDs [10]. Authors enrolled 103 subjects: 37 healthy controls, 41 SpA patients on at least 3 months of regular NSAIDs administration, and 25 SpA patients with minimal NSAID exposure who were started on NSAIDs during the study. Blood and urine samples were collected at 0,1, 6, and 12 weeks. NGAL, KIM-1, cystatin-C were measured in both urine and serum samples. Results shows that regular NSAID use in SpA patients triggered rise in biomarkers. Levels of selected markers started rising as early as 7 days of regular NSAID use and were reversible on stopping the drug [10]. Moreover, several other studies have been performed on this subject and each of them will be mentiond in the \u0026ldquo;Discussion\u0026rdquo; section. However, most authors typically evaluate only a single selected marker in one specific type of SpA. To the best of our knowledge, our study assesses the widest range of EKIMs, with a division into three distinct types of SpA.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObjective and Study Design:\u003c/h2\u003e \u003cp\u003eThe study therefore aimed to: (1) quantify serum and urinary concentrations of six EKIMs - retinol-binding protein 4 (RBP4), neutrophil gelatinase-associated lipocalin (NGAL), fibroblast growth factor 23 (FGF23), kidney injury molecule 1 (KIM-1), N-acetyl-β-D-glucosaminidase (NAG), and interleukin 18 (IL-18) in patients\u0026rsquo; serum and urine with AS, nr axSpA and PsA versus matched healthy controls; (2) compare biomarker profiles between first (NSAIDs) and second-line therapy (tsDMARDs/bDMRDs). The study was retrospective, cross-sectional, observational, and comparative in design.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Sample Selection:\u003c/h3\u003e\n\u003cp\u003eThe study population comprised randomly selected patients from the Clinic of Rheumatology and Internal Diseases at Wroclaw Medical University, enrolled between 2022 and 2024. Participants were included to the study based on the following criteria: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, Caucasian ethnicity, capacity to provide informed consent, and a prior diagnosis of ankylosing spondylitis (AS), non-radiographic axial spondyloarthritis (nr-axSpA), or psoriatic arthritis (PsA) according to specific criteria \u0026ndash; the ASAS Classification Criteria for Spondyloarthropathies (2010), the Modified New York Criteria for Ankylosing Spondylitis (1984), or the CASPAR criteria for Psoriatic Arthritis (2006). Exclusion criteria were as follow : age\u0026thinsp;\u0026lt;\u0026thinsp;18 years, active infection, any current or past kidney disease, or a history of chronic conditions that could significantly affect renal function (e.g., diabetes mellitus, systemic lupus erythematosus, systemic vasculitis, viral hepatitis, uncontrolled arterial hypertension, or other conditions deemed significant by the investigator), as well as any documented renal impairment observed in previous examinations that is currently under diagnostic evaluation. Both patients receiving first-line therapy (NSAIDs) and those treated with bDMARDs/tsDMARDs as part of a therapeutic program were recruited. No additional age limit was imposed, and patients were not excluded based on weight or well-controlled hypertension, as doing so would have adversely affected the recruitment of a sufficient number of participants for the study group. The control group consisted of healthy Caucasian volunteers, matched to the study group by age and sex.\u003c/p\u003e\n\u003ch3\u003eResearch Instruments and Procedures:\u003c/h3\u003e\n\u003cp\u003eAfter obtaining informed consent, the investigator conducted an interview with each patient to collect demographic data (age, sex, weight, height), as well as information regarding diagnosis, comorbidities, and current treatment. Subsequently, participants were evaluated for signs of potential infections that might influence study outcomes. Blood and urine samples were collected once. A portion of the collected blood and urine was centrifuged and subsequently frozen at -30\u0026deg;C. The assessment of the early kidney injury markers in serum and urine commenced after the collection of biological samples from all participants in both the study and control groups. All assays were performed in the same laboratory using commercial ELISA kits from the same manufacturer. The reference values for the early kidney injury markers were established based on the manufacturer's information provided with the commercial kits and existing literature data.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eStatistical analyses were carried out using IBM SPSS Statistics, version 26 (SPSS Inc., Chicago, IL, USA). The distribution of all datasets was first evaluated with the Shapiro\u0026ndash;Wilk test. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables were reported as counts and percentages. For comparisons between two groups, the independent samples t-test was applied when data followed a normal distribution, whereas the Mann\u0026ndash;Whitney U test was used in cases of non-normal distribution. Associations between variables were examined using Pearson\u0026rsquo;s correlation coefficient for parametric data and Spearman\u0026rsquo;s rank correlation coefficient for non-parametric data.\u003c/p\u003e \u003cp\u003eFor analyses involving more than two groups, ANOVA was employed for normally distributed variables, while the Kruskal\u0026ndash;Wallis test was used for non-normally distributed ones. When relevant, post-hoc testing with Bonferroni correction was performed to account for multiple comparisons. Relationships between categorical variables were assessed with the Chi-square test. A p-value of \u0026le;\u0026thinsp;0.05 was considered the threshold for statistical significance in all tests.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations:\u003c/h3\u003e\n\u003cp\u003e The study was conducted in accordance with the World Medical Association Declaration of Helsinki. The study received approval on 07.11.2022 from the Ethics Committee of Wroclaw Medical University (KB-826/2022). All participants provided informed consent prior to inclusion in the study, and patient data were anonymized prior to statistical analysis.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive statistics of the study population and control group:\u003c/h2\u003e\n \u003cp\u003eThe study group comprised 125 patients with spondyloarthropathies, including 50 with ankylosing spondylitis, 41 with psoriatic arthritis, and 34 with non-radiographic spondyloarthritis, with a mean BMI of 27.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97 and 43 (34.4%) of the participants being female, 82 (65.6%) being male. Regarding treatment, 64 patients were receiving NSAIDs (with or without csDMARDs), while 61 of patients were receiving second line therapy \u0026ndash; tsDMARDs/ bDMARDs at the sample collections (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.) 