Parkinson’s disease is a progressive neurodegenerative disorder that has no known cure yet. The current treatment options are aimed at slowing the progression of the disease from the stage involving non-motor symptoms to the stage where the motor neurons are affected and Lewy body dementia sets in.
In a recent study published in Nature Communications, a team of researchers used multiplexed mass spectrometry data and a machine learning model to identify biomarkers that can predict Parkinson’s disease in the pre-motor symptom or early stages and help implement interventions to prevent or slow the progression of the disease into the motor symptom stages.
Study: Plasma proteomics identify biomarkers predicting Parkinson’s disease up to 7 years before symptom onset. Image Credit: Orawan Pattarawimonchai/Shutterstock.com
Background
Parkinson’s disease is a progressive neurodegenerative disease that affects the central nervous system and is characterized by the formation of Lewy bodies, which are protein masses formed due to the accumulation of α-synuclein protein.
It is a complex disease with substantial clinical heterogeneity, which has resulted in a lack of neuroprotective strategies and treatments that can significantly slow the progression of the disease.
Developing and approving neuroprotective strategies requires identifying effective biomarkers and therapeutic targets that are directly linked to the clinical phenotypes and pathophysiology of Parkinson's disease.
The in vivo identification of α-synuclein through α-synuclein seed amplification assays is effective for disease stratification.
Still, it requires cerebrospinal fluid, which can only be obtained through the highly invasive and high-risk process of lumbar punctures, making it unsuitable for large-scale screening and clinical use.
About the Study
The present study used proteomic phenotyping data from mass spectrometry to identify potential blood biomarkers to detect Parkinson's disease in the early stages.
The early, non-motor symptoms of Parkinson's disease involve disorders affecting rapid eye movement (REM) sleep.
Therefore, the discovery stage of the study involved a cohort of newly diagnosed Parkinson's disease patients and a control cohort of healthy individuals. At the same time, the validation stage of the study also included an additional cohort consisting of patients diagnosed with isolated REM sleep behavior disorder.
Proteins thought to be putatively involved in the early inflammatory stages of Parkinson's disease were identified from the mass-spectrometry data obtained from plasma samples. These proteins were then targeted in the validation phase of the study using a targeted, high-throughput proteomic assay.
A bottom-up analysis of the plasma samples was conducted to obtain proteomic data after major proteins in blood were removed from the plasma samples using liquid chromatography fractionation and quadrupole time-of-flight mass spectrometry.
The proteins that were differentially expressed in the de novo Parkinson's disease cohort as compared to the control cohort were selected for pathway analysis to determine associations with inflammatory pathways.
The proteins identified in the discovery phase and several identified in previous studies to be associated with Parkinson's disease, aging, and Alzheimer's disease were used to develop the targeted proteomic assay for the validation phase.
Out of a panel of 121 proteins used in the targeted proteomic assay, the biomarkers measured at reliable values in the validation cohorts were then used for independent analysis in a larger cohort.
This cohort consisted of high-risk subjects who had polysomnography-confirmed isolated REM sleep behavior disorder and for whom longitudinal follow-up data was available for up to seven years.
Major Findings
The study identified eight protein biomarkers that could distinguish between patients with early Parkinson's disease and healthy controls with 100% specificity.
These biomarkers were also able to identify patients with isolated REM sleep behavior disorder for up to seven years before they developed motor-associated Parkinson's disease symptoms or Lewy body dementia.
The study found that the protein profile from patients with isolated REM sleep behavior disorder was significantly different from that of healthy controls, with lower levels of progranulin and mannan-binding serine peptidase 2 and elevated levels of serine protease inhibitors and complement factor C3, indicating the early activation of inflammatory pathways.
The Wnt-related proteins were also down-regulated in patients with early Parkinson's disease symptoms.
The protein biomarker panel identified changes in the blood proteins of eight of the 11 isolated REM sleep behavior disorder patients for up to seven years before they developed motor symptoms or Lewy body dementia.
Conclusions
In summary, using multiplexed mass-spectrometry data, the study identified a panel of blood biomarkers that can be reliably used to detect Parkinson's disease in the early stages when patients experience isolated REM sleep behavior disorder.
This protein biomarker panel also presents a less invasive method of screening for Parkinson's disease and implementing interventions in the early stages to delay or slow the progression of the disease into the motor-symptom stage or Lewy body dementia.
DeepUrfold: A New AI Model Uncovers Hidden Protein Relationships and Redefines Fold Space