PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease

Can Biomarkers Help the Early Diagnosis of Parkinson's Disease?

Parkinson's disease (PD) is a complex neurodegenerative disease with progressive loss of dopamine neurons. PD patients usually manifest a series of motor and non-motor symptoms. In order to provide better early diagnosis and subsequent disease-modifying therapies for PD patients, there is an urgent need to identify sensitive and specific biomarkers. Biomarkers can be divided into four categories: clinical, imaging, biochemical, and genetic. Ideal biomarkers not only improve our understanding of PD pathogenesis and progression, but also provide benefits for early risk evaluation and clinical diagnosis of PD. Although many efforts have been made and several biomarkers have been extensively investigated, few if any have been found useful for early diagnosis. Here, we summarize recent developments in the discovered biomarkers of PD and discuss their merits and limitations for the early diagnosis of PD.

The emerging science of precision medicine and pharmacogenomics for Parkinson's disease

Current therapies for Parkinson's disease are problematic because they are symptomatic and have adverse effects. New drugs have failed in clinical trials because of inadequate efficacy. At the core of the problem is trying to make one drug work for all Parkinson's disease patients, when we know this premise is wrong because (1) Parkinson's disease is not a single disease, and (2) no two individuals have the same biological makeup. Precision medicine is the goal to strive for, but we are only at the beginning stages of building the infrastructure for one of the most complex projects in the history of science, and it will be a long time before Parkinson's disease reaps the benefits. Pharmacogenomics, a cornerstone of precision medicine, has already proven successful for many conditions and could also propel drug discovery and improve treatment for Parkinson's disease. To make progress in the pharmacogenomics of Parkinson's disease, we need to change course from small inconclusive candidate gene studies to large-scale rigorously planned genome-wide studies that capture the nuclear genome and the microbiome. Pharmacogenomic studies must use homogenous subtypes of Parkinson's disease or apply the brute force of statistical power to overcome heterogeneity, which will require large sample sizes achievable only via internet-based methods and electronic databases. Large-scale pharmacogenomic studies, together with biomarker discovery efforts, will yield the knowledge necessary to design clinical trials with precision to alleviate confounding by disease heterogeneity and interindividual variability in drug response, two of the major impediments to successful drug discovery and effective treatment. © 2017 International Parkinson and Movement Disorder Society.

Subtle motor disturbances in PREDICT-PD participants

OBJECTIVE

The PREDICT-PD study aims to identify increased risk of Parkinson''s disease (PD) using online assessments of previously identified risk and early features of PD and an evidence-based scoring algorithm. We sought to determine whether higher risk participants (defined as those above the 15th centile of risk estimates) were more likely to have mild parkinsonian signs compared with lower risk participants.

METHODS

Video recordings of neurological examinations, including the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III, of 208 individuals who had previously completed an online risk assessment were scored blindly and independently by two movement-disorders experts. Higher risk and lower risk subjects were compared for MDS-UPDRS part III score (and derivations of this) to identify subclinical parkinsonism, and association of risk estimates with MDS-UPDRS III scores assessed.

RESULTS

Higher risk subjects had significantly higher median UPDRS part III scores (3, IQR 1-5.5) than lower risk subjects (1, IQR 0-3.0; p<0.001), and there was a significantly greater proportion of individuals classified as having subclinical parkinsonism. 18% of the higher risk subjects and 6% of the lower risk subjects exceeded the most stringent published cut-off for subtle parkinsonism of three definitions examined (p=0.027). Linear regression analysis demonstrated a continuous relationship of log-transformed risk estimates with UPDRS part III scores (increase in MDS-UPDRS per doubling of odds 0.52, 95% CI 0.31 to 0.72; p<0.001), which remained after adjustment for multiple vascular risk factors and scores on the Montreal Cognitive Assessment (0.58, 95% CI 0.30 to 0.87; p<0.001).

CONCLUSIONS

The PREDICT-PD algorithm identifies a population with an increased rate of motor disturbances.