Urinary Metabolomic Analysis Research Articles

Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease.

The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease.

A multi-omic investigation of male lower urinary tract symptoms: Potential role for JC virus.

Male lower urinary tract symptoms (LUTS) comprise a common syndrome of aging that negatively impacts quality of life. The etiology of LUTS is multifactorial, involving benign prostatic hyperplasia, smooth muscle and neurologic dysfunction, inflammation, sexually transmitted infections, fibrosis, and potentially dysbiosis, but this aspect remains poorly explored. We investigated whether the presence of infectious agents in urine might be associated with LUTS by combining next-generation DNA sequencing for virus discovery, microbiome analysis for characterization of bacterial communities, and mass spectrometry-based metabolomics. In urine from 29 LUTS cases and 9 controls from Wisconsin, we found a statistically significant association between a diagnosis of LUTS and the presence of JC virus (JCV), a common neurotropic human polyomavirus (Polyomaviridae, Betapolyomavirus) linked to severe neurologic disease in rare cases. This association (based on metagenomics) was not borne out when specific polymerase chain reaction (PCR) testing was applied to this set of samples, likely due to the greater sensitivity of PCR. Interestingly, urine metabolomics analysis identified dysregulation of metabolites associated with key LUTS processes. Microbiome analysis found no evidence of microbial community dysbiosis in LUTS cases, but JCV-positive samples contained more Anaerococcus species, which are involved in polymicrobial infections of the urinary tract. Neither age nor body mass index were significantly associated with the presence of urinary JCV—in the initial group or in an additional, regionally distinct group. These data provide preliminary support the hypothesis that viruses such as JCV may play a role in the development or progression of LUTS, together with other infectious agents and host metabolic responses.

Ayurvedic Herbal Preparation Supplementation Does Not Improve Metabolic Health in Impaired Glucose Tolerance Subjects; Observations from a Randomised Placebo Controlled Trial.

The increased usage of alternative Ayurvedic treatments as potential health-beneficial therapies emphasizes the importance of studying its efficacy in sound placebo-controlled intervention trials. An example of such a traditional Ayurvedic herbal preparation is Mohana Choorna, a mixture composed of 20 different herbs and used to prevent and treat type 2-diabetes (T2D). We studied the efficacy of “Mohana Choorna” on T2D-related parameters in subjects with impaired glucose tolerance. In a double blind, placebo-controlled cross-over trial, 19 overweight (BMI > 27 kg/m2) subjects aged 50–70 years with an impaired glucose tolerance received two four-week interventions, i.e., herbal or placebo with a four-week wash-out between interventions. HbA1c, glucose, insulin, triglycerides, cholesterol, blood pressure and augmentation index were measured before and after both interventions at fasting and during a glucose tolerance test. After both interventions, urine was collected to measure treatment exposure using LCMS-based metabolomics and whole genome gene-expression in adipose tissue of 13 subjects. The herbal intervention did not affect plasma glucose triglycerides, cholesterol, blood pressure or the augmentation index but showed a trend towards an increased insulin, HOMA-IR and postprandial insulin levels (p = 0.054, p = 0.056 and p = 0.095 respectively). An increase in expression of inflammation-related gene sets in adipose tissue was observed after the herbal intervention compared to placebo. Urine metabolomic analysis did not reveal a correlation of the presence of specific plant metabolites with “health markers”. Our findings suggest that there is no substantiating evidence to claim that four weeks’ use of the Ayurvedic herbal supplement Mohana Choorna beneficially affects glucose homeostasis.

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

Evaluation of Serum/Urine Genomic and Metabolomic Profiles to Improve the Adherence to Sildenafil Therapy in Patients with Erectile Dysfunction.

