Urinary Metabolomic Profiles Research Articles

Effects of Preanalytical Sample Collection and Handling on Comprehensive Metabolite Measurements in Human Urine Biospecimens.

Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1048 altered metabolites (adjusted P<0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (≥63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.

Sarcosine, Trigonelline and Phenylalanine as Urinary Metabolites Related to Visceral Fat in Overweight and Obesity.

The objective of the present study is to analyze the urinary metabolome profile of patients with obesity and overweight and relate it to different obesity profiles. This is a prospective, cross-sectional study in which patients with a body mass index (BMI) ≥25 kg/m were selected. Anthropometric data were assessed by physical examination and body composition was obtained by bioimpedance (basal metabolic rate, body fat percentile, skeletal muscle mass, gross fat mass and visceral fat). Urine was collected for metabolomic analysis. Patients were classified according to abdominal circumference measurements between 81 and 93, 94 and 104, and >104 cm; visceral fat up to 16 kilos and less than; and fat percentiles of <36%, 36-46% and >46%. Spectral alignment of urinary metabolite signals and bioinformatic analysis were carried out to select the metabolites that stood out. NMR spectrometry was used to detect and quantify the main urinary metabolites and to compare the groups. Seventy-five patients were included, with a mean age of 38.3 years, and 72% females. The urinary metabolomic profile showed no differences in BMI, abdominal circumference and percentage of body fat. Higher concentrations of trigonelline (p = 0.0488), sarcosine (p = 0.0350) and phenylalanine (p = 0.0488) were associated with patients with visceral fat over 16 kg. The cutoff points obtained by the ROC curves were able to accurately differentiate between patients according to the amount of visceral fat: sarcosine 0.043 mg/mL; trigonelline 0.068 mg/mL and phenylalanine 0.204 mg/mL. In conclusion, higher visceral fat was associated with urinary levels of metabolites such as sarcosine, related to insulin resistance; trigonelline, related to muscle mass and strength; and phenylalanine, related to glucose metabolism and abdominal fat. Trigonelline, sarcosine and phenylalanine play significant roles in regulating energy balance and metabolic pathways essential for controlling obesity. Our findings could represent an interesting option for the non-invasive estimation of visceral fat through biomarkers related to alterations in metabolic pathways involved in the pathophysiology of obesity.

Urine metabolomic profiles of autism and autistic traits-A twin study.

Currently, there are no reliable biomarkers for autism diagnosis. The heterogeneity of autism and several co-occurring conditions are key challenges to establishing these. Here, we used untargeted mass spectrometry-based urine metabolomics to investigate metabolic differences for autism diagnosis and autistic traits in a well-characterized twin cohort (N = 105). We identified 208 metabolites in the urine samples of the twins. No clear, significant metabolic drivers for autism diagnosis were detected when controlling for other neurodevelopmental conditions. However, we identified nominally significant changes for several metabolites. For instance, phenylpyruvate (p = 0.019) and taurine (p = 0.032) were elevated in the autism group, while carnitine (p = 0.047) was reduced. We furthermore accounted for the shared factors, such as genetics within the twin pairs, and report additional metabolite differences. Based on the nominally significant metabolites for autism diagnosis, the arginine and proline metabolism pathway (p = 0.024) was enriched. We also investigated the association between quantitative autistic traits, as measured by the Social Responsiveness Scale 2nd Edition, and metabolite differences, identifying a greater number of nominally significant metabolites and pathways. A significant positive association between indole-3-acetate and autistic traits was observed within the twin pairs (adjusted p = 0.031). The utility of urine biomarkers in autism, therefore, remains unclear, with mixed findings from different study populations.

Propionic Acidemia, Methylmalonic Acidemia, and Cobalamin C Deficiency: Comparison of Untargeted Metabolomic Profiles.

