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In recent years, numerous clinical trials for disease modification in Parkinson’s disease (PD) have failed, possibly because of a “one-size-fits all” approach. Alternatively, a precision medicine approach, which customises treatments based on patients’ individual genotype, may help reach disease modification. Here, we review clinical trials that target genetic forms of PD, i.e., GBA -associated and LRRK2 -associated PD. In summary, six ongoing studies which explicitely recruit GBA -PD patients, and two studies which recruit LRRK2 -PD patients, were identified. Available data on mechanisms of action, study design, and challenges of therapeutic trials are discussed.

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. Synopsis This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson’s disease (PD). The presented workflow allows quantification of more than 2,000 proteins in urine. Lysosomal dysregulation is reflected in the urinary proteomes of individuals with the pathogenic LRRK2 G2019S mutation. Machine learning on the urinary proteome classifies LRRK2 mutation and PD disease states with sensitivities of 78% and 74% and specificities of 73% and 84%, respectively. The neurotrophic factor VGF was identified as the most important feature to discriminate manifesting from non‐manifesting LRRK2 G2019S carriers. Graphical Abstract This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson’s disease (PD).

Parkinson's disease (PD) is a common neurodegenerative disease whose pathological hallmark is the accumulation of intracellular α-synuclein aggregates in Lewy bodies. Lipid metabolism dysregulation may play a significant role in PD pathogenesis; however, large plasma lipidomic studies in PD are lacking. In the current study, we analyzed the lipidomic profile of plasma obtained from 150 idiopathic PD patients and 100 controls, taken from the 'Spot' study at Columbia University Medical Center in New York. Our mass spectrometry based analytical panel consisted of 520 lipid species from 39 lipid subclasses including all major classes of glycerophospholipids, sphingolipids, glycerolipids and sterols. Each lipid species was analyzed using a logistic regression model. The plasma concentrations of two lipid subclasses, triglycerides and monosialodihexosylganglioside (GM3), were different between PD and control participants. GM3 ganglioside concentration had the most significant difference between PD and controls (1.531±0.037 pmol/μl versus 1.337±0.040 pmol/μl respectively; p-value = 5.96E-04; q-value = 0.048; when normalized to total lipid: p-value = 2.890E-05; q-value = 2.933E-03). Next, we used a collection of 20 GM3 and glucosylceramide (GlcCer) species concentrations normalized to total lipid to perform a ROC curve analysis, and found that these lipids compare favorably with biomarkers reported in previous studies (AUC = 0.742 for males, AUC = 0.644 for females). Our results suggest that higher plasma GM3 levels are associated with PD. GM3 lies in the same glycosphingolipid metabolic pathway as GlcCer, a substrate of the enzyme glucocerebrosidase, which has been associated with PD. These findings are consistent with previous reports implicating lower glucocerebrosidase activity with PD risk.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. While there is no curative treatment, the immune system's involvement with autoimmune T cells that recognize the protein α-synuclein (α-syn) in a subset of individuals suggests new areas for therapeutic strategies. As not all patients with PD have T cells specific for α-syn, we explored additional autoantigenic targets of T cells in PD. We generated 15-mer peptides spanning several PD-related proteins implicated in PD pathology, including glucosylceramidase β 1 (GBA), superoxide dismutase 1 (SOD1), PTEN induced kinase 1 (PINK1), Parkin RBR E3 ubiquitin protein ligase (parkin), oxoglutarate dehydrogenase (OGDH), and leucine rich repeat kinase 2 (LRRK2). Cytokine production (IFN-γ, IL-5, IL-10) against these proteins was measured using a fluorospot assay and PBMCs from patients with PD and age-matched healthy controls. We identified PINK1, a regulator of mitochondrial stability, as an autoantigen targeted by T cells, as well as its unique epitopes, and their HLA restriction. The PINK1-specific T cell reactivity revealed sex-based differences, as it was predominantly found in male patients with PD, which may contribute to the heterogeneity of PD. Identifying and characterizing PINK1 and other autoinflammatory targets may lead to antigen-specific diagnostics, progression markers, and/or novel therapeutic strategies for PD.

Parkinson’s disease, the most common age-related movement disorder, is a progressive neurodegenerative disease with unclear etiology. Better understanding of the underlying disease mechanism(s) is an urgent need for the development of disease-modifying therapeutics. Limited studies have been performed in large patient cohorts to identify protein alterations in cerebrospinal fluid (CSF), a proximal site to pathology. We set out to identify disease-relevant protein changes in CSF to gain insights into the etiology of Parkinson’s disease and potentially assist in disease biomarker identification. In this study, we used liquid chromatography-tandem mass spectrometry in data-independent acquisition (DIA) mode to identify Parkinson’s-relevant biomarkers in cerebrospinal fluid. We quantified 341 protein groups in two independent cohorts (n = 196) and a longitudinal cohort (n = 105 samples, representing 40 patients) consisting of Parkinson’s disease and healthy control samples from three different sources. A first cohort of 53 Parkinson’s disease and 72 control samples was analyzed, identifying 53 proteins with significant changes (p < 0.05) in Parkinson’s disease relative to healthy control. We established a biomarker signature and multiple protein ratios that differentiate Parkinson’s disease from healthy controls and validated these results in an independent cohort. The second cohort included 28 Parkinson’s disease and 43 control samples. Independent analysis of these samples identified 41 proteins with significant changes. Evaluation of the overlapping changes between the two cohorts identified 13 proteins with consistent and significant changes (p < 0.05). Importantly, we found the extended granin family proteins as reduced in disease, suggesting a potential common mechanism for the biological reduction in monoamine neurotransmission in Parkinson’s patients. Our study identifies several novel protein changes in Parkinson’s disease cerebrospinal fluid that may be exploited for understanding etiology of disease and for biomarker development.

