Diagnostic biomarkers for Parkinson's disease at a glance: where are we?

The potential of arts therapies in Parkinson's disease rehabilitation: A comprehensive review

Background and purpose

Parkinson's disease (PD) causes a decline in motor function, cognitive decline, and impacts the mental health of patients. Due to the high cost and side effects of conventional treatments, the medical community has begun to explore safer and more cost-effective alternative therapies. In this context, arts therapies have gained increasing attention as innovative treatments. This review plans to explore the role and potential of various arts therapies in the rehabilitation of PD patients by analyzing existing literature and case studies.

Methods

This review comprehensively searched the literature in several databases, including PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Infrastructure, to assess the effectiveness of different arts therapies in the rehabilitation of patients with PD.

Results

From 3440 articles screened, 16 met the inclusion criteria. These studies included a variety of therapies, including music, meditation, yoga, art, dance, theatre, video games and play therapy. These different types of arts therapies had a positive impact on the motor, psychological and cognitive rehabilitation of PD patients, respectively.

Conclusion

The existing literature highlights the great potential of arts therapies in the rehabilitation of people with PD, further confirming the efficacy of arts therapies in enhancing the motor, psychological and cognitive rehabilitation process of people with PD. In addition, this review identifies research gaps in the use of color therapy in PD rehabilitation and highlights the need for further exploration of various arts therapies modalities.

Metabolic Disturbances in a Mouse Model of MPTP/Probenecid-Induced Parkinson's Disease: Evaluation Using Liquid Chromatography-Mass Spectrometry

Purpose

Parkinson's disease (PD) is a common neurodegenerative disease that severely affects patients' daily lives and places a significant burden on the global economy. There are currently no specific biomarkers for distinguishing between the different stages of PD.

Methods

We divided 78 mice into six equal groups, including five model PD groups (W1-W5; based on the PD stage induced by length of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine/propofol induction time) and a control group. Then, we used metabolomics technology to detect the serum small-molecule metabolites present in each group. Ultimately, we screened for potential biomarkers using the variable importance in the projection of the orthogonal partial least squares discriminant analysis and the coefficient value of LASSO ordinal logistic regression.

Results

We identified 12 potential biomarkers, including dehydroepiandrosterone sulfate, pipecolic acid, N-acetylleucine, 2-aminoadipic acid, L-tyrosine, uric acid, and 5-hydroxyindoleacetaldehyde. Pathway analysis revealed their involvement in amino acid metabolism, caffeine metabolism, steroid hormone biosynthesis, and purine metabolism. Additionally, the receiver operating characteristic curve indicated that a biomarker panel comprising the 12 biomarkers could differentiate between the different PD stages.

Conclusion

Different PD stages are characterized by different metabolites. The biomarkers identified in this study are helpful to understand the PD process.

Importance of health history analysis in Parkinson's disease

The objective of this research article is to investigate the impact of various health history factors on the risk of developing Parkinson's disease (PD). From the medical history we can identify PD Symptoms and this also help to detect the progression of PD symptoms. By conducting statistical analyses, the study seeks to identify independent risk and protective factors for Parkinson's disease (PD), considering variations in impact across genders and BMI categories.

Introduction

In the diagnosis of PD the analysis of previous health history is very rare in practice while the main diagnosis have been done through the different motor and non-motor symptoms taking in consideration besides the cardinal symptoms of PD for identification and determination the stages of PD. Here we have analyzed the impact of 56 different diseases, symptoms, and surgeries which a subject may have experienced in their life before PD, considered as a health history.

Methods

The behavioral impact for each types of health history have been analyzed statistically with 31,265 subjects including PD, and Control. In this analysis we have calculated the variation of impact for both the Male, and Female, as well as subjects BMI.

Results

98.12 % PD patients, where 97.63 % Male PD, and 98.71 % Female PD were found with at least one health history record. Coronary heart disease odds ratio (OR) 2.15 (1.85-2.51), Colon Cancer OR 2.11 (1.45-3.05), Cranial brain surgery OR 6.21 (5.11-7.56) have the higher risks to PD. Having the history of Asthma OR 0.66 (0.6-0.72), Anemia OR 0.56 (0.51-0.63), Cirrhosis in Liver OR 0.7 (0.57-0.86), Cosmetic surgery OR 0.7 (0.64-0.77), and Gastritis OR 0.78 (0.71-0.87) have been found to be protective to PD. The risk of developing PD varies between male, and female including subjects BMI for each individual health history types. The diseases which reduce the oxygen saturation in blood like, anemia, asthma, and thalassemia act as protective to PD.

Conclusions

In this study we have analyzed fifty six diseases which include surgeries as a health history of PD patients. Study suggests that a thorough health history could greatly aid in understanding the onset and progression of Parkinson's disease (PD).

Identification and validation of N-acetylputrescine in combination with non-canonical clinical features as a Parkinson's disease biomarker panel

Parkinson's disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3-25% of sporadic Parkinson's patients present with genetic abnormalities that could represent a risk factor, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson's disease, which highlights the critical need for biomarkers. In the present study, we prospectively collected and analyzed plasma samples from 194 Parkinson's disease patients and 197 age-matched non-diseased controls. N-acetyl putrescine (NAP) in combination with sense of smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements demonstrated combined diagnostic utility. NAP was increased by 28% in Parkinsons disease patients and exhibited an AUC of 0.72 as well as an OR of 4.79. The clinical and NAP panel demonstrated an area under the curve, AUC = 0.9 and an OR of 20.4. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson's disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.

Amyloid-β and Phosphorylated Tau are the Key Biomarkers and Predictors of Alzheimer's Disease

Alzheimer's disease (AD) is a age-related neurodegenerative disease and is a major public health concern both in Texas, US and Worldwide. This neurodegenerative disease is mainly characterized by amyloid-beta (Aβ) and phosphorylated Tau (p-Tau) accumulation in the brains of patients with AD and increasing evidence suggests that these are key biomarkers in AD. Both Aβ and p-tau can be detected through various imaging techniques (such as positron emission tomography, PET) and cerebrospinal fluid (CSF) analysis. The presence of these biomarkers in individuals, who are asymptomatic or have mild cognitive impairment can indicate an increased risk of developing AD in the future. Furthermore, the combination of Aβ and p-tau biomarkers is often used for more accurate diagnosis and prediction of AD progression. Along with AD being a neurodegenerative disease, it is associated with other chronic conditions such as cardiovascular disease, obesity, depression, and diabetes because studies have shown that these comorbid conditions make people more vulnerable to AD. In the first part of this review, we discuss that biofluid-based biomarkers such as Aβ, p-Tau in cerebrospinal fluid (CSF) and Aβ & p-Tau in plasma could be used as an alternative sensitive technique to diagnose AD. In the second part, we discuss the underlying molecular mechanisms of chronic conditions linked with AD and how they affect the patients in clinical care.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model

Background

Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health.

