Biomarker For Parkinson's Disease Research Articles

Poly (ADP-Ribose) and α-synuclein extracellular vesicles in patients with Parkinson disease: A possible biomarker of disease severity.

Background/ObjectiveDespite multiple attempts, no surrogate biomarker of Parkinson disease (PD) has been definitively identified. Alternatively, identifying a non-invasive biomarker is crucial to understanding the natural history, severity, and progression of PD and to guide future therapeutic trials. Recent work highlighted alpha synuclein-containing extracellular vesicles and Poly (ADP-ribose) polymerase (PARP-1) activity as drivers of PD pathogenesis and putative PD biomarkers. This exploratory study evaluated the role of alpha-synuclein-positive extracellular vesicles and PARP-1 activity in the plasma of PD patients as non-invasive markers of the disease’s severity and progression.MethodsWe collected plasma of 57 PD patients (discovery cohort 20, replication cohort 37) and compared it with 20 unaffected individuals, 20 individuals with clinically diagnosed Alzheimer’s disease, and 20 individuals with dementia with Lewy bodies. We analyzed alpha-synuclein-positive extracellular vesicles from platelet-free plasma by nanoscale flow cytometry and blood concentrations of poly ADP-ribose using sandwich ELISA kits.ResultsMedian concentration of α-synuclein extracellular vesicles was significantly higher in PD patients compared to the other groups (Kruskal-Wallis, p < .0001). In the discovery cohort, patients with higher α-synuclein extracellular vesicles had a higher Unified Parkinson Disease Rating Scale score (UPDRS III median = 22 vs. 5, p = 0.045). Seven out of 20 patients (35%) showed detectable PAR levels, with positive patients showing significantly higher levels of α-synuclein extracellular vesicles. In the replication cohort, we did not observe a significant difference in the PAR-positive cases in relationship with UPDRS III.ConclusionsNon-invasive determination of α-synuclein-positive extracellular vesicles may provide a potential non-invasive marker of PD disease severity, and longitudinal studies are needed to evaluate the role of α-synuclein-positive extracellular vesicles as a marker of disease progression.

Diagnostic Test to Identify Parkinson's Disease from the Blood Sera of Chinese Population: A Cross-Sectional Study.

Background Parkinson's disease (PD) is a neurodegenerative disease, a hallmark by the formation of misfolded and aggregated α-synuclein proteins. The expression of potential microRNA (miRNA) candidates isolated from serum and cerebrospinal fluid (CSF) exosomes of PD patients was assessed for their diagnostic value and their potential role as biomarkers for PD was explored. In this study, we characterize the expression level of miRNAs in the exosomes of blood sera and cerebrospinal fluid and explore their potential role as biomarkers for PD. Materials and Methods A total of 209 patients having an onset of PD, along with 60 neurodegenerative (ND) disorders and 50 healthy controls were enrolled. Blood samples and CSF samples were collected and exosomes were isolated. The isolated exosomes were characterized using CD63 detection and exosomal RNA was extracted. Serum miRNA profiling was carried out by synthesizing cDNA from the purified RNA and miRNA transcripts were determined by qRT-PCR using SYBR Green PremixScript. microRNA profiling strategy was employed for extracting the exosomal miRNAs from the exosomes. Results Five common miRNAs viz. miR-151a-5p, miR-24, mir-485-5p, mir-331-5p, and mir-214 were found to be upregulated with statistical significance in both the serum exosome and CSF exosomes. The investigation revealed that serum and CSF exosomal miRNA molecules are definitive biomarkers for PD with proper specificity and sensitivity. Conclusions The significant level of miR-151a-5p, miR-24, mir-485-5p, mir-331-5p, and mir-214 was observed in the serum and CSF which may be established as a biomarker for the diagnosis of PD.

The Integration of a Canonical Workflow Framework with an Informatics System for Disease Area Research

