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      Gene-Diet Interaction and Precision Nutrition in Obesity

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          Abstract

          The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.

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          A genome-wide association study identifies novel risk loci for type 2 diabetes.

          Robert Sladek, Ghislain Rocheleau, Johan Rung … (2007)
          Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case-control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing beta-cells, and two linkage disequilibrium blocks that contain genes potentially involved in beta-cell development or function (IDE-KIF11-HHEX and EXT2-ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.
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            Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

            Benjamin Voight, Laura J. Scott, Valgerdur Steinthorsdottir … (2010)
            By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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              Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis

              Marta Guasch-Ferré, Adela Hruby, Estefanía Toledo … (2016)
              OBJECTIVE To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite. RESULTS We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 [1.24–1.48]; I 2 = 9.5%), 36% for leucine (1.36 [1.17–1.58]; I 2 = 37.4%), 35% for valine (1.35 [1.19–1.53]; I 2 = 45.8%), 36% for tyrosine (1.36 [1.19–1.55]; I 2 = 51.6%), and 26% for phenylalanine (1.26 [1.10–1.44]; I 2 = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 [0.81–0.96] and 0.85 [0.82–0.89], respectively; both I 2 = 0.0%). CONCLUSIONS In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.
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                Author and article information

                Contributors
                Lynnette Ferguson: Role: Academic Editor
                Virginia R. Parslow: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): Int J Mol Sci
                Journal ID (iso-abbrev): Int J Mol Sci
                Journal ID (publisher-id): ijms
                Title: International Journal of Molecular Sciences
                Publisher: MDPI
                ISSN (Electronic): 1422-0067
                Publication date (Electronic): 07 April 2017
                Publication date Collection: April 2017
                Volume: 18
                Issue: 4
                Electronic Location Identifier: 787
                Affiliations
                [1 ]Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA; yheianza@ 123456tulane.edu
                [2 ]Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                Author notes
                [* ]Correspondence: lqi1@ 123456tulane.edu ; Tel.: +1-504-988-3549; Fax: +1-504-988-1568
                Article
                Publisher ID: ijms-18-00787
                DOI: 10.3390/ijms18040787
                PMC ID: 5412371
                PubMed ID: 28387720
                SO-VID: 5bae1a97-29eb-41f8-a5ae-1b45b910b36f
                Copyright © © 2017 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 22 February 2017
                Date accepted : 03 April 2017
                Categories
                Subject: Review

                ScienceOpen disciplines: Molecular biology
                Keywords: gene-diet interactions,weight loss,obesity
                Data availability:
                ScienceOpen disciplines: Molecular biology
                Keywords: gene-diet interactions, weight loss, obesity

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