Urinary vesicle biomarkers and kidney function – Results from the German AugUR study

Urinary vesicle biomarkers and kidney function – Results from the German AugUR 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 Urinary vesicle biomarkers and kidney function – Results from the German AugUR study Luisa Schnobrich, Hannah C de Hesselle, Lorena Mornhiniweg, Rike Felgner, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8650516/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Progression into more severe stages of chronic kidney disease (CKD) based on estimated glomerular filtration rate (eGFR) and albuminuria are associated with increased risk of end-stage renal failure, cardiovascular diseases, and mortality. Vesicles in the urine are cell-derived particles containing constituents of the cells of origin. Little is known about the prognostic capacity of urinary vesicles for CKD progression. Purpose To evaluate the association between components of urinary vesicles and incident CKD. Methods In the AugUR study, a prospective population-based cohort study in individuals aged 70-95 years at baseline, we isolated and characterized urinary vesicles from 580 participants at two timepoints. In cross-sectional data, influences of age, sex and established kidney biomarkers on vesicular albumin and podocalyxin were characterised. Longitudinal data were used to test associations of vesicular albumin and podocalyxin with incident eGFR-based CKD and albuminuria. Results Cross-sectionally, urinary vesicle albumin and urinary vesicle-bound podocalyxin were moderately correlated with each other and with urinary albumin and alpha1-microglobulin, but not with eGFR. Vesicular albumin concentrations were influenced by sex, whereas age showed an effect on podocalyxin. After adjusting for age and sex, higher vesicular albumin was associated with higher urinary albumin and lower eGFR. Higher vesicular podocalyxin concentrations were associated with higher urinary albumin but not with eGFR. Both markers showed identical associations with urinary alpha1-microglobulin. In longitudinal analyses, baseline vesicular albumin showed association with incident CKD based on eGFR. This association was no longer present after adjustment for baseline eGFR. In contrast, higher baseline podocalyxin concentrations were predictive for decreased risk of incident albuminuria after adjustment for baseline free urinary albumin. Baseline-adjusted change in urinary vesicle albumin and urinary vesicle-bound podocalyxin were both associated with incident albuminuria, independent of free urinary albumin and other kidney biomarkers. Here, increase in follow-up versus baseline values were associated with higher risk for incident albuminuria, with higher effect sizes for vesicular albumin. Conclusion This study indicates that higher vesicular podocalyxin at baseline might be a potential predictor for lower risk for albuminuria over three years in an old-aged cohort. In contrast, longitudinal increase in urinary vesicle biomarkers, especially vesicular albumin, might be diagnostic markers for incident albuminuria in the elderly. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Cohort study old-aged population urinary vesicles vesicular albumin podocalyxin kidney function Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 KEY Message What is known According to a previous study in animals, accumulation of albumin in the subpodocyte space leads to subsequent endocytosis by the podocytes. Podocyte-produced vesicles contain potential biomarkers of the deterioration of kidney function in humans. What is new Biomarkers from urinary vesicles can be quantified from biobanked human samples. Higher vesicular podocalyxin at baseline might be a potential predictor for lower risk for albuminuria over three years in an old-aged cohort. Changes in urinary vesicle biomarkers over time, especially vesicular albumin, are associated with incident albuminuria independent of eGFR and free urinary albumin. INTRODUCTION Chronic kidney disease (CKD), which is defined by reduced kidney function and/or kidney damage persisting for over three months [ 1 ], is a serious and growing global health problem, affecting more than 10% of the adult global population [ 2 ]. Kidney function is assessed by the determination of the glomerular filtration rate (GFR), estimated with serum biomarkers creatinine or cystatin C (eGFR) and by the degree of albuminuria, measured as the urinary albumin-creatinine-ratio (uACR) [ 1 ]. The urinary albumin excretion is determined by the degree of glomerular albumin filtration and the extent of tubular reabsorption, predominantly in the proximal tubule [ 3 ]. Based on its size and charge specificity, the intact glomerular filtration barrier (GFB) is largely impermeable for macromolecules, such as albumin. The GFB consists of three layers: the glomerular endothelial cells, basement membrane and podocytes. The latter are important for the function of the GFB, as they form a size barrier with slit diaphragms for free filtration [ 4 ]. Compromised functions of the GFB and the concomitant increase in albumin filtration are usually masked for prolonged periods of time due to albumin reabsorption by renal proximal tubule cells, mediated by receptor-mediated endocytosis [ 5 ]. Once the tubular reabsorptive capacity is saturated, albumin excretion in the urine increases [ 6 ]. The commonly used limit for microalbuminuria is uACR > 30 mg/g, but even lower uACR values elevate the risk for cardiovascular diseases and mortality [ 7 , 8 ]. In addition, limitations of formulas for eGFR might lead to an under-diagnosis of early functional changes in the kidney, thus leading to the delayed initiation of reno-protective therapeutic measures [ 9 ]. Therefore, there is apparently the need for new non-invasive diagnostic and prognostic biomarkers for CKD. In this context, extracellular vesicles (EVs) detectable in the urine have gained increasing interest [ 10 ]. EVs are defined as particles with a lipid bilayer released from cells and, in the context of the kidney, can be easily recovered from urine samples [ 11 ]. Furthermore, EVs carry markers specific for the cells of origin and provide information about the content of the parental cells. Thus, the assessment of changes in EV cargo in combination with the origin of the urinary EVs might generate specific insights into localization, cause and progression of different kidney diseases [ 12 , 13 ]. According to a previous study in animals [ 14 ], changes in the permeability of the GFB lead to the accumulation of serum albumin in the subpodocyte space and the subsequent endocytosis by the podocytes [ 15 ]. Part of the endocytosed albumin is degraded in lysosomes, whereas the majority is released into the urinary space via transcytosis as albumin containing EVs [ 14 ]. It is assumed that only a small proportion of the vesicles is reabsorbed along the proximal tubule, while the majority is excreted in the urine [ 15 ]. Accordingly, albumin and the podocyte-specific protein podocalyxin [ 10 ], were detected in EVs isolated from the urine of the animals [ 14 ]. In view of these results, we hypothesized that urinary vesicular albumin and podocalyxin can be used as novel diagnostic and prognostic markers for the deterioration of kidney function in humans. So far, there are no measurements of urinary vesicular albumin and podocalyxin and evaluations of their relationship to kidney function markers in a human observational study available. Such markers could be particularly informative in old-aged individuals where the prevalence of albuminuria or low eGFR is higher than in younger individuals. We thus measured urinary vesicular albumin and podocalyxin at baseline and follow-up of a population-based cohort study in the elderly (i.e. age 70–95 year at baseline), characterised these novel kidney biomarkers and evaluated their association with eGFR-based CKD and albuminuria cross-sectionally and longitudinally. MATERIALS AND METHODS AugUR cohort study description The German AugUR study ( A ltersbezogene U ntersuchungen zur G esundheit der U niversität R egensburg) is a prospective study of the general old-aged population in and around the city of Regensburg, Bavaria. AugUR focuses on chronic diseases and associated risk factors in the population aged 70 to 95 years at baseline. Details on the study were published earlier [ 16 – 20 ]. In brief, 1,133 participants were included in the first AugUR survey between 2013 and 2015. A three year follow-up was conducted between 2016 and 2018 with 733 participants. The AugUR study was approved by the Ethics Committee of the University of Regensburg, Germany (vote 12-101-0258). The study complies with the 1964 Helsinki declaration and its later amendments. All participants provided informed written consent. AugUR study program General medical examinations at the study centre included blood pressure, height, weight, waist and hip circumference amongst others. Obesity was defined as body mass index (BMI) ≥ 30 kg/m². Systolic and diastolic blood pressures (SBP and DBP) were measured by an automatic device three times after > 5 min resting, using the average of the second and third measurements in the analyses. Mean arterial pressure (MAP) was calculated by DBP + ((SBP – DBP)/3). A questionnaire conducted as in-person interview included information on general chronic diseases, medication intake and lifestyle factors like smoking. Coronary artery disease (CAD) was defined if at least one of the following conditions was reported by the participants: myocardial infarction, percutaneous coronary intervention, or coronary artery bypass surgery. Cardiovascular disease (CVD) was defined as CAD or stroke. Hypertension was defined as blood pressure ≥ 140/90 mmHg or if the individual reported a prior hypertension diagnosis and antihypertensive medication intake [ 21 ]. Diabetes was defined as self-reported diagnosis of diabetes or the use of antidiabetic medication [ 22 ]. AugUR biomarker assessment Non-fasting blood samples were drawn in a sitting position after at least 5 min of resting. Mild venous stasis was applied for a maximum duration of 1 min. Blood was taken using a 21G multifly needle. Midstream urine was sampled. Biobanked samples (stored at -80°C) were used for laboratory analyses for creatinine, cystatin C, albumin and α1-microglobulin on a Siemens Dimension Vista 1500 (Siemens Healthcare, Erlangen, Germany). Analyses were performed in compliance with the “Guidelines of the German Medical Association for Quality Assurance of Medical Laboratory Tests” (RiLiBäK) at the Central Laboratory of the University Hospital Regensburg, which is accredited in accordance with the standard DIN EN ISO 15189. Serum cystatin C was measured with an immunoassay (assay CYSC, [mg/l]). Creatinine from serum and urine was enzymatically measured (assay ECREA, [mg/dl]). Urine albumin was measured with an immunoassay (assay MALB, [mg/l] with a limit of detection (LoD) of 5 mg/l) and α1-microglobulin (α1M, assay A1MIC, [mg/l], LoD = 7.8 mg/l) with nephelometry. Urinary albumin and α1M were normalized to urinary creatinine, i.e. urinary albumin-to-creatinine-ratio (uACR) and urinary α1M-to-creatinine-ratio (uα1MCR) and expressed in [mg/g]. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation [ 23 ] was used to derive eGFR [ml/min/1.73m²] from serum creatinine and cystatin C. CKD based on eGFR was defined with values < 60 ml/min/1.73m². Incident CKD was defined as eGFR 60 ml/min/1.73m² at baseline. Microalbuminuria was defined as uACR 30–300 mg/g and macroalbuminuria as uACR > 300 mg/g [ 1 ]. Incident albuminuria was defined as no albuminuria at baseline and micro- or macroalbuminuria at follow-up. Isolation of EVs from urine EVs from urine were isolated employing a differential centrifugation protocol. Urine (7 ml), stored at -80°C, was thawed in a water bath at 37°C and samples were homogenized by inverting. The urine was treated with EDTA, 100x protease and phosphatase inhibitor (PI) (Thermo Fisher Scientific, 78446), and PonceauS (PonS) (Sigma Aldrich, P7170), so that EDTA and PI were present in 1x concentration and PonS in a ~ 1:115 dilution. Samples were subsequently centrifuged at 3,234 g and 4°C for 20 minutes (Eppendorf 5804R Centrifuge, S-4-72) to remove cells and cell fragments. The resulting pellet was discarded, and 7 ml of supernatant were ultracentrifuged at 329,000 g and 4°C for 1h (Optima L-80 XP ultracentrifuge, Optima LE 80-K ultracentrifuge, Centrikon T-1170 ultracentrifuge, 70.1 Ti rotor, TFT 70.13 rotor). The supernatant was discarded, and the pellet was washed twice with PBS. For this, 7 ml of PBS in combination with 60 µl PonS and/or 1,2 ml of PBS in combination with 10 µl PonS were used. Samples were subsequently centrifuged for 1h at 4°C and 329,000g (Optima L-80 XP ultracentrifuge, Optima LE 80-K ultracentrifuge, Centrikon T-1170 ultracentrifuge, 70.1 Ti rotor, TFT 70.13 rotor) and/or 186,000 g (Optima™ MAX-E ultracentrifuge, TLA-55 rotor). Prior to centrifugation in the Optima™ MAX-E ultracentrifuge pellets were transferred into new cups using 3 x 400 µl PBS. The isolated EV pellet was suspended and transferred into a new tube using 2 x 25 µl of a solution containing PBS and 1x PI. Isolated samples were stored at -80°C until further processing. EV sample preparation For downstream analysis, 15 µl of the resuspended EV volume was preserved. The remaining volume was lysed using 10x RIPA buffer (abcam, ab156034). To support the lysis of EV membranes, samples were vortexed and subsequently shaken at 1400 rpm and 4°C for 20 min. Fluorescence microscope In a pilot study, EVs from one young subject were characterized employing the MemGlow™ 560 probe (Cytoskeleton Inc., MG02-02), a fluorogenic probe that integrates into lipid bilayers [ 24 ], in combination with an antibody against the podocyte-specific marker protein podocalyxin. Podocalyxin was detected by abcam Rabbit recombinant monoclonal podocalyxin antibody conjugated to Alexa Fluor® 488 (= PODXL488) (ab208254). 