Prognostic MRI and Blood Markers of Wound Healing in Diabetic Foot Ulcers

项目总结/摘要 这项提案的目的是建立和翻译新的非侵入性成像方法和血液 糖尿病足溃疡（DFU）环境中伤口愈合不完全的预后标志物。超过3000万 在美国，人们患有糖尿病。DFU占所有糖尿病相关医院费用的25-50% 在美国，每年总计约400亿美元，通常是即将到来的肢体瘫痪的第一个公认的迹象。 损失DFU的终生风险高达25%，其治疗是资源密集型的，需要抗生素， 频繁的伤口护理和外科手术，但1年愈合率仅为75%。一旦溃疡形成， 肢体丧失的风险很高，高达20%的患者接受下肢截肢。强大的基础和 临床证据表明，溃疡的发生和伤口愈合不良都是由组织减少引起的 灌注和缺血。在细胞水平，不愈合DFU反映了慢性炎症状态。过渡 从促炎症/促修复状态到抗炎/促修复状态-伤口愈合所需的-依赖于足够的 募集的中性粒细胞和单核细胞的免疫代谢编程。糖酵解和氧化 在糖尿病中，这些骨髓亚群的代谢得到了充分的证明，这与受损的 伤口愈合，但其在DFU环境中的确切影响尚不清楚。目前没有有效 用于预测溃疡愈合的临床工具。我们假设使用灌注的多参数方法 磁共振成像（MRI）和免疫代谢分析将改善对不良伤口的诊断 在DFU愈合，并告知更有针对性的治疗策略，在这种复杂的条件。我们的NIDDK糖尿病 并发症联盟的初步研究表明，使用灌注MRI和单核细胞 用于预测伤口愈合DFU的呼吸测定法。有了这个技术基础， 将扩展这些方法，以改进DFU中不完全伤口修复的表征。我们将奉行 三个相关的具体目标在目标1中，我们将对灌注MRI的灵敏度和再现性进行基准测试 测量以表征DFU患者的微循环异常并预测伤口愈合。 在目标2中，我们将建立DFU中伤口愈合不良的全身和局部代谢标志物。在目标3中， 将多参数统计学习应用于灌注MRI和血液标记物，以开发预测模型 伤口愈合受损的案例我们希望这项工作能提供新的信息，更有效地 DFU的治疗策略如果成功，该分类平台将改善DFU患者的表型 以获得更个性化的治疗策略，并改善DFU的临床管理。

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.