Precision medicine in Parkinson's disease patients with LRRK2 and GBA risk variants - Let's get even more personal

Clinical, mechanistic, biomarker, and therapeutic advances in GBA1-associated Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease. The development of PD is closely linked to genetic and environmental factors, with GBA1 variants being the most common genetic risk. Mutations in the GBA1 gene lead to reduced activity of the coded enzyme, glucocerebrosidase, which mediates the development of PD by affecting lipid metabolism (especially sphingolipids), lysosomal autophagy, endoplasmic reticulum, as well as mitochondrial and other cellular functions. Clinically, PD with GBA1 mutations (GBA1-PD) is characterized by particular features regarding the progression of symptom severity. On the therapeutic side, the discovery of the relationship between GBA1 variants and PD offers an opportunity for targeted therapeutic interventions. In this review, we explore the genotypic and phenotypic correlations, etiologic mechanisms, biomarkers, and therapeutic approaches of GBA1-PD and summarize the current state of research and its challenges.

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Pharmacotherapy for Disease Modification in Early Parkinson's Disease: How Early Should We Be?

Slowing or halting progression continues to be a major unmet medical need in Parkinson's disease (PD). Numerous trials over the past decades have tested a broad range of interventions without ultimate success. There are many potential reasons for this failure and much debate has focused on the need to test 'disease-modifying' candidate drugs in the earliest stages of disease. While generally accepted as a rational approach, it is also associated with significant challenges around the selection of trial populations as well as trial outcomes and durations. From a health care perspective, intervening even earlier and before at-risk subjects have gone on to develop overt clinical disease is at the heart of preventive medicine. Recent attempts to develop a framework for a biological definition of PD are aiming to enable 'preclinical' and subtype-specific diagnostic approaches. The present review addresses past efforts towards disease-modification, including drug targets and reasons for failure, as well as novel targets that are currently being explored in disease-modification trials in early established PD. The new biological definitions of PD may offer new opportunities to intervene even earlier. We critically discuss the potential and challenges around planning 'disease-prevention' trials in subjects with biologically defined 'preclinical' or prodromal PD.

Recalibrating the Why and Whom of Animal Models in Parkinson Disease: A Clinician's Perspective

Animal models have been used to gain pathophysiologic insights into Parkinson's disease (PD) and aid in the translational efforts of interventions with therapeutic potential in human clinical trials. However, no disease-modifying therapy for PD has successfully emerged from model predictions. These translational disappointments warrant a reappraisal of the types of preclinical questions asked of animal models. Besides the limitations of experimental designs, the one-size convergence and oversimplification yielded by a model cannot recapitulate the molecular diversity within and between PD patients. Here, we compare the strengths and pitfalls of different models, review the discrepancies between animal and human data on similar pathologic and molecular mechanisms, assess the potential of organoids as novel modeling tools, and evaluate the types of questions for which models can guide and misguide. We propose that animal models may be of greatest utility in the evaluation of molecular mechanisms, neural pathways, drug toxicity, and safety but can be unreliable or misleading when used to generate pathophysiologic hypotheses or predict therapeutic efficacy for compounds with potential neuroprotective effects in humans. To enhance the translational disease-modification potential, the modeling must reflect the biology not of a diseased population but of subtypes of diseased humans to distinguish What data are relevant and to Whom.

Unravelling the Parkinson's puzzle, from medications and surgery to stem cells and genes: a comprehensive review of current and future management strategies

Parkinson's disease (PD) is a neurodegenerative disorder, prevalent in the elderly population. Neuropathological hallmarks of PD include loss of dopaminergic cells in the nigro-striatal pathway and deposition of alpha-synuclein protein in the neurons and synaptic terminals, which lead to a complex presentation of motor and non-motor symptoms. This review focuses on various aspects of PD, from clinical diagnosis to currently accepted treatment options, such as pharmacological management through dopamine replacement and surgical techniques such as deep brain stimulation (DBS). The review discusses in detail the potential of emerging stem cell-based therapies and gene therapies to be adopted as a cure, in contrast to the present symptomatic treatment in PD. The potential sources of stem cells for autologous and allogeneic stem cell therapy have been discussed, along with the progress evaluation of pre-clinical and clinical trials. Even though recent techniques hold great potential to improve the lives of PD patients, we present the importance of addressing the safety, efficacy, ethical, cost, and regulatory concerns before scaling them to clinical use.

