Urinary metabolomics profile in children with ureteropelvic junction obstruction

Urinary metabolomics profile in children with ureteropelvic junction obstruction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Urinary metabolomics profile in children with ureteropelvic junction obstruction Lucas Henrique Ferreira da Silva, Marcos Figueiredo Mello, Felipe Guilherme Hamoy Kataoka, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9107811/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Introduction Fetal hydronephrosis (HN) is detected in about 0.25–1% of fetuses, where ureteropelvic junction obstruction (UPJO) is the most commonly found congenital urinary tract anomaly, remaining the leading cause of kidney failure in infants and children. Since it is a spectral disease, UPJO represents a particularly challenging management. Severe UPJO must be treated surgically to avoid impairment of renal function, but children with non-obstructive hydronephrosis can be treated conservatively. There has been controversy regarding the indication for surgical intervention in asymptomatic patients. Objectives This pilot study aimed at investigating urine samples from children with UPJO using gas- and high-performance liquid-chromatography coupled to mass spectrometry (GC- and HPLC-MS) via untargeted metabolomics to prospect discriminatory metabolites indicative of hydronephrosis spectral progression that may contribute to optimize clinical decision-making. Methods Thirty-seven patients, whose clinical characteristics were recorded upon visits, had urinary samples collected, processed and analyzed by both GC- and HPLC-MS analytical platforms. Three distinct age-matched groups were inspected: 10 children with obstructive HN (OHN) established at initial imaging diagnosis, 15 asymptomatic children with non-obstructive HN (NOHN), and 12 children without any urinary tract problems as control group (CTR). Commonly used univariate (ANOVA and post-hoc tests) and multivariate (PLS-DA) procedures were applied to the metabolomics data, and ROC curves were generated to validate the models. Results Metabolomics profiling revealed distinct expression patterns upon selected group comparisons. The inspection of discriminatory metabolites across the different study groups allowed a clear visualization of the disease progression at the molecular level. Moreover, the discrimination of asymptomatic patients (NOHN group) from those requiring surgical intervention (OHN group) is of upmost relevance and could also be delineated here. All univariate and multivariate methods applied to both GC- and HPLC-MS data exhibited good statistical performance (Q 2 >0.71, accuracy>92%, and AUC>0.92) resulting in a total of 94 discriminatory metabolites, suggesting strong potential clinical value as a diagnostic/prognostic signature. Worth mentioning cystine and methionine sulfoxide exhibiting the largest positive fold change score, 156% and 152%, respectively (both from OHN vs CTR group comparison). From the total of 94 discriminatory metabolites, 17 metabolites had particular statistical relevance since they were confirmed by both univariate and multivariate methods combined. Three metabolites, 1-methylhistidine-3-methylhistidine, 1-methyhistamine-3methylhistamine, and glutamyl-hydroxyproline (dipeptide), discriminate NOHN vs CTR groups whereas nine metabolites, mandelic acid, pantothenic acid, furoylglicine, gluconic acid, 3-hydroxyphenylacetate, 4-hydroxyphenylacetate, glutamylhydroxyproline, 3-hydroxy-3-methylglutaric acid, ribitol/arabitol/xylitol (pentose alcohols) and tagalose discriminate OHN vs CTR groups; those metabolites may serve as diagnostics purposes, being the first group comparison (NOHN vs CTR) indicative of the initial metabolic perturbation caused by the UPJO and the latter group comparison (OHN vs CTR) indicative of the impact UPJO had exerted on the children metabolism at long term. More importantly, five metabolites, ascorbic acid, furoylglycine, gluconic acid, and ribose/arabinose/xylose (pentoses), already discriminating OHN vs CTR groups, appear again in the OHN vs NOHN group comparison, in addition to threitol; those metabolites might be useful to support clinical decisions whether a patient should undergo surgery. Metabolomic pathway analyses revealed an alteration of the beta-alanine metabolism at early stages of HN, progressing to further compromising of tyrosine, phenylalanine, alanine/aspartate/glutamate, and amino sugar/nucleotide sugar metabolisms. Several other important metabolic pathways were further compromised at advanced stages of HN revealing the overall impact of UPJO at the basal metabolism of healthy children. Conclusion The statistical quality of this study that certified 94 discriminant metabolites from NOHN, OHN, and CTR group comparisons allows us to infer that among them there will certainly be biomarkers of the obstructive HN setting up and progression. biomarker metabolomics obstructive hydronephrosis ureteropelvic junction obstruction Figures Figure 1 Figure 2 Figure 3 1 Introduction Fetal hydronephrosis is detected in about 0.25–1% of fetuses by antenatal ultrasonographic screening [ 1 ], where ureteropelvic junction obstruction (UPJO) is the most commonly found congenital urinary tract anomaly, with a prevalence of 1 in 1500 live births, remaining the leading cause of kidney failure in infants and children [ 2 ]. Since it is a spectral disease, UPJO represents a particularly challenging management and approximately 20% of children with this anomaly will require surgical intervention [ 3 ]. In this scenario, not all hydronephrosis (HN) represents a harmful state for the kidneys. Severe UPJO must be treated surgically to avoid impairment of renal function, but children with non-obstructive hydronephrosis (NOHN) can be treated conservatively [ 4 ]. There has been controversy regarding the indication for surgical intervention in asymptomatic patients. Some authors have proposed initial non-operative management along with intensive imaging protocols. Surgical intervention is indicated primarily on decreased ipsilateral differential renal function or increased drainage interval on nuclear scans [ 5 – 6 ]. Diagnostic methods currently used as reference standards to detect relevant UPJO are unsatisfactory and there is a great demand for non-invasive and reliable tests to predict which patient requires surgical intervention at early stages. To achieve this goal, researchers have evaluated the morphological changes associated with urinary obstruction, including tubular dilation and atrophy, thickening of the basement membrane, and interstitial fibrosis [ 7 ]. A few prospective disease-related biomolecules, such as retinol-binding protein-4 (RBP-4), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), transforming growth factor beta-1 (TGF- β1), monocyte chemoattractant protein-1 (MCP-1), endothelin-1 (ET-1), as well as epidermal growth factor (EGF) have all been screened as potential urinary biomarkers for obstructive hydronephrosis (OHN) [ 8 ]. However, due to modest results and limited availability, none of them have been incorporated into clinical practice. Metabolomics is a methodological approach that investigates in a comparative manner the entire set of low molecular-mass metabolites expressed by an individual or organism (metabolome) in pre-selected conditions [ 9 ]. Both formats, untargeted (hypothesis generating) and targeted (hypothesis driven) metabolomics have the potential to function as a diagnostic/prognostic tool that can support clinical decisions regarding patient treatment. Metabolomics has been applied to several areas of medicine, urology in special, addressing several aspects of renal diseases, including the differentiation of end-stage renal disease from chronic kidney disease CKD [ 10 ], the prognosis of acute kidney injury (AKI) [ 11 ], and the inspection of patients with normoalbuminuric diabetic kidney disease [ 12 ], to cite a few. Pediatric renal diseases under the scrutiny of metabolomics have been revised recently, including AKI, kidney transplantation, CKD, renal dysplasia, vesicoureteral reflux, and lithiasis [ 13 ]. The urinary proteome signature of UPJO was first investigated by Mishak group in 2006, using capillary electrophoresis coupled to mass spectrometry (CE-MS) [ 14 ]. Since this pioneer work, a few transcriptomics and proteomics studies followed [ 15 – 17 ]. Specifically concerning metabolomics, there are studies involving targeted and untargeted analysis using either nuclear magnetic resonance spectroscopy (NMR) or separation hyphenated-MS techniques. A panel of 15 metabolites, including creatinine, tryptophan, choline, and aspartate have been monitored by targeted metabolomics with hydrophilic-interaction liquid- chromatography coupled to mass spectrometry (HILIC-MS) in the serum of neonates and infants as an attempt to differentiating patients that require surgery from those following systematic monitoring, revealing significant metabolite perturbations [ 18 ]. In an untargeted metabolomics study, NMR spectra were acquired from urine of newborns with prenatally diagnosed unilateral renal pelvis dilatation and healthy controls to identify specific urinary biomarkers for UPJO [ 19 ]. Two main metabolic pathways were found compromised in this study, amino acid and betaine metabolisms. The present work aimed at investigating the urinary profile of children with UPJO (including asymptomatic patients) using an untargeted metabolomics approach supported by GC-MS and HPLC-MS (reversed-phase mode) data to prospect metabolites for discriminating OHN from NOHN patients and both from healthy individuals. To our knowledge, no categorical untargeted metabolomics study, using multiplatform analytical methods to enhance the metabolome coverage, has been applied to UPJO so far. Moreover, the discrimination at the molecular level of asymptomatic patients from those requiring surgical intervention is of upmost relevance and it has been investigated here as well. 2 Materials and methods 2.1 Cohort & metabolomics grouping A prospective observational cohort study was performed at the Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (Brazil) and approved by the local ethics committee (protocol 62235816.8.0000.0068). This study was carried out between September 2016 and June 2019 and comprised 37 children who had unilateral NH due to UPJO and controls, categorized by age in groups from 2 to 12 years old, according to the Food and Drug administration criteria [ 20 ]. All children’s caregivers were interviewed and signed out an informed consent form attesting to the children participation in the study. Patients were initially evaluated (according to our examination protocol) and then followed-up for at least 6 months at regular clinical visits, when renal ultrasonography (US) and radionuclide examinations were registered and urine samples collected. Age, age at diagnosis, gender, and renal side were recorded, and at each reassessment, blood pressure and general physical conditions recorded as well. The diagnosis of UPJO was initially suggested by renal ultrasound, with measurement of the anteroposterior pelvic diameter (APD), and the degree of hydronephrosis was graded according to the Society for Fetal Urology (SFU) classification on renal US [ 21 ]. To confirm the diagnosis, radioisotope