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Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

ABSTRACT: BACKGROUND:Better understanding and prediction of progression of Parkinson's disease could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in Parkinson's disease. We also sought to assess the use of these models in the design of treatment trials in Parkinson's disease. METHODS:A Bayesian multivariate predictive inference platform was applied to data from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from patients with Parkinson's disease and healthy controls to construct an ensemble of models to predict the annual rate of change in combined scores from the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and III. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort from the Longitudinal and Biomarker Study in Parkinson's disease (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomised placebo-controlled trials within the out-of-sample LABS-PD cohort. FINDINGS:117 healthy controls and 312 patients with Parkinson's disease from the PPMI study were available for analysis, and 317 patients with Parkinson's disease from LABS-PD were available for validation. Our model ensemble showed strong performance within the PPMI cohort (five-fold cross-validated R2 41%, 95% CI 35-47) and significant-albeit reduced-performance in the LABS-PD cohort (R2 9%, 95% CI 4-16). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort. These included significant replication of higher baseline MDS-UPDRS motor score, male sex, and increased age, as well as a novel Parkinson's disease-specific epistatic interaction, all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2·9%, 95% CI 1·5-4·3). CSF biomarkers at baseline showed a more modest (0·3%, 95% CI 0·1-0·5) but still significant effect on prediction of motor progression. The simulations (n=5000) showed that incorporating the predicted rates of motor progression (as assessed by the annual change in MDS-UPDRS score) into the final models of treatment effect reduced the variability in the study outcome, allowing significant differences to be detected at sample sizes up to 20% smaller than in naive trials. INTERPRETATION:Our model ensemble confirmed established and identified novel predictors of Parkinson's disease motor progression. Improvement of existing prognostic models through machine-learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment. FUNDING:Michael J Fox Foundation for Parkinson's Research and National Institute of Neurological Disorders and Stroke.

SUBMITTER: Latourelle JC

PROVIDER: S-EPMC5693218 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

Latourelle Jeanne C JC Beste Michael T MT Hadzi Tiffany C TC Miller Robert E RE Oppenheim Jacob N JN Valko Matthew P MP Wuest Diane M DM Church Bruce W BW Khalil Iya G IG Hayete Boris B Venuto Charles S CS

The Lancet. Neurology 20170925 11

<h4>Background</h4>Better understanding and prediction of progression of Parkinson's disease could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in Parkinson's disease. We also sought to assess the use of these models in the design of treatment trials in Parkinson's disease.<h4>Methods</h4>A Bayesian multivariat ...[more]

PMID: 28958801

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Project description:ObjectiveThe objective of this study was to develop valid prognostic models to predict mortality, dependency, and "death or dependency" for use in newly diagnosed Parkinson's disease (PD).MethodsThe models were developed in the Parkinsonism Incidence in North-East Scotland study (UK, 198 patients) and validated in the ParkWest study (Norway, 192 patients), cohorts that attempted to identify and follow-up all new PD cases in the study area. Dependency was defined using the Schwab & England scale. We selected variables measured at time of diagnosis to include in the models. Internal validation and external validation were performed by calculating C-statistics (discrimination) and plotting observed versus predicted risk in quantiles of predicted risk (calibration).ResultsOlder age, male sex, increased severity of axial features, and Charlson comorbidity index were independent prognostic factors in the mortality model. Increasing age, higher smoking history, increased severity of axial features, and lower MMSE score were independent predictors in the models of dependency and "death or dependency." Each model had very good internal calibration and very good or good discrimination (internal and external C-statistics for the models were 0.73-0.75 and 0.68-0.78, respectively). Although each model clearly separated patients into groups according to risk, they tended to overestimate risk in ParkWest. The models were recalibrated to the baseline risk in the ParkWest study and then calibrated well in this cohort.ConclusionsWe have developed prognostic models for predicting medium-term risk of important clinical outcomes in newly diagnosed PD. These models have validity for use for stratification of randomization, confounder adjustment, and case-mix correction, but they are inadequate for individualized prognostication. © 2017. The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Project description:Mild cognitive impairment in Parkinson's disease is associated with progression to dementia (Parkinson's disease dementia) in a majority of patients. Determining structural imaging biomarkers associated with prodromal Parkinson's disease dementia may allow for the earlier identification of those at risk, and allow for targeted disease modifying therapies. One hundred and five non-demented subjects with newly diagnosed idiopathic Parkinson's disease and 37 healthy matched controls had serial 3 T structural magnetic resonance imaging scans with clinical and neuropsychological assessments at baseline, which were repeated after 18 months. The Movement Disorder Society Task Force criteria were used to classify the Parkinson's disease subjects into Parkinson's disease with mild cognitive impairment (n = 39) and Parkinson's disease with no cognitive impairment (n = 66). Freesurfer image processing software was used to measure cortical thickness and subcortical volumes at baseline and follow-up. We compared regional percentage change of cortical thinning and subcortical atrophy over 18 months. At baseline, cases with Parkinson's disease with mild cognitive impairment demonstrated widespread cortical thinning relative to controls and atrophy of the nucleus accumbens compared to both controls and subjects with Parkinson's disease with no cognitive impairment. Regional cortical thickness at baseline was correlated with global cognition in the combined Parkinson's disease cohort. Over 18 months, patients with Parkinson's disease with mild cognitive impairment demonstrated more severe cortical thinning in frontal and temporo-parietal cortices, including hippocampal atrophy, relative to those with Parkinson's disease and no cognitive impairment and healthy controls, whereas subjects with Parkinson's disease and no cognitive impairment showed more severe frontal cortical thinning compared to healthy controls. At baseline, Parkinson's disease with no cognitive impairment converters showed bilateral temporal cortex thinning relative to the Parkinson's disease with no cognitive impairment stable subjects. Although loss of both cortical and subcortical volume occurs in non-demented Parkinson's disease, our longitudinal analyses revealed that Parkinson's disease with mild cognitive impairment shows more extensive atrophy and greater percentage of cortical thinning compared to Parkinson's disease with no cognitive impairment. In particular, an extension of cortical thinning in the temporo-parietal regions in addition to frontal atrophy could be a biomarker in therapeutic studies of mild cognitive impairment in Parkinson's disease for progression towards dementia.

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Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

Publications

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

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