Methods

This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model.

Results

In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively.

Conclusions

The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

A Comprehensive Review of the Role of Biomarkers in Early Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a complex neurological, degenerative clinical condition depicted by the advancing loss of dopaminergic neurons in the substantia nigra pars compacta, which manifests itself as a myriad of sensorimotor and non-motor signs in patients. The disease occurs due to the reduced levels of the neurotransmitter dopamine in the brain, which is primarily associated with functional characteristics regarding mobility and cognition. The basal ganglion is mainly involved in the generation of cognitive functions and therefore is the most significantly associated area in PD. Since the classical diagnosis and assessment of PD depends majorly on the appearance of motor characteristics, which only arise when ~60-80% of the dopamine neuronal cell death has already occurred, it is imperative we focus on identifying biomarkers that can help us assess and diagnose PD in the earlier stages of disease progression, thus providing a better prognosis for the patients. This review article will focus on the different biomarkers that are currently available and in use, divided under the headings of clinical, biological, imaging, and genetic biomarkers, and assess their specificity and sensitivity toward providing an early assessment of Parkinson's for the patients and the future of preclinical diagnostics using molecular biomarkers. PD affects over 1% of the population worldwide and only ranks second to Alzheimer's disease in the context of its incidence and consequent socioeconomic burden. While recent breakthroughs in biomarkers have dramatically improved patients' odds of survival and prognosis, it still remains primarily a symptomatic diagnostic tool. It is an area of research that requires to focus on creating more advanced approaches toward diagnosing PD early, involving clinical diagnostics, neuroimaging technology, and molecular biology collaborations to provide the highest degree of care and quality of life that a Parkinson's patient deserves.

Epworth sleepiness scale is associated with increased striatal dopamine uptake in Parkinson's disease: a cross-sectional study

PURPOSE

A cross-sectional study was designed to investigate the possible association between Epworth sleepiness scale (ESS) scores and striatal dopamine uptake in the early stages of Parkinson's disease (PD).

METHODS

Two groups of PD patients (n = 464) and healthy controls (HC) (n = 162) were enrolled in the current study from the Parkinson's progression markers initiative cohort ( https://www.ppmi-info.org ). All the subjects were evaluated for excessive daytime sleepiness (EDS) using the ESS. They also completed specific measures to be assessed for motor and non-motor symptoms, including cognitive, gait, autonomic, and olfactory dysfunction. Dopamine transporter (DaT) scans were used to identify dopamine transporter impairments. Spatial normalization for DaTscan imaging of participants was performed to reach consistent orientation.

RESULTS

A significant correlation was found between ESS score and right putamen (P < 0.001; correlation coefficient = 0.186) and left putamen (P = 0.003; correlation coefficient = 0.139) dopamine uptake in PD patients. The same results were revealed after adjusted Pearson's correlation for the effects of handedness, age, gender, and education. No significant correlation was found between the ESS score and the amount of caudate nucleus dopamine uptake in PD patients. Moreover, there was a significant association between caudate nucleus dopamine uptake and ESS score in neither PD patients nor HCs.

CONCLUSIONS

The results of our study suggest that increased dopaminergic function of putamen nuclei may be associated with ESS scores in the early stages of PD. Further studies are needed at different PD stages and evaluate PD progression as a possible confounder.

Dopaminergic damage pattern predicts phenoconversion time in isolated rapid eye movement sleep behavior disorder

PURPOSE

The exact phenoconversion time from isolated rapid eye movement (REM) sleep behavior disorder (iRBD) to synucleinopathies remains unpredictable. This study investigated whole-brain dopaminergic damage pattern (DDP) with disease progression and predicted phenoconversion time in individual patients.

METHODS

Age-matched 33 iRBD patients and 20 healthy controls with 11C-CFT-PET scans were enrolled. The patients were followed up 2-10 (6.7 ± 2.0) years. The phenoconversion year was defined as the base year, and every 2 years before conversion was defined as a stage. Support vector machine with leave-one-out cross-validation strategy was used to perform prediction.

RESULTS

Dopaminergic degeneration of iRBD was found to occur about 6 years before conversion and then abnormal brain regions gradually expanded. Using DDP, area under curve (AUC) was 0.879 (90% sensitivity and 88.3% specificity) for predicting conversion in 0-2 years, 0.807 (72.7% sensitivity and 83.3% specificity) in 2-4 years, 0.940 (100% sensitivity and 84.6% specificity) in 4-6 years, and 0.879 (100% sensitivity and 80.7% specificity) over 6 years. In individual patients, predicted stages correlated with whole-brain dopaminergic levels (r =  - 0.740, p < 0.001).

CONCLUSION

Our findings suggest that DDP could accurately predict phenoconversion time of individual iRBD patients, which may help to screen patients for early intervention.

Clinical biomarkers for Lewy body diseases

Synucleinopathies are a group of neurodegenerative disorders characterized by pathologic aggregates of neural and glial α-synuclein (α-syn) in the form of Lewy bodies (LBs), Lewy neurites, and cytoplasmic inclusions in both neurons and glia. Two major classes of synucleinopathies are LB disease and multiple system atrophy. LB diseases include Parkinson's disease (PD), PD with dementia, and dementia with LBs. All are increasing in prevalence. Effective diagnostics, disease-modifying therapies, and therapeutic monitoring are urgently needed. Diagnostics capable of differentiating LB diseases are based on signs and symptoms which might overlap. To date, no specific diagnostic test exists despite disease-specific pathologies. Diagnostics are aided by brain imaging and cerebrospinal fluid evaluations, but more accessible biomarkers remain in need. Mechanisms of α-syn evolution to pathologic oligomers and insoluble fibrils can provide one of a spectrum of biomarkers to link complex neural pathways to effective therapies. With these in mind, we review promising biomarkers linked to effective disease-modifying interventions.

Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.

