Comparative analysis of four disease prediction models of Parkinson’s disease

2015 ◽  
Vol 411 (1-2) ◽  
pp. 127-134
Author(s):  
Nadella Kumudini ◽  
Shaik Mohammad Naushad ◽  
Balraj Alex Stanley ◽  
Manoharan Niveditha ◽  
Gunasekaran Sharmila ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Disease Prediction

scholarly journals Excess of singleton loss-of-function variants in Parkinson’s disease contributes to genetic risk

2020 ◽  
Vol 57 (9) ◽  
pp. 617-623 ◽  
Author(s):  
Dheeraj Reddy Bobbili ◽  
Peter Banda ◽  
Rejko Krüger ◽  
Patrick May
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rare Variant ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Classification Model ◽  
Polygenic Risk Score ◽  
Disease Prediction ◽  
Common Variants ◽  
Loss Of Function

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies.MethodsWe studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson’s Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods.ResultsWe observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone.ConclusionThe main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.

Development of standardized regression-based formulas to assess meaningful cognitive change in early Parkinson’s disease

2020 ◽  
Author(s):  
Hannah L Combs ◽  
Kate A Wyman-Chick ◽  
Lauren O Erickson ◽  
Michele K York
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Test Scores ◽  
Prediction Models ◽  
Early Stage ◽  
Cognitive Change ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Change Over Time ◽  
Over Time

Abstract Objective Longitudinal assessment of cognitive and emotional functioning in patients with Parkinson’s disease (PD) is helpful in tracking progression of the disease, developing treatment plans, evaluating outcomes, and educating patients and families. Determining whether change over time is meaningful in neurodegenerative conditions, such as PD, can be difficult as repeat assessment of neuropsychological functioning is impacted by factors outside of cognitive change. Regression-based prediction formulas are one method by which clinicians and researchers can determine whether an observed change is meaningful. The purpose of the current study was to develop and validate regression-based prediction models of cognitive and emotional test scores for participants with early-stage idiopathic PD and healthy controls (HC) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). Methods Participants with de novo PD and HC were identified retrospectively from the PPMI archival database. Data from baseline testing and 12-month follow-up were utilized in this study. In total, 688 total participants were included in the present study (NPD = 508; NHC = 185). Subjects from both groups were randomly divided into development (70%) and validation (30%) subsets. Results Early-stage idiopathic PD patients and healthy controls were similar at baseline. Regression-based models were developed for all cognitive and self-report mood measures within both populations. Within the validation subset, the predicted and observed cognitive test scores did not significantly differ, except for semantic fluency. Conclusions The prediction models can serve as useful tools for researchers and clinicians to study clinically meaningful cognitive and mood change over time in PD.

scholarly journals Longitudinal prediction of falls and near falls frequencies in Parkinson’s disease: a prospective cohort study

2020 ◽  
Author(s):  
Beata Lindholm ◽  
Christina Brogårdh ◽  
Per Odin ◽  
Peter Hagell
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Prospective Cohort ◽  
Prediction Models ◽  
Evidence Base ◽  
History Of ◽  
Short And Long Term ◽  
Near Falls ◽  
Short Time

Abstract Introduction and objective Several prediction models for falls/near falls in Parkinson’s disease (PD) have been proposed. However, longitudinal predictors of frequency of falls/near falls are poorly investigated. Therefore, we aimed to identify short- and long-term predictors of the number of falls/near falls in PD. Methods A prospective cohort of 58 persons with PD was assessed at baseline (mean age and PD duration, 65 and 3.2 years, respectively) and 3.5 years later. Potential predictors were history of falls and near falls, comfortable gait speed, freezing of gate, dyskinesia, retropulsion, tandem gait (TG), pain, and cognition (Mini-Mental State Exam, MMSE). After each assessment, the participants registered a number of falls/near falls during the following 6 months. Multivariate Poisson regression was used to identify short- and long-term predictors of a number of falls/near falls. Results Baseline median (q1–q3) motor (UPDRS) and MMSE scores were 10 (6.75–14) and 28.5 (27–29), respectively. History of falls was the only significant short-time predictor [incidence rate ratio (IRR), 15.17] for the number of falls/near falls during 6 months following baseline. Abnormal TG (IRR, 3.77) and lower MMSE scores (IRR, 1.17) were short-term predictors 3.5 years later. Abnormal TG (IRR, 7.79) and lower MMSE scores (IRR, 1.49) at baseline were long-term predictors of the number of falls/near falls 3.5 years later. Conclusion Abnormal TG and MMSE scores predict the number of falls/near falls in short and long term, and may be indicative of disease progression. Our observations provide important additions to the evidence base for clinical fall prediction in PD.

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