Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ASCO Annual Meeting 2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

2024 ASCO Annual Meeting

Keep up to date with all the top stories and latest evidence with our hub page, including official congress videos and expert takeaways.
ADVANCED SEARCH
Log in
Top
Diabetologia
Published in: Diabetologia 3/2017

Open Access 01-03-2017 | Article

The heritable basis of gene–environment interactions in cardiometabolic traits

Authors: Alaitz Poveda, Yan Chen, Anders Brändström, Elisabeth Engberg, Göran Hallmans, Ingegerd Johansson, Frida Renström, Azra Kurbasic, Paul W. Franks

Published in: Diabetologia | Issue 3/2017

Login to get access

Abstract

Aims/hypothesis

Little is known about the heritable basis of gene–environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype–environment interactions.

Methods

Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software.

Results

All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2) ranging from 24% to 47%. Genotype–environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype–age interactions for weight and systolic BP, genotype–sex interactions for BMI and triacylglycerols and genotype–alcohol intake interactions for weight remained significant after multiple test correction.

Conclusions/interpretation

Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.
Appendix
Available only for authorised users
Literature
1.
2.
Leal J, Luengo-Fernandez R, Gray A, Petersen S, Rayner M (2006) Economic burden of cardiovascular diseases in the enlarged European Union. Eur Heart J 27:1610–1619CrossRefPubMed
3.
4.
Santos DM, Katzmarzyk PT, Diego VP et al (2014) Genotype by energy expenditure interaction and body composition traits: the Portuguese Healthy Family Study. Biomed Res Int 2014:845207PubMedPubMedCentral
5.
Santos DM, Katzmarzyk PT, Diego VP et al (2014) Genotype by sex and genotype by age interactions with sedentary behavior: the Portuguese Healthy Family Study. PLoS One 9, e110025CrossRefPubMedPubMedCentral
6.
Santos DM, Katzmarzyk PT, Diego VP et al (2013) Genotype by energy expenditure interaction with metabolic syndrome traits: the Portuguese healthy family study. PLoS One 8, e80417CrossRefPubMedPubMedCentral
7.
Martin LJ, Kissebah AH, Sonnenberg GE, Blangero J, Comuzzie AG (2003) Genotype–by–smoking interaction for leptin levels in the metabolic risk complications of obesity genes project. Int J Obes Relat Metab Disord 27:334–340CrossRefPubMed
8.
Towne B, Siervogel RM, Blangero J (1997) Effects of genotype–by–sex interaction on quantitative trait linkage analysis. Genet Epidemiol 14:1053–1058CrossRefPubMed
9.
Kurbasic A, Poveda A, Chen Y et al (2014) Gene–lifestyle interactions in complex diseases: design and description of the GLACIER and VIKING studies. Curr Nutr Rep 3:400–411CrossRefPubMedPubMedCentral
10.
Hallmans G, Agren A, Johansson G et al (2003) Cardiovascular disease and diabetes in the Northern Sweden Health and Disease Study Cohort – evaluation of risk factors and their interactions. Scand J Publ Health Suppl 61:18–24CrossRef
11.
Ahmad S, Poveda A, Shungin D et al (2016) Established BMI-associated genetic variants and their prospective associations with BMI and other cardiometabolic traits: the GLACIER Study. Int J Obes 40:1346–1352CrossRef
12.
WHO (1999) Definition, diagnosis and classification of diabetes mellitus and its complications: Part 1: diagnosis and classification of diabetes mellitus. World Health Organization, Geneva
13.
Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:499–502PubMed
14.
Wu J, Province MA, Coon H et al (2007) An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study. BMC Genet 8:60CrossRefPubMedPubMedCentral
15.
