Epigenetic Contributions to Clinical Risk Prediction of Cardiovascular Disease

Abstract

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN—a cardiovascular risk prediction tool recommended for use in Scotland—was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort. METHODS: Previously generated DNA methylation–derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (n cases ≥1274; n controls ≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI ( P <0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P =3.7×10 − 3 ; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.

Epigenetic scores derived in saliva are associated with gestational age at birth

“…The 104 EpiScores we tested explain 1–58% of variance of protein levels [ 26 – 28 , 45 ]. However, even those that capture a relatively low proportion of the variance associate with incident diseases such as cardiovascular disease, type 2 diabetes, cognitive function and brain health [ 26 , 115 – 117 ]. This magnitude of variance explained is also comparable to that achieved with polygenic risk scores, which have proved useful in risk stratification [ 118 – 120 ].…”

DNA Methylation-Based Biomarkers of Protein Levels and Cardiovascular Disease Risk: Opportunities and Challenges for Precision Cardiology

“…19,20 Moreover, because DNA methylation is mitotically heritable, it may be a more stable marker of risk than fluctuations in plasma protein concentrations. Findings presented in the study by Chybowska et al 7 provide some evidence that DNA methylationbased biomarkers of protein concentrations may predict CVD risk beyond existing screening tools. Furthermore, leveraging DNA methylation measured with nonspecific genome-wide microarrays provides cost-effective opportunities for calculating biomarkers of multiple diseases with a single biospecimen and assay.…”

“…5,17 These tests can complement other tools in precision oncology to inform decision-making and improve patient outcomes. As highlighted by Chybowska et al, 7 CVD may also be a suitable candidate for DNA methylation-based biomarker development and clinical application.…”