Type V-phosphodiesterase-inhibitors (PDE5i) are the first choice drugs in the treatment of erectile dysfunction (ED), being effective in 60–70% of patients. However, approximately 50% of patients per year discontinue the treatment with PDE5i after reporting poor drug efficacy or major adverse drug reactions (ADR). To identify early markers of efficacy/safety for the treatment of ED with PDE5i, the basal clinical characteristics of patients, integrated with metabolomics analysis of serum and urine and genomic data, were here correlated with the PDE5i efficacy and the occurrence of ADR upon administration. Thirty-six males with new diagnosis of ED were consecutively recruited and characterized at baseline for anthropometrics, blood pressure, blood glucose, lipid profile, serum levels of thyroid/sex hormones and erectile function evaluated by IIEF-15 questionnaire. Targeted Next Generation Sequencing (NGS) was applied to genes involved in PDE5i pharmacodynamics and pharmacokinetics. Fasting metabolic profiles of serum and urine were assessed by nuclear magnetic resonance (NMR)-based metabolomics analysis. Patients were prescribed on-demand therapy with Sildenafil oro-dispersible film and followed-up after 3 months from recruitment. Baseline data were compared with IIEF-15 score at follow-up and with the occurrence of ADR recorded by a dedicated questionnaire. Twenty-eight patients were finally included in the analysis. Serum LDL-cholesterol levels were increased in those reporting ADR (143.3 ± 13.2 mg/dl ADR vs. 133.1 ± 12.4 mg/dl No ADR; p = 0.046). NGS data showed that specific variants of PDE11A and CYP2D7 genes were more represented in drug responders (both relative risk = 2.7 [0.9–5.1]; p = 0.04). NMR-based metabolomics showed the highest association between serum LDL-cholesterol metabolites and the occurrence of ADR (Hazard ratio = 17.5; p = 0.019). The association between lipid profile and the ADR pattern suggests major cues in the tailoring of ED therapy with PDE5i.

PSXII-19 Urine Metabolomics Analysis Associated with Feed Efficiency on Crossbred Steers during the Growing and Finishing Period on Forage- and Concentrate- Based Diets

Abstract A discovery project to identify non-invasive biomarkers that can detect subtle metabolic discrepancies for cattle feed efficiency was performed using untargeted and targeted urine metabolomics by ultra-performance liquid chromatography-mass spectrometry. Individual feed intake and body weight gain were measured in crossbred steers (n = 80) on a forage-based growing ration (stage-1) followed by a high-concentrate diet finishing ration (stage-2). Urine was collected on study days 0, 21, 42, 63, and 83 for each dietary stage. In total, 28 steers with the greatest and the least average daily-gain (ADG) within 0.32 SD of the mean of dry-matter-intake (DMI) were used. A principal component analysis of the untargeted metabolites fully segregated the highest-ADG and lowest-ADG animals, with overlap across diets (both stages). The urinary untargeted metabolites that segregated the ADG-groups (n = 199; P < 0.05), included steroid-hormones, bile-acids, alpha-linolenic acid metabolites, vitamin-B6, along with products of glycine, serine and threonine metabolism (metabolic pathway analysis: impact-value > 0.50; FDR < 0.10). Bile acids and steroids were then quantified in urine and their associations with animal performance and carcass composition evaluated by correlation and multiple logistic regression AUC-ROC curve analyses. In stage-1, urine concentration of cortisone was associated (P < 0.05) with ADG (r = -0.28), DMI (r = -0.40) and ribeye-area (r = -0.28); cortisol was associated with DMI (r = -0.32; P < 0.01) and testosterone was associated with ADG (r = -0.28; P < 0.01). The urine concentrations of 18 measured bile acids were negatively associated (P < 0.05) with DMI, and secondary bile acids were negatively associated (P < 0.01) with marbling and hot-carcass-weight. In stage-2, negative association between the bile acids glycocholic acid and deoxycholic acid with marbling and hot-carcass-weight were identified. Urine metabolomics provide new insight into the physiological mechanisms and potential biomarkers of cattle feed efficiency. USDA is an equal opportunity provider and employer.

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels.

Aortic valve stenosis (AVS) is a prevalent condition among the elderly population that eventually requires aortic valve replacement. The lack of reliable biomarkers for AVS poses a challenge for its early diagnosis and the application of preventive measures. Untargeted gas chromatography mass spectrometry (GC-MS) metabolomics was applied in 46 AVS cases and 46 controls to identify plasma and urine metabolites underlying AVS risk. Multivariate data analyses were performed on pre-processed data (e.g. spectral peak alignment), in order to detect changes in metabolite levels in AVS patients and to evaluate their performance in group separation and sensitivity of AVS prediction, followed by regression analyses to test for their association with AVS. Through untargeted analysis of 190 urine and 130 plasma features that could be detected and quantified in the GC-MS spectra, we identified contrasting levels of 22 urine and 21 plasma features between AVS patients and control subjects. Following metabolite assignment, we observed significant changes in the concentration of known metabolites in urine (n = 14) and plasma (n = 15) that distinguish the metabolomic profiles of AVS patients from healthy controls. Associations with AVS were replicated in both plasma and urine for about half of these metabolites. Among these, 2-Oxovaleric acid, elaidic acid, myristic acid, palmitic acid, estrone, myo-inositol showed contrasting trends of regulation in the two biofluids. Only trans-Aconitic acid and 2,4-Di-tert-butylphenol showed consistent patterns of regulation in both plasma and urine. These results illustrate the power of metabolomics in identifying potential disease-associated biomarkers and provide a foundation for further studies towards early diagnostic applications in severe heart conditions that may prevent surgery in the elderly.

