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Wuni R, Ventura EF, Curi-Quinto K, Murray C, Nunes R, Lovegrove JA, Penny M, Favara M, Sanchez A, Vimaleswaran KS. Interactions between genetic and lifestyle factors on cardiometabolic disease-related outcomes in Latin American and Caribbean populations: A systematic review. Front Nutr 2023; 10:1067033. [PMID: 36776603 PMCID: PMC9909204 DOI: 10.3389/fnut.2023.1067033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The prevalence of cardiometabolic diseases has increased in Latin American and the Caribbean populations (LACP). To identify gene-lifestyle interactions that modify the risk of cardiometabolic diseases in LACP, a systematic search using 11 search engines was conducted up to May 2022. Methods Eligible studies were observational and interventional studies in either English, Spanish, or Portuguese. A total of 26,171 publications were screened for title and abstract; of these, 101 potential studies were evaluated for eligibility, and 74 articles were included in this study following full-text screening and risk of bias assessment. The Appraisal tool for Cross-Sectional Studies (AXIS) and the Risk Of Bias In Non-Randomized Studies-of Interventions (ROBINS-I) assessment tool were used to assess the methodological quality and risk of bias of the included studies. Results We identified 122 significant interactions between genetic and lifestyle factors on cardiometabolic traits and the vast majority of studies come from Brazil (29), Mexico (15) and Costa Rica (12) with FTO, APOE, and TCF7L2 being the most studied genes. The results of the gene-lifestyle interactions suggest effects which are population-, gender-, and ethnic-specific. Most of the gene-lifestyle interactions were conducted once, necessitating replication to reinforce these results. Discussion The findings of this review indicate that 27 out of 33 LACP have not conducted gene-lifestyle interaction studies and only five studies have been undertaken in low-socioeconomic settings. Most of the studies were cross-sectional, indicating a need for longitudinal/prospective studies. Future gene-lifestyle interaction studies will need to replicate primary research of already studied genetic variants to enable comparison, and to explore the interactions between genetic and other lifestyle factors such as those conditioned by socioeconomic factors and the built environment. The protocol has been registered on PROSPERO, number CRD42022308488. Systematic review registration https://clinicaltrials.gov, identifier CRD420223 08488.
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Affiliation(s)
- Ramatu Wuni
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | - Eduard F Ventura
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | | | - Claudia Murray
- Department of Real Estate and Planning, University of Reading, Reading, United Kingdom
| | - Richard Nunes
- Department of Real Estate and Planning, University of Reading, Reading, United Kingdom
| | - Julie A Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | - Mary Penny
- Instituto de Investigación Nutricional, Lima, Peru
| | - Marta Favara
- Oxford Department of International Development, University of Oxford, Oxford, United Kingdom
| | - Alan Sanchez
- Grupo de Análisis para el Desarrollo (GRADE), Lima, Peru
| | - Karani Santhanakrishnan Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom.,Institute for Food, Nutrition and Health (IFNH), University of Reading, Reading, United Kingdom
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2
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Roa-Díaz ZM, Teuscher J, Gamba M, Bundo M, Grisotto G, Wehrli F, Gamboa E, Rojas LZ, Gómez-Ochoa SA, Verhoog S, Vargas MF, Minder B, Franco OH, Dehghan A, Pazoki R, Marques-Vidal P, Muka T. Gene-diet interactions and cardiovascular diseases: a systematic review of observational and clinical trials. BMC Cardiovasc Disord 2022; 22:377. [PMID: 35987633 PMCID: PMC9392936 DOI: 10.1186/s12872-022-02808-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Both genetic background and diet are important determinants of cardiovascular diseases (CVD). Understanding gene-diet interactions could help improve CVD prevention and prognosis. We aimed to summarise the evidence on gene-diet interactions and CVD outcomes systematically. METHODS We searched MEDLINE® via Ovid, Embase, PubMed®, and The Cochrane Library for relevant studies published until June 6th 2022. We considered for inclusion cross-sectional, case-control, prospective cohort, nested case-control, and case-cohort studies as well as randomised controlled trials that evaluated the interaction between genetic variants and/or genetic risk scores and food or diet intake on the risk of related outcomes, including myocardial infarction, coronary heart disease (CHD), stroke and CVD as a composite outcome. The PROSPERO protocol registration code is CRD42019147031. RESULTS AND DISCUSSION We included 59 articles based on data from 29 studies; six articles involved multiple studies, and seven did not report details of their source population. The median sample size of the articles was 2562 participants. Of the 59 articles, 21 (35.6%) were qualified as high quality, while the rest were intermediate or poor. Eleven (18.6%) articles adjusted for multiple comparisons, four (7.0%) attempted to replicate the findings, 18 (30.5%) were based on Han-Chinese ethnicity, and 29 (49.2%) did not present Minor Allele Frequency. Fifty different dietary exposures and 52 different genetic factors were investigated, with alcohol intake and ADH1C variants being the most examined. Of 266 investigated diet-gene interaction tests, 50 (18.8%) were statistically significant, including CETP-TaqIB and ADH1C variants, which interacted with alcohol intake on CHD risk. However, interactions effects were significant only in some articles and did not agree on the direction of effects. Moreover, most of the studies that reported significant interactions lacked replication. Overall, the evidence on gene-diet interactions on CVD is limited, and lack correction for multiple testing, replication and sample size consideration.
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Affiliation(s)
- Zayne M Roa-Díaz
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland. .,Graduate School for Health Sciences, University of Bern, Bern, Switzerland.
| | - Julian Teuscher
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
| | - Magda Gamba
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland.,Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Marvin Bundo
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland.,Graduate School for Health Sciences, University of Bern, Bern, Switzerland.,Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Giorgia Grisotto
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland.,Graduate School for Health Sciences, University of Bern, Bern, Switzerland.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Faina Wehrli
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
| | - Edna Gamboa
- School of Nutrition and Dietetics, Health Faculty, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Lyda Z Rojas
- Nursing Research and Knowledge Development Group GIDCEN, Fundación Cardiovascular de Colombia, Floridablanca, Colombia
| | - Sergio A Gómez-Ochoa
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
| | - Sanne Verhoog
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland.,Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Beatrice Minder
- Public Health & Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Oscar H Franco
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
| | - Raha Pazoki
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK.,Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,CIRTM Centre for Inflammation Research and Translational Medicine, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
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3
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The aetiology and molecular landscape of insulin resistance. Nat Rev Mol Cell Biol 2021; 22:751-771. [PMID: 34285405 DOI: 10.1038/s41580-021-00390-6] [Citation(s) in RCA: 211] [Impact Index Per Article: 70.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2021] [Indexed: 02/07/2023]
Abstract
Insulin resistance, defined as a defect in insulin-mediated control of glucose metabolism in tissues - prominently in muscle, fat and liver - is one of the earliest manifestations of a constellation of human diseases that includes type 2 diabetes and cardiovascular disease. These diseases are typically associated with intertwined metabolic abnormalities, including obesity, hyperinsulinaemia, hyperglycaemia and hyperlipidaemia. Insulin resistance is caused by a combination of genetic and environmental factors. Recent genetic and biochemical studies suggest a key role for adipose tissue in the development of insulin resistance, potentially by releasing lipids and other circulating factors that promote insulin resistance in other organs. These extracellular factors perturb the intracellular concentration of a range of intermediates, including ceramide and other lipids, leading to defects in responsiveness of cells to insulin. Such intermediates may cause insulin resistance by inhibiting one or more of the proximal components in the signalling cascade downstream of insulin (insulin receptor, insulin receptor substrate (IRS) proteins or AKT). However, there is now evidence to support the view that insulin resistance is a heterogeneous disorder that may variably arise in a range of metabolic tissues and that the mechanism for this effect likely involves a unified insulin resistance pathway that affects a distal step in the insulin action pathway that is more closely linked to the terminal biological response. Identifying these targets is of major importance, as it will reveal potential new targets for treatments of diseases associated with insulin resistance.
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4
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Osazuwa-Peters OL, Waken RJ, Schwander KL, Sung YJ, de Vries PS, Hartz SM, Chasman DI, Morrison AC, Bierut LJ, Xiong C, de Las Fuentes L, Rao DC. Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures. Genet Epidemiol 2020; 44:629-641. [PMID: 32227373 PMCID: PMC7717887 DOI: 10.1002/gepi.22292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/30/2019] [Accepted: 03/06/2020] [Indexed: 12/27/2022]
Abstract
Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.
