Coronary heart disease (CHD) threatens human health. The discovery and assessment of potential biometabolic markers for different syndrome types of CHD may contribute to decipher pathophysiological mechanisms and identify new targets for diagnosis and treatment. On the basis of UPLC-Q-TOF/MS metabolomics technology, urine samples of 1072 participants from nine centers, including normal control, phlegm and blood stasis (PBS) syndrome and Qi and Yin deficiency (QYD) syndrome, and other syndromes of CHD, were conducted to find biomarkers. Among them, the discovery set ( n = 125) and the test set ( n = 337) were used to identify and validate biomarkers, and the validation set ( n = 610) was used for the application and evaluation of the support vector machine (SVM) prediction model. We discovered 15 CHD-PBS syndrome biomarkers and 12 CHD-QYD syndrome biomarkers, and the receiver-operator characteristic (ROC) area-under-the-curve (AUC) values of them were 0.963 and 0.990. The established SVM model has a good diagnostic ability and can well distinguish the two syndromes of CHD with a high predicted accuracy >98.0%. The discovery of biomarkers and metabolic pathways in different syndrome types of CHD provides a basis for the diagnosis and evaluation of CHD, thereby improving the accurate diagnosis and precise treatment level of Chinese medicine.
Keywords: Qi and Yin deficiency; UPLC-Q-TOF/MS; biomarkers; coronary heart disease; metabolomics; phlegm and blood stasis; support vector machine.