Analyzing postprandial metabolomics data using multiway models: a simulation study

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 94 - 22
• Main Authors: Li, Lu, Yan, Shi, Bakker, Barbara M., Hoefsloot, Huub, Chawes, Bo, Horner, David, Rasmussen, Morten A., Smilde, Age K., Acar, Evrim
• Format: Journal Article
• Language: English
• Published: London BioMed Central 04.03.2024; BMC
• Subjects: Algorithms; Bioinformatics; Biomarkers; Biomedical and Life Sciences; CANDECOMP/PARAFAC (CP); Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data analysis; Diabetes; Enzyme kinetics; Fasting; Glucose; Insulin resistance; Life Sciences; Meal challenge test; Metabolic disorders; Metabolism; Metabolites; Metabolomics; Microarrays; Musculoskeletal system; Ordinary differential equations; Performance assessment; Postprandial metabolomics data; Principal component analysis (PCA); Principal components analysis; Simulation; Tensor factorizations (multiway data analysis); Time-resolved metabolomics data; Variance analysis; CANDECOMP/PARAFAC (CP); Meal challenge test; Whole-body metabolic model; Time-resolved metabolomics data; Tensor factorizations (multiway data analysis); Postprandial metabolomics data; Principal component analysis (PCA)
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05686-w

Background Analysis 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 a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. Results 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 of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-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 versus healthy groups. Conclusions Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data 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 of T0-corrected or full-dynamic data. 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.