Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases

Abstract

Background: Cardiovascular diseases (CVD) are a major global health issue strongly associated with altered lipid metabolism. However, lipid metabolism-related pharmacological targets remain limited, leaving the therapeutic challenge of residual lipid-associated cardiovascular risk. The purpose of this study is to identify potentially novel lipid metabolism-related genes by systematic genomic and phenomics analysis, with an aim to discovering potentially new therapeutic targets and diagnosis biomarkers for CVD. Methods: In this study, we conducted a comprehensive and multidimensional evaluation of 881 lipid metabolism-related genes. Using genome-wide association study (GWAS)-based mendelian randomization (MR) causal inference methods, we screened for genes causally linked to the occurrence and development of CVD. Further validation was performed through colocalization analysis in 2 independent cohorts. Then, we employed reverse screening using phenonome-wide association studies (PheWAS) and a drug target-drug association analysis. Finally, we integrated serum proteomic data to develop a machine learning model comprising 5 proteins for disease prediction. Results: Our initial screening yielded 54 genes causally linked to CVD. Colocalization analysis in validation cohorts prioritized this to 29 genes marked correlated with CVD. Comparison and interaction analysis identified 13 therapeutic targets with potential for treating CVD and its complications. A machine learning model incorporating 5 proteins for CVD prediction achieved a high accuracy of 96.1%, suggesting its potential as a diagnostic tool in clinical practice. Conclusion: This study comprehensively reveals the complex relationship between lipid metabolism regulatory targets and CVD. Our findings provide new insights into the pathogenesis of CVD and identify potential therapeutic targets and drugs for its treatment. Additionally, the machine learning model developed in this study offers a promising tool for the diagnosis and prediction of CVD, paving the way for future research and clinical applications.

Fig. 1.

Study design for prioritization of lipid metabolism targets for the diagnosis and treatment of CVDs. Our study encompasses 2 primary components: Initially, we assign precedence to potential diagnostic and therapeutic biomarkers for CVD within lipid metabolism-regulating targets, employing techniques including GWAS analysis, MR analysis, colocalization analysis, and PheWAS analysis. Subsequently, utilizing drug–target interaction databases and serum proteomic datasets, we pinpoint probable targeted therapeutic agents and predictive diagnostic biomarkers for CVD based on these prioritized targets.

Fig. 2.

Summary of findings from MR and colorization studies on links between lipid metabolism regulatory targets and CVD risk. (A and B) The volcano plot illustrates the gene-wide MR analysis results for CVD, utilizing the discovery cohort ebi-a-GCST90086053 (n = 56,637) and finn-b-I9_CVD (n = 218,792). (C) The Upset plot visualizes MR analysis outcomes across various cohorts. (D) A Forest plot shows identified MR associations between lipid metabolism regulatory targets and CVD risk, based on the discovery cohort data. (E) Colocalization analysis results are presented using expanded cohort data (ebi-a-GCST90038595, n = 484,598; ebi-a-GCST90029019, n = 477,807). (F) A comparison of associations from the expanded cohort analysis is provided, based on ebi-a-GCST90038595 and ebi-a-GCST90029019 data.

Fig. 3.

Phenome-level analysis for reverse identification of potential targets for CVD. (A) Manhattan plot for PheWAS of lipid metabolism regulation genes associated with CVD. (B) Molecular ranking chart Illustrating the contribution ranking of potential drug target genes. (C) Molecular ranking chart displaying the significance ranking of phenotypes associated with the genes.

Fig. 4.

Analysis of associations and interactions involving 13 potential drug targets in CVD. (A) Results of MR analysis for 13 genes targeted by drugs and their associations with CVD and 13 related complications. (B) Summary of the influence of genes implicated in CVD on various complications. (C) Identified potential drug–target–trait–disease association network. AF, atrial fibrillation; CA, coronary atherosclerosis; CAD, coronary artery disease; HDL, HDL cholesterol; HF, heart failure; HL, hyperlipidemia; HT, hypertension; IS, ischemic stroke; LDL, LDL cholesterol; MI, myocardial infarction; PVD, peripheral vascular disease; TC, total cholesterol levels; TG, total triglyceride levels.

Fig. 5.

CVD prediction model using 5-protein machine learning. (A) Violin plot illustrating the serum proteome study of 13 drug target proteins in CVD patients versus healthy individuals. (B) Ranking of SHAP values predicted by machine learning. (C) ROC analysis plots and AUC values for five potential biomarkers associated with CVD. (D) Confusion matrix demonstrating the accuracy of the machine learning model in predicting CVD for the test and prediction sets. (E) ROC curve for CVD prediction based on the 5-protein model.