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Abstract Background Intrahepatic cholestasis of pregnancy (ICP) is a prevalent pregnancy-specific complication that presents with maternal itching and elevated serum bile acid levels. ICP is associated with unfavorable pregnancy outcomes, severely decreasing the pregnant woman’s quality of life. Timely identification of ICP is crucial for effective management and improved outcomes. Methods We collected urine samples from 8 patients with ICP and 8 healthy individuals. We used Liquid Chromatography-Mass Spectrometry (LC-MS) to detect metabolite expression levels, then conducted a series of bioinformatic analyses to explore the potential biological meanings of differentially expressed metabolites, and preliminarily discovered several candidate biomarkers. To validate these candidate biomarkers, we performed Gas Chromatography-Mass Spectrometry (GC-MS) detection and analyzed their diagnostic values using receiver operating characteristic (ROC) curve. Results Untargeted metabolomics data showed that 6129 positive peaks and 6218 negative peaks were extracted from each specimen. OPLS-DA analysis and the heat map for cluster analysis showed satisfactory capability in discriminating ICP specimens from controls. Subsequent analysis extracted 64 significantly differentially expressed metabolites, which could be potential biomarkers for diagnosis of ICP. Based on the KEGG enrichment analyses, six candidate biomarkers were preliminarily identified. Two most promising biomarkers (3-hydroxypropionic acid and uracil) were validated by targeted metabolomics analyses with the area under the curve (AUC) of 0.920 and 0.850 respectively. Conclusion Based on preliminary screening from untargeted metabolomics and subsequent validation through targeted metabolomics, 3-hydroxypropionic acid and uracil were identified as promising diagnostic biomarkers for ICP.

Background: Intrahepatic cholestasis of pregnancy (ICP) is a prevalent pregnancy-specific complication that presents with maternal itching and elevated serum bile acid levels. ICP is associated with unfavorable pregnancy outcomes, severely decreasing the pregnant woman's quality of life. Timely identification of ICP is crucial for effective management and improved outcomes. Methods: We collected urine samples from 8 patients with ICP and 8 healthy individuals. We used Liquid Chromatography-Mass Spectrometry (LC-MS) to detect metabolite expression levels, then conducted a series of bioinformatic analyses to explore the potential biological meanings of differentially expressed metabolites, and preliminarily discovered several candidate biomarkers. To validate these candidate biomarkers, we performed Gas Chromatography-Mass Spectrometry (GC-MS) detection and analyzed their diagnostic values using receiver operating characteristic (ROC) curve. Results: Untargeted metabolomics data showed that 6129 positive peaks and 6218 negative peaks were extracted from each specimen. OPLS-DA analysis and the heat map for cluster analysis showed satisfactory capability in discriminating ICP specimens from controls. Subsequent analysis extracted 64 significantly differentially expressed metabolites, which could be potential biomarkers for diagnosis of ICP. Based on the KEGG enrichment analyses, six candidate biomarkers were preliminarily identified. Two most promising biomarkers (3-hydroxypropionic acid and uracil) were validated by targeted metabolomics analyses with the area under the curve (AUC) of 0.920 and 0.850 respectively. Conclusion: Based on preliminary screening from untargeted metabolomics and subsequent validation through targeted metabolomics, 3-hydroxypropionic acid and uracil were identified as promising diagnostic biomarkers for ICP. [ABSTRACT FROM AUTHOR]

Abstract Background Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers. Methods Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high‐resolution liquid chromatography–mass spectrometry (LC–MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects. Results Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p‐anisic acid) with CV183 > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age‐related metabolites, 18 BMI‐related metabolites, and 42 sex‐related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3‐methylcrotonylglycine and N‐acetyl‐aspartic acid. Pearson's coefficients demonstrated that some age‐, BMI‐, and sex‐related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly. Conclusion Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases.

Background: Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers. Methods: Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high‐resolution liquid chromatography–mass spectrometry (LC–MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects. Results: Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p‐anisic acid) with CV183 > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age‐related metabolites, 18 BMI‐related metabolites, and 42 sex‐related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3‐methylcrotonylglycine and N‐acetyl‐aspartic acid. Pearson's coefficients demonstrated that some age‐, BMI‐, and sex‐related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly. Conclusion: Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases. [ABSTRACT FROM AUTHOR]

High-level sport requires analysis of athletes' metabolic conditions in order to improve the training. Raman spectroscopy can be used to assess urinary composition advantageously when compared to conventional methods of urinalysis. In this work, Raman spectroscopy has been employed to detect creatine in urine of professional swimmers before and after training compared to sedentaries. It has been collected urine samples from five swimmers before and immediately after 150 min of swimming and submitted to Raman spectroscopy (830 nm excitation, 350 mW laser power, 20 s integration time) and compared to the urine from a control group (14 sedentary subjects). The Raman spectra of urine from four swimmers after training showed peaks related to creatine at 829, 915, 1049, and 1397 cm-1, besides peaks referred to urea, creatinine, ketone bodies, and phosphate. A spectral model estimated the concentration of creatine to be from 0.26 to 0.72 g/dL in the urine of these athletes. The presence of this metabolic biomarker in the urine of some swimmers suggests a metabolic profile influenced by the diet, supplementation, individual metabolism, and the self-response to the training. Raman spectroscopy allows a rapid and reliable detection of creatine excreted in the urine of swimming athletes, which may be used to adjust the nutrition/supplementation of each individual as well as the individual response and energy consumption depending on the type and duration of the training. [ABSTRACT FROM AUTHOR]

