Genetic Stratification of Age-Dependent Parkinson's Disease Risk by Polygenic Hazard Score

Immunotherapy: An emerging treatment option for neurodegenerative diseases

Accumulation of misfolded proteins and protein aggregates leading to degeneration of neurons is a hallmark of several neurodegenerative diseases. Therapy mostly relies on symptomatic relief. Immunotherapy offers a promising approach for the development of disease-modifying routes. Such strategies have shown remarkable results in oncology, and this promise is increasingly being realized for neurodegenerative diseases in advanced preclinical and clinical studies. This review highlights cases of passive and active immunotherapies in Parkinson's and Alzheimer's diseases. The reasons for success and failure, wherever available, and strategies to cross the blood-brain barrier, are discussed. The need for conditional modulation of the immune response is also reflected on.

Common genetic risk for Parkinson's disease and dysfunction of the endo-lysosomal system

Parkinson's disease is a progressive neurological disorder, characterized by prominent movement dysfunction. The past two decades have seen a rapid expansion of our understanding of the genetic basis of Parkinson's, initially through the identification of monogenic forms and, more recently, through genome-wide association studies identifying common risk variants. Intriguingly, a number of cellular pathways have emerged from these analysis as playing central roles in the aetiopathogenesis of Parkinson's. In this review, the impact of data deriving from genome-wide analyses for Parkinson's upon our functional understanding of the disease will be examined, with a particular focus on examples of endo-lysosomal and mitochondrial dysfunction. The challenges of moving from a genetic to a functional understanding of common risk variants for Parkinson's will be discussed, with a final consideration of the current state of the genetic architecture of the disorder. This article is part of a discussion meeting issue 'Understanding the endo-lysosomal network in neurodegeneration'.

Computer-aided optimization of carbidopa/levodopa orally disintegrating tablets

OBJECTIVE

This study aimed to optimize the formulation of carbidopa/levodopa orally disintegrating tablets (ODTs) in order to improve their disintegration performance, and facilitate easier medication intake for Parkinson's patients.

METHOD

The response surface methodology (RSM) was used to optimize the formulation, with the content of cross-linked polyvinylpyrrolidone (PVPP), microcrystalline cellulose (MCC), and mannitol (MNT) as independent variables, and disintegration time as the response parameter. Python was utilized to model Carr Indices and mixing time to determine the suitable mixing time. Direct compression (DC) was used for the preparation of ODTs.

RESULT

The optimization process resulted in the following values for the independent variables: 7.04% PVPP, 22.02% MCC, and 16.21% MNT. By optimizing the mixing time using Python, it was reduced to 14.19 min. The ODTs prepared using the optimized formulation and a mixing time of 14.19 min exhibited disintegration times of 16.74 s in vitro and 17.63 s in vivo. The content uniformity of levodopa and carbidopa was found to be 100.83% and 99.48%, respectively.

CONCLUSION

The ODTs optimized using RSM and Python demonstrated excellent disintegration performance, leading to a decrease in the time the drug exists in solid form in the oral cavity. This improvement in disintegration time reduced the difficulty of swallowing for patients and enhanced medication compliance, while still ensuring that ODTs prepared by DC had sufficient mechanical strength to meet storage and transportation requirements.

Association of Family History and Polygenic Risk Score With Longitudinal Prognosis in Parkinson Disease

Background and Objectives

Evidence suggests that either family history or polygenic risk score (PRS) is associated with developing Parkinson disease (PD). However, little is known about the longitudinal prognosis of PD according to family history and higher PRS.

Methods

From the Parkinson's Progression Markers Initiative database, 395 patients with PD who followed up for more than 2 years were grouped into those with family history within first-degree, second-degree, and third-degree relatives (N = 127 [32.2%]) vs those without (N = 268 [67.8%]). The PRS of 386 patients was computed using whole-genome sequencing data. Longitudinal assessment of motor, cognition, and imaging based on dopaminergic degeneration was conducted during the regular follow-up period. Effects of family history, PRS, or both on longitudinal changes of cognition, motor severity, and nigrostriatal degeneration were tested using a linear mixed model. The risk of freezing of gait (FOG) according to family history was assessed using the Kaplan-Meier analysis and Cox regression models.

Results

During a median follow-up of 9.1 years, PD with positive family history showed a slower decline of caudate dopamine transporter uptake (β estimate of family history × time = 0.02, 95% CI = 0.002-0.036, p = 0.027). Family history of PD and higher PRS were independently associated with a slower decline of Montreal Cognitive Assessment (β estimate of family history × time = 0.12, 95% CI = 0.02-0.22, p = 0.017; β estimate of PRS × time = 0.09, 95% CI = 0.03-0.16, p = 0.006). In those 364 patients without FOG at baseline, PD with positive family history had a lower risk of FOG (hazard ratio of family history = 0.57, 95% CI = 0.38-0.84, p = 0.005).

