Association of Polygenic Risk Score With Cognitive Decline and Motor Progression in Parkinson Disease

Genetic analysis and natural history of Parkinson's disease due to the LRRK2 G2019S variant

The LRRK2 G2019S variant is the most common cause of monogenic Parkinson's disease (PD); however, questions remain regarding the penetrance, clinical phenotype and natural history of carriers. We performed a 3.5-year prospective longitudinal online study in a large number of 1286 genotyped LRRK2 G2019S carriers and 109 154 controls, with and without PD, recruited from the 23andMe Research Cohort. We collected self-reported motor and non-motor symptoms every 6 months, as well as demographics, family histories and environmental risk factors. Incident cases of PD (phenoconverters) were identified at follow-up. We determined lifetime risk of PD using accelerated failure time modelling and explored the impact of polygenic risk on penetrance. We also computed the genetic ancestry of all LRRK2 G2019S carriers in the 23andMe database and identified regions of the world where carrier frequencies are highest. We observed that despite a 1 year longer disease duration (P = 0.016), LRRK2 G2019S carriers with PD had similar burden of motor symptoms, yet significantly fewer non-motor symptoms including cognitive difficulties, REM sleep behaviour disorder (RBD) and hyposmia (all P-values ≤ 0.0002). The cumulative incidence of PD in G2019S carriers by age 80 was 49%. G2019S carriers had a 10-fold risk of developing PD versus non-carriers. This rose to a 27-fold risk in G2019S carriers with a PD polygenic risk score in the top 25% versus non-carriers in the bottom 25%. In addition to identifying ancient founding events in people of North African and Ashkenazi descent, our genetic ancestry analyses infer that the G2019S variant was later introduced to Spanish colonial territories in the Americas. Our results suggest LRRK2 G2019S PD appears to be a slowly progressive predominantly motor subtype of PD with a lower prevalence of hyposmia, RBD and cognitive impairment. This suggests that the current prodromal criteria, which are based on idiopathic PD, may lack sensitivity to detect the early phases of LRRK2 PD in G2019S carriers. We show that polygenic burden may contribute to the development of PD in the LRRK2 G2019S carrier population. Collectively, the results should help support screening programmes and candidate enrichment strategies for upcoming trials of LRRK2 inhibitors in early-stage disease.

The causal effects of intelligence and fluid intelligence on Parkinson's disease: a Mendelian randomization study

Background

Parkinson's disease (PD) is a chronic neurodegenerative disease that affects the central nervous system, primarily the motor nervous system, and occurs most often in older adults. A large number of studies have shown that high intelligence leads to an increased risk of PD. However, whether there is a causal relationship between intelligence on PD has not yet been reported.

Methods

In this study, Mendelian randomization (MR) analysis was performed with intelligence (ebi-a-GCST006250) and fluid intelligence score (ukb-b-5238) as exposure factors and PD (ieu-b-7) as an outcome, which the datasets were mined from the IEU OpenGWAS database. MR analysis was performed through 3 methods [MR Egger, weighted median, inverse variance weighted (IVW)], of which IVW was the primary method. In addition, the reliability of the results of the MR analysis was assessed via the heterogeneity test, the horizontal polytropy test, and Leave-One-Out (LOO). Finally, based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, the genes corresponding to intelligence and fluid intelligence score related to SNPs were enriched for functional features and pathways.

Results

The results of MR analysis suggested that elevated intelligence indicators can increase the risk of PD [p = 0.015, Odd Ratio (OR) = 1.316]. Meanwhile, fluid intelligence score was causally associated with the PD (p = 0.035), which was a risk factor (OR = 1.142). The reliability of the results of MR analysis was demonstrated by sensitivity analysis. Finally, the results of GO enrichment analysis for 87 genes corresponding to intelligence related SNPs mainly included regulation of synapse organization, developmental cell growth, etc. These genes were enriched in the synaptic vessel cycle, polycomb expressive complex in KEGG. Similarly, 44 genes corresponding to SNPs associated with fluid intelligence score were used for enrichment analysis. Based on the GO database, these genes were mainly enriched in regulation of developmental growth, negative regulation of neuron projection development, etc. In KEGG, 44 genes corresponding to SNPs associated with fluid intelligence score were enriched in signaling pathways including Alzheimer's disease, the cellular senescence, etc.

Conclusion

The causal relationships between intelligence and fluid intelligence scores, and PD were demonstrated through MR analysis, providing an important reference and evidence for the study of PD.

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

Refining the clinical diagnosis of Parkinson's disease

Our ability to define, understand, and classify Parkinson's disease (PD) has undergone significant changes since the disorder was first described in 1817. Clinical features and neuropathologic signatures can now be supplemented by in-vivo interrogation of genetic and biological substrates of disease, offering great opportunity for further refining the diagnosis of PD. In this mini-review, we discuss the historical perspectives which shaped our thinking surrounding the definition and diagnosis of PD. We highlight the clinical, genetic, pathologic and biologic diversity which underpins the condition, and proceed to discuss how recent developments in our ability to define biologic and pathologic substrates of disease might impact PD definition, diagnosis, individualised prognostication, and personalised clinical care. We argue that Parkinson's 'disease', as currently diagnosed in the clinic, is actually a syndrome. It is the outward manifestation of any array of potential dysfunctional biologic processes, neuropathological changes, and disease aetiologies, which culminate in common outward clinical features which we term PD; each person has their own unique disease, which we can now define with increasing precision. This is an exciting time in PD research and clinical care. Our ability to refine the clinical diagnosis of PD, incorporating in-vivo assessments of disease biology, neuropathology, and neurogenetics may well herald the era of biologically-based, precision medicine approaches PD management. With this however comes a number of challenges, including how to integrate these technologies into clinical practice in a way which is acceptable to patients, promotes meaningful changes to care, and minimises health economic impact.

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'.

Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline

PURPOSE

To evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment.

METHODS

Using OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted.

RESULTS

Higher Alzheimer's disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson's disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements.

CONCLUSIONS AND RELEVANCE

Retinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.

