Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease
Abstract Background Coronary artery disease (CAD) remains a leading cause of mortality in developed nations. While previous genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) linked to CAD, their impact on disease progression requires trans-omics validation. Metho...
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BMC
2025-08-01
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| Online Access: | https://doi.org/10.1186/s40364-025-00821-y |
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| author | Paul Wei-Che Hsu Chi-Hsiao Yeh Chi-Jen Lo Tsung-Hsien Tsai Yun-Hsuan Chan Yi-Ju Chou Ning-I Yang Mei-Ling Cheng Wayne Huey-Herng Sheu Chi-Chun Lai Huey-Kang Sytwu Ting-Fen Tsai |
| author_facet | Paul Wei-Che Hsu Chi-Hsiao Yeh Chi-Jen Lo Tsung-Hsien Tsai Yun-Hsuan Chan Yi-Ju Chou Ning-I Yang Mei-Ling Cheng Wayne Huey-Herng Sheu Chi-Chun Lai Huey-Kang Sytwu Ting-Fen Tsai |
| author_sort | Paul Wei-Che Hsu |
| collection | DOAJ |
| description | Abstract Background Coronary artery disease (CAD) remains a leading cause of mortality in developed nations. While previous genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) linked to CAD, their impact on disease progression requires trans-omics validation. Methods This study merges whole genome SNP analysis and metabolomic profiling to distinguish CAD patients from high-risk and healthy individuals. A cross-sectional study was conducted, enrolling participants from the Northeastern Taiwan Community Medicine Research Cohort, which spans the period between August 2013 and November 2020. A total of 781 participants were included in the study and categorized into three groups: control (n = 271), high-risk (n = 363), and CAD (n = 147) groups, following a stratification protocol. The study integrated K-clustering of metabolomics and SNP datasets. Subsequently, a machine-learning (ML)-assisted prediction model was developed specifically for CAD identification. Results Four significant findings emerged. Firstly, plasma levels of phospholipids decline from healthy controls to high-risk individuals and then decline further among CAD patients. This indicates that plasma phospholipids have potential as biomarkers and implies that they have a role in CAD progression. Secondly, five genes are linked to lipidomic alterations via their top-ranking among CAD-associated SNPs. Thirdly, a specific LPCAT1 haplotype is associated with CAD using a trans-omics approach. Lastly, an ML-assisted trans-omics prediction model for CAD was developed, which achieves an area under the curve of 0.917, with LPCAT1 among the 16 top-ranked predictive features. Conclusion This study highlights the usefulness of a multi-omics signature when discriminating CAD patients and suggests that abnormalities in phospholipid metabolism are influenced by LPCAT1 genetic variants. Our findings underscore the potential of multi-omics approaches to our understanding and identification of critical factors in CAD development. Trial registration number and date of registration ClinicalTrials.gov Identifier: NCT04839796; Aug 2013. |
| format | Article |
| id | doaj-art-ed9e23ef0d48440eaa0a6a5a1dc4ff36 |
| institution | Kabale University |
| issn | 2050-7771 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| series | Biomarker Research |
| spelling | doaj-art-ed9e23ef0d48440eaa0a6a5a1dc4ff362025-08-24T11:41:52ZengBMCBiomarker Research2050-77712025-08-0113111810.1186/s40364-025-00821-yTrans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery diseasePaul Wei-Che Hsu0Chi-Hsiao Yeh1Chi-Jen Lo2Tsung-Hsien Tsai3Yun-Hsuan Chan4Yi-Ju Chou5Ning-I Yang6Mei-Ling Cheng7Wayne Huey-Herng Sheu8Chi-Chun Lai9Huey-Kang Sytwu10Ting-Fen Tsai11Institute of Molecular and Genomic Medicine, National Health Research InstitutesDepartment of Thoracic and Cardiovascular Surgery, Chang Gung Memorial HospitalMetabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung UniversityAdvanced Tech BU, Acer IncAdvanced Tech BU, Acer IncInstitute of Molecular and Genomic Medicine, National Health Research InstitutesCommunity Medicine Research Center, Chang Gung Memorial HospitalMetabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung UniversityInstitute of Molecular and Genomic Medicine, National Health Research InstitutesCommunity Medicine Research Center, Chang Gung Memorial HospitalNational Institute of Infectious Diseases and Vaccinology, National Health Research InstitutesInstitute of Molecular and Genomic Medicine, National Health Research InstitutesAbstract Background Coronary artery disease (CAD) remains a leading cause of mortality in developed nations. While previous genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) linked to CAD, their impact on disease progression requires trans-omics validation. Methods This study merges whole genome SNP analysis and metabolomic profiling to distinguish CAD patients from high-risk and healthy individuals. A cross-sectional study was conducted, enrolling participants from the Northeastern Taiwan Community Medicine Research Cohort, which spans the period between August 2013 and November 2020. A total of 781 participants were included in the study and categorized into three groups: control (n = 271), high-risk (n = 363), and CAD (n = 147) groups, following a stratification protocol. The study integrated K-clustering of metabolomics and SNP datasets. Subsequently, a machine-learning (ML)-assisted prediction model was developed specifically for CAD identification. Results Four significant findings emerged. Firstly, plasma levels of phospholipids decline from healthy controls to high-risk individuals and then decline further among CAD patients. This indicates that plasma phospholipids have potential as biomarkers and implies that they have a role in CAD progression. Secondly, five genes are linked to lipidomic alterations via their top-ranking among CAD-associated SNPs. Thirdly, a specific LPCAT1 haplotype is associated with CAD using a trans-omics approach. Lastly, an ML-assisted trans-omics prediction model for CAD was developed, which achieves an area under the curve of 0.917, with LPCAT1 among the 16 top-ranked predictive features. Conclusion This study highlights the usefulness of a multi-omics signature when discriminating CAD patients and suggests that abnormalities in phospholipid metabolism are influenced by LPCAT1 genetic variants. Our findings underscore the potential of multi-omics approaches to our understanding and identification of critical factors in CAD development. Trial registration number and date of registration ClinicalTrials.gov Identifier: NCT04839796; Aug 2013.https://doi.org/10.1186/s40364-025-00821-yCoronary artery disease (CAD)LPCAT1Single-nucleotide polymorphism (SNP)MetabolomicsMachine learningBiomarker |
| spellingShingle | Paul Wei-Che Hsu Chi-Hsiao Yeh Chi-Jen Lo Tsung-Hsien Tsai Yun-Hsuan Chan Yi-Ju Chou Ning-I Yang Mei-Ling Cheng Wayne Huey-Herng Sheu Chi-Chun Lai Huey-Kang Sytwu Ting-Fen Tsai Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease Biomarker Research Coronary artery disease (CAD) LPCAT1 Single-nucleotide polymorphism (SNP) Metabolomics Machine learning Biomarker |
| title | Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease |
| title_full | Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease |
| title_fullStr | Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease |
| title_full_unstemmed | Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease |
| title_short | Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease |
| title_sort | trans omics analyses identify the biochemical network of lpcat1 associated with coronary artery disease |
| topic | Coronary artery disease (CAD) LPCAT1 Single-nucleotide polymorphism (SNP) Metabolomics Machine learning Biomarker |
| url | https://doi.org/10.1186/s40364-025-00821-y |
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