Urinary Metabolic Profiles Research Articles

Dynamic variations of plasma and urine metabolic profiles in left anterior descending coronary artery ligation-induced myocardial infarction rats and the regulatory effects of Chinese patent medicine Xin-Ke-Shu

Myocardial infarction (MI) is one of the most severe cardiovascular diseases (CVD). Traditional Chinese medicines have unique advantages in the treatment of CVD, with Xin-Ke-Shu (XKS) being a commonly used Chinese patent medicine for the prevention and treatment of MI patients. This study aimed to investigate the dynamic metabolic profiles of plasma and urine in left anterior descending coronary artery ligation (LAD) -induced MI rats at days 3, 12, and 21 after surgery, and to evaluate the regulatory effects of XKS at these time points using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) metabolomics. The metabolic profiles of plasma and urine in the LAD-induced MI rats showed significant variations at days 3, 12, and 21 after MI. We identified a total of 23 plasma metabolites and 12 urine metabolites as potential pathological markers related to MI progression. These metabolites were mainly involved in pathways such as TCA cycle, arachidonic acid metabolism, glutathione metabolism, glycerophospholipid metabolism, sphingolipid metabolism, and fatty acid metabolism, all of which were associated with imbalance of myocardial energy metabolism, oxidative stress, and calcium overload. Disturbances in the TCA cycle, arachidonic acid metabolism, glutathione metabolism, and purine metabolism in plasma and urine were observed as early as day 3 after MI. By day 12, we noted significant changes in fatty acid metabolism in plasma and urine, along with notable alterations in sphingolipid metabolism in plasma. Disorders in plasma glycerophospholipid metabolism were first evident at day 12 and reached their peak severity by day 21. Treatments with XKS significantly regulated the disturbances in the plasma and urine metabolic profiles of MI rats at days 3, 12, and 21, with medium dose of XKS displaying a particularly strong regulatory effect, especially at day 12. Our study demonstrates that host metabolism undergoes dynamical changes following MI with most metabolic disorders manifesting in the early stage of MI. XKS effectively regulates nearly all of these disturbances and can be administered as soon as possible after MI. These findings provide valuable insights into the metabolic progression of MI and highlight the therapeutic potential of XKS in the treatment of MI.

Metabolomic characterization of COVID-19 survivors in Jilin province

BackgroundThe COVID-19 pandemic has escalated into a severe global public health crisis, with persistent sequelae observed in some patients post-discharge. However, metabolomic characterization of the reconvalescent remains unclear.MethodsIn this study, serum and urine samples from COVID-19 survivors (n = 16) and healthy subjects (n = 16) underwent testing via the non-targeted metabolomics approach using UPLC-MS/MS. Univariate and multivariate statistical analyses were conducted to delineate the separation between the two sample groups and identify differentially expressed metabolites. By integrating random forest and cluster analysis, potential biomarkers were screened, and the differential metabolites were subsequently subjected to KEGG pathway enrichment analysis.ResultsSignificant differences were observed in the serum and urine metabolic profiles between the two groups. In serum samples, 1187 metabolites were detected, with 874 identified as significant (457 up-regulated, 417 down-regulated); in urine samples, 960 metabolites were detected, with 39 deemed significant (12 up-regulated, 27 down-regulated). Eight potential biomarkers were identified, with KEGG analysis revealing significant enrichment in several metabolic pathways, including arginine biosynthesis.ConclusionsThis study offers an overview of the metabolic profiles in serum and urine of COVID-19 survivors, providing a reference for post-discharge monitoring and the prognosis of COVID-19 patients.