renal scans were performed, and the standards used were static renogram (DMSA radionuclide renal scans) and diuretic renography (DTPA radionuclide renal scans). DMSA was interpreted as differential renal function (DRF), where a dilation of the affected kidney less than 40% was considered abnormal. DTPA curves were classified according to Lee [ 22 ]. The indication for surgical intervention (pyeloplasty) was based on available guidelines [ 23 – 25 ]. Symptomatic obstruction (low back pain, urinary tract infection, urinary stones, or hematuria), impaired DRF (less than 40%), more than 10% decrease in renal function on subsequent investigations, drainage function deficiency after diuretic administration, increased DPA or worsening of hydronephrosis to SFU grades III and IV on US constitute the current proposed indications for surgical intervention [ 25 ]. Exclusion criteria included associated anomalies, such as vesicoureteral reflux, obstruction of the ureterovesical junction and obstruction of the posterior urethral valves, bilateral NH, previous operation of the urinary system and other deformations of the external genital organs, deformations in the lower part of the ureter, bladder and urethra, urinary stones, urinary tract infection and neurogenic bladder dysfunction. For the metabolomics study, patients were divided into three groups based on clinical and imaging findings, including APD, DRF, and drainage curve on radioisotope renal scintigraphy. The groups were classified as: a) OHN group: children with unilateral OHN due to UPJO who underwent dismembered pyeloplasty using the Anderson-Hynes method; b) NOHN group: children considered for nonoperative treatment of unilateral UPJO (clinically asymptomatic and stable according to imaging studies during follow-up); and c) Control Group (CTR): healthy children, paired by age and sex with no underlying pathologies. The OHN group was composed of 10 children (4 boys, 6 girls; median age 4.9; 2–10 years old). The NOHN group included 15 children (9 boys, 6 girls; median age 6.9; 2–10 years old). The CTR consisted of 12 children without any urinary tract problems (8 boys, 4 girls; median age 6.3; 3–10 years old). 2.2 Sample collection and analysis Urine samples were collected according to the patient age and ability to urinate spontaneously, with preference for midstream samples (when possible). Children who were not toilet trained had their urine collected using a collection bag, and some patients, who had difficulty collecting in the pre-operative period, had their urine collected after anesthetic induction through bladder catheterization. Urinary culture was mandatory to exclude active urinary infection. In the OHN group, urine samples were taken before surgical repair of the UPJO. In the NOHN group, voided urine samples were collected at the time of diagnosis. For the control group, urine samples were also collected only once. Urine was collected aseptically, mixed, and centrifuged [3,000 rpm, 10 min]; the uppernatant was stored at − 80°C. For GC-MS analysis, urine samples were thawed and vortexed. A 100 µL aliquot was taken and transferred to an Eppendorf tube. Samples were treated with urease solution (10 mg/mL final concentration) and the tube was placed in a thermostatic bath at 37°C for 1 h. After the treatment with urease, cold isopropanol (10°C) in a 1:6 urine:isopropanol proportion was added for protein precipitation. Samples were again vortexed and placed in a freezer (-20°C) for 30 min. Samples were then centrifuged at 12,000 rpm for 10 min at 4°C. Exactly 200 µL supernatant were removed and transferred to 300 µL inserts and evaporated in an Eppendorf concentrator plus (Hamburg, Germany) in two cycles of 20 min each at 35 o C. Further sample treatment steps include derivatization reactions. For the methoxymation reaction, 20 µL methoxyamine hydrochloride in pyridine (15 mg/mL) were added to each insert containing the urease-treated urine (dried sample). The inserts were placed in 2 mL vials, vortexed and submitted to ultrasound for 10 s. The vials were covered with aluminum foil and kept for 16 h at room temperature and protected from light. For the silylation reaction, after this 16 h period, 20 µL BSTFA (N,O-bis-(trimethylsilyl)trifluoroacetamide) with 1% TMCS (chlorotrimethylsilane) were added to each sample; samples were again homogenized and placed in a bath at 70°C for 1 h. Samples were then diluted in 100 µL heptane and analyzed by GC-MS. GC-MS analyses were conducted in a gas chromatograph coupled to a quadrupole mass spectrometer (Shimadzu GC-2010 Plus, Barueri, Brazil). Exactly 1 µL of the derivatized sample was injected into a HP5-MS column (30 m x 0.25 mm, 0.25 µm film) from Agilent Technologies (Santa Clara, U.S.A.). The carrier gas was helium at a flow rate of 1 mL/min, and the injector temperature was set at 250 ºC. The oven was kept initially at 60 o C for 1 min and raised to 300 o C at a rate of 10 o C/min. Total analysis time was 25 min. For the HPLC-MS analysis, 100 µL sample aliquots were diluted in 300 µL methanol containing p -fluorophenylalanine (stock at 66.7 µmol/L; 50.0 µmol/L final concentration in urine). Samples were then stored at -20°C for 30 min. After this period, samples were centrifuged at 1200 rpm for 10 min at 4°C and the uppernatants separated and injected (0.50 µL) into the equipment (Agilent 1260 Infinity II chromatograph coupled to Agilent 6530 mass spectrometer with a quadrupole time-of-flight mass analyzer, Q-TOF). A Phenomenex Kinetex® PFP column (150 x 2.1 mm, 2.6 µm film) column maintained at 40 ºC was used. Mobile phase was comprised of solvent A (0.10% v/v formic acid in water) and solvent B (0.1% v/v formic acid in methanol), eluted in gradient: 0–1 min (0% B), 1–2.5 min (0–25% B), 2.5–3 min (25–90% B), 3–5 min (90–100% B), 5–8 min (100% B), 8–8.1 min (100–0% B), 8.1–17.5 min (0% B). A quality control (QC) sample was prepared for each technique. For this purpose, 15 µL of all urine samples under consideration in this study were mixed together, separated into two portions and processed exactly by the same procedures individual urine samples did. Three samples containing only water were subjected to urease treatment and subsequently to derivatization steps and analyzed as a prepared blank solution in GC-MS. QC samples were injected sequentially, one QC injection after every five regular samples. 2.3 Statistical analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, version 17.0 (SPSS, Chicago, IL, U.S.A.), SIMCA P + 12.0.1 and the free access software packages, XCMS in the R platform (R Project for Statistical Computing, v.4.4.2) and MetaboAnalyst 6.0. 3 Results and Discussion Demographic characteristics and clinical data of the patient and control groups under investigation here are presented in Table 1. All patients had a normal estimated glomerular filtration rate (eGFR) >90 ml/min/1:73m 2 calculated using the Schwartz formula [26]. In the OHN group, 4 patients were symptomatic: 2 had low back pain, 1 with febrile UTI, and 1 with non-febrile UTI; in the NOHN group, 2 patients were symptomatic, all had non-febrile UTI that resolved on follow-ups. There was a statistically significant difference between the groups in relation to symptoms, degree of hydronephrosis on the SFU classification scale in the US, DRF on DMSA, and pattern of renal scan curves on DTPA. All ten children in the OHN group underwent surgical intervention (Anderson-Hynes dismembered pyeloplasty) based on the criteria described previously. The indications for pyeloplasty were: 4 patients due to symptomatic obstruction, 3 patients due to impaired DRF (less than 40%), and 3 patients due to a decrease in more than 10% renal function on subsequent investigations or worsening of hydronephrosis to SFU grades III and IV on US, due to poor drainage function following diuretic administration on DTPA radionuclide renal examinations. 3.1 Multivariate analysis Multivariate analysis was performed using unsupervised (principal component analysis, PCA) and supervised methods (partial least-squares discriminant analysis, PLS-DA) for both GC- and LC-MS acquired data. For both instrumental techniques, PCA was first applied to the overall data including QC samples to reveal separation patterns, trends and outliers. QC samples were clustered at the center of the PCA ellipsoid plot (not shown) indicating that there was instrumental stability during data acquisition ensuring reliability for further data treatment. PLS-DA was then applied to group comparisons (NOHN vs CTR, OHN vs NOHN, and OHN vs CTR) to reveal the discriminant metabolites based upon the most important variables for separation (VIP, variable importance in projection). Figure 1 depicts PLS-DA score plots for both GC- and HPLC-MS acquired data and corresponding model statistics for the comparisons NOHN vs CTR, OHN vs NOHNR, and OHN vs CTR. The most relevant parameters to evaluate the PLS-DA model quality are Q 2 , which demonstrates the model's ability to predict which group the sample belongs to, and R 2 , which provides the variance explained by the model. All models, including the ANOVA of cross-validation residuals, presented acceptable values, except for the comparison NOHN vs CTR via GC-MS data (overfitted model, not shown), highlighting the importance of using more than one technique for metabolomics analysis. A permutation test was carried out using 200 permutations and no permutation Q 2 was greater than that obtained for the original model, reinforcing the validity of the original model [27]. Using the MetaboAnalyst platform, ROC curves were plotted for the molecular features identified as significant (VIP>1) by the PLS-DA model. The PLS-DA classification model was used and the method for the hierarchical position of resources (ranking feature) was the built-in PLS-DA. Figure 2 depicts ROC curves for specific PLS-DA models obtained by treating GC-MS and LC-MS data. The satisfactory AUC results shown in Figure 2 for all group comparisons demonstrate the excellent ability of these sets of metabolites to distinguish between test and control groups, validating the results of the PLS-DA models. 3.2 Univariate analysis The univariate approach uses traditional statistical methods that consider one variable at-a-time, and the advantage over the multivariate approach is the elimination of overfitting. For parametric variables, the Shapiro-Wilk normality test was applied, followed by the Bartelett test to distinguish between equal and different variances. The ANOVA p -value was used to evaluate the difference between three or more groups, to find possible biomarkers and to confirm significant effects between variables. When the effect found was significant, Tukey's post-hoc test was used to discover which groups the difference was applicable to. These tests were used to correct type I error. The Tukey test uses the adjusted p -value (also known as q -value) to determine whether differences between groups are statistically significant [28]. ROC curves were then plotted to determine the ideal cutoff point (using the Youden index) for each significant molecular feature found previously by multivariate analysis (not shown). In the multivariate approach, it was not possible to find separation patterns between NOHN vs CTR groups for the analyses performed by GC-MS. However, when conducting an investigation using the univariate approach, significant metabolites were identified. This highlights the complementarity of multivariate and univariate statistical tools. 