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Affiliation(s)
| | - R J Waken
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Karen L Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Daniel I Chasman
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Lisa de Las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
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5
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Blakeway H, Van‐de-Velde V, Allen V, Kravvas G, Palla L, Page M, Flohr C, Weller R, Irvine A, McPherson T, Roberts A, Williams H, Reynolds N, Brown S, Paternoster L, Langan S. What is the evidence for interactions between filaggrin null mutations and environmental exposures in the aetiology of atopic dermatitis? A systematic review. Br J Dermatol 2020; 183:443-451. [PMID: 31794059 PMCID: PMC7496176 DOI: 10.1111/bjd.18778] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Epidemiological studies indicate that gene-environment interactions play a role in atopic dermatitis (AD). OBJECTIVES To review the evidence for gene-environment interactions in AD aetiology, focusing on filaggrin (FLG) loss-of-function mutations. METHODS A systematic search from inception to September 2018 in Embase, MEDLINE and BIOSIS was performed. Search terms included all synonyms for AD and filaggrin/FLG; any genetic or epidemiological study design using any statistical methods were included. Quality assessment using criteria modified from guidance (ROBINS-I and Human Genome Epidemiology Network) for nonrandomized and genetic studies was completed, including consideration of power. Heterogeneity of study design and analyses precluded the use of meta-analysis. RESULTS Of 1817 papers identified, 12 studies fulfilled the inclusion criteria required and performed formal interaction testing. There was some evidence for FLG-environment interactions in six of the studies (P-value for interaction ≤ 0·05), including early-life cat ownership, older siblings, water hardness, phthalate exposure, higher urinary phthalate metabolite levels (which all increased AD risk additional to FLG null genotype) and prolonged breastfeeding (which decreased AD risk in the context of FLG null genotype). Major limitations of published studies were the low numbers of individuals (ranging from five to 94) with AD and FLG loss-of-function mutations and exposure to specific environmental factors, and variation in exposure definitions. CONCLUSIONS Evidence on FLG-environment interactions in AD aetiology is limited. However, many of the studies lacked large enough sample sizes to assess these interactions fully. Further research is needed with larger sample sizes and clearly defined exposure assessment. Linked Comment: Park and Seo. Br J Dermatol 2020; 183:411.
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Affiliation(s)
- H. Blakeway
- Faculty of Health SciencesUniversity of BristolBristol Medical SchoolOakfield HouseOakfield GroveBristolBS8 2BNU.K.
| | - V. Van‐de-Velde
- Department of DermatologyLauriston Building, Lauriston PlaceEdinburghEH3 9HAU.K.
| | - V.B. Allen
- Department of InfectionSt. Thomas’ HospitalWestminster Bridge RdLambeth, LondonSE1 7EHU.K.
| | - G. Kravvas
- Department of DermatologyLauriston Building, Lauriston PlaceEdinburghEH3 9HAU.K.
| | - L. Palla
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonU.K.
| | - M.J. Page
- School of Public Health and Preventive MedicineMonash UniversityLevel 4, 553 St Kilda RoadMelbourne3004Australia
| | - C. Flohr
- Unit for Population‐Based Dermatology ResearchSt John's Institute of DermatologyGuy's & St Thomas’ NHS Foundation Trust & King's College LondonStrand, LondonWC2R 2LSU.K.
| | - R.B. Weller
- Department of DermatologyLauriston Building, Lauriston PlaceEdinburghEH3 9HAU.K.
| | - A.D. Irvine
- Clinical MedicineTrinity College DublinDublinIreland,The National Children's Research CentreCrumlinIreland,DermatologyChildren's Health IrelandCrumlinIreland
| | - T. McPherson
- Churchill HospitalOld RoadHeadington, OxfordOX3 7LEU.K.
| | - A. Roberts
- Nottingham Support Group for Carers of Children with EczemaNottinghamU.K.
| | - H.C. Williams
- Centre of Evidence‐Based DermatologyUniversity of NottinghamNottinghamNG7 2NRU.K.
| | - N. Reynolds
- DermatologyRoyal Victoria InfirmaryNHS Foundation TrustNewcastle upon TyneU.K.,Institute of Cellular MedicineFaculty of Medical SciencesNewcastle UniversityNewcastle upon TyneU.K.
| | - S.J. Brown
- Skin Research Group, Division of Molecular and Clinical MedicineSchool of MedicineUniversity of DundeeDundeeDD1 9SYU.K.,Department of DermatologyNinewells HospitalDundeeDD1 9SYU.K.
| | - L. Paternoster
- MRC Integrative Epidemiology Unit at the University of BristolPopulation Health SciencesBristol Medical School, Oakfield House, Oakfield GroveBristolBS8 2BNU.K.
| | - S.M. Langan
- Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonWC1E 7HTU.K.,Health Data Research UKLondonU.K.
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6
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Osazuwa-Peters OL, Schwander K, Waken RJ, de Las Fuentes L, Kilpeläinen TO, Loos RJF, Racette SB, Sung YJ, Rao DC. The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions. Hum Hered 2019; 83:315-332. [PMID: 31167214 DOI: 10.1159/000499711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/19/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci. OBJECTIVES This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification. METHOD For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error. RESULTS In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power. CONCLUSION SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.
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Affiliation(s)
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - R J Waken
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Lisa de Las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.,Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri, USA
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, New York, USA.,Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, New York, USA
| | - Susan B Racette
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, Missouri, USA.,Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
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7
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Oelsner EC, Balte PP, Cassano PA, Couper D, Enright PL, Folsom AR, Hankinson J, Jacobs DR, Kalhan R, Kaplan R, Kronmal R, Lange L, Loehr LR, London SJ, Navas Acien A, Newman AB, O’Connor GT, Schwartz JE, Smith LJ, Yeh F, Zhang Y, Moran AE, Mwasongwe S, White WB, Yende S, Barr RG. Harmonization of Respiratory Data From 9 US Population-Based Cohorts: The NHLBI Pooled Cohorts Study. Am J Epidemiol 2018; 187:2265-2278. [PMID: 29982273 PMCID: PMC6211239 DOI: 10.1093/aje/kwy139] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 04/13/2018] [Accepted: 04/17/2018] [Indexed: 12/13/2022] Open
Abstract
Chronic lower respiratory diseases (CLRDs) are the fourth leading cause of death in the United States. To support investigations into CLRD risk determinants and new approaches to primary prevention, we aimed to harmonize and pool respiratory data from US general population-based cohorts. Data were obtained from prospective cohorts that performed prebronchodilator spirometry and were harmonized following 2005 ATS/ERS standards. In cohorts conducting follow-up for noncardiovascular events, CLRD events were defined as hospitalizations/deaths adjudicated as CLRD-related or assigned relevant administrative codes. Coding and variable names were applied uniformly. The pooled sample included 65,251 adults in 9 cohorts followed-up for CLRD-related mortality over 653,380 person-years during 1983-2016. Average baseline age was 52 years; 56% were female; 49% were never-smokers; and racial/ethnic composition was 44% white, 22% black, 28% Hispanic/Latino, and 5% American Indian. Over 96% had complete data on smoking, clinical CLRD diagnoses, and dyspnea. After excluding invalid spirometry examinations (13%), there were 105,696 valid examinations (median, 2 per participant). Of 29,351 participants followed for CLRD hospitalizations, median follow-up was 14 years; only 5% were lost to follow-up at 10 years. The NHLBI Pooled Cohorts Study provides a harmonization standard applied to a large, US population-based sample that may be used to advance epidemiologic research on CLRD.
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MESH Headings
- Adolescent
- Adult
- Aged
- Aged, 80 and over
- Body Weights and Measures
- Bronchiectasis/epidemiology
- Bronchiectasis/physiopathology
- Chronic Disease
- Cohort Studies
- Ethnicity/statistics & numerical data
- Female
- Hispanic or Latino/statistics & numerical data
- Hospitalization/statistics & numerical data
- Humans
- Indians, North American/statistics & numerical data
- Inhalation Exposure/statistics & numerical data
- Lung Diseases, Obstructive/epidemiology
- Lung Diseases, Obstructive/ethnology
- Lung Diseases, Obstructive/mortality
- Lung Diseases, Obstructive/physiopathology
- Male
- Middle Aged
- National Heart, Lung, and Blood Institute (U.S.)/organization & administration
- National Heart, Lung, and Blood Institute (U.S.)/standards
- Phenotype
- Racial Groups/statistics & numerical data
- Respiratory Function Tests
- Risk Factors
- Smoking/epidemiology
- Socioeconomic Factors
- United States/epidemiology
- White People/statistics & numerical data
- Young Adult
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Affiliation(s)
- Elizabeth C Oelsner
- Division of General Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Pallavi P Balte
- Division of General Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Patricia A Cassano
- Division of Nutritional Sciences, Weill Cornell Medical College, Ithaca, New York
| | - David Couper
- Collaborative Studies Coordinating Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina
| | - Paul L Enright
- Department of Medicine, College of Medicine, University of Arizona, Tucson, Arizona
| | - Aaron R Folsom
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | | | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | | | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York
| | - Richard Kronmal
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Denver, Colorado
| | - Laura R Loehr
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina
| | - Stephanie J London
- National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina
| | - Ana Navas Acien
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York
| | - Anne B Newman
- Department of Epidemiology, Pitt Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - George T O’Connor
- Department of Medicine, School of Medicine, Boston University, Boston, Massachusetts
| | - Joseph E Schwartz
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stony Brook University, Stony Brook, New York
| | | | - Fawn Yeh
- Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Yiyi Zhang
- Division of General Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Andrew E Moran
- Division of General Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | | | - Wendy B White
- Jackson Heart Study, Undergraduate Training and Education Center, Tougaloo College, Tougaloo, Mississippi
| | - Sachin Yende
- Division of Pulmonary and Critical Care, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - R Graham Barr
- Division of General Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
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8
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Li SX, Imamura F, Ye Z, Schulze MB, Zheng J, Ardanaz E, Arriola L, Boeing H, Dow C, Fagherazzi G, Franks PW, Agudo A, Grioni S, Kaaks R, Katzke VA, Key TJ, Khaw KT, Mancini FR, Navarro C, Nilsson PM, Onland-Moret NC, Overvad K, Palli D, Panico S, Quirós JR, Rolandsson O, Sacerdote C, Sánchez MJ, Slimani N, Sluijs I, Spijkerman AM, Tjonneland A, Tumino R, Sharp SJ, Riboli E, Langenberg C, Scott RA, Forouhi NG, Wareham NJ. Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct. Am J Clin Nutr 2017; 106:263-275. [PMID: 28592605 PMCID: PMC5486199 DOI: 10.3945/ajcn.116.150094] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Accepted: 04/26/2017] [Indexed: 12/12/2022] Open
Abstract
Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date.Objective: We aimed to identify existing evidence for gene-macronutrient interactions and T2D and to examine the reported interactions in a large-scale study.Design: We systematically reviewed studies reporting gene-macronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution.Results: Thirteen observational studies met the eligibility criteria (n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (ω-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction < 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates.Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.