Interspecific differences in xenobiotic metabolism are a key to determining relative sensitivities of animals to xenobiotics. However, information on domesticated livestock, companion animals, and captive and free-ranging wildlife is incomplete. The present study evaluated interspecific differences in phase II conjugation using pyrene as a nondestructive biomarker of polycyclic aromatic hydrocarbon (PAH) exposure. Polycyclic aromatic hydrocarbons and their metabolites have carcinogenic and endocrine-disrupting effects in humans and wildlife and can have serious consequences. The authors collected urine from 16 mammalian species and analyzed pyrene metabolites. Interspecific differences in urinary pyrene metabolites, especially in the concentration and composition of phase II conjugated metabolites, were apparent. Glucuronide conjugates are dominant metabolites in the urine of many species, including deer, cattle, pigs, horses, and humans. However, they could not be detected in ferret urine even though the gene for ferret Uridine 5′-diphospho-glucuronosyltransferase (UDP-glucuronosyltransferase, UGT) 1A6 is not a pseudogene. Sulfate conjugates were detected mainly in the urine of cats, ferrets, and rabbits. Interestingly, sulfate conjugates were detected in pig urine. Although pigs are known to have limited aryl sulfotransferase activity, the present study demonstrated that pig liver was active in 1-hydroxypyrene sulfation. The findings have some application for biomonitoring environmental pollution. Environ Toxicol Chem 2014; 33:2062-2069. © 2014 SETAC [ABSTRACT FROM AUTHOR]

Abstract: A method to obtain high reproducibility of 1H NMR chemical shift of peaks of biofluid metabolites, by simple acidification with HCl is evaluated. Biofluid 1H NMR analysis is indeed spoiled by a strong chemical shift dependence of metabolite peaks on parameters such as ionic strength, concentration of some earth alkali cations and, mostly, on pH of samples. The resulting chemical shift variations, as large as 0.1ppm, generate misalignments of homogeneous peaks, artifacts and misinterpretations. Reproducible alignment is essential in 1H NMR based metabonomics, where peak misalignments prevent even very wide bins (i.e., 0.04ppm, as elsewhere proposed) from being used to integrate spectral data for multivariate statistical analysis. Here is demonstrated that routine acidification with HCl to 1.2≤pH≤2.0 ensures highly reproducible peak alignment of urine 1H NMR spectra. In this respect, simple inspection of citrate peaks in the urine can be used to measure pH, as it will be extensively discussed, in that at such low pH they show no dependency on other urine components as reported at higher pH. Under these conditions, in as many as 493 urine samples, in which concentrations of Ca2+, Mg2+, K+, Na+, Cl−, phosphate, and creatinine and ionic strength measured by means of well standardized conventional procedures, showed very wide ranges, peaks align within a SD always lower than 0.002ppm, thus allowing the use of integration bins at least five times narrower than 0.04ppm. [Copyright &y& Elsevier]

High-throughput profiling of metabolites has been used to identify metabolic changes in murine models as a response to the infection by the parasitic trematode Schistosoma. These investigations have contributed to our understanding on the pathogenesis of this tropical neglected disease, with a potential of helping diagnosis. Here, our study aimed to investigate the application of gas chromatography–mass spectrometry (GC/MS) on the profiling of urine metabolites from mice carrying infections by Schistosoma mansoni. Two larval infection doses created distinctive infection intensities in mice, whereby the heavily infected animals were found to release 25 times more eggs in faeces than lightly infected animals. Over 200 urine metabolites were identified from these animals by GC/MS, following two complementary derivatisation methods. A list of 14 individual metabolites with altered relative abundances between groups were identified. Most of the altered metabolites showed a trend of increased abundances in response to infection intensity, indicating host-specific metabolic alterations as a result of the disease. Hippurate, a metabolite which concentration is intimately modulated by the gut microbiota, was found to be highly correlated to infection intensity. Our study showed that urine metabolic profiling by GC/MS can distinguish non-infected animals from those carrying light and heavy infections by S. mansoni , revealing metabolites associated to the infection and providing insights on the pathogenesis of schistosomiasis. Unlabelled Image • Mice with light and heavy infections by Schistosoma mansoni were established. • Metabolic profiling by GC/MS discriminated non-infected from infected animals. • Specific metabolites were associated to the infection and infection intensity. • Alterations were related to host- and microbiota-specific metabolic disturbances. • Hippurate was a marked urine metabolite, highly correlated to infection intensity. [ABSTRACT FROM AUTHOR]

The evaluation of new tuberculosis (TB) therapies is limited by the paucity of biomarkers to monitor treatment response. Previous work detected an uncharacterized urine metabolite with a molecular mass of 874.3547 Da that showed promise as a biomarker for successful TB treatment. Using mass spectrometry combined with enzymatic digestions, the metabolite was structurally characterized as a seryl-leucine core 1 O-glycosylated peptide (SLC1G) of human origin. Examination of SLC1G in urine revealed a significant abundance increase in individuals with active TB versus their household contacts and healthy controls. Moreover, differential decreases in SLC1G levels were observed by week one in TB patients during successful treatment versus those that failed treatment. The SLC1G levels were also associated with clinical parameters used to measure bacterial burden (GeneXpert) and inflammation (positron emission tomography-computed tomography (PET-CT)). These results demonstrate the importance of metabolite identification and provide strong evidence for applying SLC1G as a biomarker of TB treatment response.