50 µl of vesicle suspension (1:5 diluted) were incubated over night at 4°C and 300 rpm with the primary antibody (diluted 1:50). To wash the vesicles, the volume was brought to 1,2 ml and the suspension was subsequently centrifuged at 186,000 g and 4°C for 1h (Optima™ MAX-E ultracentrifuge, TLA-55 rotor). The resulting pellet was resuspended in 50 µl PBS, incubated with 0.2 µM MemGlow™ at 50 rpm for 30 minutes in the dark. Afterwards the previously described washing step was repeated. The stained EV pellet was resuspended in 10 µl of PBS and 3 µl of solution were applied to a microscope slide. Samples were mounted after drying for a few minutes. To identify the proportion of podocyte-derived vesicles in the total population, the dyed EVs were visualized using a Laser Scanning microscope (LSM710, Zeiss, Jena). Data processing and analysis was performed using the Zeiss Zen lite (ZEN 3.9) software. Vesicles that were stained with MemGlow™, and vesicles stained with both the membrane dye and the antibody, were counted in six equally sized squares, which were randomly positioned in the image. Afterwards the proportion of double-positive (and therefore podocyte-derived) vesicles from the MemGlow TM -positive vesicles was calculated (Supplementary Fig. 1). Quantification of EV-albumin and EV-podocalyxin via ELISA EV-derived albumin and podocalyxin concentrations were quantified using the commercially available human albumin ELISA kit (abcam, ab227933) and the human podocalyxin ELISA kit (reddot Biotech Inc., RD-PCX-Hu), respectively. Lysed samples were diluted in the buffers included in the kits. Standards were solved and diluted, so that the same concentrations of RIPA, PBS and phosphatase and protease inhibitor and PonS were present in both standards and the samples. Samples were initially diluted 1:20 for the albumin ELISA and 1:10 for the podocalyxin ELISA, samples with concentrations above the standard curve were diluted further. All standards were assayed in duplicate. The protein concentrations of the samples were calculated based on the standard curve. Afterwards, the normalized vesicular albumin and podocalyxin concentrations per mL of urine were calculated (see supplementary information). The 0.5% extreme values at the upper end of the distribution for each EV marker at each point in time were removed. To determine the intra- and inter-assay coefficient of variability (CV) for both vesicular albumin and podocalyxin, samples were measured in triplicate (n = 26). After the initial determination of vesicular albumin and podocalyxin concentration lysed samples were stored for up to 17 months at -80°C. Subsequently, vesicular albumin and podocalyxin concentrations were determined in triplicate on two different days, one week apart. Here, samples with high, medium and low concentrations of vesicular albumin and podocalyxin were diluted 1:40 and 1:60 for the triplicate measurements, respectively. Samples were also stored at -80°C between triplicate measurements. The intra- and inter-assay coefficient of variability (CV) were calculated based on protein concentration prior to normalization (see supplementary information). Inter-assay CV was calculated between the two triplicate measurements one week apart. Additionally, inter-assay CV was calculated between the two triplicate measurements and initial vesicular albumin and podocalyxin measurement, thus reflecting the stability of vesicular albumin and podocalyxin concentrations over long storage periods. Since spot urine samples were collected, vesicular albumin and podocalyxin values were normalized to urinary creatinine (termed vACR and vPCR, respectively; see supplementary information). Both, vACR and vPCR were not normally distributed. Therefore, for regression analyses ln-scales values were used and graphic representations were given on log-scale. Analysis sample For the present analysis, we included data from all AugUR participants with available urine biomarkers and eGFR for baseline and three-year follow-up (n = 580). Statistical analysis Data management and statistical analyses were performed using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA) and IBM SPSS Statistics for Windows, Version 29.0.2 (IBM Corp., Armonk, NY, USA). Categorical variables were described with absolute numbers and percent. Normally distributed variables were given as mean ± standard deviation (SD). The median and interquartile range were used to describe the distribution of skewed variables. Estimates were given with 95% confidence intervals (CI). Spearman’s correlation coefficient r was reported with 95% CI calculated after r-to-z transformation with estimated standard errors. Linear and logistic regression analyses were performed to obtain estimates and 95% CI for continuous and binary outcomes, respectively. Explained variance in linear regression models was expressed as R². Cox proportional hazard regression analyses were used to obtain hazard ratios (HR) and 95% CI. RESULTS Study sample characteristics A total of 580 AugUR participants aged 70 to 95 years at baseline (59% men) were included in the study (Table 1 ). Median follow-up time was 3.2 years. Kidney function relevant biomarkers were measured at baseline and follow-up. Table 1 Characteristics of AugUR participants at baseline and follow-up. Characteristic Baseline (n = 580) Follow-up (n = 580) Missing General descriptives Age, yrs (min-max) 76.6 ± 4.4 (70.3–95.0) 79.8 ± 4.4 (73.4–98.3) 0/0 Sex, men 340 (58.6) 0 BMI, kg/m² 27.8 ± 4.2 27.7 ± 4.3 0/2 Obesity 151 (26.0) 150 (26.0) 0/2 Never smoked 321 (55.3) 321 (55.3) 0 Former smokers 227 (39.1) 234 (40.3) 0 Current smokers 32 (5.5) 25 (4.3) 0 Systolic blood pressure, mmHg 132.9 ± 18.5 129.3 ± 18.0 0/0 Diastolic blood pressure, mmHg 78.6 ± 11.0 73.9 ± 10.7 0/0 MAP, mmHg 96.7 ± 12.7 92.3 ± 12.2 0/0 Medication High ceiling diuretics 64 (11.1) 78 (13.4) 1/0 Any diuretics 213 (36.8) 241 (41.6) 1/0 Antihypertensive 394 (68.0) 420 (72.4) 1/0 Antidiabetics 97 (16.8) 98 (16.9) 1/0 Statins 195 (33.7) 209 (36.0) 1/0 Urine biomarkers Creatinine, mg/dl 76.7 [44.9/120.0] 75.3 [46.4/123.0] 0/0 Albumin, mg/l 9.6 [5.5/20.0] 12.0 [6.4/26.7] 0/0 uACR, mg/g 14.9 [9.1/27.3] 17.6 [9.9/32.9] 0/0 α1M, mg/l 7.8 [7.8/12.0] 7.9 [7.8/13.9] 0/0 uα1MCR, mg/g 14.4 [9.1/21.6] 14.9 [9.5/22.9] 0/0 Vesicular albumin, ng/l 492.9 [175.8/1400.4] 627.6 [193.1/1669.5] 0/0 vACR, ng/g 670.7 [241.8/2138.6] 797.3 [302.3/2014.7] 0/0 Vesicular podocalyxin, ng/l 135.6 [58.1/303.0] 139.3 [69.6/299.7] 0/0 vPCR, ng/g 186.7 [92.9/408.6] 220.9 [97.4/433.0] 0/0 Serum biomarkers Creatinine, mg/dl 0.98 ± 0.26 1.00 ± 0.35 0/0 Cystatin C, mg/l 1.11 ± 0.26 1.18 ± 0.32 0/0 eGFR, ml/min/1.73 m² 72.7 ± 16.0 68.9 ± 16.9 0/0 Diseases CKD 125 (21.6) 160 (27.6) 0/0 Incident CKD - 54 (11.9) 125 Microalbuminuria 121 (20.9) 149 (25.7) 0/0 Macroalbuminuria 11 (1.9) 13 (2.2) 0/0 Incident albuminuria - 77 (17.2) 132 Diabetes mellitus 118 (20.3) 131 (22.6) 0/1 Hypertension 430 (74.3) 436 (75.2) 1/0 CAD 86 (14.8) 98 (16.9) 0/1 CVD 119 (20.5) 137 (23.7) 0/2 Continuous values are means ± standard deviation or median [25th /75th percentile]; categorial variables are total numbers and percent in brackets. Missing values at baseline/follow-up is given in the last row. BMI, body mass index; MAP, mean arterial pressure; uACR, urinary albumin-to-creatinine ratio; α1M, alpha1-microglobulin; uα1MCR, urinary α1M-to-creatinine ratio; vACR, vesicular albumin-to-creatinine ratio; vPCR, vesicular podocalyxin-to-creatinine ratio; eGFR, estimated glomerular filtration rate based on combined serum creatinine and cystatin C (CKDEpi 2021); CKD, chronic kidney disease defined as eGFR < 60 ml/min/1.73m²; incident CKD defined as eGFR 300 mg/g; incident albuminuria defined as uACR > 30 mg/g at follow-up and not at baseline; CAD, coronary artery disease; CVD, cardiovascular disease. Characteristics of vesicular albumin and podocalyxin at baseline From biobanked urinary samples, extracellular vesicles could be isolated (Supplementary Fig. 1). Vesicular albumin and podocalyxin concentrations ranged from 45 to 18,849 ng/l and 11 to 10,499 ng/l, respectively. The stability of the quantitation results for vesicular albumin and podocalyxin in repeat measurements under different storage times was assessed (Supplementary Table 1). The intra-assay coefficient of variability (CV) was below 10% for both markers, whereas the inter-assay CV was higher for vesicular albumin compared to podocalyxin (27% and 13%, respectively). To correct for urine volume, vesicular albumin and podocalyxin were normalized to urinary creatinine (vACR, vPCR). All further analyses were performed with vACR and vPCR, respectively. At baseline, the range for vACR was 37 ng/g to 42,890 ng/g and 6.4 ng/g to 40,272 ng/g for vPCR. To gain normal distributions for regression analyses, baseline vACR and vPCR were ln-transformed resulting in ranges of 3.6 to 10.7 for vACR and 1.9 to 10.6 for vPCR, respectively. We analysed the associations of age and sex with vACR and vPCR (Table 2 ). Whereas linear regression using ln-scaled vACR as outcome revealed no association with age (p = 0.671), ln-scaled vPCR was significantly associated with age (b = 0.038 per year, 95% CI = 0.014 to 0.062, p = 0.002, R²=1.6%). We found a strong sex difference for vACR (b= -1.031 on ln-scale for men compared to women, 95% CI= -1.244 to -0.818, p = 5.31*10 − 20 ), explaining 13.5% (R²) of vACR variability at baseline. In contrast, no association between vPCR and sex (p = 0.775) was detected. Supplementary Fig. 2 shows the baseline distributions of vACR and vPCR between age groups and sex. Correlations of vesicular albumin and podocalyxin with each other and with kidney function biomarkers at baseline Vesicular albumin and podocalyxin were moderately correlated (Fig. 1 ), indicating possible independent biological mechanisms of both markers. To evaluate the influence of sex on this correlation, we calculated the explained variance (R²) separately for men and women. R² on ln-scale of vACR and vPCR was 12.7% (7.6% for women and 20.1% for men), possibly resulting from the sex-dependency of vACR. There was no correlation of both vACR and vPCR with eGFR at baseline (Fig. 2 A, D). Correlations of vACR and vPCR, respectively, with uACR was moderate (Fig. 2 B, E), and low with ua1MCR (Fig. 2 C, F). For the correlation of vACR with uACR, Spearman’s correlation coefficient r was 0.59 [95% CI = 0.53–0.64] and for the correlation of vPCR with uACR, r was 0.33 [95% CI = 0.26–0.41]. R² for vACR and uACR on ln-scale was 22.5% for women and 49.3% for men (36.1% for both sexes combined). R² for vPCR and uACR on ln-scale was 22.3% for women and 13.8% for men (16.1% for both sexes combined). Association of age, sex and known kidney markers with vesicular albumin and podocalyxin at baseline Age was associated with vPCR and sex with vACR. To analyse the influence of established kidney biomarkers on vesicular albumin and podocalyxin, we tested the association of uACR, uα1MCR as well as eGFR with vACR and vPCR, respectively, adjusted for age and sex via linear regression at baseline (Table 2 ). After adjusting for age and sex, uACR was positively associated with vACR as well as vPCR, with stronger effects on vACR. Additionally, uα1MCR, a marker for tubular function, was associated with both vesicular markers in the same direction and with comparable effect sizes. In contrast to vPCR, eGFR was negatively associated with vACR, indicating that higher vACR values were associated with lower eGFR after age- and sex-adjustment. Significant correlation of vACR with eGFR was not observed in unadjusted analyses (Fig. 2 A). However, the effect of eGFR on vACR was weak (b= -0.013) and seemed to be mainly driven by sex, indicating a sex-eGFR interaction effect on vACR in cross-sectional data. Table 2 Associations of age, sex and quantitative kidney relevant biomarkers with vACR and vPCR at baseline. Variable vACR (ln) vPCR (ln) Age b= -0.006 [-0.031;0.020] p = 0.671 b = 0.038 [0.014;0.062] p = 0.002 Sex b= -1.031 [-1.244;-0.818] p = 5.31*10 − 20 b = 0.032 [-0.185;0.248] p = 0.775 uACR (ln) * b = 0.849 [0.763;0.935] p = 6.57*10 − 65 b = 0.536 [0.434;0.639] p = 5.75*10 − 23 uα1MCR (ln) * b = 0.475 [0.318;0.632] p = 4.56*10 − 9 b = 0.468 [0.310;0.626] p = 1.00*10 − 8 eGFR * b= -0.013 [-0.020;-0.006] p = 3.19*10 − 4 b = 0.002 [-0.005;0.009] p = 0.550 Shown are beta estimates (b) and 95% CI in square brackets as well as p-values from linear regression models for age, sex and quantitative kidney biomarker and vACR/vPCR. Significant findings (p < 0.05) are presented in bold font. For vACR, vPCR, uACR and uα1MCR ln-scaled values were used in analyses. *Association results of uACR, uα1MCR and eGFR on vACR and vPCR, respectively, are presented after age- and sex-adjustment. Association of baseline vesicular albumin and podocalyxin with incident eGFR-based CKD and incident albuminuria To assess the potential predictive ability of the two vesicular biomarkers, we restricted to individuals that had (i) no CKD (i.e. eGFR > 60 ml/min/1.73 m²) or (ii) no albuminuria (i.e. uACR < 30mg/g) at baseline and observed a total of n = 54 incident CKD and n = 77 incident albuminuria cases. We tested associations of vACR and vPCR at baseline with incident eGFR-based CKD and albuminuria and found significantly elevated risk for eGFR-based CKD with higher vACR levels at baseline in the unadjusted model (HR = 1.218, p = 0.047) and after adjustment for age and sex (HR = 1.268, p = 0.021) (Table 3 ). However, a predictive marker for CKD should be independent of baseline eGFR and uACR. The effect of vACR on incident eGFR-based CKD disappeared when including baseline eGFR and uACR into the model (HR = 1.053, p = 0.704) (Table 3 ). In contrast, higher vPCR levels at baseline were associated with lower risk of incident albuminuria after adjustment for eGFR and uACR (HR = 0.763, p = 0.017). This corresponded with lower vPCR by one unit on ln-scale to confer an HR of 1.3 and thus a 30% increased risk of albuminuria within 3 years (over a range of 8.7 ln-units of vPCR in our data). Additional adjustment for uα1MCR did not change the effect estimate markedly, as well as adding vACR to the model (Table 3 ). This latter model 6 accounted for a potential confounding effect of vACR on vPCR association with incident albuminuria. In summary, our fully adjusted model indicated a potential predictive power of higher vesicular podocalyxin at baseline for lower incident albuminuria risk. Table 3 Associations of vACR and vPCR at baseline on ln-scale with incident eGFR-based CKD and incident albuminuria. Incident CKD Incident albuminuria Model HR [95% CI] p-value HR [95% CI] p-value Unadjusted vACR 1.218 [1.002;1.479] 0.047 1.121 [0.932;1.349] 0.227 vPCR 0.926 [0.749;1.144] 0.476 0.860 [0.710;1.041] 0.123 Model 1 vACR 1.268 [1.036;1.553] 0.021 1.058 [0.856;1.309] 0.601 vPCR 0.877 [0.708;1.086] 0.230 0.849 [0.698;1.031] 0.099 Model 2 vACR 1.191 [0.985;1.441] 0.072 1.064 [0.858;1.318] 0.574 vPCR 0.960 [0.770;1.197] 0.716 0.899 [0.734;1.102] 0.307 Model 3 vACR 1.208 [0.928;1.574] 0.160 0.869 [0.686;1.101] 0.245 vPCR 0.809 [0.647;1.011] 0.062 0.718 [0.580;0.890] 0.002 Model 4 vACR 1.053 [0.805;1.379] 0.704 0.895 [0.708;1.132] 0.354 vPCR 0.878 [0.701;1.099] 0.256 0.763 [0.611;0.953] 0.017 Model 5 vACR 1.076 [0.820;1.413] 0.596 0.873 [0.685;1.111] 0.269 vPCR 0.875 [0.698;1.098] 0.249 0.766 [0.614;0.955] 0.018 Model 6 vACR 1.143 [0.859;1.520] 0.359 0.945 [0.740;1.208] 0.654 vPCR 0.847 [0.668;1.075] 0.172 0.778 [0.617;0.980] 0.033 Shown are hazard ratios (HR) with 95% confidence intervals (CI) as well as p-values from Cox regression models with follow-up time used for incident CKD (n = 54) and incident albuminuria (n = 77) for vACR and vPCR on ln-scale in different models. Significant findings (p < 0.05) are highlighted in bold font. Model 1: Age, sex Model 2: Age, sex, eGFR at baseline Model 3: Age, sex, uACR (ln) at baseline Model 4: Age, sex, eGFR, uACR (ln) at baseline Model 5: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline Model 6: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline; vACR (ln) and vPCR (ln) at baseline, respectively, added to the model for vPCR and vACR, respectively Associations of changes in vesicular biomarkers with incident albuminuria and CKD When analysing vACR and vPCR changes over time, we observed a different pattern regarding incident albuminuria. Across the distribution of vACR at baseline, more participants with higher vACR values at follow-up compared to baseline developed incident albuminuria (n = 63) in contrast to those with lower follow-up values (n = 14) (Fig. 3 ). Similarly, for vPCR participants with higher values at follow-up compared to baseline developed more often incident albuminuria (n = 55) in contrast to those with lower follow-up values (n = 22) (Fig. 4 ). An illustration of vACR and vPCR changes between baseline and follow-up over age is given in Supplementary Fig. 3. For better interpretation, difference of vACR at baseline and follow-up was z-transformed. Difference of vACR at baseline and follow-up was a predictor for incident albuminuria (HR = 1.445 [95%CI = 1.260; 1.659], p = 1.50*10 − 7 ). After adjustment of age and sex, the association was still significant (HR = 1.465 [1.271; 1.688], p = 1.29*10 − 7 ). After additional adjustment for uACR (ln-scale) at baseline, the effect was stronger (HR = 1.703 [1.437; 2.019], p = 8.09*10 − 10 ). Additionally adding vACR at baseline (ln-scale) to the model did not alter the effect substantially (HR = 1.770 [1.469; 2.133]; p = 2.00*10 − 9 ). To normalize for baseline vACR values, we further used \(\:\frac{\varDelta\:\:vACR\:(FU-BL)}{vACR\:\left(BL\right)}\) . We defined vACR change as ‘stable’ in the range of -1 to 1 after normalization on vACR at baseline (n = 393) and ‘increase’ with values > 1 (n = 187). Figure 5 shows incident albuminuria cases at follow-up over the distribution of \(\:\frac{\varDelta\:\:vACR\:(FU-BL)}{vACR\:\left(BL\right)}\) . Z-transformation was also computed for vPCR. Difference of vPCR at baseline and follow-up