PROJECT SUMMARY / ABSTRACT The objective of this proposal is to establish and translate novel noninvasive imaging methods and blood markers prognostic of incomplete wound healing in the setting of diabetic foot ulcers (DFU). Over 30 million people live with diabetes in the United States. DFUs account for 25-50% of all diabetes-related hospital costs in the US totaling approximately $40 billion per year and are often the first recognized sign of impending limb loss. The lifetime risk for DFU is up to 25% and its treatment is resource-intensive requiring antibiotics, frequent wound care, and surgical procedures, yet 1-year healing rates are only 75%. Once an ulcer forms, the risk of limb loss is high with up to 20% of patients undergoing lower-extremity amputation. Strong basic and clinical evidence demonstrates both initiation of ulcers and poor wound healing arise from reduced tissue perfusion and ischemia. At the cellular level, non-healing DFU reflect a chronic inflammatory state. Transition from a pro- to an anti-inflammatory/pro-repair state—required for wound resolution—relies on the adequate immunometabolic programming of recruited neutrophils and monocytes. Altered glycolysis and oxidative metabolism is well-documented in diabetes across these myeloid subsets, and this is associated with impaired wound healing, however their precise impact in the setting of DFU is unknown. Currently there are no effective clinical tools for predicting ulcer healing. We hypothesize that a multiparametric approach using perfusion magnetic resonance imaging (MRI) and immunometabolic profiling will improve diagnosis of poor wound healing in DFU and inform more targeted treatment strategies in this complex condition. Our NIDDK Diabetic Complications Consortium pilot study shows feasibility of using perfusion MRI and monocyte cellular respirometry for predicting wound healing DFU. With this technical foundation established, the proposed study will extend these methods for improved characterization of incomplete wound repair in DFUs. We will pursue three related specific aims. In Aim 1, we will benchmark sensitivity and reproducibility of perfusion MRI measurements to characterize microcirculatory abnormalities in patients with DFUs and predict wound healing. In Aim 2, we will establish systemic and local metabolic markers of poor wound healing in DFU. In Aim 3, we will apply multiparametric statistical learning to perfusion MRI and blood markers to develop predictive models of impaired wound healing in DFU. We expect this work to provide novel information for more effective treatment strategies of DFU. If successful, this classification platform will improve phenotyping of DFU patients for more personalized treatment strategies and lead to improved clinical management of DFU.