Subgroups at high risk for ischaemic heart disease:identification and validation in 67 000 individuals from the general population

International Journal of Epidemiology, 2015, 117–128 doi: 10.1093/ije/dyu215 Advance Access Publication Date: 30 October 2014 Original article Cardiovascular Disease and Cardiovascular Risk Factors Subgroups at high risk for ischaemic heart disease: identification and validation in 67 000 individuals from the general population Ruth Frikke-Schmidt,1,2* Anne Tybjærg-Hansen,1,2,3 Greg Dyson,4 Christiane L Haase,1 Marianne Benn,5 Børge G Nordestgaard2,3,6 and Charles F Sing7 1 Department of Clinical Biochemistry, Rigshospitalet, 2The Copenhagen General Population Study, Herlev Hospital, 3The Copenhagen City Heart Study, Frederiksberg Hospital, Copenhagen, Denmark, 4 Department of Oncology, Wayne State University, Detroit, USA, 5Department of Clinical Biochemistry, Gentofte Hospital, 6Department of Clinical Biochemistry, Herlev Hospital, Copenhagen, Denmark, 7 Department of Human Genetics, University of Michigan, Ann Arbor, USA and 1–3,5–6Copenhagen University Hospital and Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. *Corresponding author. Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100, Copenhagen, Denmark. E-mail: Accepted 9 October 2014 Abstract Background The aetiology of ischaemic heart disease (IHD) is complex and is influenced by a spectrum of environmental factors and susceptibility genes. Traditional statistical modelling considers such factors to act independently in an additive manner. The Patient Rule-Induction Method (PRIM) is a multi-model building strategy for evaluating risk attributable to context-dependent gene and environmental effects. Methods PRIM was applied to 9073 participants from the prospective Copenhagen City Heart Study (CCHS). Gender-specific cumulative incidences were estimated for subgroups defined by categories of age, smoking, hypertension, diabetes, body mass index, total cholesterol, high-density lipoprotein cholesterol and triglycerides and by 94 single nucleotide variants (SNVs).Cumulative incidences for subgroups were validated using an independently ascertained sample of 58 240 participants from the Copenhagen General Population Study (CGPS). Results In the CCHS the overall cumulative incidences were 0.17 in women and 0.21 in men. PRIM identified six and four mutually exclusive subgroups in women and men, respectively, with cumulative incidences of IHD ranging from 0.02 to 0.34. Cumulative incidences of IHD generated by PRIM in the CCHS were validated in four of the six subgroups of women and two of the four subgroups of men in the CGPS. Conclusions PRIM identified high-risk subgroups characterized by specific contexts of selected values of traditional risk factors and genetic variants. These subgroups were validated in an independently ascertained cohort study. Thus, a multi-model strategy may C The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association V 117 118 International Journal of Epidemiology, 2015, Vol. 44, No. 1 identify groups of individuals with substantially higher risk of IHD than the overall risk for the general population. Key words: Cardiovascular disease, risk factor, genetic epidemiology Key Messages • The present application of the Patient Rule-Induction Method (PRIM) for IHD shows that PRIM is able to identify high- risk subgroups of individuals characterized by selected values of traditional risk factors and candidate genetic variants. • These findings are novel, and suggest that a multi-model strategy is able to identify groups of individuals character- treatments. Introduction Ischaemic heart disease (IHD) is the leading cause of morbidity and mortality worldwide.1 The aetiology of IHD is complex and is influenced by a spectrum of environmental factors and susceptibility genes.2 Traditional statistical modelling considers such factors to act independently in an additive manner, and assumes that the expected relationship between disease status and variation in genetic and environmental risk factors is the same for all individuals in the population under study. This perspective does not take into account the fact that the effects of a particular genetic variant on an individual’s risk of disease may depend on context, defined by established environmental risk factors, and by the background genotype.3,4 Currently, over 1800 genome-wide association studies (GWAS) have reported validated associations between common single nucleotide variants (SNVs) and complex disorders including cardiovascular disease, cancer, diabetes and psychiatric diseases [(http//:www.genome.gov/gwasstudies]. As these diseases are common, they place the greatest public health burden on society.4 However, in every case substantial heritable variation in risk of disease and in biological risk factors for disease is not explained by common SNVs identified by the GWAS.2,5–8 Possible explanations for this ‘missing heritability’ include rare variant effects, the effects of gene-gene and gene-environment interactions, and aetiological heterogeneity (i.e. different combinations of genes and environments influence risk in different subgroups of the population) that are not considered by GWAS.5,9 The systematic mapping of regions of transcription, transcription factor association, chromatin structure and histone modification, recently published by the Encyclopedia of DNA Elements (ENCODE) project, revealed that the function of the human transcriptional regulatory network is highly context-specific.10,11 Analytical strategies to investigate the context-dependent effects of genomic variations on the risk of a common disease having a complex multifactorial aetiology are currently in their infancy.2,5,9 The Patient Rule-Induction method (PRIM)12–15 is a model-building strategy for evaluating risk that acknowledges context-dependent gene and environmental effects and aetiological heterogeneity whereby different combinations of genetic and environmental risk factors are predictive of disease outcome in different genetic and environmentally defined subgroups of the population. This strategy makes possible the identification of combinations of risk factor values, environmental strata and/or genetic variants that characterize mutually exclusive subgroups of individuals that differ in average risk as measured by the cumulative incidence of the disease of interest. In this paper we consider traditional IHD risk factors and 94 SNVs in 22 candidate genes in an application of PRIM to modelling the cumulative incidence of IHD in subgroups of a population-based sample of 9073 individuals enrolled in the prospective Copenhagen City Heart Study (CCHS). We validated the resultant models in an independently ascertained cohort of 58 240 individuals from the Copenhagen General Population Study (CGPS). Methods Participants Studies were approved by institutional review boards and Da (...truncated)