Finding useful biomarkers for Parkinson's disease

α-Synuclein Conformations in Plasma Distinguish Parkinson's Disease from Dementia with Lewy Bodies

Spread and aggregation of misfolded α-synuclein (aSyn) within the brain is the pathologic hallmark of Lewy body diseases (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). While evidence exists for multiple aSyn protein conformations, often termed "strains" for their distinct biological properties, it is unclear whether PD and DLB result from aSyn strain differences, and biomarkers that differentiate PD and DLB are lacking. Moreover, while pathological forms of aSyn have been detected outside the brain ( e.g., in skin, gut, blood), the functional significance of these peripheral aSyn species is unclear. Here, we developed assays using monoclonal antibodies selective for two different aSyn species generated in vitro - termed Strain A and Strain B - and used them to evaluate human brain tissue, cerebrospinal fluid (CSF), and plasma, through immunohistochemistry, enzyme-linked immunoassay, and immunoblotting. Surprisingly, we found that plasma aSyn species detected by these antibodies differentiated individuals with PD vs. DLB in a discovery cohort (UPenn, n=235, AUC 0.83) and a multi-site replication cohort (Parkinson's Disease Biomarker Program, or PDBP, n=200, AUC 0.72). aSyn plasma species detected by the Strain A antibody also predicted rate of cognitive decline in PD. We found no evidence for aSyn strains in CSF, and ability to template aSyn fibrillization differed for species isolated from plasma vs. brain, and in PD vs. DLB. Taken together, our findings suggest that aSyn conformational differences may impact clinical presentation and cortical spread of pathological aSyn. Moreover, the enrichment of these aSyn strains in plasma implicates a non-central nervous system source.

The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease

The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.

Mitochondrial complex I deficiency stratifies idiopathic Parkinson's disease

Idiopathic Parkinson's disease (iPD) is believed to have a heterogeneous pathophysiology, but molecular disease subtypes have not been identified. Here, we show that iPD can be stratified according to the severity of neuronal respiratory complex I (CI) deficiency, and identify two emerging disease subtypes with distinct molecular and clinical profiles. The CI deficient (CI-PD) subtype accounts for approximately a fourth of all cases, and is characterized by anatomically widespread neuronal CI deficiency, a distinct cell type-specific gene expression profile, increased load of neuronal mtDNA deletions, and a predilection for non-tremor dominant motor phenotypes. In contrast, the non-CI deficient (nCI-PD) subtype exhibits no evidence of mitochondrial impairment outside the dopaminergic substantia nigra and has a predilection for a tremor dominant phenotype. These findings constitute a step towards resolving the biological heterogeneity of iPD with implications for both mechanistic understanding and treatment strategies.

Plasma extracellular vesicle synaptic proteins as biomarkers of clinical progression in patients with Parkinson's disease

Synaptic dysfunction plays a key role in Parkinson's disease (PD), and plasma extracellular vesicle (EV) synaptic proteins are emerging as biomarkers for neurodegenerative diseases. Assessment of plasma EV synaptic proteins for their efficacy as biomarkers in PD and their relationship with disease progression was conducted. In total, 144 participants were enrolled, including 101 people with PD (PwP) and 43 healthy controls (HCs). The changes in plasma EV synaptic protein levels between baseline and 1-year follow-up did not differ significantly in both PwP and HCs. In PwP, the changes in plasma EV synaptic protein levels were significantly associated with the changes in Unified Parkinson's Disease Rating Scale (UPDRS)-II and III scores. Moreover, PwP with elevated levels (first quartile) of any one plasma EV synaptic proteins (synaptosome-associated protein 25, growth-associated protein 43 or synaptotagmin-1) had significantly greater disease progression in UPDRS-II score and the postural instability and gait disturbance subscore in UPDRS-III than did the other PwP after adjustment for age, sex, and disease duration. The promising potential of plasma EV synaptic proteins as clinical biomarkers of disease progression in PD was suggested. However, a longer follow-up period is warranted to confirm their role as prognostic biomarkers.

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.

ErbB2pY -1248 as a predictive biomarker for Parkinson's disease based on research with RPPA technology and in vivo verification

AIMS

This study aims to reveal a promising biomarker for Parkinson's disease (PD) based on research with reverse phase protein array (RPPA) technology for the first time and in vivo verification, which gains time for early intervention in PD, thus increasing the effectiveness of treatment and reducing disease morbidity.

METHODS AND RESULTS

We employed RPPA technology which can assess both total and post-translationally modified proteins to identify biomarker candidates of PD in a cellular PD model. As a result, the phosphorylation (pY-1248) of the epidermal growth factor receptor (EGFR) ErbB2 is a promising biomarker candidate for PD. In addition, lapatinib, an ErbB2 tyrosine kinase inhibitor, was used to verify this PD biomarker candidate in vivo. We found that lapatinib-attenuated dopaminergic neuron loss and PD-like behavior in the zebrafish PD model. Accordingly, the expression of ErbB2pY-1248 significantly increased in the MPTP-induced mouse PD model. Our results suggest that ErbB2pY-1248 is a predictive biomarker for PD.

CONCLUSIONS

In this study, we found that ErbB2pY-1248 is a predictive biomarker of PD by using RPPA technology and in vivo verification. It offers a new perspective on PD diagnosing and treatment, which will be essential in identifying individuals at risk of PD. In addition, this study provides new ideas for digging into biomarkers of other neurodegenerative diseases.

Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression

Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

Aggregation and beyond: alpha-synuclein-based biomarkers in synucleinopathies

Parkinson's disease is clinically known for the loss of dopaminergic neurons in the substantia nigra pars compacta and accumulation of intraneuronal cytoplasmic inclusions rich in alpha-synuclein called 'Lewy bodies' and 'Lewy neurites'. Together with dementia with Lewy bodies and multiple system atrophy, Parkinson's disease is part of a group of disorders called synucleinopathies. Currently, diagnosis of synucleinopathies is based on the clinical assessment which often takes place in advanced disease stages. While the causal role of alpha-synuclein aggregates in these disorders is still debatable, measuring the levels, types or seeding properties of different alpha-synuclein species hold great promise as biomarkers. Recent studies indicate significant differences in peptide, protein and RNA levels in blood samples from patients with Parkinson's disease. Seed amplification assays using CSF, blood, skin biopsy, olfactory swab samples show great promise for detecting synucleinopathies and even for discriminating between different synucleinopathies. Interestingly, small extracellular vesicles, such as exosomes, display differences in their cargoes in Parkinson's disease patients versus controls. In this update, we focus on alpha-synuclein aggregation and possible sources of disease-related species released in extracellular vesicles, which promise to revolutionize the diagnosis and the monitoring of disease progression.

