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Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients

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@article{3c6175a2dea241878487ec12d71f2091,
title = "Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients",
abstract = "Several 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 method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. 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. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R2), and intra- and inter-day repeatability of each metabolite. The method's 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, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.",
keywords = "Clinical diagnostics, Diabetes, Mass spectrometry, Metabolomics",
author = "Linda Ahonen and Sirkku J{\"a}ntti and Tommi Suvitaival and Simone Theilade and Claudia Risz and Risto Kostiainen and Peter Rossing and Matej Orešič and Tuulia Hy{\"o}tyl{\"a}inen",
year = "2019",
month = "9",
day = "14",
doi = "10.3390/metabo9090184",
language = "English",
volume = "9",
journal = "Metabolites",
issn = "2218-1989",
publisher = "M D P I AG",
number = "9",

}

RIS

TY - JOUR

T1 - Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients

AU - Ahonen, Linda

AU - Jäntti, Sirkku

AU - Suvitaival, Tommi

AU - Theilade, Simone

AU - Risz, Claudia

AU - Kostiainen, Risto

AU - Rossing, Peter

AU - Orešič, Matej

AU - Hyötyläinen, Tuulia

PY - 2019/9/14

Y1 - 2019/9/14

N2 - Several 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 method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. 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. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R2), and intra- and inter-day repeatability of each metabolite. The method's 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, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

AB - Several 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 method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. 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. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R2), and intra- and inter-day repeatability of each metabolite. The method's 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, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

KW - Clinical diagnostics

KW - Diabetes

KW - Mass spectrometry

KW - Metabolomics

UR - http://www.scopus.com/inward/record.url?scp=85073386415&partnerID=8YFLogxK

U2 - 10.3390/metabo9090184

DO - 10.3390/metabo9090184

M3 - Journal article

VL - 9

JO - Metabolites

JF - Metabolites

SN - 2218-1989

IS - 9

M1 - 184

ER -

ID: 58000878