14 of all patients reported temporary use of GCs at the time of sample collections. Kidney function assessment in the study group revealed a median serum creatinine of 0.81 mg/dL and an estimated glomerular filtration rate (eGFR) of 98.58\u0026thinsp;\u0026plusmn;\u0026thinsp;17.56 mL/min/1.73 m\u0026sup2;. The control group comprised 53 healthy individuals, 15 (28,3%) women and 38 (71.7%) men, with a mean BMI of 2456\u0026thinsp;\u0026plusmn;\u0026thinsp;3,50. The results of the concentration measurements of individual EKIMs in both the study and control groups are presented in the Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. and 3. below.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTreatment \u0026ndash; Study group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etsDMARDs/bDMARDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNF-alfa inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-17 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-23 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecsDMARDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSAIDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eTabela 1. Treatment \u0026ndash; Study group.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLegend: tsDMARDs - Targeted synthetic disease-modifying anti-rheumatic drugs; bDMARDs - Biological disease-modifying anti-rheumatic drugs; csDMARDs \u0026ndash; classic synthetic disease-modifying anti-rheumatic drugs; NSAIDs - Non-steroidal anti-inflammatory drugs; GCs \u0026ndash; Glucocorticoids; TNF-alpha - Tumor necrosis factor alpha; IL -Interleukin.\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEarly kidney injury markers in serum and urine \u0026ndash; Study group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarker [unit]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMiss\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP75\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e906.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-S [mg/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1050.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-S [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-S [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNGAL-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1025.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e444.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-U [mg/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e421.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-U [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Legend: IL-18 \u0026ndash; Interleukin-18, RBP4 \u0026ndash; Retinol-binding protein 4, FGF23 \u0026ndash; Fibroblast growth factor 23, NAG \u0026ndash; N-acetyl-\u0026beta;-D-glucosaminidase, KIM1 \u0026ndash; Kidney injury molecule 1, NGAL \u0026ndash; Neutrophil gelatinase-associated lipocalin, S \u0026ndash; serum, U \u0026ndash; urine, SD \u0026ndash; standard deviation, P25\u0026ndash;25ᵗʰ percentile, P75\u0026ndash;75ᵗʰ percentile\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEarly kidney injury markers in serum and urine \u0026ndash; Healthy control\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarker [unit]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMiss\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP75\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e887.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-S [mg/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-S [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-S [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNGAL-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e851.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-U [mg/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-U [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Legend: IL-18 \u0026ndash; Interleukin-18, RBP4 \u0026ndash; Retinol-binding protein 4, FGF23 \u0026ndash; Fibroblast growth factor 23, NAG \u0026ndash; N-acetyl-\u0026beta;-D-glucosaminidase, KIM1 \u0026ndash; Kidney injury molecule 1, NGAL \u0026ndash; Neutrophil gelatinase-associated lipocalin, S \u0026ndash; serum, U \u0026ndash; urine, SD \u0026ndash; standard deviation, P25\u0026ndash;25ᵗʰ percentile, P75\u0026ndash;75ᵗʰ percentile\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eGroups comparisons and key findings:\u003c/h3\u003e\n\u003cp\u003eThere were no statistically significant differences between patients with seronegative spondyloarthropathies and healthy controls in terms of age (p\u0026thinsp;=\u0026thinsp;0.097) or sex distribution (p\u0026thinsp;=\u0026thinsp;0.110)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. SpA vs Healthy Control\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferences in mean concentrations of early kidney injury markers \u0026ndash; SpA vs healthy control (U Mann-Whitney)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarker\u003c/p\u003e\n \u003cp\u003e[unit]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpA (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHC (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281.08\u0026thinsp;\u0026plusmn;\u0026thinsp;207.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262.40\u0026thinsp;\u0026plusmn;\u0026thinsp;196.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-S [mg/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.30\u0026thinsp;\u0026plusmn;\u0026thinsp;134.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.43\u0026thinsp;\u0026plusmn;\u0026thinsp;99.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-S [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.20\u0026thinsp;\u0026plusmn;\u0026thinsp;33.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.55\u0026thinsp;\u0026plusmn;\u0026thinsp;29.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-S [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNGAL-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.75\u0026thinsp;\u0026plusmn;\u0026thinsp;202.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.92\u0026thinsp;\u0026plusmn;\u0026thinsp;189.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.08\u0026thinsp;\u0026plusmn;\u0026thinsp;45.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.69\u0026thinsp;\u0026plusmn;\u0026thinsp;18.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.38\u0026thinsp;\u0026plusmn;\u0026thinsp;70.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-U [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.34\u0026thinsp;\u0026plusmn;\u0026thinsp;36.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.02\u0026thinsp;\u0026plusmn;\u0026thinsp;32.