Parkinson's disease pathology is directly correlated to SIRT3 in human subjects and animal models: Implications for AAV.SIRT3-myc as a disease-modifying therapy

In Parkinson's disease (PD), post-mortem studies in affected brain regions have demonstrated a decline in mitochondrial number and function. This combined with many studies in cell and animal models suggest that mitochondrial dysfunction is central to PD pathology. We and others have shown that the mitochondrial protein deacetylase, SIRT3, has neurorestorative effects in PD models. In this study, to determine whether there is a link between PD pathology and SIRT3, we analysed SIRT3 levels in human subjects with PD, and compared to age-matched controls. In the SNc of PD subjects, SIRT3 was reduced by 56.8 ± 15.5% compared to control, regardless of age (p < 0.05, R = 0.6539). Given that age is the primary risk factor for PD, this finding suggests that reduced SIRT3 may contribute to PD pathology. Next, we measured whether there was a correlation between α-synuclein and SIRT3. In a parallel study, we assessed the disease-modifying potential of SIRT3 over-expression in a seeding model of α-synuclein. In PFF rats, infusion of rAAV1.SIRT3-myc reduced abundance of α-synuclein inclusions by 30.1 ± 18.5%. This was not observed when deacetylation deficient SIRT3H248Y was transduced, demonstrating the importance of SIRT3 deacetylation in reducing α-synuclein aggregation. These studies confirm that there is a clear difference in SIRT3 levels in subjects with PD compared to age-matched controls, suggesting a link between SIRT3 and the progression of PD. We also demonstrate that over-expression of SIRT3 reduces α-synuclein aggregation, further validating AAV.SIRT3-myc as a potential disease-modifying solution for PD.

High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression

BACKGROUND AND OBJECTIVES

This study aimed to identify CSF proteomic signatures characteristic of Parkinson disease (PD) and evaluate their clinical utility.

METHODS

This observational study used data from the Parkinson's Progression Markers Initiative (PPMI), which enrolled patients with PD, healthy controls (HCs), and non-PD participants carrying GBA1, LRRK2, and/or SNCA pathogenic variants (genetic prodromals) at international sites. Study participants were chosen from PPMI enrollees based on the availability of aptamer-based CSF proteomic data, quantifying 4,071 proteins, and classified as patients with PD without GBA1, LRRK2, and/or SNCA pathogenic variants (nongenetic PD), HCs, patients with PD carrying the aforementioned pathogenic variants (genetic PD), or genetic prodromals. Differentially expressed protein (DEP) analysis and the least absolute shrinkage and selection operator (LASSO) were applied to the data from nongenetic PD and HCs. Signatures characteristics of nongenetic PD were quantified as a PD proteomic score (PD-ProS), validated internally and then externally using data of 1,556 CSF proteins from the LRRK2 Cohort Consortium (LCC). We further tested the PD-ProS in genetic PD and genetic prodromals and examined associations with clinical progression.

RESULTS

Data from 279 patients with nongenetic PD (mean ± SD, age 62.0 ± 9.6 years; male 67.7%) and 141 HCs (age 60.5 ± 11.9 years; male 64.5%) were used for PD-ProS derivation. From 23 DEPs, LASSO determined weights of 14 DEPs for the PD-ProS (area under the curve [AUC] 0.83, 95% CI 0.78-0.87), validated in an independent internal validation cohort of 71 patients with nongenetic PD and 35 HCs (AUC 0.81, 95% CI 0.73-0.90). In the LCC, only 5 of the 14 DEPs were also measured. Notably, these 5 DEPs still distinguished 34 patients with nongenetic PD from 31 HCs with the same weights (AUC 0.75, 95% CI 0.63-0.87). Furthermore, the PD-ProS distinguished 258 patients with genetic PD from 365 genetic prodromals. Finally, regardless of genetic status, the PD-ProS independently predicted both cognitive and motor decline in PD (dementia, adjusted hazard ratio in the highest quintile [aHR-Q5] 2.8 [95% CI 1.6-5.0]; Hoehn and Yahr stage IV, aHR-Q5 2.1 [95% CI 1.1-4.0]).

DISCUSSION

By integrating high-throughput proteomics with machine learning, we identified PD-associated CSF proteomic signatures crucial for PD development and progression.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov (NCT01176565). A link to the trial registry page is clinicaltrials.gov/ct2/show/NCT01141023.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that the CSF proteome contains clinically important information regarding the development and progression of Parkinson disease that can be deciphered by a combination of high-throughput proteomics and machine learning.

Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis

BACKGROUND

The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis.

METHODS

Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database.

RESULTS

PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb).

CONCLUSION

Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.

Cerebrospinal fluid GFAP is a predictive biomarker for conversion to dementia and Alzheimer's disease-associated biomarkers alterations among de novo Parkinson's disease patients: a prospective cohort study

BACKGROUND

Dementia is a prevalent non-motor manifestation among individuals with advanced Parkinson's disease (PD). Glial fibrillary acidic protein (GFAP) is an inflammatory marker derived from astrocytes. Research has demonstrated the potential of plasma GFAP to forecast the progression to dementia in PD patients with mild cognitive impairment (PD-MCI). However, the predictive role of cerebrospinal fluid (CSF) GFAP on future cognitive transformation and alterations in Alzheimer's disease (AD)-associated CSF biomarkers in newly diagnosed PD patients has not been investigated.

METHODS

210 de novo PD patients from the Parkinson's Progression Markers Initiative were recruited. Cognitive progression in PD participants was evaluated using Cox regression. Cross-sectional and longitudinal associations between baseline CSF GFAP and cognitive function and AD-related CSF biomarkers were evaluated using multiple linear regression and generalized linear mixed model.

RESULTS

At baseline, the mean age of PD participants was 60.85 ± 9.78 years, including 142 patients with normal cognition (PD-NC) and 68 PD-MCI patients. The average follow-up time was 6.42 ± 1.69 years. A positive correlation was observed between baseline CSF GFAP and age (β = 0.918, p < 0.001). There was no statistically significant difference in baseline CSF GFAP levels between PD-NC and PD-MCI groups. Higher baseline CSF GFAP predicted greater global cognitive decline over time in early PD patients (Montreal Cognitive Assessment, β = - 0.013, p = 0.014). Furthermore, Cox regression showed that high baseline CSF GFAP levels were associated with a high risk of developing dementia over an 8-year period in the PD-NC group (adjusted HR = 3.070, 95% CI 1.119-8.418, p = 0.029). In addition, the baseline CSF GFAP was positively correlated with the longitudinal changes of not only CSF α-synuclein (β = 0.313, p < 0.001), but also CSF biomarkers associated with AD, namely, amyloid-β 42 (β = 0.147, p = 0.034), total tau (β = 0.337, p < 0.001) and phosphorylated tau (β = 0.408, p < 0.001).

CONCLUSIONS

CSF GFAP may be a valuable prognostic tool that can predict the severity and progression of cognitive deterioration, accompanied with longitudinal changes in AD-associated pathological markers in early PD.

Physical activity and lifestyle modifications in the treatment of neurodegenerative diseases

Neurodegenerative diseases have reached alarming numbers in the past decade. Unfortunately, clinical trials testing potential therapeutics have proven futile. In the absence of disease-modifying therapies, physical activity has emerged as the single most accessible lifestyle modification with the potential to fight off cognitive decline and neurodegeneration. In this review, we discuss findings from epidemiological, clinical, and molecular studies investigating the potential of lifestyle modifications in promoting brain health. We propose an evidence-based multidomain approach that includes physical activity, diet, cognitive training, and sleep hygiene to treat and prevent neurodegenerative diseases.

