title: Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.
abstract: The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.
authors: Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria.
authors: Marc
authors: M
authors: Breit
authors: Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria.
authors: Michael
authors: M
authors: Netzer
authors: Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria; sAnalytiCo Ltd, Belfast, United Kingdom.
authors: Klaus M
authors: KM
authors: Weinberger
authors: Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering (IEBE), University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria; Institute of Health Care Engineering with European Notified Body of Medical Devices, Graz University of Technology, Graz, Austria.
authors: Christian
authors: C
authors: Baumgartner
mesh_terms: D000328:Adult; D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism*; D005260:Female; D005947:Glucose / Q000378:metabolism; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D055442:Metabolome / Q000502:physiology*; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D008954:Models, Biological; D009043:Motor Activity / Q000502:physiology*; D053719:Tandem Mass Spectrometry; D055815:Young Adult
chemical_list: D015415:Biomarkers; D005947:Glucose