Accumulation of Elevated Risk Associated with Treatment-Escalation Differences in patients with Type 2 Diabetes (ACCELERATED-T2D)

About This Grant

PROJECT SUMMARY/ABSTRACT Treatment intensification (Trt-I), the adjustment of antidiabetic medication (ADM) usage in response to inadequate glycemic control, is an essential component to effectively manage type 2 diabetes (T2D). However, the selection of ADM and the timing of Trt-I exhibit significant divergences that cannot be solely explained by clinical motivations. Numerous studies have indicated that the diverse Trt-I patterns can be significantly attributed to socioeconomic status (SES). Nevertheless, there remains a gap in understanding whether differences in Trt-I across various SES backgrounds persist over the long-term trajectory of diabetes care, which ultimately modifies the risk of costly and often irreversible complications. To address this knowledge gap, the investigators propose a study aiming to elucidate the pathways from SES to glycemic control and the complications through Trt-I. The research plan includes: (1) providing a comprehensive summary of diverse Trt-I patterns and ADM selection across SES indicators using multi-site electronic health records (EHR); (2) assessing the marginal and intermediary impact of Trt-I on the association between SES and diabetes outcomes using a structural equation modeling approach; and (3) testing the predictive capabilities of SES, Trt- I patterns, and glucose outcomes in relation to the early onset of diabetes complications using machine learning methods. The outlined research will provide insight into the cascading effects from SES to glycemic controls mediated by Trt-I modality, and its impact on diabetic complications. This career development plan is closely aligned with the aims of the research. Both research and training will be mentored by internationally-renowned and well-versed experts in the field of health outcomes research, clinical sciences in endocrinology, causal-inference modeling, machine learning and informatics: Dr. Todd Lee, Head of Pharmacy Systems, Outcomes and Policy of the UIC; Dr. Brian Layden, Chief of the UI Health Endocrinology Division and a steering committee member of the Chicago Diabetes & Training Center; Dr. Ali Cinar, Endowed Chair Professor of the Illinois Institute of Health Technology and Director of an Engineering Center for Diabetes Research and Education; Dr. Gegory Calip, Global Epidemiology Lead of Abbvie Inc.; and Dr. Jacob Krive, lead bioinformatician at UIHealth and Northshore University Health System. Combined with formal didactics, they will provide the support needed to achieve the training aims, developing skills and knowledge in the following areas: (1) Healthcare data analytics using structured equation models within the context of causal inference; (2) Contemporary use of ADM and clinical consensus on treatment decision; (3) Computational science and machine learning techniques specifically applicable to healthcare data analysis; and (4) leadership and grantsmanship skills necessary for directing future research programs. Upon completion of the training aims, I will be uniquely positioned as a lead investigator, having acquired rigorous experiential knowledge in various research methods for real-world health data science.