3.3 Identification of metabolites Only molecular features that presented relative standard deviation of peak intensities smaller than 30% (for QC samples) were considered for identification. For GC-MS, the molecular features that showed high spectral similarity with the NIST MS Seach 2.0 library and/or Fiehn library (Score>80) were identified (level 1 according to Metabolomics Standards Initiative, MSI). Fiehn library also takes into account the retention time match of the derivative with the library standard. For HPLC-MS, molecular features were putatively annotated (level 2 according to MSI) searching the exact molecular mass into available databases (HMDB, Metlin, KEGG, among others). A complete set of the identified metabolites are compiled in the supplementary material (Table S1 for the comparison NOHN vs CTR, Table S2 for the comparison OHN vs NOHN, and Table S3 for the comparison OHN vs CTR, displaying both GC-MS and HPLC-MS techniques results as well as uni- and multi-variate parameters. All uni-variate and multi-variate methods applied to both GC- and HPLC-MS data exhibited good statistical performance (Q 2 >0.71, accuracy>92%, and AUC>0.92) resulting in a total of 94 discriminatory metabolites, suggesting strong potential clinical value as a diagnostic/prognostic signature. A more concise display containing only the most relevant metabolites in terms of statistical relevance (when they appear by both uni- and multi-variate analyses), fold change (percentage increase or decrease of a given metabolite in one studied test group related to the control or reference group), and associated metabolic routes are depicted in Table 2 for the same group comparisons. Worth mentioning cystine and methionine sulfoxide exhibiting the largest positive fold change score, 156% and 152%, respectively (both from OHN vs CTR group comparison). Moreover, from the total of 94 discriminatory metabolites, 17 metabolites had particular statistical relevance since they were confirmed by both uni-variate and multi-variate methods jointly. Three metabolites, 1-methylhistidine-3-methylhistidine, 1-methyhistamine-3mthylhistamine, and glutamyl-hydroxyproline (dipeptide), discriminate NOHN vs CTR groups whereas nine metabolites, mandelic acid, pantothenic acid, furoylglicine, gluconic acid, 3-hydroxyphenylacetate, 4-hydroxyphenylacetate, glutamylhydroxyproline, 3-hydroxy-3-methylglutaric acid, ribitol/arabitol/xylitol (pentose alcohols), and tagalose discriminate OHN vs CTR groups; those metabolites may serve as diagnostics purposes, being the former group comparison indicative of the initial metabolic perturbation UPJO imparted and the latter group comparison indicative of the impact UPJO had exerted on the children metabolism at long term. More importantly, five metabolites, ascorbic acid, furoylglycine, gluconic acid, and ribose/arabinose/xylose (pentoses), already discriminating OHN vs CTR groups, appear again in the OHN vs NOHN group comparison, in addition to threitol; those metabolites might be useful to support clinical decisions whether a patient should undergo surgery. 3.4 Biological interpretation In this work, several potential biomarkers that could provide information about diagnosis and prediction of renal outcome were identified. However, these potential biomarkers must have some biological plausibility. The key to understanding the pathophysiological situation is the analysis of the metabolic pathways. Again, statistical comparisons between OHN vs CTR groups and NOHN vs CTR groups will provide discriminant metabolites that may serve for diagnostics purposes. This is not so important once UPJO can be assertively diagnosed by other means. Nevertheless, the metabolites from OHN vs CTR group comparison will be indicative of the impact UPJO exerted on the children metabolism in a long term. The statistical comparison between OHN vs NOHN groups is on the other hand quite relevant because it will provide discriminant metabolites that might be useful to support the decision whether a patient should undergo surgery. Moreover, if a single metabolite or a set of metabolites appears throughout both NOHN vs CTR and OHN vs NOHN group comparisons these metabolites become relevant because they might be at the same time indicative of the disease implantation as well as the disease progression. With these assumptions in mind, discriminatory metabolites between all group comparisons were examined. The Metabolomic Pathway Analysis (MetPA) within the MetaboAnalyst platform was used to perform topographic analysis of the metabolites identified in each group comparison (Figure 3). Fisher's test enrichment method was chosen, the statistical significance test in the analysis of contingency tables in the topological analysis was out-degree centrality and the library of homo sapiens metabolic pathways from KEGG database was selected. It is worth mentioning that all metabolites derived from both GC- and HPLC-MS analysis as well as both uni- and multi-variate statistical approaches were considered altogether for the impact calculation that generated Figure 3. From inspection of Figure 3, it is possible to visualize the most important metabolic routes associated with all group comparisons. In the NOHN vs CTR comparison, only the metabolic pathway of beta-alanine shows a significant statistical probability. That might be explained by the fact that some obstructions are asymptomatic, not leading to renal function loss or significant health impairment. Nevertheless, although mildly, the metabolism has already been altered at this stage, which might be viewed as the disease implantation effect. The comparison of OHN vs NOHN groups reveals several important affected metabolic routes: 1- tyrosine, phenylalanine, 2- alanine, aspartate, glutamate, and 3- amino sugar and nucleotide sugar metabolisms, as it denotes the disease progression. The comparison OHN vs CTR groups shows the overall impact of UPJO in the metabolism, with the disease at its most severe stage, presenting the largest number of altered metabolic routes. Although the beta-alanine metabolic pathway was the only one significantly altered in the MetPA of NOHN vs CTR group comparison, it has also appeared in the MetPA of OHN vs NOHN group comparison (Fig. 3B&3A). This result is interesting, as it suggests that increased oxidative stress may be related to disease progression. Studies indicate that carnosine, derived from beta-alanine, plays a crucial role in protecting against oxidative stress, a factor relevant to various pathologies, including chronic kidney diseases [29]. As observed in the metabolite compilation of Table 2, a significant fold-change decrease in beta-alanine was found (36%) for the OHN vs NOHN comparison, indicating that this metabolite has been accumulated in the urine of the NOHN group. Pantothenic acid has also been found to be decreased in the comparison NOHN vs CTR (-49%) and OHN vs CTR (-59%). This metabolite is an essential precursor for the synthesis of coenzyme A (CoA) and may protect cell membranes from oxidative stress. Therefore, considering that beta-alanine pathway is altered in both MetPA of NOHN vs CTR and OHN vs NOHN group comparisons, it can be inferred that these alterations are associated with increased oxidative stress, which seems to intensify with disease progression. Moreover, beta-alanine derivatives exert a direct molecular protective action on podocytes, an essential part of the glomerular filtration membrane [30]. The metabolomic profiling conducted in this study revealed further distinct expression patterns between OHN and NOHN groups (Table 2). Analysis of the urine samples suggested specific discriminators for OHN; these include 4-hydroxyphenylacetic acid, furoglycin, gluconic acid, N -acetyl-L-aspartic acid, and threitol. All these urinary metabolomics-based analytes had an AUC above 0.80 (Table S2), suggesting strong potential clinical value as a disease progression signature. Other metabolites also present satisfactory results should be included in the discussion to allow a more thorough evaluation of the disease progression. For instance, 4-hydroxybenzoic acid was considered statistically relevant from both uni-variate (AUC 0.72) and multi-variate (AUC 0.923) analyses, which reinforces the importance of this metabolite in separating OHN from NOHN groups. One of the products of the phenylalanine metabolic pathway is tyrosine. Tyrosine and phenylalanine pathways are therefore intrinsically linked to each other. In GC-MS data analysis, 4-hydroxyphenylacetic acid was identified as the major metabolite of the tyrosine pathway and 3-(3-hydroxyphenyl) propanoic acid, 4-methoxyphenylacetic acid, and 4-hydroxyphenylacetic acid as key metabolites of the phenylalanine pathway. As a matter of fact, 4-hydroxyphenylacetic acid is known to be essential for the proper functioning of the tyrosine and phenylalanine metabolic pathways. This metabolite showed the greatest expression variation in different group comparisons (increased fold change of 112% for OHN vs NOHN and 84% for OHN vs CTR). Its reliable identification in the database (net score of 96, Tables S2 and S3) confirms that 4-hydroxyphenylacetic acid is, in fact, a discriminatory metabolite between both NOHN vs CTR and OHN vs NOHN groups. Disturbances in the metabolism of the amino acids tyrosine and phenylalanine may be related to kidney diseases [31]. Furthermore, in cases of chronic renal failure, there is a change in the conversion of phenylalanine into tyrosine and an increase in oxidative stress with harmful and toxic metabolic effects [32]. The metabolite 4-hydroxyphenylacetic acid is increased in patients with decline in renal function [33], a glomerular disease characterized by an excessive amount of protein in the urine (proteinuria) and damage to podocytes, cells that line the urinary surface of the glomerular capillary tuft, which constitute the barrier of glomerular filtration, ensuring its selective permeability. The increased production of this antioxidant metabolite suggests a defense mechanism against increased oxidative stress. Although this metabolite has been described as a marker of glomerular pathology, our results suggest that it may also play a discriminatory role in tubular pathologies such as obstructive uropathy. It has been demonstrated that elevation of 4-hydroxyphenylacetic acid during sepsis could inhibit necroptosis of renal proximal tubule epithelial cells and exert a nephroprotective effect [34]. In the alanine, aspartate and glutamate pathway, N -acetyl- L -aspartic acid was the most statistically significant metabolite with increased expression in the OHN group. This metabolite is associated with oxidative stress and inflammatory conditions in rats with chronic kidney disease and renal failure [35]. It is also a possible serum biomarker of diabetic nephropathy [36]. In the amino sugar and nucleotide sugar pathway, the metabolites that stand out are alpha-glucosamine 1-phosphate (increased in OHN group vs NOHN) and N -acetyl-D-mannosamine (decreased in OHN group vs NOHN). N -acetyl-D-mannosamine is the precursor molecule of sialic acids. Sialic acids are present in the glomerular