Genetics in Parkinson's disease, state-of-the-art and future perspectives

BACKGROUND

Parkinson's disease (PD) is the second most common neurodegenerative disorder and is clinically characterized by the presence of motor (bradykinesia, rigidity, rest tremor and postural instability) and non-motor symptoms (cognitive impairment, autonomic dysfunction, sleep disorders, depression and hyposmia). The aetiology of PD is unknown except for a small but significant contribution of monogenic forms.

SOURCES OF DATA

No new data were generated or analyzed in support of this review.

AREAS OF AGREEMENT

Up to 15% of PD patients carry pathogenic variants in PD-associated genes. Some of these genes are associated with mendelian inheritance, while others act as risk factors. Genetic background influences age of onset, disease course, prognosis and therapeutic response.

AREAS OF CONTROVERSY

Genetic testing is not routinely offered in the clinical setting, but it may have relevant implications, especially in terms of prognosis, response to therapies and inclusion in clinical trials. Widely adopted clinical guidelines on genetic testing are still lacking and open to debate. Some new genetic associations are still awaiting confirmation, and selecting the appropriate genes to be included in diagnostic panels represents a difficult task. Finally, it is still under study whether (and to which degree) specific genetic forms may influence the outcome of PD therapies.

GROWING POINTS

Polygenic Risk Scores (PRS) may represent a useful tool to genetically stratify the population in terms of disease risk, prognosis and therapeutic outcomes.

AREAS TIMELY FOR DEVELOPING RESEARCH

The application of PRS and integrated multi-omics in PD promises to improve the personalized care of patients.

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.

Genetic Testing of Movements Disorders: A Review of Clinical Utility

Currently, pathogenic variants in more than 500 different genes are known to cause various movement disorders. The increasing accessibility and reducing cost of genetic testing has resulted in increasing clinical use of genetic testing for the diagnosis of movement disorders. However, the optimal use case(s) for genetic testing at a patient level remain ill-defined. Here, we review the utility of genetic testing in patients with movement disorders and also highlight current challenges and limitations that need to be considered when making decisions about genetic testing in clinical practice.

Highlights

The utility of genetic testing extends across multiple clinical and non-clinical domains. Here we review different aspects of the utility of genetic testing for movement disorders and the numerous associated challenges and limitations. These factors should be weighed on a case-by-case basis when requesting genetic tests in clinical practice.

Parkinson's Disease is Predominantly a Genetic Disease

The discovery of a pathogenic variant in the alpha-synuclein (SNCA) gene in the Contursi kindred in 1997 indisputably confirmed a genetic cause in a subset of Parkinson's disease (PD) patients. Currently, pathogenic variants in one of the seven established PD genes or the strongest known risk factor gene, GBA1, are identified in ∼15% of PD patients unselected for age at onset and family history. In this Debate article, we highlight multiple avenues of research that suggest an important - and in some cases even predominant - role for genetics in PD aetiology, including familial clustering, high rates of monogenic PD in selected populations, and complete penetrance with certain forms. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development.

Elucidating distinct molecular signatures of Lewy body dementias

Dementia with Lewy bodies and Parkinson's disease dementia are common neurodegenerative diseases that share similar neuropathological profiles and spectra of clinical symptoms but are primarily differentiated by the order in which symptoms manifest. The question of whether a distinct molecular pathological profile could distinguish these disorders is yet to be answered. However, in recent years, studies have begun to investigate genomic, epigenomic, transcriptomic and proteomic differences that may differentiate these disorders, providing novel insights in to disease etiology. In this review, we present an overview of the clinical and pathological hallmarks of Lewy body dementias before summarizing relevant research into genetic, epigenetic, transcriptional and protein signatures in these diseases, with a particular interest in those resolving "omic" level changes. We conclude by suggesting future research directions to address current gaps and questions present within the field.

Lysosomal polygenic risk is associated with the severity of neuropathology in Lewy body disease

Intraneuronal accumulation of misfolded α-synuclein is the pathological hallmark of Parkinson's disease and dementia with Lewy bodies, often co-occurring with variable degrees of Alzheimer's disease related neuropathology. Genetic association studies have successfully identified common variants associated with disease risk and phenotypic traits in Lewy body disease, yet little is known about the genetic contribution to neuropathological heterogeneity. Using summary statistics from Parkinson's disease and Alzheimer's disease genome-wide association studies, we calculated polygenic risk scores and investigated the relationship with Lewy, amyloid-β and tau pathology. Associations were nominated in neuropathologically defined samples with Lewy body disease from the Netherlands Brain Bank (n = 217) and followed up in an independent sample series from the Mayo Clinic Brain Bank (n = 394). We also generated stratified polygenic risk scores based on single-nucleotide polymorphisms annotated to eight functional pathways or cell types previously implicated in Parkinson's disease and assessed for association with Lewy pathology in subgroups with and without significant Alzheimer's disease co-pathology. In an ordinal logistic regression model, the Alzheimer's disease polygenic risk score was associated with concomitant amyloid-β and tau pathology in both cohorts. Moreover, both cohorts showed a significant association between lysosomal pathway polygenic risk and Lewy pathology, which was more consistent than the association with a general Parkinson's disease risk score and specific to the subset of samples without significant concomitant Alzheimer's disease related neuropathology. Our findings provide proof of principle that the specific risk alleles a patient carries for Parkinson's and Alzheimer's disease also influence key aspects of the underlying neuropathology in Lewy body disease. The interrelations between genetic architecture and neuropathology are complex, as our results implicate lysosomal risk loci specifically in the subset of samples without Alzheimer's disease co-pathology. Our findings hold promise that genetic profiling may help predict the vulnerability to specific neuropathologies in Lewy body disease, with potential relevance for the further development of precision medicine in these disorders.

Parkinson's disease genome-wide association study-linked PARK16 variant is associated with a lower risk of cognitive impairment: A 4-year observational study

BACKGROUND AND PURPOSE

A genome-wide association study-linked variant (PARK16 rs6679073) modulates the risk of Parkinson's disease (PD). We postulate that there may be differences in clinical characteristics between PARK16 rs6679073 carriers and noncarriers. In a prospective study, we investigate the clinical characteristics between PARK16 rs6679073 A allele carriers and noncarriers over 4 years.

METHODS

A total of 204 PD patients, comprising 158 PARK16 rs6679073 A allele carriers and 46 noncarriers, were recruited. All patients underwent motor and nonmotor symptom and cognitive assessments yearly over 4 years.

