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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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
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| Series: | Biomarker Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40364-025-00821-y |
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| Summary: | 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. |
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| ISSN: | 2050-7771 |