Morphometric and Nanomechanical Screening of Peripheral Blood Cells with Atomic Force Microscopy for Label-Free Assessment of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Neurodegenerative disorders (NDDs) are complex, multifactorial disorders with significant social and economic impact in today's society. NDDs are predicted to become the second-most common cause of death in the next few decades due to an increase in life expectancy but also to a lack of early diagnosis and mainly symptomatic treatment. Despite recent advances in diagnostic and therapeutic methods, there are yet no reliable biomarkers identifying the complex pathways contributing to these pathologies. The development of new approaches for early diagnosis and new therapies, together with the identification of non-invasive and more cost-effective diagnostic biomarkers, is one of the main trends in NDD biomedical research. Here we summarize data on peripheral biomarkers, biofluids (cerebrospinal fluid and blood plasma), and peripheral blood cells (platelets (PLTs) and red blood cells (RBCs)), reported so far for the three most common NDDs-Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). PLTs and RBCs, beyond their primary physiological functions, are increasingly recognized as valuable sources of biomarkers for NDDs. Special attention is given to the morphological and nanomechanical signatures of PLTs and RBCs as biophysical markers for the three pathologies. Modifications of the surface nanostructure and morphometric and nanomechanical signatures of PLTs and RBCs from patients with AD, PD, and ALS have been revealed by atomic force microscopy (AFM). AFM is currently experiencing rapid and widespread adoption in biomedicine and clinical medicine, in particular for early diagnostics of various medical conditions. AFM is a unique instrument without an analog, allowing the generation of three-dimensional cell images with extremely high spatial resolution at near-atomic scale, which are complemented by insights into the mechanical properties of cells and subcellular structures. Data demonstrate that AFM can distinguish between the three pathologies and the normal, healthy state. The specific PLT and RBC signatures can serve as biomarkers in combination with the currently used diagnostic tools. We highlight the strong correlation of the morphological and nanomechanical signatures between RBCs and PLTs in PD, ALS, and AD.

Neuroinflammation and Mitochondrial Dysfunction in Parkinson's Disease: Connecting Neuroimaging with Pathophysiology

There is a pressing need for disease-modifying therapies in patients suffering from neurodegenerative diseases, including Parkinson's disease (PD). However, these disorders face unique challenges in clinical trial designs to assess the neuroprotective properties of potential drug candidates. One of these challenges relates to the often unknown individual disease mechanisms that would, however, be relevant for targeted treatment strategies. Neuroinflammation and mitochondrial dysfunction are two proposed pathophysiological hallmarks and are considered to be highly interconnected in PD. Innovative neuroimaging methods can potentially help to gain deeper insights into one's predominant disease mechanisms, can facilitate patient stratification in clinical trials, and could potentially map treatment responses. This review aims to highlight the role of neuroinflammation and mitochondrial dysfunction in patients with PD (PwPD). We will specifically introduce different neuroimaging modalities, their respective technical hurdles and challenges, and their implementation into clinical practice. We will gather preliminary evidence for their potential use in PD research and discuss opportunities for future clinical trials.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

A metabolomics approach to the validation of predictive metabolites and phenotypic expression in non-alcoholic fatty liver disease

AIMS

Nonalcoholic fatty liver disease (NAFLD) is becoming more common and severe. Individuals with NAFLD have an altered composition of gut- microbial metabolites. We used metabolomics profiling to identify microbial metabolites that could indicate gut-liver metabolic severity. Noninvasive biomarkers are required for NAFLD, especially for patients at high risk of disease progression.

MAIN METHODS

We compared the stool metabolomes, untargeted metabolomics, and clinical data of 80 patients. Patients with nonalcoholic fatty liver (NAFL: n = 16), nonalcoholic steatohepatitis (NASH: n = 26), and cirrhosis (n = 19) and healthy control individuals (HC: n = 19) were enrolled. The identified metabolites in NAFLD were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography coupled to time-of-flight-mass spectrometry (LC-TOF-MS) were used to analyze metabolites.

KEY FINDINGS

Untargeted metabolomics was used to identify and quantify 103 metabolites. Principal component analysis (PCA) was used to assess the metabolic discrimination of NAFL, NASH, and cirrhosis. Short-chain fatty acids (SCFA) levels were significantly lower in NAFLD patients, including those of acetate (p = 0.03), butyrate (p = 0.0008), and propionate. The stool cholic acid (p = 0.001) level was significantly increased in NAFLD patients. Palmitoylcarnitine and l-carnitine levels were significantly increased in NASH and cirrhosis patients. The phenotypic expression of these metabolites was linked to β-oxidation.

SIGNIFICANCE

We demonstrated a distinct metabolome profile in NAFLD patients with NAFL, NASH, and cirrhosis. We also discovered that the expression of certain metabolites and metabolic pathways was linked to NAFLD.

Biochemical and behavioral effects of rosmarinic acid treatment in an animal model of Parkinson's disease induced by MPTP

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. The main therapeutic approach available nowadays relieves motor symptoms but does not prevent or stop neurodegeneration. Rosmarinic acid (RA), an ester of caffeic and 3,4-dihydroxyphenylacetic acids, is obtained from numerous plant species such as Salvia officinalis L. (sage) and Rosmarinus officinalis (rosemary). This compound has a wide spectrum of biological activities, such as antioxidant and anti-inflammatory, and could be an additional therapy for neurodegenerative disorders. Here we evaluated the potential neuroprotective effects of RA treatment in a murine model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Mice were separated into four groups: CN, Control/saline; RA, Rosmarinic acid/vehicle; MPTP, MPTP/saline; MPTP+RA, MPTP/RA. RA (20 mg/kg, or vehicle) was administered orally by intra-gastric gavage for 14 days, one hour before MPTP or saline injection. MPTP groups received the drug (30 mg/kg, intraperitoneally) once a day for five days (fourth to the eighth day of the experiment). MPTP-treated animals displayed hyperlocomotion behavior, which was significantly prevented by RA treatment. In addition, RA treatment increased dopaminergic signaling in the parkinsonian mice and improved the monoaminergic system in healthy animals. Analysis of alterations in the striatal mRNA expression of dopaminergic system components showed that MAO-A expression was increased in the MPTP+AR group. Overall, this study brings new evidence of the potential neuroprotective properties of RA not only in preventing behavioral features observed in PD, but also by improving neurotransmission in the healthy brain.

Promising biomarkers and therapeutic targets for the management of Parkinson's disease: recent advancements and contemporary research

Parkinson's disease (PD) is one of the progressive neurological diseases which affect around 10 million population worldwide. The clinical manifestation of motor symptoms in PD patients appears later when most dopaminergic neurons have degenerated. Thus, for better management of PD, the development of accurate biomarkers for the early prognosis of PD is imperative. The present work will discuss the potential biomarkers from various attributes covering biochemical, microRNA, and neuroimaging aspects (α-synuclein, DJ-1, UCH-L1, β-glucocerebrosidase, BDNF, etc.) for diagnosis, recent development in PD management, and major limitations with current and conventional anti-Parkinson therapy. This manuscript summarizes potential biomarkers and therapeutic targets, based on available preclinical and clinical evidence, for better management of PD.

