Unknown

Dataset Information

0

Genetically-informed prediction of short-term Parkinson's disease progression.


ABSTRACT: Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

SUBMITTER: Sadaei HJ 

PROVIDER: S-EPMC9613892 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

Genetically-informed prediction of short-term Parkinson's disease progression.

Sadaei Hossein J HJ   Cordova-Palomera Aldo A   Lee Jonghun J   Padmanabhan Jaya J   Chen Shang-Fu SF   Wineinger Nathan E NE   Dias Raquel R   Prilutsky Daria D   Szalma Sandor S   Torkamani Ali A  

NPJ Parkinson's disease 20221028 1


Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were define  ...[more]

Similar Datasets

| S-EPMC6683156 | biostudies-literature
| S-EPMC5956077 | biostudies-literature
| S-EPMC5984171 | biostudies-literature
| S-EPMC8531078 | biostudies-literature
| S-EPMC4107740 | biostudies-literature
| S-EPMC9678913 | biostudies-literature
| S-EPMC7197868 | biostudies-literature
| S-EPMC8901688 | biostudies-literature
| S-EPMC5353743 | biostudies-literature
| S-EPMC9868647 | biostudies-literature