Luxembourg Parkinson's study -comprehensive baseline analysis of Parkinson's disease and atypical parkinsonism

Background

Deep phenotyping of Parkinson's disease (PD) is essential to investigate this fastest-growing neurodegenerative disorder. Since 2015, over 800 individuals with PD and atypical parkinsonism along with more than 800 control subjects have been recruited in the frame of the observational, monocentric, nation-wide, longitudinal-prospective Luxembourg Parkinson's study.

Objective

To profile the baseline dataset and to explore risk factors, comorbidities and clinical profiles associated with PD, atypical parkinsonism and controls.

Methods

Epidemiological and clinical characteristics of all 1,648 participants divided in disease and control groups were investigated. Then, a cross-sectional group comparison was performed between the three largest groups: PD, progressive supranuclear palsy (PSP) and controls. Subsequently, multiple linear and logistic regression models were fitted adjusting for confounders.

Results

The mean (SD) age at onset (AAO) of PD was 62.3 (11.8) years with 15% early onset (AAO < 50 years), mean disease duration 4.90 (5.16) years, male sex 66.5% and mean MDS-UPDRS III 35.2 (16.3). For PSP, the respective values were: 67.6 (8.2) years, all PSP with AAO > 50 years, 2.80 (2.62) years, 62.7% and 53.3 (19.5). The highest frequency of hyposmia was detected in PD followed by PSP and controls (72.9%; 53.2%; 14.7%), challenging the use of hyposmia as discriminating feature in PD vs. PSP. Alcohol abstinence was significantly higher in PD than controls (17.6 vs. 12.9%, p = 0.003).

Conclusion

Luxembourg Parkinson's study constitutes a valuable resource to strengthen the understanding of complex traits in the aforementioned neurodegenerative disorders. It corroborated several previously observed clinical profiles, and provided insight on frequency of hyposmia in PSP and dietary habits, such as alcohol abstinence in PD.Clinical trial registration: clinicaltrials.gov, NCT05266872.

A blood-based marker of mitochondrial DNA damage in Parkinson's disease

Parkinson's disease (PD) is the most common neurodegenerative movement disorder, and neuroprotective or disease-modifying interventions remain elusive. High-throughput markers aimed at stratifying patients on the basis of shared etiology are required to ensure the success of disease-modifying therapies in clinical trials. Mitochondrial dysfunction plays a prominent role in the pathogenesis of PD. Previously, we found brain region-specific accumulation of mitochondrial DNA (mtDNA) damage in PD neuronal culture and animal models, as well as in human PD postmortem brain tissue. To investigate mtDNA damage as a potential blood-based marker for PD, we describe herein a PCR-based assay (Mito DNADX) that allows for the accurate real-time quantification of mtDNA damage in a scalable platform. We found that mtDNA damage was increased in peripheral blood mononuclear cells derived from patients with idiopathic PD and those harboring the PD-associated leucine-rich repeat kinase 2 (LRRK2) G2019S mutation in comparison with age-matched controls. In addition, mtDNA damage was elevated in non-disease-manifesting LRRK2 mutation carriers, demonstrating that mtDNA damage can occur irrespective of a PD diagnosis. We further established that Lrrk2 G2019S knock-in mice displayed increased mtDNA damage, whereas Lrrk2 knockout mice showed fewer mtDNA lesions in the ventral midbrain, compared with wild-type control mice. Furthermore, a small-molecule kinase inhibitor of LRRK2 mitigated mtDNA damage in a rotenone PD rat midbrain neuron model and in idiopathic PD patient-derived lymphoblastoid cell lines. Quantifying mtDNA damage using the Mito DNADX assay may have utility as a candidate marker of PD and for measuring the pharmacodynamic response to LRRK2 kinase inhibitors.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

International Genetic Testing and Counseling Practices for Parkinson's Disease

BACKGROUND

There is growing clinical and research utilization of genetic testing in Parkinson's disease (PD), including direct-to-consumer testing.

