TY - JOUR
T1 - Urine biomarkers discovery by metabolomics and machine learning for Parkinson's disease diagnoses
AU - Wang, Xiaoxiao
AU - Hao, Xinran
AU - Yan, Jie
AU - Xu, Ji
AU - Hu, Dandan
AU - Ji, Fenfen
AU - Zeng, Ting
AU - Wang, Fuyue
AU - Wang, Bolun
AU - Fang, Jiacheng
AU - Ji, Jing
AU - Luan, Hemi
AU - Hong, Yanjun
AU - Zhang, Yanhao
AU - Chen, Jinyao
AU - Li, Min
AU - Yang, Zhu
AU - Zhang, Doudou
AU - Liu, Wenlan
AU - Cai, Xiaodong
AU - Cai, Zongwei
N1 - Funding Information:
The authors would like to acknowledge the financial support from the Collaborative Research Fund (No. C2011–21GF) and from Guangdong Province Basic and Applied Basic Research Foundation (No. 2021B1515120051).
Publisher Copyright:
© 2023 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - Biomarker
KW - High-resolution mass spectrometry
KW - Machine learning
KW - Metabolomic
KW - Parkinson's disease
UR - http://www.scopus.com/inward/record.url?scp=85164722666&partnerID=8YFLogxK
U2 - 10.1016/j.cclet.2023.108230
DO - 10.1016/j.cclet.2023.108230
M3 - Journal article
AN - SCOPUS:85164722666
SN - 1001-8417
VL - 34
JO - Chinese Chemical Letters
JF - Chinese Chemical Letters
IS - 10
M1 - 108230
ER -