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Affiliation(s)
- Sherly X Li
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Fumiaki Imamura
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Zheng Ye
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Düsseldorf, Germany
| | - Jusheng Zheng
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Eva Ardanaz
- Navarre Public Health Institute (ISPN), Pamplona, Spain
- Center for Biomedical Research in Network Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Larraitz Arriola
- Center for Biomedical Research in Network Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Public Health Division of Gipuzkoa, San Sebastian, Spain
- Bio-Donostia Institute, Basque Government, San Sebastian, Spain
| | - Heiner Boeing
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Courtney Dow
- French National Institute of Health and Medical Research (INSERM) U1018, Institut Gustave Roussy, Center for Research in Epidemiology and Population Health (CESP), Villejuif, France
- University Paris-Saclay, University Paris-Sud, Villejuif, France
| | - Guy Fagherazzi
- French National Institute of Health and Medical Research (INSERM) U1018, Institut Gustave Roussy, Center for Research in Epidemiology and Population Health (CESP), Villejuif, France
- University Paris-Saclay, University Paris-Sud, Villejuif, France
| | - Paul W Franks
- Lund University, Malmö, Sweden
- Umeå University, Umeå, Sweden
| | - Antonio Agudo
- Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Sara Grioni
- Epidemiology and Prevention Unit, Milan, Italy
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena A Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Kay Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Francesca R Mancini
- French National Institute of Health and Medical Research (INSERM) U1018, Institut Gustave Roussy, Center for Research in Epidemiology and Population Health (CESP), Villejuif, France
- University Paris-Saclay, University Paris-Sud, Villejuif, France
| | - Carmen Navarro
- Center for Biomedical Research in Network Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, Biomedical Research Institute of Murcia (IMIB)-Arrixaca, Murcia, Spain
- Unit of Preventive Medicine and Public Health, School of Medicine, University of Murcia, Murcia, Spain
| | | | | | - Kim Overvad
- Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Aalborg University Hospital, Aalborg, Denmark
| | - Domenico Palli
- Cancer Research and Prevention Institute (ISPO), Florence, Italy
| | - Salvatore Panico
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | | | | | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, City of Health and Science Hospital, University of Turin, Torino, Italy
- Center for Cancer Prevention (CPO), Torino, Italy
- Human Genetics Foundation (HuGeF), Torino, Italy
| | - María-José Sánchez
- Center for Biomedical Research in Network Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Andalusian School of Public Health, Granada, Spain
- Biosanitary Research Institute of Granada (Granada.ibs), Granada, Spain
| | - Nadia Slimani
- International Agency for Research on Cancer, Lyon, France
| | - Ivonne Sluijs
- University Medical Center Utrecht, Utrecht, Netherlands
| | | | | | - Rosario Tumino
- Provincial Healthcare Company (ASP) Ragusa, Vittoria, Italy; and
| | - Stephen J Sharp
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Elio Riboli
- School of Public Health, Imperial College London, London, United Kingdom
| | - Claudia Langenberg
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Robert A Scott
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nita G Forouhi
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom;
| | - Nicholas J Wareham
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
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9
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Franks PW, Poveda A. Lifestyle and precision diabetes medicine: will genomics help optimise the prediction, prevention and treatment of type 2 diabetes through lifestyle therapy? Diabetologia 2017; 60:784-792. [PMID: 28124081 PMCID: PMC6518113 DOI: 10.1007/s00125-017-4207-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 12/16/2016] [Indexed: 12/17/2022]
Abstract
Precision diabetes medicine, the optimisation of therapy using patient-level biomarker data, has stimulated enormous interest throughout society as it provides hope of more effective, less costly and safer ways of preventing, treating, and perhaps even curing the disease. While precision diabetes medicine is often framed in the context of pharmacotherapy, using biomarkers to personalise lifestyle recommendations, intended to lower type 2 diabetes risk or to slow progression, is also conceivable. There are at least four ways in which this might work: (1) by helping to predict a person's susceptibility to adverse lifestyle exposures; (2) by facilitating the stratification of type 2 diabetes into subclasses, some of which may be prevented or treated optimally with specific lifestyle interventions; (3) by aiding the discovery of prognostic biomarkers that help guide timing and intensity of lifestyle interventions; (4) by predicting treatment response. In this review we overview the rationale for precision diabetes medicine, specifically as it relates to lifestyle; we also scrutinise existing evidence, discuss the barriers germane to research in this field and consider how this work is likely to proceed.
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Affiliation(s)
- Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Skåne University Hospital, Malmö, SE-20502, Sweden.
- Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Alaitz Poveda
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Skåne University Hospital, Malmö, SE-20502, Sweden
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10
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Franks PW, McCarthy MI. Exposing the exposures responsible for type 2 diabetes and obesity. Science 2016; 354:69-73. [DOI: 10.1126/science.aaf5094] [Citation(s) in RCA: 161] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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11
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Abstract
The genome is often the conduit through which environmental exposures convey their effects on health and disease. Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined. Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes. It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered. As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.
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Affiliation(s)
- Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Sciences, Clinical Research Center, Skåne University Hospital Malmö, Lund University, Building 91, Level 10, Jan Waldenströms gata 35, 205 02, Malmö, Sweden.
- Department of Public Health and Clinical Medicine, Umeå University, 90188, Umeå, Sweden.
- Department of Nutrition, Harvard School of Public Health, Boston, MA, 02115, USA.
| | - Guillaume Paré
- Population Health Research Institute, McMaster University, Hamilton General Hospital Campus, DB-CVSRI, 237 Barton Street East, Room C3103, Hamilton, ON, L8L 2X2, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
- Department of Clinical Epidemiology and Biostatistics, Population Genomics Program, McMaster University, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
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12
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Epistasis Test in Meta-Analysis: A Multi-Parameter Markov Chain Monte Carlo Model for Consistency of Evidence. PLoS One 2016; 11:e0152891. [PMID: 27045371 PMCID: PMC4821560 DOI: 10.1371/journal.pone.0152891] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 03/21/2016] [Indexed: 11/19/2022] Open
Abstract
Conventional genome-wide association studies (GWAS) have been proven to be a successful strategy for identifying genetic variants associated with complex human traits. However, there is still a large heritability gap between GWAS and transitional family studies. The "missing heritability" has been suggested to be due to lack of studies focused on epistasis, also called gene-gene interactions, because individual trials have often had insufficient sample size. Meta-analysis is a common method for increasing statistical power. However, sufficient detailed information is difficult to obtain. A previous study employed a meta-regression-based method to detect epistasis, but it faced the challenge of inconsistent estimates. Here, we describe a Markov chain Monte Carlo-based method, called "Epistasis Test in Meta-Analysis" (ETMA), which uses genotype summary data to obtain consistent estimates of epistasis effects in meta-analysis. We defined a series of conditions to generate simulation data and tested the power and type I error rates in ETMA, individual data analysis and conventional meta-regression-based method. ETMA not only successfully facilitated consistency of evidence but also yielded acceptable type I error and higher power than conventional meta-regression. We applied ETMA to three real meta-analysis data sets. We found significant gene-gene interactions in the renin-angiotensin system and the polycyclic aromatic hydrocarbon metabolism pathway, with strong supporting evidence. In addition, glutathione S-transferase (GST) mu 1 and theta 1 were confirmed to exert independent effects on cancer. We concluded that the application of ETMA to real meta-analysis data was successful. Finally, we developed an R package, etma, for the detection of epistasis in meta-analysis [etma is available via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/etma/index.html].
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13
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Tillmann T, Gibson AR, Scott G, Harrison O, Dominiczak A, Hanlon P. Systems Medicine 2.0: potential benefits of combining electronic health care records with systems science models. J Med Internet Res 2015; 17:e64. [PMID: 25831125 PMCID: PMC4387294 DOI: 10.2196/jmir.3082] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 09/05/2014] [Accepted: 09/12/2014] [Indexed: 12/25/2022] Open
Abstract
Background The global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment. Objective Our aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases. Methods We undertook an unsystematic review of peer-reviewed literature and technology websites. Results Our review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome. Conclusions Systems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.
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Affiliation(s)
- Taavi Tillmann
- Department of Epidemiology & Public Health, University College London, London, United Kingdom.
