Analyzing postprandial metabolomics data using multiway models: A simulation study

Abstract

AbstractAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early-diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as asubjectsbymetabolitesbytime pointsarray. Traditional analysis methods are limited in revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. In this paper, we introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis offasting-statedata using Principal Component Analysis,T0-correcteddata (i.e., data corrected by subtractingfasting-statedata) using a CP model andfull-dynamic(i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased vs. healthy groups. Our simulations also show that it is crucial to analyze bothfasting-stateandT0-correcteddata for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models ofT0-correctedorfull-dynamicdata. Furthermore, we show that the proposed approach reveals differences between subject groups (according to body mass index) in terms of their response to a real meal challenge test. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.Author summaryPostprandial metabolomics data can mirror the ability of the human body to cope with a meal challenge. Analysis of such data can reveal differences among subject groups, discover biomarkers related to (early-onset) disease and holds the promise to guide personalized nutrition. However, traditional analysis methods are limited in terms of revealing the underlying patterns from such time-resolved data which is inherently a multiway array: asubjectsbymetabolitesbytime pointsarray. In this paper, we introduce an unsupervised approach based on multiway data analysis for postprandial metabolomics data, and assess its performance using simulated data based on a comprehensive human metabolic model. We demonstrate that the overall picture of metabolic differences among subject groups (e.g., healthy vs. diseased) and the underlying metabolic processes can be captured through the analysis offasting-statedata and multiway analysis of thedynamicdata. We show the promise of the proposed approach through real postprandial metabolomics data analysis and discuss the patterns revealing metabolic differences among subject groups (according to body mass index (BMI)) in terms of their response to a meal challenge test.