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Legend: IL-18 \u0026ndash; Interleukin-18, RBP4 \u0026ndash; Retinol-binding protein 4, FGF23 \u0026ndash; Fibroblast growth factor 23, NAG \u0026ndash; N-acetyl-\u0026beta;-D-glucosaminidase, KIM1 \u0026ndash; Kidney injury molecule 1, NGAL \u0026ndash; Neutrophil gelatinase-associated lipocalin, S \u0026ndash; serum, U \u0026ndash; urine, HC \u0026ndash; healthy controls, SpA \u0026ndash; spondyloarthritis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. AS vs PsA vs nr-axSpA vs Healthy Control\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferences in mean concentrations of selected markers \u0026ndash; SpA, PsA, nr-axSpA, healthy control \u0026ndash; only statistically significant results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarker [unit]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAS\u003c/p\u003e\n \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePsA\u003c/p\u003e\n \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enr-axSpA\u003c/p\u003e\n \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost- Hoc\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4- S [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22 (0,26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21 (0,21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68 (0,28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37 (0,25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsA vs AS \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs nr-axSpA \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs HC 0,004\u003c/p\u003e\n \u003cp\u003eAS vs Nr-axSpA NS\u003c/p\u003e\n \u003cp\u003eAS vs HC 0,002\u003c/p\u003e\n \u003cp\u003enr-axSpA vs HC NS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23- S\u003c/p\u003e\n \u003cp\u003e[pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,7 (36,04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,87 (32,93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72,92 (109,30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67,43 (99,79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsA vs AS \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs nr-axSpA \u0026ndash; 0,028\u003c/p\u003e\n \u003cp\u003ePsA vs HC 0,028\u003c/p\u003e\n \u003cp\u003eAS vs Nr-axSpA - NS\u003c/p\u003e\n \u003cp\u003eAS vs HC - NS\u003c/p\u003e\n \u003cp\u003enr-axSpA vs HC -NS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23- U\u003c/p\u003e\n \u003cp\u003e[pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,41 (74,64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,63 (76,85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,09 (52,78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,24 (16,32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsA vs AS \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs nr-axSpA \u0026ndash; 0,023\u003c/p\u003e\n \u003cp\u003ePsA vs HC \u0026minus;\u0026thinsp;0,002\u003c/p\u003e\n \u003cp\u003eAS vs Nr-axSpA -NS\u003c/p\u003e\n \u003cp\u003eAS vs HC - NS\u003c/p\u003e\n \u003cp\u003enr-axSpA vs HC - NS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG- S\u003c/p\u003e\n \u003cp\u003e[U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,97 (33,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65,88 (32,93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66,18 (32,55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74,55 (29,03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsA vs AS \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs nr-axSpA \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs HC \u0026minus;\u0026thinsp;0,013\u003c/p\u003e\n \u003cp\u003eAS vs Nr-axSpA - NS\u003c/p\u003e\n \u003cp\u003eAS vs HC - NS\u003c/p\u003e\n \u003cp\u003enr-axSpA vs HC - NS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18- U\u003c/p\u003e\n \u003cp\u003e[pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38,87 (65,94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,84 (24,65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,89 (16,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,24 (16,32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsA vs AS \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs nr-axSpA \u0026ndash; NS\u003c/p\u003e\n \u003cp\u003ePsA vs HC 0,012\u003c/p\u003e\n \u003cp\u003eAS vs Nr-axSpA - NS\u003c/p\u003e\n \u003cp\u003eAS vs HC - NS\u003c/p\u003e\n \u003cp\u003enr-axSpA vs HC -NS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Legend: IL-18 \u0026ndash; Interleukin-18, RBP4 \u0026ndash; Retinol-binding protein 4, FGF23 \u0026ndash; Fibroblast growth factor 23, NAG \u0026ndash; N-acetyl-\u0026beta;-D-glucosaminidase, S \u0026ndash; serum, U \u0026ndash; urine, AS \u0026ndash; ancylosing spondylitis, PsA- psoriatic arthritis, nr-axSpA \u0026ndash; non-radiographic spondyloarthritis, HC \u0026ndash; healthy controls, NS - not significant.\u003c/p\u003e\n\u003cp\u003e*Kruskal-Wallis Test for Independent Samples\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. First line therapy vs second line therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferences in mean concentrations of selected markers \u0026ndash; the first line therapy vs bDMARDs/tsDMARDs for at least 6 months\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarker [unit]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNSAIDs\u0026thinsp;\u0026plusmn;\u0026thinsp;csDMARDs (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ebDMARDs/tsDMARDs (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246.82\u0026thinsp;\u0026plusmn;\u0026thinsp;208.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e307.34\u0026thinsp;\u0026plusmn;\u0026thinsp;215.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-S [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-S [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.99\u0026thinsp;\u0026plusmn;\u0026thinsp;126.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.00\u0026thinsp;\u0026plusmn;\u0026thinsp;51.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-S [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.18\u0026thinsp;\u0026plusmn;\u0026thinsp;33.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.76\u0026thinsp;\u0026plusmn;\u0026thinsp;32.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-S [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNGAL-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.80\u0026thinsp;\u0026plusmn;\u0026thinsp;158.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.91\u0026thinsp;\u0026plusmn;\u0026thinsp;168.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-18-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.99\u0026thinsp;\u0026plusmn;\u0026thinsp;66.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.15\u0026thinsp;\u0026plusmn;\u0026thinsp;25.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBP4-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF23-U [pg/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.43\u0026thinsp;\u0026plusmn;\u0026thinsp;58.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.15\u0026thinsp;\u0026plusmn;\u0026thinsp;75.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAG-U [U/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.45\u0026thinsp;\u0026plusmn;\u0026thinsp;36.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.68\u0026thinsp;\u0026plusmn;\u0026thinsp;38.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIM1-U [ng/mL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Legend: IL-18 \u0026ndash; Interleukin-18, RBP4 \u0026ndash; Retinol-binding protein 4, FGF23 \u0026ndash; Fibroblast growth factor 23, NAG \u0026ndash; N-acetyl-\u0026beta;-D-glucosaminidase, KIM1 \u0026ndash; Kidney injury molecule 1, NGAL \u0026ndash; Neutrophil gelatinase-associated lipocalin, S \u0026ndash; serum, U \u0026ndash; urine, NSAIDs \u0026ndash; nonsteroidal anti-inflammatory drugs, csDMARDs \u0026ndash; conventional synthetic disease-modifying antirheumatic drugs, bDMARDs \u0026ndash; biologic DMARDs, tsDMARDs \u0026ndash; targeted synthetic DMARDs.