Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease
ObjectiveTo identify key cerebrospinal fluid (CSF) metabolomic biomarkers associated with Parkinson’s disease (PD) and prodromal PD, providing insights for intervention strategy development.MethodsSix hundred and thirty-nine participants from the Parkinson’s Progression Markers Initiative (PPMI) coh...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1582362/full |
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| author | Yifan Zhang Yifan Zhang Yuexin Yan Xiangxu Kong Haijun Zhang Shengyuan Su |
| author_facet | Yifan Zhang Yifan Zhang Yuexin Yan Xiangxu Kong Haijun Zhang Shengyuan Su |
| author_sort | Yifan Zhang |
| collection | DOAJ |
| description | ObjectiveTo identify key cerebrospinal fluid (CSF) metabolomic biomarkers associated with Parkinson’s disease (PD) and prodromal PD, providing insights for intervention strategy development.MethodsSix hundred and thirty-nine participants from the Parkinson’s Progression Markers Initiative (PPMI) cohort were included: 300 PD patients, 112 healthy controls (HC), and 227 prodromal PD patients. Regression analysis and OPLS-DA identified metabolic biomarkers, while pathway analysis examined their relationship to clinical features. An XGBoost-based early prediction model was developed to assess the distinction between PD, prodromal PD, and HC. A two-sample bidirectional Mendelian randomization analysis was performed to examine the association between differential metabolites and Parkinson’s disease.ResultsSixty-four metabolites were significantly altered in PD patients compared to HC, with 58 elevated and 6 reduced. In prodromal PD, 19 metabolites were increased, and 34 were decreased. Key metabolic pathways involved glutathione and amino acid metabolism. Dopamine 3-O-sulfate correlated with PD progression, levodopa-equivalent dose, and non-motor symptoms. The XGBoost model demonstrated high specificity in predicting the onset of PD. The MR analysis results showed a positive correlation between higher genetic predictions of dopamine 3-O-sulfate levels and the risk of Parkinson’s disease. In contrast, the reverse MR analysis found that Parkinson’s disease susceptibility significantly increased dopamine 3-O-sulfate levels.ConclusionThe differential expression of CSF metabolites reveals early cellular metabolic changes, providing insights for early diagnosis and monitoring PD progression. A bidirectional causal relationship exists between genetically determined PD susceptibility and metabolites. |
| format | Article |
| id | doaj-art-2e80033a9529463084ef445f619dd6e8 |
| institution | DOAJ |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-2e80033a9529463084ef445f619dd6e82025-08-20T03:05:53ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-05-011710.3389/fnagi.2025.15823621582362Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s diseaseYifan Zhang0Yifan Zhang1Yuexin Yan2Xiangxu Kong3Haijun Zhang4Shengyuan Su5Department of Intensive Care Medicine, Shenzhen Baoan People’s Hospital, Shenzhen, ChinaDepartment of Neurology, Shenzhen Baoan People’s Hospital, Shenzhen, ChinaDepartment of Intensive Care Medicine, Shenzhen Baoan People’s Hospital, Shenzhen, ChinaDepartment of Intensive Care Medicine, Shenzhen Baoan People’s Hospital, Shenzhen, ChinaDepartment of Neurology, Shenzhen Baoan People’s Hospital, Shenzhen, ChinaDepartment of Intensive Care Medicine, Shenzhen Baoan People’s Hospital, Shenzhen, ChinaObjectiveTo identify key cerebrospinal fluid (CSF) metabolomic biomarkers associated with Parkinson’s disease (PD) and prodromal PD, providing insights for intervention strategy development.MethodsSix hundred and thirty-nine participants from the Parkinson’s Progression Markers Initiative (PPMI) cohort were included: 300 PD patients, 112 healthy controls (HC), and 227 prodromal PD patients. Regression analysis and OPLS-DA identified metabolic biomarkers, while pathway analysis examined their relationship to clinical features. An XGBoost-based early prediction model was developed to assess the distinction between PD, prodromal PD, and HC. A two-sample bidirectional Mendelian randomization analysis was performed to examine the association between differential metabolites and Parkinson’s disease.ResultsSixty-four metabolites were significantly altered in PD patients compared to HC, with 58 elevated and 6 reduced. In prodromal PD, 19 metabolites were increased, and 34 were decreased. Key metabolic pathways involved glutathione and amino acid metabolism. Dopamine 3-O-sulfate correlated with PD progression, levodopa-equivalent dose, and non-motor symptoms. The XGBoost model demonstrated high specificity in predicting the onset of PD. The MR analysis results showed a positive correlation between higher genetic predictions of dopamine 3-O-sulfate levels and the risk of Parkinson’s disease. In contrast, the reverse MR analysis found that Parkinson’s disease susceptibility significantly increased dopamine 3-O-sulfate levels.ConclusionThe differential expression of CSF metabolites reveals early cellular metabolic changes, providing insights for early diagnosis and monitoring PD progression. A bidirectional causal relationship exists between genetically determined PD susceptibility and metabolites.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1582362/fullParkinson diseasesmetabolomic biomarkersearly prediction modelbidirectional Mendelian randomizationPD susceptibility |
| spellingShingle | Yifan Zhang Yifan Zhang Yuexin Yan Xiangxu Kong Haijun Zhang Shengyuan Su Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease Frontiers in Aging Neuroscience Parkinson diseases metabolomic biomarkers early prediction model bidirectional Mendelian randomization PD susceptibility |
| title | Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease |
| title_full | Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease |
| title_fullStr | Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease |
| title_full_unstemmed | Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease |
| title_short | Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease |
| title_sort | potential cerebrospinal fluid metabolomic biomarkers and early prediction model for parkinson s disease |
| topic | Parkinson diseases metabolomic biomarkers early prediction model bidirectional Mendelian randomization PD susceptibility |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1582362/full |
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