Integrative analysis of host omics and microbiome data provides hints at common pathways and biomarkers for diabetes risk

Abstract

Abstract Background Type 2 Diabetes (T2D) is a complex metabolic disease whose associated pathways and biomarkers need to be explained using muti-omics integration approach for getting a holistic view for T2D. Methods In this study, publicly available host omics data (RNA-seq, proteome, metabolome, and cytokines) were integrated with microbiome 16S rRNA sequencing data from both gut and the nasal cavity of 291 prediabetic and 39 control samples. Results Our study uncovered four main insights; first, 27 common pathways between all previous omics data were enriched for functional categories related to amino acids, carbohydrates, and lipid metabolism that are mainly affected by the disruption of the insulin level leading to risk of T2D incident. Second, the metabolome data shared the greatest number of significant pathways with the microbiome data, followed by RNA-seq data, with 14 and 3 pathways, respectively. Third, Glycerophospholipid metabolism was the only pathway that was common between metabolome, RNA-seq, and microbiome data of gut and nasal cavity. Last, the metabolome was the best omic data that was able to distinguish between prediabetic and control samples, with an area under the curve score of 0.98. Conclusion Our study succeeded to achieve the host-microbiome integration through finding common pathways and diagnostic biomarkers for progression of prediabetes to T2D.