\u003c/p\u003e\n\u003cp\u003e*U Mann-Whitney\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study represents so far one of the most comprehensive analyses of early kidney injury markers in patients with seronegative spondyloarthropathies (SpA), assessing both established and emerging biomarkers of renal dysfunction in a real-world, cross-sectional cohort. Given the increased risk of kidney involvement in SpA due to chronic inflammation, comorbidity, and pharmacotherapy, early detection remains of utmost importance. Commonly used kidney function indices (e.g., serum creatinine) often detect renal impairment only after substantial nephron loss [9, 11]. Researchers are therefore actively investigating biomarkers of subclinical kidney injury that may help predict future irreversible renal damage [9, 11, 12]. In this context, our study evaluated the performance of NGAL, KIM-1, RBP4, FGF23, NAG, and IL-18 in SpA patients compared to healthy control. Below, we discuss the existing literature and our findings for each marker individually.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNGAL (Neutrophil Gelatinase-Associated Lipocalin)\u003c/h2\u003e \u003cp\u003eNeutrophil gelatinase-associated lipocalin (NGAL) is a 25-kDa protein belonging to the lipocalin superfamily, which is involved in transport of hydrophilic substances through membranes to maintain cellular homeostasis [13]. NGAL is also recognized as one of the most well-established biomarkers of both AKI and CKD [9]. It is produced in several tissues, including the lungs, gastrointestinal tract, liver, and kidneys. Its expression increases significantly in damaged epithelial cells due to injury, inflammation, or neoplastic transformation. While both plasma and urine NGAL have been studied as markers of kidney injury, urinary NGAL is more specific to kidney damage. Animal studies have shown that NGAL is one of the earliest EKIMs activated after tubular injury, especially in the distal nephron. Urinary NGAL levels rise as early as 2 hours after kidney injury in mouse models [14] and in children developing AKI after cardiac surgery [15]. In chronic kidney disease, urinary NGAL is negatively correlated with eGFR and positively correlated with interstitial fibrosis and tubular atrophy [9].\u003c/p\u003e \u003cp\u003eShukla et al. in 2017 performed a prospective biomarker study in SpA patients on NSAIDs [10]. Serum and urine NGAL levels were measured at baseline and follow-ups. Importantly, urinary and serum NGAL were elevated ~ 2–3 fold in SpA patients on chronic NSAIDs compared to both controls and NSAID-naive SpA patients, despite normal serum creatinine and eGFR [10]. After initiating NSAIDs, NGAL levels rose as early as 1 week and became significantly elevated by 6 weeks, then declined after NSAID cessation, indicating the changes were reversible [10]. This suggests NGAL can detect subclinical tubular injury due to NSAIDs in SpA before creatinine rises [10].\u003c/p\u003e \u003cp\u003eIn psoriatic disease, NGAL seems to be promising marker of early kidney injury. Kucukyangoz et al., in 2024 [16], studied 44 psoriasis patients without clinical kidney disease and 44 controls, measuring urine NGAL levels [16]. Psoriasis patients had significantly higher proteinuria than controls and urinary NGAL (normalized to creatinine) correlated positively with proteinuria (r ≈ 0.36, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.02) [16]. About one in four psoriasis patients have PsA [17]. Notably, Kucukyangoz et al. study didn’t raise a subject of arthritis in study sample, however elevation in NGAL likely reflects occult tubular injury from chronic inflammation or nephrotoxic therapy, used in both psoriasis and PsA [16].\u003c/p\u003e \u003cp\u003eIn contrast, our study did not detect significant differences in urinary NGAL between SpA patients and controls (p = 0.82), nor between subtypes. The high variability in urinary NGAL values within both groups may have masked subtle differences. Several factors may account for the discrepancy with previous literature: (1) our cohort excluded patients with overt renal disease, potentially lowering baseline biomarker expression; (2) cross-sectional sampling may have missed transient NGAL elevations linked to short-term drug exposure or inflammatory flares. Notably, according to Canki et al. [18], NGAL is characterized by high intra-individual variability, which may affect the accuracy of its measurements and effectively mask subtle differences within the studied cohort. Moreover, the analysis by Seibert et al. [19] demonstrated that although urinary NGAL levels are significantly higher in CKD compared with healthy controls, it had no diagnostic value in differentiating stable chronic kidney diseases of inflammatory versus non-inflammatory origin [19]. Due to technical issue, we were unable to measure serum NGAL levels, which represents a major limitation in the interpretation of our findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eKIM-1 (Kidney Injury Molecule-1)\u003c/h2\u003e \u003cp\u003eKIM-1 is a membrane protein highly upregulated in proximal tubules following toxic, ischemic or inflammatory injury [20, 21]. Its urinary excretion reflects ongoing tubular repair or chronic low-grade injury rather than filtration impairment [12]. KIM-1 has been shown to be a highly sensitive and specific marker of kidney injury in several animal models of kidney disease, including models of injury due to ischemia [20, 21] and various nephrotoxins [9, 12]. Canki et al. describe KIM-1 as the most promising marker for the diagnosis of CKD [18]. Studies in rheumatoid arthritis and SLE further reinforce KIM-1’s sensitivity for inflammatory kidney injury, and reviews recommend its use for both monitoring and research in patients exposed to nephrotoxic medications [22]. In psoriasis, KIM-1 has shown similar utility. Kucukyangoz et al. [16] found urinary KIM-1 and creatinine was elevated in psoriatic patients and correlated with proteinuria (r ≈ 0.37, \u003cem\u003ep\u003c/em\u003e = 0.013) [16]. Patients had no clinical CKD, so higher KIM-1 signals early tubular damage [16]. In the 2017 study by Shukla et al. assessed both urinary and serum KIM-1 levels in SpA patients on NSAIDs [10]. Regular NSAID users had ~ 2–3× higher KIM-1 (both serum and urine) than controls or NSAID-naive patients (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) despite normal renal function tests [10]. Initiating NSAIDs led to a rise in KIM-1 within one week, significant by 6 weeks, and levels dropped after NSAID withdrawal, indicating subclinical AKI induced by NSAIDs [10]. Mentioned above studies showed that in chronic inflammatory diseases, including SpA, KIM-1 is elevated even in patients without clear eGFR reduction, emphasizing its value in identifying hidden tubular dysfunction before clinical CKD is apparent [10, 16].