Tobin MD, Sheehan NA, Scurrah KJ, Burton PR (2005) Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med 24:2911–2935CrossRefPubMed
16.
Johansson I, Hallmans G, Wikman A, Biessy C, Riboli E, Kaaks R (2002) Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr 5:487–496CrossRefPubMed
17.
Wennberg M, Vessby B, Johansson I (2009) Evaluation of relative intake of fatty acids according to the Northern Sweden FFQ with fatty acid levels in erythrocyte membranes as biomarkers. Public Health Nutr 12:1477–1484CrossRefPubMed
18.
Poveda A, Koivula RW, Ahmad S et al (2016) Innate biology versus lifestyle behaviour in the aetiology of obesity and type 2 diabetes: the GLACIER Study. Diabetologia 59:462–471CrossRefPubMed
19.
InterAct Consortium, Peters T, Brage S et al (2012) Validity of a short questionnaire to assess physical activity in 10 European countries. Eur J Epidemiol 27:15–25CrossRef
20.
R Development Core (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
21.
Blangero J, Diego VP, Dyer TD et al (2013) A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genet 81:1–31PubMedPubMedCentral
22.
Beasley TM, Erickson S, Allison DB (2009) Rank-based inverse normal transformations are increasingly used, but are they merited? Behav Genet 39:580–595CrossRefPubMedPubMedCentral
23.
Sargolzaei M, Iwaisaki H, Colleau JJ (2006) CFC (contribution, inbreeding (F), coancestry) release 1.0. A software package for pedigree analysis and monitoring genetic diversity. In: Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil
24.
25.
Blangero J (1993) Statistical genetic approaches to human adaptability. Hum Biol 65:941–966PubMed
26.
Blangero J (2009) Statistical genetic approaches to human adaptability, 1993. Hum Biol 81:523–546CrossRefPubMed
27.
Diego VP, Almasy L, Dyer TD, Soler JM, Blangero J (2003) Strategy and model building in the fourth dimension: a null model for genotype x age interaction as a Gaussian stationary stochastic process. BMC genetics 4 Suppl 1: S34
28.
Nyholt DR (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 74:765–769CrossRefPubMedPubMedCentral
29.
Patel CJ, Chen R, Kodama K, Ioannidis JP, Butte AJ (2013) Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus. Hum Genet 132:495–508CrossRefPubMedPubMedCentral
30.
Franks PW, Pearson E, Florez JC (2013) Gene–environment and gene–treatment interactions in type 2 diabetes: progress, pitfalls, and prospects. Diabetes Care 36:1413–1421CrossRefPubMedPubMedCentral
31.
Ahmad S, Varga TV, Franks PW (2013) Gene x environment interactions in obesity: the state of the evidence. Hum Hered 75:106–115CrossRefPubMed
32.
Franks PW, Poveda A (2011) Gene–lifestyle and gene–pharmacotherapy interactions in obesity and its cardiovascular consequences. Curr Vasc Pharmacol 9:401–456CrossRefPubMed
33.
Joseph PG, Pare G, Anand SS (2013) Exploring gene–environment relationships in cardiovascular disease. Can J Cardiol 29:37–45CrossRefPubMed
34.
Mustelin L, Silventoinen K, Pietilainen K, Rissanen A, Kaprio J (2009) Physical activity reduces the influence of genetic effects on BMI and waist circumference: a study in young adult twins. Int J Obes (2005) 33:29–36CrossRef
35.
Silventoinen K, Hasselbalch AL, Lallukka T et al (2009) Modification effects of physical activity and protein intake on heritability of body size and composition. Am J Clin Nutr 90:1096–1103CrossRefPubMedPubMedCentral
36.
Martin LJ, Cole SA, Hixson JE et al (2002) Genotype by smoking interaction for leptin levels in the San Antonio Family Heart Study. Genet Epidemiol 22:105–115CrossRefPubMed
37.
Bouchard C, Tremblay A, Despres JP et al (1994) The response to exercise with constant energy intake in identical twins. Obes Res 2:400–410CrossRefPubMed
38.
Karnehed N, Tynelius P, Heitmann BL, Rasmussen F (2006) Physical activity, diet and gene–environment interactions in relation to body mass index and waist circumference: the Swedish young male twins study. Public Health Nutr 9:851–858CrossRefPubMed
