Print Email Facebook Twitter Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study) Title Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study) Author van der Kloet, F.M. Tempels, F.W.A. Ismail, N. van der Heijden, R. Kasper, P.T. Rojas-Cherto, M. van Doorn, R. Spijksma, G. Koek, M. van der Greef, J. Mäkinen, V.P. Forsblom, C. Holthöfer, H. Groop, P.H. Reijmers, T.H. Hankemeier, T. Publication year 2012 Abstract Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies. © 2011 The Author(s). Subject LifeMSB - Microbiology and Systems BiologyEELS - Earth, Environmental and Life SciencesHealthy LivingDiabetic kidney diseaseGC-MSLC-MSMetabolite profileMetabolomicsMultivariate data analysisNephropathyUrine To reference this document use: http://resolver.tudelft.nl/uuid:b53e65d0-ba7c-4dc1-9fcf-9dffa0af44bd DOI https://doi.org/10.1007/s11306-011-0291-6 TNO identifier 446457 ISSN 1573-3882 Source Metabolomics, 8 (1), 109-119 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.