Predicting progression in patients with Parkinson's disease

The emerging postural instability phenotype in idiopathic Parkinson disease

Identification of individuals at high risk for rapid progression of motor and cognitive signs in Parkinson disease (PD) is clinically significant. Postural instability and gait dysfunction (PIGD) are associated with greater motor and cognitive deterioration. We examined the relationship between baseline clinical factors and the development of postural instability using 5-year longitudinal de-novo idiopathic data (n = 301) from the Parkinson’s Progressive Markers Initiative (PPMI). Logistic regression analysis revealed baseline features associated with future postural instability, and we designated this cohort the emerging postural instability (ePI) phenotype. We evaluated the resulting ePI phenotype rating scale validity in two held-out populations which showed a significantly higher risk of postural instability. Emerging PI phenotype was identified before onset of postural instability in 289 of 301 paired comparisons, with a median progression time of 972 days. Baseline cognitive performance was similar but declined more rapidly in ePI phenotype. We provide an ePI phenotype rating scale (ePIRS) for evaluation of individual risk at baseline for progression to postural instability.

SNCA and mTOR Pathway Single Nucleotide Polymorphisms Interact to Modulate the Age at Onset of Parkinson's Disease

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the α-synuclein (SNCA) gene are associated with differential risk and age at onset (AAO) of both idiopathic and Leucine-rich repeat kinase 2 (LRRK2)-associated Parkinson's disease (PD). Yet potential combinatory or synergistic effects among several modulatory SNPs for PD risk or AAO remain largely underexplored.

OBJECTIVES

The mechanistic target of rapamycin (mTOR) signaling pathway is functionally impaired in PD. Here we explored whether SNPs in the mTOR pathway, alone or by epistatic interaction with known susceptibility factors, can modulate PD risk and AAO.

METHODS

Based on functional relevance, we selected a total of 64 SNPs mapping to a total of 57 genes from the mTOR pathway and genotyped a discovery series cohort encompassing 898 PD patients and 921 controls. As a replication series, we screened 4170 PD and 3014 controls available from the International Parkinson's Disease Genomics Consortium.

RESULTS

In the discovery series cohort, we found a 4-loci interaction involving STK11 rs8111699, FCHSD1 rs456998, GSK3B rs1732170, and SNCA rs356219, which was associated with an increased risk of PD (odds ratio = 2.59, P < .001). In addition, we also found a 3-loci epistatic combination of RPTOR rs11868112 and RPS6KA2 rs6456121 with SNCA rs356219, which was associated (odds ratio = 2.89; P < .0001) with differential AAO. The latter was further validated (odds ratio = 1.56; P = 0.046-0.047) in the International Parkinson's Disease Genomics Consortium cohort.

CONCLUSIONS

These findings indicate that genetic variability in the mTOR pathway contributes to SNCA effects in a nonlinear epistatic manner to modulate differential AAO in PD, unraveling the contribution of this cascade in the pathogenesis of the disease. © 2019 International Parkinson and Movement Disorder Society.

Parkinson's progression prediction using machine learning and serum cytokines

The heterogeneous nature of Parkinson’s disease (PD) symptoms and variability in their progression complicates patient treatment and interpretation of clinical trials. Consequently, there is much interest in developing models that can predict PD progression. In this study we have used serum samples from a clinically well characterized longitudinally followed Michael J Fox Foundation cohort of PD patients with and without the common leucine-rich repeat kinase 2 (LRRK2) G2019S mutation. We have measured 27 inflammatory cytokines and chemokines in serum at baseline and after 1 year to investigate cytokine stability. We then used the baseline measurements in conjunction with machine learning models to predict longitudinal clinical outcomes after 2 years follow up. Using the normalized root mean square error (NRMSE) as a measure of performance, the best prediction models were for the motor symptom severity scales, with NRMSE of 0.1123 for the Hoehn and Yahr scale and 0.1193 for the unified Parkinson’s disease rating scale part three (UPDRS III). For each model, the top variables contributing to prediction were identified, with the chemokines macrophage inflammatory protein one alpha (MIP1α), and monocyte chemoattractant protein one (MCP1) making the biggest peripheral contribution to prediction of Hoehn and Yahr and UPDRS III, respectively. These results provide information on the longitudinal assessment of peripheral inflammatory cytokines in PD and give evidence that peripheral cytokines may have utility for aiding prediction of PD progression using machine learning models.