Targeting Sigma Receptors for the Treatment of Neurodegenerative and Neurodevelopmental Disorders

The sigma-1 receptor is a 223 amino acid-long protein with a recently identified structure. The sigma-2 receptor is a genetically unrelated protein with a similarly shaped binding pocket and acts to influence cellular activities similar to the sigma-1 receptor. Both proteins are highly expressed in neuronal tissues. As such, they have become targets for treating neurological diseases, including Alzheimer's disease (AD), Huntington's disease (HD), Parkinson's disease (PD), multiple sclerosis (MS), Rett syndrome (RS), developmental and epileptic encephalopathies (DEE), and motor neuron disease/amyotrophic lateral sclerosis (MND/ALS). In recent years, there have been many pre-clinical and clinical studies of sigma receptor (1 and 2) ligands for treating neurological disease. Drugs such as blarcamesine, dextromethorphan and pridopidine, which have sigma-1 receptor activity as part of their pharmacological profile, are effective in treating multiple aspects of several neurological diseases. Furthermore, several sigma-2 receptor ligands are under investigation, including CT1812, rivastigmine and SAS0132. This review aims to provide a current and up-to-date analysis of the current clinical and pre-clinical data of drugs with sigma receptor activities for treating neurological disease.

Seed amplification assay for the detection of pathologic alpha-synuclein aggregates in cerebrospinal fluid

Misfolded alpha-synuclein (αSyn) aggregates are a hallmark event in Parkinson's disease (PD) and other synucleinopathies. Recently, αSyn seed amplification assays (αSyn-SAAs) have shown promise as a test for biochemical diagnosis of synucleinopathies. αSyn-SAAs use the intrinsic self-replicative nature of misfolded αSyn aggregates (seeds) to multiply them in vitro. In these assays, αSyn seeds circulating in biological fluids are amplified by a cyclical process that includes aggregate fragmentation into smaller self-propagating seeds, followed by elongation at the expense of recombinant αSyn (rec-αSyn). Amplification of the seeds allows detection by fluorescent dyes specific for amyloids, such as thioflavin T. Several αSyn-SAA reports have been published in the past under the names 'protein misfolding cyclic amplification' (αSyn-PMCA) and 'real-time quaking-induced conversion'. Here, we describe a protocol for αSyn-SAA, originally reported as αSyn-PMCA, which allows detection of αSyn aggregates in cerebrospinal fluid samples from patients affected by PD, dementia with Lewy bodies or multiple-system atrophy (MSA). Moreover, this αSyn-SAA can differentiate αSyn aggregates from patients with PD versus those from patients with MSA, even in retrospective samples from patients with pure autonomic failure who later developed PD or MSA. We also describe modifications to the original protocol introduced to develop an optimized version of the assay. The optimized version shortens the assay length, decreases the amount of rec-αSyn required and reduces the number of inconclusive results. The protocol has a hands-on time of ~2 h per 96-well plate and can be performed by personnel trained to perform basic experiments with specimens of human origin.

Time perception reflects individual differences in motor and non-motor symptoms of Parkinson's disease

Dopaminergic signaling in the striatum has been shown to play a critical role in the perception of time. Decreasing striatal dopamine efficacy is at the core of Parkinson's disease (PD) motor symptoms and changes in dopaminergic action have been associated with many comorbid non-motor symptoms in PD. We hypothesize that patients with PD perceive time differently and in accordance with their specific comorbid non-motor symptoms and clinical state. We recruited patients with PD and compared individual differences in patients' clinical features with their ability to judge millisecond to second intervals of time (500ms-1100ms) while on or off their prescribed dopaminergic medications. We show that individual differences in comorbid non-motor symptoms, PD duration, and prescribed dopaminergic pharmacotherapeutics account for individual differences in time perception performance. We report that comorbid impulse control disorder is associated with temporal overestimation; depression is associated with decreased temporal accuracy; and PD disease duration and prescribed levodopa monotherapy are associated with reduced temporal precision and accuracy. Observed differences in time perception are consistent with hypothesized dopaminergic mechanisms thought to underlie the respective motor and non-motor symptoms in PD, but also raise questions about specific dopaminergic mechanisms. In future work, time perception tasks like the one used here, may provide translational or reverse translational utility in investigations aimed at disentangling neural and cognitive systems underlying PD symptom etiology.

One Sentence Summary

Quantitative characterization of time perception behavior reflects individual differences in Parkinson's disease motor and non-motor symptom clinical presentation that are consistent with hypothesized neural and cognitive mechanisms.

Plasma extracellular vesicle tau, β-amyloid, and α-synuclein and the progression of Parkinson's disease: a follow-up study

Background

Plasma extracellular vesicle (EV) contents are promising biomarkers of Parkinson's disease (PD). The pathognomonic proteins of PD, including α-synuclein, tau, and β-amyloid, are altered in people with PD (PwP) and are associated with clinical presentation in previous cross-sectional studies. However, the dynamic changes in these plasma EV proteins in PwP and their correlation with clinical progression remain unclear.

Objective

We investigated the dynamic changes in plasma EV α-synuclein, tau, and β-amyloid and their correlation with/prediction of clinical progression in PwP.

Design

A cohort study.

Methods

In total, 103 PwP and 37 healthy controls (HCs) completed baseline assessment and 1-year follow-up. Clinical assessments included Unified Parkinson's Disease Rating Scale (UPDRS) parts II and III, Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Plasma EVs were isolated, and immunomagnetic reduction-based immunoassay was used to assess α-synuclein, tau, and β-amyloid 1-42 (Aβ1-42) levels within the EVs.

Results

Compared with HCs, significant differences were noted in the annual changes in all three EV pathognomonic proteins in PwP. Although the absolute changes in plasma EV pathognomonic proteins did not significantly correlate with clinical changes, PwP with elevated baseline plasma EV tau (upper-half) levels demonstrated significantly greater decline in motor and cognition, and increased plasma EV α-synuclein levels were associated with postural instability and the gait disturbance motor subtype. For PwP with elevated levels of all three biomarkers, clinical deterioration was significant, as indicated by UPDRS-II scores, postural instability and gait disturbance subscores of UPDRS-III, and MMSE score.

Conclusion

The combination of plasma EV α-synuclein, tau, and Aβ1-42 may identify PwP with a high risk of deterioration. Our findings can elucidate the interaction between these pathognomonic proteins, and they may serve as treatment response markers and can be applied in treatment approaches for disease modification.

Cerebrospinal fluid neurofilament dynamic profiles predict cognitive progression in individuals with de novo Parkinson's disease

Background

Neurofilament light chain protein (NfL) in cerebrospinal fluid (CSF) reflects the severity of neurodegeneration, with its altered concentrations discovered in Parkinson's disease (PD) and Parkinson's disease dementia (PD-D).

Objective

To determine whether CSF NfL, a promising biomarker of neuronal/axonal damage, can be used to monitor cognitive progression in de novo Parkinson's disease and predict future cognitive decline.

Methods

A total of 259 people were recruited in this study, including 85 healthy controls (HC) and 174 neonatal PD patients from the Parkinson's Progression Markers Initiative (PPMI). Multiple linear regression and linear mixed effects models were used to examine the associations of baseline/longitudinal CSF NfL with cognitive decline and other CSF biomarkers. Kaplan-Meier analysis and log-rank test were used to compare the cumulative probability risk of cognition progression during the follow-up. Multivariate cox regression was used to detect cognitive progression in de novo PD.