was not a predictor for incident albuminuria (HR = 1.141 [95%CI = 0.833; 1.562], p = 0.411). After adjustment for age and sex, the association was still not significant (HR = 1.127 [0.809; 1.570], p = 0.481). Additional adjustment for uACR (ln-scale) at baseline, did not alter the effect (HR = 1.115 [0.792; 1.572], p = 0.532). Taking additionally vPCR at baseline (ln-scale) into the model did not alter the effect substantially (HR = 1.083 [0.736; 1.592]; p = 0.686). As for vACR, to normalize for baseline vPCR, we further used \(\:\frac{\varDelta\:\:vPCR\:(FU-BL)}{vPCR\:\left(BL\right)}\) . We defined vPCR change as ‘stable’ in the range of -1 to 1 after normalization on vPCR at baseline (n = 426) and ‘increase’ with values > 1 (n = 154). Figure 6 shows incident albuminuria cases at follow-up over the distribution of \(\:\frac{\varDelta\:\:vPCR\:(FU-BL)}{vPCR\:\left(BL\right)}\) . Change in vPCR shows less pronounced pattern of incident albuminuria distribution over the distribution of \(\:\frac{\varDelta\:\:vPCR\:(FU-BL)}{vPCR\:\left(BL\right)}\) . (Fig. 6 ). The dichotomized variables (stable between follow-up and baseline versus higher values in follow-up) of \(\:\frac{\varDelta\:\:vACR\:(FU-BL)}{vACR\:\left(BL\right)}\) and \(\:\frac{\varDelta\:\:vPCR\:(FU-BL)}{vPCR\:\left(BL\right)}\) were tested for association with incident albuminuria and CKD (Table 4 ). Both, vACR- and vPCR-based analyses showed associations with incident albuminuria, but not with CKD. Participants with increasing vACR and vPCR levels over time showed an increased risk for incident albuminuria. Table 4 Associations of dichotomized \(\:\frac{\varDelta\:\:vACR\:(FU-BL)}{vACR\:\left(BL\right)}\) and \(\:\frac{\varDelta\:\:vPCR\:(FU-BL)}{vPCR\:\left(BL\right)}\) with incident eGFR-based CKD and incident albuminuria. Incident CKD Incident albuminuria Model OR [95% CI] p-value OR [95% CI] p-value Unadjusted vACR change 1.003 [0.544; 1.849] 0.993 4.262 [2.545; 7.136] 3.56*10 − 8 vPCR change 0.621 [0.309; 1.246] 0.180 1.826 [1.097; 3.041] 0.021 Model 1 vACR change 1.028 [0.553; 1.913] 0.929 4.417 [2.618; 7.453] 2.60*10 − 8 vPCR change 0.667 [0.329; 1.351] 0.261 1.932 [1.152; 3.240] 0.013 Model 2 vACR change 0.976 [0.500; 1.905] 0.976 4.315 [2.550; 7.304] 5.16*10 − 8 vPCR change 0.578 [0.273; 1.226] 0.153 1.969 [1.169; 3.319] 0.011 Model 3 vACR change 1.149 [0.610; 2.162] 0.668 5.585 [3.155; 9.886] 3.55*10 − 9 vPCR change 0.699 [0.343; 1.424] 0.324 2.004 [1.163; 3.454] 0.012 Model 4 vACR change 1.160 [0.583; 2.309] 0.627 5.453 [3.064; 9.704] 8.11*10 − 9 vPCR change 0.606 [0.283; 1.295] 0.196 2.076 [1.195; 3.607] 0.009 Model 5 vACR change 1.158 [0.581; 2.307] 0.677 5.464 [3.070; 9.723] 7.75*10 − 9 vPCR change 0.620 [0.288; 1.333] 0.221 2.064 [1.185; 3.595] 0.010 Model 6 vACR change 1.161 [0.583; 2.313] 0.671 5.108 [2.850; 9.156] 4.31*10 − 8 vPCR change 0.656 [0.301; 1.429] 0.288 1.824 [1.027; 3.238] 0.040 Shown are the results from logistic regression models for the association of dichotomized \(\:\frac{\varDelta\:\:vACR\:(FU-BL)}{vACR\:\left(BL\right)}\) (vACR change) and \(\:\frac{\varDelta\:\:vPCR\:(FU-BL)}{vPCR\:\left(BL\right)}\) (vPCR change) with incident CKD (n = 54) and incident albuminuria (n = 77), respectively, in different models (odds ratio (OR) with 95% confidence intervals (CI) for stable versus increased). Significant findings (p < 0.05) are highlighted in bold font. FU, follow-up; BL, baseline. Model 1: Age, sex Model 2: Age, sex, eGFR at baseline Model 3: Age, sex, uACR (ln) at baseline Model 4: Age, sex, eGFR, uACR (ln) at baseline Model 5: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline Model 6: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline; simple vACR and vPCR change (follow-up minus baseline), respectively, added to the model for vPCR and vACR change, respectively In summary, baseline-normalized changes of both vesicular markers, albumin and podocalyxin, between baseline and follow-up were associated with incident albuminuria but not with incident CKD. After dichotomization, increased versus stable levels of vesicular albumin and podocalyxin were associated with incident albuminuria independently of established kidney function biomarkers (Table 4 ). Effect of vesicular albumin on incident albuminuria was higher (OR = 5.5) compared to podocalyxin (OR = 2.1) in the fully adjusted model 5. Model 6 with accounting for the respective other vesicular marker did not markedly alter the associations of both vACR and vPCR change with incident albuminuria. DISCUSSION CKD is defined by the progressive loss of kidney function, as determined by a decline in GFR below and an increase in urinary albumin excretion over a threshold for at least three months [ 1 , 25 ]. As a drawback, the sensitivity of both parameters is limited, and changes in kidney function remain frequently undetected in the early phase of the course of the disease [ 26 ]. Consequently, patients would benefit from novel diagnostic and prognostic biomarkers of kidney function to overcome the limitations of the established markers, such as eGFR and albuminuria. Early biomarkers of kidney disease may also facilitate the initiation of reno-protective measures in a timely manner. The potential of the RNA and protein content, as well as changed concentration levels of podocyte-derived vesicles as biomarkers of podocyte injury, glomerulonephritis and other kidney diseases has been suggested in several studies [ 27 ]. Constituents of podocyte-derived vesicles may serve as biomarkers for kidney disease of various aetiology [ 28 , 29 ], childhood nephrotic syndrome [ 30 ], diabetic nephropathy [ 31 ], disease activity in systemic lupus erythematosus [ 32 ], and renovascular hypertension [ 33 ]. Podocyte-derived vesicles were shown to contain e.g. CD2AP and WT1 mRNA as well as WT1 protein [ 30 , 31 , 34 ]. Levels of urinary exosomal WT1 mRNA have been found to predict eGFR decline in patients with diabetic nephropathy [ 34 ]. Furthermore, the content of podocyte-derived vesicles facilitated differentiating between focal segmental glomerulosclerosis and steroid-sensitive nephrotic syndrome [ 35 ]. In addition to the diagnosis of kidney disease, the prognostic potential of podocalyxin-positive vesicles has been addressed before. Miller et al. observed that a model including podocalyxin-positive exosomes was able to predict the development of acute kidney injury after cardiac surgery [ 36 ]. Furthermore, urinary podocalyxin concentration was elevated in diabetic patients, and patients with various glomerular diseases in comparison to normal controls, thus indicating its potential as a biomarker for diabetic nephropathy and other glomerular diseases [ 37 ]. In this study, we assessed the potential of two vesicular proteins as prognostic markers of kidney function. Vesicular podocalyxin and vesicular albumin are known to be derived from podocytes [ 10 , 14 ], which functionally and structurally contribute to the integrity of the glomerular filtration barrier of the kidney [ 15 ]. We found that about 19% of urinary vesicles were podocyte-derived (Supplementary Fig. 1). Additionally, the endocytotic activity of podocytes is assumed to play a role in the clearing of the glomerular filtration barrier from accumulating proteins. Thus, an impaired or saturated clearance mechanism might increase the probability for glomerular injuries [ 38 , 39 ]. Our analyses in an old-aged cohort showed an association between changes in vACR and vPCR with newly occurring albuminuria, suggesting that increasing levels of vesicular albumin and podocalyxin may be indicative of declined kidney function based on the increasing urinary albumin excretion (uACR). Conversely, no such association was found for changes in eGFR, suggesting that the changes in vACR and vPCR over time are more sensitive to changes in uACR than in eGFR. In conclusion, the presence of albumin-containing podocyte-derived vesicles is an early marker of podocyte injury, whereas eGFR rather reflects renal function decline in the advanced stages of kidney disease [ 40 ], thus no association between the vesicular markers and eGFR could be observed within this time frame. Interestingly, we found an association between higher levels of vPCR at baseline and a reduced risk for incident albuminuria in the models adjusting for baseline uACR, eGFR and ua1MGCR suggesting that the association may be podocyte-specific. The association between higher levels of vPCR and a reduced risk for incident albuminuria may also be related to a higher number of viable and endocytic-active podocytes. Consequently, the clearance of the filtration barrier from accumulating proteins, in particular in the subpodocyte space, may be protective for the function of the GFB [ 38 ]. In contrast, higher vACR and also vPCR levels in the follow-up compared to baseline were associated with an increased risk for incident albuminuria. The formation of albumin-containing podocyte-derived vesicles may indicate an increasing number of accumulating proteins in the subpodocyte space, resulting in saturation of the clearance mechanism. This may lead to the deterioration of the integrity of the glomerular filter, thus explaining the increased risk of albuminuria. Furthermore, albumin exposure at high concentrations has been shown to elicit apoptosis and injury in podocytes [ 41 – 45 ]. In addition albumin overload induced morphological changes in podocytes, e.g. a disrupted cytoskeleton [ 42 , 44 , 45 ], which plays a crucial role in normal podocyte structure and function. Disruption of the cytoskeleton might lead to podocyte foot process effacement and subsequently to albuminuria/proteinuria [ 45 , 46 ]. The present study showed a marked sex difference in vACR, being significantly lower in men compared to women, whereas vPCR did not differ between the sexes. The difference in the sex-dependency of vACR and vPCR suggest that both markers are influenced by independent biological mechanisms. Podocalyxin is a highly specific marker of podocytes [ 10 ]. For vesicular albumin, the situation is more complex. Vesicular albumin is generated via cellular uptake and transcytosis by podocytes, as shown in animal studies [ 14 ]. Furthermore, vesicular albumin may also be part of the vesicular protein corona [ 47 – 51 ], which has been demonstrated to form spontaneously around the surface of vesicles in biological fluids [ 52 ]. The latter would occur independently of podocytes and is presumably dependent on the competition between albumin and other proteins for vesicular binding site. Consequently, differences in the global protein status between men and women may be causal for the observed sex differences in vesicular albumin. In our longitudinal analyses, we have identified subjects with increased vACR and vPCR levels in the follow-up compared to baseline, but also a high proportion of participants with stable or even declining levels. For both markers, in the group with increased levels, the chance for albuminuria was significantly higher compared to subjects with stable parameters. Therefore, the temporal trajectories in vesicular biomarkers indicate (patho-)physiological changes, as described in more detail above, that increase the risk of albuminuria. We also found that considering baseline levels of the vesicular biomarkers and of uACR, improved the association with incident albuminuria. Therefore, the state of the kidney, especially the glomerulus, at baseline might be a modifying factor of the observed associations. Strengths and limitations of the study. We acknowledge some limitations of the current study. First, the AugUR population does not represent the general population aged 70+, since based on the requirement for visits at the study centre, there presumably is some selection for mobile and healthy elderly persons. Therefore, the data may not represent the entire aged population, and samples from subjects with a higher degree of comorbidities and age-related degenerative processes are likely underrepresented. Second, we observed that applying our EV isolation protocol, 19% of urinary EVs were podocalyxin-positive, and, consequently, of podocyte origin. Thus, as expected, a heterogenous population of various vesicle subtypes, originating from different cell types of the urogenital tract, was isolated employing an ultracentrifugation protocol without additional affinity-based purification [ 53 , 54 ]. The percentage of podocyte-derived EVs was in a similar range as reported previously, e.g. 11.4 ± 6.4% for patients suffering from renovascular hypertension and 6.8 ± 3,4 % for halthy subjects [ 33 ]. Similarly, 23.3% of all EVs isolated by the use of a sucrose gradient were of glomerular origin [ 55 ]. Nevertheless, as vesicular albumin and podocalyxin, which are highly podocyte-specific, were determined as potential markers of kidney function, the background caused by non-glomerular EVs likely is of limited relevance. The strength of our study is the analysis of a large number of samples of an old-aged population, with an increased incidence of apparently and inapparently compromised kidney function and an increased risk of the development of kidney disease compared to the normal population. Consequently, we analysed samples from a population that is particularly suited for the assessment of diagnostic and prognostic biomarkers of changes in kidney function. In summary, we identified higher level of baseline vesicular podocalyxin as a predictor for reduced risk of incident albuminuria. In contrast, we found strong association between increase in vesicular albumin and podocalyxin over time with higher risk for albuminuria. Increasing vACR and vPCR levels were associated with 5.5- and 2-fold higher risk for newly occurring albuminuria compared to stable levels of vACR and vPCR, respectively. Based on our results, baseline uACR levels even below 30 mg/g should be considered for risk prediction models, since they enhance the effect of vACR and vPCR changes on albuminuria. No association between changes in vACR and vPCR levels and newly occurring eGFR-based CKD was detected, suggesting that eGFR is not predominantly determined by podocyte-specific effects, whereas uACR is dependent on the integrity of podocytes. Further studies with longer follow-ups are needed to elucidate the effect of change in vACR and vPCR with incident albuminuria. The aim of those studies should be to investigate the predictive power of differences of vACR and vPCR between two time points for future development of albuminuria. Furthermore, the present study was focused on vesicular albumin and podocalyxin as novel diagnostic and prognostic biomarkers of kidney function. The content of podocyte-derived vesicles, however, contains an array of further podocyte-specific markers, some of which may be indicative of changes in podocyte function, and, more general, may provide information about the integrity of the glomerular filtration barrier of the kidney. These parameters should be addressed in future studies. Declarations Consent to publish Not applicable. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of the University of Regensburg (IRB number: 12-101-0258). Consent statement Written informed consent was obtained from all individual participants included in the study. Availability of data and materials The individual data generated and analysed during the current study are not publicly available due to data privacy of study participants. Summary statistics are available from the corresponding author on request. Funding The AugUR study was supported by grants from the German Federal Ministry of Education and Research (BMBF 01ER1206 and BMBF 01ER1507) to I.M.H., by the German Research Foundation (DFG HE 3690/7-1 and BR 6028/2-1) to I.M.H. and C.B. and by institutional budget (University of Regensburg). The project was funded by grants from the German Research Foundation (TRR 374 project-ID 50914993) to I.H.M (TRR 374 projects B2, C6, and INF) and to H.C. (TRR 374 project B2). Conflict of interest Author I.M.H. has received support from Roche Diagnostics for a biomarker project in the AugUR study, but unrelated to the work presented here. Authors´ Contributions All authors have contributed to interpreting results and manuscript writing. All authors have read and approved the manuscript. Further contributions are: L.S.: laboratory measurements, data analysis, statistical analysis, manuscript writing H.C.dH.: data analysis, statistical analysis, manuscript writing L.M.: laboratory measurements, manuscript writing R.F.: laboratory measurements, manuscript writing M.E.Z.: data management, data analysis, manuscript writing C.B.: study physician, overall medical program study, manuscript writing I.M.H.: project PI, study PI, project supervision, manuscript design, manuscript writing H.C.: project initiation, project PI, project supervision, manuscript writing K.J.S.: study coordination, project supervision, data management, statistical analysis, manuscript design, manuscript writing Acknowledgements The authors greatly appreciate the outstanding and committed study assistance of Lydia Mayerhofer, Magdalena Scharl, Sabine Schelter and Josef Simon. We would like to express our special thanks to the study participants for contributing to the AugUR study. 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Urinary extracellular vesicles and micro-RNA as markers of acute kidney injury after cardiac surgery. Sci. Rep. 12 , 10402. https://doi.org/10.1038/s41598-022-13849-z (2022). Hara, M. et al. Urinary podocalyxin is an early marker for podocyte injury in patients with diabetes: establishment of a highly sensitive ELISA to detect urinary podocalyxin. Diabetologia 55 , 2913–2919. https://doi.org/10.1007/s00125-012-2661-7 (2012). He, F-F. et al. A New Pathogenesis of Albuminuria: Role of Transcytosis. Cell. Physiol. Biochem. 47 , 1274–1286. https://doi.org/10.1159/000490223 (2018). Akilesh, S. et al. Podocytes use FcRn to clear IgG from the glomerular basement membrane. Proc. Natl. Acad. Sci. U S A . 105 , 967–972. https://doi.org/10.1073/pnas.0711515105 (2008). Lu, J. et al. Urinary levels of podocyte-derived microparticles are associated with the progression of chronic kidney disease. Ann. Transl Med. 7 , 445. https://doi.org/10.21037/atm.2019.08.78 (2019). Okamura, K. et al. Endocytosis of albumin by podocytes elicits an inflammatory response and induces apoptotic cell death. PLoS One . 8 , e54817. https://doi.org/10.1371/journal.pone.0054817 (2013). Yoshida, S. et al. Podocyte injury induced by albumin overload in vivo and in vitro: involvement of TGF-beta and p38 MAPK. Nephron Exp. Nephrol. 108 , e57–68. https://doi.org/10.1159/000124236 (2008). Pawluczyk, I. Z. A. et al. The effect of albumin on podocytes: the role of the fatty acid moiety and the potential role of CD36 scavenger receptor. Exp. Cell. Res. 326 , 251–258. https://doi.org/10.1016/j.yexcr.2014.04.016 (2014). Chen, S. et al. Calcium entry via TRPC6 mediates albumin overload-induced endoplasmic reticulum stress and apoptosis in podocytes. Cell. Calcium . 50 , 523–529. https://doi.org/10.1016/j.ceca.2011.08.008 (2011). He, F-F. et al. Role of CD2-associated protein in albumin overload-induced apoptosis in podocytes. Cell. Biol. Int. 35 , 827–834. https://doi.org/10.1042/CBI20100411 (2011). Blaine, J. & Dylewski, J. Regulation of the Actin Cytoskeleton in Podocytes. Cells 9 . https://doi.org/10.3390/cells9071700 (2020). Singh, P. et al. Removal and identification of external protein corona members from RBC-derived extracellular vesicles by surface manipulating antimicrobial peptides. J. Extracell. Biol. 2 , e78. https://doi.org/10.1002/jex2.78 (2023). Dietz, L. et al. Uptake of extracellular vesicles into immune cells is enhanced by the protein corona. J. Extracell. Vesicles . 12 , e12399. https://doi.org/10.1002/jev2.12399 (2023). Liam-Or, R. et al. Cellular uptake and in vivo distribution of mesenchymal-stem-cell-derived extracellular vesicles are protein corona dependent. Nat. Nanotechnol. 1–10. https://doi.org/10.1038/s41565-023-01585-y (2024). Wolf, M. et al. A functional corona around extracellular vesicles enhances angiogenesis, skin regeneration and immunomodulation. J. Extracell. Vesicles . 11 , e12207. https://doi.org/10.1002/jev2.12207 (2022). Gomes, F. G. et al. Synergy of Human Platelet-Derived Extracellular Vesicles with Secretome Proteins Promotes Regenerative Functions. Biomedicines 10. (2022). https://doi.org/10.3390/biomedicines10020238 Tóth, E. Á. et al. Formation of a protein corona on the surface of extracellular vesicles in blood plasma. J. Extracell. Vesicles . 10 , e12140. https://doi.org/10.1002/jev2.12140 (2021). Liangsupree, T., Multia, E. & Riekkola, M-L. Modern isolation and separation techniques for extracellular vesicles. J. Chromatogr. A . 1636 , 461773. https://doi.org/10.1016/j.chroma.2020.461773 (2021). Maggio, S. et al. Current Methods for the Isolation of Urinary Extracellular Vesicles. Methods Mol. Biol. 2292 , 153–172. https://doi.org/10.1007/978-1-0716-1354-2_14 (2021). Hogan, M. C. et al. Subfractionation, characterization, and in-depth proteomic analysis of glomerular membrane vesicles in human urine. Kidney Int. 85 , 1225–1237. https://doi.org/10.1038/ki.2013.422 (2014). Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 05 Feb, 2026 Editor invited by journal 03 Feb, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 20 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8650516","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587164906,"identity":"c182e9b1-e3fe-4869-a24f-32d2167d3bf6","order_by":0,"name":"Luisa Schnobrich","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Luisa","middleName":"","lastName":"Schnobrich","suffix":""},{"id":587164907,"identity":"e8f98d70-3fc6-4cb0-a9a3-f58ecfaa5bec","order_by":1,"name":"Hannah C de Hesselle","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"C","lastName":"de Hesselle","suffix":""},{"id":587164908,"identity":"9ce9df92-dec1-4d68-a175-ed983717969a","order_by":2,"name":"Lorena Mornhiniweg","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Lorena","middleName":"","lastName":"Mornhiniweg","suffix":""},{"id":587164909,"identity":"b8d251d7-dd7d-4a11-9558-dd07c10ea6a6","order_by":3,"name":"Rike Felgner","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Rike","middleName":"","lastName":"Felgner","suffix":""},{"id":587164910,"identity":"af4af589-f98a-40da-8c12-b7543aba7575","order_by":4,"name":"Martina E Zimmermann","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"E","lastName":"Zimmermann","suffix":""},{"id":587164911,"identity":"cc533ec5-460b-4b8e-84fd-20d850338d02","order_by":5,"name":"Caroline Brandl","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Brandl","suffix":""},{"id":587164912,"identity":"eb1ef6d5-e4c3-4635-a9ed-3226d46473b1","order_by":6,"name":"Iris M Heid","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Iris","middleName":"M","lastName":"Heid","suffix":""},{"id":587164913,"identity":"d416b373-51a2-4de2-b0e1-bfde86a701ad","order_by":7,"name":"Hayo Castrop","email":"","orcid":"","institution":"University of Regensburg","correspondingAuthor":false,"prefix":"","firstName":"Hayo","middleName":"","lastName":"Castrop","suffix":""},{"id":587164914,"identity":"a59a21c3-329b-49d9-8d83-656ae449cbdf","order_by":8,"name":"Klaus J Stark","email":"data:image/png;base64,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","orcid":"","institution":"University of Regensburg","correspondingAuthor":true,"prefix":"","firstName":"Klaus","middleName":"J","lastName":"Stark","suffix":""}],"badges":[],"createdAt":"2026-01-20 15:16:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8650516/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8650516/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102239808,"identity":"10d8d290-bd48-4c10-80a1-dbe28a88d674","added_by":"auto","created_at":"2026-02-09 16:47:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":503558,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between vACR and vPCR from 580 urinary samples at baseline. Measurements are given in ng/g and plotted on log scale. Spearman’s correlation between vACR and vPCR r was 0.31 [95% CI=0.23-0.39], p=1.8*10\u003csup\u003e-14\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/6d79b72ed706fddf3b3df96f.png"},{"id":102239813,"identity":"a3928d5b-2636-42a6-8a88-f44f542c8c93","added_by":"auto","created_at":"2026-02-09 16:47:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3774420,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between vACR, vPCR and kidney markers eGFR, uACR, and ua1MCR at baseline (n=580). On the x-axis vACR (A-C) and vPCR (D-F) are plotted in ng/g on log-scale and on the y-axis (A, D) eGFR in ml/min/1.73 m², (B, E), uACR in mg/g on log-scale, (C, F) ua1MCR in mg/g on log-scale. Spearman’s correlation coefficients r and corresponding p-values are given in the respective plots.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/7223fe27dc31e956dbea547a.png"},{"id":102297532,"identity":"67eb0f20-8b6f-4c4f-88d9-477ca610d55d","added_by":"auto","created_at":"2026-02-10 10:28:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":462973,"visible":true,"origin":"","legend":"\u003cp\u003eChange in vACR between baseline (BL) and follow-up (FU) on the y-axis (log-scale) over the range of vACR at baseline (x-axis, log-scale). Red dots indicate incident albuminuria cases (n=77) and blue dots participants without incident albuminuria (n=371).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/6330c72b2057b58614103e3c.png"},{"id":102239809,"identity":"b472a156-9cd1-4aec-b83c-be6a55acf331","added_by":"auto","created_at":"2026-02-09 16:47:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":525246,"visible":true,"origin":"","legend":"\u003cp\u003eChange in vPCR between baseline (BL) and follow-up (FU) on the y-axis (log-scale) over the range of vPCR at baseline (x-axis, log-scale). Red dots indicate incident albuminuria cases (n=77) and blue dots participants without incident albuminuria (n=371).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/6afc85d88711225d7c74c1f9.png"},{"id":102298600,"identity":"fcfdc7aa-97da-42d5-a21c-b5f436779717","added_by":"auto","created_at":"2026-02-10 10:51:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1137454,"visible":true,"origin":"","legend":"\u003cp\u003eChange of vACR between baseline (BL) and follow-up (FU) after normalization for baseline vACR and occurrence of incident albuminuria. On the y-axis follow-up uACR [mg/g] levels are plotted on log scale with solid line indicating 30 mg/g (microalbuminuria) and dashed line for 300 mg/g (macroalbuminuria). Red dots mark incident albuminuria events (n=77; n=75 incident microalbuminuria, n=2 incident macroalbuminuria) in participants without albuminuria at baseline (n=371). On the x-axis the quotient of the vACR follow-up to baseline difference and baseline vACR is plotted on log scale. Stable quotient was defined as values between -1 and +1 (blue, n=291), whereas increase was defined as quotient \u0026gt; 1 (orange, n=157). In the vACR stable region, 9.6% incident albuminuria cases (n=28) occurred, whereas 31.2% incident albuminuria cases (n=49) were observed in the vACR increase region.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/5b06eca3ff8d4e51e76522db.png"},{"id":102239811,"identity":"eaa7d27a-caf4-4330-98e9-cbc951467ba6","added_by":"auto","created_at":"2026-02-09 16:47:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1139430,"visible":true,"origin":"","legend":"\u003cp\u003eChange of vPCR between baseline (BL) and follow-up (FU) after normalization for baseline vPCR and occurrence of incident albuminuria. On the y-axis follow-up uACR [mg/g] levels are plotted on log scale with solid line indicating 30 mg/g (microalbuminuria) and dashed line for 300 mg/g (macroalbuminuria). Red dots mark incident albuminuria events (n=77; n=75 incident microalbuminuria, n=2 incident macroalbuminuria) in participants without albuminuria at baseline (n=371). On the x-axis the quotient of the vPCR follow-up to baseline difference and baseline vPCR is plotted on log scale. Stable quotient was defined as values between -1 and +1 (blue, n=317), whereas increase was defined as quotient \u0026gt; 1 (orange, n=131). In the vPCR stable region, 14.5% incident albuminuria cases (n=46) occurred, and 23.7% incident albuminuria cases (n=31) were observed in the vPCR increase region.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/65ba1a19fa6c74d325bdb2fb.png"},{"id":102301044,"identity":"0398d70c-e034-4b49-909b-6db4299fecae","added_by":"auto","created_at":"2026-02-10 11:18:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9327428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/2a6a1f3a-d304-42ef-806a-d75bf079b7db.pdf"},{"id":102297573,"identity":"66b06a11-d3a3-4a22-9795-93b58d3170c6","added_by":"auto","created_at":"2026-02-10 10:28:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":882327,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8650516/v1/92f109ef46acf9ab53964f99.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urinary vesicle biomarkers and kidney function – Results from the German AugUR study","fulltext":[{"header":"KEY Message","content":"\u003cp\u003eWhat is known\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAccording to a previous study in animals, accumulation of albumin in the subpodocyte space leads to subsequent endocytosis by the podocytes.\u003c/li\u003e\n \u003cli\u003ePodocyte-produced vesicles contain potential biomarkers of the deterioration of kidney function in humans.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhat is new\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBiomarkers from urinary vesicles can be quantified from biobanked human samples.\u003c/li\u003e\n \u003cli\u003eHigher vesicular podocalyxin at baseline might be a potential predictor for lower risk for albuminuria over three years in an old-aged cohort.\u003c/li\u003e\n \u003cli\u003eChanges in\u0026nbsp;urinary vesicle biomarkers over time, especially vesicular albumin,\u0026nbsp;are associated with incident albuminuria independent of eGFR and free urinary albumin.