Discovering Type 1 diabetes patient subgroups through integrative analysis of heterogeneous data

Abstract

Type 1 diabetes (T1D) is a disease in which the body immune system attacks the β-cells. As a result, very little, or no insulin is released to control the level of glucose in the blood. Our research investigates whether groups of patients at higher risk for developing T1D complications can be identified by integrating demographic, clinical and genetic data. Regarding this purpose, we explore two methods including Generalized Low Rank Models (GLRM) and Similarity Network Fusion (SNF) to investigate our T1D dataset and to determine groups of patients at higher risk of developing complications related to T1D. By applying the stated methods, we have identified groups of patients suffering from nerve damage, high blood pressure, dyslipidemia, and thyroid diseases. This result could be used as the basis to achieve a predictive model that could allow patients and health-care providers to take preemptive steps to reduce the risk of developing T1D related complications.