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Urine metabolome profiling of immune-mediated inflammatory diseases

View Article: PubMed Central - PubMed

ABSTRACT

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 over-representation 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.

Electronic supplementary material: The online version of this article (doi:10.1186/s12916-016-0681-8) contains supplementary material, which is available to authorized users.

No MeSH data available.


Distribution of metabolite concentrations associated to disease activity. This figure shows the logarithmic concentrations of the metabolites associated to CD disease activity normalized to the median concentration of the control cohort. White and grey bars refer to low and high activity patients, respectively
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Fig4: Distribution of metabolite concentrations associated to disease activity. This figure shows the logarithmic concentrations of the metabolites associated to CD disease activity normalized to the median concentration of the control cohort. White and grey bars refer to low and high activity patients, respectively

Mentions: In the discovery cohort, three metabolites – citrate, hippurate, and 3-hydroxyisovalerate – were found to be significantly associated with disease activity in CD after multiple-test correction (Fig. 4, Additional file 1: Table S4). In particular, CD patients with high levels of disease activity were found to have much lower levels of these three metabolites compared to patients with low disease activity. Using the validation cohort, the association between the low levels of these three metabolites in urine and high disease activity in CD was replicated (Pcitrate = 4.4 × 10–10, Phippurate = 6.0 × 10–7, and P3-hydroxyisovalerate = 1.30 × 10–5).Fig. 4


Urine metabolome profiling of immune-mediated inflammatory diseases
Distribution of metabolite concentrations associated to disease activity. This figure shows the logarithmic concentrations of the metabolites associated to CD disease activity normalized to the median concentration of the control cohort. White and grey bars refer to low and high activity patients, respectively
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016926&req=5

Fig4: Distribution of metabolite concentrations associated to disease activity. This figure shows the logarithmic concentrations of the metabolites associated to CD disease activity normalized to the median concentration of the control cohort. White and grey bars refer to low and high activity patients, respectively
Mentions: In the discovery cohort, three metabolites – citrate, hippurate, and 3-hydroxyisovalerate – were found to be significantly associated with disease activity in CD after multiple-test correction (Fig. 4, Additional file 1: Table S4). In particular, CD patients with high levels of disease activity were found to have much lower levels of these three metabolites compared to patients with low disease activity. Using the validation cohort, the association between the low levels of these three metabolites in urine and high disease activity in CD was replicated (Pcitrate = 4.4 × 10–10, Phippurate = 6.0 × 10–7, and P3-hydroxyisovalerate = 1.30 × 10–5).Fig. 4

View Article: PubMed Central - PubMed

ABSTRACT

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 over-representation 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.

Electronic supplementary material: The online version of this article (doi:10.1186/s12916-016-0681-8) contains supplementary material, which is available to authorized users.

No MeSH data available.