Structural and functional changes in the retina in Parkinson's disease

Parkinson's disease is caused by degeneration of dopaminergic neurons, originating in the substantia nigra pars compacta and characterised by bradykinesia, rest tremor and rigidity. In addition, visual disorders and retinal abnormalities are often present and can be identified by decreased visual acuity, abnormal spatial contrast sensitivity or even difficulty in complex visual task completion. Because of their early onset in patients with de novo Parkinson's disease, the anatomical retinal changes and electrophysiological modification could be valuable markers even at early stages of the disease. However, due to the concomitant occurrence of normal ageing, the relevance and specificity of these predictive values can be difficult to interpret. This review examines retinal dysfunction arising in Parkinson's disease. We highlight the electrophysiological delays and decreased amplitude in the electroretinography recorded in patients and animal models. We relate this to coexisting anatomical changes such as retinal nerve fibre layer and macular thinning, measured using optical coherence tomography, and show that functional measures are more consistent overall than optical coherence-measured structural changes. We review the underlying chemical changes seen with loss of retinal dopaminergic neurons and the effect of levodopa treatment on the retina in Parkinson's disease. Finally, we consider whether retinal abnormalities in Parkinson's disease could have a role as potential markers of poorer outcomes and help stratify patients at early stages of the disease. We emphasise that retinal measures can be valuable, accessible and cost-effective methods in the early evaluation of Parkinson's disease pathogenesis with potential for patient stratification.

Biomarkers: Role and Scope in Neurological Disorders

Neurological disorders pose a great threat to social health and are a major cause for mortality and morbidity. Effective drug development complemented with the improved drug therapy has made considerable progress towards easing symptoms associated with neurological illnesses, yet poor diagnosis and imprecise understanding of these disorders has led to imperfect treatment options. The scenario is complicated by the inability to extrapolate results of cell culture studies and transgenic models to clinical applications which has stagnated the process of improving drug therapy. In this context, the development of biomarkers has been viewed as beneficial to easing various pathological complications. A biomarker is measured and evaluated in order to gauge the physiological process or a pathological progression of a disease and such a marker can also indicate the clinical or pharmacological response to a therapeutic intervention. The development and identification of biomarkers for neurological disorders involves several issues including the complexity of the brain, unresolved discrepant data from experimental and clinical studies, poor clinical diagnostics, lack of functional endpoints, and high cost and complexity of techniques yet research in the area of biomarkers is highly desired. The present work describes existing biomarkers for various neurological disorders, provides support for the idea that biomarker development may ease our understanding underlying pathophysiology of these disorders and help to design and explore therapeutic targets for effective intervention.

A Sensor-Based Platform for Early-Stage Parkinson's Disease Monitoring

Biosensing platforms have gained much attention in clinical practice screening thousands of samples simultaneously for the accurate detection of important markers in various diseases for diagnostic and prognostic purposes. Herein, a framework for the design of an innovative methodological approach combined with data processing and appropriate software in order to implement a complete diagnostic system for Parkinson's disease exploitation is presented. The integrated platform consists of biochemical and peripheral sensor platforms for measuring biological and biometric parameters of examinees, a central collection and management unit along with a server for storing data, and a decision support system for patient's state assessment regarding the occurrence of the disease. The suggested perspective is oriented on data processing and experimental implementation and can provide a powerful holistic evaluation of personalized monitoring of patients or individuals at high risk of manifestation of the disease.

Quantitative Determination of Endogenous Tetrahydroisoquinolines, Potential Parkinson's Disease Biomarkers, in Mammals

Current diagnostic options for Parkinson's disease are very limited and primarily based on characteristic clinical symptoms. Thus, there are urgent needs for reliable biomarkers that enable us to diagnose the disease in the early stages, differentiate it from other atypical Parkinsonian syndromes, monitor its progression, increase knowledge of its pathogenesis, and improve the development of potent therapies. A promising group of potential biomarkers are endogenous tetrahydroisoquinoline metabolites, which are thought to contribute to the multifactorial etiology of Parkinson's disease. The aim of this critical review is to highlight trends and limitations of available traditional and modern analytical techniques for sample pretreatment (extraction and derivatization procedures) and quantitative determination of tetrahydroisoquinoline derivatives in various types of mammalian fluids and tissues (urine, plasma, cerebrospinal fluid, brain tissue, liver tissue). Particular attention is paid to the most sensitive and specific analytical techniques, involving immunochemistry and gas or liquid chromatography coupled with mass spectrometric, fluorescence, or electrochemical detection. The review also includes a discussion of other relevant agents proposed and tested in Parkinson's disease.

An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms

Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson’s Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting.

Role of the Gut-Brain Axis, Gut Microbial Composition, Diet, and Probiotic Intervention in Parkinson's Disease

Parkinson's disease (PD) is the second-most prevalent neurodegenerative or neuropsychiatric disease, affecting 1% of seniors worldwide. The gut microbiota (GM) is one of the key access controls for most diseases and disorders. Disturbance in the GM creates an imbalance in the function and circulation of metabolites, resulting in unhealthy conditions. Any dysbiosis could affect the function of the gut, consequently disturbing the equilibrium in the intestine, and provoking pro-inflammatory conditions in the gut lumen, which send signals to the central nervous system (CNS) through the vagus enteric nervous system, possibly disturbing the blood-brain barrier. The neuroinflammatory conditions in the brain cause accumulation of α-syn, and progressively develop PD. An important aspect of understanding and treating the disease is access to broad knowledge about the influence of dietary supplements on GM. Probiotics are live microorganisms which, when administered in adequate amounts, confer a health benefit on the host. Probiotic supplementation improves the function of the CNS, and improves the motor and non-motor symptoms of PD. Probiotic supplementation could be an adjuvant therapeutic method to manage PD. This review summarizes the role of GM in health, the GM-brain axis, the pathogenesis of PD, the role of GM and diet in PD, and the influence of probiotic supplementation on PD. The study encourages further detailed clinical trials in PD patients with probiotics, which aids in determining the involvement of GM, intestinal mediators, and neurological mediators in the treatment or management of PD.

Biomarkers of Neurodegenerative Diseases: Biology, Taxonomy, Clinical Relevance, and Current Research Status

The understanding of neurodegenerative diseases, traditionally considered to be well-defined entities with distinguishable clinical phenotypes, has undergone a major shift over the last 20 years. The diagnosis of neurodegenerative diseases primarily requires functional brain imaging techniques or invasive tests such as lumbar puncture to assess cerebrospinal fluid. A new biological approach and research efforts, especially in vivo, have focused on biomarkers indicating underlying proteinopathy in cerebrospinal fluid and blood serum. However, due to the complexity and heterogeneity of neurodegenerative processes within the central nervous system and the large number of overlapping clinical diagnoses, identifying individual proteinopathies is relatively difficult and often not entirely accurate. For this reason, there is an urgent need to develop laboratory methods for identifying specific biomarkers, understand the molecular basis of neurodegenerative disorders and classify the quantifiable and readily available tools that can accelerate efforts to translate the knowledge into disease-modifying therapies that can improve and simplify the areas of differential diagnosis, as well as monitor the disease course with the aim of estimating the prognosis or evaluating the effects of treatment. The aim of this review is to summarize the current knowledge about clinically relevant biomarkers in different neurodegenerative diseases.

Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree

Identifying genes related to Parkinson’s disease (PD) is an active research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. Recently, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long noncoding (lncRNA) genes, which play an essential role in biological processes and the transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the University of California Santa Cruz (UCSC) genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using the PyFeat method with the AdaBoost as feature selection. These selected features achieved promising results compared with extracted features from some state-of-the-art feature extraction techniques. Finally, the features were fed to the gradient-boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics were used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy of 78.6%, the area under the curve equals 84.5%, the area under precision-recall (AUPR) equals 85.3%, F1-score equals 78.3%, Matthews correlation coefficient (MCC) equals 0.575, sensitivity (SEN) equals 77.1%, and specificity (SPC) equals 80.2%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.

Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study

BACKGROUND

Current clinical assessments of essential tremor (ET) are primarily based on expert consultation combined with reviewing patient complaints, physician expertise, and diagnostic experience. Thus, traditional evaluation methods often lead to biased diagnostic results. There is a clinical demand for a method that can objectively quantify the severity of the patient's disease.

METHODS

This study aims to develop an artificial intelligence-aided diagnosis method based on multi-sensory fusion wearables. The experiment relies on a rigorous clinical trial paradigm to collect multi-modal fusion of signals from 98 ET patients. At the same time, three clinicians scored independently, and the consensus score obtained was used as the ground truth for the machine learning models.

RESULTS

Sixty kinematic parameters were extracted from the signals recorded by the nine-axis inertial measurement unit (IMU). The results showed that most of the features obtained by IMU could effectively characterize the severity of the tremors. The accuracy of the optimal model for three tasks classifying five severity levels was 97.71%, 97.54%, and 97.72%, respectively.

CONCLUSIONS

This paper reports the first attempt to combine multiple feature selection and machine learning algorithms for fine-grained automatic quantification of postural tremor in ET patients. The promising results showed the potential of the proposed approach to quantify the severity of ET objectively.

Development of early diagnosis of Parkinson's disease on animal models based on the intranasal administration of α-methyl-p-tyrosine methyl ester in a gel system

The fight against neurodegenerative diseases, including Parkinson's disease (PD), is a global challenge of this century. The effectiveness of current PD therapy is limited, since it is diagnosed many years after the onset, following the death of most nigrostriatal dopaminergic neurons regulating motor function. PD treatment could be greatly improved if it was started at an early (preclinical) stage. For this purpose, it is necessary to develop an early diagnosis of PD, which is the goal of our study. We have developed an early diagnosis of PD on animal models using a provocative test by intranasal administration of α-methyl-p-tyrosine methyl ester (αMPTME), a reversible inhibitor of dopamine synthesis. First, we produced the provocative agent, αMPTME in gel, and showed its safety and penetration into the brain bypassing the blood-brain barrier. Then, the optimal dose of αMPTME and time after administration were selected, at which the level of dopamine in the striatum of intact animals decreases, but does not reach the 30% threshold for the appearance of motor disorders in PD patients. Finally, we proved on animal models that intranasal administration of αMPTME can serve as a diagnostic test for preclinical PD. Indeed, intranasal administration of αMPTME to mice in a model of PD at the preclinical stage reversibly reduced the dopamine level in the striatum to the 30% threshold causing short-term motor disorders. Thus, using animal models of PD, we have developed a provocative test for the preclinical diagnosis of PD, a fundamentally new technology in neurology.

Proteomics Challenges for the Assessment of Synuclein Proteoforms as Clinical Biomarkers in Parkinson’s Disease

Parkinson’s disease is a complex neurodegenerative disorder resulting in a multifaceted clinical presentation which includes bradykinesia combined with either rest tremor, rigidity, or both, as well as many non-motor symptoms. The motor features of the disorder are associated with the pathological form of alpha synuclein aggregates and fibrils in Lewy bodies and loss of dopaminergic neurons in the substantia nigra. Parkinson’s disease is increasingly considered as a group of underlying disorders with unique genetic, biological, and molecular abnormalities that are likely to respond differentially to a given therapeutic approach. For this reason, it is clinically challenging to treat and at present, no therapy can slow down or arrest the progression of Parkinson’s disease. There is a clear unmet clinical need to develop reliable diagnostic and prognostic biomarkers. When disease-modifying treatments become available, prognostic biomarkers are required to support a definitive diagnosis and clinical intervention during the long prodromal period as no clinical implications or symptoms are observed. Robust diagnostic biomarkers would also be useful to monitor treatment response. Potential biomarkers for the sporadic form of Parkinson’s disease have mostly included synuclein species (monomer, oligomer, phosphorylated, Lewy Body enriched fraction and isoforms). In this review, we consider the analysis of synuclein and its proteoforms in biological samples using proteomics techniques (immunoassay and mass spectrometry) applied to neurodegenerative disease research.

Predictors of RBD progression and conversion to synucleinopathies

Purpose of review

Rapid eye movement (REM) sleep behaviour disorder (RBD) is considered the expression of the initial neurodegenerative process underlying synucleinopathies and constitutes the most important marker of their prodromal phase. This article reviews recent research from longitudinal research studies in isolated RBD (iRBD) aiming to describe the most promising progression biomarkers of iRBD and to delineate the current knowledge on the level of prediction of future outcome in iRBD patients at diagnosis.

Recent findings

Longitudinal studies revealed the potential value of a variety of biomarkers, including clinical markers of motor, autonomic, cognitive, and olfactory symptoms, neurophysiological markers such as REM sleep without atonia and electroencephalography, genetic and epigenetic markers, cerebrospinal fluid and serum markers, and neuroimaging markers to track the progression and predict phenoconversion. To-date the most promising neuroimaging biomarker in iRBD to aid the prediction of phenoconversion is striatal presynaptic striatal dopaminergic dysfunction.

Summary

There is a variety of potential biomarkers for monitoring disease progression and predicting iRBD conversion into synucleinopathies. A combined multimodal biomarker model could offer a more sensitive and specific tool. Further longitudinal studies are warranted to iRBD as a high-risk population for early neuroprotective interventions and disease-modifying therapies.