\u003c/p\u003e \u003cp\u003eOur findings align with this perspective, demonstrating a significantly higher serum KIM-1 in SpA compared with HC (p = 0.03) and higher urinary KIM-1 in SpA compared with HC (p = 0.02), despite preserved eGFR. Interestingly, KIM-1 levels did not differ significantly between patients on first-line (NSAIDs ± csDMARDs) and second-line (bDMARDs/tsDMARDs) therapy. This may indicate that subclinical tubular injury is not limited to NSAID use but could also arise from biologic therapy or disease-related factors such as persistent low-grade inflammation. It is also noteworthy that the study by Seibert et al. [19] did not demonstrate KIM-1 to differentiate between stable CKD of inflammatory and non-inflammatory origin [19]. These observations may support the idea that proximal tubular injury in this context may occur independently of the specific inflammatory etiology or treatment strategy. Clinically, present findings support the inclusion of KIM-1 in biomarker panels for SpA, particularly for long-term renal safety surveillance across treatment modalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRBP4 (Retinol Binding Protein 4)\u003c/h2\u003e \u003cp\u003eRBP4 is a low-molecular-weight protein primarily secreted by the liver and adipose tissue. [23] It functions as the principal carrier of retinol in the bloodstream. Once retinol is delivered to target cells, the remaining apo-RBP4 (retinol-free form) is freely filtered by the glomeruli, reabsorbed by proximal tubular cells, and subsequently degraded [23]. The presence of RBP4 in urine, or alterations in its serum concentration, are indicative of proximal tubular dysfunction. Notably, the homeostasis of RBP4 is influenced by both hepatic and renal function [23]. Literature supports its utility as part of a biomarker panel for early detection and risk monitoring in CKD patients [22–24]. Some studies have noted RBP4’s sensitivity in reflecting tubular injury across a range of etiologies, including drug toxicity and systemic inflammation [23, 24]. In a large study conducted in 2018 by Wu et al. [25], involving 926 patients with AS, the authors aimed to identify risk factors for renal involvement, defined as hematuria, proteinuria, and/or estimated glomerular filtration rate (eGFR) \u0026lt; 60 mL/min/1.73 m² [25]. Among various laboratory parameters assessed, serum RBP4 was included. Interestingly, RBP4 did not emerge as a strong indicator of kidney involvement. In univariate analysis, mean serum RBP4 levels were slightly lower in AS patients with renal abnormalities compared to those without (p \u0026lt; 0.05) [25]. In multivariate logistic regression RBP4 was not independently predictive for renal involvement [25].This is in line with earlier observations that urinary RBP excretion is typically normal in AS patients without overt renal disease [26]. However, RBP4 remains of interest in SpA as an adipokine and metabolic marker. For example, a proteomic study suggested that higher baseline serum RBP4 levels predicted better response to TNF inhibitor therapy in patients with axial SpA [24].\u003c/p\u003e \u003cp\u003eAccording to our study, urinary RBP4 was significantly higher in SpA compared with HC (p = 0.00), while serum RBP4 was significantly lower in SpA patients (p \u0026lt; 0.001). Although the absolute difference in urinary levels is moderate (~ 18%), it may still indicate subtle impairment of tubular reabsorption. This divergence between serum and urinary values may reflect altered tubular handling and systemic metabolism in the context of chronic inflammation. Notably, significant differences in serum RBP4 were observed between PsA and HC (p = 0.00) and between AS and HC (p = 0.00), with lower values in both SpA subtypes compared to HC, whereas urinary RBP4 did not differ significantly among SpA subtypes. These findings highlight the need for dual-compartment analysis when interpreting RBP4 in inflammatory diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFGF23 (Fibroblast growth factor 23)\u003c/h2\u003e \u003cp\u003eFGF23 is a bone-derived phosphaturic hormone that regulates phosphate and vitamin D metabolism. It is a well-established marker and key driver of CKD–mineral and bone disorder (CKD-MBD) and has been associated with the progression of CKD and increased cardiovascular morbidity and mortality in adults with renal impairment [27–29]. Elevated FGF23 levels are also observed in various chronic inflammatory conditions, including rheumatoid arthritis, where subclinical renal disturbances are common, even in the absence of CKD [30]. Chronic inflammation and persistent changes in vitamin D metabolism in SpA may potentially drive altered FGF23 secretion, although the exact pathophysiologic mechanisms in this patient population remain unclear [29].\u003c/p\u003e \u003cp\u003eTo date, there are no published studies directly examining FGF23 as a biomarker of early kidney injury specifically in seronegative spondyloarthropathies. Most available data come from general CKD cohorts or other rheumatic diseases, and data extrapolation must be done with caution [28–30]. Some data suggest that in systemic inflammatory states, FGF23 may increase even before overt declines in eGFR, possibly reflecting a dynamic response to subclinical pertubations in mineral metabolism, inflammation, or early tubular stress.\u003c/p\u003e \u003cp\u003ePresent study represents the first comprehensive evaluation of FGF23 in patients with SpA and provides novel insights into its role as an early kidney injury marker in this cohort. We observed a statistically significant elevation in urinary FGF23 concentrations in SpA patients compared to healthy controls (p \u0026lt; 0.001). This finding suggests early tubular dysfunction or altered renal handling of FGF23 in SpA, even in the absence of clinically recognized kidney disease. Conversely, serum FGF23 levels did not significantly differ between the overall SpA group and healthy controls (p = 0.101).\u003c/p\u003e \u003cp\u003eFurther stratification of the cohort revealed specific differences among SpA subtypes. While overall serum FGF23-S levels showed a statistically significant difference across the four groups (AS, PsA, nr-axSpA, and HC; p = 0.013p = 0.013), post-hoc analysis indicated that was primarily driven by lower FGF23-S in PsA compared to both nr-axSpA (p = 0.028) and HC (p = 0.028). Similarly, urinary FGF23 levels were also significantly different across the groups (p = 0.002), with PsA showing lower levels compared to nr-axSpA (p = 0.023) and HC (p = 0.002). These results suggest potential disease-specific variations in FGF23 metabolism within different SpA subtypes.