Hypercholesterolemia Risk Prediction from Serum Metabolomics Using a Metabolic Pathway-Integrated Graph Neural Network

In this paper, we presents a new machine learning framework called the Metabolic Pathway Graph Neural Network (MP-GNN), aimed at predicting hypercholesterolemia risk based on serum metabolomics data. Many existing analytical frameworks for analyzing metabolites in clinical applications overlook the complex biochemical relationships that exist among metabolites. MP-GNN explicitly takes advantage of existing knowledge about metabolic pathways by incorporating that information into graph templates where metabolites are represented as nodes and metabolic pathway interactions between metabolites are represented as edges. A comparative analysis of MP-GNN was conducted with a large-scale study of population metabolomics against several conventional machine learning and several state-of-the-art machine learning methods. The results of the simulation indicated that MP-GNN was able to provide for highly accurate prediction of risk, and importantly, provide interpretability that was biologically meaningful based on findings in the literature. Importantly, the analysis revealed several key metabolites, and also several biological metabolic pathways that were found to be significant related to prediction, which were consistent with findings in biological studies. The findings support the potential of MP-GNN to leverage prior biological knowledge to enhance predictive performance and expand our ability to gain insight into complex diseases.