39.
Kilpelainen TO, Qi L, Brage S et al (2011) Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med 8, e1001116CrossRefPubMedPubMedCentral
40.
Andreasen CH, Stender-Petersen KL, Mogensen MS et al (2008) Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes 57:95–101CrossRefPubMed
41.
Rampersaud E, Mitchell BD, Pollin TI et al (2008) Physical activity and the association of common FTO gene variants with body mass index and obesity. Arch Intern Med 168:1791–1797CrossRefPubMedPubMedCentral
42.
Young AI, Wauthier F, Donnelly P (2016) Multiple novel gene–by–environment interactions modify the effect of FTO variants on body mass index. Nat Commun 7:12724CrossRefPubMedPubMedCentral
43.
Sonestedt E, Roos C, Gullberg B, Ericson U, Wirfalt E, Orho-Melander M (2009) Fat and carbohydrate intake modify the association between genetic variation in the FTO genotype and obesity. Am J Clin Nutr 90:1418–1425CrossRefPubMed
44.
Rukh G, Sonestedt E, Melander O et al (2013) Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmö Diet and Cancer Study. Genes Nutr 8:535–547CrossRefPubMedPubMedCentral
45.
Sanchez-Moreno C, Ordovas JM, Smith CE, Baraza JC, Lee YC, Garaulet M (2011) APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population. J Nutr 141:380–385CrossRefPubMedPubMedCentral
46.
Lamri A, Abi Khalil C, Jaziri R et al (2005) Dietary fat intake and polymorphisms at the PPARG locus modulate BMI and type 2 diabetes risk in the DESIR prospective study. Int J Obes 36:218–224CrossRef
47.
Nettleton JA, Follis JL, Ngwa JS et al (2015) Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry. Hum Mol Genet 24:4728–4738CrossRefPubMedPubMedCentral
48.
Bender R, Lange S (2001) Adjusting for multiple testing—when and how? J Clin Epidemiol 54:343–349CrossRefPubMed
49.
Murakami K, Livingstone MB (2015) Prevalence and characteristics of misreporting of energy intake in US adults: NHANES 2003-2012. Br J Nutr 114:1294–1303CrossRefPubMed
50.
Nikolaidis PT (2011) Familial aggregation and maximal heritability of exercise participation: a cross-sectional study in schoolchildren and their nuclear families. Sci Sports 26:157–165CrossRef
51.
Hasselbalch AL, Heitmann BL, Kyvik KO, Sorensen TI (2008) Studies of twins indicate that genetics influence dietary intake. J Nutr 138:2406–2412CrossRefPubMed
Metadata
Title
The heritable basis of gene–environment interactions in cardiometabolic traits
Authors
Alaitz Poveda
Yan Chen
Anders Brändström
Elisabeth Engberg
Göran Hallmans
Ingegerd Johansson
Frida Renström
Azra Kurbasic
Paul W. Franks
Publication date
01-03-2017
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 3/2017
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-016-4184-0

Other articles of this Issue 3/2017

Diabetologia 3/2017 Go to the issue

Article

Detection of enteroviruses in stools precedes islet autoimmunity by several months: possible evidence for slowly operating mechanisms in virus-induced autoimmunity

Article

APPL1 prevents pancreatic beta cell death and inflammation by dampening NFκB activation in a mouse model of type 1 diabetes

Commentary

Adding exercise or subtracting sitting time for glycaemic control: where do we stand?

Article

Reversal of type 2 diabetes in youth who adhere to a very-low-energy diet: a pilot study

Article

Interrupting prolonged sitting in type 2 diabetes: nocturnal persistence of improved glycaemic control

Article

Associations of maternal BMI and insulin resistance with the maternal metabolome and newborn outcomes
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Reserve your place

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

Read more

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine
Image Credits