OBJECTIVES

The aim is to determine the international landscape of genetic testing in PD to inform future worldwide recommendations.

METHODS

A web-based survey assessing current practices, concerns, and barriers to genetic testing and counseling was administered to the International Parkinson and Movement Disorders Society membership.

RESULTS

Common hurdles across sites included cost and access to genetic testing, and counseling, as well as education on genetic counseling. Region-dependent differences in access to and availability of testing and counseling were most notable in Africa. High-income countries also demonstrated heterogeneity, with European nations more likely to have genetic testing covered through insurance than Pan-American and Asian countries.

CONCLUSIONS

This survey highlights not only diversity of barriers in different regions but also the shared and highly actionable needs for improved education and access to genetic counseling and testing for PD worldwide. © 2023 International Parkinson and Movement Disorder Society.

Astrocytes Differentiated from LRRK2-I1371V Parkinson's-Disease-Induced Pluripotent Stem Cells Exhibit Similar Yield but Cell-Intrinsic Dysfunction in Glutamate Uptake and Metabolism, ATP Generation, and Nrf2-Mediated Glutathione Machinery

Owing to the presence of multiple enzymatic domains, LRRK2 has been associated with a diverse set of cellular functions and signaling pathways. It also has several pathological mutant-variants, and their incidences show ethnicity biases and drug-response differences with expression in dopaminergic-neurons and astrocytes. Here, we aimed to assess the cell-intrinsic effect of the LRRK2-I1371V mutant variant, prevalent in East Asian populations, on astrocyte yield and biology, involving Nrf2-mediated glutathione machinery, glutamate uptake and metabolism, and ATP generation in astrocytes derived from LRRK2-I1371V PD patient iPSCs and independently confirmed in LRRK2-I1371V-overexpressed U87 cells. Astrocyte yield (GFAP-immunopositive) was comparable between LRRK2-I1371V and healthy control (HC) populations; however, the astrocytic capability to mitigate oxidative stress in terms of glutathione content was significantly reduced in the mutant astrocytes, along with a reduction in the gene expression of the enzymes involved in glutathione machinery and nuclear factor erythroid 2-related factor 2 (Nrf2) expression. Simultaneously, a significant decrease in glutamate uptake was observed in LRRK2-I1371V astrocytes, with lower gene expression of glutamate transporters SLC1A2 and SLC1A3. The reduction in the protein expression of SLC1A2 was also directly confirmed. Enzymes catalyzing the generation of γ glutamyl cysteine (precursor of glutathione) from glutamate and the metabolism of glutamate to enter the Krebs cycle (α-ketoglutaric acid) were impaired, with significantly lower ATP generation in LRRK2-I1371V astrocytes. De novo glutamine synthesis via the conversion of glutamate to glutamine was also affected, indicating glutamate metabolism disorder. Our data demonstrate for the first time that the mutation in the LRRK2-I1371V allele causes significant astrocytic dysfunction with respect to Nrf2-mediated antioxidant machinery, AT -generation, and glutamate metabolism, even with comparable astrocyte yields.

LRRK2 and Parkinson's disease: from genetics to targeted therapy

LRRK2 variants are implicated in both familial and sporadic PD. LRRK2-PD has a generally benign clinical presentation and variable pathology, with inconsistent presence of Lewy bodies and marked Alzheimer's disease pathology. The mechanisms underlying LRRK2-PD are still unclear, but inflammation, vesicle trafficking, lysosomal homeostasis, and ciliogenesis have been suggested, among others. As novel therapies targeting LRRK2 are under development, understanding the role and function of LRRK2 in PD is becoming increasingly important. Here, we outline the epidemiological, pathophysiological, and clinical features of LRRK2-PD, and discuss the arising therapeutic approaches targeting LRRK2 and possible future directions for research.

Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson's Disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success.

