DNA Methylation Predicts Adverse Outcomes of Coronary Artery Disease

Background Adverse outcomes including myocardial infarction and stroke render coronary artery disease (CAD) a leading cause of death worldwide. Biomarkers that predict such adversity enable closer medical supervision and opportunities for improved outcomes. Methods and results We present a study of genome-wide DNA methylation profiling in 933 CAD patients with up to 13 years of clinical follow-up. We discovered 115 methylation sites associated with poor prognosis and inferred that cellular senescence, inflammation, and high-density lipoprotein mediated the adversity. We built succinct prognostic models combining a few methylation sites and clinical features, which could stratify patients of different risks. Furthermore, we assessed genetic regulation of the differential methylation by interrogating QTL effects. Prognostic genes such as FKBP5 , UBE2E2 and AUTS2 appeared recurrently in various analyses and were validated in patients of myocardial infarction and stroke. Conclusions Our study provides prognostic models for clinical application and revealed methylation biomarkers and mechanisms of CAD adverse outcomes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported by the National Natural Science Foundation of China (No. 82274016, 32270626, 81872934), the Key-Area Research and Development Program of Guangdong Province, China (No. 2019B020229003), Greater Bay Area Research Institute of Precision Medicine (Guangzhou) Research Grants (I0005, R2001), National key research and development program (No. 2017YFC0909301) and the Science and Technology Program of Guangzhou (No. 2023B03J1251). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Medical Research Ethics Committee of Guangdong Provincial People's Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Gene regulatory elements were obtained from ENCODE5 catalog (). Enhancer-gene predictions by the ABC models in 131 biosamples were obtained from the ENGREITZ LAB (). CpG sites associated with diseases and traits were download from EWAS Catalog () and EWAS Atlas (). meQTL summary statistic data were obtained from Pan-mQTL (). eQTL summary statistics were derived from eQTLGen (). Single-nuclei RNA sequencing data of MI patients were available from Zenodo data archive (). Blood derived bulk RNA sequencing data were obtained from GEO database (GSE61144 and GSE16561). Calculation of DNA methylation age of GrimAge, Hannum clock, Hovath clock, and PhenoAge were performed by DNA Methylation Age Calculator ().