Longitudinal multi-omics of host–microbe dynamics in prediabetes - Zhou, Wenyu; Sailani, M. Reza; Contrepois, Kévin; Zhou, Yanjiao; Ahadi, Sara; Leopold, Shana R.; Zhang, Martin J.; Rao, Varsha; Avina, Monika; Mishra, Tejaswini; Johnson, Jethro; Lee-McMullen, Brittany; et al. (Nature (London), 2019)
Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states. Deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, alongside changes in the microbiome, in samples from individuals with and without prediabetes reveal insights into inter-individual variability and associations between changes in the microbiome and other factors.