\u003cbr\u003e\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eChronic kidney disease (CKD), which is defined by reduced kidney function and/or kidney damage persisting for over three months [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], is a serious and growing global health problem, affecting more than 10% of the adult global population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Kidney function is assessed by the determination of the glomerular filtration rate (GFR), estimated with serum biomarkers creatinine or cystatin C (eGFR) and by the degree of albuminuria, measured as the urinary albumin-creatinine-ratio (uACR) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The urinary albumin excretion is determined by the degree of glomerular albumin filtration and the extent of tubular reabsorption, predominantly in the proximal tubule [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Based on its size and charge specificity, the intact glomerular filtration barrier (GFB) is largely impermeable for macromolecules, such as albumin. The GFB consists of three layers: the glomerular endothelial cells, basement membrane and podocytes. The latter are important for the function of the GFB, as they form a size barrier with slit diaphragms for free filtration [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Compromised functions of the GFB and the concomitant increase in albumin filtration are usually masked for prolonged periods of time due to albumin reabsorption by renal proximal tubule cells, mediated by receptor-mediated endocytosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Once the tubular reabsorptive capacity is saturated, albumin excretion in the urine increases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The commonly used limit for microalbuminuria is uACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g, but even lower uACR values elevate the risk for cardiovascular diseases and mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, limitations of formulas for eGFR might lead to an under-diagnosis of early functional changes in the kidney, thus leading to the delayed initiation of reno-protective therapeutic measures [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, there is apparently the need for new non-invasive diagnostic and prognostic biomarkers for CKD. In this context, extracellular vesicles (EVs) detectable in the urine have gained increasing interest [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEVs are defined as particles with a lipid bilayer released from cells and, in the context of the kidney, can be easily recovered from urine samples [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, EVs carry markers specific for the cells of origin and provide information about the content of the parental cells. Thus, the assessment of changes in EV cargo in combination with the origin of the urinary EVs might generate specific insights into localization, cause and progression of different kidney diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to a previous study in animals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], changes in the permeability of the GFB lead to the accumulation of serum albumin in the subpodocyte space and the subsequent endocytosis by the podocytes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Part of the endocytosed albumin is degraded in lysosomes, whereas the majority is released into the urinary space via transcytosis as albumin containing EVs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is assumed that only a small proportion of the vesicles is reabsorbed along the proximal tubule, while the majority is excreted in the urine [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Accordingly, albumin and the podocyte-specific protein podocalyxin [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], were detected in EVs isolated from the urine of the animals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn view of these results, we hypothesized that urinary vesicular albumin and podocalyxin can be used as novel diagnostic and prognostic markers for the deterioration of kidney function in humans. So far, there are no measurements of urinary vesicular albumin and podocalyxin and evaluations of their relationship to kidney function markers in a human observational study available. Such markers could be particularly informative in old-aged individuals where the prevalence of albuminuria or low eGFR is higher than in younger individuals. We thus measured urinary vesicular albumin and podocalyxin at baseline and follow-up of a population-based cohort study in the elderly (i.e. age 70\u0026ndash;95 year at baseline), characterised these novel kidney biomarkers and evaluated their association with eGFR-based CKD and albuminuria cross-sectionally and longitudinally.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAugUR cohort study description\u003c/h2\u003e \u003cp\u003eThe German AugUR study (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003eltersbezogene \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eU\u003c/span\u003entersuchungen zur \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eG\u003c/span\u003eesundheit der \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eU\u003c/span\u003eniversit\u0026auml;t \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eR\u003c/span\u003eegensburg) is a prospective study of the general old-aged population in and around the city of Regensburg, Bavaria. AugUR focuses on chronic diseases and associated risk factors in the population aged 70 to 95 years at baseline. Details on the study were published earlier [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In brief, 1,133 participants were included in the first AugUR survey between 2013 and 2015. A three year follow-up was conducted between 2016 and 2018 with 733 participants.\u003c/p\u003e \u003cp\u003e The AugUR study was approved by the Ethics Committee of the University of Regensburg, Germany (vote 12-101-0258). The study complies with the 1964 Helsinki declaration and its later amendments. All participants provided informed written consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAugUR study program\u003c/h3\u003e\n\u003cp\u003eGeneral medical examinations at the study centre included blood pressure, height, weight, waist and hip circumference amongst others. Obesity was defined as body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;. Systolic and diastolic blood pressures (SBP and DBP) were measured by an automatic device three times after \u0026gt;\u0026thinsp;5 min resting, using the average of the second and third measurements in the analyses. Mean arterial pressure (MAP) was calculated by DBP + ((SBP \u0026ndash; DBP)/3).\u003c/p\u003e \u003cp\u003eA questionnaire conducted as in-person interview included information on general chronic diseases, medication intake and lifestyle factors like smoking. Coronary artery disease (CAD) was defined if at least one of the following conditions was reported by the participants: myocardial infarction, percutaneous coronary intervention, or coronary artery bypass surgery. Cardiovascular disease (CVD) was defined as CAD or stroke. Hypertension was defined as blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg or if the individual reported a prior hypertension diagnosis and antihypertensive medication intake [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Diabetes was defined as self-reported diagnosis of diabetes or the use of antidiabetic medication [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAugUR biomarker assessment\u003c/h3\u003e\n\u003cp\u003eNon-fasting blood samples were drawn in a sitting position after at least 5 min of resting. Mild venous stasis was applied for a maximum duration of 1 min. Blood was taken using a 21G multifly needle. Midstream urine was sampled.\u003c/p\u003e \u003cp\u003eBiobanked samples (stored at -80\u0026deg;C) were used for laboratory analyses for creatinine, cystatin C, albumin and α1-microglobulin on a Siemens Dimension Vista 1500 (Siemens Healthcare, Erlangen, Germany). Analyses were performed in compliance with the \u0026ldquo;Guidelines of the German Medical Association for Quality Assurance of Medical Laboratory Tests\u0026rdquo; (RiLiB\u0026auml;K) at the Central Laboratory of the University Hospital Regensburg, which is accredited in accordance with the standard DIN EN ISO 15189. Serum cystatin C was measured with an immunoassay (assay CYSC, [mg/l]). Creatinine from serum and urine was enzymatically measured (assay ECREA, [mg/dl]). Urine albumin was measured with an immunoassay (assay MALB, [mg/l] with a limit of detection (LoD) of 5 mg/l) and α1-microglobulin (α1M, assay A1MIC, [mg/l], LoD\u0026thinsp;=\u0026thinsp;7.8 mg/l) with nephelometry. Urinary albumin and α1M were normalized to urinary creatinine, i.e. urinary albumin-to-creatinine-ratio (uACR) and urinary α1M-to-creatinine-ratio (uα1MCR) and expressed in [mg/g].\u003c/p\u003e \u003cp\u003eChronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was used to derive eGFR [ml/min/1.73m\u0026sup2;] from serum creatinine and cystatin C. CKD based on eGFR was defined with values\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2;. Incident CKD was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; at follow-up and eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; at baseline. Microalbuminuria was defined as uACR 30\u0026ndash;300 mg/g and macroalbuminuria as uACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Incident albuminuria was defined as no albuminuria at baseline and micro- or macroalbuminuria at follow-up.\u003c/p\u003e\n\u003ch3\u003eIsolation of EVs from urine\u003c/h3\u003e\n\u003cp\u003eEVs from urine were isolated employing a differential centrifugation protocol. Urine (7 ml), stored at -80\u0026deg;C, was thawed in a water bath at 37\u0026deg;C and samples were homogenized by inverting. The urine was treated with EDTA, 100x protease and phosphatase inhibitor (PI) (Thermo Fisher Scientific, 78446), and PonceauS (PonS) (Sigma Aldrich, P7170), so that EDTA and PI were present in 1x concentration and PonS in a\u0026thinsp;~\u0026thinsp;1:115 dilution. Samples were subsequently centrifuged at 3,234 g and 4\u0026deg;C for 20 minutes (Eppendorf 5804R Centrifuge, S-4-72) to remove cells and cell fragments. The resulting pellet was discarded, and 7 ml of supernatant were ultracentrifuged at 329,000 g and 4\u0026deg;C for 1h (Optima L-80 XP ultracentrifuge, Optima LE 80-K ultracentrifuge, Centrikon T-1170 ultracentrifuge, 70.1 Ti rotor, TFT 70.13 rotor). The supernatant was discarded, and the pellet was washed twice with PBS. For this, 7 ml of PBS in combination with 60 \u0026micro;l PonS and/or 1,2 ml of PBS in combination with 10 \u0026micro;l PonS were used. Samples were subsequently centrifuged for 1h at 4\u0026deg;C and 329,000g (Optima L-80 XP ultracentrifuge, Optima LE 80-K ultracentrifuge, Centrikon T-1170 ultracentrifuge, 70.1 Ti rotor, TFT 70.13 rotor) and/or 186,000 g (Optima\u0026trade; MAX-E ultracentrifuge, TLA-55 rotor). Prior to centrifugation in the Optima\u0026trade; MAX-E ultracentrifuge pellets were transferred into new cups using 3 x 400 \u0026micro;l PBS. The isolated EV pellet was suspended and transferred into a new tube using 2 x 25 \u0026micro;l of a solution containing PBS and 1x PI. Isolated samples were stored at -80\u0026deg;C until further processing.\u003c/p\u003e\n\u003ch3\u003eEV sample preparation\u003c/h3\u003e\n\u003cp\u003eFor downstream analysis, 15 \u0026micro;l of the resuspended EV volume was preserved. The remaining volume was lysed using 10x RIPA buffer (abcam, ab156034). To support the lysis of EV membranes, samples were vortexed and subsequently shaken at 1400 rpm and 4\u0026deg;C for 20 min.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFluorescence microscope\u003c/h2\u003e \u003cp\u003eIn a pilot study, EVs from one young subject were characterized employing the MemGlow\u0026trade; 560 probe (Cytoskeleton Inc., MG02-02), a fluorogenic probe that integrates into lipid bilayers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], in combination with an antibody against the podocyte-specific marker protein podocalyxin. Podocalyxin was detected by abcam Rabbit recombinant monoclonal podocalyxin antibody conjugated to Alexa Fluor\u0026reg; 488 (=\u0026thinsp;PODXL488) (ab208254). 50 \u0026micro;l of vesicle suspension (1:5 diluted) were incubated over night at 4\u0026deg;C and 300 rpm with the primary antibody (diluted 1:50). To wash the vesicles, the volume was brought to 1,2 ml and the suspension was subsequently centrifuged at 186,000 g and 4\u0026deg;C for 1h (Optima\u0026trade; MAX-E ultracentrifuge, TLA-55 rotor). The resulting pellet was resuspended in 50 \u0026micro;l PBS, incubated with 0.2 \u0026micro;M MemGlow\u0026trade; at 50 rpm for 30 minutes in the dark. Afterwards the previously described washing step was repeated. The stained EV pellet was resuspended in 10 \u0026micro;l of PBS and 3 \u0026micro;l of solution were applied to a microscope slide. Samples were mounted after drying for a few minutes. To identify the proportion of podocyte-derived vesicles in the total population, the dyed EVs were visualized using a Laser Scanning microscope (LSM710, Zeiss, Jena). Data processing and analysis was performed using the Zeiss Zen lite (ZEN 3.9) software. Vesicles that were stained with MemGlow\u0026trade;, and vesicles stained with both the membrane dye and the antibody, were counted in six equally sized squares, which were randomly positioned in the image. Afterwards the proportion of double-positive (and therefore podocyte-derived) vesicles from the MemGlow\u003csup\u003eTM\u003c/sup\u003e-positive vesicles was calculated (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuantification of EV-albumin and EV-podocalyxin via ELISA\u003c/h3\u003e\n\u003cp\u003eEV-derived albumin and podocalyxin concentrations were quantified using the commercially available human albumin ELISA kit (abcam, ab227933) and the human podocalyxin ELISA kit (reddot Biotech Inc., RD-PCX-Hu), respectively. Lysed samples were diluted in the buffers included in the kits. Standards were solved and diluted, so that the same concentrations of RIPA, PBS and phosphatase and protease inhibitor and PonS were present in both standards and the samples. Samples were initially diluted 1:20 for the albumin ELISA and 1:10 for the podocalyxin ELISA, samples with concentrations above the standard curve were diluted further. All standards were assayed in duplicate. The protein concentrations of the samples were calculated based on the standard curve. Afterwards, the normalized vesicular albumin and podocalyxin concentrations per mL of urine were calculated (see supplementary information). The 0.5% extreme values at the upper end of the distribution for each EV marker at each point in time were removed.\u003c/p\u003e \u003cp\u003eTo determine the intra- and inter-assay coefficient of variability (CV) for both vesicular albumin and podocalyxin, samples were measured in triplicate (n\u0026thinsp;=\u0026thinsp;26). After the initial determination of vesicular albumin and podocalyxin concentration lysed samples were stored for up to 17 months at -80\u0026deg;C. Subsequently, vesicular albumin and podocalyxin concentrations were determined in triplicate on two different days, one week apart. Here, samples with high, medium and low concentrations of vesicular albumin and podocalyxin were diluted 1:40 and 1:60 for the triplicate measurements, respectively. Samples were also stored at -80\u0026deg;C between triplicate measurements. The intra- and inter-assay coefficient of variability (CV) were calculated based on protein concentration prior to normalization (see supplementary information). Inter-assay CV was calculated between the two triplicate measurements one week apart. Additionally, inter-assay CV was calculated between the two triplicate measurements and initial vesicular albumin and podocalyxin measurement, thus reflecting the stability of vesicular albumin and podocalyxin concentrations over long storage periods.\u003c/p\u003e \u003cp\u003eSince spot urine samples were collected, vesicular albumin and podocalyxin values were normalized to urinary creatinine (termed vACR and vPCR, respectively; see supplementary information). Both, vACR and vPCR were not normally distributed. Therefore, for regression analyses ln-scales values were used and graphic representations were given on log-scale.\u003c/p\u003e\n\u003ch3\u003eAnalysis sample\u003c/h3\u003e\n\u003cp\u003eFor the present analysis, we included data from all AugUR participants with available urine biomarkers and eGFR for baseline and three-year follow-up (n\u0026thinsp;=\u0026thinsp;580).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData management and statistical analyses were performed using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA) and IBM SPSS Statistics for Windows, Version 29.0.2 (IBM Corp., Armonk, NY, USA). Categorical variables were described with absolute numbers and percent. Normally distributed variables were given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The median and interquartile range were used to describe the distribution of skewed variables. Estimates were given with 95% confidence intervals (CI). Spearman\u0026rsquo;s correlation coefficient r was reported with 95% CI calculated after r-to-z transformation with estimated standard errors. Linear and logistic regression analyses were performed to obtain estimates and 95% CI for continuous and binary outcomes, respectively. Explained variance in linear regression models was expressed as R\u0026sup2;. Cox proportional hazard regression analyses were used to obtain hazard ratios (HR) and 95% CI.