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14
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15
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Qi Q, Kilpeläinen TO, Downer MK, Tanaka T, Smith CE, Sluijs I, Sonestedt E, Chu AY, Renström F, Lin X, Ängquist LH, Huang J, Liu Z, Li Y, Asif Ali M, Xu M, Ahluwalia TS, Boer JMA, Chen P, Daimon M, Eriksson J, Perola M, Friedlander Y, Gao YT, Heppe DHM, Holloway JW, Houston DK, Kanoni S, Kim YM, Laaksonen MA, Jääskeläinen T, Lee NR, Lehtimäki T, Lemaitre RN, Lu W, Luben RN, Manichaikul A, Männistö S, Marques-Vidal P, Monda KL, Ngwa JS, Perusse L, van Rooij FJA, Xiang YB, Wen W, Wojczynski MK, Zhu J, Borecki IB, Bouchard C, Cai Q, Cooper C, Dedoussis GV, Deloukas P, Ferrucci L, Forouhi NG, Hansen T, Christiansen L, Hofman A, Johansson I, Jørgensen T, Karasawa S, Khaw KT, Kim MK, Kristiansson K, Li H, Lin X, Liu Y, Lohman KK, Long J, Mikkilä V, Mozaffarian D, North K, Pedersen O, Raitakari O, Rissanen H, Tuomilehto J, van der Schouw YT, Uitterlinden AG, Zillikens MC, Franco OH, Shyong Tai E, Ou Shu X, Siscovick DS, Toft U, Verschuren WMM, Vollenweider P, Wareham NJ, Witteman JCM, Zheng W, Ridker PM, Kang JH, Liang L, Jensen MK, Curhan GC, Pasquale LR, Hunter DJ, Mohlke KL, Uusitupa M, Cupples LA, Rankinen T, Orho-Melander M, Wang T, Chasman DI, Franks PW, Sørensen TIA, Hu FB, Loos RJF, Nettleton JA, Qi L. FTO genetic variants, dietary intake and body mass index: insights from 177,330 individuals. Hum Mol Genet 2014; 23:6961-72. [PMID: 25104851 DOI: 10.1093/hmg/ddu411] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 × 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 × 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.
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Affiliation(s)
- Qibin Qi
- Department of Epidemiology, Albert Einstein College of Medicine, Bronx, NY, USA Department of Nutrition and
| | - Tuomas O Kilpeläinen
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences and
| | | | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Caren E Smith
- Nutrition and Genomics Laboratory, Jean Mayer USDA HNRCA at Tufts University, Boston, MA, USA
| | - Ivonne Sluijs
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Emily Sonestedt
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | - Frida Renström
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Xiaochen Lin
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Lars H Ängquist
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
| | - Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhonghua Liu
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | | | | | - Min Xu
- Department of Nutrition and
| | - Tarunveer Singh Ahluwalia
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences and Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Danish Pediatric Asthma Center, Gentofte Hospital, The Capital Region, Copenhagen, Denmark
| | - Jolanda M A Boer
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Peng Chen
- Saw Swee Hock School of Public Health and
| | - Makoto Daimon
- Department of Endocrinology and Metabolism, Graduate School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan Department of Neurology, Hematology, Metabolism, Endocrinology and Diabetology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Johan Eriksson
- Department of General Practice and Primary Health Care National Institute for Health and Welfare, Helsinki, Finland
| | - Markus Perola
- Institute for Molecular Medicine National Institute for Health and Welfare, Helsinki, Finland Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Yechiel Friedlander
- School of Public Health, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yu-Tang Gao
- Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Denise H M Heppe
- The Generation R Study Group Department of Epidemiology Department of Pediatrics
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Denise K Houston
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK
| | - Yu-Mi Kim
- Department of Preventive Medicine, Dong-A University College of Medicine, Busan, Korea
| | | | - Tiina Jääskeläinen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Nanette R Lee
- USC Office of Population Studies Foundation, Inc., University of San Carlos, Cebu, Philippines
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere, Finland
| | | | - Wei Lu
- Shanghai Institute of Preventive Medicine, Shanghai, China
| | - Robert N Luben
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Ani Manichaikul
- Center for Public Health Genomics Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, USA
| | - Satu Männistö
- National Institute for Health and Welfare, Helsinki, Finland
| | - Pedro Marques-Vidal
- Institute of Social and Preventive Medicine, Bâtiment Biopôle 2, Route de la Corniche 10, CH-1010 Lausanne, Switzerland Department of Medicine, CHUV, Rue du Bugnon 21, CH-1011 Lausanne, Switzerland
| | - Keri L Monda
- Department of Epidemiology Center for Observational Research, Amgen, Inc., Thousand Oaks, CA, USA
| | - Julius S Ngwa
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Louis Perusse
- Department of Kinesiology, Laval University, Ste-Foy, QC, Canada
| | - Frank J A van Rooij
- Department of Epidemiology The Netherlands Genomics Initiative sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Yong-Bing Xiang
- Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Mary K Wojczynski
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Jingwen Zhu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Ingrid B Borecki
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK National Institute for Health Research Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK National Institute for Health Research Musculoskeletal Biomedical Research Unit, University of Oxford, Oxford OX3 7LE, UK
| | - George V Dedoussis
- Department of Dietetics-Nutrition, Harokopio University, 70 El. Venizelou Str, Athens, Greece
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD) and
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences and
| | - Lene Christiansen
- The Danish Twin Registry, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Albert Hofman
- Department of Epidemiology The Netherlands Genomics Initiative sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | | | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
| | - Shigeru Karasawa
- Department of Neurology, Hematology, Metabolism, Endocrinology and Diabetology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Mi-Kyung Kim
- Department of Preventive Medicine, HanYang University College of Medicine, Seoul, Korea
| | | | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Yongmei Liu
- Department of Epidemiology, Division of Public Health Sciences
| | - Kurt K Lohman
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Vera Mikkilä
- Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Dariush Mozaffarian
- Department of Nutrition and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA Channing Division of Network Medicine, Department of Medicine Division of Cardiovascular Medicine Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Kari North
- Department of Epidemiology Carolina Center for Genome Sciences
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences and
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Harri Rissanen
- National Institute for Health and Welfare, Helsinki, Finland
| | - Jaakko Tuomilehto
- National Institute for Health and Welfare, Helsinki, Finland Diabetes Research Group, King Abdulaziz University, 21589 Jeddah, Saudi Arabia Centre for Vascular Prevention, Danube-University Krems, 3500 Krems, Austria Instituto de Investigacion Sanitaria del Hospital Universario LaPaz (IdiPAZ), Madrid, Spain
| | - Yvonne T van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - André G Uitterlinden
- Department of Epidemiology The Netherlands Genomics Initiative sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - M Carola Zillikens
- The Netherlands Genomics Initiative sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Oscar H Franco
- Department of Epidemiology The Netherlands Genomics Initiative sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - E Shyong Tai
- Saw Swee Hock School of Public Health and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Xiao Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - David S Siscovick
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Ulla Toft
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
| | - W M Monique Verschuren
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Peter Vollenweider
- Department of Medicine, CHUV, Rue du Bugnon 21, CH-1011 Lausanne, Switzerland
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Jacqueline C M Witteman
- Department of Epidemiology The Netherlands Genomics Initiative sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Paul M Ridker
- Division of Preventive Medicine Division of Cardiovascular Medicine
| | - Jae H Kang
- Channing Division of Network Medicine, Department of Medicine
| | - Liming Liang
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Majken K Jensen
- Department of Nutrition and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Gary C Curhan
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA Channing Division of Network Medicine, Department of Medicine
| | - Louis R Pasquale
- Channing Division of Network Medicine, Department of Medicine Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - David J Hunter
- Department of Nutrition and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA Channing Division of Network Medicine, Department of Medicine
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland Research Unit, Kuopio University Hospital, Kuopio, Finland
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA The Framingham Heart Study, Framingham, MA, USA
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Marju Orho-Melander
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Tao Wang
- Department of Epidemiology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Daniel I Chasman
- Division of Preventive Medicine Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paul W Franks
- Department of Nutrition and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden Department of Public Health and Clinical Medicine, Genetic Epidemiology and Clinical Research Group, Umeå University, Umeå, Sweden
| | - Thorkild I A Sørensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences and Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
| | - Frank B Hu
- Department of Nutrition and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA Channing Division of Network Medicine, Department of Medicine
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK The Genetics of Obesity and Related Metabolic Traits Program, The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, Department of Preventive Medicine, Mount Sinai School of Medicine, New York City, NY, USA and
| | - Jennifer A Nettleton
- Division of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center, Houston, TX, USA
| | - Lu Qi
- Department of Nutrition and Channing Division of Network Medicine, Department of Medicine
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16
