Abstract

T2DM is the fasting growing chronic disease worldwide and poses a substantial burden to the patient and healthcare system. Thus, the clinical management of T2DM is of global concern. However, due to the complex nature of the disease progression, the relationship between comorbidities and glycemic control remain poorly understood. The ability to improve our understanding of the common disease trajectories starting from diagnosis would provide new insights into disease phenotypes, risk factors, and provide opportunities to develop personalized treatment plans. Additionally, one clinical area of concern within diabetes management is the risk of fragility fractures. While patients with T2DM often have normal or even increased BMD, studies consistently show these patients have an increased risk of fragility fracture. While a number of studies have examined common fracture risk factors for fracture, observational and animal studies are conflicting. Thus, through the collaboration with the SDSC we aim to explore new methods to capture the complex and dynamic nature of patient trajectories. To achieve this aim, this collaboration grant will bring together experts in pharmacoepidemiology, real-world data analytics, social network analysis, and machine learning to develop interpretable models that will serve as an important step towards identifying high-risk patients and subsequently prevent adverse health outcomes. In particular, PolyNet has two primary research objectives to solve the above identified gaps in T2DM care: 1) To explore new methodologies to characterize and visualize common disease and comorbidity trajectories in patients, and 2) To develop longitudinal models to address important clinical questions in T2DM – predicting glycemic control changes and fragility fracture risk. All projects will leverage data from the world’s largest primary care database, the UK Clinical Practice Research Datalink, and will include substantial interaction with the SDSC.