Motor Progression in Early-Stage Parkinson's Disease: A Clinical Prediction Model and the Role of Cerebrospinal Fluid Biomarkers

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development.

Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

A progression analysis of motor features in Parkinson's disease based on the mapper algorithm

Background

Parkinson's disease (PD) is a neurodegenerative disease with a broad spectrum of motor and non-motor symptoms. The great heterogeneity of clinical symptoms, biomarkers, and neuroimaging and lack of reliable progression markers present a significant challenge in predicting disease progression and prognoses.

Methods

We propose a new approach to disease progression analysis based on the mapper algorithm, a tool from topological data analysis. In this paper, we apply this method to the data from the Parkinson's Progression Markers Initiative (PPMI). We then construct a Markov chain on the mapper output graphs.

Results

The resulting progression model yields a quantitative comparison of patients' disease progression under different usage of medications. We also obtain an algorithm to predict patients' UPDRS III scores.

Conclusions

By using mapper algorithm and routinely gathered clinical assessments, we developed a new dynamic models to predict the following year's motor progression in the early stage of PD. The use of this model can predict motor evaluations at the individual level, assisting clinicians to adjust intervention strategy for each patient and identifying at-risk patients for future disease-modifying therapy clinical trials.

Multi-predictor modeling for predicting early Parkinson’s disease and non-motor symptoms progression

Background

Identifying individuals with high-risk Parkinson’s disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.

Methods

We retrieved the data from the Parkinson’s Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for “optimism” or overfitting with the bootstrap resampling approach.

Results

For constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91–0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.

Conclusion

The findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.

Detraining Slows and Maintenance Training Over 6 Years Halts Parkinsonian Symptoms-Progression

Introduction: There are scant data to demonstrate that the long-term non-pharmaceutical interventions can slow the progression of motor and non-motor symptoms and lower drug dose in Parkinson's disease (PD). Methods: After randomization, the Exercise-only (E, n = 19) group completed an initial 3-week-long, 15-session supervised, high-intensity sensorimotor agility exercise program designed to improve the postural stability. The Exercise + Maintenance (E + M, n = 22) group completed the 3-week program and continued the same program three times per week for 6 years. The no exercise and no maintenance control (C, n = 26) group continued habitual living. In each patient, 11 outcomes were measured before and after the 3-week initial exercise program and then, at 3, 6, 12, 18, 24, 36, 48, 60, and 72 months. Results: The longitudinal linear mixed effects modeling of each variable was fitted with maximum likelihood estimation and adjusted for baseline and covariates. The exercise program strongly improved the primary outcome, Motor Experiences of Daily Living, by ~7 points and all secondary outcomes [body mass index (BMI), disease and no disease-specific quality of life, depression, mobility, and standing balance]. In E group, the detraining effects lasted up to 12 months. E+M group further improved the initial exercise-induced gains up to 3 months and the gains were sustained until year 6. In C group, the symptoms worsened steadily. By year 6, levodopa (L-dopa) equivalents increased in all the groups but least in E + M group. Conclusion: A short-term, high-intensity sensorimotor agility exercise program improved the PD symptoms up to a year during detraining but the subsequent 6-year maintenance program was needed to further increase or sustain the initial improvements in the symptoms, quality of life, and drug dose.