Metabolomic signatures for diagnosis and clinical severity in Parkinson's disease.
Metabolomic signatures for diagnosis and clinical severity in Parkinson's disease.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Jinan, CN · Author affiliation
Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Department of Neurology, Qilu Hospital, Shandong University, Jinan, Shandong, China.Location evidence
CN · Author affiliation · country only
Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.Location evidence
Hengyang, CN · Author affiliation
The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders and Department of Neurology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.Location evidence
A plain-language reading has not been prepared for this paper yet.
Original abstract
BACKGROUND: Parkinson's disease (PD) remains challenging to diagnose at early stages owing to subtle and heterogeneous clinical manifestations and the lack of reliable biomarkers. Metabolomics offers a powerful approach to capture disease-related biochemical alterations that reflect underlying pathophysiology. This study aimed to identify robust plasma metabolic signatures for PD diagnosis and to elucidate metabolic alterations associated with clinical severity. METHODS: We performed untargeted plasma metabolomic profiling using ultra-high performance liquid chromatography-tandem mass spectrometry in a large Chinese population comprising two independent early-stage PD groups, one of which consisted of drug-naïve, de novo patients. Integrative statistical analyses, pathway enrichment, and machine learning-based diagnostic modelling were applied to identify discriminative metabolites and characterise disease- and treatment-related metabolic changes. FINDINGS: In the two case-control datasets, 111 metabolites were consistently altered in early-stage PD, among which 12-hydroxyeicosatetraenoic acid, spermine, and niacinamide emerged as key differential metabolites. Pathway enrichment analysis highlighted sphingolipid metabolism as a major dysregulated pathway in early-stage PD. Using machine learning-based models, a classification model based on six metabolites achieved strong performance (area under the receiver operating characteristic curve [AUC] = 0.976), while individual metabolites also demonstrated good discriminative ability, with the highest AUC reaching 0.916. We further observed that antiparkinsonian medication was significantly associated with metabolic alterations in tyrosine, tryptophan, and polyamine pathways. In addition, gut microbiota-derived metabolites, particularly phenylacetylglutamine and p-Cresol glucuronide, were markedly elevated in PD and associated with both motor and non-motor symptom severity, suggesting a potential contribution to clinical heterogeneity. INTERPRETATION: These findings indicate reproducible plasma metabolic differences associated with early PD and suggest the potential diagnostic value of internally validated classifiers for disease diagnosis. Alterations in gut microbiota-derived metabolites correlate with clinical severity, highlighting the need for further mechanistic and translational research. FUNDING: This study was supported by the National Natural Science Foundation of China (82271281 and 82471267), the Science and Technology Major Project of Hunan Provincial Science and Technology Department (2021SK1010), and the National Key Research and Development Program of China (2021YFC2501204).