Discussion

Having a family history of PD predicts slower progression of cognitive decline and caudate dopaminergic degeneration, and less FOG compared with those without a family history independent of PRS. Taken together, information on family history could be used as a proxy for the clinical heterogeneity of PD.

Trial Registration Information

The study was registered at clinicaltrials.gov (NCT01141023), and the enrollment began June 1, 2010.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

A Breast Cancer Polygenic Risk Score Is Feasible for Risk Stratification in the Norwegian Population

BACKGROUND

Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population.

AIM

To evaluate the performance of PRS models for BC in a Norwegian dataset.

METHODS

We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool.

RESULTS

The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406-1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples.

CONCLUSION

The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

Parkinson's disease therapy: what lies ahead?

The worldwide prevalence of Parkinson's disease (PD) has been constantly increasing in the last decades. With rising life expectancy, a longer disease duration in PD patients is observed, further increasing the need and socioeconomic importance of adequate PD treatment. Today, PD is exclusively treated symptomatically, mainly by dopaminergic stimulation, while efforts to modify disease progression could not yet be translated to the clinics. New formulations of approved drugs and treatment options of motor fluctuations in advanced stages accompanied by telehealth monitoring have improved PD patients care. In addition, continuous improvement in the understanding of PD disease mechanisms resulted in the identification of new pharmacological targets. Applying novel trial designs, targeting of pre-symptomatic disease stages, and the acknowledgment of PD heterogeneity raise hopes to overcome past failures in the development of drugs for disease modification. In this review, we address these recent developments and venture a glimpse into the future of PD therapy in the years to come.

Cerebrospinal Fluid-Basic Concepts Review

Cerebrospinal fluid plays a crucial role in protecting the central nervous system (CNS) by providing mechanical support, acting as a shock absorber, and transporting nutrients and waste products. It is produced in the ventricles of the brain and circulates through the brain and spinal cord in a continuous flow. In the current review, we presented basic concepts related to cerebrospinal fluid history, cerebrospinal fluid production, circulation, and its main components, the role of the blood-brain barrier and the blood-cerebrospinal fluid barrier in the maintenance of cerebrospinal fluid homeostasis, and the utility of Albumin Quotient (Q_Alb) evaluation in the diagnosis of CNS diseases. We also discussed the collection of cerebrospinal fluid (type, number of tubes, and volume), time of transport to the laboratory, and storage conditions. Finally, we briefly presented the role of cerebrospinal fluid examination in CNS disease diagnosis of various etiologies and highlighted that research on identifying cerebrospinal fluid biomarkers indicating disease presence or severity, evaluating treatment effectiveness, and enabling understanding of pathogenesis and disease mechanisms is of great importance. Thus, in our opinion, research on cerebrospinal fluid is still necessary for both the improvement of CNS disease management and the discovery of new treatment options.

Disease mechanisms as subtypes: Microbiome

Abnormalities in gut microbiota have been suggested to be involved in the pathophysiology and progression of Parkinson's disease (PD). Gastrointestinal nonmotor symptoms often precede the onset of motor features in PD, suggesting a role for gut dysbiosis in neuroinflammation and α-synuclein (α-syn) aggregation. In the first part of this chapter, we analyze critical features of healthy gut microbiota and factors (environmental and genetic) that modify its composition. In the second part, we focus on the mechanisms underlying the gut dysbiosis and how it alters anatomically and functionally the mucosal barrier, triggering neuroinflammation and subsequently α-syn aggregation. In the third part, we describe the most common alterations in the gut microbiota of PD patients, dividing the gastrointestinal system in higher and lower tract to examine the association between microbiota abnormalities and clinical features. In the final section, we report on current and future therapeutic approaches to gut dysbiosis aiming to either reduce the risk for PD, modify the disease course, or improve the pharmacokinetic profile of dopaminergic therapies. We also suggest that further studies will be needed to clarify the role of the microbiome in PD subtyping and of pharmacological and nonpharmacological interventions in modifying specific microbiota profiles in individualizing disease-modifying treatments in PD.

