Proteomic Biomarkers Prognostic for Diabetic Wound Healing

预测糖尿病伤口愈合的蛋白质组生物标志物

• 批准号: 10396875
• 负责人: Monika Anna Niewczas
• 金额: $ 56.87万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-25 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10396875
• 关键词: Address; Amputation; Basic Science; Biological Markers; Biological Process; Biometry; Carboxypeptidase; Cell Adhesion Molecules; Clinical; Clinical Research; Complement; Complex; Complications of Diabetes Mellitus; Data; Development; Diabetes Mellitus; Diabetic Foot; Diabetic Foot Ulcer; Discrimination; Disease; Immunoassay; Impaired wound healing; Individual; Inflammatory; Knowledge; Logistic Models; Logistic Regressions; Lower Extremity; Machine Learning; Measurement; Methods; Modeling; Outcome; Pathway Analysis; Pathway interactions; Performance; Phase; Phenotype; Pilot Projects; Play; Prognostic Marker; Prospective cohort; Proteins; Proteome; Proteomics; Public Health; Quality of life; Reporting; Reproducibility; Research Personnel; Risk; Role; Sensitivity and Specificity; Target Populations; Testing; Translational Research; Validation; Work; beta catenin; biomarker panel; biomarker signature; candidate marker; cell type; clinical trial enrollment; cohort; cross reactivity; detection method; diabetic wound healing; drug development; high risk; interest; machine learning method; mortality risk; prognostic; prognostic signature; prognostication; prospective; protein biomarkers; random forest; research clinical testing; single-cell RNA sequencing; statistics; support tools; targeted biomarker; tool; transcription factor; wound healing

PROJECT SUMMARY / ABSTRACT Impaired wound healing is an alarming problem in diabetes, attributed to the development of over 750,000 diabetic foot ulcerations (DFU) and 70,000 lower extremity amputations per year in the USA. DFU is a heterogeneous disease with a variable clinical course and there is an urgent, unmet need to identify subjects who are at higher risk of an impaired wound healing. Our preliminary collaborative study of the circulating proteome points to the interesting candidate biomarker proteins prognostic of the DFU course. In our pilot, study we have identified proteins associated with the prospective wound healing outcome belonging to such classes as adhesion molecules, carboxypeptidases, and inflammatory proteins among others. Pathway network analyses seeded with these proteins revealed β catenin and cellular Myc (c-Myc) among the most connected nodes. Interestingly, Wnt/β catenin pathway and transcription factor, c-Myc have been extensively implicated to play a role in the DFU including work of the Diabetic Foot Consortium (DFC) investigators. The DFU course is heterogeneous and multi-factorial, thus we hypothesize that multi-biomarker panel will offer the most optimal prognostication. Biological processes reflected by protein levels are often connected, thus we hypothesize that by employing parallal biostatistical and machine learning approach will offer us tools to build a robust signature. This proposal has the three aims: Aim 1 (R61 phase) will focus on refining candidate protein exemplars for a prospective wound healing among subjects with diabetes. We will perform semi-targeted measurements of our candidate proteins (identified in our preliminary study) in subjects of the DFC cohort followed for a 3-month disease course. We will employ biostatistical tools supported by machine learning methods, adequate for correlated data to finally determine key protein exemplars. In Aim 2, (R61 phase), we will develop a focused, proteomics biomarker signature comprising of a targeted quantitative panel of 10-12 proteins. For that purpose, we will perform an extensive analytical validation and subsequently we will build a focused and quantitative multi- biomarker panel. Finally, in Aim 3 (R33 phase) will initiate determination of the clinical utility of our prognostic biomarker signature for the DFU course. To this end, we will perform targeted biomarker measurements in the DFC cohort. We will employ rigorous biostatistical metrics to evaluate the biomarker signature’s performance (integrated discrimination ability, Akake criterion among others). Advancements in this project will identify and initially determine a value of the biomarker signature prognostic for the prospective diabetic wound healing course. These efforts will aid in refining a target population with a phenotype of interest by providing objective quantifiable metrics that can enhance clinical trial enrollment criteria for Phase 2/3 studies.

项目概要/摘要 伤口愈合受损是糖尿病中一个令人担忧的问题，归因于超过 750,000 在美国，每年有 70,000 例糖尿病足溃疡 (DFU) 和 70,000 例下肢截肢。 DFU 是一个 具有不同临床病程的异质性疾病，并且迫切需要确定受试者，但尚未得到满足 伤口愈合受损的风险较高的人。我们对流通的初步合作研究 蛋白质组指出了 DFU 过程预后的有趣候选生物标志物蛋白质。在我们的试点中，研究 我们已经鉴定出与属于此类的预期伤口愈合结果相关的蛋白质 如粘附分子、羧肽酶和炎症蛋白等。通路网络 用这些蛋白质进行的分析表明，β 连环蛋白和细胞 Myc (c-Myc) 是最相关的 节点。有趣的是，Wnt/β 连环蛋白途径和转录因子 c-Myc 与 在 DFU 中发挥作用，包括糖尿病足联盟 (DFC) 研究人员的工作。 DFU课程是 异质性和多因素，因此我们假设多生物标志物组将提供最佳的 预测。蛋白质水平反映的生物过程通常是相互关联的，因此我们假设 通过采用并行生物统计和机器学习方法将为我们提供构建强大签名的工具。 该提案具有三个目标： 目标 1（R61 阶段）将侧重于提炼候选蛋白质范例 糖尿病患者的预期伤口愈合。我们将对我们的产品进行半针对性测量 对 DFC 队列受试者的候选蛋白质（在我们的初步研究中确定）进行了为期 3 个月的跟踪 病程。我们将采用由机器学习方法支持的生物统计工具，足以满足 相关数据最终确定关键蛋白质范例。在目标 2（R61 阶段）中，我们将开发一个有重点的、 蛋白质组学生物标志物特征由 10-12 种蛋白质的靶向定量组组成。为此， 我们将进行广泛的分析验证，随后我们将建立一个有针对性的定量多 生物标志物面板。最后，目标 3（R33 阶段）将开始确定我们的预后的临床效用 DFU 课程的生物标记签名。为此，我们将在 DFC队列。我们将采用严格的生物统计指标来评估生物标志物特征的性能 （综合辨别能力、Akake 标准等）。该项目的进展将确定并 初步确定生物标志物特征对糖尿病伤口愈合的预测价值 课程。这些努力将通过提供客观的结果来帮助完善具有感兴趣表型的目标人群 可量化的指标，可以提高 2/3 期研究的临床试验招募标准。