RESULTS

PARK16 rs6679073 carriers were less likely to have mild cognitive impairment (MCI) compared to noncarriers at both baseline (48.1% vs. 67.4%, p = 0.027) and 4-year follow-up (29.3% vs. 58.6%, p = 0.007).

CONCLUSIONS

PD PARK16 rs6679073 carriers had significantly lower frequency of MCI in a 4-year follow-up study, suggesting that the variant may have a neuroprotective effect on cognitive functions.

Modeling Parkinson's disease in LRRK2 rodents

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are associated with familial and sporadic forms of Parkinson's disease (PD). Sporadic PD and LRRK2 PD share main clinical and neuropathological features, namely hypokinesia, degeneration of nigro-striatal dopamine neurons and α-synuclein aggregates in the form of Lewy bodies. Animals harboring the most common LRRK2 mutations, i.e. p.G2019S and p.R1441C/G, have been generated to replicate the parkinsonian phenotype and investigate the underlying pathogenic mechanisms. Disappointingly, however, LRRK2 rodents did not consistently phenocopy hypokinesia and nigro-striatal degeneration, or showed Lewy body-like aggregates. Instead, LRRK2 rodents manifested non-motor signs and dysregulated transmission at dopaminergic and non-dopaminergic synapses that are reminiscent of behavioral and functional network changes observed in the prodromal phase of the disease. LRRK2 rodents also manifested greater susceptibility to different parkinsonian toxins or stressors when subjected to dual-hit or multiple-hit protocols, confirming LRRK2 mutations as genetic risk factors. In conclusion, LRRK2 rodents represent a unique tool to identify the molecular mechanisms through which LRRK2 modulates the course and clinical presentations of PD and to study the interplay between genetic, intrinsic and environmental protective/risk factors in PD pathogenesis.

The combined effect of lifestyle factors and polygenic scores on age at onset in Parkinson's disease

Objective

To investigate the association between a Parkinson's disease (PD)-specific polygenic score (PGS) and protective lifestyle factors on age at onset (AAO) in PD.

Methods

We included data from 4375 patients with idiopathic PD, 167 patients with GBA1-PD, and 3091 healthy controls of European ancestry from AMP-PD, PPMI, and Fox Insight cohorts. The PGS was calculated based on a previously proposed composition of 1805 variants. The association between PGS and lifestyle factors (i.e., coffee, tobacco, and aspirin) on AAO was assessed with linear and Cox proportional hazards models.

Results

The PGS showed a negative association with AAO (β=-1.07, p=6×10-7). The use of one, two, or three of the protective lifestyle factors showed a reduction in the hazard ratio by 21% (p=0.0001), 45% (p<2×10-16), and 55% (p<2×10-16), respectively, compared to no use. An additive effect of aspirin (β=7.61, p=8×10-7) and PGS (β=-1.63, p=0.0112) was found for AAO without an interaction (p=0.9789) in the linear regressions, and similar effects were seen for tobacco. Aspirin is shown to be a better predictor of AAO (R2=0.1740) compared to coffee and tobacco use (R2=0.0243, R2=0.0295) or the PGS (R2=0.0141). In contrast, no association between aspirin and AAO was found in GBA1-PD (p>0.05).

Interpretation

In our cohort, coffee, tobacco, aspirin, and PGS are independent predictors of PD AAO. Additionally, lifestyle factors seem to have a greater influence on AAO than common genetic risk variants with aspirin presenting the largest effect. External validation of our findings is needed.

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.

Defining the Riddle in Order to Solve It: There Is More Than One "Parkinson's Disease"

BACKGROUND

More than 200 years after James Parkinsondescribed a clinical syndrome based on his astute observations, Parkinson's disease (PD) has evolved into a complex entity, akin to the heterogeneity of other complex human syndromes of the central nervous system such as dementia, motor neuron disease, multiple sclerosis, and epilepsy. Clinicians, pathologists, and basic science researchers evolved arrange of concepts andcriteria for the clinical, genetic, mechanistic, and neuropathological characterization of what, in their best judgment, constitutes PD. However, these specialists have generated and used criteria that are not necessarily aligned between their different operational definitions, which may hinder progress in solving the riddle of the distinct forms of PD and ultimately how to treat them.

OBJECTIVE

This task force has identified current in consistencies between the definitions of PD and its diverse variants in different domains: clinical criteria, neuropathological classification, genetic subtyping, biomarker signatures, and mechanisms of disease. This initial effort for "defining the riddle" will lay the foundation for future attempts to better define the range of PD and its variants, as has been done and implemented for other heterogeneous neurological syndromes, such as stroke and peripheral neuropathy. We strongly advocate for a more systematic and evidence-based integration of our diverse disciplines by looking at well-defined variants of the syndrome of PD.

CONCLUSION

Accuracy in defining endophenotypes of "typical PD" across these different but interrelated disciplines will enable better definition of variants and their stratification in therapeutic trials, a prerequisite for breakthroughs in the era of precision medicine. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Proximity to residential and workplace pesticides application and the risk of progression of Parkinson's diseases in Central California

BACKGROUND

Pesticide exposure has consistently been associated with Parkinson's disease (PD) onset. Yet, fewer epidemiologic studies have examined whether pesticides influence PD motor and non-motor symptom progression.

OBJECTIVES

Using a geographic information system tool that integrates agricultural pesticide use reports and land use records to derive ambient exposures at residences and workplaces, we assessed associations between specific pesticides previously related to PD onset with PD symptom progression in two PD patient cohorts living in agricultural regions of California.

METHODS

We calculated the pounds of pesticide applied agriculturally near each participant's residential or occupational addresses from 1974 to the year of PD diagnosis, using a geographic information system tool that links the California Pesticide Use Reports database to land use data. We examined 53 pesticides selected a priori as they have previously been associated with PD onset. We longitudinally followed two PD patient cohorts (PEG1 N = 242, PEG2 N = 259) for an average of 5.0 years (SD ± 3.5) and 2.7 years (SD ± 1.6) respectively and assessed PD symptoms using the movement disorder specialist-administered Unified Parkinson's disease Rating Scale part III (UPDRS), Mini-Mental State Examination (MMSE), and Geriatric Depression Scale (GDS). Weighted time-to-event regression models were implemented to estimate effects.