Effect of Defatted Dabai Pulp Extract in Urine Metabolomics of Hypercholesterolemic Rats.

A source of functional food can be utilized from a source that might otherwise be considered waste. This study investigates the hypocholesterolemic effect of defatted dabai pulp (DDP) from supercritical carbon dioxide extraction and the metabolic alterations associated with the therapeutic effects of DDP using 1H NMR urinary metabolomic analysis. Male-specific pathogen-free Sprague–Dawley rats were fed with a high cholesterol diet for 30 days to induce hypercholesterolemia. Later, the rats were administered with a 2% DDP treatment diet for another 30 days. Supplementation with the 2% DDP treatment diet significantly reduced the level of total cholesterol (TC), triglyceride, low-density lipoprotein (LDL), and inflammatory markers (C-reactive protein (CRP), interleukin 6 (IL6) and tumour necrosis factor-α (α-TNF)) and significantly increased the level of antioxidant profile (total antioxidant status (TAS), superoxide dismutase (SOD), glutathione peroxide (GPX), and catalase (CAT)) compared with the positive control group (PG) group (p < 0.05). The presence of high dietary fibre (28.73 ± 1.82 g/100 g) and phenolic compounds (syringic acid, 4-hydroxybenzoic acid and gallic acid) are potential factors contributing to the beneficial effect. Assessment of 1H NMR urinary metabolomics revealed that supplementation of 2% of DDP can partially recover the dysfunction in the metabolism induced by hypercholesterolemia via choline metabolism. 1H-NMR-based metabolomic analysis of urine from hypercholesterolemic rats in this study uncovered the therapeutic effect of DDP to combat hypercholesterolemia.

A Urinary Metabolomics Analysis Based on UPLC-MS and Effects of Moxibustion in APP/PS1 Mice.

Alzheimer's disease (AD) is a common neurodegenerative disorder with the symptoms of cognitive impairment and decreased learning and memory abilities. Metabolomics can reflect the related functional status and physiological and pathological changes in the process of AD. Moxibustion is a unique method in traditional Chinese medicine, which has been used in the treatment and prevention of diseases for thousands of years. A total of 32 APP/PS1 mice were randomly divided into the model group, moxibustion group, moxa smoke group and smoke-free moxibustion group (n=8/group), using the random number table method, while eight C57BL/6 mice were used as the control group. The five groups were measured for 20 min/day, 6 days/week, for 4 weeks. After 4 weeks' experiment, all the mice were placed in metabolic cages to collect urine continuously for 24 hours, for UPLC-MS analysis. Principal component analysis (PCA) was used to identify the different metabolites among the five groups, and partial least squares discriminant analysis (PLS-DA) was performed to reveal the effects on the metabolic variance. Sixteen potential biomarkers were identified among the five groups, primarily related to amino acid metabolism, starch metabolism, sucrose metabolism, interconversion of pentose and glucuronate, and aminoacyl biosynthesis. There were 17 differences in the potential metabolites between the control and model groups, involving the metabolism of amino acid, purine, pyrimidine, nicotinic acid and nicotinamide, and biosynthesis of pantothenate and coenzyme A. Fifteen potential biomarkers were identified between the model and moxibustion groups, related to starch metabolism, sucrose metabolism, interconversion of pentose and glucuronate, glyoxylate, dicarboxylate anions and some amino acid metabolism. Moxibustion can regulate the metabolism of substance and energy by improving the synthesis and decomposition of carbohydrates and amino acids in APP/PS1 transgenic AD model mice.

Targeted Metabolomics With Ultraperformance Liquid Chromatography-Mass Spectrometry (UPLC-MS) Highlights Metabolic Differences in Healthy and Atopic Staffordshire Bull Terriers Fed Two Different Diets, A Pilot Study.

