Multimodal analysis of the "honeymoon period" in autoimmune diabetes

这项提案的最终目标是定义可用于改善结果的复合生物标记物 在未来的1型糖尿病(T1D)临床试验中。T1D是青少年糖尿病的主要原因。它的特点是 由自身免疫介导的ç细胞破坏引起的终生胰岛素不足。尽管付出了相当大的努力 在过去的30多年里，仍然缺乏有效的治疗方法，迫切需要治愈。天然 病史研究表明，T1D进展的速度在个体之间差异很大，无论是在以前，还是在 发病后。事实上，目前缺乏经过验证的能够准确预测的机械性生物标记物 “慢”或“快”的进展是找到治愈方法的主要障碍。 至少40%-60%的患者在开始治疗后的头6个月内经历了一段时间的部分缓解期 正在服用胰岛素。这段“蜜月期”变化很大，从几周到几年不等。喜欢 T1D，控制PRM发生和持续的因素尚不完全清楚。最初，人们认为 PRM仅仅是一种代谢现象，但越来越多的证据表明，免疫系统也 扮演着积极的角色。这导致了支撑我们建议的主要假设：确定 与PRM持续时间相关的免疫学、代谢和人口统计学特征将使 改良的临床可操作的T1D复合生物标志物的开发。 我们的研究有一个特定的目标，即定义和验证一个或多个可以 根据基线数据准确预测发病后头两年的T1D进展的快慢。这将是 通过对100名受试者的外周血液进行深入的多模式分析而实现的 最近被诊断为T1D。将在确诊后3-6个月进行单次抽签，范围为 使用DNA、RNA、蛋白质和功能读数进行分析，其复杂性从单一的 单细胞转录本的分析物。PRM持续时间将根据在以下时间收集的临床数据确定 紧随1.5-2年。受试者将被随机分配到与年龄、性别、 以及“快”和“慢”进步语的内容。分析数据中的要素将用于生成 使用DIFAcTO预测PRM持续时间的模型，DIFAcTO是一种结合单变量的机器学习算法 过滤、层次聚类和套索回归，以选择非冗余要素 最优模型。最终模型的性能将通过将它们应用于独立的 验证队列。 结果模型中保留的特征将是需要改进的复合生物标志物的主要候选者 招聘时的科目分层，以及未来应答者和非应答者的辅助识别 临床试验。因此，如果成功，我们的研究应该会对该领域产生重大影响。

The ultimate goal of this proposal is to define composite biomarkers that can be used to improve outcomes in future type 1 diabetes (T1D) clinical trials. T1D is the major cause of diabetes in youth. It is characterized by life-long insulin insufficiency due to autoimmune mediated ß cell destruction. Despite considerable efforts over the past 30+ years, effective therapies are still lacking and there is an urgent need for a cure. Natural history studies indicate that the rate of T1D progression varies greatly between individuals, both before, and after onset. Indeed, the current paucity of validated mechanistic biomarkers that can accurately predict “slow” or “fast” progression is a major impediment to finding a cure. At least 40-60% of patients experience a period of partial remission (PRM) in the first 6 mo after they begin taking insulin. This “honeymoon period” is highly variable, ranging from a few weeks to several years. Like T1D, the factors that govern the onset and duration of PRM are not fully understood. Initially it was believed that PRM is solely a metabolic phenomenon, but there is increasing evidence that the immune system also plays an active part. This leads to the primary hypothesis that underpins our proposal: identification of immunological, metabolic, and demographic features that associate with PRM duration will enable the development of improved clinically actionable composite biomarkers for T1D. Our study has a single specific aim, namely, to define and validate one or more classifiers that can accurately predict fast or slow progression of T1D in the first 2y post-onset from baseline data. This will be achieved through an in depth multimodal analysis of peripheral blood drawn from a cohort of 100 subjects with a recent diagnosis of T1D. A single draw will be made at 3-6 months post diagnosis, and a range of assays performed with DNA, RNA, protein and functional readouts, and ranging in complexity from single analytes to single cell transcriptomes. PRM duration will be determined from clinical data collected over the following 1.5-2y. Subjects will be randomized to training and validation cohorts matched for age, gender, and content of “fast” and “slow” progressors. Features from the analytical data will be used to generate models that predict PRM duration using DIFAcTO, a machine learning algorithm that combines univariate filtering, hierarchical clustering, and LASSO regression, to select non-redundant features that result in an optimal model. Performance of the final models will be evaluated by applying them to the independent validation cohort. The features retained in the resulting models will be prime candidates as composite biomarkers to improve subject stratification at recruitment, and aid identification of responders and non-responders, in future clinical trials. Thus, if successful, our study should have significant impact on the field.