Urinary Metabolomic Profiles Research Articles

The Clinical Experiences of Urine Metabolomics of Genitourinary Urothelial Cancer in a Tertiary Hospital in Taiwan.

Few studies have addressed the impact of diagnostic urine metabolites and the clinical outcomes associated with genitourinary urothelial (GU) cancer to date. Furthermore, longitudinal analysis of the dynamics of urine metabolites contributing to the detection of GU cancer has not yet been fully investigated; therefore, the discovery of novel diagnostic urine biomarkers is of enormous interest. We explored the correlation of the urine metabolomic profiles to GU cancers. The aqueous metabolites of the GU cancer and the control were also identified and analyzed through high-resolution1H nuclear magnetic resonance (NMR) spectroscopy. Compared with the control, the urine metabolites of the tumor were studied in relation to changes over time in a linear mixed model for repeated measures. The urine metabolites of sixty-three (44 male and 19 female) patients with GU cancers were systemically analyzed. The urine metabolite profile in GU cancer was significantly higher than those in the control group (p<0.05). Sevenurine metabolites including histidine, propylene glycol, valine, leucine, acetylsalicylate, glycine, and isoleucine as well as other pathways were identified statistically and were significantly associated with GU cancer detection with longitudinal analysis. We discovered that histidine, propylene glycol, valine, leucine, acetylsalicylate, glycine, isoleucine, succinic acid, lysine2-aminobutyric acid, and acetic acid are involved significantly in all types of male patients in whom the type (upper tract) of urine metabolites were found to be statistically significant compared with the control. We did not find any statistical significance in urine biomarkers between female and male patients. However, a statistically insignificant correlation was found among the grade and stage with the metabolites.

Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study.

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, 1H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual’s stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51–0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual’s urinary NMR metabolome.

Serum and urinary metabolites discriminate disease activity in ANCA associated glomerulonephritis in a pilot study.

Renal biopsy is currently the gold standard for diagnosing active renal vasculitis. In this pilot study, metabolomics analysis was used to investigate the differences in metabolic profiles between paired patients' serum and urine samples collected during both the active and the remission phase of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV). Ten patients with AAV renal disease were included. Mean age was 61years, with 6 patients each being male and Caucasian. Mean Birmingham Vasculitis Activity Score (BVAS) and mean glomerular filtration rate (GFR) were 17 and 28, respectively. We found that while the citric acid cycle intermediates citrate, iso-citrate and oxaloacetate had lower intensities in the active phase samples as compared to the remission phase samples. The intensities of other metabolites of carbohydrate metabolism, amino acid metabolism, and nucleotide synthesis were significantly higher in the active phase samples, indicating the upregulation of these pathways for the production of energy and other biomolecules such as proteins and nucleic acids during the active phase of AAV. This pilot studysuggests that serum and urinary metabolomic profiling may be usefulto monitor disease activity in renal AAV.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics.

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Urinary metabolomic profiling reveals difference between two traditional Chinese medicine subtypes of coronary heart disease

The World Health Organization has shown that coronary heart disease (CHD) is a more common cause of death than cancer. In traditional Chinese medicine (TCM), CHD is classified as a form of thoracic obstruction that can be divided in different subtypes including Qi stagnation with blood stasis (QS) and Qi deficiency with blood stasis (QD). Different treatment strategies are used based on this subtyping. Owing to the lack of scientific markers in the diagnosis of these subtypes, subjective judgments made by clinicians have limited the objective manner for utility of TCM in the treatment of CHD. Untargeted (UHPLC-QTOF-MS) and targeted (UHPLC-MS/MS) metabolomics approaches were employed to search significantly different metabolites related to the QS or QD subtypes of CHD with angina pectoris in this study. A total of 42 metabolites were obtained in the untargeted metabolomics analysis and 34 amino acids were detected in the targeted metabolomics analysis. In total, 16 metabolites were found significantly different among different groups. The results showed distinct metabolic profiles of urine samples not only between CHD patients and healthy controls, but also between the two subtypes of CHD. Pathway analysis of the significantly varied metabolites revealed that there were subtype-related differences in the activity of pathways. Therefore, urinary metabolomics can reveal the pathological changes of CHD in different subtypes, make the diagnosis of CHD in different subtypes in an objective manner and comprehensive and contribute to personalized treatment by providing scientific evidence.