RESULTS

Ten agricultural pesticides, including copper sulfate (pentahydrate), 2-methyl-4-chlorophenoxyacetic acid (MCPA) dimethylamine salt, tribufos, sodium cacodylate, methamidophos, ethephon, propargite, bromoxynil octanoate, monosodium methanearsonate (MSMA), and dicamba, were associated with faster symptom progression. Among these pesticides, residential or workplace proximity to higher amounts of copper sulfate (pentahydrate) and MCPA (dimethylamine salt) was associated with all three progression endpoints (copper sulfate: HRs = 1.22-1.36, 95 % CIs = 1.03-1.73; MCPA: HRs = 1.27-1.35, 95 % CIs = 1.02-1.70).

CONCLUSIONS

Our findings suggest that pesticide exposure may not only be relevant for PD onset but also PD progression phenotypes. We have implicated ten specific pesticide active ingredients in faster PD motor and non-motor decline.

The evolution of comprehensive genetic analysis in neurology: Implications for precision medicine

Technological advancements have facilitated the availability of reliable and thorough genetic analysis in many medical fields, including neurology. In this review, we focus on the importance of selecting the appropriate genetic test to aid in the accurate identification of disease utilizing currently employed technologies for analyzing monogenic neurological disorders. Moreover, the applicability of comprehensive analysis via NGS for various genetically heterogeneous neurological disorders is reviewed, revealing its efficiency in clarifying a frequently cloudy diagnostic picture and delivering a conclusive and solid diagnosis that is essential for the proper management of the patient. The feasibility and effectiveness of medical genetics in neurology require interdisciplinary cooperation among several medical specialties and geneticists, to select and perform the most relevant test according to each patient's medical history, using the most appropriate technological tools. The prerequisites for a comprehensive genetic analysis are discussed, highlighting the utility of appropriate gene selection, variant annotation, and classification. Moreover, genetic counseling and interdisciplinary collaboration could improve diagnostic yield further. Additionally, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, with a focus on neurology-related genes, to clarify the value of suitable variant categorization. Finally, we review the current applications of genetic analysis in the diagnosis and personalized management of neurological patients and the advances in the research and scientific knowledge of hereditary neurological disorders that are evolving the utility of genetic analysis towards the individualization of the treatment strategy.

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants

Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.

Immune system disruptions implicated in whole blood epigenome-wide association study of depression among Parkinson's disease patients

Although Parkinson's Disease (PD) is typically described in terms of motor symptoms, depression is a common feature. We explored whether depression influences blood-based genome-wide DNA methylation (DNAm) in 692 subjects from a population-based PD case-control study, using both a history of clinically diagnosed depression and current depressive symptoms measured by the geriatric depression scale (GDS). While PD patients in general had more immune activation and more accelerated epigenetic immune system aging than controls, the patients experiencing current depressive symptoms (GDS≥5) showed even higher levels of both markers than patients without current depressive symptoms (GDS<5). For PD patients with a history of clinical depression compared to those without, we found no differences in immune cell composition. However, a history of clinical depression among patients was associated with differentially methylated CpGs. Epigenome-wide association analysis (EWAS) revealed 35 CpGs associated at an FDR≤0.05 (569 CpGs at FDR≤0.10, 1718 CpGs at FDR≤0.15). Gene set enrichment analysis implicated immune system pathways, including immunoregulatory interactions between lymphoid and non-lymphoid cells (p-adj = 0.003) and cytokine-cytokine receptor interaction (p-adj = 0.004). Based on functional genomics, 25 (71%) of the FDR≤0.05 CpGs were associated with genetic variation at 45 different methylation quantitative trait loci (meQTL). Twenty-six of the meQTLs were also expression QTLs (eQTLs) associated with the abundance of 53 transcripts in blood and 22 transcripts in brain (substantia nigra, putamen basal ganglia, or frontal cortex). Notably, cg15199181 was strongly related to rs823114 (SNP-CpG p-value = 3.27E-310), a SNP identified in a PD meta-GWAS and related to differential expression of PM20D1, RAB29, SLC41A1, and NUCKS1. The entire set of genes detected through functional genomics was most strongly overrepresented for interferon-gamma-mediated signaling pathway (enrichment ratio = 18.8, FDR = 4.4e-03) and T cell receptor signaling pathway (enrichment ratio = 13.2, FDR = 4.4e-03). Overall, the current study provides evidence of immune system involvement in depression among Parkinson's patients.

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Parkinson's disease (PD) is associated with clinical depression prior to PD onset and depressive symptoms after PD diagnosis.

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Epigenome-wide analysis revealed CpGs related to current depressive symptoms and a history of clinical depression among PD patients.

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Patients experiencing current depressive symptoms had the highest epigenetic-based neutrophil-to-lymphocyte ratio on average.

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Patients with a history of clinical depression had differentially methylated CpGs in genes enriched for immune system pathways.

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Many of the depression associated CpGs were linked to differential expression through meQTL/eQTLs, which included GWAS variants.

Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients.

Polygenic Resilience Modulates the Penetrance of Parkinson Disease Genetic Risk Factors

OBJECTIVE

The aim of the current study is to understand why some individuals avoid developing Parkinson disease (PD) despite being at relatively high genetic risk, using the largest datasets of individual-level genetic data available.

METHODS

We calculated polygenic risk score to identify controls and matched PD cases with the highest burden of genetic risk for PD in the discovery cohort (International Parkinson's Disease Genomics Consortium, 7,204 PD cases and 9,412 controls) and validation cohorts (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease, 8,968 cases and 7,598 controls; UK Biobank, 2,639 PD cases and 14,301 controls; Accelerating Medicines Partnership-Parkinson's Disease Initiative, 2,248 cases and 2,817 controls). A genome-wide association study meta-analysis was performed on these individuals to understand genetic variation associated with resistance to disease. We further constructed a polygenic resilience score, and performed multimarker analysis of genomic annotation (MAGMA) gene-based analyses and functional enrichment analyses.

RESULTS

A higher polygenic resilience score was associated with a lower risk for PD (β = -0.054, standard error [SE] = 0.022, p = 0.013). Although no single locus reached genome-wide significance, MAGMA gene-based analyses nominated TBCA as a putative gene. Furthermore, we estimated the narrow-sense heritability associated with resilience to PD (h2  = 0.081, SE = 0.035, p = 0.0003). Subsequent functional enrichment analysis highlighted histone methylation as a potential pathway harboring resilience alleles that could mitigate the effects of PD risk loci.

