Given the projected increasing burden of diabetes in the future, understanding modifiable risk factors for its development is important for public health in order to develop appropriate and targeted preventive strategies. While it has been well described that obesity is a risk factor for type 2 diabetes development, critical questions regarding the relationship of body composition and diabetes remain unanswered, particularly given that research linking body-mass index (BMI) to diabetes only partially addresses the role of body composition and that BMI may also be influenced by muscle mass. More specifically: 1) How do changes in lean body (muscle) mass over time, particularly declines in lean body mass, relate to the risk of incident diabetes?; 2) Is the relationship of lean body mass changes to the development of diabetes stronger in older versus younger ages?; 3) Are there bidirectional influences of lean body mass with elevated glucose levels that may suggest a vicious cycle of decline? We propose to use complex, state-of-the art, statistical methodology to explore these questions in the ongoing Baltimore Longitudinal Study of Aging (BLSA), a cohort of community-dwelling individuals with extensive follow-up of over fifty years. The quality of the outcomes data, the frequency of glycemic assessments, and the duration of follow-up in BLSA are unparalleled. Our proposed study has the following aims: Aim 1: To describe the degree to which specific patterns of change in lean body mass over time (using dual-energy X-ray absorptiometry) are related to the development of diabetes, accounting for changes in total fat mass, using innovative latent class analytic methodology. In a Secondary Aim, we will explore the degree to which the relationship of lean body mass changes to the risk of diabetes differs in older versus younger age. Aim 2: To characterize the dynamic, bidirectional association between lean body mass and elevated glucose levels (dysglycemia) after accounting for changes in total body fat. We will use lead-lag structural equation modeling, a novel statistical technique that is particularly well suited for this purpose, to provide further insights. These analyses will serve two important purposes. First, this will be the first longitudinal data to demonstrate a relationship of adverse changes in lean body mass to incident diabetes, in order to refine the population for a clinical trial targeting novel prevention strategies for diabetes. Second, characterizing the potential bidirectional relationships of lean body mass and dysglycemia can inform the clinical management of elevated blood glucoses, particularly in those at high-risk for functional decline. This research should definitely answer the relevant questions that can be answered with observational data and are essential to move the field forward. Investigating the relationship of lean body mass changes to the development of diabetes is critically important to develop novel preventive strategies that can reduce the burden of this disease and its complications in the future.