\u003c/p\u003e \u003cp\u003eAdditionally, present analysis showed that FGF23-S concentrations were significantly lower in patients receiving bDMARDs/tsDMARDs for at least 6 months compared to those on first-line therapy (NSAIDs ± csDMARDs) (p = 0.018). Both Schnedl et al. [31] and Zhang and Qin [9] provide extensive descriptions of the role of FGF23 as a marker in AKI/CKD and its association with inflammation, indicating that it is induced by pro-inflammatory cytokines [9, 31]. In the context of lower serum FGF23 levels observed in patients receiving bDMARDs/tsDMARDs, our findings are consistent with the hypothesis that inflammation may be a key regulatory factor of FGF23 in SpA. Furthermore, Mattinzoli et al. [32] discuss the potential role of FGF23 as a “risk biomarker\u003cem\u003e”\u003c/em\u003e in AKI, as well as the interactions between mineral bone disorders and cardiac injury, which opens new perspectives for future research on the bone–kidney–heart axis in SpA [32]. Further research targeting longitudinal measurement of FGF23 in well-defined SpA populations, especially with integrated assessment of CKD-MBD and inflammatory status, is necessary to clarify its clinical value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNAG (N-acetyl-β-D-glucosaminidase)\u003c/h2\u003e \u003cp\u003eNAG is a lysosomal enzyme present in high concentrations in renal tubular cells, and its urinary excretion increases rapidly with tubular cell damage due to toxins, inflammation or ischemia [11]. Several studies affirm that NAG is a sensitive marker for the earliest forms of tubular injury [22]. In chronic inflammatory and immune-mediated diseases such as lupus nephritis, heightened urinary NAG excretion has been observed and is frequently linked to long-term disease activity and renal prognosis [11]. However, data specifically in SpA are scant, representing a clear area for future research.\u003c/p\u003e \u003cp\u003eOur results showed significantly lower serum NAG in SpA compared with HC (p = 0.01), with no significant urinary differences. The absence of urinary differences and the unexpected reduction in serum NAG may be due to large interindividual variability and the influence of extra-renal sources on serum NAG concentrations. This non-intuitive observation also gains context in the study by Canki et al. [18], who indicate that NAG activity may be altered by nephrotoxins, making it a less reliable marker of CKD. This suggests that extra-renal factors or interactions with pharmacotherapy in chronic inflammatory diseases such as SpA may have influenced its serum concentration, thereby masking potential changes related to tubular injury. Additionally, significant differences in serum NAG were found between PsA and HC (p = 0.01), with lower values in PsA. These results underscore the need for disease-specific validation before adopting NAG as a biomarker in SpA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIL-18 (Interleukin-18)\u003c/h2\u003e \u003cp\u003eIL-18 is a pro-inflammatory cytokine belonging to the IL-1 family, known for its role in the pathogenesis of immune-mediated kidney injury [33, 34]. Studies show that both serum and urinary IL-18 rise in the very early phase of ischemic AKI, prior to conventional lab changes and often before NGAL or KIM-1 [34]. In systemic autoimmune and inflammatory conditions, increased IL-18 is associated with disease activity and renal risk [22].\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated elevated serum IL-18 levels in patients with PsA, correlating both with higher disease activity and an unfavorable metabolic - atherogenic profile [35[Waszczykowski, 2020 #56]]. In our study, no significant differences in serum IL-18 were observed between SpA and HC or among subtypes. Urinary IL-18 did not differ significantly between SpA and HC overall. However, urinary IL-18 was significantly higher in PsA compared to HC (p = 0.01). This may suggest that in PsA, urinary IL-18 appears earlier than it is reflected in the systemic circulation. This observation is supported by the literature, where urinary IL-18 is considered a very early marker of inflammatory stress and tubulointerstitial injury. Studies by Mattinzoli et al. [32] and Canki et al. [18] emphasize its role as a pro-inflammatory cytokine and a sensitive indicator of AKI, while the work of Hirooki [36] highlights its utility in identifying early tubular changes. In the context of PsA, elevated urinary IL-18 may therefore reflect a specific, localized inflammatory process in the kidneys that is not yet detected by serum levels, giving it potential as a biomarker of early PsA-specific inflammation-related renal stress.\u003c/p\u003e \u003c/div\u003e "},{"header":"STRENGTHS AND LIMITATIONS","content":"\u003cp\u003eA key strength of this study is its comprehensive evaluation of multiple early kidney injury markers in a well-characterized cohort of patients with seronegative spondyloarthropathies, stratified by disease subtype and treatment regimen. The inclusion of both serum and urinary measurements for most biomarkers allowed for nuanced interpretation of renal tubular and systemic alterations. Another strength is the rigorous selection of patients without clinically overt kidney disease, enabling detection of subclinical abnormalities potentially relevant for early intervention. The use of healthy controls matched in age and sex distribution adds to the robustness of the comparisons.\u003c/p\u003e\u003cp\u003eHowever, several limitations must be acknowledged. First, the cross-sectional design precludes assessment of temporal changes in biomarker levels and their predictive value for future renal outcomes. Longitudinal follow-up would be essential to establish causality and clinical relevance. Second, the inability to measure serum NGAL limited the completeness of comparisons for this key biomarker. Third, potential confounders such as cumulative drug exposure, disease activity scores, and detailed comorbidity profiles were not controlled for in multivariate models, which may influence biomarker variability. Finally, the sample size within certain subgroups, particularly in nr-axSpA and in specific biologic therapy categories, may have limited the power to detect subtle between-group differences.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provides a comprehensive evaluation of early kidney injury markers in patients with seronegative spondyloarthropathies, a population at increased risk for renal involvement. Our findings reveal that despite preserved glomerular filtration rate, patients with SpA exhibit subclinical tubular dysfunction, as evidenced by significantly elevated serum and urinary KIM-1 levels compared to healthy controls (p = 0.039 and p = 0.024, respectively). Moreover, altered RBP4 metabolism, with significantly higher urinary (p = 0.005\u003cem\u003ep\u003c/em\u003e = 0.005) and lower serum (p \u0026lt; 0.001) levels in SpA patients, suggests disrupted tubular reabsorption and systemic changes. Notably, we observed significantly elevated urinary FGF23 in the overall SpA cohort compared to healthy controls (p \u0026lt; 0.001), indicating early tubular stress or altered mineral metabolism. While overall urinary NGAL and IL-18 did not differ significantly between SpA and healthy controls, subgroup analysis pointed to distinct biomarker profiles in specific SpA subtypes; for instance, psoriatic arthritis patients showed lower serum FGF23 (p = 0.028 vs HC) and NAG (p = 0.013 vs HC), and higher urinary IL-18 (p = 0.012 vs HC) compared to healthy individuals. These results underscore the potential of these novel biomarkers to detect early, subclinical kidney injury in SpA, offering a crucial window for intervention before overt renal damage occurs. Longitudinal