Honing Precision Medicine for Type 2 Diabetes in the Veteran Population

/ABSTRACT Significance and Impact to Veterans Healthcare: Type 2 Diabetes (T2D) represents a significant health challenge among US veterans, with 25% affected and a growing prevalence over the past decade. Managing the progression of T2D and its complications such as kidney disease, neuropathy, retinopathy, and heart attack, is essential to mitigating the health consequences for veterans – particularly those in underserved populations and marginalized communities who are at the greatest risk – while also reducing the overall costs to the VA health system. Precision medicine, which leverages multi-modal data to identify and tailor risk prediction and treatment to individual veterans, holds promise in addressing this issue, and is the cornerstone of our proposed approach. Our proposal centers around addressing two major VHA/ORD priorities for the VA in the 2022-2028 strategic plan, including (i) Health Equity to underserved communities to address disparities, and (ii) Innovation from emerging methods in Genomics, Artificial Intelligence / Machine Learning, and Personalized Medicine. Background and Innovation. Our team leads the T2D Global Genomics Initiative (T2D-GGI), aimed at improving the understanding of the genetic basis of T2D and its complications. This international effort, which includes contributions from the Million Veteran Program (MVP), has identified >600 risk loci for T2D from over 2.5 million subjects, with substantial diversity among participants. Building on this, our proposal seeks to integrate genetic data with lifestyle, dietary, and newly available methylation data to predict T2D risk and its complications among veterans. Towards these objectives, in Aim One, we will develop a multi-factorial predictor for T2D and disease progression, using genetic risk factors, lifestyle, and dietary data on T2D and its complications from ~289,000 veterans from diverse populations, further enhanced using machine learning models and methylation data from VA participants. In Aim Two, using newly generated methylation profiling in MVP, we will map genetic variants associated with methylation differences among >45,000 subjects, linking these variations to T2D and metabolic traits. In Aim Three, we will apply statistical causal inference methods to define T2D subtypes and their impact on disease progression in veterans. Quantifying the causal effects of these subtypes on disease complications will guide the application of preventive precision medicine. Particularly innovative components of the proposal include: (i) the application and use of newly generated lifestyle, dietary, and genomics data in veterans to risk prediction problem and inference of gene regulatory networks, (ii) the scale of genomics study that is representative of the diverse demographics of the US, helping to address health disparities, (iii) the focus on disease progression and development of complications, important for veterans health now and into the future, and (iv) novel application of statistical methods and machine learning approaches to prediction and causal inference problems addressed in the proposal. Path to Translation/Implementation: By understanding the interplay between genetic, lifestyle, and environmental factors, we aim to develop tailored risk prediction models which could facilitate early identification and timely intervention of high-risk veterans. Identifying genetic variation associated with methylation differences could also reveal novel therapeutic targets. Furthermore, characterizing T2D subtypes and their impact on disease progression will guide targeted interventions, bringing precision medicine closer to reality for veterans. By achieving these aims, we hope to significantly advance the field of precision medicine for T2D, providing tailored and effective interventions for veterans at high risk of T2D and its complications. Project Number: 1I01BX007140-01 | Fiscal Year: 2026 | NIH Institute/Center: Veterans Affairs (VA) | Principal Investigator: Benjamin Voight | Institution: PHILADELPHIA VA MEDICAL CENTER, PHILADELPHIA, PA | Activity Code: I01 | Study Section: Special Emphasis Panel[ZRD1 ENDA-L (01)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11177220