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Prediction of cognition in Parkinson's disease with a clinical-genetic score: a longitudinal analysis of nine cohorts.

ABSTRACT: Cognitive 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.In 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.3200 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 ?-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.Our 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.National Institutes of Health, US Department of Defense.

SUBMITTER: Liu G

PROVIDER: S-EPMC5761650 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Prediction of cognition in Parkinson's disease with a clinical-genetic score: a longitudinal analysis of nine cohorts.

Liu Ganqiang G Locascio Joseph J JJ Corvol Jean-Christophe JC Boot Brendon B Liao Zhixiang Z Page Kara K Franco Daly D Burke Kyle K Jansen Iris E IE Trisini-Lipsanopoulos Ana A Winder-Rhodes Sophie S Tanner Caroline M CM Lang Anthony E AE Eberly Shirley S Elbaz Alexis A Brice Alexis A Mangone Graziella G Ravina Bernard B Shoulson Ira I Cormier-Dequaire Florence F Heutink Peter P van Hilten Jacobus J JJ Barker Roger A RA Williams-Gray Caroline H CH Marinus Johan J Scherzer Clemens R CR

The Lancet. Neurology 20170616 8

<h4>Background</h4>Cognitive 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.<h4>Methods</h4>In 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 b ...[more]

PMID: 28629879

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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

Prediction of cognition in Parkinson's disease with a clinical-genetic score: a longitudinal analysis of nine cohorts.

Publications

Prediction of cognition in Parkinson's disease with a clinical-genetic score: a longitudinal analysis of nine cohorts.

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