Background: While anecdotal evidence has long claimed that a raw meat–based diet (RMBD) improves the metabolic health of canines, no rigorous scientific study has clarified this issue. Canine atopic dermatitis (CAD) has also been linked to metabolic health, but its relation to diet remains poorly understood. This study investigates whether dietary choice is linked to metabolic health in healthy and CAD-diagnosed canines via targeted serum and urine metabolomic analysis of polar, non-ionic metabolites, as well as whether the underlying CAD condition modulates the response to nutritional intake.Materials and Methods: Serum metabolites of client-owned Staffordshire bull terriers, divided into CAD-diagnosed (n = 14) and healthy (n = 6) cohorts, were studied. Urine metabolites of a subset of the CAD-diagnosed canines (n = 8) were also studied. The canines were split into two cohorts based on diet. The first cohort were fed a commercially available high-fat, moderate-protein, low-carbohydrate RMBD (n = 11, CAD diagnosed n = 8, healthy n = 3). Those in the second cohort were fed a commercially available moderate-fat, moderate-protein, high-carbohydrate kibble diet (KD) (n = 9: CAD diagnosed n = 6, healthy n = 3). The diet intervention period lasted approximately 4.5 months (median 135 days). Statistical analyses of the serum profiles across all dogs (n = 20) and the urine profiles of the CAD-diagnosed subset (n = 8) were performed.Results and Discussion: The KD cohort was found to have higher concentrations of methionine than the RMBD cohort, both in serum (all dogs, p < 0.0001) and in urine (CAD-only cohort, p < 0.0002), as well as cystathionine and 4-pyridoxic acid. Methionine plays important roles in homocysteine metabolism, and elevated levels have been implicated in various pathologies. The CAD (n = 14) cohort dogs showed starker metabolic changes in response to diet regarding these pathways compared to the healthy (n = 6) cohort. However, there was no significant change in CAD severity as a result of either diet. Likely due to the higher meat content of the RMBD, higher concentrations of several carnitines and creatine were found in the RMBD cohort. Citrulline was found in higher concentrations in the KD cohort. Our findings provide insight into the relationship between diet and the serum and urine metabolite profiles of canines. They also suggest that neither diet significantly affected CAD severity.

Mechanistic insight on the effects of metals and metalloids to neurodevelopment

The neurodevelopmental exposome paradigm was applied on a cohort of mother-infant pairs (n=178). Prenatal exposure to metals and metalloids was determined by measuring mercury in hair samples selected at birth, while cord blood and breast milk samples were analysed for mercury, cadmium, lead, and arsenic, as well as for essential elements (selenium, zinc, copper). Cognitive function, language, and motor development were assessed in children at the age of 18 months by the Bayley Scale for Infant Development (Bayley-III) development tool. The individual-level biological profiles were characterized using both nuclear magnetic resonance (NMR) spectroscopy and ultra‐high-performance liquid chromatography‐high resolution mass spectrometry (UPLC‐HRMS) for the untargeted urinary and plasma metabolomics analysis. LC-HRMS metabolites were assigned using the xcms and CAMERA R packages. In this study Human Metabolome DataBase (HMDB), Metlin, and LipidMaps databases were used for annotations, considering the calculated mass error in ppm, the isotope patterns, and the MS spectra. NMR spectra were preprocessed with MestreNova v.11.04 and ChenomX 8.03. Integrated pathway analysis and exposome-wide association algorithms were used for the evaluation of the associations between in utero exposure to metals and metabolic pathway dysregulation, as well as between metabolic pathway perturbations and neurodevelopment. NMR and UPLC‐HRMS analysis of plasma samples, as well as the analysis of urine samples pointed out the presence of oxoglutaric acid, oxalosuccinic acid, succinate, 2-oxoglutarate, formate, isocitrate, oxoglutaric acid, glycerol, L-carnitine, glutathione, methionine, cysteine, pyruvate, N-acetylglutamic acid, β-alanine, serine, and arginine. Τherefore, pathway analysis revealed that the most perturbed metabolic pathways from exposure to heavy metals were related to TCA cycle, purine, pyrimidine, phospholipids and carnitine metabolism, and glycolysis. The aforementioned results suggested major disturbances to cell biochemistry, which resulted in the impairment of antioxidant defense mechanisms leading to the clinically observed results in linguistic, motor development and cognitive capacity.