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample characteristics\u003c/h2\u003e \u003cp\u003eA total of 580 AugUR participants aged 70 to 95 years at baseline (59% men) were included in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Median follow-up time was 3.2 years. Kidney function relevant biomarkers were measured at baseline and follow-up.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of AugUR participants at baseline and follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline (n\u0026thinsp;=\u0026thinsp;580)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFollow-up (n\u0026thinsp;=\u0026thinsp;580)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGeneral descriptives\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yrs (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 (70.3\u0026ndash;95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 (73.4\u0026ndash;98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, men\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e340 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.9\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMedication\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh ceiling diuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny diuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidiabetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrine biomarkers\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.7 [44.9/120.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.3 [46.4/123.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, mg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6 [5.5/20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 [6.4/26.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euACR, mg/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9 [9.1/27.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6 [9.9/32.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα1M, mg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8 [7.8/12.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9 [7.8/13.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euα1MCR, mg/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4 [9.1/21.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9 [9.5/22.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVesicular albumin, ng/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492.9 [175.8/1400.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e627.6 [193.1/1669.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR, ng/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e670.7 [241.8/2138.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e797.3 [302.3/2014.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVesicular podocalyxin, ng/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135.6 [58.1/303.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.3 [69.6/299.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR, ng/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.7 [92.9/408.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220.9 [97.4/433.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSerum biomarkers\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C, mg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDiseases\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncident CKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroalbuminuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacroalbuminuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncident albuminuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e436 (75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eContinuous values are means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median [25th /75th percentile]; categorial variables are total numbers and percent in brackets. Missing values at baseline/follow-up is given in the last row.\u003c/p\u003e \u003cp\u003eBMI, body mass index; MAP, mean arterial pressure; uACR, urinary albumin-to-creatinine ratio; α1M, alpha1-microglobulin; uα1MCR, urinary α1M-to-creatinine ratio; vACR, vesicular albumin-to-creatinine ratio; vPCR, vesicular podocalyxin-to-creatinine ratio; eGFR, estimated glomerular filtration rate based on combined serum creatinine and cystatin C (CKDEpi 2021); CKD, chronic kidney disease defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2;; incident CKD defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m\u0026sup2; at follow-up and not at baseline; microalbuminuria defined as uACR 30\u0026ndash;300 mg/g; macroalbuminuria defined as uACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g; incident albuminuria defined as uACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g at follow-up and not at baseline; CAD, coronary artery disease; CVD, cardiovascular disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of vesicular albumin and podocalyxin at baseline\u003c/h2\u003e \u003cp\u003eFrom biobanked urinary samples, extracellular vesicles could be isolated (Supplementary Fig.\u0026nbsp;1). Vesicular albumin and podocalyxin concentrations ranged from 45 to 18,849 ng/l and 11 to 10,499 ng/l, respectively. The stability of the quantitation results for vesicular albumin and podocalyxin in repeat measurements under different storage times was assessed (Supplementary Table\u0026nbsp;1). The intra-assay coefficient of variability (CV) was below 10% for both markers, whereas the inter-assay CV was higher for vesicular albumin compared to podocalyxin (27% and 13%, respectively).\u003c/p\u003e \u003cp\u003eTo correct for urine volume, vesicular albumin and podocalyxin were normalized to urinary creatinine (vACR, vPCR). All further analyses were performed with vACR and vPCR, respectively. At baseline, the range for vACR was 37 ng/g to 42,890 ng/g and 6.4 ng/g to 40,272 ng/g for vPCR. To gain normal distributions for regression analyses, baseline vACR and vPCR were ln-transformed resulting in ranges of 3.6 to 10.7 for vACR and 1.9 to 10.6 for vPCR, respectively.\u003c/p\u003e \u003cp\u003eWe analysed the associations of age and sex with vACR and vPCR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Whereas linear regression using ln-scaled vACR as outcome revealed no association with age (p\u0026thinsp;=\u0026thinsp;0.671), ln-scaled vPCR was significantly associated with age (b\u0026thinsp;=\u0026thinsp;0.038 per year, 95% CI\u0026thinsp;=\u0026thinsp;0.014 to 0.062, p\u0026thinsp;=\u0026thinsp;0.002, R\u0026sup2;=1.6%). We found a strong sex difference for vACR (b= -1.031 on ln-scale for men compared to women, 95% CI= -1.244 to -0.818, p\u0026thinsp;=\u0026thinsp;5.31*10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e), explaining 13.5% (R\u0026sup2;) of vACR variability at baseline. In contrast, no association between vPCR and sex (p\u0026thinsp;=\u0026thinsp;0.775) was detected. Supplementary Fig.\u0026nbsp;2 shows the baseline distributions of vACR and vPCR between age groups and sex.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelations of vesicular albumin and podocalyxin with each other and with kidney function biomarkers at baseline\u003c/b\u003e \u003c/p\u003e \u003cp\u003eVesicular albumin and podocalyxin were moderately correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating possible independent biological mechanisms of both markers. To evaluate the influence of sex on this correlation, we calculated the explained variance (R\u0026sup2;) separately for men and women. R\u0026sup2; on ln-scale of vACR and vPCR was 12.7% (7.6% for women and 20.1% for men), possibly resulting from the sex-dependency of vACR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was no correlation of both vACR and vPCR with eGFR at baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, D). Correlations of vACR and vPCR, respectively, with uACR was moderate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, E), and low with ua1MCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, F).\u003c/p\u003e \u003cp\u003eFor the correlation of vACR with uACR, Spearman\u0026rsquo;s correlation coefficient r was 0.59 [95% CI\u0026thinsp;=\u0026thinsp;0.53\u0026ndash;0.64] and for the correlation of vPCR with uACR, r was 0.33 [95% CI\u0026thinsp;=\u0026thinsp;0.26\u0026ndash;0.41]. R\u0026sup2; for vACR and uACR on ln-scale was 22.5% for women and 49.3% for men (36.1% for both sexes combined). R\u0026sup2; for vPCR and uACR on ln-scale was 22.3% for women and 13.8% for men (16.1% for both sexes combined).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of age, sex and known kidney markers with vesicular albumin and podocalyxin at baseline\u003c/h2\u003e \u003cp\u003eAge was associated with vPCR and sex with vACR. To analyse the influence of established kidney biomarkers on vesicular albumin and podocalyxin, we tested the association of uACR, uα1MCR as well as eGFR with vACR and vPCR, respectively, adjusted for age and sex via linear regression at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjusting for age and sex, uACR was positively associated with vACR as well as vPCR, with stronger effects on vACR. Additionally, uα1MCR, a marker for tubular function, was associated with both vesicular markers in the same direction and with comparable effect sizes. In contrast to vPCR, eGFR was negatively associated with vACR, indicating that higher vACR values were associated with lower eGFR after age- and sex-adjustment. Significant correlation of vACR with eGFR was not observed in unadjusted analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, the effect of eGFR on vACR was weak (b= -0.013) and seemed to be mainly driven by sex, indicating a sex-eGFR interaction effect on vACR in cross-sectional data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of age, sex and quantitative kidney relevant biomarkers with vACR and vPCR at baseline.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003evACR (ln)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003evPCR (ln)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb= -0.006 [-0.031;0.020]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eb\u0026thinsp;=\u0026thinsp;0.038 [0.014;0.062]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb= -1.031 [-1.244;-0.818]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;5.31*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;20\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;0.032 [-0.185;0.248]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euACR (ln) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb\u0026thinsp;=\u0026thinsp;0.849 [0.763;0.935]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;6.57*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;65\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eb\u0026thinsp;=\u0026thinsp;0.536 [0.434;0.639]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;5.75*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;23\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euα1MCR (ln) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb\u0026thinsp;=\u0026thinsp;0.475 [0.318;0.632]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;4.56*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eb\u0026thinsp;=\u0026thinsp;0.468 [0.310;0.626]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;1.00*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eb= -0.013 [-0.020;-0.006]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;3.19*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;4\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;0.002 [-0.005;0.009]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eShown are beta estimates (b) and 95% CI in square brackets as well as p-values from linear regression models for age, sex and quantitative kidney biomarker and vACR/vPCR. Significant findings (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are presented in bold font. For vACR, vPCR, uACR and uα1MCR ln-scaled values were used in analyses. *Association results of uACR, uα1MCR and eGFR on vACR and vPCR, respectively, are presented after age- and sex-adjustment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of baseline vesicular albumin and podocalyxin with incident eGFR-based CKD and incident albuminuria\u003c/h2\u003e \u003cp\u003eTo assess the potential predictive ability of the two vesicular biomarkers, we restricted to individuals that had (i) no CKD (i.e. eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 ml/min/1.73 m\u0026sup2;) or (ii) no albuminuria (i.e. uACR\u0026thinsp;\u0026lt;\u0026thinsp;30mg/g) at baseline and observed a total of n\u0026thinsp;=\u0026thinsp;54 incident CKD and n\u0026thinsp;=\u0026thinsp;77 incident albuminuria cases. We tested associations of vACR and vPCR at baseline with incident eGFR-based CKD and albuminuria and found significantly elevated risk for eGFR-based CKD with higher vACR levels at baseline in the unadjusted model (HR\u0026thinsp;=\u0026thinsp;1.218, p\u0026thinsp;=\u0026thinsp;0.047) and after adjustment for age and sex (HR\u0026thinsp;=\u0026thinsp;1.268, p\u0026thinsp;=\u0026thinsp;0.021) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, a predictive marker for CKD should be independent of baseline eGFR and uACR. The effect of vACR on incident eGFR-based CKD disappeared when including baseline eGFR and uACR into the model (HR\u0026thinsp;=\u0026thinsp;1.053, p\u0026thinsp;=\u0026thinsp;0.704) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, higher vPCR levels at baseline were associated with lower risk of incident albuminuria after adjustment for eGFR and uACR (HR\u0026thinsp;=\u0026thinsp;0.763, p\u0026thinsp;=\u0026thinsp;0.017). This corresponded with lower vPCR by one unit on ln-scale to confer an HR of 1.3 and thus a 30% increased risk of albuminuria within 3 years (over a range of 8.7 ln-units of vPCR in our data). Additional adjustment for uα1MCR did not change the effect estimate markedly, as well as adding vACR to the model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This latter model 6 accounted for a potential confounding effect of vACR on vPCR association with incident albuminuria.\u003c/p\u003e \u003cp\u003eIn summary, our fully adjusted model indicated a potential predictive power of higher vesicular podocalyxin at baseline for lower incident albuminuria risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of vACR and vPCR at baseline on ln-scale with incident eGFR-based CKD and incident albuminuria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIncident CKD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIncident albuminuria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnadjusted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.218 [1.002;1.479]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.121 [0.932;1.349]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.926 [0.749;1.144]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.860 [0.710;1.041]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.268 [1.036;1.553]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.058 [0.856;1.309]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.877 [0.708;1.086]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.849 [0.698;1.031]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.191 [0.985;1.441]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.064 [0.858;1.318]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960 [0.770;1.197]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.899 [0.734;1.102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.208 [0.928;1.574]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869 [0.686;1.101]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809 [0.647;1.011]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.718 [0.580;0.890]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.053 [0.805;1.379]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.895 [0.708;1.132]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.878 [0.701;1.099]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.763 [0.611;0.953]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.076 [0.820;1.413]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.873 [0.685;1.111]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.875 [0.698;1.098]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.766 [0.614;0.955]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.143 [0.859;1.520]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945 [0.740;1.208]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.847 [0.668;1.075]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.778 [0.617;0.980]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eShown are hazard ratios (HR) with 95% confidence intervals (CI) as well as p-values from Cox regression models with follow-up time used for incident CKD (n\u0026thinsp;=\u0026thinsp;54) and incident albuminuria (n\u0026thinsp;=\u0026thinsp;77) for vACR and vPCR on ln-scale in different models. Significant findings (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are highlighted in bold font.