INTERPRETATION

The present study represents a novel and comprehensive assessment of heritable genetic variation contributing to PD resistance. We show that a genetic resilience score can modify the penetrance of PD genetic risk factors and therefore protect individuals carrying a high-risk genetic burden from developing PD. ANN NEUROL 2022;92:270-278.

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.

Mitochondrial and autophagy-lysosomal pathway polygenic risk scores predict Parkinson's disease

Polygenic Risk Scores (PRS), which allow assessing an individuals' genetic risk for a complex disease, are calculated as the weighted number of genetic risk alleles in an individual's genome, with the risk alleles and their weights typically derived from the results of genome-wide association studies (GWAS). Among a wide range of applications, PRS can be used to identify at-risk individuals and select them for further clinical follow-up. Pathway PRS are genetic scores based on single nucleotide polymorphisms (SNPs) assigned to genes involved in major disease pathways. The aim of this study is to assess the predictive utility of PRS models constructed based on SNPs corresponding to two cardinal pathways in Parkinson's disease (PD) including mitochondrial PRS (Mito PRS) and autophagy-lysosomal PRS (ALP PRS). PRS models were constructed using the clumping-and-thresholding method in a German population as prediction dataset that included 371 cases and 249 controls, using SNPs discovered by the most recent PD-GWAS. We showed that these pathway PRS significantly predict the PD status. Furthermore, we demonstrated that Mito PRS are significantly associated with later age of onset in PD patients. Our results may add to the accumulating evidence for the contribution of mitochondrial and autophagy-lysosomal pathways to PD risk and facilitate biologically relevant risk stratification of PD patients.

Genetic Determinants of Survival in Parkinson's Disease in the Asian Population

BACKGROUND

Patients with Parkinson's disease (PD) have reduced life expectancy compared to the general population. Genetic variation was shown to play a role in the heterogeneity of survival for patients with PD, although the underlying genetic background remains poorly studied.

OBJECTIVE

The aim was to explore the genetic determinants influencing the survival of PD.

METHODS

We performed a genome-wide association analysis using a Cox proportional hazards model in a longitudinal cohort of 1080 Chinese patients with PD. Furthermore, we built a clinical-genetic model to predict the survival of patients using clinical variables combined with polygenic risk score (PRS) of survival of PD.

RESULTS

The cohort was followed up for an average of 7.13 years, with 85 incidents of death. One locus rs12628329 (RPL3) was significantly associated with reduced survival time by ~10.8 months (P = 2.72E-08, β = 1.79, standard error = 0.32). Functional exploration suggested this variant could upregulate the expression of RPL3 and induce apoptosis and cell death. In addition, adding PRS of survival in the prediction model substantially improved survival predictability (concordance index [Cindex]: 0.936) compared with the clinical model (Cindex: 0.860).

CONCLUSIONS

These findings improve the current understanding of the genetic cause of survival of PD and provide a novel target RPL3 for further research on PD pathogenesis and potential therapeutic options. Our results also demonstrate the potential utility of PRS of survival in identifying patients with shorter survival and providing personalized clinical monitoring and treatment. © 2022 International Parkinson and Movement Disorder Society.

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.

A Polygenic Score for Type 2 Diabetes Improves Risk Stratification Beyond Current Clinical Screening Factors in an Ancestrally Diverse Sample

A substantial proportion of the adult United States population with type 2 diabetes (T2D) are undiagnosed, calling into question the comprehensiveness of current screening practices, which primarily rely on age, family history, and body mass index (BMI). We hypothesized that a polygenic score (PGS) may serve as a complementary tool to identify high-risk individuals. The T2D polygenic score maintained predictive utility after adjusting for family history and combining genetics with family history led to even more improved disease risk prediction. We observed that the PGS was meaningfully related to age of onset with implications for screening practices: there was a linear and statistically significant relationship between the PGS and T2D onset (-1.3 years per standard deviation of the PGS). Evaluation of U.S. Preventive Task Force and a simplified version of American Diabetes Association screening guidelines showed that addition of a screening criterion for those above the 90th percentile of the PGS provided a small increase the sensitivity of the screening algorithm. Among T2D-negative individuals, the T2D PGS was associated with prediabetes, where each standard deviation increase of the PGS was associated with a 23% increase in the odds of prediabetes diagnosis. Additionally, each standard deviation increase in the PGS corresponded to a 43% increase in the odds of incident T2D at one-year follow-up. Using complications and forms of clinical intervention (i.e., lifestyle modification, metformin treatment, or insulin treatment) as proxies for advanced illness we also found statistically significant associations between the T2D PGS and insulin treatment and diabetic neuropathy. Importantly, we were able to replicate many findings in a Hispanic/Latino cohort from our database, highlighting the value of the T2D PGS as a clinical tool for individuals with ancestry other than European. In this group, the T2D PGS provided additional disease risk information beyond that offered by traditional screening methodologies. The T2D PGS also had predictive value for the age of onset and for prediabetes among T2D-negative Hispanic/Latino participants. These findings strengthen the notion that a T2D PGS could play a role in the clinical setting across multiple ancestries, potentially improving T2D screening practices, risk stratification, and disease management.

Effects of Alzheimer's genetic risk scores and CSF biomarkers in de novo Parkinson's Disease

Coexisting Alzheimer's disease (AD) pathology is common in Parkinson's disease (PD). However, the implications of genetic risk scores (GRS) for AD have not been elucidated in PD. In 413 de novo PD and 195 healthy controls from the Parkinson's Progression Marker Initiative database, the effects of GRS for AD (GRS-AD) and PD (GRS-PD) on the risk of PD and longitudinal CSF biomarkers and clinical outcomes were explored. Higher GRS-PD and lower baseline CSF α-synuclein were associated with an increased risk of PD. In the PD group, GRS-AD was correlated positively with CSF p-tau/Aβ and negatively with CSF α-synuclein. Higher GRS-PD was associated with faster CSF p-tau/Aβ increase, and GRS-AD and GRS-PD were interactively associated with CSF α-synuclein. In the PD group, higher GRS-AD was associated with poor visuospatial function, and baseline CSF p-tau/Aβ was associated with faster cognitive decline. Higher GRS-PD was associated with better semantic fluency and frontal-related cognition and motor function given the same levels of CSF biomarkers and dopamine transporter uptake. Taken together, our results suggest that higher GRS-AD and CSF p-tau/Aβ, reflecting AD-related pathophysiology, may be associated with cognitive decline in PD patients.