Results

We found PD patients with mild cognitive impairment (PD-MCI) was higher than with normal cognition (PD-NC) in terms of CSF NfL baseline levels (p = 0.003) and longitudinal increase rate (p = 0.034). Both baseline CSF NfL and its rate of change predicted measurable cognitive decline in de novo PD (MoCA, β = -0.010, p = 0.011; β = -0.0002, p < 0.001, respectively). The predictive effects in de novo PD patients aged >65, male, ill-educated (<13 years) and without carrying Apolipoprotein E ε4 (APOE ε4) seemed to be more obvious and reflected in more domains investigated. We also observed that CSF NfL levels predicted progression in de novo PD patients with different cognitive diagnosis and amyloid status. After an average follow-up of 6.66 ± 2.54 years, higher concentration above the median of baseline CSF NfL was associated with a future high risk of PD with dementia (adjusted HR 2.82, 95% CI: 1.11-7.20, p = 0.030).

Conclusion

Our results indicated that CSF NfL is a promising prognostic predictor of PD, and its concentration and dynamics can monitor the severity and progression of cognitive decline in de novo PD patients.

Predictive modelling of Parkinson's disease progression based on RNA-Sequence with densely connected deep recurrent neural networks

The advent of recent high throughput sequencing technologies resulted in unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson's disease (PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient's next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients without revealing any race, sex, or age information with a variable number of visits and 34,682 predictor variables for 4 years. We propose a predictive model based on deep Recurrent Neural Network (RNN) with the addition of dense connections and batch normalization into RNN layers. The results show that the proposed architecture can predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and a rank-order correlation of (r = 0.83, p < 0.0001) between the predicted and actual disease status of PD.

How Well Do Rodent Models of Parkinson's Disease Recapitulate Early Non-Motor Phenotypes? A Systematic Review

The prodromal phase of Parkinson's disease (PD) is characterised by many non-motor symptoms, and these have recently been posited to be predictive of later diagnosis. Genetic rodent models can develop non-motor phenotypes, providing tools to identify mechanisms underlying the early development of PD. However, it is not yet clear how reproducible non-motor phenotypes are amongst genetic PD rodent models, whether phenotypes are age-dependent, and the translatability of these phenotypes has yet to be explored. A systematic literature search was conducted on studies using genetic PD rodent models to investigate non-motor phenotypes; cognition, anxiety/depressive-like behaviour, gastrointestinal (GI) function, olfaction, circadian rhythm, cardiovascular and urinary function. In total, 51 genetic models of PD across 150 studies were identified. We found outcomes of most phenotypes were inconclusive due to inadequate studies, assessment at different ages, or variation in experimental and environmental factors. GI dysfunction was the most reproducible phenotype across all genetic rodent models. The mouse model harbouring mutant A53T, and the wild-type hα-syn overexpression (OE) model recapitulated the majority of phenotypes, albeit did not reliably produce concurrent motor deficits and nigral cell loss. Furthermore, animal models displayed different phenotypic profiles, reflecting the distinct genetic risk factors and heterogeneity of disease mechanisms. Currently, the inconsistent phenotypes within rodent models pose a challenge in the translatability and usefulness for further biomechanistic investigations. This review highlights opportunities to improve phenotype reproducibility with an emphasis on phenotypic assay choice and robust experimental design.

Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Performance of αSynuclein RT-QuIC in relation to neuropathological staging of Lewy body disease

Currently, there is a need for diagnostic markers in Lewy body disorders (LBD). α-synuclein (αSyn) RT-QuIC has emerged as a promising assay to detect misfolded αSyn in clinically or neuropathologically established patients with various synucleinopathies. In this study, αSyn RT-QuIC was used to analyze lumbar CSF in a clinical cohort from the Swedish BioFINDER study and postmortem ventricular CSF in a neuropathological cohort from the Arizona Study of Aging and Neurodegenerative Disorders/Brain and Body Donation Program (AZSAND/BBDP). The BioFINDER cohort included 64 PD/PDD, 15 MSA, 15 PSP, 47 controls and two controls who later converted to PD/DLB. The neuropathological cohort included 101 cases with different brain disorders, including LBD and controls. In the BioFINDER cohort αSyn RT-QuIC identified LBD (i.e. PD, PDD and converters) vs. controls with a sensitivity of 95% and a specificity of 83%. The two controls that converted to LBD were αSyn RT-QuIC positive. Within the AZSAND/BBDP cohort, αSyn RT-QuIC identified neuropathologically verified "standard LBD" (i.e. PD, PD with AD and DLB; n = 25) vs. no LB pathology (n = 53) with high sensitivity (100%) and specificity (94%). Only 57% were αSyn RT-QuIC positive in the subgroup with "non-standard" LBD (i.e., AD with Lewy Bodies not meeting criteria for DLB or PD, and incidental LBD, n = 23). Furthermore, αSyn RT-QuIC reliably identified cases with LB pathology in the cortex (97% sensitivity) vs. cases with no LBs or LBs present only in the olfactory bulb (93% specificity). However, the sensitivity was low, only 50%, for cases with LB pathology restricted to the brainstem or amygdala, not affecting the allocortex or neocortex. In conclusion, αSyn RT-QuIC of CSF samples is highly sensitive and specific for identifying cases with clinicopathologically-defined Lewy body disorders and shows a lower sensitivity for non-standard LBD or asymptomatic LBD or in cases with modest LB pathology not affecting the cortex.

Plasma MIA, CRP, and Albumin Predict Cognitive Decline in Parkinson's Disease

OBJECTIVE

Using a multi-cohort, discovery-replication-validation design, we sought new plasma biomarkers that predict which individuals with Parkinson's disease (PD) will experience cognitive decline.

METHODS

In 108 discovery cohort PD individuals and 83 replication cohort PD individuals, we measured 940 plasma proteins on an aptamer-based platform. Using proteins associated with subsequent cognitive decline in both cohorts, we trained a logistic regression model to predict which patients with PD showed fast (> = 1 point drop/year on Montreal Cognitive Assessment [MoCA]) versus slow (< 1 point drop/year on MoCA) cognitive decline in the discovery cohort, testing it in the replication cohort. We developed alternate assays for the top 3 proteins and confirmed their ability to predict cognitive decline - defined by change in MoCA or development of incident mild cognitive impairment (MCI) or dementia - in a validation cohort of 118 individuals with PD. We investigated the top plasma biomarker for causal influence by Mendelian randomization (MR).

RESULTS

A model with only 3 proteins (melanoma inhibitory activity protein [MIA], C-reactive protein [CRP], and albumin) separated fast versus slow cognitive decline subgroups with an area under the curve (AUC) of 0.80 in the validation cohort. The individuals with PD in the validation cohort in the top quartile of risk for cognitive decline based on this model were 4.4 times more likely to develop incident MCI or dementia than those in the lowest quartile. Genotypes at MIA single nucleotide polymorphism (SNP) rs2233154 associated with MIA levels and cognitive decline, providing evidence for MIA's causal influence.

CONCLUSIONS

An easily obtained plasma-based predictor identifies individuals with PD at risk for cognitive decline. MIA may participate causally in development of cognitive decline. ANN NEUROL 2022.

Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT

Purpose

FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans.

Methods

We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale.

Results

The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model.

Conclusion

A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05816-7.

The Chinese Parkinson's Disease Registry (CPDR): Study Design and Baseline Patient Characteristics

BACKGROUND

There is a lack of large multicenter Parkinson's disease (PD) cohort studies and limited data on the natural history of PD in China.

