Multimodal analysis of the "honeymoon period" in autoimmune diabetes

该提案的最终目标是定义可用于改善结局的复合生物标志物 在未来的1型糖尿病（T1 D）临床试验中。T1 D是青年糖尿病的主要原因。它的特点 由于自身免疫介导的胰岛β细胞破坏导致的终身胰岛素不足。尽管付出了巨大的努力 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。自然 历史研究表明，T1 D进展的速度在个体之间差异很大，无论是之前， 发病后。事实上，目前缺乏有效的机制生物标志物，可以准确地预测 “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前6个月内经历部分缓解（PRM）期 注射胰岛素这个“蜜月期”变化很大，从几周到几年不等。像 T1 D，支配PRM发作和持续时间的因素尚未完全了解。起初人们认为 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 发挥积极作用。这就引出了支撑我们建议的主要假设： 与PRM持续时间相关免疫学、代谢和人口统计学特征将使 开发改进的T1 D临床可行的复合生物标志物。 我们的研究有一个特定的目标，即定义和验证一个或多个分类器， 从基线数据准确预测T1 D发病后前2年的快速或缓慢进展。这将是 通过对从100名受试者的队列中抽取的外周血进行深入的多模式分析来实现 最近被诊断为T1 D在诊断后3-6个月进行一次抽签， 使用DNA、RNA、蛋白质和功能读数进行的分析，其复杂性从单个 分析物到单细胞转录组。PRM持续时间将根据在整个研究期间收集的临床数据确定。 在1.5- 2 y之后。受试者将被随机分配至年龄、性别、 以及“快”和“慢”进展者的内容。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 过滤、分层聚类和LASSO回归，以选择非冗余特征，从而 最优模型最终模型的性能将通过将其应用于独立的 验证队列。 在所得模型中保留的特征将是作为复合生物标志物的主要候选者，以改善 招募时的受试者分层，并在未来帮助识别应答者和非应答者 临床试验因此，如果成功，我们的研究应该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.