Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases
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 th...
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| Format: | Article |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Online Access: | https://spj.science.org/doi/10.34133/research.0618 |
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| author | Zhihua Wang Shuo Chen Fanshun Zhang Shamil Akhmedov Jianping Weng Suowen Xu |
| author_facet | Zhihua Wang Shuo Chen Fanshun Zhang Shamil Akhmedov Jianping Weng Suowen Xu |
| author_sort | Zhihua Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1b2c661651043efbec7f3c7b2b31892 |
| institution | OA Journals |
| issn | 2639-5274 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj-art-d1b2c661651043efbec7f3c7b2b318922025-08-20T02:29:29ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0618Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular DiseasesZhihua Wang0Shuo Chen1Fanshun Zhang2Shamil Akhmedov3Jianping Weng4Suowen Xu5Department of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.Department of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.Department of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia.Department of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.Department of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.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.https://spj.science.org/doi/10.34133/research.0618 |
| spellingShingle | Zhihua Wang Shuo Chen Fanshun Zhang Shamil Akhmedov Jianping Weng Suowen Xu Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases Research |
| title | Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases |
| title_full | Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases |
| title_fullStr | Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases |
| title_full_unstemmed | Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases |
| title_short | Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases |
| title_sort | prioritization of lipid metabolism targets for the diagnosis and treatment of cardiovascular diseases |
| url | https://spj.science.org/doi/10.34133/research.0618 |
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