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Langenberg C, Sharp SJ, Franks PW, Scott RA, Deloukas P, Forouhi NG, Froguel P, Groop LC, Hansen T, Palla L, Pedersen O, Schulze MB, Tormo MJ, Wheeler E, Agnoli C, Arriola L, Barricarte A, Boeing H, Clarke GM, Clavel-Chapelon F, Duell EJ, Fagherazzi G, Kaaks R, Kerrison ND, Key TJ, Khaw KT, Kröger J, Lajous M, Morris AP, Navarro C, Nilsson PM, Overvad K, Palli D, Panico S, Quirós JR, Rolandsson O, Sacerdote C, Sánchez MJ, Slimani N, Spijkerman AMW, Tumino R, van der A DL, van der Schouw YT, Barroso I, McCarthy MI, Riboli E, Wareham NJ. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study. PLoS Med 2014; 11:e1001647. [PMID: 24845081 PMCID: PMC4028183 DOI: 10.1371/journal.pmed.1001647] [Citation(s) in RCA: 155] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 04/11/2014] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction = 1.50×10-3) and waist circumference (p for interaction = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
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Affiliation(s)
- Claudia Langenberg
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Stephen J. Sharp
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Paul W. Franks
- Lund University, Malmö, Sweden
- Umeå University, Umeå, Sweden
| | - Robert A. Scott
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Panos Deloukas
- The Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - Nita G. Forouhi
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | | | - Leif C. Groop
- University Hospital Scania, Malmö, Sweden
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Luigi Palla
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Science, University of Aarhus, Aarhus, Denmark
- Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Maria-Jose Tormo
- Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain
- Consorcio de Investigación Biomédica de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Department of Health and Social Sciences, Universidad de Murcia, Spain
| | - Eleanor Wheeler
- The Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | | | - Larraitz Arriola
- Consorcio de Investigación Biomédica de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Public Health Division of Gipuzkoa, San Sebastian, Spain
- Instituto de Investigación Sanitaria BioDonostia, Basque Government, San Sebastian, Spain
| | - Aurelio Barricarte
- Consorcio de Investigación Biomédica de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Navarre Public Health Institute, Pamplona, Spain
| | - Heiner Boeing
- German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
| | - Geraldine M. Clarke
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | | | - Eric J. Duell
- Catalan Institute of Oncology, Bellvitge Biomedical Research Institute, Barcelona, Spain
| | - Guy Fagherazzi
- Inserm, CESP U1018, Villejuif, France
- Université Paris-Sud, UMRS 1018, Villejuif, France
| | - Rudolf Kaaks
- German Cancer Research Center, Heidelberg, Germany
| | - Nicola D. Kerrison
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | | | - Kay Tee Khaw
- University of Cambridge, Cambridge, United Kingdom
| | - Janine Kröger
- German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
| | - Martin Lajous
- Inserm, CESP U1018, Villejuif, France
- Center for Research on Population Health, National Institute of Public Health of Mexico, Cuernavaca, Mexico
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Carmen Navarro
- Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain
- Consorcio de Investigación Biomédica de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Unit of Preventive Medicine and Public Health, School of Medicine, University of Murcia, Murcia, Spain
| | | | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Aalborg University Hospital, Aalborg, Denmark
| | - Domenico Palli
- Cancer Research and Prevention Institute, Florence, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy
| | | | | | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Azienda Ospedaliero Universitaria Città della Salute e della Scienza, University of Turin, Turin, Italy
- Piedmont Reference Center for Epidemiology and Cancer Prevention, Torino, Italy
- Human Genetics Foundation, Torino, Italy
| | - María-José Sánchez
- Consorcio de Investigación Biomédica de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Andalusian School of Public Health, Granada, Spain
| | - Nadia Slimani
- International Agency for Research on Cancer, Lyon, France
| | | | - Rosario Tumino
- Azienda Sanitaria Provinciale di Ragusa, Ragusa, Italy
- Aire Onlus, Ragusa, Italy
| | - Daphne L. van der A
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - Inês Barroso
- The Wellcome Trust Sanger Institute, Cambridge, United Kingdom
- University of Cambridge Metabolic Research Laboratories, Cambridge, United Kingdom
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford, United Kingdom
| | - Elio Riboli
- School of Public Health, Imperial College London, London, United Kingdom
| | - Nicholas J. Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
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17
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Shang Q, Wang H, Song Y, Wei L, Lavebratt C, Zhang F, Gu H. Serological data analyses show that adenovirus 36 infection is associated with obesity: a meta-analysis involving 5739 subjects. Obesity (Silver Spring) 2014; 22:895-900. [PMID: 23804409 DOI: 10.1002/oby.20533] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Revised: 05/23/2013] [Accepted: 05/26/2013] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Serological studies on the relationship between adenovirus 36 (Ad36) and an increased risk of obesity development have shown conflicting results. We reviewed the published studies and carried out a meta-analysis to explore this relationship. METHODS PubMed was searched until December 2012 for the relative references with sufficient information to estimate odds ratios (ORs) and 95% confidence intervals (CIs). A total of 11 case-control studies, including 2508 obese subjects and 3005 controls, were selected. RESULTS Compared with nonobese controls, Ad36 infection significantly increased the obesity risk by a pooled OR of 1.60 (95% CI = 1.14-2.25; P < 0.01). Meta-regression showed that the types of subject and obesity assessments were potential risk factors. In the subgroup analysis, a significantly increased risk was found in children (OR = 1.95; 95% CI = 1.34-2.85; z = 3.45; P < 0.01) and those with an obesity assessment of BMI ≥ 30 kg/cm2 (OR = 1.89; 95% CI = 1.15-3.10; P < 0.05). CONCLUSIONS Ad36 infection is associated with an increased risk of obesity development. To our knowledge, this is the first report to reveal the significant relationship in children with a serological data analysis.
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Affiliation(s)
- Qinglong Shang
- Department of Microbiology, Harbin Medical University, Harbin, 150081, China; Heilongjiang key Laboratory of Infection and Immunity, Heilongjiang Province, China; Pathogenic-Biological key laboratory, Heilongjiang Higher Education Institutions, 150081, Harbin, China
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18
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van der Mei IAF, Otahal P, Simpson S, Taylor B, Winzenberg T. Meta-analyses to investigate gene-environment interactions in neuroepidemiology. Neuroepidemiology 2013; 42:39-49. [PMID: 24356062 DOI: 10.1159/000355439] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Most chronic neurological diseases are caused by a combination of multiple genetic and environmental factors. Increasingly, gene-environment interactions (GxE) are being examined, providing opportunities to combine studies systematically using meta-analysis. METHODS Systematic review of the literature on how to examine GxE using observational study designs, and how to conduct a meta-analysis of studies on GxE. RESULTS Most methods and challenges related to a standard meta-analysis apply to a GxE meta-analysis. There are, however, some substantive differences. With GxE, there is the capability of using a case-only design. Research on GxE interactions may be more prone to publication bias, since interactions are usually not the primary hypothesis and only 'exciting' significant GxE findings are reported out of a range of secondary analyses. In disease aetiology research, there has been debate whether to measure interaction on a multiplicative or additive scale. There are some significant challenges associated with measuring interaction on an additive scale, and thus the uptake of the measures of additive interaction has been limited. As a result, the methods of analysing interaction have been less consistent and reporting has been highly variable. We suggest using the STROBE/STREGA reporting guidelines to allow evaluation of interaction on both scales. CONCLUSIONS We identified a number of differences of a GxE meta-analysis over a standard meta-analysis. Awareness of these issues is important. Using established reporting guidelines for GxE studies is recommended. The development of consortia for neurological disorders that include both genetic and environmental data might offer benefits for GxE meta-analyses in the future.
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Affiliation(s)
- I A F van der Mei
- Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tas., Australia
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19
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Common Sources of Bias in Gene–Lifestyle Interaction Studies of Cardiometabolic Disease. Curr Nutr Rep 2013. [DOI: 10.1007/s13668-013-0056-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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20
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Ahmad S, Varga TV, Franks PW. Gene × environment interactions in obesity: the state of the evidence. Hum Hered 2013; 75:106-15. [PMID: 24081226 DOI: 10.1159/000351070] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS Obesity is a pervasive and highly prevalent disease that poses substantial health risks to those it affects. The rapid emergence of obesity as a global epidemic and the patterns and distributions of the condition within and between populations suggest that interactions between inherited biological factors (e.g. genes) and relevant environmental factors (e.g. diet and physical activity) may underlie the current obesity epidemic. METHODS We discuss the rationale for the assertion that gene × lifestyle interactions cause obesity, systematically appraise relevant literature, and consider knowledge gaps future studies might seek to bridge. RESULTS We identified >200 relevant studies, of which most are relatively small scale and few provide replication data. CONCLUSION Although studies on gene × lifestyle interactions in obesity point toward the presence of such interactions, improved data standardization, appropriate pooling of data and resources, innovative study designs, and the application of powerful statistical methods will be required if translatable examples of gene × lifestyle interactions in obesity are to be identified. Future studies, of which most will be observational, should ideally be accompanied by appropriate replication data and, where possible, by analogous findings from experimental settings where clinically relevant traits (e.g. weight regain and weight cycling) are outcomes.