Genetic and environmental influences on covariation in reproducible diet–metabolite associations

Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet-metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points. This research aims to examine the genetic and environmental factors influencing covariation in diet-metabolite associations that are reproducible over time in healthy twins. The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites. The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15-0.49). For 8 diet-metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (β: 0.40-0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (β: -0.50, P<0.001) and β-hydroxybutyrate (β: -0.46, P<0.001), as well as intakes of saturated fat and acetoacetate (β: 0.47, P<0.001) and β-hydroxybutyrate (β: 0.52, P<0.001). For these diet-metabolite associations a common shared environmental factor explained 66-69% of variance in the metabolites. This study shows that diet-metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment.

NMR-based serum and urine metabolomic profile reveals suppression of mitochondrial pathways in experimental sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a significant problem in the critically ill that causes increased death. Emerging understanding of this disease implicates metabolic dysfunction in its pathophysiology. This study sought to identify specific metabolic pathways amenable to potential therapeutic intervention. Using a murine model of sepsis, blood and tissue samples were collected for assessment of systemic inflammation, kidney function, and renal injury. Nuclear magnetic resonance (NMR)-based metabolomics quantified dozens of metabolites in serum and urine that were subsequently submitted to pathway analysis. Kidney tissue gene expression analysis confirmed the implicated pathways. Septic mice had elevated circulating levels of inflammatory cytokines and increased levels of blood urea nitrogen and creatinine, indicating both systemic inflammation and poor kidney function. Renal tissue showed only mild histological evidence of injury in sepsis. NMR metabolomic analysis identified the involvement of mitochondrial pathways associated with branched-chain amino acid metabolism, fatty acid oxidation, and de novo NAD+ biosynthesis in SA-AKI. Renal cortical gene expression of enzymes associated with those pathways was predominantly suppressed. Renal cortical fatty acid oxidation rates were lower in septic mice with high inflammation, and this correlated with higher serum creatinine levels. Similar to humans, septic mice demonstrated renal dysfunction without significant tissue disruption, pointing to metabolic derangement as an important contributor to SA-AKI pathophysiology. Metabolism of branched-chain amino acid and fatty acids and NAD+ synthesis, which all center on mitochondrial function, appeared to be suppressed. Developing interventions to activate these pathways may provide new therapeutic opportunities for SA-AKI.NEW & NOTEWORTHY NMR-based metabolomics revealed disruptions in branched-chain amino acid metabolism, fatty acid oxidation, and NAD+ synthesis in sepsis-associated acute kidney injury. These pathways represent essential processes for energy provision in renal tubular epithelial cells and may represent targetable mechanisms for therapeutic intervention.

Urinary Biomarkers for Early Diagnosis of Lung Cancer.

Lung cancer is the leading cause of cancer deaths worldwide. Its early detection has the potential to significantly impact the burden of the disease. The screening and diagnostic techniques in current use suffer from limited specificity. The need therefore arises for a reliable biomarker to identify the disease earlier, which can be integrated into a test. This test would also allow for the recurrence risk after surgery to be stratified. In this context, urine could represent a non-invasive alternative matrix, with the urinary metabolomic profile offering a potential source for the discovery of diagnostic biomarkers. This paper aims to examine the current state of research and the potential for translation into clinical practice.

Urinary Metabolomic Profiling after Administration of Corydalis Tuber and Pharbitis Seed Extract in Healthy Korean Volunteers.

Pharmacometabolomics is a useful tool to identify biomarkers that can assess and predict response after drug administration. The primary purpose of pharmacometabolomics is to better understand the mechanisms and pathways of a drug by searching endogenous metabolites that have significantly changed after drug administration. DA-9701, a prokinetic agent, consists of Pharbitis seed and Corydalis tube extract and it is known to improve the gastrointestinal motility. Although the overall mechanism of action of DA-9701 remains unclear, its active ingredients, corydaline and chlorogenic acid, act as a 5-HT3 and D2 receptor antagonist and 5-HT4 receptor agonist. To determine the significant metabolites after the administration of DA-9701, a qualitative analysis was carried out using ultra-high performance liquid chromatography coupled with orbitrap mass spectrometer followed by a multivariate analysis. Seven candidates were selected and a statistical analysis of fold change was performed over time. Our study concluded that all the seven selected metabolites were commonly involved in lipid metabolism and purine metabolism.