Methylmalonic acidemia (MMA), propionic acidemia (PA), and cobalamin C deficiency (cblC) share a defect in propionic acid metabolism. In addition, cblC is also involved in the process of homocysteine remethylation. These three diseases produce various phenotypes and complex downstream metabolic effects. In this study, we used an untargeted metabolomics approach to investigate the biochemical differences and the possible connections among the pathophysiology of each disease. The significantly changed metabolites in the untargeted urine metabolomic profiles of 21 patients (seven MMA, seven PA, seven cblC) were identified through statistical analysis (p < 0.05; log2FC > |1|) and then used for annotation. Annotated features were associated with different metabolic pathways potentially involved in the disease's development. Comparative statistics showed markedly different metabolomic profiles between MMA, PA, and cblC, highlighting the characteristic species for each disease. The most affected pathways were related to the metabolism of organic acids (all diseases), amino acids (all diseases), and glycine and its conjugates (in PA); the transsulfuration pathway; oxidative processes; and neurosteroid hormones (in cblC). The untargeted metabolomics study highlighted the presence of significant differences between the three diseases, pointing to the most relevant contrast in the cblC profile compared to MMA and PA. Some new biomarkers were proposed for PA, while novel data regarding the alterations of steroid hormone profiles and biomarkers of oxidative stress were obtained for cblC disease. The elevation of neurosteroids in cblC may indicate a potential connection with the development of ocular and neuronal deterioration.

Urine metabolite changes after cardiac surgery predict acute kidney injury.

Acute kidney injury (AKI) is a serious complication in patients undergoing cardiac surgery, with the underlying mechanism remaining elusive and a lack of specific biomarkers for cardiac surgery-associated AKI (CS-AKI). We performed an untargeted metabolomics analysis of urine samples procured from a cohort of patients with or without AKI at 6 and 24 h following cardiac surgery. Based on the differential urinary metabolites discovered, we further examined the expressions of the key metabolic enzymes that regulate these metabolites in kidney during AKI using a mouse model of ischemia-reperfusion injury (IRI) and in hypoxia-treated tubular epithelial cells (TECs). The urine metabolomic profiles in AKI patients were significantly different from those in non-AKI patients, including upregulation of tryptophan metabolism- and aerobic glycolysis-related metabolites, such as l-tryptophan and d-glucose-1-phosphate, and downregulation of fatty acid oxidation (FAO) and tricarboxylic acid (TCA) cycle-related metabolites. Spearman correlation analysis showed that serum creatinine was positively correlated with urinary l-tryptophan and indole, which had high accuracy for predicting AKI. In animal experiments, we demonstrated that the expression of rate-limiting enzymes in glycolysis, such as hexokinase II (HK2), was significantly upregulated during renal IRI. However, the TCA cycle-related key enzyme citrate synthase was significantly downregulated after IRI. In vitro, hypoxia induced downregulation of citrate synthase in TECs. In addition, FAO-related gene peroxisome proliferator-activated receptor alpha (PPARα) was remarkably downregulated in kidney during renal IRI. This study presents urinary metabolites related to CS-AKI, indicating the rewiring of the metabolism in kidney during AKI, identifying potential AKI biomarkers.

Serum and Urine Metabolomic Profiles of Long-Term Ultra-Processed Food Intake in the Longitudinal Interactive Diet and Activity Tracking in AARP (IDATA) Study

Serum and Urine Metabolomic Profiles of Long-Term Ultra-Processed Food Intake in the Longitudinal Interactive Diet and Activity Tracking in AARP (IDATA) Study

#2194 Targeted metabolomics analysis identifies a specific metabolomic profile in patients with early chronic kidney disease