OBJECTIVES

The objective of this study was to launch the Chinese Parkinson's Disease Registry (CPDR) and to report its protocol, cross-sectional baseline data, and prospects for a comprehensive observational, longitudinal, multicenter study.

METHODS

The CPDR recruited PD patients from 19 clinical sites across China between January 2018 and December 2020. Clinical data were collected prospectively using at least 17 core assessment scales. Patients were followed up for clinical outcomes through face-to-face interviews biennially.

RESULTS

We launched the CPDR in China based on the Parkinson's Disease & Movement Disorders Multicenter Database and Collaborative Network (PD-MDCNC). A total of 3148 PD patients were enrolled comprising 1623 men (51.6%) and 1525 women (48.4%). The proportions of early-onset PD (EOPD, age at onset ≤50 years) and late-onset PD (LOPD) were 897 (28.5%) and 2251 (71.5%), respectively. Stratification by age at onset showed that EOPD manifested milder motor and nonmotor phenotypes and was related to increased probability of dyskinesia. Comparison across genders suggested a slightly older average age at PD onset, milder motor symptoms, and a higher rate of developing levodopa-induced dyskinesias in women.

CONCLUSIONS

The CPDR is one of the largest multicenter, observational, longitudinal, and natural history studies of PD in China. It offers an opportunity to expand the understanding of clinical features, genetic, imaging, and biological markers of PD progression. © 2022 International Parkinson and Movement Disorder Society.

Systems Biology to Address Unmet Medical Needs in Neurological Disorders

Neurological diseases are highly prevalent and constitute a significant cause of mortality and disability. Neurological disorders encompass a heterogeneous group of neurodegenerative conditions, broadly characterized by injury to the peripheral and/or central nervous system. Although the etiology of neurological diseases varies greatly, they share several characteristics, such as heterogeneity of clinical presentation, non-cell autonomous nature, and diversity of cellular, subcellular, and molecular pathways. Systems biology has emerged as a valuable platform for addressing the challenges of studying heterogeneous neurological diseases. Systems biology has manifold applications to address unmet medical needs for neurological illness, including integrating and correlating different large datasets covering the transcriptome, epigenome, proteome, and metabolome associated with a specific condition. This is particularly useful for disentangling the heterogeneity and complexity of neurological conditions. Hence, systems biology can help in uncovering pathophysiology to develop novel therapeutic targets and assessing the impact of known treatments on disease progression. Additionally, systems biology can identify early diagnostic biomarkers, to help diagnose neurological disease preceded by a long subclinical phase, as well as define the exposome, the collection of environmental toxicants that increase risk of certain neurological diseases. In addition to these current applications, there are numerous potential emergent uses, such as precision medicine.

Appropriateness of Applying Cerebrospinal Fluid Biomarker Cutoffs from Alzheimer's Disease to Parkinson's Disease

BACKGROUND

While cutoffs for abnormal levels of the cerebrospinal fluid (CSF) biomarkers amyloid-β 1-42 (Aβ142), total tau (t-tau), phosphorylated tau (p-tau), and the ratios of t-tau/Aβ142 and p-tau/Aβ142, have been established in Alzheimer's disease (AD), biologically relevant cutoffs have not been studied extensively in Parkinson's disease (PD).

OBJECTIVE

Assess the suitability and diagnostic accuracy of established AD-derived CSF biomarker cutoffs in the PD population.

METHODS

Baseline and longitudinal data on CSF biomarkers, cognitive diagnoses, and PET amyloid imaging in 423 newly diagnosed patients with PD from the Parkinson's Progression Markers Initiative (PPMI) cohort were used to evaluate established AD biomarker cutoffs compared with optimal cutoffs derived from the PPMI cohort.

RESULTS

Using PET amyloid imaging as the gold standard for AD pathology, the optimal cutoff of Aβ142 was higher than the AD cutoff, the optimal cutoffs of t-tau/Aβ142 and p-tau/Aβ142 were lower than the AD cutoffs, and their confidence intervals (CIs) did not overlap with the AD cutoffs. Optimal cutoffs for t-tau and p-tau to predict cognitive impairment were significantly lower than the AD cutoffs, and their CIs did not overlap with the AD cutoffs.

CONCLUSION

Optimal cutoffs for the PPMI cohort for Aβ142, t-tau/Aβ142, and p-tau/Aβ142 to predict amyloid-PET positivity and for t-tau and p-tau to predict cognitive impairment differ significantly from cutoffs derived from AD populations. The presence of additional pathologies such as alpha-synuclein in PD may lead to disease-specific CSF biomarker characteristics.

Autophagy-Lysosomal Pathway as Potential Therapeutic Target in Parkinson's Disease

Cellular quality control systems have gained much attention in recent decades. Among these, autophagy is a natural self-preservation mechanism that continuously eliminates toxic cellular components and acts as an anti-ageing process. It is vital for cell survival and to preserve homeostasis. Several cell-type-dependent canonical or non-canonical autophagy pathways have been reported showing varying degrees of selectivity with regard to the substrates targeted. Here, we provide an updated review of the autophagy machinery and discuss the role of various forms of autophagy in neurodegenerative diseases, with a particular focus on Parkinson's disease. We describe recent findings that have led to the proposal of therapeutic strategies targeting autophagy to alter the course of Parkinson's disease progression.

Glucocerebrosidase mutations: A paradigm for neurodegeneration pathways

Biallelic (homozygous or compound heterozygous) glucocerebrosidase gene (GBA) mutations cause Gaucher disease, whereas heterozygous mutations are numerically the most important genetic risk factor for Parkinson disease (PD) and are associated with the development of other synucleinopathies, notably Dementia with Lewy Bodies. This phenomenon is not limited to GBA, with converging evidence highlighting further examples of autosomal recessive disease genes increasing neurodegeneration risk in heterozygous mutation carriers. Nevertheless, despite extensive research, the cellular mechanisms by which mutations in GBA, encoding lysosomal enzyme β-glucocerebrosidase (GCase), predispose to neurodegeneration remain incompletely understood. Alpha-synuclein (A-SYN) accumulation, autophagic lysosomal dysfunction, mitochondrial abnormalities, ER stress and neuroinflammation have been proposed as candidate pathogenic pathways in GBA-linked PD. The observation of GCase and A-SYN interactions in PD initiated the development and evaluation of GCase-targeted therapeutics in PD clinical trials.

Parkinson Disease: Translating Insights from Molecular Mechanisms to Neuroprotection

Parkinson disease (PD) used to be considered a nongenetic condition. However, the identification of several autosomal dominant and recessive mutations linked to monogenic PD has changed this view. Clinically manifest PD is then thought to occur through a complex interplay between genetic mutations, many of which have incomplete penetrance, and environmental factors, both neuroprotective and increasing susceptibility, which variably interact to reach a threshold over which PD becomes clinically manifested. Functional studies of PD gene products have identified many cellular and molecular pathways, providing crucial insights into the nature and causes of PD. PD originates from multiple causes and a range of pathogenic processes at play, ultimately culminating in nigral dopaminergic loss and motor dysfunction. An in-depth understanding of these complex and possibly convergent pathways will pave the way for therapeutic approaches to alleviate the disease symptoms and neuroprotective strategies to prevent disease manifestations. This review is aimed at providing a comprehensive understanding of advances made in PD research based on leveraging genetic insights into the pathogenesis of PD. It further discusses novel perspectives to facilitate identification of critical molecular pathways that are central to neurodegeneration that hold the potential to develop neuroprotective and/or neurorestorative therapeutic strategies for PD. SIGNIFICANCE STATEMENT: A comprehensive review of PD pathophysiology is provided on the complex interplay of genetic and environmental factors and biologic processes that contribute to PD pathogenesis. This knowledge identifies new targets that could be leveraged into disease-modifying therapies to prevent or slow neurodegeneration in PD.