Bioinformatic Analysis of Genetic Factors from Human Blood Samples and Postmortem Brains in Parkinson's Disease

Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders characterized by motor and nonmotor symptoms due to the selective loss of midbrain dopaminergic neurons. Pharmacological and surgical interventions have not been possible to cure PD; however, the cause of neurodegeneration remains unclear. Here, we performed and tested a multitiered bioinformatic analysis using the GEO and Proteinexchange database to investigate the gene expression involved in the pathogenesis of PD. Then we further validated individual differences in gene expression in whole blood samples that we collected in the clinic. We also made an interaction analysis and prediction for these genetic factors. There were in all 1045 genes expressing differently in PD compared with the healthy control group. Protein-protein interaction (PPI) networks showed 10 top hub genes: ACO2, MDH2, SDHA, ATP5A1, UQCRC2, PDHB, SUCLG1, NDUFS3, UQCRC1, and ATP5C1. We validated the ten hub gene expression in clinical PD patients and showed the expression of MDH2 was significantly different compared with healthy control. Besides, we also identified the expression of G6PD, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were all significantly increased, and levels of MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were all significantly decreased in PD. Among them, to our knowledge, we presently have the most recent and conclusive evidence that GRID2, RIPK2, CUL4B, E2F2, and ABCA7 are possible PD indicators. We confirmed several genetic factors which may be involved in the pathogenesis of PD. They could be promising markers for discriminating the PD and potential factors that may affect PD development.

Genome Sequencing in the Parkinson Disease Clinic

Background and Objectives

Genetic variants affect both Parkinson disease (PD) risk and manifestations. Although genetic information is of potential interest to patients and clinicians, genetic testing is rarely performed during routine PD clinical care. The goal of this study was to examine interest in comprehensive genetic testing among patients with PD and document reactions to possible findings from genome sequencing in 2 academic movement disorder clinics.

Methods

In 203 subjects with PD (age = 63 years, 67% male), genome sequencing was performed and filtered using a custom panel, including 49 genes associated with PD, parkinsonism, or related disorders, as well as a 90-variant PD genetic risk score. Based on the results, 231 patients (age = 67 years, 63% male) were surveyed on interest in genetic testing and responses to vignettes covering (1) familial risk of PD (LRRK2); (2) risk of PD dementia (GBA); (3) PD genetic risk score; and (4) secondary, medically actionable variants (BRCA1).

Results

Genome sequencing revealed a LRRK2 variant in 3% and a GBA risk variant in 10% of our clinical sample. The genetic risk score was normally distributed, identifying 41 subjects with a high risk of PD. Medically actionable findings were discovered in 2 subjects (1%). In our survey, the majority (82%) responded that they would share a LRRK2 variant with relatives. Most registered unchanged or increased interest in testing when confronted with a potential risk for dementia or medically actionable findings, and most (75%) expressed interest in learning their PD genetic risk score.

Discussion

Our results highlight broad interest in comprehensive genetic testing among patients with PD and may facilitate integration of genome sequencing in clinical practice.

Association Between Dystonia-Related Genetic Loci and Parkinson's Disease in Eastern China

Background

Parkinson's disease (PD) and dystonia are closely related in terms of pathophysiology and clinical manifestations, but their common genetic characteristics remain unclear. Some genome-wide association studies (GWASs) and replication studies have revealed correlations between single nucleotide polymorphisms (SNPs) of the ARSG, BDNF, NALCN, OR4X2, KIAA1715, and OR4B1 genes and dystonia. This study was conducted to assess the association between these genetic loci and PD in a population from Eastern China.

Methods

We genotyped the SNPs (rs11655081 of ARSG; rs6265 of BDNF; rs61973742, rs1338051, rs9518384, and rs9518385 of NALCN; rs67863238 of OR4X2; rs10930717 of KIAA1715; and rs35875350 of OR4B1) in a cohort of 474 patients with PD and 439 healthy controls from East China. To determine the genotypes of these SNPs, we used an Agena MassARRAY Typer 4.0. Odds ratios (ORs) and 95% CIs were computed to evaluate the correlations between these SNPs and the risk of PD.

Results

There were significant differences in the genotype distribution (OR = 0.649, 95% CI = 0.478–0.880) and minor allele frequency (MAF) (OR = 0.703, 95% CI = 0.533–0.929) of SNP rs61973742 (NALCN) between patients with PD and healthy controls. A significant difference was detected in the genotype distribution of rs11655081 (ARSG) (OR = 1.486, 95% CI = 1.080–2.045).

Conclusion

Single nucleotide polymorphisms rs11655081 (ARSG) and rs61973742 (NALCN) may be associated with PD. The C allele of rs11655081 may increase the risk of PD, whereas the G allele of rs61973742 may be a protective factor.