filtration barrier, functional units of the kidneys, where blood filtration and elimination of metabolic waste occurs. The decrease in these acids is related to membranous nephropathy, minimal lesion disease and the loss of nephrin, a structural protein in podocytes. The synthesis of sialic acid from N -acetylneuraminic acid is regulated by the enzyme GNE. The decrease in N -acetyl-D-mannosamine precursors may be directly related to the efficiency of the GNE enzyme. Mice with proteinuria and podocytopathy had a good response to treatment when N -acetyl-D-mannosamine supplementation was administered [37,38]. All the above discussed metabolic pathways for OHN vs NOHN group comparison (tyrosine, phenylalanine, alanine, and amino sugar and nucleotide sugar metabolism), with exception of the alanine, asparate and glutamate pathways, were also seen in the impact pathway graph for OHN vs CTR group comparison. The appearance of the same metabolic routes is particularly relevant as it consolidates the hypothesis that these metabolic alterations are indicative of disease progression. Other metabolites such as alpha ketoglutaric acid, furoylglycine, 4-hydroxyphenylacetic acid, and ascorbic acid were also found altered in the comparisons between OHN vs NOHN groups and OHN vs CTR (Table 2). Furthermore, changes in their concentration (increased or decreased) coincide in both comparisons, again in consonance with the disease progression. Reports of unilateral uretheral obstruction in rats as well as patients with acute renal injury indicated an expressive decreased level in urine of ketoglutaric acid and citric acid, intermediary metabolites of Krebs cycle. This decrease has been associated to a possible compromised mitochondrial oxidative metabolism, suggesting lesion of the proximal tubule. Other studies also confirm this observation [39]. 4 Conclusion There have been advances in the evaluation of the use of urinary biomarkers in UPJO, but none of the discriminant metabolites have yet managed to be part of tests requested in clinical practice. Our findings suggest that analysis of urinary metabolites using a metabolomics strategy may lead to promising obstructive HN biomarkers that may contribute to optimize clinical decision-making. Declarations Author Contribution Information All authors contributed to the study conception and design. Material preparation, urine collection, data acquisition, statistical analysis, and biological interpretation as well as first draft elaboration were performed by LHFS and MFM. LFY, MJK, MALO, and ATF contributed with instrumental data acquisition. JPSF provided support on statistical analyses. FGHK, HFAF, FTD, and LA participated of clinical discussions and biological interpretation of the results. RIL and MFMT supervised all steps. All authors read and approved the final manuscript. Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Acknowledgements The authors wish to acknowledge the Sao Paulo Research Foundation (FAPESP 2017/08224-6; 2017/27059-6; 2023/7993-7) and National Council for Scientific and Technological Development (CNPq 133021/2018-1; 304983/2022-5) for fellowships and financial support. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. 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Biochem Medica . 2012;21(3):203-209. https://doi.org/10.11613/BM.2011.029 Calabrese V, Scuto M, Salinaro AT, et al. Hydrogen Sulfide and Carnosine: Modulation of Oxidative Stress and Inflammation in Kidney and Brain Axis. Antioxidants (Basel). 2007;18;9(12):1303. doi: 10.3390/antiox9121303 Scuto, M., Trovato SA, Modafferi, S., Carnosine activates cellular stress response in podocytes and reduces glycative and lipoperoxidative stress. Biomedicines, 8(6), 177. https://doi.org/10.3390/biomedicines80601777 Kopple JD. Phenylalanine and Tyrosine Metabolism in Chronic Kidney Failure. J Nutr . 2007;137(6):1586S-1590S. https://doi.org/10.1093/jn/137.6.1586S Chakrapani A, Holme E. Indorn Metabolic Diseases: Disorders of Tyrosine Metabolism. 1th ed. Berlin: Springer; 2006 Stec DF, Wang Suwan, Stothers Cody, et al. Alterations of urinary metabolite profile in model diabetic nephropathy. Biochem Biophys Res Commun . 2015;456(2):610-614. Sheng A, Yi Y, Junjie W, et al. Gut-derived 4-hydroxyphenylacetic acid attenuates sepsis-induced acute kidney injury by upregulating ARC to inhibit necroptosis. BBAdis 2024;1870(1), 166867. doi.org/10.1016/j.bbadis.2023.166876 Chen DQ, Chen H, Chen L, et al. The link between phenotype and fatty acid metabolism in advanced chronic kidney disease. Nephrol Dial Transplant . 2017;32(7):1154-1166. https://doi.org/10.1093/ndt/gfw415 Hirayama A, Nakashima E, Sugimoto M, et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem . 2012;404(10):3101-3109. https://doi.org/10.1007/s00216-012-6412-x Huizing M, Yardeni T, Fuentes F, et al. Rationale and Design for a Phase 1 Study of N-Acetylmannosamine for Primary Glomerular Diseases. Kidney Int Rep . 2019;4(10):1454-1462. https://doi.org/10.1016/j.ekir.2019.06.012 Ito M, Sugihara K, Asaka T, et al. Glycoprotein Hyposialylation Gives Rise to a Nephrotic-Like Syndrome That Is Prevented by Sialic Acid Administration in GNE V572L Point-Mutant Mice. PLoS One . 2012;7(1):e29873. https://doi.org/10.1371/journal.pone.0029873. Maclellan D, Mataija D, Doucette A, et al. Alterations in urinary metabolites due to unilateral ureteral obstruction in a rodent model. Mol BioSyst. 2011;7: 2181 – 2188. https://doi.org/10.1039/C1MB05080J Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIALMar122026.docx JUPTable1.docx JUPTable2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 14 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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12:48:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":548496,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-DA score plots for GC-MS and HPLC-MS acquired data.\u003c/p\u003e\n\u003cp\u003eLegend: A) GC-MS: a) OHN vs NOHN, R\u003csup\u003e2\u003c/sup\u003e=0.996, Q\u003csup\u003e2\u003c/sup\u003e=0.759, p-value=0.03, F=3 and b) OHN vs CTR, R\u003csup\u003e2\u003c/sup\u003e=0.91, Q\u003csup\u003e2\u003c/sup\u003e=0.743, p-value=0.0001, F=11.4; B) HPLC-MS: c) NOHN vs CTR, R\u003csup\u003e2\u003c/sup\u003e=0.988, Q\u003csup\u003e2\u003c/sup\u003e=0.849, p-value=0.002, F=4.9, d) OHN vs NOHN, R\u003csup\u003e2\u003c/sup\u003e=0.985, Q\u003csup\u003e2\u003c/sup\u003e=0.714, p-value=0.01, F=4.43, and e) OHN vs CTR, R\u003csup\u003e2\u003c/sup\u003e=0.984, Q\u003csup\u003e2\u003c/sup\u003e= 0.772, p-value=0.006, F=4.5.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/bed394f6cdab6d98d4669d7f.png"},{"id":105259440,"identity":"daaddf3b-bea9-41fd-a285-ffb29a368c8e","added_by":"auto","created_at":"2026-03-24 05:50:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":631906,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves validating PLS-DA models obtained by group comparison using GC-MS and HPLC-MS acquired data.\u003c/p\u003e\n\u003cp\u003eLegend: A) GC-MS: a) OHN vs NOHN, 18 metabolites, 98.3% accuracy, AUC=0.983 and b) OHN vs CTR, 26 metabolites, 92.3% accuracy, AUC=0.923; B) HPLC-MS: c) NOHN vs CTR, 11 metabolites, 99.5% accuracy, AUC= 0.995, d) OHN vs NOHN, 9 metabolites, 95.3% accuracy, AUC = 0.953, and e) OHN vs CTR, 30 metabolites, 96.6% accuracy, AUC=0.966.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/7c5beb1ccfd171604a2152d1.png"},{"id":105564585,"identity":"ef5787d6-ca18-4100-bb58-799943e484de","added_by":"auto","created_at":"2026-03-27 12:50:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280403,"visible":true,"origin":"","legend":"\u003cp\u003ePathway impact maps for the comparisons between OHN \u003cem\u003evs\u003c/em\u003eNOHN groups (A), NOHN \u003cem\u003evs\u003c/em\u003e CTR groups (B), and OHN \u003cem\u003evs\u003c/em\u003e CTR groups (C) in UPJO.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/58533052700dc234c602ad93.png"},{"id":105569430,"identity":"e1a6e48f-87a2-4b96-a4ad-442402481371","added_by":"auto","created_at":"2026-03-27 13:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1956106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/3dfc6fa3-43c8-49e9-b523-409405f1980d.pdf"},{"id":105259436,"identity":"f2ed5a48-65a1-4644-b3c3-99f867fe1021","added_by":"auto","created_at":"2026-03-24 05:50:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":73084,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALMar122026.docx","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/61e3356b7fde8ab0974b5ce8.docx"},{"id":105259434,"identity":"a8f4364d-58bc-4090-8c10-82f4453c21da","added_by":"auto","created_at":"2026-03-24 05:50:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17996,"visible":true,"origin":"","legend":"","description":"","filename":"JUPTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/fe66a4bec8a43611a3e64e73.docx"},{"id":105259439,"identity":"3738386d-2be4-46db-ad85-18ffaef55940","added_by":"auto","created_at":"2026-03-24 05:50:59","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":27080,"visible":true,"origin":"","legend":"","description":"","filename":"JUPTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9107811/v1/e5624091d9eb4aec51829304.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urinary metabolomics profile in children with ureteropelvic junction obstruction","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFetal hydronephrosis is detected in about 0.25\u0026ndash;1% of fetuses by antenatal ultrasonographic screening [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], where ureteropelvic junction obstruction (UPJO) is the most commonly found congenital urinary tract anomaly, with a prevalence of 1 in 1500 live births, remaining the leading cause of kidney failure in infants and children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Since it is a spectral disease, UPJO represents a particularly challenging management and approximately 20% of children with this anomaly will require surgical intervention [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In this scenario, not all hydronephrosis (HN) represents a harmful state for the kidneys. Severe UPJO must be treated surgically to avoid impairment of renal function, but children with non-obstructive hydronephrosis (NOHN) can be treated conservatively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. There has been controversy regarding the indication for surgical intervention in asymptomatic patients. Some authors have proposed initial non-operative management along with intensive imaging protocols. Surgical intervention is indicated primarily on decreased ipsilateral differential renal function or increased drainage interval on nuclear scans [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiagnostic methods currently used as reference standards to detect relevant UPJO are unsatisfactory and there is a great demand for non-invasive and reliable tests to predict which patient requires surgical intervention at early stages. To achieve this goal, researchers have evaluated the morphological changes associated with urinary obstruction, including tubular dilation and atrophy, thickening of the basement membrane, and interstitial fibrosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A few prospective disease-related biomolecules, such as retinol-binding protein-4 (RBP-4), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), transforming growth factor beta-1 (TGF- β1), monocyte chemoattractant protein-1 (MCP-1), endothelin-1 (ET-1), as well as epidermal growth factor (EGF) have all been screened as potential urinary biomarkers for obstructive hydronephrosis (OHN) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, due to modest results and limited availability, none of them have been incorporated into clinical practice.