Urinary metabolomic investigations in vitiligo patients

Urinary metabolomics is a useful non-invasive tool for large-scale screening of disease-related metabolites. However, no comprehensive urinary metabolomic analysis of vitiligo is presently available. To investigate the urine metabolic pattern of vitiligo patients, we conducted a combined cross-sectional and prospective self-control cohort study and an untargeted urinary metabolomic analysis. In the cross-sectional study, 295 vitiligo patients and 192 age‐ and sex‐matched controls were enrolled, and 71 differential metabolites between two groups were identified. Pathway enrichment analysis revealed that drug metabolism-cytochrome P450, biopterin metabolism, vitamin B9 (folate) metabolism, selenoamino acid metabolism, and methionine and cysteine metabolism showed significant enrichment in vitiligo patients compared with the status in healthy controls. In the self-control cohort, 46 active vitiligo patients were recruited to analyse the urinary metabolic signatures after treatment. All of these patients were asked to undertake follow-up visits every 2 months three times after first consulting and the disease stage was evaluated compared with that at the last visit. Folate metabolism, linoleate metabolism, leukotriene metabolism, alkaloid biosynthesis, and tyrosine metabolism were predicted to be involved in vitiligo activity. Our study is the first attempt to reveal urinary metabolic signatures of vitiligo patients and provides new insights into the metabolic mechanisms of vitiligo.

1H-NMR-based urinary metabolomic analysis for the preventive effects of gushudan on glucocorticoid-induced osteoporosis rats

Gushudan (GSD), a traditional Chinese medicine with a history of more than 15 years, has been shown to have anti-osteoporosis effects, but the specific therapeutic mechanism behind it is still unclear. To further elucidate the pathogenesis of osteoporosis and the preventive mechanism of GSD on glucocorticoid-induced osteoporosis (GIOP) rats, a rapid and comprehensive 1H NMR metabolomics method was established to detect urinary metabolic profiles in the control group, model group and GSD treatment group in this study. The orthogonal partial least squares discriminant analysis (OPLS-DA) was performed to investigate changes in the metabolites, and related metabolic pathways were discovered using MetaboAnalyst platform. As a result, a total of 27 differential metabolites were identified. Of these, 17 metabolites such as formate, allantoin and l-threonate were newly discovered as GIOP potential biomarkers. Energy metabolism, intestinal flora metabolism, amino acid metabolism and oxidative stress response were significantly changed in the urinary profiles of GIOP rats, and GSD could play an anti-osteoporosis role by regulating these metabolic pathways. This study compliments the earlier LC-MS based urine metabolomics research, and helps further understand the pathogenesis of osteoporosis and the potential preventive effects of GSD on GIOP rats.

Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach.

Simple SummaryThere are currently no effective specific biomarkers for the screening of early gastric cancer. Recently, metabolomics has been used to profile small endogenous metabolites, demonstrating significant potential in the diagnosis/screening of cancer, owing to its ability to conduct a noninvasive sample analysis. Here, we performed a urine metabolomics analysis in the context of an early diagnosis of gastric cancer. This approach showed very high diagnostic sensitivity and specificity and performed significantly better than the analysis of serum tumor markers modalities. An additional genomic data analysis revealed the up-regulation of several genes in gastric cancer. This metabolomics-based early diagnosis approach may have the potential for mass screening an average-risk population and may facilitate endoscopic examination through risk stratification.The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.

Urinary GC–MS steroid metabotyping in treated children with congenital adrenal hyperplasia.