Reliability of urinary charged metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Currently, large-scale cohort studies for metabolome analysis have been launched globally. However, only a few studies have evaluated the reliability of urinary metabolome analysis. This study aimed to establish the reliability of urinary metabolomic profiling in cohort studies. In the Tsuruoka Metabolomics Cohort Study, 123 charged metabolites were identified and routinely quantified using capillary electrophoresis-mass spectrometry (CE-MS). We evaluated approximately 750 quality control (QC) samples and 6,720 participants’ spot urine samples. We calculated inter- and intra-batch coefficients of variation in the QC and participant samples and technical intraclass correlation coefficients (ICC). A correlation of metabolite concentrations between spot and 24-h urine samples obtained from 32 sub-cohort participants was also evaluated. The coefficient of variation (CV) was less than 20% for 87 metabolites (70.7%) and 20–30% for 19 metabolites (15.4%) in the QC samples. There was less than 20% inter-batch CV for 106 metabolites (86.2%). Most urinary metabolites would have reliability for measurement. The 96 metabolites (78.0%) was above 0.75 for the estimated ICC, and those might be useful for epidemiological analysis. Among individuals, the Pearson correlation coefficient of 24-h and spot urine was more than 70% for 59 of the 99 metabolites. These results show that the profiling of charged metabolites using CE-MS in morning spot human urine is suitable for epidemiological metabolomics studies.

Effect of Bicarbonate on Net Acid Excretion, Blood Pressure, and Metabolism in Patients With and Without CKD: The Acid Base Compensation in CKD Study

Patients with CKD are at elevated risk of metabolic acidosis due to impaired net acid excretion (NAE). Identifying early markers of acidosis may guide prevention in chronic kidney disease (CKD). This study compared NAE in participants with and without CKD, as well as the NAE, blood pressure (BP), and metabolomic response to bicarbonate supplementation. Randomized order, cross-over study with controlled feeding. Participants consisted of 8 patients with CKD (estimated glomerular filtration rate 30-59mL/min/1.73m2 or 60-70mL/min/1.73m2 with albuminuria) and 6 patients without CKD. All participants had baseline serum bicarbonate concentrations between 20 and 28 mEq/L; they did not have diabetes mellitus and did not use alkali supplements at baseline. Participants were fed a fixed-acid-load diet with bicarbonate supplementation (7 days) and with sodium chloride control (7 days) in a randomized order, cross-over fashion. Urine NAE, 24-hour ambulatory BP, and 24-hour urine and plasma metabolomic profiles were measured after each period. During the control period, mean NAE was 28.3±10.2 mEq/d overall without differences across groups (P=0.5). Urine pH, ammonium, and citrate were significantly lower in CKD than in non-CKD (P<0.05 for each). Bicarbonate supplementation reduced NAE and urine ammonium in the CKD group, increased urine pH in both groups (but more in patients with CKD than in those without), and increased; urine citrate in the CKD group (P< 0.2 for interaction for each). Metabolomic analysis revealed several urine organic anions were increased with bicarbonate in CKD, including 3-indoleacetate, citrate/isocitrate, and glutarate. BP was not significantly changed. Small sample size and short feeding duration. Compared to patients without CKD, those with CKD had lower acid excretion in the form of ammonium but also lower base excretion such as citrate and other organic anions, a potential compensation to preserve acid-base homeostasis. In CKD, acid excretion decreased further, but base excretion (eg, citrate) increased in response to alkali. Urine citrate should be evaluated as an early and responsive marker of impaired acid-base homeostasis. National Institute of Diabetes and Digestive and Kidney Diseases and the Duke O'Brien Center for Kidney Research. Registered atClinicalTrials.gov with study number NCT02427594.

Identification of Dose-Dependent DNA Damage and Repair Responses From Subchronic Exposure to 1,4-Dioxane in Mice Using a Systems Analysis Approach.

1,4-Dioxane (1,4-DX) is an environmental contaminant found in drinking water throughout the United States. Although it is a suspected liver carcinogen, there is no federal or state maximum contaminant level for 1,4-DX in drinking water. Very little is known about the mechanisms by which this chemical elicits liver carcinogenicity. In the present study, female BDF-1 mice were exposed to 1,4-DX (0, 50, 500, and 5,000mg/L) in their drinking water for 1 or 4 weeks, to explore the toxic effects. Histopathological studies and a multi-omics approach (transcriptomics and metabolomics) were performed to investigate potential mechanisms of toxicity. Immunohistochemical analysis of the liver revealed increased H2AXγ-positive hepatocytes (a marker of DNA double-strand breaks), and an expansion of precholangiocytes (reflecting both DNA damage and repair mechanisms) after exposure. Liver transcriptomics revealed 1,4-DX-induced perturbations in signaling pathways predicted to impact the oxidative stress response, detoxification, and DNA damage. Liver, kidney, feces, and urine metabolomic profiling revealed no effect of 1,4-DX exposure, and bile acid quantification in liver and feces similarly showed no effect of exposure. We speculate that the results may be reflective of DNA damage being counterbalanced by the repair response, with the net result being a null overall effect on the systemic biochemistry of the exposed mice. Our results show a novel approach for the investigation of environmental chemicals that do not elicit cell death but have activated the repair systems in response to 1,4-DX exposure.

