Urine metabolome profiling of immune-mediated inflammatory diseases

Background: Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn?s disease, and ulcerative colitis. Methods: Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. Results: In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (PFDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (PFDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an overrepresentation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. Conclusions: This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs

Using metabolomics to monitor kidney transplantation patients by means of clustering to spot anomalous patient behavior

Background: NMR spectroscopy-based metabolomics is a system approach used to investigate the metabolic profile of biological fluids with multivariate data analysis tools. The aim of this study was to examine the kidney graft recovery process noninvasively through the examinations of urine samples using 1H NMR spectroscopy combined with chemometric tools. Methods: Urine samples were treated as the source of metabolites reflecting the pathological and clinical conditions of patients with transplanted kidneys. We observed 15 subjects (9 males and 6 females) during the graft recovery process and initial days thereafter. The patients provided at least 9 samples each, applying advanced statistical methods of analysis: Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis PLS-DA). Results: The PCA model (for all subjects exp. var. PC1 13.96% and PC2 9.88%) allowed us to clearly designate 3 stages of recovery: initially the kidney is not working; in the second stage, it regains functions, and the third stage includes follow-up during hospitalization. PCA analysis of a single patient follows graft recovery based on biochemical (metabolites) information, assigning the appropriate recuperation stage. Conclusions: NMR spectroscopy together with chemometric analysis allow monitoring of kidney graft recovery to identify patients who are not progressing within the normal range

Stability in metabolic phenotypes and inferred metagenome profiles before the onset of colitis-induced inflammation.

Inflammatory bowel disease (IBD) is associated with altered microbiota composition and metabolism, but it is unclear whether these changes precede inflammation or are the result of it since current studies have mainly focused on changes after the onset of disease. We previously showed differences in mucus gut microbiota composition preceded colitis-induced inflammation and stool microbial differences only became apparent at colitis onset. In the present study, we aimed to investigate whether microbial dysbiosis was associated with differences in both predicted microbial gene content and endogenous metabolite profiles. We examined the functional potential of mucus and stool microbial communities in the mdr1a (-/-) mouse model of colitis and littermate controls using PICRUSt on 16S rRNA sequencing data. Our findings indicate that despite changes in microbial composition, microbial functional pathways were stable before and during the development of mucosal inflammation. LC-MS-based metabolic phenotyping (metabotyping) in urine samples confirmed that metabolite profiles in mdr1a (-/-) mice were remarkably unaffected by development of intestinal inflammation and there were no differences in previously published metabolic markers of IBD. Metabolic profiles did, however, discriminate the colitis-prone mdr1a (-/-) genotype from controls. Our results indicate resilience of the metabolic network irrespective of inflammation. Importantly as metabolites differentiated genotype, genotype-differentiating metabolites could potentially predict IBD risk