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Affiliation(s)
- Shafqat Ahmad
- Genetic and Molecular Epidemiology Unit, Department of Clinical Science, Lund University, Malmö, Sweden
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21
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Smith CE, Ngwa J, Tanaka T, Qi Q, Wojczynski MK, Lemaitre RN, Anderson JS, Manichaikul A, Mikkilä V, van Rooij FJA, Ye Z, Bandinelli S, Frazier-Wood AC, Houston DK, Hu F, Langenberg C, McKeown NM, Mozaffarian D, North KE, Viikari J, Zillikens MC, Djoussé L, Hofman A, Kähönen M, Kabagambe EK, Loos RJF, Saylor GB, Forouhi NG, Liu Y, Mukamal KJ, Chen YDI, Tsai MY, Uitterlinden AG, Raitakari O, van Duijn CM, Arnett DK, Borecki IB, Cupples LA, Ferrucci L, Kritchevsky SB, Lehtimäki T, Qi L, Rotter JI, Siscovick DS, Wareham NJ, Witteman JCM, Ordovás JM, Nettleton JA. Lipoprotein receptor-related protein 1 variants and dietary fatty acids: meta-analysis of European origin and African American studies. Int J Obes (Lond) 2013; 37:1211-20. [PMID: 23357958 PMCID: PMC3770755 DOI: 10.1038/ijo.2012.215] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 11/15/2012] [Accepted: 11/28/2012] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Low-density lipoprotein-related receptor protein 1 (LRP1) is a multi-functional endocytic receptor and signaling molecule that is expressed in adipose and the hypothalamus. Evidence for a role of LRP1 in adiposity is accumulating from animal and in vitro models, but data from human studies are limited. The study objectives were to evaluate (i) relationships between LRP1 genotype and anthropometric traits, and (ii) whether these relationships were modified by dietary fatty acids. DESIGN AND METHODS We conducted race/ethnic-specific meta-analyses using data from 14 studies of US and European whites and 4 of African Americans to evaluate associations of dietary fatty acids and LRP1 genotypes with body mass index (BMI), waist circumference and hip circumference, as well as interactions between dietary fatty acids and LRP1 genotypes. Seven single-nucleotide polymorphisms (SNPs) of LRP1 were evaluated in whites (N up to 42 000) and twelve SNPs in African Americans (N up to 5800). RESULTS After adjustment for age, sex and population substructure if relevant, for each one unit greater intake of percentage of energy from saturated fat (SFA), BMI was 0.104 kg m(-2) greater, waist was 0.305 cm larger and hip was 0.168 cm larger (all P<0.0001). Other fatty acids were not associated with outcomes. The association of SFA with outcomes varied by genotype at rs2306692 (genotyped in four studies of whites), where the magnitude of the association of SFA intake with each outcome was greater per additional copy of the T allele: 0.107 kg m(-2) greater for BMI (interaction P=0.0001), 0.267 cm for waist (interaction P=0.001) and 0.21 cm for hip (interaction P=0.001). No other significant interactions were observed. CONCLUSION Dietary SFA and LRP1 genotype may interactively influence anthropometric traits. Further exploration of this, and other diet x genotype interactions, may improve understanding of interindividual variability in the relationships of dietary factors with anthropometric traits.
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Affiliation(s)
- CE Smith
- Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
| | - J Ngwa
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - T Tanaka
- Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA
| | - Q Qi
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - MK Wojczynski
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - RN Lemaitre
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - JS Anderson
- Department of Internal Medicine, Section on Cardiology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - A Manichaikul
- Center for Public Health Genomics and Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, USA
| | - V Mikkilä
- Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - FJA van Rooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Z Ye
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - S Bandinelli
- Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Florence, Italy
| | - AC Frazier-Wood
- Department of Epidemiology, Section on Statistical Genetics, and The Office of Energetics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - DK Houston
- Sticht Center on Aging, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - F Hu
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - C Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - NM McKeown
- Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
| | - D Mozaffarian
- Departments of Epidemiology and Nutrition, Harvard School of Public Health, Boston, MA, USA
- Division of Cardiovascular Medicine and Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - KE North
- Department of Epidemiology and Carolina Center for Genome Sciences; University of North Carolina; Chapel Hill, NC, USA
| | - J Viikari
- Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - MC Zillikens
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - L Djoussé
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, and Boston VA Healthcare System, Boston, MA, USA
| | - A Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - M Kähönen
- Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - EK Kabagambe
- Department of Epidemiology, Section on Statistical Genetics, and The Office of Energetics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - RJF Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - GB Saylor
- Department of Internal Medicine, Section on Cardiology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - NG Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Y Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - KJ Mukamal
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Y-DI Chen
- Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - MY Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - AG Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - O Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - CM van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - DK Arnett
- Department of Epidemiology, Section on Statistical Genetics, and The Office of Energetics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - IB Borecki
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - LA Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - L Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA
| | - SB Kritchevsky
- Sticht Center on Aging, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - T Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Lu Qi
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - JI Rotter
- Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - DS Siscovick
- Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - NJ Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - JCM Witteman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - JM Ordovás
- Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- Department of Epidemiology and Population Genetics, Centro Nacional Investigación Cardiovasculares (CNIC), Madrid, Spain
- Instituto Madrileños de Estudios Avanzados Alimentación, Madrid, Spain
| | - JA Nettleton
- Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
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22
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Franks PW, Pearson E, Florez JC. Gene-environment and gene-treatment interactions in type 2 diabetes: progress, pitfalls, and prospects. Diabetes Care 2013; 36:1413-21. [PMID: 23613601 PMCID: PMC3631878 DOI: 10.2337/dc12-2211] [Citation(s) in RCA: 110] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Paul W Franks
- Department of Clinical Science, Lund University, Malmö, Sweden.
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23
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Hruby A, Ngwa JS, Renström F, Wojczynski MK, Ganna A, Hallmans G, Houston DK, Jacques PF, Kanoni S, Lehtimäki T, Lemaitre RN, Manichaikul A, North KE, Ntalla I, Sonestedt E, Tanaka T, van Rooij FJA, Bandinelli S, Djoussé L, Grigoriou E, Johansson I, Lohman KK, Pankow JS, Raitakari OT, Riserus U, Yannakoulia M, Zillikens MC, Hassanali N, Liu Y, Mozaffarian D, Papoutsakis C, Syvänen AC, Uitterlinden AG, Viikari J, Groves CJ, Hofman A, Lind L, McCarthy MI, Mikkilä V, Mukamal K, Franco OH, Borecki IB, Cupples LA, Dedoussis GV, Ferrucci L, Hu FB, Ingelsson E, Kähönen M, Kao WHL, Kritchevsky SB, Orho-Melander M, Prokopenko I, Rotter JI, Siscovick DS, Witteman JCM, Franks PW, Meigs JB, McKeown NM, Nettleton JA. Higher magnesium intake is associated with lower fasting glucose and insulin, with no evidence of interaction with select genetic loci, in a meta-analysis of 15 CHARGE Consortium Studies. J Nutr 2013; 143:345-53. [PMID: 23343670 PMCID: PMC3713023 DOI: 10.3945/jn.112.172049] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Favorable associations between magnesium intake and glycemic traits, such as fasting glucose and insulin, are observed in observational and clinical studies, but whether genetic variation affects these associations is largely unknown. We hypothesized that single nucleotide polymorphisms (SNPs) associated with either glycemic traits or magnesium metabolism affect the association between magnesium intake and fasting glucose and insulin. Fifteen studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided data from up to 52,684 participants of European descent without known diabetes. In fixed-effects meta-analyses, we quantified 1) cross-sectional associations of dietary magnesium intake with fasting glucose (mmol/L) and insulin (ln-pmol/L) and 2) interactions between magnesium intake and SNPs related to fasting glucose (16 SNPs), insulin (2 SNPs), or magnesium (8 SNPs) on fasting glucose and insulin. After adjustment for age, sex, energy intake, BMI, and behavioral risk factors, magnesium (per 50-mg/d increment) was inversely associated with fasting glucose [β = -0.009 mmol/L (95% CI: -0.013, -0.005), P < 0.0001] and insulin [-0.020 ln-pmol/L (95% CI: -0.024, -0.017), P < 0.0001]. No magnesium-related SNP or interaction between any SNP and magnesium reached significance after correction for multiple testing. However, rs2274924 in magnesium transporter-encoding TRPM6 showed a nominal association (uncorrected P = 0.03) with glucose, and rs11558471 in SLC30A8 and rs3740393 near CNNM2 showed a nominal interaction (uncorrected, both P = 0.02) with magnesium on glucose. Consistent with other studies, a higher magnesium intake was associated with lower fasting glucose and insulin. Nominal evidence of TRPM6 influence and magnesium interaction with select loci suggests that further investigation is warranted.
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Affiliation(s)
- Adela Hruby
- Tufts University Friedman School of Nutrition Science and Policy, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA
| | - Julius S. Ngwa
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Frida Renström
- Department of Nutrition, Harvard School of Public Health, Boston, MA,Department of Clinical Sciences, Lund University, Malmö, Sweden,Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Mary K. Wojczynski
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Andrea Ganna
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden
| | - Denise K. Houston
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
| | - Paul F. Jacques
- Tufts University Friedman School of Nutrition Science and Policy, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA
| | - Stavroula Kanoni
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK,Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Terho Lehtimäki
- Fimlab Laboratories and University of Tampere, School of Medicine, and Tampere University Hospital, Tampere, Finland
| | - Rozenn N. Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Ani Manichaikul
- Center for Public Health Genomics, and Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Kari E. North
- Department of Epidemiology and Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC
| | - Ioanna Ntalla
- Clinical Research Branch, National Institute on Aging, Baltimore, MD
| | - Emily Sonestedt
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Toshiko Tanaka
- Clinical Research Branch, National Institute on Aging, Baltimore, MD
| | - Frank J. A. van Rooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands,Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Leiden, The Netherlands
| | | | - Luc Djoussé
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Massachusetts Veterans Epidemiology and Research Information Center and Geriatric Research, Education, and Clinical Center, Boston Veterans Affairs Healthcare System, Boston, MA
| | - Efi Grigoriou
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | | | - Kurt K. Lohman
- Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - James S. Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN
| | - Olli T. Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Ulf Riserus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Mary Yannakoulia
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - M. Carola Zillikens
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands,Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Leiden, The Netherlands,Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Neelam Hassanali
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Yongmei Liu
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC
| | - Dariush Mozaffarian
- Department of Epidemiology and Nutrition, Harvard School of Public Health, Boston, MA; Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | | | - Ann-Christine Syvänen
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands,Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Leiden, The Netherlands,Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jorma Viikari
- Department of Medicine, University of Turku, and Turku University Hospital, Turku, Finland
| | - Christopher J. Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands,Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Leiden, The Netherlands
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Vera Mikkilä
- Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Kenneth Mukamal
- Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, MA
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands,Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Leiden, The Netherlands
| | - Ingrid B. Borecki
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA,Framingham Heart Study, Framingham, MA
| | - George V. Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, MD
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - Erik Ingelsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and University of Tampere, Tampere, Finland
| | - W. H. Linda Kao
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | | | | | - Inga Prokopenko
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Jerome I. Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - David S. Siscovick
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA,Department of Epidemiology, University of Washington, Seattle, WA
| | - Jacqueline C. M. Witteman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands,Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Leiden, The Netherlands
| | - Paul W. Franks
- Department of Nutrition, Harvard School of Public Health, Boston, MA,Department of Clinical Sciences, Lund University, Malmö, Sweden,Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - James B. Meigs
- Harvard Medical School and General Medicine Division, Clinical Epidemiology and Diabetes Research Units, Massachusetts General Hospital, Boston, MA; and
| | - Nicola M. McKeown
- Tufts University Friedman School of Nutrition Science and Policy, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA,To whom correspondence should be addressed. E-mail:
| | - Jennifer A. Nettleton
- Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health at The University of Texas Health Science Center–Houston, Houston, TX
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24
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Abstract
Obesity is a complex multifaceted disease resulting from interactions between genetics and lifestyle. The proportion of phenotypic variance ascribed to genetic variance is 0.4 to 0.7 for obesity and recent years have seen considerable success in identifying disease-susceptibility variants. Although with the advent of genome-wide association studies the list of genetic variants predisposing to obesity has significantly increased the identified variants only explain a fraction of disease heritability. Studies of gene-environment interactions can provide more insight into the biological mechanisms involved in obesity despite the challenges associated with such designs. Epigenetic changes that affect gene function without DNA sequence modifications may be a key factor explaining interindividual differences in obesity, with both genetic and environmental factors influencing the epigenome. Disentangling the relative contributions of genetic, environmental and epigenetic marks to the establishment of obesity is a major challenge given the complex interplay between these determinants.