Evolution patterns of probable REM sleep behavior disorder predicts Parkinson's disease progression

The course of REM sleep behavior disorder (RBD) variates in the early stage of Parkinson's disease. We aimed to delineate the association between the evolution pattern of probable RBD (pRBD) and the progression of Parkinson's disease (PD). 281 de novo PD patients from the Parkinson's Progression Markers Initiative database were included. Patients were followed up for a mean of 6.8 years and were classified into different groups according to the evolution patterns of pRBD. Disease progression was compared among groups using survival analysis, where the endpoint was defined as progression to Hoehn-Yahr stage 3 or higher for motor progression and progression to mild cognitive impairment for cognitive decline. At the 4th year of follow-up, four types of pRBD evolution patterns were identified: (1) non-RBD-stable (55.5%): patients persistently free of pRBD; (2) late-RBD (12.1%): patients developed pRBD during follow-up; (3) RBD-stable (24.9%): patients showed persistent pRBD, and (4) RBD-reversion (7.5%): patients showed pRBD at baseline which disappeared during follow-up. The RBD-reversion type showed the fastest motor progression while the RBD-stable type showed the fastest cognitive decline. At baseline, the RBD-reversion type showed the most severe gray matter atrophy in the middle frontal gyrus, while the RBD-stable type showed gray matter atrophy mainly in the para-hippocampal gyrus. Four types of early pRBD evolution patterns featured different brain lesions and predicted different courses of motor and cognitive decline in PD.

Genetics of cognitive dysfunction in Parkinson's disease

Presentation and progression of cognitive symptoms in Parkinson's disease are highly variable. PD is a genetically complex disorder with multiple genetic risk factors and understanding the role that genes play in cognitive outcomes is important for patient counseling and treatment. Currently, there are seven well-described genes that increase the risk for PD, with variable levels of penetrance: SNCA, LRRK2, VPS35, PRKN, PINK1, DJ1 and GBA. In addition, large, genome-wide association studies have identified multiple loci in our DNA which increase PD risk. In this chapter, we summarize what is currently known about each of the seven strongly-associated PD genes and select PD risk variants, including PITX3, TMEM106B, SNCA Rep1, APOɛ4, COMT and MAPT H1/H1, along with their respective relationships to cognition.

The potential convergence of NLRP3 inflammasome, potassium, and dopamine mechanisms in Parkinson's disease

The pathology of Parkinson's disease (PD) is characterized by α-synuclein aggregation, microglia-mediated neuroinflammation, and dopaminergic neurodegeneration in the substantia nigra with collateral striatal dopamine signaling deficiency. Microglial NLRP3 inflammasome activation has been linked independently to each of these facets of PD pathology. The voltage-gated potassium channel Kv1.3, upregulated in microglia by α-synuclein and facilitating potassium efflux, has also been identified as a modulator of neuroinflammation and neurodegeneration in models of PD. Evidence increasingly suggests that microglial Kv1.3 is mechanistically coupled with NLRP3 inflammasome activation, which is contingent on potassium efflux. Potassium conductance also influences dopamine release from midbrain dopaminergic neurons. Dopamine, in turn, has been shown to inhibit NLRP3 inflammasome activation in microglia. In this review, we provide a literature framework for a hypothesis in which Kv1.3 activity-induced NLRP3 inflammasome activation, evoked by stimuli such as α-synuclein, could lead to microglia utilizing dopamine from adjacent dopaminergic neurons to counteract this process and fend off an activated state. If this is the case, a sufficient dopamine supply would ensure that microglia remain under control, but as dopamine is gradually siphoned from the neurons by microglial demand, NLRP3 inflammasome activation and Kv1.3 activity would progressively intensify to promote each of the three major facets of PD pathology: α-synuclein aggregation, microglia-mediated neuroinflammation, and dopaminergic neurodegeneration. Risk factors overlapping to varying degrees to render brain regions susceptible to such a mechanism would include a high density of microglia, an initially sufficient supply of dopamine, and poor insulation of the dopaminergic neurons by myelin.

Association of the Polygenic Risk Score With the Probability of Prodromal Parkinson's Disease in Older Adults

Several studies have investigated the association of the Parkinson’s disease (PD) polygenic risk score (PRS) with several aspects of well-established PD. We sought to evaluate the association of PRS with the prodromal stage of PD. We calculated PRS in a longitudinal sample (n = 1120) of community dwelling individuals ≥ 65 years from the HELIAD (The Hellenic Longitudinal Investigation of Aging and Diet) study in order to evaluate the association of this score with the probability of prodromal PD or any of the established risk and prodromal markers in MDS research criteria, using regression multi-adjusted models. Increases in PRS estimated from GWAS summary statistics’ ninety top SNPS with p < 5 × 10^–8 was associated with increased odds of having probable/possible prodromal PD (i.e., ≥ 30% probability, OR = 1.033, 95%CI: 1.009–1.057 p = 0.006). From the prodromal PD risk markers, significant association was found between PRS and global cognitive deficit exclusively (p = 0.003). To our knowledge, our study is the first population based study investigating the association between PRS scores and prodromal markers of Parkinson’s disease. Our results suggest a strong relationship between the accumulation of many common genetic variants, as measured by PRS, and cognitive deficits.

Stochastic Epigenetic Mutations Influence Parkinson's Disease Risk, Progression, and Mortality

BACKGROUND

Stochastic epigenetic mutations (SEM) reflect a deviation from normal site-specific methylation patterns. Epigenetic mutation load (EML) captures the accumulation of SEMs across an individual's genome and may reflect dysfunction of the epigenetic maintenance system in response to epigenetic challenges.

OBJECTIVE

We investigate whether EML is associated with PD risk and time to events (i.e., death and motor symptom decline).