\u003c/p\u003e \u003cp\u003eMetabolomics is a methodological approach that investigates in a comparative manner the entire set of low molecular-mass metabolites expressed by an individual or organism (metabolome) in pre-selected conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Both formats, untargeted (hypothesis generating) and targeted (hypothesis driven) metabolomics have the potential to function as a diagnostic/prognostic tool that can support clinical decisions regarding patient treatment. Metabolomics has been applied to several areas of medicine, urology in special, addressing several aspects of renal diseases, including the differentiation of end-stage renal disease from chronic kidney disease CKD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the prognosis of acute kidney injury (AKI) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and the inspection of patients with normoalbuminuric diabetic kidney disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], to cite a few. Pediatric renal diseases under the scrutiny of metabolomics have been revised recently, including AKI, kidney transplantation, CKD, renal dysplasia, vesicoureteral reflux, and lithiasis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe urinary proteome signature of UPJO was first investigated by Mishak group in 2006, using capillary electrophoresis coupled to mass spectrometry (CE-MS) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Since this pioneer work, a few transcriptomics and proteomics studies followed [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Specifically concerning metabolomics, there are studies involving targeted and untargeted analysis using either nuclear magnetic resonance spectroscopy (NMR) or separation hyphenated-MS techniques. A panel of 15 metabolites, including creatinine, tryptophan, choline, and aspartate have been monitored by targeted metabolomics with hydrophilic-interaction liquid- chromatography coupled to mass spectrometry (HILIC-MS) in the serum of neonates and infants as an attempt to differentiating patients that require surgery from those following systematic monitoring, revealing significant metabolite perturbations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In an untargeted metabolomics study, NMR spectra were acquired from urine of newborns with prenatally diagnosed unilateral renal pelvis dilatation and healthy controls to identify specific urinary biomarkers for UPJO [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Two main metabolic pathways were found compromised in this study, amino acid and betaine metabolisms.\u003c/p\u003e \u003cp\u003eThe present work aimed at investigating the urinary profile of children with UPJO (including asymptomatic patients) using an untargeted metabolomics approach supported by GC-MS and HPLC-MS (reversed-phase mode) data to prospect metabolites for discriminating OHN from NOHN patients and both from healthy individuals. To our knowledge, no categorical untargeted metabolomics study, using multiplatform analytical methods to enhance the metabolome coverage, has been applied to UPJO so far. Moreover, the discrimination at the molecular level of asymptomatic patients from those requiring surgical intervention is of upmost relevance and it has been investigated here as well.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cohort \u0026amp; metabolomics grouping\u003c/h2\u003e \u003cp\u003e A prospective observational cohort study was performed at the Hospital das Cl\u0026iacute;nicas da Faculdade de Medicina da Universidade de S\u0026atilde;o Paulo (Brazil) and approved by the local ethics committee (protocol 62235816.8.0000.0068). This study was carried out between September 2016 and June 2019 and comprised 37 children who had unilateral NH due to UPJO and controls, categorized by age in groups from 2 to 12 years old, according to the Food and Drug administration criteria [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All children\u0026rsquo;s caregivers were interviewed and signed out an informed consent form attesting to the children participation in the study.\u003c/p\u003e \u003cp\u003ePatients were initially evaluated (according to our examination protocol) and then followed-up for at least 6 months at regular clinical visits, when renal ultrasonography (US) and radionuclide examinations were registered and urine samples collected. Age, age at diagnosis, gender, and renal side were recorded, and at each reassessment, blood pressure and general physical conditions recorded as well.\u003c/p\u003e \u003cp\u003eThe diagnosis of UPJO was initially suggested by renal ultrasound, with measurement of the anteroposterior pelvic diameter (APD), and the degree of hydronephrosis was graded according to the Society for Fetal Urology (SFU) classification on renal US [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To confirm the diagnosis, radioisotope renal scans were performed, and the standards used were static renogram (DMSA radionuclide renal scans) and diuretic renography (DTPA radionuclide renal scans). DMSA was interpreted as differential renal function (DRF), where a dilation of the affected kidney less than 40% was considered abnormal. DTPA curves were classified according to Lee [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe indication for surgical intervention (pyeloplasty) was based on available guidelines [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Symptomatic obstruction (low back pain, urinary tract infection, urinary stones, or hematuria), impaired DRF (less than 40%), more than 10% decrease in renal function on subsequent investigations, drainage function deficiency after diuretic administration, increased DPA or worsening of hydronephrosis to SFU grades III and IV on US constitute the current proposed indications for surgical intervention [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExclusion criteria included associated anomalies, such as vesicoureteral reflux, obstruction of the ureterovesical junction and obstruction of the posterior urethral valves, bilateral NH, previous operation of the urinary system and other deformations of the external genital organs, deformations in the lower part of the ureter, bladder and urethra, urinary stones, urinary tract infection and neurogenic bladder dysfunction.\u003c/p\u003e \u003cp\u003eFor the metabolomics study, patients were divided into three groups based on clinical and imaging findings, including APD, DRF, and drainage curve on radioisotope renal scintigraphy. The groups were classified as: a) OHN group: children with unilateral OHN due to UPJO who underwent dismembered pyeloplasty using the Anderson-Hynes method; b) NOHN group: children considered for nonoperative treatment of unilateral UPJO (clinically asymptomatic and stable according to imaging studies during follow-up); and c) Control Group (CTR): healthy children, paired by age and sex with no underlying pathologies. The OHN group was composed of 10 children (4 boys, 6 girls; median age 4.9; 2\u0026ndash;10 years old). The NOHN group included 15 children (9 boys, 6 girls; median age 6.9; 2\u0026ndash;10 years old). The CTR consisted of 12 children without any urinary tract problems (8 boys, 4 girls; median age 6.3; 3\u0026ndash;10 years old).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection and analysis\u003c/h2\u003e \u003cp\u003eUrine samples were collected according to the patient age and ability to urinate spontaneously, with preference for midstream samples (when possible). Children who were not toilet trained had their urine collected using a collection bag, and some patients, who had difficulty collecting in the pre-operative period, had their urine collected after anesthetic induction through bladder catheterization. Urinary culture was mandatory to exclude active urinary infection. In the OHN group, urine samples were taken before surgical repair of the UPJO. In the NOHN group, voided urine samples were collected at the time of diagnosis. For the control group, urine samples were also collected only once. Urine was collected aseptically, mixed, and centrifuged [3,000 rpm, 10 min]; the uppernatant was stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003cp\u003eFor GC-MS analysis, urine samples were thawed and vortexed. A 100 \u0026micro;L aliquot was taken and transferred to an Eppendorf tube. Samples were treated with urease solution (10 mg/mL final concentration) and the tube was placed in a thermostatic bath at 37\u0026deg;C for 1 h. After the treatment with urease, cold isopropanol (10\u0026deg;C) in a 1:6 urine:isopropanol proportion was added for protein precipitation. Samples were again vortexed and placed in a freezer (-20\u0026deg;C) for 30 min. Samples were then centrifuged at 12,000 rpm for 10 min at 4\u0026deg;C. Exactly 200 \u0026micro;L supernatant were removed and transferred to 300 \u0026micro;L inserts and evaporated in an Eppendorf concentrator plus (Hamburg, Germany) in two cycles of 20 min each at 35 \u003csup\u003eo\u003c/sup\u003eC.\u003c/p\u003e \u003cp\u003eFurther sample treatment steps include derivatization reactions. For the methoxymation reaction, 20 \u0026micro;L methoxyamine hydrochloride in pyridine (15 mg/mL) were added to each insert containing the urease-treated urine (dried sample). The inserts were placed in 2 mL vials, vortexed and submitted to ultrasound for 10 s. The vials were covered with aluminum foil and kept for 16 h at room temperature and protected from light. For the silylation reaction, after this 16 h period, 20 \u0026micro;L BSTFA (N,O-bis-(trimethylsilyl)trifluoroacetamide) with 1% TMCS (chlorotrimethylsilane) were added to each sample; samples were again homogenized and placed in a bath at 70\u0026deg;C for 1 h. Samples were then diluted in 100 \u0026micro;L heptane and analyzed by GC-MS.\u003c/p\u003e \u003cp\u003eGC-MS analyses were conducted in a gas chromatograph coupled to a quadrupole mass spectrometer (Shimadzu GC-2010 Plus, Barueri, Brazil). Exactly 1 \u0026micro;L of the derivatized sample was injected into a HP5-MS column (30 m x 0.25 mm, 0.25 \u0026micro;m film) from Agilent Technologies (Santa Clara, U.S.A.). The carrier gas was helium at a flow rate of 1 mL/min, and the injector temperature was set at 250 \u0026ordm;C. The oven was kept initially at 60 \u003csup\u003eo\u003c/sup\u003eC for 1 min and raised to 300 \u003csup\u003eo\u003c/sup\u003eC at a rate of 10 \u003csup\u003eo\u003c/sup\u003eC/min. Total analysis time was 25 min.