Diabetes is one of the prevalent chronic diseases affecting the quality of patient’s life seriously. According to the World Health Organization (WHO) report, the prevalence rate of diabetes patients in the worldwide population over 18 years old was estimated at 8.5% in 2014. In Korea, that of Korean population aged >= 30 years old was reported as 13.7% in 2016, together with prevalence of ‘impaired fasting glucose’ of 24.8% that is a precursor stage of diabetes. Precision Medicine (PM) is a field of medicine that manages patient-driven omics data, ‘electronic medical records (EMRs)’ and life-style data to provides treatment tailored to each person. To overcome the lack of ‘actionable genomic findings’ in diabetes research, several approaches based on PM, such as enlargement of sample size and increase of study power, phenotype with EMRs, analysis of omics data, and machine-learning with various patient-driven data such as microbiome, were suggested and has been in progress. Because wearable devices is also valuable in PM, the development of non-invasive continuous glucose meter is ongoing to help diabetes patients. Following trends in PM on chronic diseases, Division of Genome Research, KNIH has been producing, analyzing, and curating several omics data, including genomics, tranomics, epigenomics, and metabolomics for more sophisticated analysis. As a part of PM projects, it is expected that the integrative analysis of omics data, EMRs and life log data by artificial intelligence with deep learning technics would make us possible to predict, prevent and treat the diabetes in the precise manner.