Genome-wide DNA Methylation Study Of Individual Differences In Key Metabolites Associated With The Risk Of Coronary Heart Disease With Heart Failure

Coronary artery disease(CAD)is one of the most important types of cardiovascular diseases.Patients with CAD are often accompanied by heart failure(HF)in the terminal stage,which poses a great threat to patients’ lives.In recent years,more and more metabolomics studies have shown that patients with CAD or HF are inclined to have imbalance of metabolite levels in their body.However,traditional metabolomics studies only provide correlations between metabolites and diseases.The causal relationship between them and the genetic and epigenetic mechanisms that regulate metabolite levels in vivo still remain unclear.Therefore,it is of great clinical value to explore how metabolite levels affect and cause individual differences in CAD patients with HF,and to explore the possible epigenetic regulation of DNA methylation by related metabolites.Objectives:Our aim is to identify metabolic micromolecule related to CAD with or without HF and recognize metabolic markers that predict the progression of HF.And we assess the potential causal relationship of those metabolites with the risk of HF based on the genetic risk theory of Mendelian randomization.What’s more,we investigate the epigenetic regulate on of metabolic molecules affecting individual differences in CAD complicated with HF from the perspective of epigenetic regulation of plasma metabolites in patients with CAD.Methods:(1)Following the inclusion criteria of the cohort,we collected and extracted plasma samples from 1,559 patients with CAD and CAD with HF,and conducted extensive targeted detection of metabolites by UPLC-MS/MS.Respectively in the selected single-center discovery cohort(N=1,028)and multicenter validation cohort(N=531),patients with CAD were divided into groups with or without HF,and generalized linear regression model was used to identify differential metabolites and construct risk prediction models.Then pathway enrichment analysis was performed on the differential metabolites to explore the disordered metabolic pathways.(2)An One-sample Mendelian randomization(One-sample MR)method was used to analyze the causal relationship between the six differential metabolites identified in the first part and the occurrence of HF in patients with CAD.Using publicly available large-scale GWAS data of HF and mGWAS data of large-scale healthy people,two-sample Mendelian random analysis(Two-sample MR)was conducted to further verify and explore the causal relationship between plasma metabolites and the occurrence of HF in healthy people.(3)Finally based on six differential metabolites,we conduct EWAS based on blood cell DNA methylation levels to collect information about relevant DNA methylation sites,and perform functional annotation and pathway enrichment analysis.And using the public data set in the GEO database,we analyzed the correlation between the DNA methylation levels of these metabolite significantly related sites and the expression of annotated genes.Results:(1)A total of 161 duplicate plasma metabolites were detected in both cohorts.Six metabolites were identified and replicated,including hexanoyl glycine,kynurenine,suberic acid,N2,N2-dimethylguanosine,3-indolebutyric acid,4-acetamidobutyric acid,significantly associated with the occurrence of HF in CAD patients with HF.The results of pathway enrichment analysis showed that the disturbance of arginine and proline metabolism pathways was significantly correlated with the occurrence of CAD complicated with HF.Compared with the prediction model containing traditional risk factors of HF,the inclusion of differential metabolites associated with HF significantly improved the prediction effect of the model(AUC=0.74 vs 0.68).(2)The results of One-sample MR analysis showed a nominal causal relationship between hexanoyl glycine and the risk of HF in patients with CAD in the discovery cohort(OR=1.07,P=0.032),but failed in Bonferroni’s multiple hypothesis test(P>0.05/6,i.e.P>0.0083).In the validation cohort,there was a nominal causal relationship between 3indolebutyric acid(OR=1.29,P=0.046),hexanoyl glycine(OR=1.18,P=0.015),kynurenine(OR=1.24,P=0.014)and the risk of HF in patients with CAD(0.0083<P<0.05).In addition,our use of data from public datasets combined with Two-sample MR analysis failed to provide causal evidence for the above-mentioned metabolites and the risk of HF in healthy people.Furthermore,we analyzed the causal relationship between all other metabolites and the risk of HF and found that 4-vinylphenol sulfate was significant in at least three Two-sample MR causal test models and passed the heterogeneity test and sensitivity analysis.(3)The results of EWAS showed that kynurenine,N2,N2-dimethylguanosine,and 4-acetamidobutyric acid identified a number of sites with significant correlation,while hexanoyl glycine,suberic acid,and 3indolebutyric acid did not identify DNA methylation sites with significant correlation.And the annotated genes related to the methylation sites of kynurenine and N2,N2-dimethylguanosine were enriched in the ferroptosis pathway.In addition,the methylation levels of multiple DNA methylation sites(cg17944885,cg20015729,cg26286565)significantly associated with the above metabolites were also significantly associated with the expression of their own gene(ZNF20,UBE2E2,PHTF2).Conclusions:We identified six differential metabolites that were significantly associated with HF in patients with CAD.And the model combining differential metabolites and traditional factors can better predict the risk of HF in patients with CAD.Through MR analysis,it was found that there was a nominal causal relationship between the elevated plasma hexanoyl glycine level and the increased risk of HF in patients with CAD,while there was a nominal causal relationship between the elevated plasma 4-vinylphenol sulfate level in healthy people and the increased risk of HF.What’s more,Kynurenine,N2,N2-dimethylguanosine,and 4acetamidobutyric acid may be subjected to epigenetic regulation by varying degrees of DNA methylation.It was also found that the methylation levels of multiple sites were significantly correlated with their own gene expression,suggesting that DNA methylation may have additional effects on key metabolites.And these methylation sites may regulate metabolic disorders in vivo by changing metabolite levels in the way of affecting gene expression.