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Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

ABSTRACT: BACKGROUND:Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied. OBJECTIVES:To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale. METHODS:We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81?years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes. RESULTS:Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for ?-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. CONCLUSIONS:We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

SUBMITTER: Iwaki H

PROVIDER: S-EPMC7017876 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

Iwaki Hirotaka H Blauwendraat Cornelis C Leonard Hampton L HL Kim Jonggeol J JJ Liu Ganqiang G Maple-Grødem Jodi J Corvol Jean-Christophe JC Pihlstrøm Lasse L van Nimwegen Marlies M Hutten Samantha J SJ Nguyen Khanh-Dung H KH Rick Jacqueline J Eberly Shirley S Faghri Faraz F Auinger Peggy P Scott Kirsten M KM Wijeyekoon Ruwani R Van Deerlin Vivianna M VM Hernandez Dena G DG Gibbs J Raphael JR Chitrala Kumaraswamy Naidu KN Day-Williams Aaron G AG Brice Alexis A Alves Guido G Noyce Alastair J AJ Tysnes Ole-Bjørn OB Evans Jonathan R JR Breen David P DP Estrada Karol K Wegel Claire E CE Danjou Fabrice F Simon David K DK Andreassen Ole O Ravina Bernard B Toft Mathias M Heutink Peter P Bloem Bastiaan R BR Weintraub Daniel D Barker Roger A RA Williams-Gray Caroline H CH van de Warrenburg Bart P BP Van Hilten Jacobus J JJ Scherzer Clemens R CR Singleton Andrew B AB Nalls Mike A MA

Movement disorders : official journal of the Movement Disorder Society 20190910 12

<h4>Background</h4>Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied.<h4>Objectives</h4>To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale.<h4>Methods</h4>We accumulated individual data from 12 l ...[more]

PMID: 31505070

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Project description:BackgroundCognitive decline is a debilitating manifestation of disease progression in Parkinson's disease. We aimed to develop a clinical-genetic score to predict global cognitive impairment in patients with the disease.MethodsIn this longitudinal analysis, we built a prediction algorithm for global cognitive impairment (defined as Mini Mental State Examination [MMSE] ≤25) using data from nine cohorts of patients with Parkinson's disease from North America and Europe assessed between 1986 and 2016. Candidate predictors of cognitive decline were selected through a backward eliminated Cox's proportional hazards analysis using the Akaike's information criterion. These were used to compute the multivariable predictor on the basis of data from six cohorts included in a discovery population. Independent replication was attained in patients from a further three independent longitudinal cohorts. The predictive score was rebuilt and retested in 10 000 training and test sets randomly generated from the entire study population.Findings3200 patients with Parkinson's disease who were longitudinally assessed with 27 022 study visits between 1986 and 2016 in nine cohorts from North America and Europe were assessed for eligibility. 235 patients with MMSE ≤25 at baseline and 135 whose first study visit occurred more than 12 years from disease onset were excluded. The discovery population comprised 1350 patients (after further exclusion of 334 with missing covariates) from six longitudinal cohorts with 5165 longitudinal visits over 12·8 years (median 2·8, IQR 1·6-4·6). Age at onset, baseline MMSE, years of education, motor exam score, sex, depression, and β-glucocerebrosidase (GBA) mutation status were included in the prediction model. The replication population comprised 1132 patients (further excluding 14 patients with missing covariates) from three longitudinal cohorts with 19 127 follow-up visits over 8·6 years (median 6·5, IQR 4·1-7·2). The cognitive risk score predicted cognitive impairment within 10 years of disease onset with an area under the curve (AUC) of more than 0·85 in both the discovery (95% CI 0·82-0·90) and replication (95% CI 0·78-0·91) populations. Patients scoring in the highest quartile for cognitive risk score had an increased hazard for global cognitive impairment compared with those in the lowest quartile (hazard ratio 18·4 [95% CI 9·4-36·1]). Dementia or disabling cognitive impairment was predicted with an AUC of 0·88 (95% CI 0·79-0·94) and a negative predictive value of 0·92 (95% 0·88-0·95) at the predefined cutoff of 0·196. Performance was stable in 10 000 randomly resampled subsets.InterpretationOur predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson's disease. This model could improve trials of cognitive interventions and inform on prognosis.FundingNational Institutes of Health, US Department of Defense.

Project description:Background: Different factors influence severity, progression, and outcomes in Parkinson's disease (PD). Lack of standardized clinical assessment limits comparison of outcomes and availability of well-characterized cohorts for collaborative studies. Methods: Structured clinical documentation support (SCDS) was developed within the DNA Predictions to Improve Neurological Health (DodoNA) project to standardize clinical assessment and identify molecular predictors of disease progression. The Longitudinal Clinical and Genetic Study of Parkinson's Disease (LONG-PD) was launched within the Genetic Epidemiology of Parkinson's disease (GEoPD) consortium using a Research Electronic Data Capture (REDCap) format mirroring the DodoNA SCDS. Demographics, education, exposures, age at onset (AAO), Unified Parkinson's Disease Rating Scale (UPDRS) parts I-VI or Movement Disorders Society (MDS)-UPDRS, Montreal Cognitive Assessment (MoCA)/Short Test of Mental Status (STMS)/Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Epworth Sleepiness Scale (ESS), dopaminergic therapy, family history, nursing home placement, death and blood samples were collected. DodoNA participants (396) with 6 years of follow-up and 346 LONG-PD participants with up to 3 years of follow-up were analyzed using group-based trajectory modeling (GBTM) focused on: AAO, education, family history, MMSE/MoCA/STMS, UPDRS II-II, UPDRS-III tremor and bradykinesia sub-scores, Hoehn and Yahr staging (H&Y) stage, disease subtype, dopaminergic therapy, and presence of autonomic symptoms. The analysis was performed with either cohort as the training/test set. Results: Patients are classified into slowly and rapidly progressing courses by AAO, MMSE score, H &Y stage, UPDRS-III tremor and bradykinesia sub-scores relatively early in the disease course. Late AAO and male sex assigned patients to the rapidly progressing group, whereas tremor to the slower progressing group. Classification is independent of which cohort serves as the training set. Frequencies of disease-causing variants in LRRK2 and GBA were 1.89 and 2.96%, respectively. Conclusions: Standardized clinical assessment provides accurate phenotypic characterization in pragmatic clinical settings. Trajectory analysis identified two different trajectories of disease progression and determinants of classification. Accurate phenotypic characterization is essential in interpreting genomic information that is generated within consortia, such as the GEoPD, formed to understand the genetic epidemiology of PD. Furthermore, the LONGPD study protocol has served as the prototype for collecting standardized phenotypic information at GEoPD sites. With genomic analysis, this will elucidate disease etiology and lead to targeted therapies that can improve disease outcomes.

Dataset Information

Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

Publications

Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

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