Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Stability

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

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.

Baseline predictors of progression of Parkinson's disease in a sample of Egyptian patients: clinical and biochemical

Background

Clinical progression of Parkinson’s disease (PD) is highly heterogeneous, and its predictors are generally lacking. Identifying predictors of early disease progression is important for patients’ management and follow-up. The current study aims to identify clinical, neuroimaging and biochemical baseline predictors of motor progression in patients with PD. Forty-five PD patients were assessed at baseline, 6 months and 1 year using MDS-UPDRS total and subscores, Hoehn and Yahr (H&Y), Schwab and England (S&E), International Physical Activity Questionnaire (IPAQ). Baseline New Freezing of Gait Questionnaire (NFOG-Q), Berg Balance Scale (BBS), Ten-Meter Walking Test (10-MWT), and Time Up and Go Test (TUG), Non-Motor Symptoms Scale (NMSS), Beck Depression Inventory (BDI), PD questionnaire 39 (PDQ-39), MRI brain, uric acid, lipid profile and glycated hemoglobin were performed.

Results

Significant worsening of MDS-UPDRS total, part III scores, H&Y, S&E and IPAQ (p < 0.001) was detected. One-year progression of H&Y and S&E were significantly correlated to disease duration (p = 0.014, p = 0.025, respectively). Progression of H&Y was correlated to baseline TUG (p = 0.035). S&E progression was correlated to baseline MDS-UPDRS total score (rho = 0.478, p = 0.001) and part III (rho = 0.350, p = 0.020), H&Y (rho = 0.401, p = 0.007), PIGD (rho = 0.591, p < 0.001), NFOG-Q (rho = 0.498, p = 0.001), and TUG (rho = 0.565, p = 0.001). Using linear regression, there was no predictors of clinical progression among the used baseline variables.

Conclusion

Despite the significant motor and physical activity progression over 1 year that was correlated to baseline motor and gait severity, but without predictive value, further similar and longitudinal studies are warranted to detect predictors of early progression and confirm findings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41983-022-00445-1.