Short Chain Fatty Acid Metabolism in Relation to Gut Microbiota and Genetic Variability

It is widely accepted that the gut microbiota plays a significant role in modulating inflammatory and immune responses of their host. In recent years, the host-microbiota interface has gained relevance in understanding the development of many non-communicable chronic conditions, including cardiovascular disease, cancer, autoimmunity and neurodegeneration. Importantly, dietary fibre (DF) and associated compounds digested by the microbiota and their resulting metabolites, especially short-chain fatty acids (SCFA), were significantly associated with health beneficial effects, such as via proposed anti-inflammatory mechanisms. However, SCFA metabolic pathways are not fully understood. Major steps include production of SCFA by microbiota, uptake in the colonic epithelium, first-pass effects at the liver, followed by biodistribution and metabolism at the host's cellular level. As dietary patterns do not affect all individuals equally, the host genetic makeup may play a role in the metabolic fate of these metabolites, in addition to other factors that might influence the microbiota, such as age, birth through caesarean, medication intake, alcohol and tobacco consumption, pathogen exposure and physical activity. In this article, we review the metabolic pathways of DF, from intake to the intracellular metabolism of fibre-derived products, and identify possible sources of inter-individual variability related to genetic variation. Such variability may be indicative of the phenotypic flexibility in response to diet, and may be predictive of long-term adaptations to dietary factors, including maladaptation and tissue damage, which may develop into disease in individuals with specific predispositions, thus allowing for a better prediction of potential health effects following personalized intervention with DF.

Precision medicine for Parkinson's disease: The subtyping challenge

Despite many pharmacological and surgical treatments addressing the symptoms of Parkinson's disease, there are no approved treatments that slow disease progression. Genetic discoveries in the last 20 years have increased our understanding of the molecular contributors to Parkinson's pathophysiology, uncovered many druggable targets and pathways, and increased investment in treatments that might slow or stop the disease process. Longitudinal, observational studies are dissecting Parkinson's disease heterogeneity and illuminating the importance of molecularly defined subtypes more likely to respond to targeted interventions. Indeed, clinical and pathological differences seen within and across carriers of PD-associated gene mutations suggest the existence of greater biological complexity than previously appreciated and increase the likelihood that targeted interventions based on molecular characteristics will be beneficial. This article offers our current perspective on the promise and current challenges in subtype identification and precision medicine approaches in Parkinson's disease.

Dopaminergic Neurons Differentiated from LRRK2 I1371V-Induced Pluripotent Stem Cells Display a Lower Yield, α-Synuclein Pathology, and Functional Impairment

Being a large multidomain protein, LRRK2 has several confirmed pathological mutant variants for PD, and the incidence of these variants shows ethnicity biases. I1371V, a mutation in the GTPase domain, has been reported in East-Asian populations, but there are no studies reported on dopaminergic (DA) neurons differentiated from this variant. The aim here was to assess the yield, function, and α-synuclein pathology of DA neurons differentiated from LRRK2 I1371V iPSCs. FACS analysis of neural progenitors (NPs) showed a comparable immunopositive population of cells for neural and glial progenitor markers nestin and S100β; however, NPs from I1371V iPSCs showed lower clonogenic and proliferative capacities than healthy control NPs as determined by the neurosphere assay and Ki67 expression. Floor plate cells obtained from I1371V NPs primed with FGF8 showed distinctly lower immunopositivity for FOXA2 and CLIC5 than healthy control FPCs and similar DOC2B expression. On SHH addition, a similar mature neuronal population was obtained from both groups; however, the yield of TH-immunopositive cells was significantly lower in I1371V, with lower expression of mature DA neuronal markers En1, Nurr1, and DAT. Vesicular dopamine release and intracellular Ca²⁺ response with KCl stimulation were lower in I1371V DA neurons, along with a significantly reduced expression of resting vesicle marker VMAT2. A concurrently lower expression of PSD95/Syn-I immunopositive puncta was observed in I1371V differentiated cells. Further, higher phosphorylation of α-synuclein and aggregation of oligomeric α-synuclein in I1371V DA neurons were observed. Our data demonstrated conclusively for the first time that mutations in the I1371V allele of LRRK2 showed developmental deficit from the FPC stage and generated a lower yield/number of TH-immunopositive neurons with impairment in their function and synapse density along with increased α-synuclein pathology.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Who to Enroll in Parkinson Disease Prevention Trials? The Case for Genetically At-Risk Cohorts