Gut Microbial and Metabolic Profiling Reveal the Lingering Effects of Infantile Iron Deficiency Unless Treated with Iron.

Iron deficiency (ID) compromises the health of infants worldwide. Although readily treated with iron, concerns remain about the persistence of some effects. Metabolic and gut microbial consequences of infantile ID were investigated in juvenile monkeys after natural recovery (pID) from iron deficiency or post-treatment with iron dextran and B vitamins (pID+Fe). Metabolomic profiling of urine and plasma is conducted with 1 H nuclear magnetic resonance (NMR) spectroscopy. Gut microbiota are characterized from rectal swabs by amplicon sequencing of the 16S rRNA gene. Urinary metabolic profiles of pID monkeys significantly differed from pID+Fe and continuously iron-sufficient controls (IS) with higher maltose and lower amounts of microbial-derived metabolites. Persistent differences in energy metabolism are apparent from the plasma metabolic phenotypes with greater reliance on anaerobic glycolysis in pID monkeys. Microbial profiling indicated higher abundances of Methanobrevibacter, Lachnobacterium, and Ruminococcus in pID monkeys and any history of ID resulted in a lower Prevotella abundance compared to the IS controls. Lingering metabolic and microbial effects are found after natural recovery from ID. These long-term biochemical derangements are not present in the pID+Fe animals emphasizing the importance of the early detection and treatment of early-life ID to ameliorate its chronic metabolic effects.

Changes in the Urinary Metabolites in Losartan-Treated Spontaneously Hypertensive Rats

Introduction: This study examined the association of losartan induced changes in urinary metabolomic profile with the changes in blood pressure (BP) and renin-angiotensin- aldosterone system (RAAS) in spontaneously hypertensive rats (SHR). Methods: Male SHR were administered with either 0.5 mL of distilled water (control group, n=6) or 10 mg.kg -1 of losartan (group 2, n=6) daily by oral gavage for 4 weeks. Body weight, BP, food and water intake were measured weekly. At week 4, urine was collected for urinary electrolyte analysis and metabolite profiling, after which the animals were euthanised by decapitation and blood was collected for analysis of components of RAAS and electrolyte concentrations. Urine metabolite profile of SHR was determined using proton nuclear magnetic resonance ( 1 H-NMR) spectrometry combined with multivariate data analysis. Results: At week 4, losartan- treated SHR had significantly lower BP than non-treated SHR. There were no differences in water and food intake, body weight, serum and urinary electrolyte concentrations or in their urinary excretions between the two groups. No differences were evident in the components of RAAS except that the angiotensinogen level was significantly higher in losartan-treated SHR compared to non-treated SHR. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed clear separation of urinary metabolites between control and losartan-treated SHR. Losartan-treated SHR group was separated from the control group by changes in the intermediates involved in glycine, serine and threonine metabolism. Conclusion: Antihypertensive effect of losartan in SHR seems to be associated with changes in urinary metabolite profile, particularly involving the metabolism of glycine, serine and threonine.

Study of the pathophysiological mechanisms associated with the onset and course of neurodevelopmental disorders in preterm infants (the PeriSTRESS-PremTEA study): Rationale, objectives, design and sample description

BackgroundThere are few studies exploring the pathophysiological pathways that may condition differentially the emergence/course of neurodevelopmental disorders (ND) in very preterm and extremely preterm newborns (VPTN/EPTN). Furthermore, there are no established biological markers predictive of ND in this population. The aim of this study is four-fold: in two cohorts of VPTN/EPTN (i) to characterize the emergence/course of ND up to corrected-age 6 years, (ii) to identify those factors (from prenatal stages up to age 6 years) that explain the interindividual differences related to emergence/course of ND, (iii) to identify in the first hours/days of life a urinary metabolomic biomarker profile predictive of ND, and (iv) to determine longitudinally variations in DNA methylation patterns predictive of ND. MethodsObservational, longitudinal, prospective, six-year follow-up, multicentre collaborative study. Two cohorts are being recruited: the PeriSTRESS-Valencia-cohort (n=26 VPTN, 18 EPTN, and 122 born-at-term controls), and the PremTEA-Madrid-cohort (n=49 EPTN and n=29 controls). ResultsWe describe the rationale, objectives and design of the PeriSTRESS-PremTEA project and show a description at birth of the recruited samples. ConclusionsThe PeriSTRESS-PremTEA project could help improve early identification of clinical, environmental and biological variables involved in the physiopathology of ND in VPTN/EPTN. It could also help to improve the early identification of non-invasive ND biomarkers in this population. This may allow early ND detection as well as early and personalised intervention for these children.

Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine.

Prostate cancer (PC) is one of the most common male cancers worldwide. Until now, there is no consensus about using urinary metabolomic profiling as novel biomarkers to identify PC. In this study, urine samples from 50 PC patients and 50 non-cancerous individuals (control group) were collected. Based on 1H nuclear magnetic resonance (1H-NMR) analysis, 20 metabolites were identified. Subsequently, principal component analysis (PCA), partial least squares-differential analysis (PLS-DA) and ortho-PLS-DA (OPLS-DA) were applied to find metabolites to distinguish PC from the control group. Furthermore, Wilcoxon test was used to find significant differences between the two groups in metabolite urine levels. Guanidinoacetate, phenylacetylglycine, and glycine were significantly increased in PC, while L-lactate and L-alanine were significantly decreased. The receiver operating characteristics (ROC) analysis revealed that the combination of guanidinoacetate, phenylacetylglycine, and glycine was able to accurately differentiate 77% of the PC patients with sensitivity = 80% and a specificity = 64%. In addition, those three metabolites showed significant differences in patients stratified for Gleason score 6 and Gleason score ≥7, indicating potential use to detect significant prostate cancer. Pathway enrichment analysis using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and the SMPDB (The Small Molecule Pathway Database) revealed potential involvement of KEGG “Glycine, Serine, and Threonine metabolism” in PC. The present study highlights that guanidinoacetate, phenylacetylglycine, and glycine are potential candidate biomarkers of PC. To the best knowledge of the authors, this is the first study identifying guanidinoacetate, and phenylacetylglycine as potential novel biomarkers in PC.

Urinary Metabolomic Profiling Reveals Biological Pathways and Predictive Signatures Associated with Childhood Asthma.

BackgroundDespite considerable efforts, the pathogenic mechanisms of asthma are still incompletely understood, due to its heterogeneous nature. However, metabolomics can offer a global view of a biological system, making it a valuable tool for further elucidation of mechanisms and biomarker discovery in asthma.MethodsGC-MS–based metabolomic analysis was conducted for comparison of urine metabolic profiles between asthmatic children (n=30) and healthy controls (n=30).ResultsAn orthogonal projections to latent structures discriminant–analysis model revealed a clear separation of the asthma and control groups (R2x=0.137, R2y=0.947, Q2=0.82). A total of 20 differential metabolites were identified as discriminant factors, of which eleven were significantly increased and nine decreased in the asthma group compared to the control group. Pathway-enrichment analysis based on these differential metabolites indicated that sphingolipid metabolism, protein biosynthesis, and citric acid cycle were strongly associated with asthma. Among the identified metabolites, 2-hydroxybutanoic acid showed excellent discriminatory performance for distinguishing asthma from healthy controls, with an AUC of 0.969.ConclusionOur study revealed significant changes in the urine metabolome of asthma patients. Several perturbed pathways (eg, sphingolipid metabolism and citric acid cycle) may be related to asthma pathogenesis, and 2-hydroxybutanoic acid could serve as a potential biomarker for asthma diagnosis.

Urinary Metabolomic Profiling in Streptozotocin-Induced Diabetic Mice after Treatment with Losartan