\u003c/p\u003e \u003cp\u003eModel 1: Age, sex\u003c/p\u003e \u003cp\u003eModel 2: Age, sex, eGFR at baseline\u003c/p\u003e \u003cp\u003eModel 3: Age, sex, uACR (ln) at baseline\u003c/p\u003e \u003cp\u003eModel 4: Age, sex, eGFR, uACR (ln) at baseline\u003c/p\u003e \u003cp\u003eModel 5: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline\u003c/p\u003e \u003cp\u003eModel 6: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline; vACR (ln) and vPCR (ln) at baseline, respectively, added to the model for vPCR and vACR, respectively\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of changes in vesicular biomarkers with incident albuminuria and CKD\u003c/h2\u003e \u003cp\u003eWhen analysing vACR and vPCR changes over time, we observed a different pattern regarding incident albuminuria. Across the distribution of vACR at baseline, more participants with higher vACR values at follow-up compared to baseline developed incident albuminuria (n\u0026thinsp;=\u0026thinsp;63) in contrast to those with lower follow-up values (n\u0026thinsp;=\u0026thinsp;14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, for vPCR participants with higher values at follow-up compared to baseline developed more often incident albuminuria (n\u0026thinsp;=\u0026thinsp;55) in contrast to those with lower follow-up values (n\u0026thinsp;=\u0026thinsp;22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). An illustration of vACR and vPCR changes between baseline and follow-up over age is given in Supplementary Fig.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor better interpretation, difference of vACR at baseline and follow-up was z-transformed. Difference of vACR at baseline and follow-up was a predictor for incident albuminuria (HR\u0026thinsp;=\u0026thinsp;1.445 [95%CI\u0026thinsp;=\u0026thinsp;1.260; 1.659], p\u0026thinsp;=\u0026thinsp;1.50*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). After adjustment of age and sex, the association was still significant (HR\u0026thinsp;=\u0026thinsp;1.465 [1.271; 1.688], p\u0026thinsp;=\u0026thinsp;1.29*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). After additional adjustment for uACR (ln-scale) at baseline, the effect was stronger (HR\u0026thinsp;=\u0026thinsp;1.703 [1.437; 2.019], p\u0026thinsp;=\u0026thinsp;8.09*10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e). Additionally adding vACR at baseline (ln-scale) to the model did not alter the effect substantially (HR\u0026thinsp;=\u0026thinsp;1.770 [1.469; 2.133]; p\u0026thinsp;=\u0026thinsp;2.00*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eTo normalize for baseline vACR values, we further used \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vACR\\:(FU-BL)}{vACR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e. We defined vACR change as \u0026lsquo;stable\u0026rsquo; in the range of -1 to 1 after normalization on vACR at baseline (n\u0026thinsp;=\u0026thinsp;393) and \u0026lsquo;increase\u0026rsquo; with values\u0026thinsp;\u0026gt;\u0026thinsp;1 (n\u0026thinsp;=\u0026thinsp;187). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows incident albuminuria cases at follow-up over the distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vACR\\:(FU-BL)}{vACR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eZ-transformation was also computed for vPCR. Difference of vPCR at baseline and follow-up was not a predictor for incident albuminuria (HR\u0026thinsp;=\u0026thinsp;1.141 [95%CI\u0026thinsp;=\u0026thinsp;0.833; 1.562], p\u0026thinsp;=\u0026thinsp;0.411). After adjustment for age and sex, the association was still not significant (HR\u0026thinsp;=\u0026thinsp;1.127 [0.809; 1.570], p\u0026thinsp;=\u0026thinsp;0.481). Additional adjustment for uACR (ln-scale) at baseline, did not alter the effect (HR\u0026thinsp;=\u0026thinsp;1.115 [0.792; 1.572], p\u0026thinsp;=\u0026thinsp;0.532). Taking additionally vPCR at baseline (ln-scale) into the model did not alter the effect substantially (HR\u0026thinsp;=\u0026thinsp;1.083 [0.736; 1.592]; p\u0026thinsp;=\u0026thinsp;0.686).\u003c/p\u003e \u003cp\u003eAs for vACR, to normalize for baseline vPCR, we further used \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vPCR\\:(FU-BL)}{vPCR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e. We defined vPCR change as \u0026lsquo;stable\u0026rsquo; in the range of -1 to 1 after normalization on vPCR at baseline (n\u0026thinsp;=\u0026thinsp;426) and \u0026lsquo;increase\u0026rsquo; with values\u0026thinsp;\u0026gt;\u0026thinsp;1 (n\u0026thinsp;=\u0026thinsp;154). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows incident albuminuria cases at follow-up over the distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vPCR\\:(FU-BL)}{vPCR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eChange in vPCR shows less pronounced pattern of incident albuminuria distribution over the distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vPCR\\:(FU-BL)}{vPCR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dichotomized variables (stable between follow-up and baseline versus higher values in follow-up) of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vACR\\:(FU-BL)}{vACR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vPCR\\:(FU-BL)}{vPCR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e were tested for association with incident albuminuria and CKD (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both, vACR- and vPCR-based analyses showed associations with incident albuminuria, but not with CKD. Participants with increasing vACR and vPCR levels over time showed an increased risk for incident albuminuria.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of dichotomized \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vACR\\:(FU-BL)}{vACR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vPCR\\:(FU-BL)}{vPCR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e with incident eGFR-based CKD and incident albuminuria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIncident CKD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIncident albuminuria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnadjusted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003 [0.544; 1.849]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.262 [2.545; 7.136]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.56*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.621 [0.309; 1.246]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.826 [1.097; 3.041]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.028 [0.553; 1.913]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.417 [2.618; 7.453]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.60*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.667 [0.329; 1.351]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.932 [1.152; 3.240]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.976 [0.500; 1.905]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.315 [2.550; 7.304]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.16*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.578 [0.273; 1.226]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.969 [1.169; 3.319]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.149 [0.610; 2.162]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.585 [3.155; 9.886]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.55*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.699 [0.343; 1.424]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.004 [1.163; 3.454]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.160 [0.583; 2.309]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.453 [3.064; 9.704]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.11*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.606 [0.283; 1.295]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.076 [1.195; 3.607]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.158 [0.581; 2.307]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.464 [3.070; 9.723]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.75*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.620 [0.288; 1.333]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.064 [1.185; 3.595]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evACR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.161 [0.583; 2.313]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.108 [2.850; 9.156]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.31*10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evPCR change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656 [0.301; 1.429]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.824 [1.027; 3.238]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eShown are the results from logistic regression models for the association of dichotomized \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vACR\\:(FU-BL)}{vACR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e (vACR change) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varDelta\\:\\:vPCR\\:(FU-BL)}{vPCR\\:\\left(BL\\right)}\\)\u003c/span\u003e\u003c/span\u003e (vPCR change) with incident CKD (n\u0026thinsp;=\u0026thinsp;54) and incident albuminuria (n\u0026thinsp;=\u0026thinsp;77), respectively, in different models (odds ratio (OR) with 95% confidence intervals (CI) for stable versus increased). Significant findings (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are highlighted in bold font.\u003c/p\u003e \u003cp\u003eFU, follow-up; BL, baseline.\u003c/p\u003e \u003cp\u003eModel 1: Age, sex\u003c/p\u003e \u003cp\u003eModel 2: Age, sex, eGFR at baseline\u003c/p\u003e \u003cp\u003eModel 3: Age, sex, uACR (ln) at baseline\u003c/p\u003e \u003cp\u003eModel 4: Age, sex, eGFR, uACR (ln) at baseline\u003c/p\u003e \u003cp\u003eModel 5: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline\u003c/p\u003e \u003cp\u003eModel 6: Age, sex, eGFR, uACR (ln), uα1MGCR (ln) at baseline; simple vACR and vPCR change (follow-up minus baseline), respectively, added to the model for vPCR and vACR change, respectively\u003c/p\u003e \u003cp\u003eIn summary, baseline-normalized changes of both vesicular markers, albumin and podocalyxin, between baseline and follow-up were associated with incident albuminuria but not with incident CKD. After dichotomization, increased versus stable levels of vesicular albumin and podocalyxin were associated with incident albuminuria independently of established kidney function biomarkers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Effect of vesicular albumin on incident albuminuria was higher (OR\u0026thinsp;=\u0026thinsp;5.5) compared to podocalyxin (OR\u0026thinsp;=\u0026thinsp;2.1) in the fully adjusted model 5. Model 6 with accounting for the respective other vesicular marker did not markedly alter the associations of both vACR and vPCR change with incident albuminuria.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCKD is defined by the progressive loss of kidney function, as determined by a decline in GFR below and an increase in urinary albumin excretion over a threshold for at least three months [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As a drawback, the sensitivity of both parameters is limited, and changes in kidney function remain frequently undetected in the early phase of the course of the disease [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consequently, patients would benefit from novel diagnostic and prognostic biomarkers of kidney function to overcome the limitations of the established markers, such as eGFR and albuminuria. Early biomarkers of kidney disease may also facilitate the initiation of reno-protective measures in a timely manner.\u003c/p\u003e \u003cp\u003eThe potential of the RNA and protein content, as well as changed concentration levels of podocyte-derived vesicles as biomarkers of podocyte injury, glomerulonephritis and other kidney diseases has been suggested in several studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Constituents of podocyte-derived vesicles may serve as biomarkers for kidney disease of various aetiology [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], childhood nephrotic syndrome [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], diabetic nephropathy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], disease activity in systemic lupus erythematosus [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and renovascular hypertension [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Podocyte-derived vesicles were shown to contain e.g. \u003cem\u003eCD2AP\u003c/em\u003e and \u003cem\u003eWT1\u003c/em\u003e mRNA as well as WT1 protein [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Levels of urinary exosomal \u003cem\u003eWT1\u003c/em\u003e mRNA have been found to predict eGFR decline in patients with diabetic nephropathy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, the content of podocyte-derived vesicles facilitated differentiating between focal segmental glomerulosclerosis and steroid-sensitive nephrotic syndrome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition to the diagnosis of kidney disease, the prognostic potential of podocalyxin-positive vesicles has been addressed before. Miller et al. observed that a model including podocalyxin-positive exosomes was able to predict the development of acute kidney injury after cardiac surgery [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, urinary podocalyxin concentration was elevated in diabetic patients, and patients with various glomerular diseases in comparison to normal controls, thus indicating its potential as a biomarker for diabetic nephropathy and other glomerular diseases [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we assessed the potential of two vesicular proteins as prognostic markers of kidney function. Vesicular podocalyxin and vesicular albumin are known to be derived from podocytes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which functionally and structurally contribute to the integrity of the glomerular filtration barrier of the kidney [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We found that about 19% of urinary vesicles were podocyte-derived (Supplementary Fig.\u0026nbsp;1). Additionally, the endocytotic activity of podocytes is assumed to play a role in the clearing of the glomerular filtration barrier from accumulating proteins. Thus, an impaired or saturated clearance mechanism might increase the probability for glomerular injuries [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analyses in an old-aged cohort showed an association between changes in vACR and vPCR with newly occurring albuminuria, suggesting that increasing levels of vesicular albumin and podocalyxin may be indicative of declined kidney function based on the increasing urinary albumin excretion (uACR). Conversely, no such association was found for changes in eGFR, suggesting that the changes in vACR and vPCR over time are more sensitive to changes in uACR than in eGFR. In conclusion, the presence of albumin-containing podocyte-derived vesicles is an early marker of podocyte injury, whereas eGFR rather reflects renal function decline in the advanced stages of kidney disease [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], thus no association between the vesicular markers and eGFR could be observed within this time frame.