Therapies that prevent the occurrence of Parkinson disease (PD) (primary prevention) or mitigate the progression of symptoms in those with early disease (secondary prevention) are a critical unmet need in disease management. Despite great promise, PD prevention trials have not yet demonstrated success. Initiation of treatment too late in the disease course and the heterogeneity of disease are obstacles that may have contributed to the failure. Genetically stratified groups offer many advantages to primary and secondary prevention trials. In addition to their ease of identification, they decrease disease heterogeneity on several levels. Particularly, they comprise a phenotypically and pathologically enriched group with defined clinical features, pathogenic mechanisms and associated proteins that may serve as specific trial endpoints, therapeutic targets and biomarkers for disease state, and pharmacodynamic and pharmacokinetic status. However, challenges arise from genetic variant heterogeneity, from reduced penetrance whereby many carriers will not develop PD, and in recruiting a population that will meet the desired outcome in the proposed study duration. In this review, we discussed the opportunities afforded by the enrollment of genetically stratified cohorts (i.e., leucine-rich repeat kinase 2 and glucocerebrosidase 1) into prevention trials with a primary focus on primary prevention trials. We also outlined challenges surrounding the enrollment of these cohorts and offered suggestions to leverage their many advantages.

Targeting Macroautophagy as a Therapeutic Opportunity to Treat Parkinson's Disease

Macroautophagy, an evolutionary conserved catabolic process in the eukaryotic cell, regulates cellular homeostasis and plays a decisive role in self-engulfing proteins, protein aggregates, dysfunctional or damaged organelles, and invading pathogens. Growing evidence from in vivo and in vitro models shows that autophagy dysfunction plays decisive role in the pathogenesis of various neurodegenerative diseases, including Parkinson's disease (PD). PD is an incurable and second most common neurodegenerative disease characterised by neurological and motor dysfunction accompanied of non-motor symptoms that can also reduce the life quality of patients. Despite the investment in research, the aetiology of the disease is still unknown and the therapies available are aimed mostly at ameliorating motor symptoms. Hence, therapeutics regulating the autophagy pathway might play an important role controlling the disease progression, reducing neuronal loss and even ameliorating non-motor symptoms. In this review, we highlight potential therapeutic opportunities involved in different targeting options like an initiation of autophagy, Leucine-rich repeat kinase 2 (LRRK2) inhibition, mitophagy, lysosomes, lipid metabolism, immune system, gene expression, biomarkers, and also non-pharmacological interventions. Thus, strategies to identify therapeutics targeting the pathways modulating autophagy might hold a future for therapy development against PD.

COVID-19 and Parkinsonism: A Critical Appraisal

A few cases of parkinsonism linked to COVID-19 infection have been reported so far, raising the possibility of a post-viral parkinsonian syndrome. The objective of this review is to summarize the clinical, biological, and neuroimaging features of published cases describing COVID-19-related parkinsonism and to discuss the possible pathophysiological mechanisms. A comprehensive literature search was performed using NCBI’s PubMed database and standardized search terms. Thirteen cases of COVID-19-related parkinsonism were included (7 males; mean age: 51 years ± 14.51, range 31–73). Patients were classified based on the possible mechanisms of post-COVID-19 parkinsonism: extensive inflammation or hypoxic brain injury within the context of encephalopathy (n = 5); unmasking of underlying still non-symptomatic Parkinson’s Disease (PD) (n = 5), and structural and functional basal ganglia damage (n = 3). The various clinical scenarios show different outcomes and responses to dopaminergic treatment. Different mechanisms may play a role, including vascular damage, neuroinflammation, SARS-CoV-2 neuroinvasive potential, and the impact of SARS-CoV-2 on α-synuclein. Our results confirm that the appearance of parkinsonism during or immediately after COVID-19 infection represents a very rare event. Future long-term observational studies are needed to evaluate the possible role of SARS-CoV-2 infection as a trigger for the development of PD in the long term.

A 10-Year Community-Based Study of Leucine-Rich Repeat Kinase 2 G2385R Carriers' Conversion to Parkinson's Disease

BACKGROUND

The G2385R variant of leucine-rich repeat kinase 2 (LRRK2) is mainly associated with Parkinson's disease(PD) in Asian populations.

OBJECTIVE

The aim of this study was to investigate the PD conversion rate and clinical characteristics of LRRK2 G2385R nonmanifesting carriers.