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage kidney disease. Renin–angiotensin system inhibitors such as losartan are the predominant therapeutic options in clinical practice to treat DKD. Therefore, it is necessary to identify DKD-related metabolic profiles that are affected by losartan. To investigate the change in metabolism associated with the development of DKD, we performed global and targeted metabolic profiling using 800 MHz nuclear magnetic resonance spectroscopy of urine samples from streptozotocin-induced diabetic mice (DM) with or without losartan administration. A principal component analysis plot showed that the metabolic pattern in the losartan-treated diabetic mice returned from that in the DM group toward that in the control mice (CM). We found that 33 urinary metabolites were significantly changed in DM compared with CM, and the levels of 16 metabolites among them, namely, glucose, mannose, myo-inositol, pyruvate, fumarate, 2-hydroxyglutarate, isobutyrate, glycine, threonine, dimethylglycine, methyldantoin, isoleucine, leucine, acetylcarnitine, 3-hydroxy-3-methylglutarate, and taurine, shifted closer to the control level in response to losartan treatment. Pathway analysis revealed that these metabolites were associated with branched-chain amino acid degradation; taurine and hypotaurine metabolism; glycine, serine, and threonine metabolism; the tricarboxylic acid cycle; and galactose metabolism. Our results demonstrate that metabolomic analysis is a useful tool for identifying the metabolic pathways related to the development of DKD affected by losartan administration and may contribute to the discovery of new therapeutic agents for DKD.

Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease.

ObjectiveDiabetic kidney disease (DKD) leads to low high albuminuria and gradually progresses to very high albuminuria with kidney insufficiency. However, about 20–40% of DKD is normoalbuminuric DKD (NADKD), which has impaired kidney function but normal urine albumin. This study is to investigate the urine metabolomic profiles of patients with NADKD and albuminuria DKD (ADKD).MethodsIn total, 95 patients were divided into a simple diabetes mellitus group (SDM group), an ADKD group, and a NADKD group. All subjects were analyzed for urine metabolites using non-targeted metabolomics based on ultra-performance liquid chromatography – tandem mass spectrometry.ResultsThe urine metabolomic profiles of the SDM group, NADKD group, and ADKD group were significantly different, and 65 different metabolites were identified among the three groups. Metabolic pathway analysis of these differential metabolites found that the top three significantly changed metabolic pathways were linoleic acid metabolism, citrate cycle, and, arginine and proline metabolism. There are 12 metabolites enriched in these three metabolic pathways. In detail, compared with those in the SDM group, the levels of γ-linolenic acid in the ADKD group were increased significantly, while the levels of succinic acid, cis-aconitic acid, citric acid, L-proline, L-erythro-4-hydroxyglutamate, N-methylhydantoin, N-carbamoylputrescine, spermidine, and 5-aminopentanoic acid were reduced significantly; compared with those in the NADKD group, the levels of linoleic acid, γ-linolenic acid, and L-malic acid in the ADKD group were increased significantly (P < 0.05), while the levels of L-proline, L-erythro-4-hydroxyglutamate, N-carbamoylputrescine, and spermidine were significantly reduced (P < 0.05). However, there were no significant difference between the SDM group and NADKD group (P > 0.05).ConclusionThe urine metabolomic profiles between the NADKD group and the ADKD group are significantly different. Specifically, these two groups have distinct levels of linoleic acid, γ-linolenic acid, L-malic acid, L-proline, L-erythro-4-hydroxyglutamate, N-carbamoylputrescine, and spermidine.

Abstract 3510: The metabolomic profile of urine from prostate cancer patients using capillary electrophoresis mass spectrometry (CEMS)

Abstract Background: Prostate cancer (PCa) pathogenesis is influenced by alterations in cellular metabolism. Metabolomics measures these biochemical changes to create global tissue metabolite profiles. Urinary studies are noninvasive and can potentially identify biomarkers for PCa. Objective: We used Capillary Electrophoresis Mass Spectrometry (CE-MS) to analyze urine from men undergoing prostate biopsy to investigate their metabolomic profiles. Methods: We retrospectively analyzed 40 urine samples (20 from bx demonstrating PCa, 20 from benign) that were prospectively collected from men undergoing prostate biopsy. Analysis of charged metabolites done with Agilent CE-MS system (J Proteome Res. 2:488; 2003). Screening of potential biomarkers was performed with statistical protocols and pathway analyses. Metabolites with levels below the detection limit in all samples were excluded. Relative abundances of metabolites were normalized to levels of creatinine. Pathway analysis with MetaboAnalyst (Metabolites 9:57; 2019). Conclusions: CE-MS analysis identified several metabolic pathways that were altered in urine of men with PCa. These metabolites are involved in steroid, aromatic, microorganism and SCFA processes and warrant targeted studies which are underway in our lab. If validated, they have potential to serve as non-invasive biomarkers for PCa diagnosis and therapeutics Citation Format: Alexander Buko, Leo Cheng, Andrew Gustev, Adam Feldman. The metabolomic profile of urine from prostate cancer patients using capillary electrophoresis mass spectrometry (CEMS) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3510.