Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations

• Published in: Metabolomics Vol. 20; no. 4; p. 86
• Main Authors: Li, Lu, Yan, Shi, Horner, David, Rasmussen, Morten A., Smilde, Age K., Acar, Evrim
• Format: Journal Article
• Language: English
• Published: New York Springer US 27.07.2024; Springer Nature B.V
• Subjects: Adult; Biochemistry; Biomarkers; Biomarkers - blood; Biomarkers - metabolism; Biomedical and Life Sciences; Biomedicine; Body mass index; Cell Biology; Developmental Biology; Fasting; Fasting - metabolism; Female; Humans; Life Sciences; Male; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Middle Aged; Molecular Medicine; Original Article; Phenotypes; Postprandial Period; Postprandial metabolomics data; Data fusion; Coupled matrix and tensor factorizations; Tensor factorizations; Dynamic biomarkers
• DOI: 10.1007/s11306-024-02128-9
• ISSN: 1573-3890; 1573-3882

Introduction Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects , metabolites , and time . The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Objectives Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. Methods We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC 2000 cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data ( subjects by metabolites by time ) is coupled with the fasting data ( subjects by metabolites ) in the subjects mode. Results The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. Conclusion The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications—when markers are present in both fasting and dynamic states.