Type 2 diabetes mellitus is a chronic disease that may be amenable to precision medicine approaches because it affects a large, diverse segment of the population. In fact, the VA and American Diabetes Association guidelines recommend individualization of diabetes management, yet initial diabetes treatment is rarely individualized in current routine clinical care. The vast majority of diabetes patients are initially treated with metformin, and over a quarter of these patients fail to respond to metformin alone, leading to delays in achievement of early glycemic control and potentially avoidable risk of diabetes complications. There is a paucity of validated strategies to individualize initial diabetes treatment. Thus, precision medicine approaches, which attempt to match optimal disease management strategies to characteristics of an individual patient, are ideal to address the evidence gap to systematically guide individualized diabetes care. Individualized or precision medicine treatment is common in cardiovascular disease care, where clinical risk prediction tools guide drug selection (e.g., anticoagulation in atrial fibrillation) and treatment intensity (e.g., cholesterol goals on statin therapy). We propose a similar approach for diabetes treatment individualization based on prediction tools that estimate risk of diabetes complications and glycemic response to metformin. The overall goals of this career development award (CDA) are to develop and validate prediction tools that inform individualized diabetes care. The first two aims of the proposal are designed to determine patient characteristics at the onset of treatment that predict diabetes-related complications (Aim 1) and glycemic response to metformin (Aim 2). In Aim 3, we will evaluate whether these prediction models can inform treatment approaches to achieve improvements in long-term diabetes complications. We will leverage large VA data repositories to create two independent cohorts of Veterans with type 2 diabetes to complete the aims of this proposal and form the basis of additional future observational studies. This study is innovative in that it leverages real-world clinical data from Veterans to generate evidence to guide precision medicine interventions; uses machine learning approaches and longitudinal methods to capture information from repeated measurements in routine clinical care to make maximal use of electronic health record data; and focuses on prediction tools based on data available at the time of diabetes diagnosis to guide initial treatment. The career development plan aligns research aims with training aims in order to prepare the applicant to undertake a research career focused on applications of precision medicine to improve Veteran health. The training goals of the proposal are focused in three areas that share a common theme of maximizing longitudinal VA clinical data for clinically-relevant observational research: 1) machine learning approaches applied to a clinical database; 2) longitudinal methods, including growth mixture models, that enable identification of patterns in repeated clinical measures; 3) methods for causal inference using large- scale observational data. Upon successful completion of the proposed scientific and training aims, the applicant will be prepared to pursue precision medicine studies focused on the use of newer diabetes drugs, incorporation of genetic data into clinical prediction models, and evaluation of the effect of prediction model use in routine diabetes care. The diverse mentorship team has content expertise in diabetes and cardiovascular disease, and methodological expertise in machine learning, longitudinal epidemiological studies, and causal inference. Direct mentorship will be paired with coursework and seminars to fill gaps in the applicant?s training and ensure progress towards independence as a clinician-investigator. Finally, the environment is ideal for the applicant?s career development, including resources available through the VA HSR&D Denver-Seattle Center of Innovation for Veteran-Centric and Value-Driven Care and the University of Colorado School of Medicine.

IK2CX001907

2019-04-01

2024-03-31