bioRxivBiochemistryPreprint
Targeted Clinical Metabolomics Platform for the Stratification of Diabetic Patients
L. Ahonen, S. Jantti, T. Suvitaival, S. Thelaide, C. Risz, R. Kostiainen, P. Rossing, M. Oresic, T. Hyotylainen
Abstract
BackgroundSeveral small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities and complications. Here, we report the development and validation of a novel, quantitative, analytical method for use in the diabetes clinic. This method enables the determination of a selected panel of 36 metabolite biomarkers from human plasma.\n\nMethodsBased on a review of the literature and our own data, we selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings.\n\nResultsWe validated the method in terms of limit of (a) detection (LOD), (b) limit of quantitation (LOQ), (c) linearity (R2), (d) linear range, and (e) intra- and inter-day repeatability of each metabolite. The methods performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes were associated with macro-albuminuria. Additionally, specific bile acids were associated with kidney function, anti-hypertensive medication, statin medication and clinical lipid measurements.\n\nConclusionsThe developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.
- organism: human
- assay: metabolomics
- research focus: differential expression
- research focus: cell type annotation
- research focus: clustering
- approach: experimental assay
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