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1.
Justice, A.E.* et al.: Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat. Genet. 51, 452–469 (2019)
2.
Willmann, C. et al.: Potential effects of reduced red meat compared with increased fiber intake on glucose metabolism and liver fat content: A randomized and controlled dietary intervention study. Am. J. Clin. Nutr. 109, 288-296 (2019)
3.
Wittenbecher, C.* et al.: Insulin-like growth factor binding protein 2 (IGFBP-2) and the risk of developing type 2 diabetes. Diabetes 68, 188-197 (2019)
4.
Eckel, N.* et al.: Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90 257 women (the Nurses' Health Study): 30 year follow-up from a prospective cohort study. Lancet Diabet. Endocrinol. 6, 714-724 (2018)
5.
Iqbal, K.* et al.: Comparison of metabolite networks from four German population-based studies. Int. J. Epidemiol. 47, 2070-2081 (2018)
6.
Kroeger, J.* et al.: Circulating fetuin - A and risk of type 2 diabetes : A mendelian randomization analysis. Diabetes 67, 1200-1205 (2018)
7.
Mahajan, A.* et al.: Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559-571 (2018)
8.
Herder, C.* et al.: Independent and opposite associations of serum levels of omentin-1 and adiponectin with increases of glycaemia and incident type 2 diabetes in an older population: KORA F4/FF4 Study. Eur. J. Endocrinol. 177, 277-286 (2017)
9.
Jäger, S.* et al.: Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes. Sci. Rep. 7:6037 (2017)
10.
Kantartzis, K. et al.: An extended fatty liver index to predict non-alcoholic fatty liver disease. Diabetes Metab. 43, 229-239 (2017)
11.
Molnos, S. et al.: Metabolite ratios as potential biomarkers for type 2 diabetes: A DIRECT study. Diabetologia 61, 117-129 (2017)
12.
Stefan, N. ; Häring, H.-U. & Schulze, M.B.*: Metabolically healthy obesity: The low-hanging fruit in obesity treatment? Lancet Diabet. Endocrinol. 6, 249-258 (2017)
13.
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)
14.
Eckel, N.* ; Meidtner, K.* ; Kalle-Uhlmann, T.* ; Stefan, N. & Schulze, M.B.*: Metabolically healthy obesity and cardiovascular events: A systematic review and meta-analysis. Eur. J. Prev. Cardiol. 23, 956-966 (2016)
15.
Stefan, N. ; Häring, H.-U. ; Hu, F.B.* & Schulze, M.B.*: Divergent associations of height with cardiometabolic disease and cancer: Epidemiology, pathophysiology, and global implications. Lancet Diabet. Endocrinol. 4, 457-467 (2016)
16.
Eckel, N.* et al.: Characterization of metabolically unhealthy normal-weight individuals: Risk factors and their associations with type 2 diabetes. Metab.-Clin. Exp. 64, 862-871 (2015)
17.
Jacobs, S.* et al.: Associations of erythrocyte fatty acids in the de novo lipogenesis pathway with proxies of liver fat accumulation in the EPIC-Potsdam study. PLoS ONE 10:e0127368 (2015)
18.
Kröger, J.* et al.: Erythrocyte membrane fatty acid fluidity and risk of type 2 diabetes in the EPIC-Potsdam study. Diabetologia 58, 282-289 (2015)
19.
Wittenbecher, C.* et al.: Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes. Am. J. Clin. Nutr. 101, 1241-1250 (2015)
20.
Diethelm, K.* et al.: Prospective relevance of dietary patterns at the beginning and during the course of primary school to the development of body composition. Br. J. Nutr. 111, 1488-1498 (2014)