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.
BackgroundThe relation between circulating homocysteine (hcy) and folate concentrations and risk of type 2 diabetes mellitus (T2DM) has been evaluated in several observational studies with inconsistent results; and it is unclear about their causal relationships. Our aim was to assess the causality association between circulating hcy or folate concentrations and the development of T2DM using Mendelian randomization (MR) analysis, based on results of an observational study in Chinese adults.MethodsWe conducted an observational study of 370 patients with T2DM and 402 controls after routine physical examination who consulted at the Zhongnan Hospital of Wuhan University between March 2021 and December 2021. Correlations between hcy and folate and the incidence of T2DM were quantified using logistic regression models. Two-sample MR analysis was conducted using summary statistics of genetic variants gained from 2 genome-wide association studies (GWAS) on circulating hcy and folate concentrations in individuals of European ancestry and from an independent GWAS study based on DIAMANTE meta-analysis.ResultsIn the observational study, after logistic regression with multiple adjustment, lower hcy and higher folate levels were identified to be associated with the risk of T2DM, with OR (95% CI) for hcy of 1.032 (1.003–1.060); while 0.909 (0.840–0.983) for folate. In the MR analysis, the OR for T2DM was 1.08 (95% CI: 0.95, 1.21; P = 0.249) for each SD unit increase in genetically predicted homocysteinemia and the OR for T2DM per SD increase in genetically predicted folate elevation was 0.80 (95% CI: 0.60, 1.00, P = 0.026).ConclusionsWe discovered that high circulating hcy and low folate concentrations were related with an increased risk of developing T2DM in Chinese adults. Moreover, MR analysis provided genetic evidence for a possible causal relationship between serum folate and the risk of T2DM.
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