Baseline predictors of progression of Parkinson's disease in a sample of Egyptian patients: clinical and biochemical

Background

Clinical progression of Parkinson’s disease (PD) is highly heterogeneous, and its predictors are generally lacking. Identifying predictors of early disease progression is important for patients’ management and follow-up. The current study aims to identify clinical, neuroimaging and biochemical baseline predictors of motor progression in patients with PD. Forty-five PD patients were assessed at baseline, 6 months and 1 year using MDS-UPDRS total and subscores, Hoehn and Yahr (H&Y), Schwab and England (S&E), International Physical Activity Questionnaire (IPAQ). Baseline New Freezing of Gait Questionnaire (NFOG-Q), Berg Balance Scale (BBS), Ten-Meter Walking Test (10-MWT), and Time Up and Go Test (TUG), Non-Motor Symptoms Scale (NMSS), Beck Depression Inventory (BDI), PD questionnaire 39 (PDQ-39), MRI brain, uric acid, lipid profile and glycated hemoglobin were performed.

Results

Significant worsening of MDS-UPDRS total, part III scores, H&Y, S&E and IPAQ (p < 0.001) was detected. One-year progression of H&Y and S&E were significantly correlated to disease duration (p = 0.014, p = 0.025, respectively). Progression of H&Y was correlated to baseline TUG (p = 0.035). S&E progression was correlated to baseline MDS-UPDRS total score (rho = 0.478, p = 0.001) and part III (rho = 0.350, p = 0.020), H&Y (rho = 0.401, p = 0.007), PIGD (rho = 0.591, p < 0.001), NFOG-Q (rho = 0.498, p = 0.001), and TUG (rho = 0.565, p = 0.001). Using linear regression, there was no predictors of clinical progression among the used baseline variables.

Conclusion

Despite the significant motor and physical activity progression over 1 year that was correlated to baseline motor and gait severity, but without predictive value, further similar and longitudinal studies are warranted to detect predictors of early progression and confirm findings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41983-022-00445-1.

Review of Metabolomics-Based Biomarker Research for Parkinson's Disease

Parkinson's disease (PD), as the second most common neurodegenerative disease, is seriously affecting the life quality of the elderly. However, there is still a lack of efficient medical methods to diagnosis PD before apparent symptoms occur. In recent years, clinical biomarkers including genetic, imaging, and tissue markers have exhibited remarkable benefits in assisting PD diagnoses. Due to the advantages of high-throughput detection of metabolites and almost non-invasive sample collection, metabolomics research of PD is widely used for diagnostic biomarker discovery. However, there are also a few shortages for those identified biomarkers, such as the scarcity of verifications regarding the sensitivity and specificity. Thus, reviewing the research progress of PD biomarkers based on metabolomics techniques is of great significance for developing PD diagnosis. To comprehensively clarify the progress of current metabolic biomarker studies in PD, we reviewed 20 research articles regarding the discovery and validation of biomarkers for PD diagnosis from three mainstream academic databases (NIH PubMed, ISI Web of Science, and Elsevier ScienceDirect). By analyzing those materials, we summarized the metabolic biomarkers identified by those metabolomics studies and discussed the potential approaches used for biomarker verifications. In conclusion, this review provides a comprehensive and updated overview of PD metabolomics research in the past two decades and particularly discusses the validation of disease biomarkers. We hope those discussions might provide inspiration for PD biomarker discovery and verification in the future.

Motor Dysfunction in REM Sleep Behavior Disorder: A Rehabilitation Framework for Prodromal Synucleinopathy

Parkinson disease (PD) and other related diseases with α-synuclein pathology are associated with a long prodromal or preclinical stage of disease. Predictive models based on diagnosis of idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) make it possible to identify people in the prodromal stage of synucleinopathy who have a high probability of future disease and provide an opportunity to implement neuroprotective therapies. However, rehabilitation providers may be unaware of iRBD and the motor abnormalities that indicate early motor system dysfunction related to α-synuclein pathology. Furthermore, there is no existing rehabilitation framework to guide early interventions for people with iRBD. The purpose of this work is to (1) review extrapyramidal signs of motor system dysfunction in people with iRBD and (2) propose a framework for early protective or preventive therapies in prodromal synucleinopathy using iRBD as a predictive marker. Longitudinal and cross-sectional studies indicate that the earliest emerging motor deficits in iRBD are bradykinesia, deficits performing activities of daily living, and abnormalities in speech, gait, and posture. These deficits may emerge up to 12 years before a diagnosis of synucleinopathy. The proposed rehabilitation framework for iRBD includes early exercise-based interventions of aerobic exercise, progressive resistance training, and multimodal exercise with rehabilitation consultations to address exercise prescription, progression, and monitoring. This rehabilitation framework may be used to implement neuroprotective, multidisciplinary, and proactive clinical care in people with a high likelihood of conversion to PD, dementia with Lewy bodies, or multiple systems atrophy.

Is REM sleep behavior disorder a friend or foe of obstructive sleep apnea? Clinical and etiological implications for neurodegeneration

NONE

Rapid eye movement sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia during rapid eye movement sleep, associated with complex motor enactment of dreams. Obstructive sleep apnea (OSA) is a relatively common sleep disorder characterized by repetitive episodes of upper airway obstruction while sleeping, which can result in hypoxemia and sleep fragmentation. Even though the nature of RBD and OSA is different, OSA may sometimes be accompanied by RBD symptoms. Accordingly, it is reasonable to distinguish these 2 sleep disorders in people with dream enactment behaviors. Although RBD and OSA share similar sleep phenomena, their association has yet to be elucidated. Herein we draw attention to various RBD-mimicking conditions, RBD combined with OSA, and the relationship between RBD and OSA. Furthermore, the clinical implications of OSA in neurodegeneration and the optimized management of RBD combined with OSA are also discussed in this review.

Dual Roles of Microglia in the Basal Ganglia in Parkinson's Disease

With the increasing age of the population, the incidence of Parkinson’s disease (PD) has increased exponentially. The development of novel therapeutic interventions requires an understanding of the involvement of senescent brain cells in the pathogenesis of PD. In this review, we highlight the roles played by microglia in the basal ganglia in the pathophysiological processes of PD. In PD, dopaminergic (DAergic) neuronal degeneration in the substantia nigra pars compacta (SNc) activates the microglia, which then promote DAergic neuronal degeneration by releasing potentially neurotoxic factors, including nitric oxide, cytokines, and reactive oxygen species. On the other hand, microglia are also activated in the basal ganglia outputs (the substantia nigra pars reticulata and the globus pallidus) in response to excess glutamate released from hyperactive subthalamic nuclei-derived synapses. The activated microglia then eliminate the hyperactive glutamatergic synapses. Synapse elimination may be the mechanism underlying the compensation that masks the appearance of PD symptoms despite substantial DAergic neuronal loss. Microglial senescence may correlate with their enhanced neurotoxicity in the SNc and the reduced compensatory actions in the basal ganglia outputs. The dual roles of microglia in different basal ganglia regions make it difficult to develop interventions targeting microglia for PD treatment.

Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III

BACKGROUND

Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson's disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson's disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications.

METHODS

We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time-frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time-frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data.

RESULTS

The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks.

