Yaping Shao1,2, Tianbai Li1,2, Zheyi Liu3, Xiaolin Wang3, Xiaojiao Xu1,2, Song Li1,2, Guowang Xu4, Weidong Le5,6,7. 1. Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, 193 Lianhe Road, Dalian, China. 2. Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, 193 Lianhe Road, Dalian, China. 3. CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China. 4. CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, 116023, China. xugw@dicp.ac.cn. 5. Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, 193 Lianhe Road, Dalian, China. wdle_sibs@163.com. 6. Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, 193 Lianhe Road, Dalian, China. wdle_sibs@163.com. 7. Institute of Neurology, Sichuan Academy of Medical Science-Sichuan Provincial Hospital, Medical School of UESTC, Sichuan, China. wdle_sibs@163.com.
Abstract
BACKGROUND: Parkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use. METHODS: We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts. RESULTS: A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD. CONCLUSIONS: This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.
BACKGROUND:Parkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use. METHODS: We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts. RESULTS: A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD. CONCLUSIONS: This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.
Entities:
Keywords:
Bile acid profile; Biomarker; Metabolic disturbances; Metabolomics; Parkinson’s disease
Authors: Hemi Luan; Liang-Feng Liu; Nan Meng; Zhi Tang; Ka-Kit Chua; Lei-Lei Chen; Ju-Xian Song; Vincent C T Mok; Li-Xia Xie; Min Li; Zongwei Cai Journal: J Proteome Res Date: 2014-10-09 Impact factor: 4.466
Authors: Jesper F Havelund; Andreas D Andersen; Michael Binzer; Morten Blaabjerg; Niels H H Heegaard; Egon Stenager; Nils J Faergeman; Jan Bert Gramsbergen Journal: J Neurochem Date: 2017-07-11 Impact factor: 5.372
Authors: Filip Scheperjans; Velma Aho; Pedro A B Pereira; Kaisa Koskinen; Lars Paulin; Eero Pekkonen; Elena Haapaniemi; Seppo Kaakkola; Johanna Eerola-Rautio; Marjatta Pohja; Esko Kinnunen; Kari Murros; Petri Auvinen Journal: Mov Disord Date: 2014-12-05 Impact factor: 10.338
Authors: Bruno L Santos-Lobato; Luiz Gustavo Gardinassi; Mariza Bortolanza; Ana Paula Ferranti Peti; Ângela V Pimentel; Lúcia Helena Faccioli; Elaine A Del-Bel; Vitor Tumas Journal: Mol Neurobiol Date: 2021-12-02 Impact factor: 5.590
Authors: Elena A Ostrakhovitch; Eun-Suk Song; Jessica K A Macedo; Matthew S Gentry; Jorge E Quintero; Craig van Horne; Tritia R Yamasaki Journal: Neurosci Lett Date: 2021-12-28 Impact factor: 3.046