Clemens Wittenbecher1, Kristin Mühlenbruch1, Janine Kröger1, Simone Jacobs1, Olga Kuxhaus1, Anna Floegel1, Andreas Fritsche1, Tobias Pischon1, Cornelia Prehn1, Jerzy Adamski1, Hans-Georg Joost1, Heiner Boeing1, Matthias B Schulze2. 1. From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA). 2. From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA). mschulze@dife.de.
Abstract
BACKGROUND: Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. OBJECTIVE: This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. DESIGN: Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. RESULTS: After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). CONCLUSION: In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.
BACKGROUND: Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. OBJECTIVE: This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. DESIGN: Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. RESULTS: After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). CONCLUSION: In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.
Authors: Fenglei Wang; Megu Y Baden; Marta Guasch-Ferré; Clemens Wittenbecher; Jun Li; Yanping Li; Yi Wan; Shilpa N Bhupathiraju; Deirdre K Tobias; Clary B Clish; Lorelei A Mucci; A Heather Eliassen; Karen H Costenbader; Elizabeth W Karlson; Alberto Ascherio; Eric B Rimm; JoAnn E Manson; Liming Liang; Frank B Hu Journal: Diabetologia Date: 2022-04-08 Impact factor: 10.122
Authors: Karina Meidtner; Clara Podmore; Janine Kröger; Yvonne T van der Schouw; Benedetta Bendinelli; Claudia Agnoli; Larraitz Arriola; Aurelio Barricarte; Heiner Boeing; Amanda J Cross; Courtney Dow; Kim Ekblom; Guy Fagherazzi; Paul W Franks; Marc J Gunter; José María Huerta; Paula Jakszyn; Mazda Jenab; Verena A Katzke; Timothy J Key; Kay Tee Khaw; Tilman Kühn; Cecilie Kyrø; Francesca Romana Mancini; Olle Melander; Peter M Nilsson; Kim Overvad; Domenico Palli; Salvatore Panico; J Ramón Quirós; Miguel Rodríguez-Barranco; Carlotta Sacerdote; Ivonne Sluijs; Magdalena Stepien; Anne Tjonneland; Rosario Tumino; Nita G Forouhi; Stephen J Sharp; Claudia Langenberg; Matthias B Schulze; Elio Riboli; Nicholas J Wareham Journal: Diabetes Care Date: 2017-11-22 Impact factor: 19.112
Authors: Tõnu Esko; Joel N Hirschhorn; Henry A Feldman; Yu-Han H Hsu; Amy A Deik; Clary B Clish; Cara B Ebbeling; David S Ludwig Journal: Am J Clin Nutr Date: 2017-01-11 Impact factor: 7.045