Abstract A recurring pattern of access to existing databases, data analyses, formulation of new hypotheses, use of an experimental design, institutional review board approvals, data collection, curation, and storage within trusted digital repositories is observable during clinical research work. The workflows that support the repeated nature of these activities can be ascribed as a Canonical Workflow Framework for Research (CWFR). Disease area clinical research is protocol specific, and during data collection, the electronic case report forms can use Common Data Elements (CDEs) that have precisely defined questions and are associated with the specified value(s) as responses. The CDE-based CWFR is integrated with a biomedical research informatics computing system, which consists of a complete stack of technical layers including the Protocol and Form Research Management System. The unique data dictionaries associated with the CWFR for Traumatic Brain Injury and Parkinson's Disease resulted in the development of the Federal Interagency Traumatic Brain Injury and Parkinson's Disease Biomarker systems. Due to a canonical workflow, these two systems can use similar tools, applications, and service modules to create findable, accessible, interoperable, and reusable Digital Objects. The Digital Objects for Traumatic Brain Injury and Parkinson's disease contain all relevant information needed from the time data is collected, validated, and maintained within a Storage Repository for future access. All Traumatic Brain Injury and Parkinson's Disease studies can be shared as Research Objects that can be produced by aggregating related resources as information packages and is findable on the Internet by using unique identifiers. Overall, the integration of CWFR with an informatics system has resulted in the reuse of software applications for several National Institutes of Health-supported biomedical research programs.

Methylated Cytochrome P450 and the Solute Carrier Family of Genes Correlate With Perturbations in Bile Acid Metabolism in Parkinson's Disease.

Parkinson’s disease (PD) is second most prevalent neurodegenerative disorder following Alzheimer’s disease. Parkinson’s disease is hypothesized to be caused by a multifaceted interplay between genetic and environmental factors. Herein, and for the first time, we describe the integration of metabolomics and epigenetics (genome-wide DNA methylation; epimetabolomics) to profile the frontal lobe from people who died from PD and compared them with age-, and sex-matched controls. We identified 48 metabolites to be at significantly different concentrations (FDR q < 0.05), 4,313 differentially methylated sites [5’-C-phosphate-G-3’ (CpGs)] (FDR q < 0.05) and increased DNA methylation age in the primary motor cortex of people who died from PD. We identified Primary bile acid biosynthesis as the major biochemical pathway to be perturbed in the frontal lobe of PD sufferers, and the metabolite taurine (p-value = 5.91E-06) as being positively correlated with CpG cg14286187 (SLC25A27; CYP39A1) (FDR q = 0.002), highlighting previously unreported biochemical changes associated with PD pathogenesis. In this novel multi-omics study, we identify regulatory mechanisms which we believe warrant future translational investigation and central biomarkers of PD which require further validation in more accessible biomatrices.

Alterations in Proteostasis System Components in Peripheral Blood Mononuclear Cells in Parkinson Disease: Focusing on the HSP70 and p62 Levels.

Parkinson disease (PD) is attributed to a proteostasis disorder mediated by α-synuclein accumulating in a specific brain region. PD manifestation is often related to extraneuronal alterations, some of which could be used as diagnostic or prognostic PD biomarkers. In this work, we studied the shifts in the expression of proteostasis-associated chaperones of the HSP70 family and autophagy-dependent p62 protein values in the peripheral blood mononuclear cells (PBMC) of mild to moderate PD patients. Although we did not detect any changes in the intracellular HSP70 protein pool in PD patients compared to non-PD controls, an increase in the transcriptional activity of the stress-associated HSPA1A/B and HSPA6 genes was observed in these cells. Basal p62 content was found to be increased in PD patients’ PBMC, similarly to the p62 level in substantia nigra neural cells in PD. Moreover, the spontaneous apoptosis level was increased among PBMC and positively correlated with the p62 intracellular level in the PD group. A combined HSPA6- and p62-based analysis among 26 PD patients and 36 age-matched non-PD controls pointed out the diagnostic significance of these markers, with intermediate sensitivity and high specificity of this combination when observing patients diagnosed with PD.

Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials

Electrophysiological biomarkers reflecting the pathological activities in the basal ganglia are essential to gain an etiological understanding of Parkinson's disease (PD) and develop a method of diagnosing and treating the disease. Previous studies that explored electrophysiological biomarkers in PD have focused mainly on oscillatory or periodic activities such as beta and gamma oscillations. Emerging evidence has suggested that the nonoscillatory, aperiodic component reflects the firing rate and synaptic current changes corresponding to cognitive and pathological states. Nevertheless, it has never been thoroughly examined whether the aperiodic component can be used as a biomarker that reflects pathological activities in the basal ganglia in PD. In this study, we examined the parameters of the aperiodic component in hemiparkinsonian rats and tested its practicality as an electrophysiological biomarker of pathological activity. We found that a set of aperiodic parameters, aperiodic offset and exponent, were significantly decreased by the nigrostriatal lesion. To further prove the usefulness of the parameters as biomarkers, acute levodopa treatment reverted the aperiodic offset. We then compared the aperiodic parameters with a previously established periodic biomarker of PD, beta frequency oscillation. We found a significantly low negative correlation with beta power. We showed that the performance of the machine learning-based prediction of pathological activities in the basal ganglia can be improved by using both beta power and the aperiodic component, which showed a low correlation with each other. We suggest that the aperiodic component will provide a more sensitive measurement to early diagnosis PD and have the potential to use as the feedback parameter for the adaptive deep brain stimulation.