BackgroundTreatment of children with classic congenital adrenal hyperplasia (CAH) is a difficult balance between hypercortisolism and hyperandrogenism. Biochemical monitoring of treatment is not well defined. ObjectiveCluster analysis of the urinary steroid metabolome obtained by targeted gas chromatography–mass spectrometry (GC–MS) for treatment monitoring of children with CAH. MethodsWe evaluated 24-h urinary steroid metabolome analyses of 109 prepubertal children aged 7.0 ± 1.6 years with classic CAH due to 21-hydroxylase deficiency treated with hydrocortisone and fludrocortisone. 24-h urinary steroid metabolite excretions were transformed into CAH-specific z-scores. Subjects were divided into groups (metabotypes) by k-means clustering algorithm. Urinary steroid metabolome and clinical data of patients of each metabotype were analyzed. ResultsFour unique metabotypes were generated. Metabotype 1 (N = 21 (19%)) revealed adequate metabolic control with low cortisol metabolites (mean: −0.57z) and suppressed androgen and 17α-hydroxyprogesterone (17OHP) metabolites (−0.79z). Metabotype 2 (N = 23 (21%)) showed overtreatment consisting of a constellation of elevated urinary cortisol metabolites (0.62z) and low metabolites of androgens and 17OHP (−0.75z). Metabotype 3 (N = 32 (29%)) demonstrated undertreated patients with low cortisol metabolites (−0.69z) and elevated metabolites of androgens and 17OHP (0.50z). Metabotype 4 (N = 33 (30%)) presented patients with treatment failure reflected by unsuppressed androgen- and 17OHP metabolites (0.71z) despite elevated urinary cortisol metabolites (0.39z). ConclusionMetabotyping, which means grouping metabolically similar individuals, helps to monitor treatment of children with CAH using GC–MS urinary steroid metabolome analysis. This method allows classification in adequately-, over-, or undertreated children as well as identification of patients with treatment failure.

Urinary 1H-NMR Metabolic Signature in Subjects Undergoing Colonoscopy for Colon Cancer Diagnosis

Metabolomics represents a promising non-invasive approach that can be applied to identify biochemical changes in colorectal cancer patients (CRC) and is potentially useful for diagnosis and follow-up. Despite the literature regarding metabolomics CRC-specific profiles, discrimination between metabolic changes specifically related to CRC and intra-individual variability is still a problem to be solved. This was a preliminary case-control study, in which 1H-NMR spectroscopy combined with multivariate statistical analysis was used to profile urine metabolites in subjects undergoing colonoscopy for colon cancer diagnosis. To reduce intra-individual variability, metabolic profiles were evaluated in participants’ urine samples, collected just before the colonoscopy and after a short-term dietary regimen required for the endoscopy procedure. Data obtained highlighted different urinary metabolic profiles between CRC and unaffected subjects (C). The metabolites altered in the CRC urine (acetoacetate, creatine, creatinine, histamine, phenylacetylglycine, and tryptophan) significantly correlated with colon cancer and discriminated with accuracy CRC patients from C patients (receiver operator characteristic (ROC) curve with an area under the curve (AUC) of 0.875; 95% CI: 0.667–1). These results confirm that urinary metabolomic analysis can be a valid tool to improve CRC diagnosis, prognosis, and response to therapy, representing a noninvasive approach that could precede more invasive tests.

An Untargeted Urine Metabolomics Approach for Autologous Blood Transfusion Detection.

Autologous blood transfusion is performance enhancing and prohibited in sport but remains difficult to detect. This study explored the hypothesis that an untargeted urine metabolomics analysis can reveal one or more novel metabolites with high sensitivity and specificity for detection of autologous blood transfusion. In a randomized, double-blinded, placebo-controlled, crossover design, exercise-trained men (n = 12) donated 900 mL blood or were sham phlebotomized. After 4 wk, red blood cells or saline were reinfused. Urine samples were collected before phlebotomy and 2 h and 1, 2, 3, 5, and 10 d after reinfusion and analyzed by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. Models of unique metabolites reflecting autologous blood transfusion were attained by partial least-squares discriminant analysis. The strongest model was obtained 2 h after reinfusion with a misclassification error of 6.3% and 98.8% specificity. However, combining only a few of the strongest metabolites selected by this model provided a sensitivity of 100% at days 1 and 2 and 66% at day 3 with 100% specificity. Metabolite identification revealed the presence of secondary di-2-ethylhexyl phtalate metabolites and putatively identified the presence of (iso)caproic acid glucuronide as the strongest candidate biomarker. Untargeted urine metabolomics revealed several plasticizers as the strongest metabolic pattern for detection of autologous blood transfusion for up to 3 d. Importantly, no other metabolites in urine seem of value for antidoping purposes.

Comparative Blood and Urine Metabolomics Analysis of Healthy Elderly and Young Male Singaporeans.

