An Integrative Network Approach for Longitudinal Stratification in Parkinson’s Disease
Barry Ryan, Riccardo Marioni, T. Ian Simpson.
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms resulting from the loss of dopamine-producing neurons in the brain. Currently, there is no cure for the disease which is in part due to the heterogeneity in patient symptoms, trajectories and manifestations. There is a known genetic component of PD and genomic datasets have helped to uncover some aspects of the disease. Understanding the longitudinal variability of PD is essential as it has been theorised that there are different triggers and underlying disease mechanisms at different points during disease progression.
Introduction
Parkinson’s disease (PD) is a heterogenous, progressive, multisystem neurological disorder that affects the nervous system. It is most commonly characterised by a range of motor symptoms, primarily involving difficulties with movement, however a wide variety of non-motor symptoms also exist. PD has a complex pathophysiology, but these disease pathways culminate in the gradual death of neuronal cells, causing a deficit in dopamine.
Materials and Methods:
There are two main components in the MOGDx framework; PSN generation and Graph Convolutional Network with Multi-Modal Encoder training and classification. A single PSN is built per modality. Feature selection is performed, and similarity is measured between these features using Pearson correlation, where suitable, otherwise Euclidean distance. Each PSN is constructed from the similarity matrix using the k nearest neighbours algorithm. Similarity Network Fusion (SNF) is then used to combine individual PSN’s into a single network. As is common practice, all patient information is used to construct the network, with train, validation and test labels created during the training phase of the GCN-MME.
Discussion
In this paper, we applied an integrative network framework and artificial intelligence to the PPMI dataset. The PPMI dataset is an observational, international study, consisting of multiple data modalities, with the goal of identifying markers of PD to accelerate disease modifying clinical trials [17]. We used clinical, genomic, and proteomic data to include numerous patient samples and conducted cross-sectional and longitudinal stratification of participants who have PD, have an early indication of developing PD (PL), or were a HC.
Citation: Ryan B, Marioni R, Simpson TI (2025) An integrative network approach for longitudinal stratification in Parkinson’s disease. PLoS Comput Biol 21(3): e1012857. https://doi.org/10.1371/journal.pcbi.1012857
Editor: Sushmita Roy
Received: May 10, 2024; Accepted: February 6, 2025; Published: March 28, 2025.
Copyright: © 2025 Ryan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data used in the preparation of this article were obtained on April, 5th 2022 from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data), RRID:SCR 006431. Using the following datasets: Project 133 RNA Sequencing Methods Project 133 Small RNA Transcriptome Sequencing Read Counts Project 140: Comprehensive Methylation Profiling of the PPMI Cohort Project 107: NeuroX SNP Data Project 151 Identification of proteins & protein networks & pQTL analysis in CSF All code is available for download from a dedicated GitHub repository - https://github.com/biomedicalinformaticsgroup/MOGDx-PPMI.
Funding: This work was supported by the UKRI Centre for Doctoral Training in Biomedical Artificial Intelligence (EP/S02431X/1, TIS & BR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: REM is a scientific advisor to Optima Partners and the Epigenetic Clock Development Foundation.