studies are warranted to further elucidate the predictive value of these markers for long-term renal outcomes and to assess their utility in guiding therapeutic strategies in patients with spondyloarthropathies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study was conducted in accordance with the World Medical Association Declaration of Helsinki. The study received approval on 07.11.2022 from the Ethics Committee of Wroclaw Medical University (KB-826/2022). All participants provided informed consent prior to inclusion in the study, and patient data were anonymized prior to statistical analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eI declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Wroclaw Medical University (subsidy number: SUBK.A270.23.049). The funding body had no involvement in the study design, data collection, analysis, interpretation of data, the decision to publish, or the preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKMT conceived and designed the study, obtained ethical approval, recruited study participants and controls, secured funding, and drafted the manuscript. PKK performed the statistical analyses. HA-B provided nephrological expertise and supervision. MŻ, DB, and KK-K conducted the laboratory analyses. JŚ provided rheumatological expertise and supervision. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to express their sincere gratitude to all patients who agreed to participate in this study, as well as to all volunteers who formed the control group. The authors also wish to thank the medical and technical staff for their valuable assistance and organizational support during the conduct of the research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWalsh JA, Magrey M: Clinical Manifestations and Diagnosis of Axial Spondyloarthritis. \u003cem\u003eJ Clin Rheumatol\u003c/em\u003e 2021, 27(8):e547\u0026ndash;e560.\u003c/li\u003e\n\u003cli\u003eTaurog JD, Gensler LS, Haroon N: Spondyloarthritis. In: \u003cem\u003eHarrison's Principles of Internal Medicine, 21e.\u003c/em\u003e Edited by Loscalzo J, Fauci A, Kasper D, Hauser S, Longo D, Jameson JL. 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involvement in seronegative spondyloarthropathies: a systematic review. \u003cem\u003eRheumatol Int\u003c/em\u003e 2025, 45(6):151.\u003c/li\u003e\n\u003cli\u003eHaroon NN, Paterson JM, Li P, Inman RD, Haroon N: Patients With Ankylosing Spondylitis Have Increased Cardiovascular and Cerebrovascular Mortality: A Population-Based Study. \u003cem\u003eAnn Intern Med\u003c/em\u003e 2015, 163(6):409\u0026ndash;416.\u003c/li\u003e\n\u003cli\u003eHwang S, Kim YJ, Ahn SM, Koo BS: Incidence rate of and risk factors for glomerulonephritis in patients with axial spondyloarthritis: a nationwide population-based study. \u003cem\u003eTher Adv Musculoskelet Dis\u003c/em\u003e 2025, 17:1759720x251320328.\u003c/li\u003e\n\u003cli\u003eZhang WR, Parikh CR: Biomarkers of Acute and Chronic Kidney Disease. \u003cem\u003eAnnu Rev Physiol\u003c/em\u003e 2019, 81:309\u0026ndash;333.\u003c/li\u003e\n\u003cli\u003e Shukla A, Rai MK, Prasad N, Agarwal V: Short-Term Non-Steroid Anti-Inflammatory Drug Use in 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systematic review and meta-analysis of observational and clinical studies. \u003cem\u003eJ Am Acad Dermatol\u003c/em\u003e 2019, 80(1):251\u0026ndash;265.e219.\u003c/li\u003e\n\u003cli\u003e Canki E, Kho E, Hoenderop JGJ: Urinary biomarkers in kidney disease. \u003cem\u003eClin Chim Acta\u003c/em\u003e 2024, 555:117798.\u003c/li\u003e\n\u003cli\u003e Seibert FS, Sitz M, Passfall J, Haesner M, Laschinski P, Buhl M, Bauer F, Rohn B, Babel N, Westhoff TH: Urinary calprotectin, NGAL, and KIM-1 in the differentiation of primarily inflammatory vs. non-inflammatory stable chronic kidney diseases. \u003cem\u003eRenal Failure\u003c/em\u003e 2021, 43(1):417\u0026ndash;424.\u003c/li\u003e\n\u003cli\u003e Han WK, Bailly V, Abichandani R, Thadhani R, Bonventre JV: Kidney Injury Molecule-1 (KIM-1): A novel biomarker for human renal proximal tubule injury. \u003cem\u003eKidney International\u003c/em\u003e 2002, 62(1):237\u0026ndash;244.\u003c/li\u003e\n\u003cli\u003e Ichimura T, Bonventre JV, Bailly V, Wei H, Hession CA, Cate RL, Sanicola M: Kidney Injury Molecule-1 (KIM-1), a Putative Epithelial Cell Adhesion Molecule Containing a Novel Immunoglobulin Domain, Is Up-regulated in Renal Cells after Injury *. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e 1998, 273(7):4135\u0026ndash;4142.\u003c/li\u003e\n\u003cli\u003e Wasung ME, Chawla LS, Madero M: Biomarkers of renal function, which and when? \u003cem\u003eClinica Chimica Acta\u003c/em\u003e 2015, 438:350\u0026ndash;357.\u003c/li\u003e\n\u003cli\u003e Frey SK, Nagl B, Henze A, Raila J, Schlosser B, Berg T, Tepel M, Zidek W, Weickert MO, Pfeiffer AF \u003cem\u003eet al\u003c/em\u003e: Isoforms of retinol binding protein 4 (RBP4) are increased in chronic diseases of the kidney but not of the liver. \u003cem\u003eLipids Health Dis\u003c/em\u003e 2008, 7:29.\u003c/li\u003e\n\u003cli\u003e Wu J, Wu X, Chen Z, Lv Q, Yang M, Zheng X, Li Q, Zhang Y, Wei Q, Cao S \u003cem\u003eet al\u003c/em\u003e: Circulating Retinol-Binding Protein 4 as a Possible Biomarker of Treatment Response for Ankylosing Spondylitis: An Array-Based Comparative Study. \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e 2020, 11.\u003c/li\u003e\n\u003cli\u003e Wu Y, Zhang G, Wang N, Xue Q: Risk Factors of Renal Involvement Based on Different Manifestations in Patients with Ankylosing Spondylitis. \u003cem\u003eKidney Blood Press Res\u003c/em\u003e 2018, 43(2):367\u0026ndash;377.\u003c/li\u003e\n\u003cli\u003e Vilar MJ, Cury SE, Ferraz MB, Sesso R, Atra E: Renal abnormalities in ankylosing spondylitis. \u003cem\u003eScand J Rheumatol\u003c/em\u003e 1997, 26(1):19\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003e Cannata-And\u0026iacute;a JB, Mart\u0026iacute;n-Carro B, Mart\u0026iacute;n-V\u0026iacute;rgala J, Rodr\u0026iacute;guez-Carrio J, Bande-Fern\u0026aacute;ndez JJ, Alonso-Montes C, Carrillo-L\u0026oacute;pez N: Chronic Kidney Disease-Mineral and Bone Disorders: Pathogenesis and Management. \u003cem\u003eCalcif Tissue Int\u003c/em\u003e 2021, 108(4):410\u0026ndash;422.\u003c/li\u003e\n\u003cli\u003e Isakova T, Wahl P, Vargas GS, Guti\u0026eacute;rrez OM, Scialla J, Xie H, Appleby D, Nessel L, Bellovich K, Chen J \u003cem\u003eet al\u003c/em\u003e: Fibroblast growth factor 23 is elevated before parathyroid hormone and phosphate in chronic kidney disease. \u003cem\u003eKidney Int\u003c/em\u003e 2011, 79(12):1370\u0026ndash;1378.\u003c/li\u003e\n\u003cli\u003e Gercik O, Solmaz D, Coban E, Iptec BO, Avcioglu G, Bayindir O, Kabadayi G, Topal FE, Kozaci D, Akar S: Evaluation of serum fibroblast growth factor-23 in patients with axial spondyloarthritis and its association with sclerostin, inflammation, and spinal damage. \u003cem\u003eRheumatol Int\u003c/em\u003e 2019, 39(5):835\u0026ndash;840.\u003c/li\u003e\n\u003cli\u003e Sato H, Kazama JJ, Murasawa A, Otani H, Abe A, Ito S, Ishikawa H, Nakazono K, Kuroda T, Nakano M \u003cem\u003eet al\u003c/em\u003e: Serum Fibroblast Growth Factor 23 (FGF23) in Patients with Rheumatoid Arthritis. \u003cem\u003eIntern Med\u003c/em\u003e 2016, 55(2):121\u0026ndash;126.