Abstract Background and Aims The complex interconnections between various metabolites and the kidney play a major role in the pathogenesis of chronic kidney disease (CKD). Hence, by the assessment of the relationship between metabolites and the kidney, more accurate biomarkers could be discovered to prevent and detect CKD at an early stage. The primary goal of this study was to find new serum and urine biomarkers for the diagnosis of early CKD by conducting a metabolomic profiling through targeted metabolomics of both blood and urine in patients with CKD. Method This cross-sectional study comprised 80 patients with CKD who were categorized into six subgroups (G1-G6) based on their eGFR according to the KDIGO Guidelines. Additionally, 20 healthy individuals were included as healthy control participants (group C). Ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry was used to perform serum and urine metabolomic profiling. The 6 metabolites evaluated by this study were selected based on the results of a previous untargeted analysis research. Both blood and urine samples underwent multivariate analysis, which was subsequently followed by univariate analysis and quantitative analysis. Initially, the PLSDA score plot and VIP score were utilized. The application of the cross-validation procedure to the initial set of 6 molecules provided outstanding results, including high accuracy, high R² values, and a significant Q2 value. Hence, the model might be deemed as predictive. Random Forest analysis was utilized to conduct biomarker analysis and prediction. The specificity and sensitivity of the compounds identified as possible biomarkers were assessed using the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC). Results There were strong positive correlations between eGFR and serum levels of Arginine (p &amp;lt; 0.05) and L-Acetylcarnitine (p &amp;lt; 0.05). Of note, the amounts of L-Methionine, L-Phenylalanine, Kynurenic acid, and Indoxyl sulfate in serum showed a progressive increase from group C to subgroups G1-G5. Furthermore, L-Methionine, L-Phenylalanine, Kynurenic acid, and Indoxyl sulfate exhibited a negative correlation with eGFR (p &amp;lt; 0.05). In addition, there was a positive correlation between the levels of urine Arginine, L-Methionine, and L-Phenylalanine and eGFR (p &amp;lt; 0.05). Urine levels of Kynurenic acid, Indoxyl sulfate, and L-Acetylcarnitine were higher in groups G4 and G5, as compared to groups G1-G3b and C, respectively. The urinary concentrations of Indoxyl sulfate, Kynurenic acid, Indoxyl sulfate, and L-Acetylcarnitine exhibited a negative correlation with eGFR (p &amp;lt; 0.05). Conclusion The current study shows that serum and urinary metabolites levels in patients with CKD display an alteration in production, secretion, and excretion in all CKD stages. There was demonstrated a distinct metabolomic profile in the course of CKD which points to novel biomarkers useful for the diagnosis and progression evaluation of CKD from its early stages.

Changes in urinary metabolomic profile show the effectiveness of a nutritional intervention in children 6-12 years old: The ALINFA study.

Diet plays an essential role in health and disease. Therefore, its determination is an important component of many investigations. The aim of the study was to evaluate the effect of a nutritional intervention on the urinary metabolome in children aged 6-12 years. Also, it was intended to identify biomarkers of diet quality and dietary intake. A 2-month, randomized, controlled, parallel trial was conducted in Spanish children. The analyses focused on the ALINFA group, which followed a full-fixed meal plan including healthy products, ready-to-eat meals, and healthy recipes. Diet quality was assessed by the KIDMED index and dietary intake by a food frequency questionnaire. Untargeted metabolomic analysis on urine samples was carried out, and multivariate analyses were performed for pattern recognition and characteristic metabolite identification. PLS-DA and Volcano plot analyses were performed to identify the discriminating metabolites of this group. 12 putative metabolites were found to be the most relevant to this intervention. Most of them were products derived from protein and amino acid metabolism (N-Ribosylhistidine, indolacrylic acid, and peptides) and lipid metabolism (3-oxo-2-pentylcyclopentane-1-hexanoic acid methyl, Suberoyl-L-carnitine, and 7-Dehydrodichapetalin E). All these metabolites decreased after the intervention, which was mainly associated with a decrease in the consumption of fatty meat and total fat, especially saturated fat. In turn, N-Ribosylhistidine and Suberoyl-L-carnitine were negatively associated with diet quality, as well as able to predict the change in KIDMED index. In conclusion, the changes observed in urinary metabolome demonstrate the effectiveness of the ALINFA nutritional intervention.

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Metabolomic Analysis Reveals the Association of Severe Bronchopulmonary Dysplasia with Gut Microbiota and Oxidative Response in Extremely Preterm Infants.