A Secretory Vesicle Failure in Parkinson's Disease Occurs in Human Platelets.

ObjectiveThe presence of elevated dopamine (DA) and its major metabolites in the cytosol of neurons has been associated with their vulnerability in Parkinson's disease (PD). Over 99% of the cell's amines are confined to secretory vesicles (SVs), making these structures fundamental in the regulation of cytosolic DA levels. SVs of platelets use similar, if not the same mechanisms to accumulate serotonin in SVs as dopaminergic neurons do to store DA. Hence, any functional defects in platelets probably mirrors events in DA neurons.MethodsWe have isolated fresh platelets from the blood of 75 PD patients, 116 matched controls and 24 patients with Parkinsonism, assaying serotonin handling (basal content, accumulation, secretion and spontaneous leakage).ResultsWe found a dramatic decrease in the serotonin content and uptake by SVs, as well as decreased thrombin‐induced release by platelets from PD patients but not in those from most Parkinsonism cases. Platelets from PD patients also failed to retain serotonin in SVs.InterpretationThese findings indicate a functional impairment in the handling of amines by SVs in PD patients. This defect may serve as a biomarker of PD, and the approach described here may be potentially used for the subclinical detection of PD and to establish a platform to assay disease modifying drugs. ANN NEUROL 2022

Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.

Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

Tau and Amyloid-β Peptides in Serum of Patients With Parkinson's Disease: Correlations With CSF Levels and Clinical Parameters.

Relevance of blood-based biomarkers is increasing into the neurodegenerative diseases field, but data on Parkinson's disease (PD) remain still scarce. In this study, we used the SiMoA technique to measure serum content of total tau protein and amyloid-β peptides (Aβ-42, Aβ-40) in 22 PD patients and ten control subjects. Serum levels of each biomarker were correlated with the respective CSF levels in both the groups; in PD patients, also the correlations between serum biomarkers and main clinical parameters were tested (motor, non-motor, cognitive scores and levodopa equivalent daily dose). Serum biomarkers did not exhibit quantitative differences between patients and controls; however, only PD patients had inter-fluids (serum-CSF) associations in tau and amyloid-β-42 levels. Moreover, serum content of tau protein was inversely correlated with cognitive performances (MoCA score). These findings, albeit preliminary, indicate that brain-derived peptides may change in parallel in both peripheral blood and CSF of PD patients, eventually even in association with some clinical features. Further studies are now needed to validate the use of blood-based biomarkers in PD.

Cytokines, miRNAs, and Antioxidants as Combined Non-invasive Biomarkers for Parkinson's Disease.

Parkinson's disease (PD) is one of the most common long-term degenerative disorders of the CNS that primarily affects the human locomotor system. Owing to the heterogeneity of PD etiology and the lack of appropriate diagnostic tests, blood-based biomarkers became the most promising method for diagnosing PD. Even though various biomarkers for PD have been found, their specificity and sensitivity are not optimum when used alone. Therefore, the aim of this study was directed to evaluate changes in a group of sensitive blood-based biomarkers in the same PD patients compared to healthy individuals. Serum samples were collected from 20 PD patients and 15 age-matched healthy controls. We analyzed serum levels of cytokines (IL10, IL12, and TNF-α), α-synuclein proteins, miRNAs (miR-214, miR-221, and miR-141), and antioxidants (UA, PON1, ARE). Our results showed an increase in sera levels of cytokines in PD patients as well as a positive correlation among them. Also, we found a significant increase in sera levels of α-synuclein protein associated with a decrease in miR-214 which regulates its gene expression. Lastly, we observed a decrease in sera levels of miR-221, miR-141, UA, PON1, and ARE, which have a prominent role against oxidative stress. Because of the many etiologies of PD, a single measure is unlikely to become a useful biomarker. Therefore, to correctly predict disease state and progression, a mix of noninvasive biomarkers is required. Although considerable work has to be done, this study sheds light on the role of certain biomarkers in the diagnosis of PD.

Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson’s disease using two-layer hybrid feature selection

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.

Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD). For this purpose, we trained auto-encoders on parameter maps of healthy controls (n = 56) and tested them on those of de novo PD patients (n = 129). We considered large reconstruction errors between the original and reconstructed images to be anomalies that, when quantified, allow discerning between de novo PD patients and healthy controls. The most discriminating brain macro-region was found to be the white matter with a ROC-AUC 68.3 (IQR 5.4) and the best subcortical structure, the GPi (ROC-AUC 62.6 IQR 5.4). Our results indicate that our deep learning-based model can detect potentially pathological regions in de novo PD patients, without requiring any expert delineation. This may enable extracting neuroimaging biomarkers of PD in the future, but further testing on larger cohorts is needed. Such models can be seamlessly extended with additional parameter maps and applied to study the physio-pathology of other neurological diseases.

A metabolic biomarker predicts Parkinson's disease at the early stages in patients and animal models.

BackgroundCare management of Parkinson’s disease (PD) patients currently remains symptomatic, mainly because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Earlier detection of PD therefore represents a key step for developing therapies able to delay or slow down its progression.MethodsWe investigated metabolic markers in 3 different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 3 cohorts of de novo PD patients and matched controls (n = 129). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics.ResultsOur translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, nonmotor signs and, very interestingly, also discriminated PD patients from healthy subjects.ConclusionFrom our translational study, which included 3 animal models and 3 de novo PD patient cohorts, we propose a promising biomarker exhibiting a high accuracy for de novo PD diagnosis that may possibly predict early PD development, before motor symptoms appear.FundingFrench National Research Agency (ANR), DOPALCOMP, Institut National de la Santé et de la Recherche Médicale, Université Grenoble Alpes, Association France Parkinson.

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson\u2019s disease patients

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.

Erythrocytic α-Synuclein Species for Parkinson's Disease Diagnosis and the Correlations With Clinical Characteristics.

BackgroundErythrocytes contain most of the peripheral α-synuclein (α-syn), which is the key pathological molecular of α-synucleinopathies including Parkinson’s disease (PD). Our objectives were to assess the efficiency of erythrocytic total and oligomeric α-syn levels as PD diagnostic biomarkers, and to identify the correlations between erythrocytic α-syn levels and physiological/psychiatrical assessment scales.MethodsHome-brewed electrochemiluminescence assays were applied to assess the concentrations of erythrocytic total and oligomeric α-syn levels in a cohort including 124 patients with PD and 79 healthy controls (HCs). The correlations between erythrocytic α-syn levels and clinical measurements were assessed using Spearman’s rank test.ResultsBoth the erythrocytic total and oligomeric α-syn levels were significantly higher in PD patients than HCs. The biomarkers adjusted for age and sex discriminated PDs from HCs well with 80% sensitivity, 89% specificity and 79% sensitivity, 83% specificity, respectively. Combining erythrocytic total and oligomeric α-syn levels by using binary logistic regression analysis with the controlling of age and sex generated a factor discriminates PDs from HCs with 88% sensitivity and 85% specificity. The erythrocytic total but not oligomeric α-syn levels adjusted for age and sex significantly correlated with anxiety scales and the MDS-UPDRS III scales in PD patients, respectively.ConclusionWe showed the usefulness of erythrocytic total and oligomeric α-syn levels as biomarkers for PD. Our results also suggest the capability of erythrocytic α-syn as a potential pathological factor and therapeutic target for psychiatric symptoms in PD patients.

A digital seed amplification assay for the quantitative diagnosis of Parkinson’s disease

The process of forming amyloid fibrils from misfolded proteins occurs through a nucleation-dependent polymerization reaction, which has also been studied in the context of neurodegenerative diseases, including Parkinson's disease (PD). PD severity is strongly correlated with the accumulation of intraneuronal α-synuclein aggregates, making pathological α-synuclein isoforms promising biomarkers for PD. α-synuclein aggregates can be detected using seed amplification assays (SAAs), however, these assays are not quantitative and suffer from a lack of reproducibility.

Novel approaches for quantifying beta synchrony in Parkinson's disease.