Untargeted metabolomics analysis of the urinary metabolic signature of acute and chronic gout

BackgroundGout is a common kind of inflammatory arthritis with metabolic disorders. However, the detailed pathogenesis of gout is complex and not fully clear. We investigated the urine metabolic profiling of gout patients by ultra-performance liquid chromatograph quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). MethodUrine metabolites were extracted from 26 acute gout patients, 31 chronic gout patients, and 32 healthy controls. Metabolite extracts were analyzed by UPLC-Q-TOF-MS for untargeted metabolomics. The peak area of creatinine was used to correct the content variations of urine samples for the semi-quantitative analysis. The value of variable importance in the projection (VIP) was obtained through the orthogonal partial least squares-discrimination analysis (OPLS-DA), and several differential metabolites were screened out. ResultsThe potential metabolic markers of gout in different stages were found based on the t-test. Finally, 18 different metabolites were identified through Human Metabolome Database (HMDB) and Targeted-MS/MS. The receiver operating characteristic (ROC) curve results revealed that all the screened biomarkers exerted high accuracy and diagnostic value. Pathway analysis indicated that the significantly different metabolites were mainly involved in purine metabolism and amino acid metabolism. ConclusionThe identified potential biomarkers are mainly involved in purine metabolism and amino acid metabolism, which leads us to further explore the pathogenesis of gout. This will lead us to further explore the pathogenesis of gout and provide the basis and ideas for the prevention and treatment of gout.

Urinary metabolic profile and its predictive indexes after MSG consumption in rat.

Monosodium glutamate (MSG) is a widely used food additive with conflicting evidence regarding its potential effects on human health, with proposed relevance for obesity and metabolic syndrome (MetS) or chronic kidney disease. As being able to accurately quantify the MSG dietary intake would help clarify the open issues, we constructed a predictive formula to estimate the daily intake of MSG in a rat model based on the urinary metabolic profile. Adult male Wistar rats were divided into groups receiving different daily amounts of MSG in drinking water (0.5, 1.5, and 3.0 g%), no MSG, and MSG withdrawal after 3.0% MSG treatment for 4 weeks. We then analyzed 24-hour urine samples for chemistries and metabolites using 1H NMR spectrometry and observed a strong correlation between urine pH, sodium, bicarbonate, alpha-ketoglutarate, citrate, fumarate, glutamate, methylamine, N-methyl-4-pyridone-3-carboxamide, succinate, and taurine and the daily MSG intake. Following the multiple linear regression analysis a simple formula model based on urinary Na+, citrate, and glutamate was most accurate and could be validated for estimating daily MSG intake. In conclusion, we propose that the daily MSG intake correlates with urinary metabolites in a rat model and that this new tool for monitoring the impact of MSG on health measures.

Metabolic dynamics in chronic gastritis: Examining urinary profiles post Helicobacter pylori eradication

Chronic gastritis is the persistent and insidious inflammation of the gastric lining. Helicobacter pylori (H. pylori ) has been identified as the most common cause of chronic gastritis and consequently elimination of H. pylori can lead to its cure. This editorial explores the use of urinary metabolic profiles before and after eradication to identify biomarkers that can aid in prognosis and treatment. Despite providing promising insights, there are limitations such as a small sample size (17 patients), a narrow treatment period of 2 wk, and treatment heterogeneity, which raise concerns. Nevertheless, these findings have opened a gateway to enhancing the treatment and prognosis of chronic gastritis through urinary metabolomics.

Dried Blood Spot Postmortem Metabolic Autopsy With Genotype Validation for Sudden Unexpected Deaths in Infancy and Childhood in Hong Kong.

Background Inborn errors of metabolism (IEM) are collectively rare but potentially preventable causes of sudden unexpected death (SUD) in infancy or childhood, and metabolic autopsy serves as the final tool for establishing the diagnosis. We conducted a retrospective review of the metabolic and molecular autopsy on SUD and characterized the biochemical and genetic findings. Methodology A retrospective review of postmortem metabolic investigations (dried blood spot acylcarnitines and amino acid analysis, urine metabolic profiling where available, and next-generation sequencing on a panel of 75 IEM genes) performed for infants and children who presented with SUD between October 2016 and December 2021 with inconclusive autopsy findings or autopsy features suspicious of underlying IEM in our locality was conducted. Clinical and autopsy findings were reviewed for each case. Results A total of 43 infants and children aged between zero days to 10 years at the time of death were referred to the authors' laboratories throughout the study period. One positive case of multiple acyl-CoA dehydrogenase deficiency was diagnosed. Postmortem reference intervals for dried blood spot amino acids and acylcarnitines profile were established based on the results from the remaining patients. Conclusions Our study confirmed the importance of metabolic autopsy and the advantages of incorporating biochemical and genetic testing in this setting.

