Observational studyNeurology2023
High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression.
Observational study in Neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 21 papers.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Parkinson's Progression Markers Initiative (PPMI)
Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH)-II: A Phase III Randomized Multicenter Clinical Trial of Blood Pressure Reduction for Hypertension in Acute Intracerebral Hemorrhage
Who cites it
21 citing papers in PubMed, 26 citations in OpenAlex.
- CSF proteomic pathway alterations and caudate dopamine transporter deficits predict cognitive vulnerability in early Parkinson's disease.European journal of nuclear medicine and molecular imaging · 2026Article
- The complement C3-microglial axis in depression of Parkinson's disease: from mechanism to therapeutic intervention.EBioMedicine · 2026Article
- Accelerating Medicines Partnership® Parkinson's Disease Proteomics: A Comprehensive Resource for Advancing Parkinson's Disease Research.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- A neuroimmune-enriched multibiofluid proteomic index associated with cognitive-motor progression in early Parkinson's disease: a longitudinal PPMI cohort study.Frontiers in immunology · 2026Article
- Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease.PloS one · 2026Article
- Proteins Associated with Cognition in Amyloid-beta Positive Elders Based on Cerebrospinal Fluid Proteomics.Molecular neurobiology · 2025Article
- Predictive modeling of motor symptom severity and stage classification in Parkinson's disease using machine learning methods with selected multiple serological biomarkers.BMC neurology · 2025Article
- Orthogonal validation of PD Biomarkers: Multi-platform proteomics profiling of CSF, Plasma, and Urine confirms DDC as a consistent candidate.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.Scientific reports · 2025Article
- Cross-organ hierarchy of HLA molecular mismatches in donor-specific antibody development in solid organ transplantations.Cell reports. Medicine · 2025Article
- Identification of Parkinson's disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach.Frontiers in aging neuroscience · 2025Article
- A lightweight cerebrospinal fluid biomarker-based model for first-diagnosis prediction of Parkinson's disease: model development, external validation, and local deployment.Frontiers in aging neuroscience · 2025Article
- Mechanism of action and experimental validation of key genes common to diabetic retinopathy and coronary heart disease based on multiple bioinformatics investigations.Frontiers in genetics · 2025Article
- Screening and identification of novel protein markers of early-stage lung cancer and construction and application of screening models.Frontiers in oncology · 2025Article
- Programmed cell death signatures-driven microglial transformation in Alzheimer's disease: single-cell transcriptomics and functional validation.Frontiers in immunology · 2025Article
- Development and validation of a novel scoring system integrating MIBG scintigraphy and SPECT imaging for differentiating Parkinson's disease.Frontiers in neurology · 2025Article
- 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
- Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods.Neurology · 2024Article
- Dysregulated Wnt and NFAT signaling in a Parkinson's disease LRRK2 G2019S knock-in model.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesThis study aimed to identify CSF proteomic signatures characteristic of Parkinson disease (PD) and evaluate their clinical utility.
methodsThis observational study used data from the Parkinson's Progression Markers Initiative (PPMI), which enrolled patients with PD, healthy controls (HCs), and non-PD participants carrying
resultsData from 279 patients with nongenetic PD (mean ± SD, age 62.0 ± 9.6 years; male 67.7%) and 141 HCs (age 60.5 ± 11.9 years; male 64.5%) were used for PD-ProS derivation. From 23 DEPs, LASSO determined weights of 14 DEPs for the PD-ProS (area under the curve [AUC] 0.83, 95% CI 0.78-0.87), validated in an independent internal validation cohort of 71 patients with nongenetic PD and 35 HCs (AUC 0.81, 95% CI 0.73-0.90). In the LCC, only 5 of the 14 DEPs were also measured. Notably, these 5 DEPs still distinguished 34 patients with nongenetic PD from 31 HCs with the same weights (AUC 0.75, 95% CI 0.63-0.87). Furthermore, the PD-ProS distinguished 258 patients with genetic PD from 365 genetic prodromals. Finally, regardless of genetic status, the PD-ProS independently predicted both cognitive and motor decline in PD (dementia, adjusted hazard ratio in the highest quintile [aHR-Q5] 2.8 [95% CI 1.6-5.0]; Hoehn and Yahr stage IV, aHR-Q5 2.1 [95% CI 1.1-4.0]). DISCUSSION: By integrating high-throughput proteomics with machine learning, we identified PD-associated CSF proteomic signatures crucial for PD development and progression. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov (NCT01176565). A link to the trial registry page is clinicaltrials.gov/ct2/show/NCT01141023. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the CSF proteome contains clinically important information regarding the development and progression of Parkinson disease that can be deciphered by a combination of high-throughput proteomics and machine learning.
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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.