METHODS

All participants were from the community-based longitudinal cohort of Shanghai Ruijin Hospital. The G2385R carriers and noncarriers were screened by Sanger sequencing and received face-to-face interviews at baseline and follow-up assessments. The Kaplan-Meier method was used to compare the conversion rate of PD. Cox regression models were used to estimate the risk of G2385R variant for PD.

RESULTS

In the combined cohort, 26 (7.9%) people developed PD in 329 carriers versus 9 (2.6%) in 345 noncarriers (P = 0.0016). Cox regression model confirmed that the G2385R variant was a strong risk factor for PD in a Chinese population older than 50 years (hazard ratio, 3.314; 95% confidence interval, 1.551-7.078; P = 0.002). No difference was found in clinical symptoms between carriers and noncarriers.

CONCLUSIONS

We confirmed an increased conversion of PD in leucine-rich repeat kinase 2 G2385R carriers during a 10-year follow-up. © 2022 International Parkinson and Movement Disorder Society.

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

Prodromal Parkinson's disease: hype or hope for disease-modification trials?

The ultimate goal in Parkinson's disease (PD) research remains the identification of treatments that are capable of slowing or even halting the progression of the disease. The failure of numerous past disease-modification trials in PD has been attributed to a variety of factors related not only to choosing wrong interventions, but also to using inadequate trial designs and target populations. In patients with clinically established PD, neuronal pathology may already have advanced too far to be modified by any intervention. Based on such reasoning, individuals in yet prediagnostic or prodromal disease stages, may provide a window of opportunity to test disease-modifying strategies. There is now sufficient evidence from prospective studies to define diagnostic criteria for prodromal PD and several approaches have been studied in observational cohorts. These include the use of PD-risk algorithms derived from multiple established risk factors for disease as well as follow-up of cohorts with single defined prodromal markers like hyposmia, rapid eye movement sleep behavior disorders, or PD gene carriers. In this review, we discuss recruitment strategies for disease-modification trials in various prodromal PD cohorts, as well as potential trial designs, required trial durations, and estimated sample sizes. We offer a concluding outlook on how the goal of implementing disease-modification trials in prodromal cohorts might be achieved in the future.

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

Polygenic Risk Scores Contribute to Personalized Medicine of Parkinson's Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder characterized by the loss of dopaminergic neurons. The vast majority of PD patients develop the disease sporadically and it is assumed that the cause lies in polygenic and environmental components. The overall polygenic risk is the result of a large number of common low-risk variants discovered by large genome-wide association studies (GWAS). Polygenic risk scores (PRS), generated by compiling genome-wide significant variants, are a useful prognostic tool that quantifies the cumulative effect of genetic risk in a patient and in this way helps to identify high-risk patients. Although there are limitations to the construction and application of PRS, such as considerations of limited genetic underpinning of diseases explained by SNPs and generalizability of PRS to other populations, this personalized risk prediction could make a promising contribution to stratified medicine and tailored therapeutic interventions in the future.

New therapeutic approaches to Parkinson's disease targeting GBA, LRRK2 and Parkin

Parkinson's disease (PD) is defined as a complex disorder with multifactorial pathogenesis, yet a more accurate definition could be that PD is not a single entity, but rather a mixture of different diseases with similar phenotypes. Attempts to classify subtypes of PD have been made based on clinical phenotypes or biomarkers. However, the most practical approach, at least for a portion of the patients, could be to classify patients based on genes involved in PD. GBA and LRRK2 mutations are the most common genetic causes or risk factors of PD, and PRKN is the most common cause of autosomal recessive form of PD. Patients carrying variants in GBA, LRRK2 or PRKN differ in some of their clinical characteristics, pathology and biochemical parameters. Thus, these three PD-associated genes are of special interest for drug development. Existing therapeutic approaches in PD are strictly symptomatic, as numerous clinical trials aimed at modifying PD progression or providing neuroprotection have failed over the last few decades. The lack of precision medicine approach in most of these trials could be one of the reasons why they were not successful. In the current review we discuss novel therapeutic approaches targeting GBA, LRRK2 and PRKN and discuss different aspects related to these genes and clinical trials.

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.