Soluble Triggering Receptor Expressed on Myeloid Cells 2 From Cerebrospinal Fluid in Sleep Disorders Related to Parkinson's Disease

Background: Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglial receptor exclusively expressed in the central nervous system (CNS). It contributes to abnormal protein aggregation in neurodegenerative disorders, but its role in Parkinson’s disease (PD) is still unclear.

Methods: In this case-control study, we measured the concentration of the soluble fragment of TREM2 (sTREM2) in PD patients, evaluated their sleep conditions by the PD sleep scale (PDSS), and analyzed the relationship between sTREM2 and PD symptoms.

Results: We recruited 80 sporadic PD patients and 65 healthy controls without disease-related variants in TREM2. The concentration of sTREM2 in the CSF was significantly higher in PD patients than in healthy controls (p < 0.01). In the PD group, the concentration of sTREM2 had a positive correlation with α-syn in the CSF (Pearson r = 0.248, p = 0.027). Receiver operating characteristic curve (ROC) analyses showed that sTREM2 in the CSF had a significant diagnostic value for PD (AUC, 0.791; 95% CI, 0.711–0.871, p < 0.05). The subgroup analysis showed that PD patients with sleep disorders had a significantly higher concentration of sTREM2 in their CSF (p < 0.01). The concentration of sTREM2 in the CSF had a negative correlation with the PDSS score in PD patients (Pearson r = −0.555, p < 0.01). The ROC analyses showed that sTREM2 in the CSF had a significant diagnostic value for sleep disorders in PD (AUC, 0.733; 95% CI, 0.619–0.846, p < 0.05).

Conclusion: Our findings suggest that CSF sTREM2 may be a potential biomarker for PD and it could help predict sleep disorders in PD patients, but multicenter prospective studies with more participants are still needed to confirm its diagnostic value in future.

Mitochondrial Dysfunction, Protein Misfolding and Neuroinflammation in Parkinson's Disease: Roads to Biomarker Discovery

Parkinson’s Disease (PD) is a highly prevalent neurodegenerative disease among older adults. PD neuropathology is marked by the progressive loss of the dopaminergic neurons of the substantia nigra pars compacta and the widespread accumulation of misfolded intracellular α-synuclein (α-syn). Genetic mutations and post-translational modifications, such as α-syn phosphorylation, have been identified among the multiple factors supporting α-syn accrual during PD. A decline in the clearance capacity of the ubiquitin-proteasome and the autophagy-lysosomal systems, together with mitochondrial dysfunction, have been indicated as major pathophysiological mechanisms of PD neurodegeneration. The accrual of misfolded α-syn aggregates into soluble oligomers, and the generation of insoluble fibrils composing the core of intraneuronal Lewy bodies and Lewy neurites observed during PD neurodegeneration, are ignited by the overproduction of reactive oxygen species (ROS). The ROS activate the α-syn aggregation cascade and, together with the Lewy bodies, promote neurodegeneration. However, the molecular pathways underlying the dynamic evolution of PD remain undeciphered. These gaps in knowledge, together with the clinical heterogeneity of PD, have hampered the identification of the biomarkers that may be used to assist in diagnosis, treatment monitoring, and prognostication. Herein, we illustrate the main pathways involved in PD pathogenesis and discuss their possible exploitation for biomarker discovery.

Extracellular Vesicles as Novel Diagnostic and Prognostic Biomarkers for Parkinson's Disease

The elderly population will significantly increase in the next decade and, with it, the proportion of people affected by age-related diseases. Among them, one of the most invalidating is Parkinson's disease (PD), characterized by motor- and non-motor dysfunctions which strongly impair the quality of life of affected individuals. PD is characterized by the progressive degeneration of dopaminergic neurons, with consequent dopamine depletion, and the accumulation of misfolded α-synuclein aggregates. Although 150 years have passed since PD first description, no effective therapies are currently available, but only palliative treatments. Importantly, PD is often diagnosed when the neuronal loss is elevated, making difficult any therapeutic intervention. In this context, two key challenges remain unanswered: (i) the early diagnosis to avoid the insurgence of irreversible symptoms; and (ii) the reliable monitoring of therapy efficacy. Research strives to identify novel biomarkers for PD diagnosis, prognosis, and therapeutic follow-up. One of the most promising sources of biomarkers is represented by extracellular vesicles (EVs), a heterogeneous population of nanoparticles, released by all cells in the microenvironment. Brain-derived EVs are able to cross the blood-brain barrier, protecting their payload from enzymatic degradation, and are easily recovered from biofluids. Interestingly, EV content is strongly influenced by the specific pathophysiological status of the donor cell. In this manuscript, the role of EVs as source of novel PD biomarkers is discussed, providing all recent findings concerning relevant proteins and miRNAs carried by PD patient-derived EVs, from several biological specimens. Moreover, the contribution of mitochondria-derived EVs will be dissected. Finally, the promising possibility to use EVs as source of markers to monitor PD therapy efficacy will be also examined. In the future, larger cohort studies will help to validate these EV-associated candidates, that might be effectively used as non-invasive and robust source of biomarkers for PD.

Serum Neurofilament Dynamics Predicts Cognitive Progression in de novo Parkinson's Disease

BACKGROUND

Neurofilament light (NfL) can reflect the extent of neuron/axon damage, thus providing an opportunity to examine the severity and progression of the diseases with such damage.

OBJECTIVE

Whether serum NfL can be used as an indicator to monitor the cognitive progress of de novo Parkinson's disease (PD) remains unclear.

METHODS

In this research, 144 healthy controls and 301 de novo PD patients from Parkinson's Progression Markers Initiative (PPMI) were recruited. Linear mixed effects models were used to examine the associations of baseline/longitudinal serum NfL with cognitive decline. Cox regression was used to detect cognitive progression in PD participants.

RESULTS

We found PD patients had higher serum NfL than controls at baseline (p = 0.031), and NfL increase was faster in PD group (p < 0.001). Both baseline serum NfL and its rate of change predicted measurable cognitive decline in early PD (MoCA, β= -0.014, p < 0.001; β= -0.002, p < 0.001, respectively). Additionally, we observed that NfL levels were also able to predict progression in different diagnostic groups and Amyloid- PD and Amyloid+PD groups. After an average follow-up of 6.37±1.84 years, the baseline NfL of the third tertile of high concentrations was associated with a future high risk of PD dementia (adjusted HR 6.33, 95% CI 2.62-15.29, p < 0.001).