Comparative metabolomics analysis of biofluids could provide information about the metabolic alterations in aging. To investigate the signature of multiple metabolic profiles associated with aging in an Asian population, we performed a pilot study in healthy Singaporeans, including 33 elderly and 33 young males. Fasting whole bloods were analyzed by routine hematology; the serum and urine metabolome profiles were obtained using NMR-based nontargeted metabolomics analysis and targeted lipoprotein analysis. Among the 90 identified compounds in serum and urine samples, 32 were significantly different between the two groups. The most obvious age-related metabolic signatures include decreased serum levels of albumin lysyl and essential amino acids and derivatives but increased levels of N-acetyl glycoproteins and several lipids and elevated urine levels of trimethylamine N-oxide, scyllo-inositol, citrate, and ascorbic acid but decreased levels of several amino acids, acetate, etc. Among 112 lipoprotein subfractions, 65 were elevated, and 2 were lower in the elderly group. These significantly age-varying metabolites, especially in the amino acid and fatty acid metabolism pathways, suggest that the regulation of these pathways contributes to the aging process in Chinese Singaporeans. Further multiomics studies including the gut microbiome and intervention studies in a larger cohort are needed to elucidate the possible mechanisms in the aging process.

Development of urine metabolomic biomarkers for ifosfamide-induced encephalopathy (IIE) in soft tissue sarcoma patients.

e23534 Background: In the treatment of sarcomas, high doses of ifosfamide in combination with other chemotherapeutic agents remains a backbone of cytoreductive therapy. Unfortunately, IIE remains a prevalent treatment side effect with a reported prevalence of 10-20%. Although mild hypoalbuminemia has previously been implicated as a risk factor, its risk contribution is modest. There are no clinically significant biomarkers which may help identify IIE patents. Given the correlation with hypoalbuminemia to renal function, we hypothesized that urinary metabolites may serve as potential early biomarkers for IIE. Here we report our interim biomarker findings as part of our sarcoma metabolomic biocollection protocol. Methods: All sarcoma patients that received ifosfamide were identified (2011-2020) through a sarcoma registry (OSU-14242). Development of IIE was obtained through chart review. For consented patients undergoing adriamycin and ifosfamide treatment, urine samples were obtained over the course of 5 days and two treatment cycles (OSU IRB: 2014C0181). Urine samples were subjected to targeted metabolomic analysis for known ifosfamide metabolites and untargeted metabolomic profiling. LC-MS was performed on a Sciex TripleTOF 5600 and data preprocessing with XCMS. For each metabolite, mean abundance and slope, as determined by linear regression across all time points, was compared to development of IIE coded as a binary variable. Results: A total of 70 sarcoma patients were identified, of whom 11 developed IIE for a prevalence of 15.7%. Baseline values at the start of ifosfamide treatment were a mean albumin of 3.7 g/dL (+/- 0.38) and a mean serum creatinine of 0.79 mg/dL (+/- 0.26). Both values are within our “normal range”. For seven of these patients, of whom one developed IIE, we successfully collected additional metabolomic profiling. From these data, 6,030 features (representing metabolites or metabolite fragments) were measured. Time course plots of drug metabolites for which pure standards were run showed an increase of expected chemotherapy-related metabolites at the beginning of each cycle, with inter- and intra-patient variability. IIE was associated with 70 metabolite features (by mean abundance, FDR &lt; 5%). Metabolite identification of putative features based on tandem mass spectrometry is in process. Conclusions: This pilot study indicates that urine metabolomic analysis is feasible and may identify IIE biomarkers. We expect to confirm these results once ongoing specimen analysis is complete.

A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry.

Human urine gives evidence of the metabolism in the body and contains different metabolites at various concentrations. A number of analytical techniques including mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been used to obtain metabolites levels in urine samples. However, gas chromatography-mass spectrometry (GC-MS) is one of the most widely used techniques for urinary metabolomics studies due to its higher sensitivity, resolution, reproducibility, reliability, relatively low cost and ease of operation compared to liquid chromatography-mass spectrometry and NMR. This review looks at various aspects of urine preparation prior to analysis by GC-MS including sample storage, urease pretreatment, derivatization, use of internal standard and quality control samples for data correction. In addition, most common types of inlet liners, ionization techniques and columns are discussed and a summary of mass analyzers are also highlighted. Lastly, the role of retention index in metabolite identification and data normalization methods are presented. The purpose of this review is summarizing methods of sample storage, pretreatment, and GC-MS analysis that are mostly used in urine metabolomics studies. Specific emphasis is given to the critical steps within the GC-MS urine metabolomics that those new to this field need to be aware of and the remaining challenges that require further attention and studies.