CONCLUSION

Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.

Proteomics Profiling of Neuron-Derived Small Extracellular Vesicles from Human Plasma: Enabling Single-Subject Analysis

Small extracellular vesicles have been intensively studied as a source of biomarkers in neurodegenerative disorders. The possibility to isolate neuron-derived small extracellular vesicles (NDsEV) from blood represents a potential window into brain pathological processes. To date, the absence of sensitive NDsEV isolation and full proteome characterization methods has meant their protein content has been underexplored, particularly for individual patients. Here, we report a rapid method based on an immunoplate covalently coated with mouse monoclonal anti-L1CAM antibody for the isolation and the proteome characterization of plasma-NDsEV from individual Parkinson’s disease (PD) patients. We isolated round-shaped vesicles with morphological characteristics consistent with exosomes. On average, 349 ± 38 protein groups were identified by liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis, 20 of which are annotated in the Human Protein Atlas as being highly expressed in the brain, and 213 were shared with a reference NDsEV dataset obtained from cultured human neurons. Moreover, this approach enabled the identification of 23 proteins belonging to the Parkinson disease KEGG pathway, as well as proteins previously reported as PD circulating biomarkers.

Metabolomics of sebum reveals lipid dysregulation in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder, which is characterised by degeneration of distinct neuronal populations, including dopaminergic neurons of the substantia nigra. Here, we use a metabolomics profiling approach to identify changes to lipids in PD observed in sebum, a non-invasively available biofluid. We used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Pathway enrichment analysis shows alterations in lipid metabolism related to the carnitine shuttle, sphingolipid metabolism, arachidonic acid metabolism and fatty acid biosynthesis. This study shows sebum can be used to identify potential biomarkers for PD.

Distinguishing normal and aggregated alpha-synuclein interaction on gold nanorod incorporated zinc oxide nanocomposite by electrochemical technique

Misfolding and accumulation of the protein alpha synuclein in the brain cells characterize Parkinson's disease (PD). Electrochemical based aluminum interdigitated electrodes (ALIDEs) was fabricated by using conventional photolithography method and modified the surfaces with zinc oxide and gold nanorod by using spin coating method for the analysis of PD protein biomarker. The device surface modified with gold nanorod of 25 nm diameter was used. The bare devices and the surface modified devices were characterized by Scanning Electron Microscope, 3D-Profilometer, Atomic Force Microscope and high-power microscope. The above measurement was also performed to measure the interaction of antibody with aggregated alpha-synuclein for normal, aggregated and aggregated alpha synuclein in human serum and distinguished against 3 control proteins (PARK1, DJ-1 and Factor IX). The detection limit for normal alpha synuclein was 1 f. with the sensitivity of 1 f. on a linear regression (R² = 0.9759). The detection limit for aggregated alpha synuclein was 10 aM with the sensitivity of 1 aM on a linear regression (R² = 0.9797). Also, the detection limit of aggregated alpha synuclein in serum was 10 aM with the sensitivity of 1 aM on a linear regression (R² = 0.9739). These results however indicate that, serum has only minimal amount of alpha synuclein.

Neurons under genetic control: What are the next steps towards the treatment of movement disorders?

Since the implementation of deep-brain stimulation as a therapy for movement disorders, there has been little progress in the clinical application of novel alternative treatments. Movement disorders are a group of neurological conditions, which are characterised with impairment of voluntary movement and share similar anatomical loci across the basal ganglia. The focus of the current review is on Parkinson's disease and Huntington's disease as they are the most investigated hypokinetic and hyperkinetic movement disorders, respectively. The last decade has seen enormous advances in the development of laboratory techniques that control neuronal activity. The two major ways to genetically control the neuronal function are: 1) expression of light-sensitive proteins that allow for the optogenetic control of the neuronal spiking and 2) expression or suppression of genes that control the transcription and translation of proteins. However, the translation of these methodologies from the laboratories into the clinics still faces significant challenges. The article summarizes the latest developments in optogenetics and gene therapy. Here, I compare the physiological mechanisms of established electrical deep brain stimulation to the experimental optogenetical deep brain stimulation. I compare also the advantages of DNA- and RNA-based techniques for gene therapy of familial movement disorders. I highlight the benefits and the major issues of each technique and I discuss the translational potential and clinical feasibility of optogenetic stimulation and gene expression control. The review emphasises recent technical breakthroughs that could initiate a notable leap in the treatment of movement disorders.

Urinary Biomarkers for Neurodegenerative Diseases

Global incidence of neurodegenerative diseases (NDDs) such as Alzheimer's disease (AD) and Parkinson's disease (PD) is rapidly increasing, but the diagnosis of these diseases at their early stage is challenging. Therefore, the availability of reproducible and reliable biomarkers to diagnose such diseases is more critical than ever. In addition, biomarkers could be used not only to diagnose diseases but also to monitor the development of disease therapeutics. Urine is an excellent biofluid that can be utilized as a source of biomarker to diagnose not only several renal diseases but also other diseases because of its abundance in invasive sampling. However, urine was conventionally regarded as inappropriate as a source of biomarker for neurodegenerative diseases because it is anatomically distant from the central nervous system (CNS), a major pathologic site of NDD, in comparison to other biofluids such as cerebrospinal fluid (CSF) and plasma. However, recent studies have suggested that urine could be utilized as a source of NDD biomarker if an appropriate marker is predetermined by metabolomic and proteomic approaches in urine and other samples. In this review, we summarize such studies related to NDD.

Post-mortem ventricular cerebrospinal fluid cell-free-mtDNA in neurodegenerative disease

Cell-free mitochondrial DNA (cfmtDNA) is detectable in almost all human body fluids and has been associated with the onset and progression of several complex traits. In-life assessments indicate that reduced cfmtDNA is a feature of neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease and multiple sclerosis. However, whether this feature is conserved across all neurodegenerative diseases and how it relates to the neurodegenerative processes remains unclear. In this study, we assessed the levels of ventricular cerebrospinal fluid-cfmtDNA (vCSF-cfmtDNA) in a diverse group of neurodegenerative diseases (NDDs) to determine if the in-life observations of reduced cfmtDNA seen in lumbar CSF translated to the post-mortem ventricular CSF. To investigate further, we compared vCSF-cfmtDNA levels to known protein markers of neurodegeneration, synaptic vesicles and mitochondrial integrity. Our data indicate that reduced vCSF-cfmtDNA is a feature specific to Parkinson's and appears consistent throughout the disease course. Interestingly, we observed increased vCSF-cfmtDNA in the more neuropathologically severe NDD cases, but no association to protein markers of neurodegeneration, suggesting that vCSF-cfmtDNA release is more complex than mere cellular debris produced following neuronal death. We conclude that vCSF-cfmtDNA is reduced in PD, but not other NDDs, and appears to correlate to pathology. Although its utility as a prognostic biomarker is limited, our data indicate that higher levels of vCSF-cfmtDNA is associated with more severe clinical presentations; suggesting that it is associated with the neurodegenerative process. However, as vCSF-cfmtDNA does not appear to correlate to established indicators of neurodegeneration or indeed indicators of mitochondrial mass, further work to elucidate its exact role is needed.