Bronchopulmonary dysplasia (BPD) is a chronic lung disease mainly affecting premature infants needing ventilation or oxygen for respiratory distress. This study aimed to evaluate the molecular linkages for BPD in very and extremely preterm infants using a metabolomics-based approach. A case-control study of enrolling preterm infants born before 32 weeks gestational age (GA) was prospectively performed. These preterm infants were subsequently stratified into the following two groups for further analysis: no or mild BPD, and moderate or severe BPD based on the 2019 NICHD criteria. Urinary metabolomic profiling was performed using 1H-Nuclear magnetic resonance (NMR) spectroscopy coupled with partial least squares discriminant analysis (PLS-DA) at a corrected age of 6 months. Metabolites significantly differentially related to GA and BPD severity were performed between groups, and their roles in functional metabolic pathways were also assessed. A total of 89 preterm infants born before 32 weeks gestation and 50 infants born at term age (above 37 completed weeks' gestation) served as controls and were enrolled into the study. There were 21 and 24 urinary metabolites identified to be significantly associated with GA and BPD severity, respectively (p < 0.05). Among them, N-phenylacetylglycine, hippurate, acetylsalicylate, gluconate, and indoxyl sulfate were five metabolites that were significantly higher, with the highest importance in both infants with GA < 28 weeks and those with moderate to severe BPD, whereas betaine and N,N-dimethylglycine were significantly lower (p < 0.05). Furthermore, ribose and a gluconate related pentose phosphate pathway were strongly associated with these infants (p < 0.01). In conclusion, urinary metabolomic analysis highlights the crucial role of gut microbiota dysbiosis in the pathogenesis of BPD in preterm infants, accompanied by metabolites related to diminished antioxidative capacity, prompting an aggressive antioxidation response in extremely preterm infants with severe BPD.

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.

Taurine Deficiency Is a Hallmark of Injured Kidney Allografts.

Taurine is one of the most abundant amino acids in humans. Low taurine levels are associated with cellular senescence, mitochondrial dysfunction, DNA damage, and inflammation in mouse, all of which can be reversed by supplementation. It is unknown whether taurine metabolism is associated with kidney allograft function and survival. We performed urine metabolomic profiling of kidney transplant recipients in the early and late phases after transplantation combined with transcriptomic analysis of human kidney allografts. Single-nucleus RNA sequencing data sets of mouse kidneys after ischemia-reperfusion injury were analyzed. We analyzed the association of urinary taurine levels and taurine metabolism genes with kidney function, histology, and graft survival. Urine taurine concentrations were significantly lower in kidney transplant recipients who experienced delayed graft function. In a mouse model of ischemia-reperfusion injury, the taurine biosynthesis gene, CSAD , but not the taurine transporter SLC6A6 , was repressed. In the late stage of transplantation, low level of taurine in urine was associated with impaired kidney function and chronic structural changes. Urine taurine level in the lowest tertile was predictive of graft loss. Expression of the taurine transporter SLC6A6 in the upper median, but not CSAD , was associated with chronic kidney injury and was predictive of graft loss. Low urine taurine level is a marker of injury in the kidney allograft, is associated with poor kidney function, is associated with chronic histological changes, and is predictive of graft survival. The differential expression of CSAD and SLC6A6 , depending on the time after transplantation and marks of injury, highlights different mechanisms affecting taurine metabolism.

Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder.

Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML. Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identify metabolic pathways associated with autism. A total of 52 autistic children and 40 typically developing children were enrolled. Lasso regression identified ninety-two urinary metabolites that significantly differed between the two groups. Distinct metabolites, such as prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, were revealed to be associated with autism through the application of four different ML methods (p<0.05). The alterations observed in the phosphatidylinositol and inositol phosphate metabolism pathways were linked to the pathophysiology of autism (p<0.05). Significant urinary metabolites, including prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, exhibit associations with autism. Additionally, the involvement of the phosphatidylinositol and inositol phosphate pathways suggests their potential role in the pathophysiology of autism.

Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry.

Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88-0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Diet and Chronic Disease Research in the Women’s Health Initiative

The Women’s Health Initiative (WHI) has been a major contributor to diet and chronic disease research among postmenopausal US women over its 30+ year history (1993 to present). The WHI program included full-scale randomized trials of a low-fat dietary pattern high in fruits, vegetables, and grains, and of calcium and vitamin D supplementation, each with designated primary and secondary chronic disease outcomes. The history of these trials will be briefly reviewed here, along with principal findings that included evidence for breast cancer-related benefits for each of the 2 interventions. In recent years, WHI investigators have developed an active research program in nutritional biomarker development and in the application of these biomarkers in WHI cohorts, among various other nutritional epidemiology uses of WHI observational study resources. The intake biomarker work, which primarily relies on blood and urine metabolomics profiles, lends support to the low-fat dietary pattern trial results, and supports chronic disease benefits of higher carbohydrate diets more generally, especially through the fiber component of carbohydrate.

Changes in urine metabolite concentration as a minimally invasive marker of ovarian serous adenocarcinoma

Introduction. Detection of ovarian cancer (OC) at the earliest possible stages is a priority for gynecological oncology, since 5-year survival rates decrease significantly with the progression of the disease. Currently, there is a huge need for more effective diagnostic methods and approaches. In recent years, fluid biopsy has received increasing attention in precision medicine because it is minimally invasive and can be repeated many times, allowing for realtime disease monitoring.Aim. Study of the urine metabolomic profile of patients with ovarian carcinoma.Materials and methods. To perform metabolomic analysis, 50 urine samples from patients with a diagnosis of serous ovarian carcinoma and 20 samples from apparently healthy individuals were selected. For protein precipitation, 300 mkl of urine was mixed with 600 mkl of a solution of acetonitrile LC-MS (Merck, Germany) and methanol LC-MS (Merck, Germany) (3:1 ratio). Chromatographic separation of metabolites was performed on a Vanquish Flex UHPLC System chromatograph (Thermo Scientific, Germany). The chromatograph was coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Scientific, Germany) equipped with an electrospray ionization source. Chromatographic separation was carried out on a Hypersil GOLD™ C18 column (1.9mkm, 10 x 2.1 mm) using the following eluents: A, 0.1 % formic acid; B, acetonitrile containing 0.1 % formic acid.Results. A total of 417 metabolites of various classes were identified by HPLC-MS. It was shown that in the urine of patients with OC 14 metabolites (kynurenine, phenylalanyl-valine, lysophosphatidylcholine (18:3), lysophosphatidylcholine (18:2), alanyl-leucine, lysophosphatidylcholine (20:4), L-phenylalanine, phosphatidylinositol (34:1), 5-methoxytryptophan, 2-hydroxymyristic acid, 3-oxocholic acid, lysophosphatidylcholine (14:0), indoleacrylic acid, lysophosphatidylserine (20:4)) had a significantly higher concentration compared to apparently healthy individuals. The content of 12 compounds, on the contrary, was reduced (L-beta-aspartyl-L-phenylalanine, myristic acid, decanoylcarnitine, aspartyl-glycine, malonylcarnitine, 3-hydroxybutyrylcarnitine, 3-methylxanthine, 2,6-dimethylheptanoylcarnitine, 3-oxododecanoic acid, N-acetylproline, L-octanoylcarnitine, capryloylglycine). This indicates a significant metabolomic imbalance in patients with OC.Conclusion. The metabolomic profile study of urine by UHPLC-MS showed that in patients with serous ovarian carcinoma there is an imbalance in the content of certain fatty acids and their derivatives, acylcarnitines, phospholipids, amino acids and their derivatives, as well as some derivatives of nitrogenous bases. At the same time, 26 metabolites with abnormal concentrations in urine may have some potential as non-invasive biomarkers of OC in women belonging to high-risk groups.

Metabolomics Meets Clinics: A Multivariate Analysis of Plasma and Urine Metabolic Signatures in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a severe, multifactorial, and frequently misdiagnosed disorder. The aim of this observational study was to compare the plasma and urine metabolomic profiles of PAH patients and healthy control subjects. Plasma and urine metabolomic profiles were analyzed using the GC-MS technique. Correlations between metabolite levels and clinical parameters among PAH patients, as well as the between-group differences, were evaluated. The linear discriminant analysis model, which allows for subject classification in terms of PAH with the highest possible precision, was developed and proposed. Plasma pyruvic acid, cholesterol, threonine, urine 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, butyric acid, 1,2-benzenediol, glucoheptonic acid, and 2-oxo-glutaric acid were found to build a relatively accurate classification model for PAH patients. The model reached an accuracy of 91% and significantly improved subject classification (OR = 119 [95% CI: 20.3-698.3], p < 0.0001). Five metabolites were detected in urine that provide easily available and noninvasive tests as compared to right heart catheterization. The selected panel of metabolites has potential for early recognition of patients with dyspnea and faster referral to a reference center.