Urine metabolic profile in rats with arterial hypertension of different genesis.

The diversity of pathogenetic mechanisms underlying arterial hypertension leads to the necessity to devise a personalized approach to the diagnosis and treatment of the disease. Metabolomics is one of the promising methods for personalized medicine, as it provides a comprehensive understanding of the physiological processes occurring in the body. The metabolome is a set of low-molecular substances available for detection in a sample and representing intermediate and final products of cell metabolism. Changes in the content and ratio of metabolites in the sample mark the corresponding pathogenetic mechanisms by highlighting them, which is especially important for such a multifactorial disease as arterial hypertension. To identify metabolomic markers for hypertensive conditions of different origins, three forms of arterial hypertension (AH) were studied: rats with hereditary AH (ISIAH rat strain); rats with AH induced by L-NAME administration (a model of endothelial dysfunction with impaired NO production); rats with AH caused by the administration of deoxycorticosterone in combination with salt loading (hormone-dependent form - DOCA-salt AH). WAG rats were used as normotensive controls. 24-hour urine samples were collected from all animals and analyzed by quantitative NMR spectroscopy for metabolic profiling. Then, potential metabolomic markers for the studied forms of hypertensive conditions were identified using multivariate statistics. Analysis of the data obtained showed that hereditary stress-induced arterial hypertension in ISIAH rats was characterized by a decrease in the following urine metabolites: nicotinamide and 1-methylnicotinamide (markers of inflammatory processes), N- acetylglutamate (nitric oxide cycle), isobutyrate and methyl acetoacetate (gut microbiota). Pharmacologically induced forms of hypertension (the L-NAME and DOCA+NaCl groups) do not share metabolomic markers with hereditary AH. They are differentiated by N,N-dimethylglycine (both groups), choline (the L-NAME group) and 1-methylnicotinamide (the group of rats with DOCA-salt hypertension).

Regulatory effect of Tingli Dazao Xiefei Decoction on asthmatic rats by urine metabolomics

Urine metabolomics based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS) was utilized to investigate the metabolic regulation mechanism of Tingli Dazao Xiefei Decoction(TLDZ) in rats with allergic asthma. SD male rats were divided into a normal group, a model group, a dexamethasone group, and a TLDZ group. The allergic asthma model was established by intraperitoneal injection of ovalbumin(OVA) to induce allergy, combined with atomization excitation. Urine metabolites from all rats were collected by UHPLC-Q-TOF-MS. The metabolic profiles of rats in each group were built by principal component analysis(PCA). Besides, the differential metabolites between the model group and the TLDZ group were selected by orthogonal partial least squares discriminant analysis(OPLS-DA), t-test(P<0.05), and variable importance in the projection(VIP) values of more than 3. The differential metabolites were identified through HMDB, METLIN, and other online databa-ses. Heat maps and clustering analysis for relative quantitative information of biomarkers in each group were drawn by MeV 4.8.0 software. Finally, MetaboAnalyst, MBRole, and KEGG databases were used to enrich related metabolic pathways and construct metabolic networks. The result demonstrated that TLDZ could effectively regulate the disordered urine metabolic profiles of asthmatic rats. Combined with multivariate statistical analysis and online databases, a total of 45 differential metabolites with significant changes(P<0.05) between the model group and the TLDZ group were screened out. Metabolic pathways including histidine metabolism, tryptophan metabolism, and arginine and proline metabolism were enriched. TLDZ could improve asthma by regulating related metabolic pathways and interfering with pathological processes such as immune homeostasis airway inflammation. The study investigates the molecular mechanism of anti-asthma of TLDZ from the perspective of urine metabolomics, and combined with previous pharmacological studies, it provides a scientific basis for the clinical development and application of TLDZ in the treatment of asthma.