METHODS

We employed logistic regression and Cox proportional hazards regression to assess the association between EML and several outcomes. Our analyses are based on 568 PD patients and 238 controls from the Parkinson's disease, Environment and Genes (PEG) study, for whom blood-based methylation data was available.

RESULTS

We found an association for PD onset and EML in all genes (OR = 1.90; 95%CI 1.52-2.37) and PD-related genes (OR = 1.87; 95%CI 1.50-2.32). EML was also associated with time to a minimum score of 35 points on the motor UPDRS exam (OR = 1.28; 95%CI 1.06-1.56) and time to death (OR = 1.29, 95%CI 1.11-1.49). An analysis of PD related genes only revealed five intragenic hotspots of high SEM density associated with PD risk.

CONCLUSION

Our findings suggest an enrichment of methylation dysregulation in PD patients in general and specifically in five PD related genes. EML may also be associated with time to death and motor symptom progression in PD patients.

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.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

The effect of polygenic risk on white matter microstructural degeneration in Parkinson's disease: A longitudinal Diffusion Tensor Imaging study

BACKGROUND AND PURPOSE

This study was undertaken to investigate the effect of genetic risk on whole brain white matter (WM) integrity in patients with Parkinson disease (PD).

METHODS

Data were acquired from the Parkinson's Progression Markers Initiative (PPMI) database. Polygenic load was estimated by calculating weighted polygenic risk scores (PRS) using (i) all available 26 PD-risk single nucleotide polymorphisms (SNPs) (PRS1) and (ii) 23 SNPs with minor allele frequency (MAF) > 0.05 (PRS2). According to the PRS2, and combined with clinical and diffusion tensor imaging (DTI) data over 3-year follow-up, 60 PD patients were screened and assigned to the low-PRS group (n = 30) and high-PRS group (n = 30) to investigate intergroup differences in clinical profiles and WM microstructure measured by DTI cross-sectionally and longitudinally.

RESULTS

PRS were associated with younger age at onset in patients with PD (PRS1, Spearman ρ = -0.190, p = 0.003; PRS2, Spearman ρ = -0.189, p = 0.003). The high-PRS group showed more extensive WM microstructural degeneration compared with the low-PRS group, mainly involving the anterior thalamic radiation (AThR) and inferior fronto-occipital fasciculus (IFOF) (p < 0.05). Furthermore, WM microstructural changes in AThR correlated with declining cognitive function (r = -0.401, p = 0.028) and increasing dopaminergic deficits in caudate (r = -0.405, p = 0.030).

CONCLUSIONS

These findings suggest that PD-associated polygenic load aggravates the WM microstructural degeneration and these changes may lead to poor cognition with continuous dopamine depletion. This study provides advanced evidence that combined with a cumulative PRS and DTI methods may predict disease progression in PD patients.

Validity and Prognostic Value of a Polygenic Risk Score for Parkinson's Disease

Idiopathic Parkinson’s disease (PD) is a complex multifactorial disorder caused by the interplay of both genetic and non-genetic risk factors. Polygenic risk scores (PRSs) are one way to aggregate the effects of a large number of genetic variants upon the risk for a disease like PD in a single quantity. However, reassessment of the performance of a given PRS in independent data sets is a precondition for establishing the PRS as a valid tool to this end. We studied a previously proposed PRS for PD in a separate genetic data set, comprising 1914 PD cases and 4464 controls, and were able to replicate its ability to differentiate between cases and controls. We also assessed theoretically the prognostic value of the PD-PRS, i.e., its ability to predict the development of PD in later life for healthy individuals. As it turned out, the PD-PRS alone can be expected to perform poorly in this regard. Therefore, we conclude that the PD-PRS could serve as an important research tool, but that meaningful PRS-based prognosis of PD at an individual level is not feasible.

Alpha-Synuclein and Cognitive Decline in Parkinson Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder in elderly people. It is characterized by the aggregation of misfolded alpha-synuclein throughout the nervous system. Aside from cardinal motor symptoms, cognitive impairment is one of the most disabling non-motor symptoms that occurs during the progression of the disease. The accumulation and spreading of alpha-synuclein pathology from the brainstem to limbic and neocortical structures is correlated with emerging cognitive decline in PD. This review summarizes the genetic and pathophysiologic relationship between alpha-synuclein and cognitive impairment in PD, together with potential areas of biomarker advancement.

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.

Accelerated hematopoietic mitotic aging measured by DNA methylation, blood cell lineage, and Parkinson's disease

BACKGROUND

Aging and inflammation are important components of Parkinson's disease (PD) pathogenesis and both are associated with changes in hematopoiesis and blood cell composition. DNA methylation (DNAm) presents a mechanism to investigate inflammation, aging, and hematopoiesis in PD, using epigenetic mitotic aging and aging clocks. Here, we aimed to define the influence of blood cell lineage on epigenetic mitotic age and then investigate mitotic age acceleration with PD, while considering epigenetic age acceleration biomarkers.

RESULTS

We estimated epigenetic mitotic age using the "epiTOC" epigenetic mitotic clock in 10 different blood cell populations and in a population-based study of PD with whole-blood. Within subject analysis of the flow-sorted purified blood cell types DNAm showed a clear separation of epigenetic mitotic age by cell lineage, with the mitotic age significantly lower in myeloid versus lymphoid cells (p = 2.1e-11). PD status was strongly associated with accelerated epigenetic mitotic aging (AccelEpiTOC) after controlling for cell composition (OR = 2.11, 95 % CI = 1.56, 2.86, p = 1.6e-6). AccelEpiTOC was also positively correlated with extrinsic epigenetic age acceleration, a DNAm aging biomarker related to immune system aging (with cell composition adjustment: R = 0.27, p = 6.5e-14), and both were independently associated with PD. Among PD patients, AccelEpiTOC measured at baseline was also associated with longitudinal motor and cognitive symptom decline.

CONCLUSIONS

The current study presents a first look at epigenetic mitotic aging in PD and our findings suggest accelerated hematopoietic cell mitosis, possibly reflecting immune pathway imbalances, in early PD that may also be related to motor and cognitive progression.

Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson's disease

Background

Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.

Results

Using eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered.

Conclusions

Overall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00357-w.

