Utilizing islet autoantibody signatures for precision medicine in type 2 diabetes

利用胰岛自身抗体特征进行 2 型糖尿病精准医疗

• 批准号: 10096119
• 负责人: Nisa M Maruthur
• 金额: $ 47.94万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10096119
• 关键词: Address; Affect; Antibodies; Antigens; Autoantibodies; Autoimmune; Autoimmunity; Bayesian Modeling; Beta Cell; Biological Assay; Blood specimen; Caring; Cell physiology; Classification; Clinical; Cluster Analysis; Complications of Diabetes Mellitus; Data; Data Display; Diabetes Mellitus; Diagnosis; Disease; Disease model; Epidemiology; Epitopes; Failure; Future; Goals; Health; Heterogeneity; Immune system; Immunoglobulin G; Immunologics; Insulin Resistance; Knowledge; Laboratories; Longitudinal cohort; Malignant neoplasm of prostate; Measures; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Onset of illness; Outcome; Overweight; Participant; Patients; Pattern; Phenotype; Pilot Projects; Play; Prevalence; Prognosis; Research Project Grants; Risk; Role; Scleroderma; Self-Direction; Specificity; Structure of beta Cell of islet; Subgroup; Techniques; Testing; Time; Visualization; Visualization software; Work; adult obesity; base; clinical effect; clinical phenotype; clinically significant; cohort; data visualization; follow-up; glycemic control; high risk; improved; individualized medicine; insulinoma; islet autoimmunity; islet cell antibody; lifestyle intervention; mortality; novel; precision medicine; preservation; randomized trial; response; tool; trend; zinc-binding protein

For a substantial proportion of patients diagnosed with type 2 diabetes, islet autoimmunity plays an important role in disease onset, risk of complications, and progression to pancreatic β-cell failure. However, advances in understanding the heterogeneity within type 2 diabetes have not yet been harnessed to improve clinical diabetes care. In this proposal, we will use state-of-the-art assays and novel statistical techniques in Look AHEAD (Action for Health in Diabetes), a well-characterized longitudinal cohort in order to develop a precision medicine approach to islet autoimmunity in type 2 diabetes care. We will measure islet autoantibody assays currently available for clinical use (antibodies to GAD65, insulinoma antigen 2, and zinc transporter 8), and novel assays that are critical for determining clinical significance and could be widely available in the near future. Novel assays include the GAD65 antibody epitope specificity and IgG subclass, which are key determinants of immunologic activity. We will also measure an antibody to novel epitopes of insulinoma antigen 2 which has been shown to greatly expand its sensitivity and improve diabetes classification. With these rich islet autoantibody data, we will be able to identify patterns of islet autoimmunity that more accurately predict a patient’s diabetes phenotype than traditional measures. We will accomplish this in Look AHEAD, a U.S.-based randomized trial investigated the effects of an intensive lifestyle intervention versus self-directed care among 5,145 overweight/obese adults with type 2 diabetes. Participants have been followed for >15 years with detailed longitudinal ascertainment of clinical and laboratory data, diabetes complications, and mortality. Our pilot data has identified a 6.4% prevalence of traditional islet autoantibodies and associations with mortality and other outcomes. Informed by our work in precision medicine for other disease models, we will use advanced statistical techniques and interactive visualizations to develop a platform for clinicians to interpret the significance of islet autoimmunity. Bayesian hierarchical models will jointly estimate patterns of autoimmunity and associations with a patient’s health trajectory, and we will develop a visualization tool to present these predictions for clinicians. We have developed such visualization tools for prostate cancer, scleroderma, and other disease states which are currently implemented in Johns Hopkins Precision Medicine Centers of Excellence. Our specific aims are: Aim 1) To identify, among Look AHEAD participants, islet autoantibody signatures (patterns of antibody positivity, epitope specificity, titer, and IgG subclass) and determine their association and clustering with clinical features; Aim 2) Determine the impact of islet autoimmunity on outcomes; and Aim 3) To develop clinically useful a data visualization to display these findings to clinicians. Through these aims, we will achieve our overall goal to advance the epidemiologic knowledge and statistical tools for understanding islet autoimmunity in type 2 diabetes so that it can be used for precision diabetes care.

对于大部分诊断为2型糖尿病的患者，胰岛自身免疫在疾病发作、并发症风险和胰腺β细胞衰竭进展中起重要作用。然而，在了解2型糖尿病异质性方面的进展尚未被用于改善临床糖尿病护理。在这项提案中，我们将在Look AHEAD（糖尿病健康行动）中使用最先进的测定和新的统计技术，这是一个具有良好特征的纵向队列，旨在开发2型糖尿病护理中胰岛自身免疫的精确医学方法。 我们将测量目前可用于临床的胰岛自身抗体检测（GAD 65抗体，胰岛素瘤抗原2和锌转运蛋白8），以及对确定临床意义至关重要的新检测，并可能在不久的将来广泛使用。新的检测方法包括GAD 65抗体表位特异性和IgG亚类，这是免疫活性的关键决定因素。我们还将测量针对胰岛素瘤抗原2的新表位的抗体，该抗体已被证明可以大大扩展其灵敏度并改善糖尿病分类。有了这些丰富的胰岛自身抗体数据，我们将能够确定胰岛自身免疫的模式，比传统的措施更准确地预测患者的糖尿病表型。 我们将在展望未来中实现这一目标，美国-一项随机试验研究了5，145名超重/肥胖的2型糖尿病成年人中强化生活方式干预与自我护理的效果。参与者已被跟踪超过15年，详细纵向确定临床和实验室数据，糖尿病并发症和死亡率。我们的试点数据已经确定了传统胰岛自身抗体的患病率为6.4%，并与死亡率和其他结局相关。根据我们在其他疾病模型的精准医学方面的工作，我们将使用先进的统计技术和交互式可视化为临床医生开发一个平台，以解释胰岛自身免疫的意义。贝叶斯分层模型将联合估计自身免疫的模式和与患者健康轨迹的关联，我们将开发一个可视化工具来为临床医生提供这些预测。我们已经为前列腺癌、硬皮病和其他疾病状态开发了这样的可视化工具，这些工具目前在约翰霍普金斯精准医学卓越中心实施。 我们的具体目标是：目的1）在Look AHEAD参与者中识别胰岛自身抗体特征（抗体阳性、表位特异性、滴度和IgG亚类的模式），并确定其与临床特征的关联和聚类;目的2）确定胰岛自身免疫对结局的影响;目的3）开发临床有用的数据可视化，以向临床医生显示这些结果。通过这些目标，我们将实现我们的总体目标，即推进流行病学知识和统计工具，以了解2型糖尿病中的胰岛自身免疫，使其可用于精确的糖尿病护理。