| Urine biomarkers discovery by metabolomics and machine learning for Parkinson's disease diagnoses |
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| 作者姓名： | Xiaoxiao Wang Xinran Hao Jie Yan Ji Xu Dandan Hu Fenfen Ji Ting Zeng Fuyue Wang Bolun Wang Jiacheng Fang Jing Ji Hemi Luan Yanjun Hong Yanhao Zhang Jinyao Chen Min Li Zhu Yang Doudou Zhang Wenlan Liu Xiaodong Cai Zongwei Cai |
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| 作者单位： | 1. State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University;4. Department of Nutrition, Food Safety and Toxicology, West China School of Public Health, Sichuan University |
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| 基金项目： | Guangdong Province Basic and Applied Basic Research Foundation (No. 2021B1515120051)； |
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| 摘 要： | Parkinson’s disease(PD) is a complex neurological disorder that typically worsens with age. A wide range of pathologies makes PD a very heterogeneous condition, and there are currently no reliable diagnostic tests for this disease. The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites. In this study, urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were ass...
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| 收稿时间： | 1 November 2022 |
Urine biomarkers discovery by metabolomics and machine learning for Parkinson's disease diagnoses |
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| Xiaoxiao Wang,Xinran Hao,Jie Yan,Ji Xu,Dandan Hu,Fenfen Ji,Ting Zeng,Fuyue Wang,Bolun Wang,Jiacheng Fang,Jing Ji,Hemi Luan,Yanjun Hong,Yanhao Zhang,Jinyao Chen,Min Li,Zhu Yang,Doudou Zhang,Wenlan Liu,Xiaodong Cai,Zongwei Cai.Urine biomarkers discovery by metabolomics and machine learning for Parkinson's disease diagnoses[J].Chinese Chemical Letters,2023,34(10):108230-113. |
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| Institution: | 1. State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China;2. Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People''s Hospital, Futian District, Shenzhen 518035, China;3. The Central Laboratory, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People''s Hospital, Shenzhen 518035, China;4. Department of Nutrition, Food Safety and Toxicology, West China School of Public Health, Sichuan University, Chengdu 610041, China;5. Mr. and Mrs. Ko Chi Ming Centre for Parkinson''s Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China;1. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China;2. State Key Laboratory of Molecular Oncology, Cancer Institute, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China;3. Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), National Ethnic Affairs Commission, Beijing 100081, China;4. Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China;1. State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China;2. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China;3. School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China;1. Department of Chemistry and the MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China;2. Department of Chemistry, Taiyuan Normal University, Jinzhong 030619, China;3. Department of Electronic Science, Xiamen University, Xiamen 361005, China |
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| Abstract: | Parkinson's disease (PD) is a complex neurological disorder that typically worsens with age. A wide range of pathologies makes PD a very heterogeneous condition, and there are currently no reliable diagnostic tests for this disease. The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites. In this study, urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were assessed based on liquid chromatography-mass spectrometry. The urine metabolic profile was first evaluated with partial least-squares discriminant analysis, and then we integrated the metabolomic data with ensemble machine learning techniques using the voting strategy to achieve better predictive performance. A combination of 8-metabolite predictive panel performed well with an accuracy of over 90.7%. Compared to control subjects, PD patients had higher levels of 3-methoxytyramine, N-acetyl-l-tyrosine, orotic acid, uric acid, vanillic acid, and xanthine, and lower levels of 3,3-dimethylglutaric acid and imidazolelactic acid in their urine. The multi-metabolite prediction model developed in this study can serve as an initial point for future clinical studies. |
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