Prediction of Parkinson's Disease Risk Based on Genetic Profile and Established Risk Factors

Background: Parkinson’s disease (PD) is a neurodegenerative disorder, and literature suggests that genetics and lifestyle/environmental factors may play a key role in the triggering of the disease. This study aimed to evaluate the predictive performance of a 12-Single Nucleotide Polymorphisms (SNPs) polygenic risk score (PRS) in combination with already established PD-environmental/lifestyle factors. Methods: Genotypic and lifestyle/environmental data on 235 PD-patients and 464 controls were obtained from a previous study carried out in the Cypriot population. A PRS was calculated for each individual. Univariate logistic-regression analysis was used to assess the association of PRS and each risk factor with PD-status. Stepwise-regression analysis was used to select the best predictive model for PD combining genetic and lifestyle/environmental factors. Results: The 12-SNPs PRS was significantly increased in PD-cases compared to controls. Furthermore, univariate analyses showed that age, head injury, family history, depression, and Body Mass Index (BMI) were significantly associated with PD-status. Stepwise-regression suggested that a model which includes PRS and seven other independent lifestyle/environmental factors is the most predictive of PD in our population. Conclusions: These results suggest an association between both genetic and environmental factors and PD, and highlight the potential for the use of PRS in combination with the classical risk factors for risk prediction of PD.

The Genetics of Parkinson's Disease and Implications for Clinical Practice

The genetic landscape of Parkinson’s disease (PD) is characterised by rare high penetrance pathogenic variants causing familial disease, genetic risk factor variants driving PD risk in a significant minority in PD cases and high frequency, low penetrance variants, which contribute a small increase of the risk of developing sporadic PD. This knowledge has the potential to have a major impact in the clinical care of people with PD. We summarise these genetic influences and discuss the implications for therapeutics and clinical trial design.

Genome-wide survival study identifies a novel synaptic locus and polygenic score for cognitive progression in Parkinson's disease

A key driver of patients' well-being and clinical trials for Parkinson's disease (PD) is the course that the disease takes over time (progression and prognosis). To assess how genetic variation influences the progression of PD over time to dementia, a major determinant for quality of life, we performed a longitudinal genome-wide survival study of 11.2 million variants in 3,821 patients with PD over 31,053 visits. We discover RIMS2 as a progression locus and confirm this in a replicate population (hazard ratio (HR) = 4.77, P = 2.78 × 10-11), identify suggestive evidence for TMEM108 (HR = 2.86, P = 2.09 × 10-8) and WWOX (HR = 2.12, P = 2.37 × 10-8) as progression loci, and confirm associations for GBA (HR = 1.93, P = 0.0002) and APOE (HR = 1.48, P = 0.001). Polygenic progression scores exhibit a substantial aggregate association with dementia risk, while polygenic susceptibility scores are not predictive. This study identifies a novel synaptic locus and polygenic score for cognitive disease progression in PD and proposes diverging genetic architectures of progression and susceptibility.

Prodromal Parkinson disease subtypes - key to understanding heterogeneity

In Parkinson disease (PD), pathological processes and neurodegeneration begin long before the cardinal motor symptoms develop and enable clinical diagnosis. In this prodromal phase, risk and prodromal markers can be used to identify individuals who are likely to develop PD, as in the recently updated International Parkinson and Movement Disorders Society research criteria for prodromal PD. However, increasing evidence suggests that clinical and prodromal PD are heterogeneous, and can be classified into subtypes with different clinical manifestations, pathomechanisms and patterns of spatial and temporal progression in the CNS and PNS. Genetic, pathological and imaging markers, as well as motor and non-motor symptoms, might define prodromal subtypes of PD. Moreover, concomitant pathology or other factors, including amyloid-β and tau pathology, age and environmental factors, can cause variability in prodromal PD. Patients with REM sleep behaviour disorder (RBD) exhibit distinct patterns of α-synuclein pathology propagation and might indicate a body-first subtype rather than a brain-first subtype. Identification of prodromal PD subtypes and a full understanding of variability at this stage of the disease is crucial for early and accurate diagnosis and for targeting of neuroprotective interventions to ensure efficacy.

Interaction of CSF α-synuclein and amyloid beta in cognition and cortical atrophy

INTRODUCTION

Lewy body-related pathology is commonly observed at autopsy in individuals with dementia, but in vivo biomarkers for α-synucleinopathy are lacking.

METHODS

Baseline cerebrospinal fluid (CSF) biomarkers, polygenic risk score (PRS) for Parkinson's disease (PRS-PD) and Alzheimer's disease (PRS-AD), longitudinal cognitive scores, and magnetic resonance imaging were measured in 217 participants from the Alzheimer's Disease Neuroimaging Initiative. Linear mixed models were used to find the relationship of CSF biomarkers and the PRS with cognition and cortical atrophy.

RESULTS

Higher PRS-PD and PRS-AD were associated with lower CSF α-synuclein and amyloid beta (Aβ), respectively. Lower CSF α-synuclein and the interaction of CSF α-synuclein and Aβ were associated with lower cognitive scores and global cortical atrophy most prominently in the occipital cortex.

DISCUSSION

Lower CSF α-synuclein could be a biomarker for α-synucleinopathy, and the simultaneous evaluation of CSF biomarkers for AD and CSF α-synuclein could reveal the independent and interactive effects on cognition and cortical atrophy.

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.

Advancing Personalized Medicine in Common Forms of Parkinson's Disease through Genetics: Current Therapeutics and the Future of Individualized Management

Parkinson’s disease (PD) is a condition with heterogeneous clinical manifestations that vary in age at onset, rate of progression, disease course, severity, motor and non-motor symptoms, and a variable response to antiparkinsonian drugs. It is considered that there are multiple PD etiological subtypes, some of which could be predicted by genetics. The characterization and prediction of these distinct molecular entities provides a growing opportunity to use individualized management and personalized therapies. Dissecting the genetic architecture of PD is a critical step in identifying therapeutic targets, and genetics represents a step forward to sub-categorize and predict PD risk and progression. A better understanding and separation of genetic subtypes has immediate implications in clinical trial design by unraveling the different flavors of clinical presentation and development. Personalized medicine is a nascent area of research and represents a paramount challenge in the treatment and cure of PD. This manuscript summarizes the current state of precision medicine in the PD field and discusses how genetics has become the engine to gain insights into disease during our constant effort to develop potential etiological based interventions.