CONCLUSION

In conclusion, our results indicated that the serum NfL concentration could function as an easily accessible biomarker to monitor the severity and progression of cognitive decline in PD.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Imaging of Substantia Nigra in Parkinson's Disease: A Narrative Review

Parkinson's disease (PD) is a progressive neurodegenerative disorder, characterized by motor and non-motor symptoms due to the degeneration of the pars compacta of the substantia nigra (SNc) with dopaminergic denervation of the striatum. Although the diagnosis of PD is principally based on a clinical assessment, great efforts have been expended over the past two decades to evaluate reliable biomarkers for PD. Among these biomarkers, magnetic resonance imaging (MRI)-based biomarkers may play a key role. Conventional MRI sequences are considered by many in the field to have low sensitivity, while advanced pulse sequences and ultra-high-field MRI techniques have brought many advantages, particularly regarding the study of brainstem and subcortical structures. Nowadays, nigrosome imaging, neuromelanine-sensitive sequences, iron-sensitive sequences, and advanced diffusion weighted imaging techniques afford new insights to the non-invasive study of the SNc. The use of these imaging methods, alone or in combination, may also help to discriminate PD patients from control patients, in addition to discriminating atypical parkinsonian syndromes (PS). A total of 92 articles were identified from an extensive review of the literature on PubMed in order to ascertain the-state-of-the-art of MRI techniques, as applied to the study of SNc in PD patients, as well as their potential future applications as imaging biomarkers of disease. Whilst none of these MRI-imaging biomarkers could be successfully validated for routine clinical practice, in achieving high levels of accuracy and reproducibility in the diagnosis of PD, a multimodal MRI-PD protocol may assist neuroradiologists and clinicians in the early and differential diagnosis of a wide spectrum of neurodegenerative disorders.

Hippocampal correlates of episodic memory in Parkinson's disease: A systematic review of magnetic resonance imaging studies

The present review asks whether magnetic resonance imaging (MRI) studies are able to define neural correlates of episodic memory within the hippocampus in Parkinson's disease (PD). Systematic searches were performed in PubMed, Web of Science, Medline, CINAHL, and EMBASE using search terms related to structural and functional MRI (fMRI), the hippocampus, episodic memory, and PD. Risk of bias was assessed for each study using the Newtown-Ottawa Scale. Thirty-nine studies met inclusion criteria; eight fMRI, seven diffusion MRI (dMRI), and 24 structural MRI (14 exploring whole hippocampus and 10 exploring hippocampal subfields). Critical analysis of the literature revealed mixed evidence from functional and dMRI, but stronger evidence from sMRI of the hippocampus as a biomarker for episodic memory impairment in PD. Hippocampal subfield studies most often implicated CA1, CA3/4, and subiculum volume in episodic memory and cognitive decline in PD. Despite differences in imaging methodology, study design, and sample characteristics, MRI studies have helped elucidate an important neural correlate of episodic memory impairment in PD with both clinical and theoretical implications. Natural progression of this work encourages future research on hippocampal subfield function as a potential biomarker of, or therapeutic target for, episodic memory dysfunction in PD.

Simplified quantification of [18F]FE-PE2I PET in Parkinson's disease: Discriminative power, test-retest reliability and longitudinal validity during early peak and late pseudo-equilibrium

Quantification of dopamine transporter (DAT) availability with [¹⁸F]FE-PE2I PET enables the detection of presynaptic dopamine deficiency and provides a potential progression marker for Parkinson`s disease (PD). Simplified quantification is feasible, but the time window of short acquisition protocols may have a substantial impact on the reliability of striatal binding estimates. Dynamic [¹⁸F]FE-PE2I PET data of cross-sectional (33 PD patients, 24 controls), test-retest (9 patients), and longitudinal (12 patients) cohorts were used to assess the variability and reliability of specific binding ratios (SBR) measured during early peak and late pseudo-equilibrium. Receiver operating characteristics area under the curve (PD vs. controls) was high for early (0.996) and late (0.991) SBR. Early SBR provided more favourable effect size, absolute variability, and standard error of measurement than late SBR (caudate: 1.29 vs. 1.23; 6.9% vs. 9.8%; 0.09 vs. 0.20; putamen: 1.75 vs. 1.67; 7.7% vs. 14.0%; 0.08 vs. 0.17). The annual percentage change was comparable for both time windows (-7.2%-8.5%), but decline was significant only for early SBR. Whereas early and late [¹⁸F]FE-PE2I PET acquisitions have similar discriminative power to separate PD patients and controls, the early peak equilibrium acquisition can be recommended if [¹⁸F]FE-PE2I is used to measure longitudinal changes of DAT availability.

Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead

Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.

The Quebec Parkinson Network: A Researcher-Patient Matching Platform and Multimodal Biorepository

BACKGROUND

Genetic, biologic and clinical data suggest that Parkinson's disease (PD) is an umbrella for multiple disorders with clinical and pathological overlap, yet with different underlying mechanisms. To better understand these and to move towards neuroprotective treatment, we have established the Quebec Parkinson Network (QPN), an open-access patient registry, and data and bio-samples repository.

OBJECTIVE

To present the QPN and to perform preliminary analysis of the QPN data.

METHODS

A total of 1,070 consecutively recruited PD patients were included in the analysis. Demographic and clinical data were analyzed, including comparisons between males and females, PD patients with and without RBD, and stratified analyses comparing early and late-onset PD and different age groups.

RESULTS

QPN patients exhibit a male:female ratio of 1.8:1, an average age-at-onset of 58.6 years, an age-at-diagnosis of 60.4 years, and average disease duration of 8.9 years. REM-sleep behavior disorder (RBD) was more common among men, and RBD was associated with other motor and non-motor symptoms including dyskinesia, fluctuations, postural hypotension and hallucinations. Older patients had significantly higher rates of constipation and cognitive impairment, and longer disease duration was associated with higher rates of dyskinesia, fluctuations, freezing of gait, falls, hallucinations and cognitive impairment. Since QPN's creation, over 60 studies and 30 publications have included patients and data from the QPN.

CONCLUSIONS

The QPN cohort displays typical PD demographics and clinical features. These data are open-access upon application (http://rpq-qpn.ca/en/), and will soon include genetic, imaging and bio-samples. We encourage clinicians and researchers to perform studies using these resources.

Parkinson's Disease Drug Development Since 1999: A Story of Repurposing and Relative Success

Background:

A global overview of drug development programs in Parkinson’s disease over the last few decades is lacking, while such programs are challenging given the multifaceted and heterogeneous nature of the disease.

Objective:

To indirectly assess drug development programs in Parkinson’s disease, exploring some factors associated with compound attrition at different trial phases.

Methods:

We assessed all Parkinson’s disease trials in the WHO trials portal, from inception (1999) to September 2019. Independent authors selected trials and extracted data. The success rate was the number of compounds that progressed to the next drug development phase divided by the number of compounds in that phase.

Results:

Overall, 357 trials (studying 152 compounds) fulfilled our inclusion criteria, with 62 (17.3%) phase 1 trials, 135 (37.8%) phase 2 trials, 85 (23.8%) phase 3 trials, and 53 (14.8%) phase 4 trials. The success rate was 42.4% from phase 2 to 3. Original compounds received regulatory approval by the FDA in 21.4% of cases, compared with 6.7% of repurposed compounds, representing an overall success rate of 14.9%. We found 172 trials (48.2%) conducted for repurposing previously licensed compounds. These figures were approximately the same regarding approval by the EMA. Most compounds were approved to treat parkinsonism and motor fluctuations.