Metabolic signatures of prenatal exposure to 'Cocktails' of benzotriazoles and benzothiazoles and its health implications

Prenatal exposure to benzotriazoles and benzothiazoles (collectively as BTs) was associated with pregnancy complications. Identifying the metabolites associated with prenatal BTs exposure may help elucidate the mechanism and characterize the exposure risk. In this prospective study of 158 pregnant women from Wuhan, China, urinary BTs were repeatedly measured across three trimesters to provide an accurate estimation of exposure during pregnancy. We conducted high-throughput targeted metabolomics with great coverage and high accuracy to characterize the urinary metabolic profile in late pregnancy. We first identified the perturbed metabolites of cocktail BTs exposure and then pinned down to the pairwise associations between individual BTs and the identified metabolites. A total of 44 metabolites were identified as perturbed biomarkers of cocktail BTs exposure based on the variable influence on projection (VIP > 1.2) score. Further pairwise associations analysis showed positive association of BTs with oxidative stress related biomarkers and negative association of BTs with neuronal function metabolites. The shared metabolic signatures among BTs in the co-occurrence network of pairwise association analysis may partially be attributed to the correlation among cocktail BTs exposure. The findings provide the potential mechanisms of BTs-associated pregnancy complications and offer insight into the health implications for prenatal BTs exposure. Furthermore, the framework we employed, which integrates both cocktail exposure and individual exposure, may illuminate future epidemiological research that seeks to incorporate exposure to mixtures and omics scale data.

Exploring potential predictors of Henoch-Schönlein purpura nephritis: a pilot investigation on urinary metabolites.

Henoch-Schönlein purpura nephritis (HSPN) is the most severe manifestation of Henoch-Schönlein purpura (HSP). This study aimed to determine the role of urine metabolomics in predicting HSPN and explore the potential mechanisms of HSP. A liquid chromatography-tandem mass spectrometry-based untargeted metabolomics analysis was performed to investigate the urinary metabolic profiles of 90 participants, comprising 30 healthy children (group CON) and 60 patients with HSP, including 30 HSP patients without renal involvement (group H) and 30 HSPN patients (group HSPN). The differentially expressed metabolites (DEMs) were identified using orthogonal partial least squares discriminant analysis (OPLS-DA), and subsequent bioinformatics analysis was conducted to elucidate the perturbed metabolic pathways. A total of 43 DEMs between H and HSPN groups were analyzed by the Kyoto Encyclopedia of Gene and Genome (KEGG) database, and the result indicates that glycine, serine and threonine metabolism, and cysteine and methionine metabolism were significantly disturbed. A composite model incorporating propionylcarnitine and indophenol sulfate was developed to assess the risk of renal involvement in pediatric patients with HSP. Conclusion: This study reveals the metabolic alterations in healthy children, HSPN patients, and HSP patients without renal involvement. Furthermore, propionylcarnitine and indophenol sulfate may be potential predictive biomarkers of the occurrence of HSPN. What is Known: • HSP is the predominant type of vasculitis observed in children. The long-term prognosis of HSP is contingent upon the extent of renal impairment. In severe nephritis, a delay in appropriate treatment may lead to fibrosis progression and subsequent development of chronic kidney disease (CKD), even leading to renal failure. • The application of metabolomics in investigating diverse renal disorders has been documented. Urine is a robust and sensitive medium for metabolomics detection. What is New: • The metabolic profiles were identified in urine samples of healthy children and those with HSP at the early stage of the disease. Different metabolites were identified between HSP patients without nephritis and those who developed HSPN. • These different metabolites may affect oxidative stress in the progression of HSPN.

