DNA methylation and gene expression integration in cardiovascular disease

Abstract

Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.

Integration strategies of multi-omics data for machine learning analysis

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DNA Hypomethylation as a Potential Link between Excessive Alcohol Intake and Cardiometabolic Dysfunction in Morbidly Obese Adults

“…The role of DNA methylation as a predictive marker and a therapeutic target in cardiovascular diseases was highlighted in the systematic review/meta-analysis by Palou-Márquez et al [ 64 ] where DNA methylation data from the Framingham Offspring Study were integrated with gene expression data to identify biomarkers of cardiovascular risk. This multi-omics data integration approach identified several DNA methylation patterns in genes involved in inflammation, endothelium homeostasis, and cardiac remodeling that were key players in determining cardiovascular risk.…”

Epigenetic regulation in cardiovascular disease: mechanisms and advances in clinical trials

“…Recent multifactorial approaches integrating DNA methylation and gene expression data provide new insights into the pathogenesis of the cardiovascular disease. 69 Thirty-four new DNA methylation sites associated with AMI were identified in two-stage Epigenome-wide association studies. Four of them were associated with coronary heart disease.…”