Javier Delgado-Lista1, Pablo Perez-Martinez, Juan Solivera, Antonio Garcia-Rios, A I Perez-Caballero, Julie A Lovegrove, Christian A Drevon, Catherine Defoort, Ellen E Blaak, Aldona Dembinska-Kieć, Ulf Risérus, Ezequiel Herruzo-Gomez, Antonio Camargo, Jose M Ordovas, Helen Roche, José Lopez-Miranda. 1. Lipids and Atherosclerosis Unit (J.D.-L., P.P-M., A.G.-R., A.I.P.-C., A.C., J.L.-M.), Department of Medicine, and Neurosurgery Unit (J.S.), Instituto Maimónedes de Investigación Biomédica de Córdoba/Hospital Universitario Reina Sofía/Universidad de Córdoba, 14004 Cordoba, Spain and Centro de Investigación Biomédica en Red Fisiopatologia Obesidad y Nutricion, Instituto de Salud Carlos III, 28029 Madrid, Spain; Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (J.A.L.), University of Reading, Whiteknights, Reading RG6 6AP, United Kingdom; Department of Nutrition (C.A.D.), Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway; Unité Mixte de Recherche, Inserm 1062 (C.D.), Research Unit in Nutrition, Obesity and Risk of Thrombosis, Faculté de Médecine, Aix-Marseille University, F-13385 Marseille, France; Department of Human Biology (E.E.B.), Nutrition and Toxicology Research Institute, Maastricht, 6200 MD Maastricht, The Netherlands; Department of Clinical Biochemistry (A.D.-K.), Jagiellonian University Medical College, 31-008, Krakow, Poland; Clinical Nutrition and Metabolism (U.R.), Department of Public Health and Caring Sciences, Faculty of Medicine, Uppsala University, 753 12 Uppsala, Sweden; Department of Computer Engineering (E.H.-G.), University of Cordoba, 14071 Cordoba, Spain; Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University (J.M.O.), Boston, Massachusetts 20111; and Nutrigenomics Research Group (H.R.), UCD School of Public Health and Population Science, UCD Conway Institute, University College Dublin 2010, Ireland.
Abstract
RATIONALE: Metabolic syndrome (MetS) is a high-prevalence condition characterized by altered energy metabolism, insulin resistance, and elevated cardiovascular risk. OBJECTIVES: Although many individual single nucleotide polymorphisms (SNPs) have been linked to certain MetS features, there are few studies analyzing the influence of SNPs on carbohydrate metabolism in MetS. METHODS: A total of 904 SNPs (tag SNPs and functional SNPs) were tested for influence on 8 fasting and dynamic markers of carbohydrate metabolism, by performance of an intravenous glucose tolerance test in 450 participants in the LIPGENE study. FINDINGS: From 382 initial gene-phenotype associations between SNPs and any phenotypic variables, 61 (16% of the preselected variables) remained significant after bootstrapping. Top SNPs affecting glucose metabolism variables were as follows: fasting glucose, rs26125 (PPARGC1B); fasting insulin, rs4759277 (LRP1); C-peptide, rs4759277 (LRP1); homeostasis assessment of insulin resistance, rs4759277 (LRP1); quantitative insulin sensitivity check index, rs184003 (AGER); sensitivity index, rs7301876 (ABCC9), acute insulin response to glucose, rs290481 (TCF7L2); and disposition index, rs12691 (CEBPA). CONCLUSIONS: We describe here the top SNPs linked to phenotypic features in carbohydrate metabolism among approximately 1000 candidate gene variations in fasting and postprandial samples of 450 patients with MetS from the LIPGENE study.
RATIONALE: Metabolic syndrome (MetS) is a high-prevalence condition characterized by altered energy metabolism, insulin resistance, and elevated cardiovascular risk. OBJECTIVES: Although many individual single nucleotide polymorphisms (SNPs) have been linked to certain MetS features, there are few studies analyzing the influence of SNPs on carbohydrate metabolism in MetS. METHODS: A total of 904 SNPs (tag SNPs and functional SNPs) were tested for influence on 8 fasting and dynamic markers of carbohydrate metabolism, by performance of an intravenous glucose tolerance test in 450 participants in the LIPGENE study. FINDINGS: From 382 initial gene-phenotype associations between SNPs and any phenotypic variables, 61 (16% of the preselected variables) remained significant after bootstrapping. Top SNPs affecting glucose metabolism variables were as follows: fasting glucose, rs26125 (PPARGC1B); fasting insulin, rs4759277 (LRP1); C-peptide, rs4759277 (LRP1); homeostasis assessment of insulin resistance, rs4759277 (LRP1); quantitative insulin sensitivity check index, rs184003 (AGER); sensitivity index, rs7301876 (ABCC9), acute insulin response to glucose, rs290481 (TCF7L2); and disposition index, rs12691 (CEBPA). CONCLUSIONS: We describe here the top SNPs linked to phenotypic features in carbohydrate metabolism among approximately 1000 candidate gene variations in fasting and postprandial samples of 450 patients with MetS from the LIPGENE study.
Authors: Jie Qu; Sarah Fourman; Maureen Fitzgerald; Min Liu; Supna Nair; Juan Oses-Prieto; Alma Burlingame; John H Morris; W Sean Davidson; Patrick Tso; Aditi Bhargava Journal: Sci Rep Date: 2021-06-24 Impact factor: 4.379