The Effect of Genetic and Environmental Factors, and their Interaction on Prevalent and Incident Cardiometabolic Disease

As the most important cardiometabolic diseases, cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) share common features, including high incidence, and being the leading cause of mortality and disability worldwide. CVD has caused 17.79 million deaths1 and 366 million global disability-adjusted life-years (DALYs)2 in 2017. T2DM is seen as a major public health issue around the world, with 425 million cases worldwide in 20173. It is one of the major causes of mortality and has emerged as the fourth leading cause of disability globally4. Increasing evidence suggests a common pathogenesis of metabolic abnormalities between CVD and T2DM. They are often accompanied with the manifestation of a much broader set of underlying disturbances5,6, commonly described as metabolic syndrome (MetS), a highly prevalent, multifaceted cluster of abnormalities that include abdominal obesity, hypertension, dyslipidemia and elevated fasting plasma glucose. 
Cardiometabolic diseases are multifactorial diseases with complex inheritance, determined by large number of genes, multiple environmental factors (health behaviors, exposures) and their interaction. For a range of cardiometabolic traits (e.g. body mass index and blood pressure), it has been shown that they are up to 50% heritable7, i.e. the variance of these traits is for a considerable part determined by additive genetic factors. Meanwhile, cardiometabolic diseases, such as coronary artery disease (CAD) and T2DM, also have substantial heritability and a polygenic architecture. However, it is unclear to what extent CAD and T2DM cluster in families. Furthermore, previous heritability estimates may have been subject to sampling error. Heritability estimates from a large representative cohort are needed to inform gene-discovering efforts such as the genome-wide association study (GWAS). 
Sequencing of the human genome and efforts to discover single nucleotide polymorphisms (SNPs) have greatly facilitated the development of two new association approaches: gene-wide candidate gene studies and GWASs. Today, it is feasible to conduct GWAS because of rapid progress in genotyping technology and accompanying reductions in genotyping costs, which have led to an explosion in the number of newly identified genes for complex traits and diseases8,9. Currently, a total of 163 and 243 common genetic loci have now been associated with CAD10 and T2DM11, respectively. Due to the overlap between CAD, T2DM, and underlying metabolic traits, a multivariate, joint analysis may be more powerful than traditional single-trait GWAS. In such a multi-trait GWAS approach, it can be expected that even more variants can be identified12. 
Genetic data obtained from GWAS can be summarized into polygenic risk scores (PRSs). These PRSs have been shown to be related to cardiometabolic disease, including hypertension, stroke and CAD13. Khera et al. developed PRSs for CAD and T2DM and found that this approach identifies subsets of the population that are at a greater than threefold increased risk for CAD and T2DM14, which is a risk increase similar to that conveyed by monogenic mutations. Although PRSs cannot yet be used for accurate individual risk prediction, they can be used as a proxy for genetic predisposition in population-based research. 
Besides genetic factors, environmental factors also have a crucial role in the development of cardiometabolic diseases. These include smoking, regular alcohol consumption, dietary intake, socio-economic status, physical activity, psychosocial factors (e.g., exposure to stress) and many more15. Furthermore, there may be complex interactions between genetic and environmental factors. The VIKING study showed that genotype-age interactions for weight and systolic blood pressure, genotype-sex interactions for body mass index and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction7. Barcellos et al. also found a robust interaction of genes and education on health16 and our own previous work observed interactions between genetic predisposition and socio-economic status in determining risk of T2DM17,18. However, much remains unknown about the way the interplay between genetic predisposition and environmental factors (i.e. gene-environment interaction) shapes risk of cardiometabolic disease. By using PRSs as a summary of genetic predisposition to cardiometabolic disease, it may be possible to identify environmental factors that amplify genetic risk.
The research proposed here may have several implications. First, our study will assess the added value of family history in risk stratification (and clustering) of cardiometabolic diseases, and to investigate the potential impact of specifically targeting family members of cardiometabolic disease patients in screening and prevention. Additionally, the data on familial recurrence of cardiometabolic diseases may guide clinical decision-making with regards to cardiometabolic diseases diagnosis and prevention. Second, the heritability estimates provide an upper bound to how much variance of the cardiometabolic traits can be explained by genetic factors, informing future gene-discovery. Third, much of the variance in metabolic traits and diseases remains to be explained, and more novel loci need to be identified. Furthermore, it is meaningful to study the interaction between environment and genes as it may guide optimization of behavioral changes to improve health and improve prediction and prognostication of cardiometabolic disease.