1H-NMR urine metabolomic fingerprint for severity discrimination in pulmonary sarcoidosis.

Abstract

Background: Sarcoidosis is a systemic inflammatory immune disease characterised by noncaseating granulomas. Its course can vary from benign to very severe, requiring appropriate treatment. Few biomarkers are available to monitor the management of these patients and to identify those at risk of poor prognosis. Given the systemic nature of sarcoidosis, we hypothesised that the analysis of urine metabolites could provide valuable biomarkers for the management of severe disease.

Methods: We conducted a comparative analysis of urine metabolomics in a consecutive cohort of 37 well-phenotyped patients, using ¹H nuclear magnetic resonance (NMR) with multivariate statistical analysis, followed by metabolite identification. After NMR spectra acquisition, we used principal component analysis and partial least squares (PLS) discriminant analysis to generate predictive models.

Results: A urinary metabolomic signature predictive of severe pulmonary sarcoidosis was identified using a PLS model. We selected five metabolites with significant changes in samples from severe sarcoidosis defined by a composite physiologic index >40 compared to those from nonsevere sarcoidosis. These changes correspond to the decreased levels of taurine, hippurate, serine and creatinine and increased levels of 3-hydroxyisovalerate. This metabolite profile suggests an association with activated inflammatory pathways.

Conclusions: This study shows that urinary NMR metabolites can discriminate samples between nonsevere and severe sarcoidosis. It suggests that urinary metabolomic studies in sarcoidosis may be particularly useful to identify potentially relevant biomarkers. However, further validation in a larger cohort of patients at different disease stages is warranted to confirm the relevance of these NMR biomarkers for follow-up.