Comprehensive characterization of Epimedium-Rhizoma drynariae herb pair in rat plasma, urine, and feces metabolic profiles by UHPLC-Q-Orbitrap HRMS combined with diagnostic extraction strategy and multicomponent pharmacokinetic study by UHPLC-MS/MS.

Epimedium-Rhizoma drynariae (EP-RD) was a well-known herb commonly used to treat bone diseases in traditional Chinese medicine. Nevertheless, there was incomplete pharmacokinetic behavior, metabolic conversion and chemical characterization of EP-RD in vivo. Therefore, this study aimed to establish metabolic profiles combined with multicomponent pharmacokinetics to reveal the in vivo behavior of EP-RD. Firstly, the diagnostic product ions (DPIs) and neutral losses (NLs) filtering strategy combined with UHPLC-Q-Orbitrap HRMS for the in vitro chemical composition of EP-RD and metabolic profiles of plasma, urine, and feces after oral administration of EP-RD to rats were proposed to comprehensively characterize the 47 chemical compounds and the 97 exogenous in vivo (35 prototypes and 62 metabolites), and possible biotransformation pathways of EP-RD were proposed, which included phase I reactions such as hydrolysis, hydrogenation, dehydrogenation, hydroxylation, dehydroxylation, isomerization, and demethylation and phase II reactions such as glucuronidation, acetylation, methylation, and sulfation. Moreover, a UHPLC-MS/MS quantitative approach was established for the pharmacokinetic analysis of seven active components: magnoflorine, epimedin A, epimedin B, epimedin C, icariin, baohuoside II, and icariin II. Results indicated that the established method was reliably used for the quantitative study of plasma active ingredients after oral administration of EP-RD in rats. Compared to oral EP alone, the increase in area under curves and maximum plasma drug concentration (P < 0.05). This study increased the understanding of the material basis and biotransformation profiles of EP-RD in vivo, which was of great significance in exploring the pharmacological effects of EP-RD.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study.

The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied. We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures. Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel. In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD. Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

Urinary metabolomic profiles uncover metabolic pathways in children with asthma

Objective The prevalence of asthma has gradually increased worldwide in recent years, which has made asthma a global public health problem. However, due to its complexity and heterogeneity, there are a few academic debates on the pathogenic mechanism of asthma. The study of the pathogenesis of asthma through metabolomics has become a new research direction. We aim to uncover the metabolic pathway of children with asthma. Methods Liquid chromatography (LC)–mass spectrometry (MS)–based metabolomic analysis was conducted to compare urine metabolic profiles between asthmatic children (n = 30) and healthy controls (n = 10). Results Orthogonal projections to latent structures-discrimination analysis (OPLS-DA) showed that there were significant differences in metabolism between the asthma group and the control group with three different metabolites screened out, including traumatic acid, dodecanedioic acid, and glucobrassicin, and the levels of traumatic acid and dodecanedioic acid in the urine samples of asthmatic children were lower than those of healthy controls therein. Pathway enrichment analysis of differentially abundant metabolites suggested that α-linolenic acid metabolism was an asthma-related pathway. Conclusions This study suggests that there are significant metabolic differences in the urine of asthmatic children and healthy controls, and α-linolenic acid metabolic pathways may be involved in the pathogenesis of asthma.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Urinary metabolic profiles during Helicobacter pylori eradication in chronic gastritis.

