Personalized Medicine; Systems Biology; Genetics; Diabetes Type 2
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Diabetes mellitus type 2 is a global epidemic affecting over 200 million people, and there are currently no truly curative and effective interventions. To improve diagnosis and prognosis, and thus reduce diabetes incidence, a better understanding of the molecular mechanisms underlying diabetes is needed. Specifically, the role of the human microbiome in diabetes is currently poorly understood but potentially plays a large role in disease progression. Here, we propose to use personalized omics profiling of 100 subjects with high risk of developing diabetes to investigate the molecular causes of diabetes using longitudinal high throughput measurements of the human microbiome and metabolome. This will allow us to apply a personalized systems biology approach to investigate the host-microbiome interaction and observe dynamic changes of the microbiome during disease progression. Using network-based analysis on the molecular level, we expect to obtain functional insights into the microbial community structure and explore its temporal evolution on an individual basis. The proposed large-scale profiling and the longitudinal nature of the analysis will by far exceed any previous study to date both in number of time points as well as in number of molecular components measured. The deep personalized profiling will likely reveal novel interactions between microbial pathways and host metabolism, which can contribute to our understanding of diabetes and serve as biomarker signature that may be useful for the prevention of the disease.