DNA methylation and gene expression integration in cardiovascular disease

Area of Science:

• Genomics and Bioinformatics
• Cardiovascular Research
• Biomarker Discovery

Background:

• Cardiovascular diseases (CVD) present a complex challenge requiring integrated omics data for biomarker and therapeutic target identification.
• This study aimed to combine DNA methylation and gene expression data to find biomarkers for cardiovascular disease risk in a community population.
• Data from the Framingham Offspring Study, including DNA methylation and gene expression profiles, were utilized.

Purpose of the Study:

• To integrate multi-omics data (DNA methylation and gene expression) to identify novel biomarkers for cardiovascular disease (CVD) risk.
• To explore the relationship between integrated omics data and cardiovascular events in a population-based cohort.
• To assess the potential of identified omics-driven factors in improving existing cardiovascular risk prediction models.

Main Methods:

• Utilized the MOFA2 R package for unsupervised multi-omics data integration.
• Analyzed DNA methylation data from Infinium HumanMethylation450 BeadChip and gene expression data from Human Exon 1.0 ST Array.
• Performed association analyses between identified latent factors and cardiovascular disease risk, including sensitivity analyses and replication in an independent study.

Main Results:

• Four independent latent factors derived from DNA methylation were associated with cardiovascular disease risk, independent of traditional risk factors.
• Factor 21, specific to women, and factors 9 and 27 were associated with coronary heart disease risk.
• Factor 21 improved the discriminative and reclassification capacity of the Framingham risk function, while factor 27 enhanced its discrimination.

Conclusions:

• Unsupervised multi-omics integration offers valuable insights into cardiovascular disease pathogenesis.
• Identified factors highlight the roles of inflammation, endothelium homeostasis, visceral fat, cardiac remodeling, and lifestyle in cardiovascular risk.
• Two identified factors demonstrated potential to enhance the predictive power of established cardiovascular risk functions.