Despite the clinical and financial burden of Parkinson's disease (PD), there is no standardized, reliable biomarker to diagnose and track PD progression. Instead, PD is primarily assessed using subjective clinical rating scales and patient self-report. Such approaches can be imprecise, hindering diagnosis and disease monitoring. An objective biomarker would be beneficial for clinical care, refining diagnosis, and treatment. Due to widespread electrophysiological abnormalities both within and between brain structures in PD, development of electrophysiologic biomarkers may be feasible. Basal ganglia recordings acquired with neurosurgical approaches have revealed elevated power in the beta frequency range (13-30Hz) in PD, suggesting that beta power could be a putative PD biomarker. However, there are limitations to the use of beta power as a biomarker. Recent advances in analytic approaches have led to novel methods to quantify oscillatory synchrony in the beta frequency range. Here we describe some of these novel approaches in the context of PD and explore how they may serve as electrophysiological biomarkers. These novel signatures include (1) interactions between beta phase and broadband (> 50Hz, "gamma") amplitude (i.e., phase amplitude coupling, PAC), (2) asymmetries in waveform shape, (3) beta coherence, and (4) beta "bursts." Development of a robust, reliable, and readily accessible electrophysiologic biomarker would represent a major step towards more precise and personalized care in PD.

Plasma neurofilament light chain levels are associated with depressive and anxiety symptoms in Parkinson's disease.

The present study aimed to explore the association of plasma neurofilament light chain (NfL) levels with depression and anxiety in Parkinson's disease (PD). This prospective study enrolled 116 patients with PD and 38 healthy controls, and found plasma NfL levels were higher in patients with depression or anxiety than in those without these symptoms. Binary logistic regression identified NfL concentration as an independent predictor of depression and anxiety in PD. In conclusion, elevated plasma NfL may be associated with severity of depression and anxiety in PD patients and may serve as a diagnostic biomarker of PD with moderate to severe depression or anxiety.

Serum NFL discriminates Parkinson disease from essential tremor and reflect motor and cognition severity

ObjectiveTo investigate the diagnostic value of serum neurofilament light chain (NFL) for discriminating Parkinson disease (PD) from Essential tremor (ET) and healthy controls, and to evaluate its correlation with some clinical features of PD patients.MethodsThis cross-sectional study measured NFL levels with electrochemiluminescence immunoassay in serum of 146 PD patients, 82 ET patients and 60 age-matched healthy controls. We used multivariate regression analyses to examine whether NFL contributes to PD biomarkers. Disease severity were assessed by Unified Parkinson’s Disease Rating Scale part III (UPDRS III), Hoehn & Yahr (H-Y) stage and Mini-Mental State Examination (MMSE).ResultsSerum NFL levels were significantly higher in PD than in ET and healthy controls (16.6 ± 3.5, 12.2 ± 2.4 and 11.8 ± 2.4 pg/mL, respectively, p < 0.001). In patients with PD, serum NFL were markedly increased in patients with advanced H-Y stage and patients with dementia (both p < 0.001). The correlation analysis revealed that serum NFL was positively associated with UPDRS III score (r = 0.79, p < 0.001) and H-Y stage (r = 0.86, p < 0.001), and negatively correlated with MMSE scores (r = − 0.70, p < 0.001). Further multivariate regression analyses showed that serum NFL was an independent contributor to motor symptom and cognition severity in PD patients (all p < 0.01).ConclusionsSerum NFL levels were markedly elevated may be a useful clinical biomarker for discriminating PD patients from ET and controls. Serum NFL may serve as a potential blood biomarker for motor and cognition severity of PD.

Wearable Electrochemical Sensors in Parkinson's Disease.

Parkinson’s disease (PD) is a neurodegenerative disorder associated with widespread aggregation of α-synuclein and dopaminergic neuronal loss in the substantia nigra pars compacta. As a result, striatal dopaminergic denervation leads to functional changes in the cortico-basal-ganglia-thalamo-cortical loop, which in turn cause most of the parkinsonian signs and symptoms. Despite tremendous advances in the field in the last two decades, the overall management (i.e., diagnosis and follow-up) of patients with PD remains largely based on clinical procedures. Accordingly, a relevant advance in the field would require the development of innovative biomarkers for PD. Recently, the development of miniaturized electrochemical sensors has opened new opportunities in the clinical management of PD thanks to wearable devices able to detect specific biological molecules from various body fluids. We here first summarize the main wearable electrochemical technologies currently available and their possible use as medical devices. Then, we critically discuss the possible strengths and weaknesses of wearable electrochemical devices in the management of chronic diseases including PD. Finally, we speculate about possible future applications of wearable electrochemical sensors in PD, such as the attractive opportunity for personalized closed-loop therapeutic approaches.