Identification of DNA methylation-regulated genes as potential biomarkers for coronary heart disease via machine learning in the Framingham Heart Study

Abstract

Background DNA methylation-regulated genes have been demonstrated as the crucial participants in the occurrence of coronary heart disease (CHD). The machine learning based on DNA methylation-regulated genes has tremendous potential for mining non-invasive predictive biomarkers and exploring underlying new mechanisms of CHD. Results First, the 2085 age-gender-matched individuals in Framingham Heart Study (FHS) were randomly divided into training set and validation set. We then integrated methylome and transcriptome data of peripheral blood leukocytes (PBLs) from the training set to probe into the methylation and expression patterns of CHD-related genes. A total of five hub DNA methylation-regulated genes were identified in CHD through dimensionality reduction, including ATG7, BACH2, CDKN1B, DHCR24 and MPO. Subsequently, methylation and expression features of the hub DNA methylation-regulated genes were used to construct machine learning models for CHD prediction by LightGBM, XGBoost and Random Forest. The optimal model established by LightGBM exhibited favorable predictive capacity, whose AUC, sensitivity, and specificity were 0.834, 0.672, 0.864 in the validation set, respectively. Furthermore, the methylation and expression statuses of the hub genes were verified in monocytes using methylation microarray and transcriptome sequencing. The methylation statuses of ATG7, DHCR24 and MPO and the expression statuses of ATG7, BACH2 and DHCR24 in monocytes of our study population were consistent with those in PBLs from FHS. Conclusions We identified five DNA methylation-regulated genes based on a predictive model for CHD using machine learning, which may clue the new epigenetic mechanism for CHD.

Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease

“…Although there are numerous models for predicting incident CHD, 73 to our knowledge, this is the first integrated genetic–epigenetic algorithm for current CHD status. In 2022, Zhang and colleagues 74 described 3 different machine learning models that used genome‐wide methylation and expression information to predict current CHD status in the FHS. The performance metrics of each of the models were similar to the current values, but there was no external validation and no translation of either the methylation or expression assessments into a clinically implementable format.…”

The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation

“…In this regard, this may be an excellent opportunity for artificial intelligence (AI) to further its role in the derivation and implementation of this testing technology. AI technologies for aggregating and analyzing methylation and genetic data are increasingly commonplace [ 43 , 44 ]. These tools will need to be integrated with tools for parsing clinical laboratory or text data from the electronic medical records in order to create a more complete understanding of the performance and implications of these technologies in the real world [ 45 ].…”

Epigenomic insights into common human disease pathology

“…Cardiovascular disease (CVD) traits have been explored via EWAS, such as ischaemic stroke [ 229 ]. In the Framingham Study, biomarkers of CVD were identified through ML by combining DNAm and RNA data in a decision tree, light gradient-boosting machine (LightGBM) [ 230 ]. These were subsequently confirmed in isolated monocytes.…”