\u003c/p\u003e \u003cp\u003eFor the HPLC-MS analysis, 100 \u0026micro;L sample aliquots were diluted in 300 \u0026micro;L methanol containing \u003cem\u003ep\u003c/em\u003e-fluorophenylalanine (stock at 66.7 \u0026micro;mol/L; 50.0 \u0026micro;mol/L final concentration in urine). Samples were then stored at -20\u0026deg;C for 30 min. After this period, samples were centrifuged at 1200 rpm for 10 min at 4\u0026deg;C and the uppernatants separated and injected (0.50 \u0026micro;L) into the equipment (Agilent 1260 Infinity II chromatograph coupled to Agilent 6530 mass spectrometer with a quadrupole time-of-flight mass analyzer, Q-TOF). A Phenomenex Kinetex\u0026reg; PFP column (150 x 2.1 mm, 2.6 \u0026micro;m film) column maintained at 40 \u0026ordm;C was used. Mobile phase was comprised of solvent A (0.10% v/v formic acid in water) and solvent B (0.1% v/v formic acid in methanol), eluted in gradient: 0\u0026ndash;1 min (0% B), 1\u0026ndash;2.5 min (0\u0026ndash;25% B), 2.5\u0026ndash;3 min (25\u0026ndash;90% B), 3\u0026ndash;5 min (90\u0026ndash;100% B), 5\u0026ndash;8 min (100% B), 8\u0026ndash;8.1 min (100\u0026ndash;0% B), 8.1\u0026ndash;17.5 min (0% B).\u003c/p\u003e \u003cp\u003eA quality control (QC) sample was prepared for each technique. For this purpose, 15 \u0026micro;L of all urine samples under consideration in this study were mixed together, separated into two portions and processed exactly by the same procedures individual urine samples did. Three samples containing only water were subjected to urease treatment and subsequently to derivatization steps and analyzed as a prepared blank solution in GC-MS. QC samples were injected sequentially, one QC injection after every five regular samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using IBM SPSS Statistics for Windows, version 17.0 (SPSS, Chicago, IL, U.S.A.), SIMCA P\u0026thinsp;+\u0026thinsp;12.0.1 and the free access software packages, XCMS in the R platform (R Project for Statistical Computing, v.4.4.2) and MetaboAnalyst 6.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cp\u003eDemographic characteristics and clinical data of the patient and control groups under investigation here are presented in Table 1. All patients had a normal estimated glomerular filtration rate (eGFR) \u0026gt;90 ml/min/1:73m\u003csup\u003e2\u003c/sup\u003e calculated using the Schwartz formula [26]. In the OHN group, 4 patients were symptomatic: 2 had low back pain, 1 with febrile UTI, and 1 with non-febrile UTI; in the NOHN group, 2 patients were symptomatic, all had non-febrile UTI that resolved on follow-ups. There was a statistically significant difference between the groups in relation to symptoms, degree of hydronephrosis on the SFU classification scale in the US, DRF on DMSA, and pattern of renal scan curves on DTPA.\u003c/p\u003e\n\u003cp\u003eAll ten children in the OHN group underwent surgical intervention (Anderson-Hynes dismembered pyeloplasty) based on the criteria described previously. The indications for pyeloplasty were: 4 patients due to symptomatic obstruction, 3 patients due to impaired DRF (less than 40%), and 3 patients due to a decrease in more than 10% renal function on subsequent investigations or worsening of hydronephrosis to SFU grades III and IV on US, due to poor drainage function following diuretic administration on DTPA radionuclide renal examinations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Multivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate analysis was performed using unsupervised (principal component analysis, PCA) and supervised methods (partial least-squares discriminant analysis, PLS-DA) for both GC- and LC-MS acquired data. For both instrumental techniques, PCA was first applied to the overall data including QC samples to reveal separation patterns, trends and outliers. QC samples were clustered at the center of the PCA ellipsoid plot (not shown) indicating that there was instrumental stability during data acquisition ensuring reliability for further data treatment. PLS-DA was then applied to group comparisons (NOHN vs CTR, OHN vs NOHN, and OHN vs CTR) to reveal the discriminant metabolites based upon the most important variables for separation (VIP, variable importance in projection).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 depicts PLS-DA score plots for both GC- and HPLC-MS acquired data and corresponding model statistics for the comparisons NOHN vs CTR, OHN vs NOHNR, and OHN vs CTR. The most relevant parameters to evaluate the PLS-DA model quality are Q\u003csup\u003e2\u003c/sup\u003e, which demonstrates the model\u0026apos;s ability to predict which group the sample belongs to, and R\u003csup\u003e2\u003c/sup\u003e, which provides the variance explained by the model. All models, including the ANOVA of cross-validation residuals, presented acceptable values, except for the comparison NOHN vs CTR via GC-MS data (overfitted model, not shown), highlighting the importance of using more than one technique for metabolomics analysis. A permutation test was carried out using 200 permutations and no permutation Q\u003csup\u003e2\u003c/sup\u003e was greater than that obtained for the original model, reinforcing the validity of the original model [27].\u003c/p\u003e\n\u003cp\u003eUsing the MetaboAnalyst platform, ROC curves were plotted for the molecular features identified as significant (VIP\u0026gt;1) by the PLS-DA model. The PLS-DA classification model was used and the method for the hierarchical position of resources (ranking feature) was the built-in PLS-DA. Figure 2 depicts ROC curves for specific PLS-DA models obtained by treating GC-MS and LC-MS data.\u003c/p\u003e\n\u003cp\u003eThe satisfactory AUC results shown in Figure 2 for all group comparisons demonstrate the excellent ability of these sets of metabolites to distinguish between test and control groups, validating the results of the PLS-DA models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Univariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate approach uses traditional statistical methods that consider one variable at-a-time, and the advantage over the multivariate approach is the elimination of overfitting. For parametric variables, the Shapiro-Wilk normality test was applied, followed by the Bartelett test to distinguish between equal and different variances. The ANOVA \u003cem\u003ep\u003c/em\u003e-value was used to evaluate the difference between three or more groups, to find possible biomarkers and to confirm significant effects between variables. When the effect found was significant, Tukey\u0026apos;s post-hoc test was used to discover which groups the difference was applicable to. These tests were used to correct type I error. The Tukey test uses the adjusted \u003cem\u003ep\u003c/em\u003e-value (also known as \u003cem\u003eq\u003c/em\u003e-value) to determine whether differences between groups are statistically significant [28]. ROC curves were then plotted to determine the ideal cutoff point (using the Youden index) for each significant molecular feature found previously by multivariate analysis (not shown). In the multivariate approach, it was not possible to find separation patterns between NOHN vs CTR groups for the analyses performed by GC-MS. However, when conducting an investigation using the univariate approach, significant metabolites were identified. This highlights the complementarity of multivariate and univariate statistical tools.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Identification of metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly molecular features that presented relative standard deviation of peak intensities smaller than 30% (for QC samples) were considered for identification. For GC-MS, the molecular features that showed high spectral similarity with the NIST MS Seach 2.0 library and/or Fiehn library (Score\u0026gt;80) were identified (level 1 according to Metabolomics Standards Initiative, MSI). Fiehn library also takes into account the retention time match of the derivative with the library standard. For HPLC-MS, molecular features were putatively annotated (level 2 according to MSI) searching the exact molecular mass into available databases (HMDB, Metlin, KEGG, among others). A complete set of the identified metabolites are compiled in the supplementary material (Table S1 for the comparison NOHN vs CTR, Table S2 for the comparison OHN vs NOHN, and Table S3 for the comparison OHN vs CTR, displaying both GC-MS and HPLC-MS techniques results as well as uni- and multi-variate parameters. All uni-variate and multi-variate methods applied to both GC- and HPLC-MS data exhibited good statistical performance (Q\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.71, accuracy\u0026gt;92%, and AUC\u0026gt;0.92) resulting in a total of 94 discriminatory metabolites, suggesting strong potential clinical value as a diagnostic/prognostic signature. A more concise display containing only the most relevant metabolites in terms of statistical relevance (when they appear by both uni- and multi-variate analyses), fold change (percentage increase or decrease of a given metabolite in one studied test group related to the control or reference group), and associated metabolic routes are depicted in Table 2 for the same group comparisons. Worth mentioning cystine and methionine sulfoxide exhibiting the largest positive fold change score, 156% and 152%, respectively (both from OHN vs CTR group comparison). Moreover, from the total of 94 discriminatory metabolites, 17 metabolites had particular statistical relevance since they were confirmed by both uni-variate and multi-variate methods jointly. Three metabolites, 1-methylhistidine-3-methylhistidine, 1-methyhistamine-3mthylhistamine, and glutamyl-hydroxyproline (dipeptide), discriminate NOHN vs CTR groups whereas nine metabolites, mandelic acid, pantothenic acid, furoylglicine, gluconic acid, 3-hydroxyphenylacetate, 4-hydroxyphenylacetate, glutamylhydroxyproline, 3-hydroxy-3-methylglutaric acid, ribitol/arabitol/xylitol (pentose alcohols), and tagalose discriminate OHN vs CTR groups; those metabolites may serve as diagnostics purposes, being the former group comparison indicative of the initial metabolic perturbation UPJO imparted and the latter group comparison indicative of the impact UPJO had exerted on the children metabolism at long term. More importantly, five metabolites, ascorbic acid, furoylglycine, gluconic acid, and ribose/arabinose/xylose (pentoses), already discriminating OHN vs CTR groups, appear again in the OHN vs NOHN group comparison, in addition to threitol; those metabolites might be useful to support clinical decisions whether a patient should undergo surgery. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Biological interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this work, several potential biomarkers that could provide information about diagnosis and prediction of renal outcome were identified. However, these potential biomarkers must have some biological plausibility. The key to understanding the pathophysiological situation is the analysis of the metabolic pathways.\u003c/p\u003e\n\u003cp\u003eAgain, statistical comparisons between OHN vs CTR groups and NOHN vs CTR groups will provide discriminant metabolites that may serve for diagnostics purposes. This is not so important once UPJO can be assertively diagnosed by other means. Nevertheless, the metabolites from OHN vs CTR group comparison will be indicative of the impact UPJO exerted on the children metabolism in a long term. The statistical comparison between OHN vs NOHN groups is on the other hand quite relevant because it will provide discriminant metabolites that might be useful to support the decision whether a patient should undergo surgery. Moreover, if a single metabolite or a set of metabolites appears throughout both NOHN vs CTR and OHN vs NOHN group comparisons these metabolites become relevant because they might be at the same time indicative of the disease implantation as well as the disease progression. With these assumptions in mind, discriminatory metabolites between all group comparisons were examined.