Identification of novel metabolite biomarkers for type 2 diabetes

The main target of this project is the identification of metabolite patterns, which can be used to develop a diagnostic test for pre-diabetic metabolic traits. Therefore, statistics, mathematical modelling and pattern recognition are used to analyze mass spectrometry data samples of the TULIP (TUebingen Lifestyle Intervention Program) cohort. The mathematical modelling and classification is based on differentially expressed mass-profiles of individuals, who show metabolic traits, i.e. who are at high risk to develop type 2 Diabetes mellitus. Machine learning methods are applied to infer a mathematical method, which is able to classify "pre-diabetic" versus "non diabetic" probands from their mass-profile-patterns only. The next step involves the application of Evolutionary Algorithms to extract an optimal metabolite pattern, which on the one hand is able to classify with high sensitivity and specificity and, on the other hand, has minimal complexity.



Data management and workflow

The data management and workflow platform for the biomarker project can be found here.
Guests can log in with limited access rights using the the username guest and the password guest.


Project Partners:

University Hospital of Tuebingen
Helmholtz Zentrum Muenchen, Institute for Ecological Chemistry
Dalian Institute of Chemical Physics

This work is supported by the Kompetenznetz Diabetes mellitus (Competence Network for Diabetes mellitus) funded by the Federal Ministry of Education and Research (FKZ 01GI0803-04).



Publications:

[1] Erhan Kenar, Holger Franken, Sara Forcisi, Kilian W6rmann, Hans-Ulrich H4ring, Rainer Lehmann, Philippe Schmitt-Kopplin, Andreas Zell, and Oliver Kohlbacher. Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data. Molecular & Cellular Proteomics, 13(1):348-359, 2014. [ DOI | arXiv | link ]
[2] Peiyuan Yin, Andreas Peter, Holger Franken, Xinjie Zhao, Sabine Sarah Neukamm, Lars Rosenbaum, Marianna Lucio, Andreas Zell, Hans-Ulrich Häring, Guowang Xu, and Rainer Lehmann. Pre-analytical aspects and sample quality assessment in metabolomics studies of human blood. Clinical Chemistry, 59(5):833-845, May 2013.
[3] R. Lehmann, H. Franken, S. Dammeier, L. Rosenbaum, K. Kantartzis, A. Peter, A. Zell, P. Adam, J. Li, G. Xu, A. Königsrainer, J. Machann, F. Schick, M. Hrabe de Angelis, M. Schwab, H. Staiger, E. Schleicher, A. Gastaldelli, A. Fritsche, H.-U. Häring, and N. Stefan. Circulating lyso-phosphatidylcholines are markers of ametabolically benign nonalcoholic fatty liver. Diabetes Care, 36(8):2331-2338, 2013.
[4] Holger Franken, Alexander Seitz, Rainer Lehmann, Hans-Ulrich Häring, Norbert Stefan, and Andreas Zell. Inferring disease-related metabolite dependencies with a bayesian optimization algorithm. In Mario Giacobini, Leonardo Vanneschi, and William Bush, editors, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 7246 of Lecture Notes in Computer Science, pages 62-73. Springer Berlin / Heidelberg, 2012. 10.1007/978-3-642-29066-4_6. [ link ]
[5] E. Kenar, H. Franken, L. Rosenbaum, R. Lehmann, S. Forcisi, K. Wörmann, M. Lucio, A. König, J. Rahnenfüher, P. Schmidt-Kopplin, H.-U. Häring, A. Zell, and O. Kohlbacher. Mit bioinformatik zu biomarkern. Medizinische Welt, 63(5):245-250, 2012.
[6] K. Wörmann, M. Lucio, S. Forcisi, S.S. Heinzmann, E. Kenar, H. Franken, L. Rosenbaum, P. Schmitt-Kopplin, O. Kohlbacher, A. Zell, H.-U. Häring, and R. Lehmann. Metabolomics in der Diabetesforschung. Der Diabetologe, pages 1-5, 2012. 10.1007/s11428-011-0778-9. [ link ]
[7] Holger Franken, Rainer Lehmann, Hans-Ulrich Häring, Andreas Fritsche, Norbert Stefan, and Andreas Zell. Wrapper- and ensemble-based feature subset selection methods for biomarker discovery in targeted metabolomics. In Marco Loog, Lodewyk Wessels, Marcel Reinders, and Dick de Ridder, editors, Pattern Recognition in Bioinformatics, volume 7036 of Lecture Notes in Computer Science, pages 121-132. Springer Berlin / Heidelberg, 2011. 10.1007/978-3-642-24855-9_11. [ link ]
[8] Miriam Hoene, Holger Franken, Louise Fritsche, Rainer Lehmann, Ann Kathrin Pohl, Hans-Ulrich Häring, Andreas Zell, Erwin D. Schleicher, and Cora Weigert. Activation of the mitogen-activated protein kinase (MAPK) signalling pathway in the liver of mice is related to plasma glucose levels after acute exercise. Diabetologia, 53(6):1131-1141, March 2010. [ DOI | link | pdf ]

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Contact: Lars Rosenbaum, Raum A317, Tel. (07071) 29-77174, lars.rosenbaum at uni-tuebingen.de