Helicobacter pylori (H. pylori) infection is a major risk factor for chronic gastritis, affecting approximately half of the global population. H. pylori eradication is a popular treatment method for H. pylori-positive chronic gastritis, but its mechanism remains unclear. Urinary metabolomics has been used to elucidate the mechanisms of gastric disease treatment. However, no clinical study has been conducted on urinary metabolomics of chronic gastritis. To elucidate the urinary metabolic profiles during H. pylori eradication in patients with chronic gastritis. We applied LC-MS-based metabolomics and network pharmacology to investigate the relationships between urinary metabolites and H. pylori-positive chronic gastritis via a clinical follow-up study. Our study revealed the different urinary metabolic profiles of H. pylori-positive chronic gastritis before and after H. pylori eradication. The metabolites regulated by H. pylori eradication therapy include cis-aconitic acid, isocitric acid, citric acid, L-tyrosine, L-phenylalanine, L-tryptophan, and hippuric acid, which were involved in four metabolic pathways: (1) Phenylalanine metabolism; (2) phenylalanine, tyrosine, and tryptophan biosynthesis; (3) citrate cycle; and (4) glyoxylate and dicarboxylate metabolism. Integrated metabolomics and network pharmacology revealed that MPO, COMT, TPO, TH, EPX, CMA1, DDC, TPH1, and LPO were the key proteins involved in the biological progress of H. pylori eradication in chronic gastritis. Our research provides a new perspective for exploring the significance of urinary metabolites in evaluating the treatment and prognosis of H. pylori-positive chronic gastritis patients.

P324 Urine metabolite profile characterizes clinical activity and disease extent in patients with inflammatory bowel disease

Abstract Background Inflammatory Bowel Disease (IBD), which comprises Crohn's Disease (CD) and Ulcerative Colitis (UC), is a disorder in which complex interactions between genetic, environmental, and microbial factors trigger alterations in the immune intestinal response. On the other hand, nutrient catabolism from the diet carried out by the microbiota results in the formation of metabolites that also impact immune responses on the intestinal mucosa. Considering that IBD patients have altered microbiota compared to healthy controls (HC) and that the urinary metabolic profile of an individual is the result of a combination of genetic, dietary, and microbial components, we propose the hypothesis that the urinary metabolic profile distinguishes IBD patients from HC. The aim of the study was to compare metabolomic analysis of urine samples from HC and IBD patients. Methods 27 patients were included [HC (n=10), UC (n=10), and CD (n=7)]. Clinical IBD activity was evaluated with partial Mayo score (UC) and Harvey-Bradshaw index (CD). Metabolites in urine were measured using gas chromatography-mass spectrometry (GC-MS). Metabolomic analysis and machine learning were performed using the MetaboAnalyst software. PLS-DA was used for multivariate analysis, and clustering separation was performed using hierarchical clustering. To identify and interpret patterns of metabolite concentration changes in a biologically meaningful way we perform Over Representation Analysis. Results In total, 62 organic acids were detected. Comparing UC and HC revealed 11 significantly altered metabolites (9 increased and 2 decreased). Main increased metabolites comprised lactic acid (p=0.0015), pyroglutamic acid (p=0.0147), isocitric acid (p=0.0325), and phosphoric acid (p=0.0091). Interestingly, the value of adipic and suberic acids were higher in remission vs. active patients. Furthermore, adipic acid was higher in UC patients with proctitis compared to left-side colitis/extensive colitis. Comparing CD and HC revealed only 2 significantly altered metabolites, glycolic acid (p=0.0214), and lactic acid (p=0.0111). Oxalic acid was more concentrated in CD patient with perianal disease. Finally, propionic and phosphoric acid were able to discriminate UC from CD patients. The most affected metabolic pathways were related to energy metabolism and oxidative stress, including the Warburg effect (p=0.0016), gluconeogenesis (p=0.0106), pyruvate metabolism (p=0.0197) and glutathione metabolism (p=0.0039) (Fig.1). Conclusion We found that the urinary metabolic profile of IBD patients differs from the HC, particularly for UC patients. These metabolites profile could be used to improve diagnosis and monitoring in patients with IBD.

