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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.

Purpose of ReviewGBA mutations are the most common known genetic cause of Parkinson’s disease (PD). Its biological pathway may be important in idiopathic PD, since activity of the enzyme encoded by GBA, glucocerebrosidase, is reduced even among PD patients without GBA mutations. This article describes the structure and function of GBA, reviews recent literature on the clinical phenotype of GBA PD, and suggests future directions for research, counseling, and treatment.Recent FindingsSeveral longitudinal studies have shown that GBA PD has faster motor and cognitive progression than idiopathic PD and that this effect is dose dependent. New evidence suggests that GBA mutations may be important in multiple system atrophy. Further, new interventional studies focusing on GBA PD are described. These studies may increase the interest of PD patients and caregivers in genetic counseling.SummaryGBA mutation status may help clinicians estimate PD progression, though mechanisms underlying GBA and synucleinopathy require further understanding.

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.

Background Accurate identification of Parkinson’s disease (PD) in large electronic health record (EHR) population-based databases is challenging due to diagnostic heterogeneity in routine care, with a substantial proportion of individuals diagnosed with PD had not been diagnosed by a specialist. Our aim was to develop and validate a simplified rule-based medication algorithm to identify PD in a nationwide healthcare registry and apply it to estimate long-term incidence, prevalence, and pre-diagnostic diagnoses. Methods Using Clalit Health Services EHR data covering over five million individuals (2005–2025), we constructed a medication-based algorithm incorporating predefined inclusion and exclusion criteria and two levels of diagnostic certainty (probable/possible PD). Validation was performed against two independent specialist-confirmed PD cohorts and FDOPA PET/CT and a non-PD neurological cohort. Incidence rates per 100,000 were calculated annually with 95% confidence intervals (CIs) assuming a Poisson distribution. Age-adjusted incidence rates were computed using the WHO standard population. motor and non-motor diagnoses preceding PD were examined up to 18 years before the index date using matched controls. Results The algorithm identified 34,368 PD patients (56.5% male; mean age at index 75.2 ± 10.5 years). Sensitivity was 94.8% (95% CI 90.4–97.2) in the FDOPA PET/CT cohort, 94.8% (95% CI 92.1–96.6) in the private clinic cohort, and 94.7% (95% CI 90.9–96.9) in the movement disorder clinic cohort. Specificity was 85.2% (95% CI 77.8–90.6). Incidence increased markedly with age but declined significantly over time (overall annual percent change [APC] - 4.47%, 95% CI -4.90 – -4.03). Age-adjusted incidence rates (≥20 years) declined 2.4-fold between 2005 and 2024 (55 [95% CI 53–58] to 23 [95% CI 21–24] per 100,000). Overall prevalence declined modestly (APC -0.78%, 95% CI -0.84 – -0.72), with increases in younger age groups and declines in older groups. Constipation, depression, and tremor diagnoses were more frequent years before diagnosis, whereas smoking-related codes were less frequent among future PD patients. Conclusions This validated medication-based algorithm provides a reproducible framework for PD identification in large registries. Applied over two decades in a nationwide cohort, it demonstrated high diagnostic performance and revealed age-dependent declines in PD incidence alongside heterogeneous prevalence trends.

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.

Parkinson’s disease (PD) is a highly heterogeneous condition with symptoms spanning motor and non-motor domains. Clinical scales like the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) are standard in clinical trials where disease progression is monitored. They rely on summing item values, assuming uniform item importance and score increments. Here, we propose a novel data-driven approach to optimize weights for such scales–so that total scores better reflect the underlying disease severity. In a retrospective observational analysis of longitudinal cohort data from the Parkinson’s Progression Markers Initiative (PPMI), our methods identified which items (and value increments) most strongly indicate PD progression, down-weighting or excluding less informative items. The learned weights substantially improve the monotonic relationship between total scores and clinical progression. We validated our weights using both held-out PPMI data and an independent dataset (BeaT-PD), demonstrating their robustness. Applying such weights in clinical trials may increase power and reduce the required sample size 1 .