As the COVID-19 pandemic continues to affect the international community, very little is known about its impact on the health and day-to-day activities of people with Parkinson’s disease (PwPD). To better understand the emotional and behavioral consequences of the public health policies implemented to mitigate the spread of SARS-CoV-2 in PwPD, and to explore the factors contributing to accessing alternative health care mechanisms, such as telehealth, we administered an anonymous knowledge, attitude, and practice survey to PwPD and care partners, via the mailing lists of the Parkinson’s Foundation and Columbia University Parkinson’s Disease Center of Excellence with an average response rate of 19.3%. Sufficient information was provided by 1,342 PwPD to be included in the final analysis. Approximately half of respondents reported a negative change in PD symptoms, with 45–66% reporting mood disturbances. Telehealth use increased from 9.7% prior to the pandemic to 63.5% during the pandemic. Higher income and higher education were associated with telehealth use. Services were more often used for doctor’s appointment than physical, occupational, speech, or mental health therapies. Almost half (46%) of PwPD preferred to continue using telehealth always or sometimes after the coronavirus outbreak had ended. Having received support/instruction for telehealth and having a care partner, friend, or family member to help them with the telehealth visit increased the likelihood of continuous use of telehealth after the pandemic ended. Taken together, PD symptoms and management practices were markedly affected by COVID-19. Given the observed demographic limitations of telehealth, expanding its implementation to include additional physical, occupational, psychological, and speech therapies, increasing support for telehealth, as well as reaching underserved (low income) populations is urgently required.

[...]the oEV concentrations of pS129-α-syn increased in the same order, HC < PD < MSA, and were significantly higher in both disease groups (Fig. 1a). [...]pS129-α-syn concentration in nEVs was not affected by synucleinopathy, whereas in oEVs it was increased in MSA and in a subgroup of patients with PD compared to HCs. [...]unlike the oEV:nEV total α-syn ratio, the oEV:nEV pS129-α-syn ratio separated the groups only moderately. Significantly higher plasma NfL concentrations have been reported in MSA compared to HC and PD [8]. [...]we tested whether similar differences could be detected in our cohort, though we used serum rather than plasma. [...]we used the same statistical approach as in [2] to construct new models (Additional file 1:

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. Plain language summary Parkinson’s disease (PD) is a progressive neurological disorder that affects body movement because some brain cells stop producing the chemical dopamine. PD is often not diagnosed until it has advanced, making early detection crucial. To enable early detection, we investigated tiny packages called extracellular vesicles released from a variety of cells, including the brain cells, that can be found in urine as a potential source for diagnosing PD. These tiny packages contain different kinds of molecules from inside the cells. We analyzed urine samples from 138 individuals and found several proteins involved in PD development that could be biological indicators for early detection of the disease. We used various techniques to make sure that our findings were accurate. Our study suggests that looking at these proteins in urine could be a good way to detect PD in a non-invasive manner. Hadisurya et al. identify potential diagnostic biomarkers for Parkinson’s disease (PD) via mass spectrometry analysis of urinary extracellular vesicles. The authors identify multiple proteins and phosphoproteins previously found to be involved in PD pathophysiology.

Objective To determine the evolution of numerous neuropsychiatric symptoms and cognitive abilities in Parkinson disease from disease onset. Methods Prospectively collected, longitudinal (untreated, disease onset to year 5), observational data from Parkinson's Progression Markers Initiative annual visits was used to evaluate prevalence, correlates, and treatment of 10 neuropsychiatric symptoms and cognitive impairment in Parkinson disease participants and matched healthy controls. Results Of 423 Parkinson disease participants evaluated at baseline, 315 (74.5%) were assessed at year 5. Eight neuropsychiatric symptoms studied increased in absolute prevalence by 6.2–20.9% at year 5 relative to baseline, and cognitive impairment increased by 2.7–6.2%. In comparison, the frequency of neuropsychiatric symptoms in healthy controls remained stable or declined over time. Antidepressant and anxiolytic/hypnotic use in Parkinson disease were common at baseline and increased over time (18% to 27% for the former; 13% to 24% for the latter); antipsychotic and cognitive‐enhancing medication use was uncommon throughout (2% and 5% of patients at year 5); and potentially harmful anticholinergic medication use was common and increased over time. At year 5 the cross‐sectional prevalence for having three or more neuropsychiatric disorders/cognitive impairment was 56% for Parkinson disease participants versus 13% for healthy controls, and by then seven of the examined disorders had either occurred or been treated at some time point in the majority of Parkinson disease patients. Principal component analysis suggested an affective disorder subtype only. Interpretation Neuropsychiatric features in Parkinson disease are common from the onset, increase over time, are frequently comorbid, and fluctuate in severity.