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Metabolomic Pattern Predicts Incident Coronary Heart Disease
Zhe Wang1, Cong Zhu1, Vijay Nambi2,3
1From the Human Genetics Center, University of Texas Health Science Center at Houston (Z.W., C.Z., A.C.M., E.B., B.Y.).
Arteriosclerosis, Thrombosis, and Vascular Biology
|May 17, 2019
Summary
Serum metabolite profiles can predict coronary heart disease (CHD) risk. This study identified 19 key metabolites, including theophylline and erythritol, that significantly improve CHD risk prediction beyond traditional factors.
Area of Science:
- Cardiovascular Medicine
- Metabolomics
- Biochemistry
Background:
- Serum metabolomic alterations may precede coronary heart disease (CHD) onset in at-risk individuals.
- Identifying specific metabolomic signatures could elucidate CHD pathophysiology and inform prevention strategies.
Purpose of the Study:
- To identify serum metabolites associated with incident coronary heart disease (CHD).
- To develop a metabolite risk score for predicting CHD beyond traditional risk factors.
Main Methods:
- Metabolomic profiling using chromatography-mass spectrometry in a large, multi-ethnic cohort (ARIC study).
- Application of Cox regression with least absolute shrinkage and selection operator (LASSO) for metabolite selection.
- Construction and validation of a metabolite risk score for CHD prediction.
Main Results:
- Thirty-two metabolites were associated with incident CHD, with 19 showing significant individual associations.
- Theophylline and gamma-linolenic acid demonstrated the strongest positive and negative associations with CHD, respectively.
- A metabolite risk score significantly improved CHD risk prediction, outperforming traditional risk factors.
Conclusions:
- A panel of 19 metabolites from established and novel pathways collectively enhances coronary heart disease (CHD) risk prediction.
- These findings offer potential for early detection and personalized prevention of CHD through metabolomic analysis.
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