\u003c/p\u003e \u003cp\u003eInterestingly, we found an association between higher levels of vPCR at baseline and a reduced risk for incident albuminuria in the models adjusting for baseline uACR, eGFR and ua1MGCR suggesting that the association may be podocyte-specific. The association between higher levels of vPCR and a reduced risk for incident albuminuria may also be related to a higher number of viable and endocytic-active podocytes. Consequently, the clearance of the filtration barrier from accumulating proteins, in particular in the subpodocyte space, may be protective for the function of the GFB [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, higher vACR and also vPCR levels in the follow-up compared to baseline were associated with an increased risk for incident albuminuria. The formation of albumin-containing podocyte-derived vesicles may indicate an increasing number of accumulating proteins in the subpodocyte space, resulting in saturation of the clearance mechanism. This may lead to the deterioration of the integrity of the glomerular filter, thus explaining the increased risk of albuminuria. Furthermore, albumin exposure at high concentrations has been shown to elicit apoptosis and injury in podocytes [\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition albumin overload induced morphological changes in podocytes, e.g. a disrupted cytoskeleton [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which plays a crucial role in normal podocyte structure and function. Disruption of the cytoskeleton might lead to podocyte foot process effacement and subsequently to albuminuria/proteinuria [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study showed a marked sex difference in vACR, being significantly lower in men compared to women, whereas vPCR did not differ between the sexes. The difference in the sex-dependency of vACR and vPCR suggest that both markers are influenced by independent biological mechanisms. Podocalyxin is a highly specific marker of podocytes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For vesicular albumin, the situation is more complex. Vesicular albumin is generated via cellular uptake and transcytosis by podocytes, as shown in animal studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, vesicular albumin may also be part of the vesicular protein corona [\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], which has been demonstrated to form spontaneously around the surface of vesicles in biological fluids [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The latter would occur independently of podocytes and is presumably dependent on the competition between albumin and other proteins for vesicular binding site. Consequently, differences in the global protein status between men and women may be causal for the observed sex differences in vesicular albumin.\u003c/p\u003e \u003cp\u003eIn our longitudinal analyses, we have identified subjects with increased vACR and vPCR levels in the follow-up compared to baseline, but also a high proportion of participants with stable or even declining levels. For both markers, in the group with increased levels, the chance for albuminuria was significantly higher compared to subjects with stable parameters. Therefore, the temporal trajectories in vesicular biomarkers indicate (patho-)physiological changes, as described in more detail above, that increase the risk of albuminuria. We also found that considering baseline levels of the vesicular biomarkers and of uACR, improved the association with incident albuminuria. Therefore, the state of the kidney, especially the glomerulus, at baseline might be a modifying factor of the observed associations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and limitations of the study.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe acknowledge some limitations of the current study. First, the AugUR population does not represent the general population aged 70+, since based on the requirement for visits at the study centre, there presumably is some selection for mobile and healthy elderly persons. Therefore, the data may not represent the entire aged population, and samples from subjects with a higher degree of comorbidities and age-related degenerative processes are likely underrepresented.\u003c/p\u003e \u003cp\u003eSecond, we observed that applying our EV isolation protocol, 19% of urinary EVs were podocalyxin-positive, and, consequently, of podocyte origin. Thus, as expected, a heterogenous population of various vesicle subtypes, originating from different cell types of the urogenital tract, was isolated employing an ultracentrifugation protocol without additional affinity-based purification [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The percentage of podocyte-derived EVs was in a similar range as reported previously, e.g. 11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4% for patients suffering from renovascular hypertension and 6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3,4 % for halthy subjects [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, 23.3% of all EVs isolated by the use of a sucrose gradient were of glomerular origin [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Nevertheless, as vesicular albumin and podocalyxin, which are highly podocyte-specific, were determined as potential markers of kidney function, the background caused by non-glomerular EVs likely is of limited relevance.\u003c/p\u003e \u003cp\u003eThe strength of our study is the analysis of a large number of samples of an old-aged population, with an increased incidence of apparently and inapparently compromised kidney function and an increased risk of the development of kidney disease compared to the normal population. Consequently, we analysed samples from a population that is particularly suited for the assessment of diagnostic and prognostic biomarkers of changes in kidney function.\u003c/p\u003e \u003cp\u003eIn summary, we identified higher level of baseline vesicular podocalyxin as a predictor for reduced risk of incident albuminuria. In contrast, we found strong association between increase in vesicular albumin and podocalyxin over time with higher risk for albuminuria. Increasing vACR and vPCR levels were associated with 5.5- and 2-fold higher risk for newly occurring albuminuria compared to stable levels of vACR and vPCR, respectively. Based on our results, baseline uACR levels even below 30 mg/g should be considered for risk prediction models, since they enhance the effect of vACR and vPCR changes on albuminuria. No association between changes in vACR and vPCR levels and newly occurring eGFR-based CKD was detected, suggesting that eGFR is not predominantly determined by podocyte-specific effects, whereas uACR is dependent on the integrity of podocytes.\u003c/p\u003e \u003cp\u003eFurther studies with longer follow-ups are needed to elucidate the effect of change in vACR and vPCR with incident albuminuria. The aim of those studies should be to investigate the predictive power of differences of vACR and vPCR between two time points for future development of albuminuria.\u003c/p\u003e \u003cp\u003eFurthermore, the present study was focused on vesicular albumin and podocalyxin as novel diagnostic and prognostic biomarkers of kidney function. The content of podocyte-derived vesicles, however, contains an array of further podocyte-specific markers, some of which may be indicative of changes in podocyte function, and, more general, may provide information about the integrity of the glomerular filtration barrier of the kidney. These parameters should be addressed in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of the University of Regensburg (IRB number: 12-101-0258).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edata\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe individual data generated and analysed during the current study are not publicly available due to data privacy of study participants. Summary statistics are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AugUR study was supported by grants from the German Federal Ministry of Education and Research (BMBF 01ER1206 and BMBF 01ER1507) to I.M.H., by the German Research Foundation (DFG HE 3690/7-1 and BR 6028/2-1) to I.M.H. and C.B. and by institutional budget (University of Regensburg). The project was funded by grants from the German Research Foundation (TRR 374 project-ID 50914993) to I.H.M (TRR 374 projects B2, C6, and INF) and to H.C. (TRR 374 project B2).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor I.M.H. has received support from Roche Diagnostics for a biomarker project in the AugUR study, but unrelated to the work presented here.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026acute; Contributions\u003c/p\u003e\n\u003cp\u003eAll authors have contributed to interpreting results and manuscript writing. All authors have read and approved the manuscript. Further contributions are:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;L.S.: laboratory measurements, data analysis, statistical analysis, manuscript writing\u003c/p\u003e\n\u003cp\u003eH.C.dH.: data analysis, statistical analysis, manuscript writing\u003c/p\u003e\n\u003cp\u003eL.M.: laboratory measurements, manuscript writing\u003c/p\u003e\n\u003cp\u003eR.F.: laboratory measurements, manuscript writing\u003c/p\u003e\n\u003cp\u003eM.E.Z.: data management, data analysis, manuscript writing\u003c/p\u003e\n\u003cp\u003eC.B.: study physician, overall medical program study, manuscript writing\u003c/p\u003e\n\u003cp\u003eI.M.H.: project PI, study PI, project supervision, manuscript design, manuscript writing\u003c/p\u003e\n\u003cp\u003eH.C.: project initiation, project PI, project supervision, manuscript writing\u003c/p\u003e\n\u003cp\u003eK.J.S.: study coordination, project supervision, data management, statistical analysis, manuscript design, manuscript writing\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors greatly appreciate the outstanding and committed study assistance of Lydia Mayerhofer, Magdalena Scharl, Sabine Schelter and Josef Simon. We would like to express our special thanks to the study participants for contributing to the AugUR study. The authors also thank Bernhard Gess and Katharina Fremter for their technical support in the lab.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e105\u003c/b\u003e, S117\u0026ndash;S314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.kint.2023.10.018\u003c/span\u003e\u003cspan address=\"10.1016/j.kint.2023.10.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy, C. P. Epidemiology of chronic kidney disease: an update 2022. 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Subfractionation, characterization, and in-depth proteomic analysis of glomerular membrane vesicles in human urine. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e, 1225\u0026ndash;1237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ki.2013.422\u003c/span\u003e\u003cspan address=\"10.1038/ki.2013.422\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\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":"Cohort study, old-aged population, urinary vesicles, vesicular albumin, podocalyxin, kidney function","lastPublishedDoi":"10.21203/rs.3.rs-8650516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8650516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Progression into more severe stages of chronic kidney disease (CKD) based on estimated glomerular filtration rate (eGFR) and albuminuria are associated with increased risk of end-stage renal failure, cardiovascular diseases, and mortality. Vesicles in the urine are cell-derived particles containing constituents of the cells of origin. Little is known about the prognostic capacity of urinary vesicles for CKD progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e \u0026nbsp;To evaluate the association between components of urinary vesicles and incident CKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e \u0026nbsp;In the AugUR study, a prospective population-based cohort study in individuals aged 70-95 years at baseline, we isolated and characterized urinary vesicles from 580 participants at two timepoints. In cross-sectional data, influences of age, sex and established kidney biomarkers on vesicular albumin and podocalyxin were characterised. Longitudinal data were used to test associations of vesicular albumin and podocalyxin with incident eGFR-based CKD and albuminuria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e \u0026nbsp;Cross-sectionally, urinary vesicle albumin and urinary vesicle-bound podocalyxin were moderately correlated with each other and with urinary albumin and alpha1-microglobulin, but not with eGFR. Vesicular albumin concentrations were influenced by sex, whereas age showed an effect on podocalyxin. After adjusting for age and sex, higher vesicular albumin was associated with higher urinary albumin and lower eGFR. Higher vesicular podocalyxin concentrations were associated with higher urinary albumin but not with eGFR. Both markers showed identical associations with urinary alpha1-microglobulin. In longitudinal analyses, baseline vesicular albumin showed association with incident CKD based on eGFR. This association was no longer present after adjustment for baseline eGFR. In contrast, higher baseline podocalyxin concentrations were predictive for decreased risk of incident albuminuria after adjustment for baseline free urinary albumin. Baseline-adjusted change in urinary vesicle albumin and urinary vesicle-bound podocalyxin were both associated with incident albuminuria, independent of free urinary albumin and other kidney biomarkers. Here, increase in follow-up versus baseline values were associated with higher risk for incident albuminuria, with higher effect sizes for vesicular albumin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e \u0026nbsp;This study indicates that higher vesicular podocalyxin at baseline might be a potential predictor for lower risk for albuminuria over three years in an old-aged cohort. In contrast, longitudinal increase in urinary vesicle biomarkers, especially vesicular albumin, might be diagnostic markers for incident albuminuria in the elderly.\u003c/p\u003e","manuscriptTitle":"Urinary vesicle biomarkers and kidney function – Results from the German AugUR study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:47:20","doi":"10.21203/rs.3.rs-8650516/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T16:49:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T05:20:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157025176812837643607443344927799203932","date":"2026-03-29T23:15:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239430447402347384715751119758903955377","date":"2026-03-26T23:14:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T15:48:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277055256070780052776501590658374981598","date":"2026-03-05T08:44:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T14:10:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T07:21:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T05:21:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T05:18:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-20T14:08:08+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":"b303984f-3c10-4127-acf3-100bfa1cde8d","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62476844,"name":"Health sciences/Biomarkers"},{"id":62476845,"name":"Health sciences/Diseases"},{"id":62476846,"name":"Health sciences/Medical research"},{"id":62476847,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2026-05-12T17:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 16:47:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8650516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8650516","identity":"rs-8650516","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}