Small Intestinal Permeability and Metabolomic Profiles in Feces and Plasma Associate With Clinical Response in Patients With Active Psoriatic Arthritis Participating in a Fecal Microbiota Transplantation Trial: Exploratory Findings From the FLORA Trial.

We investigated intestinal permeability and fecal, plasma, and urine metabolomic profiles in methotrexate-treated active psoriatic arthritis (PsA) and how this related to clinical response following one sham or fecal microbiota transplantation (FMT). This exploratory study is based on the FLORA trial cohort, in which 31 patients with moderate-to-high peripheral PsA disease activity, despite at least 3 months of methotrexate-treatment, were included in a 26-week, double-blind, 1:1 randomized, sham-controlled trial. Participants were randomly allocated to receive either one healthy donor FMT (n=15) or sham (n=16) via gastroscopy. The primary trial end point was the proportion of treatment failures through 26 weeks. We performed a lactulose-to-mannitol ratio (LMR) test at baseline (n=31) and at week 26 (n=26) to assess small intestinal permeability. Metabolomic profiles in fecal, plasma, and urine samples collected at baseline, weeks 4, 12, and 26 were measured using 1 H Nuclear Magnetic Resonance. Trial failures (n=7) had significantly higher LMR compared with responders (n=19) at week 26 (0.027 [0.017-0.33]) vs. 0.012 [0-0.064], P = 0.013), indicating increased intestinal permeability. Multivariate analysis revealed a significant model for responders (n=19) versus failures (n=12) at all time points based on their fecal (P < 0.0001) and plasma (P = 0.005) metabolomic profiles, whereas urine metabolomic profiles did not differ between groups (P = 1). Fecal N-acetyl glycoprotein GlycA correlated with Health Assessment Questionnaire Disability Index (coefficient=0.50; P = 0.03) and fecal propionate correlated with American College of Rheumatology 20 response at week 26 (coefficient=27, P = 0.02). Intestinal permeability and fecal and plasma metabolomic profiles of patients with PsA were associated with the primary clinical trial end point, failure versus responder.

Sardinian Infants of Diabetic Mothers: A Metabolomics Observational Study.

Gestational diabetes mellitus (GDM) is a condition characterized by glucose intolerance, with hyperglycemia of varying severity with onset during pregnancy. An uncontrolled GDM can lead to an increased risk of morbidity in the fetus and newborn, and an increased risk of obesity or developing type 2 diabetes, hypertension or neurocognitive developmental impairment in adulthood. In this study, we used nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GS-MS) to analyze the urinary metabolomic profile of newborns of diabetic mothers (NDMs) with the aim of identifying biomarkers useful for the monitoring of NDMs and for early diagnosis of predisposition to develop related chronic diseases. A total of 26 newborns were recruited: 21 children of diabetic mothers, comprising 13 in diet therapy (NDM-diet) and 8 in insulin therapy (NDM-insulin), and 5 control children of non-diabetic mothers (CTR). Urine samples were collected at five time points: at birth (T1), on the third day of life (T2), one week (T3), one month (T4) and six months postpartum (T5). At T1, variations were observed in the levels of seven potential biomarkers (acetate, lactate, glycylproline/proline, isocitrate, N,N-dimethylglycine, N-acetylglucosamine and N-carbamoyl-aspartate) in NMD-insulin infants compared to NDM-diet and CTR infants. In particular, the altered metabolites were found to be involved in several metabolic pathways such as citrate metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, amino sugar and nucleotide sugar metabolism, and pyruvate metabolism. In contrast, these changes were not visible at subsequent sampling times. The impact of early nutrition (maternal and formula milk) on the metabolomic profile was considered as a potential contributing factor to this finding.