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Affiliation(s)
- Jana V. van Vliet-Ostaptchouk
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Harold Snieder
- Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Vasiliki Lagou
- Oxford Centre for Diabetes, Endocrinology, and Metabolism and Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN UK
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25
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Scott RA, Chu AY, Grarup N, Manning AK, Hivert MF, Shungin D, Tönjes A, Yesupriya A, Barnes D, Bouatia-Naji N, Glazer NL, Jackson AU, Kutalik Z, Lagou V, Marek D, Rasmussen-Torvik LJ, Stringham HM, Tanaka T, Aadahl M, Arking DE, Bergmann S, Boerwinkle E, Bonnycastle LL, Bornstein SR, Brunner E, Bumpstead SJ, Brage S, Carlson OD, Chen H, Chen YDI, Chines PS, Collins FS, Couper DJ, Dennison EM, Dowling NF, Egan JS, Ekelund U, Erdos MR, Forouhi NG, Fox CS, Goodarzi MO, Grässler J, Gustafsson S, Hallmans G, Hansen T, Hingorani A, Holloway JW, Hu FB, Isomaa B, Jameson KA, Johansson I, Jonsson A, Jørgensen T, Kivimaki M, Kovacs P, Kumari M, Kuusisto J, Laakso M, Lecoeur C, Lévy-Marchal C, Li G, Loos RJF, Lyssenko V, Marmot M, Marques-Vidal P, Morken MA, Müller G, North KE, Pankow JS, Payne F, Prokopenko I, Psaty BM, Renström F, Rice K, Rotter JI, Rybin D, Sandholt CH, Sayer AA, Shrader P, Schwarz PEH, Siscovick DS, Stancáková A, Stumvoll M, Teslovich TM, Waeber G, Williams GH, Witte DR, Wood AR, Xie W, Boehnke M, Cooper C, Ferrucci L, Froguel P, Groop L, Kao WHL, Vollenweider P, Walker M, Watanabe RM, Pedersen O, Meigs JB, Ingelsson E, Barroso I, Florez JC, Franks PW, Dupuis J, Wareham NJ, Langenberg C. No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels. Diabetes 2012; 61:1291-6. [PMID: 22415877 PMCID: PMC3331745 DOI: 10.2337/db11-0973] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 01/11/2012] [Indexed: 12/11/2022]
Abstract
Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.
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Affiliation(s)
- Robert A Scott
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK.
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26
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Cornelis MC, Hu FB. Gene-environment interactions in the development of type 2 diabetes: recent progress and continuing challenges. Annu Rev Nutr 2012; 32:245-59. [PMID: 22540253 DOI: 10.1146/annurev-nutr-071811-150648] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Type 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors. Since the advent of genome-wide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D. Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it. However, there still remain inconsistencies warranting further investigation. Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions. Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS. Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges. Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors.
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Affiliation(s)
- Marilyn C Cornelis
- Department of Nutrition, Harvard School of Public Health, Harvard University, Boston, Massachusetts 02115, USA
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27
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Abstract
People vary genetically in their susceptibility to the effects of environmental risk factors for many diseases. Genetic variation also underlies the extent to which people respond appropriately to clinical therapies. Defining the basis to the interactions between the genome and the environment may help elucidate the biologic basis to diseases such as type 2 diabetes, as well as help target preventive therapies and treatments. This review examines 1) some of the most current evidence on gene × environment interactions in relation to type 2 diabetes; 2) outlines how the availability of information on gene × environment interactions might help improve the prevention and treatment of type 2 diabetes; and 3) discusses existing and emerging strategies that might enhance our ability to detect and exploit gene × environment interactions in complex disease traits.
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Affiliation(s)
- Paul W Franks
- Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Skåne University Hospital Malmö, 205 02 Malmö, Sweden.
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28
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Kilpeläinen TO, Qi L, Brage S, Sharp SJ, Sonestedt E, Demerath E, Ahmad T, Mora S, Kaakinen M, Sandholt CH, Holzapfel C, Autenrieth CS, Hyppönen E, Cauchi S, He M, Kutalik Z, Kumari M, Stančáková A, Meidtner K, Balkau B, Tan JT, Mangino M, Timpson NJ, Song Y, Zillikens MC, Jablonski KA, Garcia ME, Johansson S, Bragg-Gresham JL, Wu Y, van Vliet-Ostaptchouk JV, Onland-Moret NC, Zimmermann E, Rivera NV, Tanaka T, Stringham HM, Silbernagel G, Kanoni S, Feitosa MF, Snitker S, Ruiz JR, Metter J, Larrad MTM, Atalay M, Hakanen M, Amin N, Cavalcanti-Proença C, Grøntved A, Hallmans G, Jansson JO, Kuusisto J, Kähönen M, Lutsey PL, Nolan JJ, Palla L, Pedersen O, Pérusse L, Renström F, Scott RA, Shungin D, Sovio U, Tammelin TH, Rönnemaa T, Lakka TA, Uusitupa M, Rios MS, Ferrucci L, Bouchard C, Meirhaeghe A, Fu M, Walker M, Borecki IB, Dedoussis GV, Fritsche A, Ohlsson C, Boehnke M, Bandinelli S, van Duijn CM, Ebrahim S, Lawlor DA, Gudnason V, Harris TB, Sørensen TIA, Mohlke KL, Hofman A, Uitterlinden AG, Tuomilehto J, Lehtimäki T, Raitakari O, Isomaa B, Njølstad PR, Florez JC, Liu S, Ness A, Spector TD, Tai ES, Froguel P, Boeing H, Laakso M, Marmot M, Bergmann S, Power C, Khaw KT, Chasman D, Ridker P, Hansen T, Monda KL, Illig T, Järvelin MR, Wareham NJ, Hu FB, Groop LC, Orho-Melander M, Ekelund U, Franks PW, Loos RJF. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med 2011; 8:e1001116. [PMID: 22069379 PMCID: PMC3206047 DOI: 10.1371/journal.pmed.1001116] [Citation(s) in RCA: 371] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2011] [Accepted: 09/23/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The FTO gene harbors the strongest known susceptibility locus for obesity. While many individual studies have suggested that physical activity (PA) may attenuate the effect of FTO on obesity risk, other studies have not been able to confirm this interaction. To confirm or refute unambiguously whether PA attenuates the association of FTO with obesity risk, we meta-analyzed data from 45 studies of adults (n = 218,166) and nine studies of children and adolescents (n = 19,268). METHODS AND FINDINGS All studies identified to have data on the FTO rs9939609 variant (or any proxy [r(2)>0.8]) and PA were invited to participate, regardless of ethnicity or age of the participants. PA was standardized by categorizing it into a dichotomous variable (physically inactive versus active) in each study. Overall, 25% of adults and 13% of children were categorized as inactive. Interaction analyses were performed within each study by including the FTO×PA interaction term in an additive model, adjusting for age and sex. Subsequently, random effects meta-analysis was used to pool the interaction terms. In adults, the minor (A-) allele of rs9939609 increased the odds of obesity by 1.23-fold/allele (95% CI 1.20-1.26), but PA attenuated this effect (p(interaction) = 0.001). More specifically, the minor allele of rs9939609 increased the odds of obesity less in the physically active group (odds ratio = 1.22/allele, 95% CI 1.19-1.25) than in the inactive group (odds ratio = 1.30/allele, 95% CI 1.24-1.36). No such interaction was found in children and adolescents. CONCLUSIONS The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of PA in particular in those genetically predisposed to obesity.
