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

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.

Automated summary

Longitudinal deep multiomics profiling holds great promise for precision health, but integrating and understanding the complexity of such data remains a challenge. In this study, an individual-focused approach was used to assess single individuals' multiomics time series and group multiple individuals based on similarity of their longitudinal deep multiomics profiles. The analysis of type 2 diabetes mellitus profiles identified individual-level immune responses and clusters of individuals with similar immune behaviors that were associated with their diabetic status.