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Background <p>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.</p> Methods <p>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 (<i>n</i> = 271), high-risk (<i>n</i> = 363), and CAD (<i>n</i> = 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.</p> Results <p>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.</p> Conclusion <p>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.</p> Trial registration number and date of registration <p>ClinicalTrials.gov Identifier: NCT04839796; Aug 2013.</p>

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Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • 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

摘要

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.