Literature DB >> 30907085

A Large-Scale, Multi-Center Urine Biomarkers Identification of Coronary Heart Disease in TCM Syndrome Differentiation.

Haonan Zhou1, Lin Li1, Huan Zhao1, Yuming Wang1, Jun Du1, Pengjie Zhang1, Chunjie Li2, Xianliang Wang3, Yuechen Liu1, Qiang Xu4, Tianpu Zhang1, Yanqi Song1, Chunquan Yu1, Yubo Li1.   

Abstract

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.

Entities:  

Keywords:  Qi and Yin deficiency; UPLC-Q-TOF/MS; biomarkers; coronary heart disease; metabolomics; phlegm and blood stasis; support vector machine

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Year:  2019        PMID: 30907085     DOI: 10.1021/acs.jproteome.8b00799

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  5 in total

1.  The RIGHT Extension Statement for Traditional Chinese Medicine: Development, Recommendations, and Explanation.

Authors:  Runsheng Xie; Yun Xia; Yaolong Chen; Hui Li; Hongcai Shang; Xinying Kuang; Linjun Xia; Yi Guo
Journal:  Pharmacol Res       Date:  2020-09-02       Impact factor: 7.658

2.  Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study.

Authors:  Olga Krysko; Elena Kondakova; Olga Vershinina; Elena Galova; Anna Blagonravova; Ekaterina Gorshkova; Claus Bachert; Mikhail Ivanchenko; Dmitri V Krysko; Maria Vedunova
Journal:  Front Immunol       Date:  2021-08-27       Impact factor: 7.561

3.  The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review.

Authors:  Hongmin Chu; Seunghwan Moon; Jeongsu Park; Seongjun Bak; Youme Ko; Bo-Young Youn
Journal:  Front Pharmacol       Date:  2022-04-01       Impact factor: 5.988

4.  A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.

Authors:  Zonghai Huang; Jiaqing Miao; Ju Chen; Yanmei Zhong; Simin Yang; Yiyi Ma; Chuanbiao Wen
Journal:  JMIR Med Inform       Date:  2022-04-06

5.  Exploring the pivotal variables of tongue diagnosis between patients with acute ischemic stroke and health participants.

Authors:  Yung-Sheng Huang; Han-Kuei Wu; Hen-Hong Chang; Tsung-Chieh Lee; Sung-Yen Huang; John Y Chiang; Po-Chi Hsu; Lun-Chien Lo
Journal:  J Tradit Complement Med       Date:  2022-04-07
  5 in total

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