\u003c/li\u003e\n\u003cli\u003e Schnedl C, Fahrleitner-Pammer A, Pietschmann P, Amrein K: FGF23 in Acute and Chronic Illness. \u003cem\u003eDis Markers\u003c/em\u003e 2015, 2015:358086.\u003c/li\u003e\n\u003cli\u003e Mattinzoli D, Molinari P, Romero-Gonz\u0026aacute;lez G, Bover J, Cicero E, Pesce F, Abinti M, Conti C, Castellano G, Alfieri C: Is there a role in acute kidney injury for FGF23 and Klotho? \u003cem\u003eClinical Kidney Journal\u003c/em\u003e 2023, 16(10):1555\u0026ndash;1562.\u003c/li\u003e\n\u003cli\u003e Parikh CR, Mishra J, Thiessen-Philbrook H, Dursun B, Ma Q, Kelly C, Dent C, Devarajan P, Edelstein CL: Urinary IL-18 is an early predictive biomarker of acute kidney injury after cardiac surgery. \u003cem\u003eKidney Int\u003c/em\u003e 2006, 70(1):199\u0026ndash;203.\u003c/li\u003e\n\u003cli\u003e Parikh CR, Abraham E, Ancukiewicz M, Edelstein CL: Urine IL-18 is an early diagnostic marker for acute kidney injury and predicts mortality in the intensive care unit. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e 2005, 16(10):3046\u0026ndash;3052.\u003c/li\u003e\n\u003cli\u003e Bonek K, Kuca-Warnawin E, Kornatka A, Zielińska A, Wisłowska M, Kontny E, Głuszko P: Associations of IL-18 with Altered Cardiovascular Risk Profile in Psoriatic Arthritis and Ankylosing Spondylitis. \u003cem\u003eJournal of Clinical Medicine\u003c/em\u003e 2022, 11(3):766.\u003c/li\u003e\n\u003cli\u003e Hirooka Y, Nozaki Y: Interleukin-18 in Inflammatory Kidney Disease. \u003cem\u003eFrontiers in Medicine\u003c/em\u003e 2021, Volume 8\u0026ndash;2021.\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7907238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7907238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients with seronegative spondyloarthropathies (SpA) are at increased risk of kidney involvement due to chronic inflammation, comorbidities and pharmacotherapy. There is a need for early kidney injury markers (EKIMs) to identify subclinical renal damage, potentially enabling timely intervention and preventing irreversible kidney disease progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to comprehensively evaluate the concentrations of various established and emerging early kidney injury markers, including Neutrophil Gelatinase-Associated Lipocalin (NGAL), Kidney Injury Molecule-1 (KIM-1), Retinol Binding Protein 4 (RBP4), Fibroblast Growth Factor 23 (FGF23), N-acetyl-β-D-glucosaminidase (NAG), and Interleukin-18 (IL-18), in serum and urine of SpA patients without clinically recognized kidney disease, comparing them to healthy controls (HC) and analyzing their associations with SpA subtypes and therapeutic regimens.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis was a retrospective cross-sectional comparative study involving 125 patients with SpA (50 with ankylosing spondylitis, 41 with psoriatic arthritis, 34 with non-radiographic spondyloarthritis) and 53 healthy individuals serving as controls. Demographic data, clinical characteristics, and treatment regimens were collected. Serum and urinary levels of EKIMs (NGAL, KIM-1, RBP4, FGF23, NAG, and IL-18) were measured. Statistical analyses, including the Mann-Whitney U test and Kruskal-Wallis test with post-hoc analysis, were performed to assess differences between groups and identify significant associations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings indicate subclinical kidney involvement in SpA patients. Serum and urinary KIM-1 levels were significantly elevated in SpA patients compared to HC (serum: p\u0026thinsp;=\u0026thinsp;0.039; urine: p\u0026thinsp;=\u0026thinsp;0.024), suggesting early tubular damage. Urinary RBP4 was significantly higher in SpA patients (p\u0026thinsp;=\u0026thinsp;0.005), while serum RBP4 was significantly lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to HC, pointing to altered RBP4 handling. Urinary FGF23 concentrations were significantly higher in SpA patients than in HC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting early tubular stress. Serum NAG levels also differed significantly between SpA and HC (p\u0026thinsp;=\u0026thinsp;0.018). While no overall differences were observed for urinary NGAL or IL-18 between the SpA group and HC, subgroup analyses revealed specific differences. Notably, psoriatic arthritis patients showed distinct profiles, including lower serum FGF23 (p\u0026thinsp;=\u0026thinsp;0.028 vs nr-axSpA and HC), lower serum NAG (p\u0026thinsp;=\u0026thinsp;0.013 vs HC), and higher urinary IL-18 (p\u0026thinsp;=\u0026thinsp;0.012 vs HC) compared to healthy individuals. Levels of most EKIMs did not significantly differ between patients on first-line versus second-line therapies, except for serum FGF23, which was lower in patients receiving bDMARDs/tsDMARDs (p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients with seronegative spondyloarthropathies without overt kidney disease demonstrate early, subclinical tubular injury and altered biomarker profiles, particularly involving KIM-1, RBP4, FGF23, and NAG. These findings underscore the potential utility of these novel markers in detecting kidney compromise at an early stage in SpA, which is crucial for timely management. Further longitudinal studies are needed to validate the predictive value of these EKIMs for long-term renal outcomes and to explore their role in monitoring disease progression and therapeutic response in SpA.\u003c/p\u003e","manuscriptTitle":"Early Kidney Injury Markers in Patients with Seronegative Spondyloarthropathies: A Retrospective Cross-Sectional Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 16:25:59","doi":"10.21203/rs.3.rs-7907238/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-03T11:03:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2330593386166555678876139380070172338","date":"2026-03-19T12:13:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T11:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121825851850978530368488563427151593843","date":"2026-03-06T13:43:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T08:46:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T08:28:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-14T04:18:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T21:18:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-11T21:15:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65faa304-8273-4e29-a597-ea2bedd1f963","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64058587,"name":"Health sciences/Biomarkers"},{"id":64058588,"name":"Health sciences/Diseases"},{"id":64058589,"name":"Biological sciences/Immunology"},{"id":64058590,"name":"Health sciences/Medical research"},{"id":64058591,"name":"Health sciences/Nephrology"},{"id":64058592,"name":"Health sciences/Rheumatology"}],"tags":[],"updatedAt":"2026-03-11T16:25:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 16:25:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7907238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7907238","identity":"rs-7907238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}