Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

• Published in: Scientific reports Vol. 12; no. 1; pp. 12098 - 13
• Main Authors: Zheng, Minzhang, Piermarocchi, Carlo, Mias, George I.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.07.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2163; 631/61/212/2166; 692/308/53/2421; 692/4017; Big Data; Diabetes; Diabetes mellitus (non-insulin dependent); Humanities and Social Sciences; Immune response; multidisciplinary; Personal health; Physiology; Science; Science (multidisciplinary); Time series; Trends
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-16326-9

Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals’ multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.