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101.
Ferrannini, E.* et al.: Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes 62, 1730-1737 (2013)
102.
Menni, C.* et al.: Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 42, 1111-1119 (2013)
103.
Menni, C.* et al.: Biomarkers for type 2 diabetes and impaired fasting glucose using a non-targeted metabolomics approach. Diabetes 62, 4270-4276 (2013)
104.
Montrone, C. et al.: HSC-Explorer: A curated database for hematopoietic stem cells. PLoS ONE 8:e70348 (2013)
105.
Raffler, J. et al.: Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med. 5:13 (2013)
106.
Then, C. et al.: Plasma metabolomics reveal alterations of sphingo- and glycerophospholipid levels in non-diabetic carriers of the transcription factor 7-like 2 polymorphism rs7903146. PLoS ONE 8:e78430 (2013)
107.
Jourdan, C. et al.: Body fat free mass is associated with the serum metabolite profile in a population-based study. PLoS ONE 7:e40009 (2012)
108.
Krug, S.* et al.: The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607-2619 (2012)
109.
Krumsiek, J. et al.: Mining the unknown: A systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8:e1003005 (2012)
110.
Krumsiek, J. ; Stückler, F. ; Kastenmüller, G. & Theis, F.J.: Systems biology meets metabolism. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: from Experiment to Systems Biology. New York: Springer, 2012. 281-313
111.
Lechner, M. et al.: CIDeR: Multifactorial interaction networks in human diseases. Genome Biol. 13:R62 (2012)
112.
Petersen, A.-K. et al.: Genetic associations with lipoprotein subfractions provide information on their biological nature. Hum. Mol. Genet. 21, 1433-1443 (2012)
113.
Altmaier, E. et al.: Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics. Eur. J. Epidemiol. 26, 145-156 (2011)
114.
Fuchs, H. et al.: Mouse phenotyping. Methods 53, 120-135 (2011)
115.
Kastenmüller, G. ; Römisch-Margl, W. ; Wägele, B. ; Altmaier, E. & Suhre, K.: metaP-Server: A web-based metabolomics data analysis tool. J. Biomed. Biotechnol. 2011:839862 (2011)
116.
Suhre, K. et al.: Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54-60 (2011)
117.
Yu, Z. et al.: Differences between human plasma and serum metabolite profiles. PLoS ONE 6:e21230 (2011)
118.
Illig, T. et al.: A genome-wide perspective of genetic variation in human metabolism. Nat. Genet. 42, 137-141 (2010)
119.
Altmaier, E. et al.: Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics. Mol. Nutr. Food Res. 53, 1357-1365 (2009)
120.
Kastenmüller, G. ; Schenk, M.E. ; Gasteiger, J.* & Mewes, H.-W.: Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes. Genome Biol. 10:R28 (2009)