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Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers.

ABSTRACT: BACKGROUND: In metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity. FINDINGS: In this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise. CONCLUSIONS: We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/.

SUBMITTER: Netzer M

PROVIDER: S-EPMC3320562 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers.

Netzer Michael M Weinberger Klaus M KM Handler Michael M Seger Michael M Fang Xiaocong X Kugler Karl G KG Graber Armin A Baumgartner Christian C

Journal of clinical bioinformatics 20111219 1

<h4>Background</h4>In metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity.<h4>Findings</h4>In th ...[more]

PMID: 22182709

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Project description: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.

Project description:BACKGROUND:New food biomarkers are needed to objectively evaluate the effect of diet on health and to check adherence to dietary recommendations and healthy eating patterns. OBJECTIVE:We developed a strategy for food biomarker discovery, which combined nutritional intervention with metabolic phenotyping and biomarker validation in a large-scale epidemiologic study. DESIGN:We administered a standardized diet to 8 individuals and established a putative urinary biomarker of fruit consumption by using (1)H nuclear magnetic resonance (NMR) spectroscopic profiling. The origin of the biomarker was confirmed by using targeted NMR spectroscopy of various fruit. Excretion kinetics of the biomarker were measured. The biomarker was validated by using urinary NMR spectra from UK participants of the INTERMAP (International Collaborative Study of Macronutrients, Micronutrients, and Blood Pressure) (n = 499) in which citrus consumption was ascertained from four 24-h dietary recalls per person. Finally, dietary patterns of citrus consumers (n = 787) and nonconsumers (n = 1211) were compared. RESULTS:We identified proline betaine as a putative biomarker of citrus consumption. High concentrations were observed only in citrus fruit. Most proline betaine was excreted < or =14 h after a first-order excretion profile. Biomarker validation in the epidemiologic data showed a sensitivity of 86.3% for elevated proline betaine excretion in participants who reported citrus consumption and a specificity of 90.6% (P < 0.0001). In comparison with noncitrus consumers, citrus consumers had lower intakes of fats, lower urinary sodium-potassium ratios, and higher intakes of vegetable protein, fiber, and most micronutrients. CONCLUSION:The biomarker identification and validation strategy has the potential to identify biomarkers for healthier eating patterns associated with a reduced risk of major chronic diseases. The trials were registered at clinicaltrials.gov as NCT01102049 and NCT01102062.

Project description:This study aimed to identify baseline metabolic biomarkers for response to methotrexate (MTX) therapy in rheumatoid arthritis (RA) using an untargeted method. In total, 82 baseline plasma samples (41 insufficient responders and 41 sufficient responders to MTX) were selected from the Treatment in the Rotterdam Early Arthritis Cohort (tREACH, trial number: ISRCTN26791028) based on patients' EULAR response at 3 months. Metabolites were assessed using high-performance liquid chromatography-quadrupole time of flight mass spectrometry. Differences in metabolite concentrations between insufficient and sufficient responders were assessed using partial least square regression discriminant analysis (PLS-DA) and Welch's t-test. The predictive performance of the most significant findings was assessed in a receiver operating characteristic plot with area under the curve (AUC), sensitivity and specificity. Finally, overrepresentation analysis was performed to assess if the best discriminating metabolites were enriched in specific metabolic events. Baseline concentrations of homocystine, taurine, adenosine triphosphate, guanosine diphosphate and uric acid were significantly lower in plasma of insufficient responders versus sufficient responders, while glycolytic intermediates 1,3-/2,3-diphosphoglyceric acid, glycerol-3-phosphate and phosphoenolpyruvate were significantly higher in insufficient responders. Homocystine, glycerol-3-phosphate and 1,3-/2,3-diphosphoglyceric acid were independent predictors and together showed a high AUC of 0.81 (95% CI: 0.72-0.91) for the prediction of insufficient response, with corresponding sensitivity of 0.78 and specificity of 0.76. The Warburg effect, glycolysis and amino acid metabolism were identified as underlying metabolic events playing a role in clinical response to MTX in early RA. New metabolites and potential underlying metabolic events correlating with MTX response in early RA were identified, which warrant validation in external cohorts.

Dataset Information

Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers.

Publications

Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers.

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