ReviewInternational journal of molecular sciences2022
Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Untargeted and Targeted Cerebrospinal Fluid Neurometabolomics via Chromatography-Mass Spectrometry-Based Methods.Molecules (Basel, Switzerland) · 2026Review
- Metabolomics of Multiple System Atrophy Patient-Derived Striatal Medium Spiny Neurons.Biomolecules · 2026Article
- Review
- Biological Function Analysis of MicroRNAs and Proteins in the Cerebrospinal Fluid of Patients with Parkinson's Disease.International journal of molecular sciences · 2024Review
- Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease.International journal of molecular sciences · 2024Review
- Comprehensive analysis of the cerebrospinal fluid and serum metabolome in neurological diseases.Journal of neuroinflammation · 2024Article
- Neuroprotective and vasoprotective effects of herb pair of Zhiqiao-Danggui in ischemic stroke uncovered by LC-MS/MS-based metabolomics approach.Metabolic brain disease · 2024Article
- Bioinformatics-based identification of key candidate genes and signaling pathways in patients with Parkinson's disease and obstructive sleep apnea.Sleep & breathing = Schlaf & Atmung · 2024Article
- Article
- Perspective Strategies for Interventions in Parkinsonism: Remedying the Neglected Role of TPPP.Cells · 2024Article
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
- Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease.Communications medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Parkinson's disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.