P216 Urine Metabolomic Characteristics of Anxiety and Depression Disorders in Patients with Inflammatory Bowel Disease

Abstract Background Up to 25%-35% of patients with inflammatory bowel disease (IBD) suffer from anxiety or depression. We aimed to depict the urine metabolic profile of IBD patients with anxiety and depression disorders and investigate the urinary metabolites that could characterize the mental disorders in IBD patients. Methods Urine samples were collected from IBD patients, and the Liquid Chromatography-Mass Spectrometry (LC-MS) was used to detect the metabolites. The Hospital Anxiety and Depression Scale (HADS) was used to assess the anxiety and depression symptoms. Least absolute shrinkage and selection operator (LASSO) regression model with 10-fold cross-validation was used to establish a metabolite-based prediction model for mental disorders in patients with IBD. Results Among the involved IBD patients (n=23, median age 41, women 61%), 35% had anxiety and 35% had depression. The urine metabolic profiles of IBD patients with anxiety or depression disorders were largely separated from those without mental symptoms (Figure 1 a-b). There were 36 metabolites up-regulated and 25 metabolites down-regulated in the anxiety group, while 10 metabolites up-regulated and 35 metabolites down-regulated in the depression group (Figure 1 c-d). The LASSO model identified the metabolites predicting IBD patients with mental disorders (Figure 2 a-b). Six metabolites (L-Carnitine, Riboflavin-5-phosphate, 2-Ethyl-2-phenylmalonamide, 3-Indoxyl sulphate, Sodium cholate, Tritylalcohol) were selected to construct the prediction model for anxiety, and another six metabolites (D-(-)-Glutamine, dAMP, DL-Stachydrine, dCMP, 5'-Deoxy-5'-(Methylthio)Adenosine, Sakuranin) were selected to construct the prediction model for depression in IBD patients. Both models showed excellent performances with an AUC of 0.992 (95%CI:0.97-1.00) and 0.933 (95%CI: 0.83-1.00) for predicting anxiety and depression, respectively, among patients with IBD (Figure 2 c-d). Conclusion We found significantly different urine metabolic profiles between IBD patients with and without anxiety and depression disorders. The specific urinary metabolites panel may become effective biomarkers for the prediction of anxiety and depression disorders in IBD patients.

Renal Transplantation in Rare Monogenic Urinary Stone Disease – A Single-center Experience

Introduction: Monogenic urinary stone disease (MUSD) tends to be more severe with early onset of symptoms and a higher risk of chronic kidney disease than sporadic USD. The literature on the outcome after renal transplant in patients with certain MUSD is scarce. Materials and Methods: This is a retrospective single-center observational study conducted in a tertiary care renal transplant unit in North India between 2018 and 2021. The renal transplant recipients who developed an end-stage renal disease (ESRD) due to renal calculus disease/nephrocalcinosis were included in the study. All the patients presented to us in an anuric state, and hence, a 24-h urine metabolic profile could not be performed. Ear, nose, and throat and ophthalmological evaluations were done to rule out extrarenal manifestations. These patients were subjected to genetic analysis, i.e., clinical exome sequencing using next-generation sequencing. Results: Out of 283 live renal transplants, 11 patients developed ESRD due to nephrocalcinosis/renal calculus disease. Out of 11, only 4 had genetic mutations and the rest did not have any identifiable genetic mutations. The gene mutations were identified in ADCY10, CLDN16, CaSR, and SLC3A4. The patient with ADCY10 mutation had a strong family history. The clinical phenotype and in silico parameters analysis predicted the variant to be damaging except the one with CaSR mutation which causes Hypocalciuric hypercalcemia syndrome, type 1. Three of four underwent surgical intervention at younger age. All underwent successful live-related renal transplantation, with good graft function on follow-up, without any recurrence of calculus in the allograft. Conclusion: Renal transplantation can be safely proceeded in patients with the above monogenic mutations. Genetic analysis should be a part of pretransplant evaluation in young onset nephrolithiasis and end-stage kidney disease patients to look for a monogenic cause, to assess the risk of recurrence postrenal transplant.

