  Parkinson's disease (PD) is a common neurodegenerative disease with a high
degree of heterogeneity in its clinical features, rate of progression, and
change of variables over time. In this work, we present a novel data-driven,
network-based Trajectory Profile Clustering (TPC) algorithm for 1)
identification of PD subtypes and 2) early prediction of disease progression in
individual patients. Our subtype identification is based not only on PD
variables, but also on their complex patterns of progression, providing a
useful tool for the analysis of large heterogenous, longitudinal data.
Specifically, we cluster patients based on the similarity of their trajectories
through a time series of bipartite networks connecting patients to demographic,
clinical, and genetic variables. We apply this approach to demographic and
clinical data from the Parkinson's Progression Markers Initiative (PPMI)
dataset and identify 3 patient clusters, consistent with 3 distinct PD
subtypes, each with a characteristic variable progression profile.
Additionally, TPC predicts an individual patient's subtype and future disease
trajectory, based on baseline assessments. Application of our approach resulted
in 74% accurate subtype prediction in year 5 in a test/validation cohort.
Furthermore, we show that genetic variability can be integrated seamlessly in
our TPC approach. In summary, using PD as a model for chronic progressive
diseases, we show that TPC leverages high-dimensional longitudinal datasets for
subtype identification and early prediction of individual disease subtype. We
anticipate this approach will be broadly applicable to multidimensional
longitudinal datasets in diverse chronic diseases.