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Affiliation(s)
- Tuomas O Kilpeläinen
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
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29
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Kanoni S, Nettleton JA, Hivert MF, Ye Z, van Rooij FJ, Shungin D, Sonestedt E, Ngwa JS, Wojczynski MK, Lemaitre RN, Gustafsson S, Anderson JS, Tanaka T, Hindy G, Saylor G, Renstrom F, Bennett AJ, van Duijn CM, Florez JC, Fox CS, Hofman A, Hoogeveen RC, Houston DK, Hu FB, Jacques PF, Johansson I, Lind L, Liu Y, McKeown N, Ordovas J, Pankow JS, Sijbrands EJ, Syvänen AC, Uitterlinden AG, Yannakoulia M, Zillikens MC, Wareham NJ, Prokopenko I, Bandinelli S, Forouhi NG, Cupples LA, Loos RJ, Hallmans G, Dupuis J, Langenberg C, Ferrucci L, Kritchevsky SB, McCarthy MI, Ingelsson E, Borecki IB, Witteman JC, Orho-Melander M, Siscovick DS, Meigs JB, Franks PW, Dedoussis GV. Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. Diabetes 2011; 60:2407-16. [PMID: 21810599 PMCID: PMC3161318 DOI: 10.2337/db11-0176] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Accepted: 06/01/2011] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Many genetic variants have been associated with glucose homeostasis and type 2 diabetes in genome-wide association studies. Zinc is an essential micronutrient that is important for β-cell function and glucose homeostasis. We tested the hypothesis that zinc intake could influence the glucose-raising effect of specific variants. RESEARCH DESIGN AND METHODS We conducted a 14-cohort meta-analysis to assess the interaction of 20 genetic variants known to be related to glycemic traits and zinc metabolism with dietary zinc intake (food sources) and a 5-cohort meta-analysis to assess the interaction with total zinc intake (food sources and supplements) on fasting glucose levels among individuals of European ancestry without diabetes. RESULTS We observed a significant association of total zinc intake with lower fasting glucose levels (β-coefficient ± SE per 1 mg/day of zinc intake: -0.0012 ± 0.0003 mmol/L, summary P value = 0.0003), while the association of dietary zinc intake was not significant. We identified a nominally significant interaction between total zinc intake and the SLC30A8 rs11558471 variant on fasting glucose levels (β-coefficient ± SE per A allele for 1 mg/day of greater total zinc intake: -0.0017 ± 0.0006 mmol/L, summary interaction P value = 0.005); this result suggests a stronger inverse association between total zinc intake and fasting glucose in individuals carrying the glucose-raising A allele compared with individuals who do not carry it. None of the other interaction tests were statistically significant. CONCLUSIONS Our results suggest that higher total zinc intake may attenuate the glucose-raising effect of the rs11558471 SLC30A8 (zinc transporter) variant. Our findings also support evidence for the association of higher total zinc intake with lower fasting glucose levels.
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Affiliation(s)
- Stavroula Kanoni
- Department of Nutrition-Dietetics, Harokopio University, Athens, Greece
- Wellcome Trust Sanger Institute, Hinxton, U.K
| | - Jennifer A. Nettleton
- Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center, Houston, Texas
| | - Marie-France Hivert
- Department of Medicine, Division of Endocrinology, Université de Sherbrooke, Sherbrooke, Canada
| | - Zheng Ye
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Frank J.A. van Rooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | - Dmitry Shungin
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Odontology, Umeå University, Umeå, Sweden
| | - Emily Sonestedt
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Julius S. Ngwa
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Mary K. Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
| | - Rozenn N. Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine and Epidemiology, University of Washington, Seattle, Washington
| | - Stefan Gustafsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Toshiko Tanaka
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - George Hindy
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Georgia Saylor
- Baptist Medical Center, Wake Forest University, Winston-Salem, North Carolina
| | - Frida Renstrom
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Amanda J. Bennett
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | - Jose C. Florez
- Diabetes Unit, Center for Human Genetic Research and Diabetes Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Caroline S. Fox
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | - Ron C. Hoogeveen
- Section of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart Center, Houston, Texas
| | - Denise K. Houston
- Sticht Center on Aging, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Paul F. Jacques
- Nutrition Epidemiology Program, U.S. Department of Agriculture Human Nutrition Research Center on Aging (USDA HNRCA) at Tufts University, Boston, Massachusetts
| | | | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina
| | - Nicola McKeown
- Nutrition Epidemiology Program, U.S. Department of Agriculture Human Nutrition Research Center on Aging (USDA HNRCA) at Tufts University, Boston, Massachusetts
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts
| | - Jose Ordovas
- Nutrition and Genomics Laboratory, Jean Mayer USDA HNRCA at Tufts University, Boston, Massachusetts
| | - James S. Pankow
- Department of Epidemiology, University of Minnesota, Minneapolis, Minnesota
| | - Eric J.G. Sijbrands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - André G. Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Mary Yannakoulia
- Department of Nutrition-Dietetics, Harokopio University, Athens, Greece
| | - M. Carola Zillikens
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - Nick J. Wareham
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Inga Prokopenko
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
| | | | - Nita G. Forouhi
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Ruth J. Loos
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Goran Hallmans
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Claudia Langenberg
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - Stephen B. Kritchevsky
- Sticht Center on Aging, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, U.K
| | - Erik Ingelsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ingrid B. Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
| | - Jacqueline C.M. Witteman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | | | - David S. Siscovick
- Cardiovascular Health Research Unit, Department of Medicine and Epidemiology, University of Washington, Seattle, Washington
| | - James B. Meigs
- General Medicine Division, Clinical Epidemiology Unit and Diabetes Research Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Paul W. Franks
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
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Rathmann W, Kowall B, Giani G. Type 2 diabetes: unravelling the interaction between genetic predisposition and lifestyle. Diabetologia 2011; 54:2217-9. [PMID: 21710289 DOI: 10.1007/s00125-011-2222-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Accepted: 05/31/2011] [Indexed: 12/20/2022]
Affiliation(s)
- W Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Centre, Auf'm Hennekamp 65, 40225 Duesseldorf, Germany.
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31
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Minelli C, Gögele M. The role of antioxidant gene polymorphisms in modifying the health effects of environmental exposures causing oxidative stress: a public health perspective. Free Radic Biol Med 2011; 51:925-30. [PMID: 21334432 DOI: 10.1016/j.freeradbiomed.2011.02.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2010] [Revised: 01/30/2011] [Accepted: 02/11/2011] [Indexed: 01/21/2023]
Affiliation(s)
- Cosetta Minelli
- Institute of Genetic Medicine, EURAC Research, Viale Druso 1, 39100 Bolzano, Italy.
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32
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Franks PW, Shungin D. The interplay of lifestyle and genetic susceptibility in Type 2 diabetes risk. ACTA ACUST UNITED AC 2011. [DOI: 10.2217/dmt.11.3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Lee YC, Lai CQ, Ordovas JM, Parnell LD. A Database of Gene-Environment Interactions Pertaining to Blood Lipid Traits, Cardiovascular Disease and Type 2 Diabetes. ACTA ACUST UNITED AC 2011; 2. [PMID: 22328972 DOI: 10.4172/2153-0602.1000106] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
As the role of the environment - diet, exercise, alcohol and tobacco use and sleep among others - is accorded a more prominent role in modifying the relationship between genetic variants and clinical measures of disease, consideration of gene-environment (GxE) interactions is a must. To facilitate incorporation of GxE interactions into single-gene and genome-wide association studies, we have compiled from the literature a database of GxE interactions relevant to nutrition, blood lipids, cardiovascular disease and type 2 diabetes. Over 550 such interactions have been incorporated into a single database, along with over 1430 instances where a lack of statistical significance was found. This database will serve as an important resource to researchers in genetics and nutrition in order to gain an understanding of which points in the human genome are sensitive to variations in diet, physical activity and alcohol use, among other lifestyle choices. Furthermore, this GxE database has been designed with future integration into a larger database of nutritional phenotypes in mind.
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Affiliation(s)
- Yu-Chi Lee
- Nutrition and Genomics Laboratory, Jean Mayer-United States Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA
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Franks PW, Nettleton JA. Invited commentary: Gene X lifestyle interactions and complex disease traits--inferring cause and effect from observational data, sine qua non. Am J Epidemiol 2010; 172:992-7; discussion 998-9. [PMID: 20847104 DOI: 10.1093/aje/kwq280] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Observational epidemiology has made outstanding contributions to the discovery and elucidation of relations between lifestyle factors and common complex diseases such as type 2 diabetes. Recent major advances in the understanding of the human genetics of this disease have inspired studies that seek to determine whether the risk conveyed by bona fide risk loci might be modified by lifestyle factors such as diet composition and physical activity levels. A major challenge is to determine which of the reported findings are likely to represent causal interactions and which might be explained by other factors. The authors of this commentary use the Bradford-Hill criteria, a set of tried-and-tested guidelines for causal inference, to evaluate the findings of a recent study on interaction between variation at the cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) locus and total energy intake with respect to prevalent metabolic syndrome and hemoglobin A₁(c) levels in a cohort of 313 Japanese men. The current authors conclude that the study, while useful for hypothesis generation, does not provide overwhelming evidence of causal interactions. They overview ways in which future studies of gene × lifestyle interactions might overcome the limitations that motivated this conclusion.
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