\u003c/p\u003e\n\u003cp\u003eThe Metabolomic Pathway Analysis (MetPA) within the MetaboAnalyst platform was used to perform topographic analysis of the metabolites identified in each group comparison (Figure 3). Fisher\u0026apos;s test enrichment method was chosen, the statistical significance test in the analysis of contingency tables in the topological analysis was out-degree centrality and the library of homo sapiens metabolic pathways from KEGG database was selected. It is worth mentioning that all metabolites derived from both GC- and HPLC-MS analysis as well as both uni- and multi-variate statistical approaches were considered altogether for the impact calculation that generated Figure 3.\u003c/p\u003e\n\u003cp\u003eFrom inspection of Figure 3, it is possible to visualize the most important metabolic routes associated with all group comparisons. In the NOHN vs CTR comparison, only the metabolic pathway of beta-alanine shows a significant statistical probability.\u0026nbsp;That might be explained by the fact that some obstructions are asymptomatic, not leading to renal function loss or significant health impairment. Nevertheless, although mildly, the metabolism has already been altered at this stage, which might be viewed as the disease implantation effect. The comparison of OHN vs NOHN groups reveals several important affected metabolic routes: 1- tyrosine, phenylalanine, 2- alanine, aspartate, glutamate, and 3- amino sugar and nucleotide sugar metabolisms, as it denotes the disease progression. The comparison OHN vs CTR groups shows the overall impact of UPJO in the metabolism, with the disease at its most severe stage, presenting the largest number of altered metabolic routes.\u003c/p\u003e\n\u003cp\u003eAlthough the beta-alanine metabolic pathway was the only one significantly altered in the MetPA of NOHN vs CTR group comparison, it has also appeared in the MetPA of OHN vs NOHN group comparison (Fig. 3B\u0026amp;3A). This result is interesting, as it suggests that increased oxidative stress may be related to disease progression. Studies indicate that carnosine, derived from beta-alanine, plays a crucial role in protecting against oxidative stress, a factor relevant to various pathologies, including chronic kidney diseases [29]. As observed in the metabolite compilation of Table 2, a significant fold-change decrease in beta-alanine was found (36%) for the OHN vs NOHN comparison, indicating that this metabolite has been accumulated in the urine of the NOHN group. Pantothenic acid has also been found to be decreased in the comparison NOHN vs CTR (-49%) and OHN vs CTR (-59%). This metabolite is an essential precursor for the synthesis of coenzyme A (CoA) and may protect cell membranes from oxidative stress. Therefore, considering that beta-alanine pathway is altered in both MetPA of NOHN vs CTR and OHN vs NOHN group comparisons, it can be inferred that these alterations are associated with increased oxidative stress, which seems to intensify with disease progression. Moreover, beta-alanine derivatives exert a direct molecular protective action on podocytes, an essential part of the glomerular filtration membrane [30].\u003c/p\u003e\n\u003cp\u003eThe metabolomic profiling conducted in this study revealed further distinct expression patterns between OHN and NOHN groups (Table 2). Analysis of the urine samples suggested specific discriminators for OHN; these include 4-hydroxyphenylacetic acid, furoglycin, gluconic acid, \u003cem\u003eN\u003c/em\u003e-acetyl-L-aspartic acid, and threitol. All these urinary metabolomics-based analytes had an AUC above 0.80 (Table S2), suggesting strong potential clinical value as a disease progression signature. Other metabolites also present satisfactory results should be included in the discussion to allow a more thorough evaluation of the disease progression. For instance, 4-hydroxybenzoic acid was considered statistically relevant from both uni-variate (AUC 0.72) and multi-variate (AUC 0.923) analyses, which reinforces the importance of this metabolite in separating OHN from NOHN groups.\u003c/p\u003e\n\u003cp\u003eOne of the products of the phenylalanine metabolic pathway is tyrosine. Tyrosine and phenylalanine pathways are therefore intrinsically linked to each other. In GC-MS data analysis, 4-hydroxyphenylacetic acid was identified as the major metabolite of the tyrosine pathway and 3-(3-hydroxyphenyl) propanoic acid, 4-methoxyphenylacetic acid, and 4-hydroxyphenylacetic acid as key metabolites of the phenylalanine pathway. As a matter of fact, 4-hydroxyphenylacetic acid is known to be essential for the proper functioning of the tyrosine and phenylalanine metabolic pathways. This metabolite showed the greatest expression variation in different group comparisons (increased fold change of 112% for OHN vs NOHN and 84% for OHN vs CTR). Its reliable identification in the database (net score of 96, Tables S2 and S3) confirms that 4-hydroxyphenylacetic acid is, in fact, a discriminatory metabolite between both NOHN vs CTR and OHN vs NOHN groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisturbances in the metabolism of the amino acids tyrosine and phenylalanine may be related to kidney diseases [31]. Furthermore, in cases of chronic renal failure, there is a change in the conversion of phenylalanine into tyrosine and an increase in oxidative stress with harmful and toxic metabolic effects [32]. The metabolite 4-hydroxyphenylacetic acid is increased in patients with decline in renal function [33], a glomerular disease characterized by an excessive amount of protein in the urine (proteinuria) and damage to podocytes, cells that line the urinary surface of the glomerular capillary tuft, which constitute the barrier of glomerular filtration, ensuring its selective permeability. The increased production of this antioxidant metabolite suggests a defense mechanism against increased oxidative stress. Although this metabolite has been described as a marker of glomerular pathology, our results suggest that it may also play a discriminatory role in tubular pathologies such as obstructive uropathy. It has been demonstrated that elevation of 4-hydroxyphenylacetic acid during sepsis could inhibit necroptosis of renal proximal tubule epithelial cells and exert a nephroprotective effect [34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the alanine, aspartate and glutamate pathway, \u003cem\u003eN\u003c/em\u003e-acetyl-\u003cem\u003eL\u003c/em\u003e-aspartic acid was the most statistically significant metabolite with increased expression in the OHN group. This metabolite is associated with oxidative stress and inflammatory conditions in rats with chronic kidney disease and renal failure [35]. It is also a possible serum biomarker of diabetic nephropathy [36].\u003c/p\u003e\n\u003cp\u003eIn the amino sugar and nucleotide sugar pathway, the metabolites that stand out are alpha-glucosamine 1-phosphate (increased in OHN group vs NOHN) and \u003cem\u003eN\u003c/em\u003e-acetyl-D-mannosamine (decreased in OHN group vs NOHN). \u003cem\u003eN\u003c/em\u003e-acetyl-D-mannosamine is the precursor molecule of sialic acids. Sialic acids are present in the glomerular filtration barrier, functional units of the kidneys, where blood filtration and elimination of metabolic waste occurs. The decrease in these acids is related to membranous nephropathy, minimal lesion disease and the loss of nephrin, a structural protein in podocytes. The synthesis of sialic acid from \u003cem\u003eN\u003c/em\u003e-acetylneuraminic acid is regulated by the enzyme GNE. The decrease in \u003cem\u003eN\u003c/em\u003e-acetyl-D-mannosamine precursors may be directly related to the efficiency of the GNE enzyme. Mice with proteinuria and podocytopathy had a good response to treatment when \u003cem\u003eN\u003c/em\u003e-acetyl-D-mannosamine supplementation was administered [37,38].\u003c/p\u003e\n\u003cp\u003eAll the above discussed metabolic pathways for OHN vs NOHN group comparison (tyrosine, phenylalanine, alanine, and amino sugar and nucleotide sugar metabolism), with exception of the alanine, asparate and glutamate pathways, were also seen in the impact pathway graph for OHN vs CTR group comparison. The appearance of the same metabolic routes is particularly relevant as it consolidates the hypothesis that these metabolic alterations are indicative of disease progression.\u003c/p\u003e\n\u003cp\u003eOther metabolites such as alpha ketoglutaric acid, furoylglycine, 4-hydroxyphenylacetic acid, and ascorbic acid were also found altered in the comparisons between OHN vs NOHN groups and OHN vs CTR (Table 2). Furthermore, changes in their concentration (increased or decreased) coincide in both comparisons, again in consonance with the disease progression. Reports of unilateral uretheral obstruction in rats as well as patients with acute renal injury indicated an expressive decreased level in urine of ketoglutaric acid and citric acid, intermediary metabolites of Krebs cycle. This decrease has been associated to a possible compromised mitochondrial oxidative metabolism, suggesting lesion of the proximal tubule. Other studies also confirm this observation [39].\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThere have been advances in the evaluation of the use of urinary biomarkers in UPJO, but none of the discriminant metabolites have yet managed to be part of tests requested in clinical practice. Our findings suggest that analysis of urinary metabolites using a metabolomics strategy may lead to promising obstructive HN biomarkers that may contribute to optimize clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution Information\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, urine collection, data acquisition, statistical analysis, and biological interpretation as well as first draft elaboration were performed by LHFS and MFM. LFY, MJK, MALO, and ATF contributed with instrumental data acquisition. JPSF provided support on statistical analyses. FGHK, HFAF, FTD, and LA participated of clinical discussions and biological interpretation of the results. RIL and MFMT supervised all steps. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors wish to acknowledge the Sao Paulo Research Foundation (FAPESP 2017/08224-6; 2017/27059-6; 2023/7993-7) and National Council for Scientific and Technological Development (CNPq 133021/2018-1; 304983/2022-5) for fellowships and financial support.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMesrobian HGO, Mirza SP. 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Serum targeted hilic-ms metabolomics-based analysi in infants with ureteropelvic junction obstruction. \u003cem\u003eJ Proteome Res. \u003c/em\u003e2020;19(6):2294\u0026ndash;2303. https://doi.org/10.1021/acs.jproteome.9b00855\u003c/li\u003e\n\u003cli\u003eScalabre A, Cl\u0026eacute;ment Y, Guilli\u0026egrave;re F, Ayciriex S, Gaillard S, Dem\u0026egrave;de D, Bouty A, Lanteri P, Mure P-Y. Early detection of ureteropelvic junction obstruction in neonates with prenatal diagnosis of renal pelvis dilatation using \u003csup\u003e1\u003c/sup\u003eH NMR urinary metabolomics. \u003cem\u003eSci Rep.