Clinical, Radiologic and Biochemical Factors Related to Diabetes Development after Acute Pancreatitis

急性胰腺炎后与糖尿病发展相关的临床、放射学和生化因素

• 批准号: 10458035
• 负责人: Rita R Kalyani
• 金额: $ 28.66万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10458035
• 关键词: Acute; Acute Necrotizing Pancreatitis; Address; Antibodies; Autoantibodies; Autoantigens; Autoimmune Diabetes; Autoimmune Diseases; Autoimmune Responses; Autologous Transplantation; Beta Cell; Biochemical; Biological Markers; Biopsy Specimen; Clinical; Clinical Data; Clinical/Radiologic; Collection; Communicable Diseases; Development; Diabetes Mellitus; Diabetes autoantibodies; Disease; Disease Progression; Fatty acid glycerol esters; Functional disorder; Future; Glucagon; Goals; Hospitals; Human; Hypoglycemia; Image; Impairment; Incidence; Individual; Inflammatory; Insulin; Insulin-Dependent Diabetes Mellitus; Lead; Life; Machine Learning; Malignant Neoplasms; Mediating; Meta-Analysis; Methodology; Morbidity - disease rate; Organ; Organ failure; Pancreas; Pancreatic Diseases; Pathology; Patients; Prediabetes syndrome; Process; Proteome; Proteomics; Public Health; Radiology Specialty; Recording of previous events; Recovery; Recurrence; Research; Research Design; Research Personnel; Resolution; Resources; Risk Factors; Role; Serology; Serum; Systemic Inflammatory Response Syndrome; Techniques; Technology; Texture; Time; Total Pancreatectomy; Visceral; X-Ray Computed Tomography; acute pancreatitis; biomarker identification; clinical center; clinical risk; experience; follow-up; impaired glucose tolerance; improved; islet; machine learning model; machine learning prediction; macrovascular disease; model development; mortality; novel; nutrient absorption; predictive modeling; radiological imaging; radiomics; subcutaneous; systematic review

Abstract Nearly 40% of patients develop diabetes after an initial episode of acute pancreatitis (AP). Various studies have evaluated risk factors for the development of diabetes but they have shown inconsistent findings, suggesting methodologic shortcomings. The present proposal consists of three specific aims where we will attempt to determine the biochemical, radiologic and clinical factors related to the development of diabetes after AP. Aim 1: To identify autoantibodies associated with the progression of type 1 diabetes in patients after AP using a large human proteome array. Aim 2: To study the role of imaging, more specifically quantitative textural analysis to predict the development of type 1 diabetes after AP. Aim 3 is to build a machine learning model to predict type 1 diabetes after AP using patient-related risk factors, textural analysis on imaging and autoantibodies involved in disease progression. Our proposal will help us better understand diabetes after AP. Successful completion of this study has the potential to improve management for AP.

摘要 近40%的患者在急性胰腺炎(AP)首次发作后发展为糖尿病。各种研究都有 评估了糖尿病发展的风险因素，但他们的结果不一致，表明 方法上的缺陷。目前的提议包括三个具体目标，我们将在这些目标中尝试 确定与AP后糖尿病发展相关的生化、放射学和临床因素。目标 1：在AP后患者中使用大量的自身抗体来确定与1型糖尿病进展相关的自身抗体 人类蛋白质组阵列。目标2：研究成像的作用，更具体地说，定量质地分析 预测AP后1型糖尿病的发展。目标3是建立一个机器学习模型来预测类型 1使用患者相关危险因素对AP后糖尿病进行成像和自身抗体的质构分析 在疾病的发展过程中。我们的建议将帮助我们更好地了解AP后的糖尿病。成功完成 这项研究的成果有可能改善AP的管理。