Protective effects of Huang-Qi-Ge-Gen decoction against diabetic liver injury through regulating PI3K/AKT/Nrf2 pathway and metabolic profiling

Ethnopharmacological relevanceHuang-Qi-Ge-Gen decoction (HGD) is a traditional Chinese medicine prescription that has been used for centuries to treat “Xiaoke” (the name of diabetes mellitus in ancient China). However, the ameliorating effects of HGD on diabetic liver injury (DLI) and its mechanisms are not yet fully understood. Aim of the studyTo elucidate the ameliorative effect of HGD on DLI and explore its material basis and potential hepatoprotective mechanism. Materials and methodsA diabetic mice model was induced by feeding a high-fat diet and injecting intraperitoneally with streptozotocin (40 mg kg−1) for five days. After the animals were in confirmed diabetic condition, they were given HGD (3 or 12 g kg−1, i. g.) for 14 weeks. The effectiveness of HGD in treating DLI mice was evaluated by monitoring blood glucose and blood lipid levels, liver function, and pathological conditions. Furthermore, UPLC-MS/MS was used to identify the chemical component profile in HGD and absorption components in HGD-treated plasma. Network pharmacology and molecular docking were performed to predict the potential pathway of HGD intervention in DLI. Then, the results of network pharmacology were validated by examining biochemical parameters and using western blotting. Lastly, urine metabolites were analyzed by metabolomics strategy to explore the effect of HGD on the metabolic profile of DLI mice. ResultsHGD exerted therapeutic potential against the disorders of glucose metabolism and lipid metabolism, liver dysfunction, liver steatosis, and fibrosis in a DLI model mice induced by HFD/STZ. A total of 108 chemical components in HGD and 18 absorption components in HGD-treated plasma were preliminarily identified. Network pharmacology and molecular docking results of the absorbed components in plasma indicated PI3K/AKT as a potential pathway for HGD to intervene in DLI mice. Further experiments verified that HGD markedly reduced liver oxidative stress in DLI mice by modulating the PI3K/AKT/Nrf2 signaling pathway. Moreover, 19 differential metabolites between normal and DLI mice were detected in urine, and seven metabolites could be significantly modulated back by HGD. ConclusionsHGD could ameliorate diabetic liver injury by modulating the PI3K/AKT/Nrf2 signaling pathway and urinary metabolic profile.

Targeting Gut Microbiome With Prebiotic in Patients With CKD: The TarGut-CKD Study

IntroductionDisruption of gut microbiota underpins some of the metabolic alterations observed in chronic kidney disease (CKD). MethodsIn a non-randomized, open-label, 3-phase pilot trial, with repeated measures within each phase, we examined the efficacy of oligofructose-enriched inulin (p-inulin) in changing the gut microbiome and their metabolic products in 15 CKD patients. The stability of microbiome and metabolome was studied during the pre-treatment phase (8 weeks), a p-inulin treatment phase (12 weeks), and a post-treatment phase (8 weeks) of the study. ResultsStudy participants completed 373 of the 420 expected study visits (88.8%). Adherence to p-inulin was 83.4%. 16S rRNA sequencing was performed in 368 stool samples. A total of 1085 stool, urine, and plasma samples were subjected to untargeted metabolomic studies. P-inulin administration altered the composition of the gut microbiota significantly, with an increase in abundance of Bifidobacterium and Anaerostipes. Inter-subject variations in microbiome and metabolome were larger than intra-subject variation, indicating the stability of the gut microbiome within each phase of the study. Overall metabolite compositions assessed by beta diversity in urine and stool metabolic profiles were significantly different across study phases. Several specific metabolites in stool, urine and plasma were significant at FDR ≤ 0.1 over phase. Specifically, there was significant enrichment in microbial metabolites derived from saccharolysis. ConclusionResults from our study highlight the stability of the gut microbiome and the expansive effect of p-inulin on microbiome and host co-metabolism in CKD patients. Findings from this study will enable rigorous design of microbiome-based intervention trials.