Conclusion:

We found a moderate-to-high success rate in all phases of drug development. This was largely based on the success of original compounds, despite almost half of the identified trials attempting compound repurposing.

Streamlined alpha-synuclein RT-QuIC assay for various biospecimens in Parkinson's disease and dementia with Lewy bodies

Definitive diagnosis of Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) relies on postmortem finding of disease-associated alpha-synuclein (αSyn^D) as misfolded protein aggregates in the central nervous system (CNS). The recent development of the real-time quaking induced conversion (RT-QuIC) assay for ultrasensitive detection of αSyn^D aggregates has revitalized the diagnostic values of clinically accessible biospecimens, including cerebrospinal fluid (CSF) and peripheral tissues. However, the current αSyn RT-QuIC assay platforms vary widely and are thus challenging to implement and standardize the measurements of αSyn^D across a wide range of biospecimens and in different laboratories. We have streamlined αSyn RT-QuIC assay based on a second generation assay platform that was assembled entirely with commercial reagents. The streamlined RT-QuIC method consisted of a simplified protocol requiring minimal hands-on time, and allowing for a uniform analysis of αSyn^D in different types of biospecimens from PD and DLB. Ultrasensitive and specific RT-QuIC detection of αSyn^D aggregates was achieved in million-fold diluted brain homogenates and in nanoliters of CSF from PD and DLB cases but not from controls. Comparative analysis revealed higher seeding activity of αSyn^D in DLB than PD in both brain homogenates and CSF. Our assay was further validated with CSF samples of 214 neuropathologically confirmed cases from tissue repositories (88 PD, 58 DLB, and 68 controls), yielding a sensitivity of 98% and a specificity of 100%. Finally, a single RT-QuIC assay protocol was employed uniformly to detect seeding activity of αSyn^D in PD samples across different types of tissues including the brain, skin, salivary gland, and colon. We anticipate that our streamlined protocol will enable interested laboratories to easily and rapidly implement the αSyn RT-QuIC assay for various clinical specimens from PD and DLB. The utilization of commercial products for all assay components will improve the robustness and standardization of the RT-QuIC assay for diagnostic applications across different sites. Due to ultralow sample consumption, the ultrasensitive RT-QuIC assay will facilitate efficient use and sharing of scarce resources of biospecimens. Our streamlined RT-QuIC assay is suitable to track the distribution of αSyn^D in CNS and peripheral tissues of affected patients. The ongoing evaluation of RT-QuIC assay of αSyn^D as a potential biomarker for PD and DLB in clinically accessible biospecimens has broad implications for understanding disease pathogenesis, improving early and differential diagnosis, and monitoring therapeutic efficacies in clinical trials.

Weighted Gene Coexpression Network Analysis Uncovers Critical Genes and Pathways for Multiple Brain Regions in Parkinson's Disease

Objective

In this study, we aimed to identify critical genes and pathways for multiple brain regions in Parkinson's disease (PD) by weighted gene coexpression network analysis (WGCNA).

Methods

From the GEO database, differentially expressed genes (DEGs) were separately identified between the substantia nigra, putamen, prefrontal cortex area, and cingulate gyrus of PD and normal samples with the screening criteria of p value < 0.05 and ∣log₂fold change (FC) | >0.585. Then, a coexpression network was presented by the WGCNA package. Gene modules related to PD were constructed. Then, PD-related DEGs were used for construction of PPI networks. Hub genes were determined by the cytoHubba plug-in. Functional enrichment analysis was then performed.

Results

DEGs were identified for the substantia nigra (17 upregulated and 52 downregulated genes), putamen (317 upregulated and 317 downregulated genes), prefrontal cortex area (39 upregulated and 72 downregulated genes), and cingulate gyrus (116 upregulated and 292 downregulated genes) of PD compared to normal samples. Gene modules were separately built for the four brain regions of PD. PPI networks revealed hub genes for the substantia nigra (SLC6A3, SLC18A2, and TH), putamen (BMP4 and SNAP25), prefrontal cortex area (SNAP25), and cingulate gyrus (CTGF, CDH1, and COL5A1) of PD. These DEGs in multiple brain regions were involved in distinct biological functions and pathways. GSEA showed that these DEGs were all significantly enriched in electron transport chain, proteasome degradation, and synaptic vesicle pathway.

Conclusion

Our findings revealed critical genes and pathways for multiple brain regions in PD, which deepened the understanding of PD-related molecular mechanisms.

Literature-based learning and experimental design model in molecular biology teaching for medical students at Tongji University

In 2018, to help undergraduate medical students strengthen their self-learning and scientific research skills, we introduced an instructional model that combined literature-based learning with experimental design. We tested this model on the molecular biology class at Tongji University. In the first step of the model, a topic is chosen, and students find, read, and evaluate scientific papers in groups and deliver presentations. In the second step, they design scientific experiments in groups and discuss their proposed experiments in class, which is to be followed by further experimental verification in the lab course. This entire activity was given 20% weightage in the final score. The model led to better student-centered teaching and self-directed learning according to quantitative and qualitative assessment. Students showed great interest in literature and research. They enjoyed group work and gained experience in organization and presentation. Apart from a significant increase in final score, assessment data from students indicated that they were satisfied with this teaching model and considered it a positive experience. Looking at the positive impact of the literature-based learning and experiment design model, we support its continued use for teaching molecular biology to undergraduate medical students.

Open science takes on Parkinson's disease

The Aligning Science Across Parkinson’s (ASAP) initiative was set up to improve understanding of the biology underlying the onset and progression of Parkinson’s disease. With an emphasis on open science and collaboration, we have assembled a research network led by nearly 100 investigators to explore the pathology of Parkinson’s disease, and this network will soon expand to include researchers working on relevant (dys)-functional neural circuits. We have also contributed to large-scale genetics and patient cohort initiatives related to the disease. We hope that these actions, and others planned for the future, will deepen our knowledge of the molecular mechanisms underlying the origin and evolution of Parkinson’s disease and, ultimately, contribute to the development of novel therapies.

Design of a virtual longitudinal observational study in Parkinson's disease (AT-HOME PD)

OBJECTIVE

The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site-centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID-19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow-up of clinical trial participants, (2) compare changes in smartphone-based assessments, online patient-reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression.

METHODS

Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT-HOME PD), a two-year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone-based assessments of motor function and socialization, and patient-reported outcomes online.

RESULTS

From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient-reported outcomes on the online platform.

INTERPRETATION

It is feasible to conduct a large-scale, international virtual observational study following the completion of participation in brick-and-mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow-up of clinical trial participants and design of future clinical trials.

Urinary proteome profiling for stratifying patients with familial Parkinson's disease

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non‐invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)‐based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non‐carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD‐associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.

Cannabinoid Receptors and Ligands: Lessons from CNS Disorders and the Quest for Novel Treatment Venues

The potential use of cannabinoids for therapeutic purposes is at the forefront of cannabinoid research which aims to develop innovative strategies to prevent, manage and treat a broad spectrum of human diseases. This chapter briefly reviews the pivotal role of the endocannabinoid system in modulating the central nervous system and its roles on neurodegenerative diseases and brain disorders. Ligand-induced modulation of cannabinoid 1 and 2 receptors to modulate immune response, decrease neurodegeneration and pain are aspects that are also discussed.