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Chak, C.M. et al.: Ageing investigation using two-time-point metabolomics data from KORA and CARLA studies. Metabolites 9:44 (2019)
Rabstein, S.* et al.: Differences in twenty-four-hour profiles of blue-light exposure between day and night shifts in female medical staff. Sci. Total Environ. 653, 1025-1033 (2019)
Hertel, J.* et al.: Dilution correction for dynamically influenced urinary analyte data. Anal. Chim. Acta 1032, 18-31 (2018)
Rotter, M. et al.: Night shift work affects urine metabolite profiles of nurses with early chronotype. Metabolites 8:45 (2018)
Saulnier-Blache, J.S.* et al.: Ldlr-/- and ApoE-/- mice better mimic the human metabolite signature of increased carotid intima media thickness compared to other animal models of cardiovascular disease. Atherosclerosis 276, 140-147 (2018)
Tsepilov, Y.A.* et al.: A network-based conditional genetic association analysis of the human metabolome. GigaScience 7, accepted (2018)
van Zuydam, N.R.* et al.: A genome-wide association study of diabetic kidney disease in subjects with type 2 diabetes. Diabetes 67, 1414-1427 (2018)
Adam, J. et al.: Response to comment on Adam et al. Metformin effect on nontargeted metabolite profiles in patients with type 2 diabetes and in multiple murine tissues. Diabetes 2016;65:3776-3785. Diabetes 66, e3-e4 (2017)
Molnos, S. et al.: Metabolite ratios as potential biomarkers for type 2 diabetes: A DIRECT study. Diabetologia 61, 117-129 (2017)
Molnos, S. et al.: pulver: An R package for parallel ultra-rapid p-value computation for linear regression interaction terms. BMC Bioinformatics 18:429 (2017)
Rotter, M. et al.: Stability of targeted metabolite profiles of urine samples under different storage conditions. Metabolomics 13:4 (2017)
Standl, M. et al.: Association of atopic dermatitis with cardiovascular risk factors and diseases. J. Invest. Dermatol. 137, 1074-1081 (2017)
Standl, M. et al.: Association of atopic dermatitis with cardiovascular risk factors and diseases. Allergy 72, 53-53 (2017)
Stratmann, B.* et al.: Metabolomic signature of coronary artery disease in type 2 diabetes mellitus. Int. J. Endocrinol. 2017:7938216 (2017)
Suarez-Diez, M.* et al.: Plasma and serum metabolite association networks: Comparibility within and between studies using NMR and MS profiling. J. Proteome Res. 16, 2547-2559 (2017)
Ward-Caviness, C.K. et al.: Improvement of myocardial infarction risk prediction via inflammation-associated metabolite biomarkers. Heart 103, 1278-1285 (2017)
Adam, J. et al.: Metformin effect on non-targeted metabolite profiles in patients with type 2 diabetes and multiple murine tissues. Diabetes 65, 3776-3785 (2016)
Auer, M.K.* et al.: 12-months metabolic changes among gender dysphoric individuals under cross-sex hormone treatment: A targeted metabolomics study. Sci. Rep. 6:37005 (2016)
Bader, E. et al.: Identification of proliferative and mature β-cells in the islets of Langerhans. Nature 535, 430-434 (2016)
Dietrich, S.* et al.: Random survival forest in practice: A method for modelling complex metabolomics data in time to event analysis. Int. J. Epidemiol. 45, 1406-1420 (2016)