\u003c/em\u003e 2022;12:13406. https://doi.org/10.1038/s41598-022-17664-4.\u003c/li\u003e\n\u003cli\u003eGeneral Clinical Pharmacology Considerations for Pediatric Studies for Drugs and Biological Products Guidance for Industry. https://www.fda.gov/media/90358/download (accessed on May05, 2025).\u003c/li\u003e\n\u003cli\u003eOnen A. An alternative grading system to refine the criteria for severity of hydronephrosis and optimal treatment guidelines in neonates with primary UPJ-type hydronephrosis. \u003cem\u003eJ Pediatr Urol\u003c/em\u003e. 2007;3(3):200-205. https://doi.org/:10.1016/j.jpurol.2006.08.002\u003c/li\u003e\n\u003cli\u003eBanker H. Sheffield EG, Cohen HL. Nuclear Renal Scan. In: StatPearls [Internet]. Treasure Island (FL): StatPearls 2023; Publishing; https://www.ncbi.nlm.nih.gov/books/NBK562236/.\u003c/li\u003e\n\u003cli\u003eRadmayr C, Bogaert G, Bujons A, et al. EAU guidelines on Paediatric Urology. https://d56bochluxqnz.cloudfront.net/documents/full-guideline/EAU-Guidelines-on-Paediatric-Urology-2024.pdf. \u003c/li\u003e\n\u003cli\u003eArora S, Yadav P, Kumar M, et al. Predictors for the need of surgery in antenatally detected hydronephrosis due to UPJ obstruction \u0026ndash; A prospective multivariate analysis. \u003cem\u003eJ Pediatr Urol\u003c/em\u003e. 2015;11(5):248.e1-248.e5. https://doi.org10.1016/j.jpurol.2015.02.008\u003c/li\u003e\n\u003cli\u003eOlsen LR, Rawasdeh YFH. Surgery of the Ureter in Children. \u003cem\u003eIn: Wein AJ Kavoussi LR Partin AW Peters CA. \u003c/em\u003eCampell Walsh Urology. Elsevier, Philadelphia; 2016.\u003c/li\u003e\n\u003cli\u003eSchwartz GJ, Work DF. Measurement and Estimation of GFR in Children and Adolescents. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2009;4(11):1832-1843. https://doi.org/10.2215/CJN.01640309\u003c/li\u003e\n\u003cli\u003eSzymanska E, et al. Double-check: Validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics, 2011;8(1), 3-16. https://doi.org/10\u003c/li\u003e\n\u003cli\u003eMcHugh ML. Multiple comparison analysis testing in ANOVA. \u003cem\u003eBiochem Medica\u003c/em\u003e. 2012;21(3):203-209. https://doi.org/10.11613/BM.2011.029\u003c/li\u003e\n\u003cli\u003eCalabrese V, Scuto M, Salinaro AT, et al. Hydrogen Sulfide and Carnosine: Modulation of Oxidative Stress and Inflammation in Kidney and Brain Axis. Antioxidants (Basel). 2007;18;9(12):1303. doi: 10.3390/antiox9121303\u003c/li\u003e\n\u003cli\u003eScuto, M., Trovato SA, Modafferi, S., Carnosine activates cellular stress response in podocytes and reduces glycative and lipoperoxidative stress. \u003cem\u003eBiomedicines, \u003c/em\u003e8(6), 177. https://doi.org/10.3390/biomedicines80601777\u003c/li\u003e\n\u003cli\u003eKopple JD. Phenylalanine and Tyrosine Metabolism in Chronic Kidney Failure. \u003cem\u003eJ Nutr\u003c/em\u003e. 2007;137(6):1586S-1590S. https://doi.org/10.1093/jn/137.6.1586S\u003c/li\u003e\n\u003cli\u003eChakrapani A, Holme E. Indorn Metabolic Diseases: Disorders of Tyrosine Metabolism. 1th ed. Berlin: Springer; 2006\u003c/li\u003e\n\u003cli\u003eStec DF, Wang Suwan, Stothers Cody, et al. Alterations of urinary metabolite profile in model diabetic nephropathy. \u003cem\u003eBiochem Biophys Res Commun\u003c/em\u003e. 2015;456(2):610-614.\u003c/li\u003e\n\u003cli\u003eSheng A, Yi Y, Junjie W, et al. Gut-derived 4-hydroxyphenylacetic acid attenuates sepsis-induced acute kidney injury by upregulating ARC to inhibit necroptosis. BBAdis 2024;1870(1), 166867. doi.org/10.1016/j.bbadis.2023.166876\u003c/li\u003e\n\u003cli\u003eChen DQ, Chen H, Chen L, et al. The link between phenotype and fatty acid metabolism in advanced chronic kidney disease. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e. 2017;32(7):1154-1166. https://doi.org/10.1093/ndt/gfw415\u003c/li\u003e\n\u003cli\u003eHirayama A, Nakashima E, Sugimoto M, et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. \u003cem\u003eAnal Bioanal Chem\u003c/em\u003e. 2012;404(10):3101-3109. https://doi.org/10.1007/s00216-012-6412-x\u003c/li\u003e\n\u003cli\u003eHuizing M, Yardeni T, Fuentes F, et al. Rationale and Design for a Phase 1 Study of N-Acetylmannosamine for Primary Glomerular Diseases. \u003cem\u003eKidney Int Rep\u003c/em\u003e. 2019;4(10):1454-1462. https://doi.org/10.1016/j.ekir.2019.06.012\u003c/li\u003e\n\u003cli\u003eIto M, Sugihara K, Asaka T, et al. Glycoprotein Hyposialylation Gives Rise to a Nephrotic-Like Syndrome That Is Prevented by Sialic Acid Administration in GNE V572L Point-Mutant Mice. \u003cem\u003ePLoS One\u003c/em\u003e. 2012;7(1):e29873. https://doi.org/10.1371/journal.pone.0029873.\u003c/li\u003e\n\u003cli\u003eMaclellan D, Mataija D, Doucette A, et al. Alterations in urinary metabolites due to unilateral ureteral obstruction in a rodent model. \u003cem\u003eMol BioSyst.\u003c/em\u003e 2011;7: 2181 \u0026ndash; 2188. https://doi.org/10.1039/C1MB05080J\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\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":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biomarker, metabolomics, obstructive hydronephrosis, ureteropelvic junction obstruction","lastPublishedDoi":"10.21203/rs.3.rs-9107811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9107811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction \u003c/strong\u003eFetal hydronephrosis (HN) is detected in about 0.25–1% of fetuses, where ureteropelvic junction obstruction (UPJO) is the most commonly found congenital urinary tract anomaly, remaining the leading cause of kidney failure in infants and children. Since it is a spectral disease, UPJO represents a particularly challenging management. Severe UPJO must be treated surgically to avoid impairment of renal function, but children with non-obstructive hydronephrosis can be treated conservatively. There has been controversy regarding the indication for surgical intervention in asymptomatic patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e This pilot study aimed at investigating urine samples from children with UPJO using gas- and high-performance liquid-chromatography coupled to mass spectrometry (GC- and HPLC-MS) via untargeted metabolomics to prospect discriminatory metabolites indicative of hydronephrosis spectral progression that may contribute to optimize clinical decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Thirty-seven patients, whose clinical characteristics were recorded upon visits, had urinary samples collected, processed and analyzed by both GC- and HPLC-MS analytical platforms. Three distinct age-matched groups were inspected: 10 children with obstructive HN (OHN) established at initial imaging diagnosis, 15 asymptomatic children with non-obstructive HN (NOHN), and 12 children without any urinary tract problems as control group (CTR). Commonly used univariate (ANOVA and post-hoc tests) and multivariate (PLS-DA) procedures were applied to the metabolomics data, and ROC curves were generated to validate the models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Metabolomics profiling revealed distinct expression patterns upon selected group comparisons. The inspection of discriminatory metabolites across the different study groups allowed a clear visualization of the disease progression at the molecular level. Moreover, the discrimination of asymptomatic patients (NOHN group) from those requiring surgical intervention (OHN group) is of upmost relevance and could also be delineated here. All univariate and multivariate methods applied to both GC- and HPLC-MS data exhibited good statistical performance (Q\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.71, accuracy\u0026gt;92%, and AUC\u0026gt;0.92) resulting in a total of 94 discriminatory metabolites, suggesting strong potential clinical value as a diagnostic/prognostic signature. Worth mentioning cystine and methionine sulfoxide exhibiting the largest positive fold change score, 156% and 152%, respectively (both from OHN vs CTR group comparison). From the total of 94 discriminatory metabolites, 17 metabolites had particular statistical relevance since they were confirmed by both univariate and multivariate methods combined. Three metabolites, 1-methylhistidine-3-methylhistidine, 1-methyhistamine-3methylhistamine, and glutamyl-hydroxyproline (dipeptide), discriminate NOHN vs CTR groups whereas nine metabolites, mandelic acid, pantothenic acid, furoylglicine, gluconic acid, 3-hydroxyphenylacetate, 4-hydroxyphenylacetate, glutamylhydroxyproline, 3-hydroxy-3-methylglutaric acid, ribitol/arabitol/xylitol (pentose alcohols) and tagalose discriminate OHN vs CTR groups; those metabolites may serve as diagnostics purposes, being the first group comparison\u0026nbsp; (NOHN vs CTR) indicative of the initial metabolic perturbation caused by the UPJO and the latter group comparison (OHN vs CTR) indicative of the impact UPJO had exerted on the children metabolism at long term. More importantly, five metabolites, ascorbic acid, furoylglycine, gluconic acid, and ribose/arabinose/xylose (pentoses), already discriminating OHN vs CTR groups, appear again in the OHN vs NOHN group comparison, in addition to threitol; those metabolites might be useful to support clinical decisions whether a patient should undergo surgery.\u0026nbsp; Metabolomic pathway analyses revealed an alteration of the beta-alanine metabolism at early stages of HN, progressing to further compromising of tyrosine, phenylalanine, alanine/aspartate/glutamate, and amino sugar/nucleotide sugar metabolisms. Several other important metabolic pathways were further compromised at advanced stages of HN revealing the overall impact of UPJO at the basal metabolism of healthy children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The statistical quality of this study that certified 94 discriminant metabolites from NOHN, OHN, and CTR group comparisons allows us to infer that among them there will certainly be biomarkers of the obstructive HN setting up and progression.\u003c/p\u003e","manuscriptTitle":"Urinary metabolomics profile in children with ureteropelvic junction obstruction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 05:50:50","doi":"10.21203/rs.3.rs-9107811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T22:14:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56137779802669356643767223779739188051","date":"2026-04-21T21:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54672691194049094315435489177841034553","date":"2026-03-20T20:59:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T12:24:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T06:20:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T06:19:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2026-03-12T19:00:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0d1a4c25-52f9-4e67-a76a-ede0aedcdc2e","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T22:14:49+00:00","index":18,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T05:50:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 05:50:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9107811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9107811","identity":"rs-9107811","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}