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

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 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.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.We thank all study participants, their families, and friends for their support and participation, and our study coordinators. The co-investigators and contributors from Parkinson's Disease Biomarkers Program, Harvard Biomarkers Study, Drug Interaction with Genes in Parkinson's Disease (DIGPD), Parkinson Research Examination of CEP-1347 Trial (PreCEPT) and a longitudinal follow-up of the PRECEPT study cohort (PostCEPT), Parkinsonism Incidence, Cognition and Non-motor heterogeneity in Cambridgeshire (PICNICS), Cambridgeshire Parkinson's Incidence from GP to Neurologist (CamPaIGN), PROfiling PARKinson's disease study (PROPARK), as well as acknowledgments for Parkinson's Progression Marker Initiative and Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonism (DATATOP) are listed in the appendix. This work was supported in part by National Institutes of Health grants U01 NS082157, U01NS095736 (to CRS), US Department of Defense grants W81XWH-1–0007 (BR) and W81XWH-15–10007 (to CRS); MEMO Hoffman Foundation (to CRS); Brigham and Women's Hospital Departmental Funds (to BB). The Harvard Biomarkers Study is supported by the Harvard NeuroDiscovery Center, the Parkinson's Disease Biomarkers Program U01 NS082157, U01NS100603 of the National Institute of Neurological Disorders and Stroke (NINDS), and the Massachusetts Alzheimer's Disease Research Center P50 AG005134 grant of the National Institute on Aging, Harvard Aging Brain Study grant P01 AG036694. The PreCEPT and PostCEPT cohort was funded by Cephalon Inc and Lundbeck for the parent PRECEPT clinical trial and follow-up PostCEPT cohort, and the Department of Defense Neurotoxin Exposure Treatment Parkinson's Research Program (W23RRYX7022N606), NINDS Data and Organizing Center's (NS050095), the Parkinson's Disease Foundation (New York, NY, USA). Additional funding information for the PreCEPT and PostCEPT cohort and corresponding investigators is listed in Ravina et al. The CamPaIGN and PICNICS studies received funding support from the Wellcome Trust, MRC, Parkinson's UK, Cure-PD, the Patrick Berthoud Trust, the Van Geest Foundation, and National Institute for Health Research funding of a Biomedical Research Centre at the University of Cambridge and Addenbrooke's Hospital. DIGPD cohort was promoted by the Assistance Publique Hôpitaux de Paris, and funded by the French clinical research hospital programme (code AOR08010). The research leading to these results has received funding from the programme Investissements d'Avenir ANR-10-IAIHU-06. DATATOP was supported by NIH grant NS24778. The PROPARK study was funded by the Prinses Beatrix Fonds (project number WAR05–0120), the van Alkemade-Keuls Foundation (Stichting Alkemade-Keuls), and the International Parkinson Foundation (Stichting ParkinsonFonds)

Specifically neuropathic Gaucher's mutations accelerate cognitive decline in Parkinson's

Objective: We hypothesized that specific mutations in the β-glucocerebrosidase gene (GBA) causing neuropathic Gaucher's disease (GD) in homozygotes lead to aggressive cognitive decline in heterozygous Parkinson's disease (PD) patients, whereas non-neuropathic GD mutations confer intermediate progression rates. Methods: A total of 2,304 patients with PD and 20,868 longitudinal visits for up to 12.8 years (median, 4.1) from seven cohorts were analyzed. Differential effects of four types of genetic variation in GBA on longitudinal cognitive decline were evaluated using mixed random and fixed effects and Cox proportional hazards models. Results: Overall, 10.3% of patients with PD and GBA sequencing carried a mutation. Carriers of neuropathic GD mutations (1.4% of patients) had hazard ratios (HRs) for global cognitive impairment of 3.17 (95% confidence interval [CI], 1.60–6.25) and a hastened decline in Mini–Mental State Exam scores compared to noncarriers (p = 0.0009). Carriers of complex GBA alleles (0.7%) had an HR of 3.22 (95% CI, 1.18–8.73; p = 0.022). By contrast, the common, non-neuropathic N370S mutation (1.5% of patients; HR, 1.96; 95% CI, 0.92–4.18) or nonpathogenic risk variants (6.6% of patients; HR, 1.36; 95% CI, 0.89–2.05) did not reach significance. Interpretation: Mutations in the GBA gene pathogenic for neuropathic GD and complex alleles shift longitudinal cognitive decline in PD into “high gear.” These findings suggest a relationship between specific types of GBA mutations and aggressive cognitive decline and have direct implications for improving the design of clinical trials. Ann Neurol 2016;80:674–685

Specifically neuropathic Gaucher's mutations accelerate cognitive decline in Parkinson's

Objective: We hypothesized that specific mutations in the β‐glucocerebrosidase gene (GBA) causing neuropathic Gaucher's disease (GD) in homozygotes lead to aggressive cognitive decline in heterozygous Parkinson's disease (PD) patients, whereas non‐neuropathic GD mutations confer intermediate progression rates. Methods: A total of 2,304 patients with PD and 20,868 longitudinal visits for up to 12.8 years (median, 4.1) from seven cohorts were analyzed. Differential effects of four types of genetic variation in GBA on longitudinal cognitive decline were evaluated using mixed random and fixed effects and Cox proportional hazards models. Results: Overall, 10.3% of patients with PD and GBA sequencing carried a mutation. Carriers of neuropathic GD mutations (1.4% of patients) had hazard ratios (HRs) for global cognitive impairment of 3.17 (95% confidence interval [CI], 1.60–6.25) and a hastened decline in Mini–Mental State Exam scores compared to noncarriers (p = 0.0009). Carriers of complex GBA alleles (0.7%) had an HR of 3.22 (95% CI, 1.18–8.73; p = 0.022). By contrast, the common, non‐neuropathic N370S mutation (1.5% of patients; HR, 1.96; 95% CI, 0.92–4.18) or nonpathogenic risk variants (6.6% of patients; HR, 1.36; 95% CI, 0.89–2.05) did not reach significance. Interpretation Mutations in the GBA gene pathogenic for neuropathic GD and complex alleles shift longitudinal cognitive decline in PD into “high gear.” These findings suggest a relationship between specific types of GBA mutations and aggressive cognitive decline and have direct implications for improving the design of clinical trials. Ann Neurol 2016;80:674–68

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

International audienceSummary Background 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. Methods A prediction algorithm for global cognitive impairment (defined as Mini Mental State Exam (MMSE) ≤25) was built using data from 1,350 patients with 5,165 longitudinal visits over 12.8 (median, 2.8) years. Age at onset, MMSE, education, motor exam score, gender, depression and GBA mutations, machine selected through stepwise Cox’ hazards analysis and Akaike’s information criterion, were used to compute the multivariable predictor. Independent validation was achieved in another 1,132 patients with 19,127 visits over 8.6 (median, 6.5) years. Findings The cognitive risk score accurately predicted cognitive impairment within ten years of disease onset with an area under the curve (AUC) of >0.85 in both the discovery (95% CI, 0.821–0.902) and validation populations (95% CI, 0.779 – 0.913). 72.6% of patients scoring in the highest quartile were cognitively impaired by ten years vs. 3.7% 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 0.877 (95% CI 0.788–0.943) and high negative predictive value (0.920, 95% 0.877–0.954) at the predefined cutoff (0.196). Performance was stable in 10,000 randomly resampled subsets. Interpretation Our predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson’s. It could improve trials of cognitive interventions and inform on prognosis