Lecture: Defining Disease with Big Data

Disease have historically been defined from their clinical presentation including symptoms and objective findings. Along with the ongoing biotechnical revolution and its inherent possibilities to characterize individual people at the most basal cellular and molecular level with respect to their entire genome, transcriptome, proteome, epigenome, microbiome, metabolome, lipidome etc., our insights into distinct disease etiologies as well as category defining and predictive biomarkers is rapidly increasing. This combined with larger, more detailed, prospective and more reliable patient registries has opened up for unique opportunities to understand the heterogeneity of current disease classifications, and subsequently to re- and subclassify diseases in new and more clinical useful and logical ways according to distinct etiology, treatment needs and prognosis etc. This lecture will illustrate where we are today with respect to classifying and subgrouping one of the largest and most common chronic diseases, namely type 2 diabetes. The respective speakers will address current and future potentials of using “big data” to understand and subclassify type 2 diabetes, and how this may provide the needed breakthroughs in precision medicine in the disease.