Metabolomics Fingerprint Induced by the Intranigral Inoculation of Exogenous Human Alpha-Synuclein Oligomers in a Rat Model of Parkinson's Disease

Parkinson’s disease (PD) is considered a synucleinopathy because of the intraneuronal accumulation of aggregated α-synuclein (αSyn). Recent evidence points to soluble αSyn-oligomers (αSynO) as the main cytotoxic species responsible for cell death. Given the pivotal role of αSyn in PD, αSyn-based models are crucial for the investigation of toxic mechanisms and the identification of new therapeutic targets in PD. By using a metabolomics approach, we evaluated the metabolic profile of brain and serum samples of rats infused unilaterally with preformed human αSynOs (HαSynOs), or vehicle, into the substantia nigra pars compacta (SNpc). Three months postinfusion, the striatum was dissected for striatal dopamine (DA) measurements via High Pressure Liquid Chromatography (HPLC) analysis and mesencephalon and serum samples were collected for the evaluation of metabolite content via gas chromatography mass spectrometry analysis. Multivariate, univariate and correlation statistics were applied. A 40% decrease of DA content was measured in the HαSynO-infused striatum as compared to the contralateral and the vehicle-infused striata. Decreased levels of dehydroascorbic acid, myo-inositol, and glycine, and increased levels of threonine, were found in the mesencephalon, while increased contents of fructose and mannose, and a decrease in glycine and urea, were found in the serum of HαSynO-infused rats. The significant correlation between DA and metabolite content indicated that metabolic variations reflected the nigrostriatal degeneration. Collectively, the metabolomic fingerprint of HαSynO-infused rats points to an increase of oxidative stress markers, in line with PD neuropathology, and provides hints for potential biomarkers of PD.

Development of early diagnosis of Parkinson's disease: Illusion or reality?

The fight against neurodegenerative diseases, Alzheimer disease and Parkinson's disease (PD), is a challenge of the 21st century. The low efficacy of treating patients is due to the late diagnosis and start of therapy, after the degeneration of most specific neurons and depletion of neuroplasticity. It is believed that the development of early diagnosis (ED) and preventive treatment will delay the onset of specific symptoms. This review evaluates methodologies for developing ED of PD. Since PD is a systemic disease, and the degeneration of certain neurons precedes that of nigrostriatal dopaminergic neurons that control motor function, the current methodology is based on searching biomarkers, such as premotor symptoms and changes in body fluids (BF) in patients. However, all attempts to develop ED were unsuccessful. Therefore, it is proposed to enhance the current methodology by (i) selecting among biomarkers found in BF in patients at the clinical stage those that are characteristics of animal models of the preclinical stage, (ii) searching biomarkers in BF in subjects at the prodromal stage, selected by detecting premotor symptoms and failure of the nigrostriatal dopaminergic system. Moreover, a new methodology was proposed for the development of ED of PD using a provocative test, which is successfully used in internal medicine.

Clinical and Imaging Markers of Prodromal Parkinson's Disease

The diagnosis of Parkinson's disease (PD) relies on the clinical effects of dopamine deficiency, including bradykinesia, rigidity and tremor, usually manifesting asymmetrically. Misdiagnosis is common, due to overlap of symptoms with other neurodegenerative disorders such as multiple system atrophy and progressive supranuclear palsy, and only autopsy can definitively confirm the disease. Motor deficits generally appear when 50–60% of dopaminergic neurons in the substantia nigra are already lost, limiting the effectiveness of potential neuroprotective therapies. Today, we consider PD to be not just a movement disorder, but rather a complex syndrome non-motor symptoms (NMS) including disorders of sleep-wake cycle regulation, cognitive impairment, disorders of mood and affect, autonomic dysfunction, sensory symptoms and pain. Symptomatic LRRK2 mutation carriers share non-motor features with individuals with sporadic PD, including hyposmia, constipation, impaired color discrimination, depression, and sleep disturbance. Following the assumption that the pre-symptomatic gene mutation carriers will eventually exhibit clinical symptoms, their neuroimaging results can be extended to the pre-symptomatic stage of PD. The long latent phase of PD, termed prodromal-PD, represents an opportunity for early recognition of incipient PD. Early recognition could allow initiation of possible neuroprotective therapies at a stage when therapies might be most effective. The number of markers with the sufficient level of evidence to be included in the MDS research criteria for prodromal PD have increased during the last 10 years. Here, we review the approach to prodromal PD, with an emphasis on clinical and imaging markers and report results from our neuroimaging study, a retrospective evaluation of a cohort of 39 participants who underwent DAT-SPECT scan as part of their follow up. The study was carried out to see if it was possible to detect subclinical signs in the preclinical (neurodegenerative processes have commenced, but there are no evident symptoms or signs) and prodromal (symptoms and signs are present, but are yet insufficient to define disease) stages of PD.

Parkinson disease: Enhance nursing knowledge

Parkinson disease (PD) is a progressive, incurable disease caused by dopamine deficiency. This article provides an overview of this neurodegenerative disorder and offers information for optimal outcomes.

Biomarkers in Stress Related Diseases/Disorders: Diagnostic, Prognostic, and Therapeutic Values

Various internal and external factors negatively affect the homeostatic equilibrium of organisms at the molecular to the whole-body level, inducing the so-called state of stress. Stress affects an organism's welfare status and induces energy-consuming mechanisms to combat the subsequent ill effects; thus, the individual may be immunocompromised, making them vulnerable to pathogens. The information presented here has been extensively reviewed, compiled, and analyzed from authenticated published resources available on Medline, PubMed, PubMed Central, Science Direct, and other scientific databases. Stress levels can be monitored by the quantitative and qualitative measurement of biomarkers. Potential markers of stress include thermal stress markers, such as heat shock proteins (HSPs), innate immune markers, such as Acute Phase Proteins (APPs), oxidative stress markers, and chemical secretions in the saliva and urine. In addition, stress biomarkers also play critical roles in the prognosis of stress-related diseases and disorders, and therapy guidance. Moreover, different components have been identified as potent mediators of cardiovascular, central nervous system, hepatic, and nephrological disorders, which can also be employed to evaluate these conditions precisely, but with stringent validation and specificity. Considerable scientific advances have been made in the detection, quantitation, and application of these biomarkers. The present review describes the current progress of identifying biomarkers, their prognostic, and therapeutic values.