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Multi-omics analysis revealed biomarkers for coronary atherosclerosis: Occurrence and development
Mengxue He1, Dongxue Wang2, Yong-Jiang Xu1
1State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Insights
New biomarkers from plasma metabolites show promise for diagnosing coronary atherosclerosis (CA) severity. This multi-omics study identified key metabolic changes to improve early detection and personalized CA management.
Area of Science:
- Cardiovascular Research
- Metabolomics
- Biomarker Discovery
Background:
- Coronary atherosclerosis (CA) is a major cause of cardiovascular disease with high mortality.
- Current diagnostic methods are often inadequate for early or subclinical CA detection.
- There is a need for sensitive and specific biomarkers for CA diagnosis and severity assessment.
Purpose of the Study:
- To discover sensitive and specific biomarkers for diagnosing coronary atherosclerosis (CA) severity.
- To evaluate the diagnostic performance of identified biomarker panels.
- To assess the potential for improving early diagnosis and personalized management of CA.
Main Methods:
- Multi-omics data integration from 443 participants across discovery, testing, and validation cohorts.
- Refinement of biomarker panels using random forest models.
- Evaluation of biomarker performance in blinded validation cohorts for CA occurrence and severity.
Main Results:
- Plasma metabolites strongly correlated with CA severity and distinguished different CA stages.
- A diagnostic model achieved an AUC > 0.933, with high sensitivity and specificity.
- Biomarker panels demonstrated good performance in a blinded validation cohort, with most remaining significant after adjusting for comorbidities.
Conclusions:
- Significant metabolic changes are associated with coronary atherosclerosis (CA) progression.
- Established biomarker panels show potential for diagnosing CA severity.
- Key metabolites identified may improve early diagnosis and personalized management of CA.
Background:
Coronary atherosclerosis (CA) is a leading cause of cardiovascular diseases with the high morbidity and mortality; however, the current diagnostic methods, primarily based on symptoms, signs, lab examination and imaging, are often inadequate for detecting subclinical or early-stage CA, costly, and inaccessible in many cases. The objective of this study was to discover sensitive and specific biomarkers for the diagnosis of CA severity.
Methods:
We enrolled 443 participants, including CA patients and healthy controls, from three independent cohorts: discovery, testing, and blinded validation. Multi-omics data integration during the discovery phase identified key features of atherosclerotic progression and potential biomarkers. Biomarker panels were refined using random forest models in the testing cohort, and their performance was evaluated in a blinded validation cohort to assess their ability to monitor the occurrence and development of CA.
Results:
Multi-omics analysis revealed that plasma metabolites exhibited the strongest correlation with CA severity, effectively distinguished different CA stages from healthy controls. Post hoc analysis confirmed the diagnostic model's robustness, with an AUC value higher than .933 (95% CI: .828-.984, sensitivity 93.75%, and specificity 80%). In the blinded validation cohort, the biomarker panel achieved AUC values of .821-.898 for CA occurrence and .649-.849 for CA severity. Notably, 90% of these biomarkers remained significant after adjusting for comorbidities (p < .05).
Conclusions:
This study identified significant metabolic changes during CA progression and established biomarker panels with potential diagnostic value for assessing CA severity. Key metabolites including cholesteryl sulphate, azelaic acid, tryptophan, arabinofuranosyluracil, TMAO, ADMA, LPC18:2, tartaric acid, L-citrulline, and L-proline, purine, sorbitol, and 2-aminoadipic acid. These findings highlight the potential of these biomarkers to improve early diagnosis and personalised management of CA.
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