Center for Identification and Study of Individuals with Atypical Diabetes Mellitus (U54)

糖尿病传统上分为两大类：自身免疫型1型和肥胖相关的2型。 许多表型和病原学上不同的形式存在并正在出现，统称为“非典型” 糖尿病“(AD)，不属于这两类中的任何一种。我们假设AD包括一个频谱，它包括 多种已知的形式(如MODY/单基因，成人潜伏性自身免疫性糖尿病，酮症易感性 糖尿病)和未知。我们建议建立非典型糖尿病研究和评估中心 (干部)。干部的目标是(1)全面识别和界定AD的形式；(2)建立 AD患者生物样本的广泛数据库和储存库。定义AD的形式是具有挑战性的 因为阿尔茨海默病的病因可以从单一基因变异到高度复杂的遗传混合， 表观遗传学和环境因素。为了识别AD患者，然后定义表单，我们将使用 双管齐下。方法(A)将对来自不同国家的基因类型和表型数据进行聚类分析 糖尿病患者群体。我们用数据招募了15个国内和国际人口队列 对超过33,000名糖尿病患者进行研究。此外，我们还招募了六家糖尿病研究诊所(贝勒学院 医学院，华盛顿大学，印第安纳大学，埃默里大学，华盛顿大学 科罗拉多州和纽约州州立大学)，他们将前瞻性地招募患者。青少年糖尿病的研究进展 基金会将促进从患者群体中招募。BCM的行政核心将与同龄人合作 和诊所获取数据。南佛罗里达大学(USF)将过滤AD的数据，并将患者分离到 表型均一簇，可根据独特的遗传和临床特征诊断 集群。方法(B)将从登记的患者中识别单基因、少基因和线粒体形式的AD 遗传学登记处(BCM)和具有遗传数据的家庭研究(波特尼亚)。BCM登记处包括患者 提到了未诊断的疾病网络。所有患者都将接受全外显子组和线粒体基因组检查。 测序(BCM核心)、单核苷酸多态变异分析(牛津)、代谢组学分析 (Duke)和C-肽、脂联素、胰岛素原、胰岛自身抗体和血清胰岛素的表型分析 DNA水平(UW和Iu的核心)。为了建立数据库和生物库，我们将邀请AD患者 通过队列和诊所参与样本捐赠和纵向随访。数据和样本将是 传输到USF的数据库和生物库核心，输入和跟踪使用 美国联邦为大型多中心试验开发的成功框架。数据将通过 NIDDK和一个干部网站和样本将通过辅助学习申请提供。正在进行中 将通过研究诊所和临床医生招募患者以扩大数据库和生物库 通过干部网站推荐。总体而言，干部将建立在现有的、成功的核心基础上 设施和计算系统，以创造一个持久的资源，将大大促进AD的研究。

Diabetes is traditionally classified in two broad categories: autoimmune Type 1 and obesity-related Type 2. Numerous phenotypically and etiologically distinct forms exist and are emerging, collectively termed “atypical diabetes” (AD), that do not fit into either category. We hypothesize that AD comprises a spectrum that includes numerous forms, both known (e.g, MODY/monogenic, Latent Autoimmune Diabetes of Adults, Ketosis-Prone Diabetes) and unknown. We propose to establish the Center for Atypical Diabetes Research and Evaluation (CADRE). The goals of CADRE are to (1) identify and define comprehensively the forms of AD; and (2) establish an extensive database and repository of biosamples from AD patients. Defining the forms of AD is challenging because the etiology of AD can range from a single gene variant to a highly complex mixture of genetics, epigenetics and environmental factors. To identify patients with AD, and then to define the forms, we will use a two-pronged approach. Approach (a) will use clustering analysis of genotypic and phenotypic data from diverse groups of patients with diabetes. We have recruited 15 domestic and international population cohorts with data on more than 33,000 patients with diabetes. Further, we have recruited six diabetes study clinics (Baylor College of Medicine [BCM], University of Washington [UW], Indiana University [IU], Emory University, University of Colorado, and SUNY Downstate) who will prospectively recruit patients. The Juvenile Diabetes Research Foundation will facilitate recruitment from patient groups. The Administrative Core at BCM will work with cohorts and clinics to obtain data. The University of South Florida (USF) will filter data for AD and separate patients into phenotypically homogenous clusters that can be diagnosed based on genetic and clinical features unique to the cluster. Approach (b) will identify monogenic, oligogenic and mitochondrial forms of AD from patients enrolled in genetics registries (BCM) and a family study with genetic data (Botnia). The BCM registries include patients referred to the Undiagnosed Disease Network. All patients will undergo whole-exome and mitochondrial genome sequencing (Cores at BCM), single-nucleotide polymorphism variant analysis (Oxford), metabolomic analysis (Duke) and phenotypic analysis of C-peptide, adiponectin, proinsulin, islet autoantibodies, and serum insulin DNA levels (Cores at UW and IU). To establish the database and biorepository, we will invite patients with AD to participate in sample donation and longitudinal follow-up via the cohorts and clinics. Data and samples will be transferred to the Database and Biorepository Core at USF, with input and tracking implemented using the successful framework developed by USF for large multicenter trials. Data will be made available through the NIDDK and a CADRE website and samples will be made available through ancillary study application. Ongoing patient recruitment to expand the database and biorepository will occur through the study clinics and clinician referral through the CADRE website. Overall, CADRE will be built on a foundation of existing, successful core facilities and computational systems to create a lasting resource that will substantially advance AD research.