Investigating Causal Relationships of Diabetes and Obesity on Degenerative Rotator Cuff Tear

项目摘要/摘要： 退行性肩袖撕裂(DCT)是导致肩痛的最常见原因之一，但人们对此知之甚少。 关于这些撕裂的遗传和身体风险因素。与之前的看法相反，DCT完全是 由于反复的微创伤，关于内在肌腱异常的新证据已经出现 可能预置DCT风险。肥胖和糖尿病是与内源性疾病相关的常见健康状况 肌腱改变(如肌腱脂肪和微细结构纤维成分异常，以及 肌腱细胞死亡和营养血管解剖异常)，并可能导致 促进肩袖损伤。一些流行病学研究将肥胖和糖尿病与袖带疾病联系起来。然而， 大多数人在建立因果联系方面受到限制，部分原因是对袖带疾病的定义不一致， 暴露和结果之间缺乏时间性，以及这些研究固有的偏见。该计划的目标是 拟议的工作是利用几个大型国际DNA和患者健康数据库，以及 正在进行的前瞻性队列研究，通过以下方式评估糖尿病和肥胖症对DTC的因果作用 根植于工具变量分析(孟德尔随机化[MR])的方法可以克服 以往研究面临的传统挑战。对于我的第一个目标，我将构建、验证和比较两个 DCT病例与非病例的分类算法。这些算法将进行适当的匹配并 适用于各种具有与电子健康记录相关联的基因数据的国际生物信息库 (电子健康记录)。使用从这些来源产生的DCT的全基因组关联研究(GWAS)数据，以及 关于肥胖和糖尿病特征的数据来自已发表的研究，我将评估因果关系的证据 应用MR技术研究糖尿病与DCT(目标2)和肥胖与DCT(目标3)之间的关系。 此外，我将确定糖尿病在肥胖和肥胖之间的潜在中介作用。 使用两步磁共振方法测量肩袖撕裂。DCT是一种长期发病率很高的衰弱疾病。 随着我们人口中糖尿病和肥胖率的不断上升，从这项工作中得出的结论(空 或其他方面)将是及时和有影响的。总而言之，这部作品触及了未知和未被充分研究的领域 糖尿病和肥胖症的肌肉骨骼后果，并将为减轻伤害风险的方法提供信息， 为未来在这些人群中治疗和预防DCT的研究打开了大门。

PROJECT SUMMARY/ABSTRACT: Degenerative rotator cuff tear (DCT) is among the most common causes of shoulder pain, yet little is known about the genetic and physical risk factors for these tears. Contrary to prior belief that DCTs were fully attributable to repetitive microtrauma, new evidence has emerged on intrinsic tendinous abnormalities that could predispose DCT risk. Obesity and diabetes are common health conditions associated with intrinsic tendinous changes (such as tendon fat and aberrant microstructural fiber composition, as well as increased tendon cell death and abnormal nutrient vessel anatomy) and may contribute to predisposing conditions that promote rotator cuff injury. Several epidemiologic studies link obesity and diabetes with cuff disease. However, most are limited in their scope to establish causal links in part due to inconsistent definitions of cuff disease, lack of temporality between exposure and outcome, and biases inherent to these studies. The objective of the proposed work is to leverage several large international DNA and patient health databases, as well as an ongoing prospective cohort, to evaluate causal roles of diabetes and obesity on DTCs, by incorporating methods rooted in instrumental variable analysis (Mendelian Randomization [MR]) which can overcome traditional challenges faced by previous studies. For my first aim, I will build, validate and compare two algorithms to classify cases and non-cases of DCT. These algorithms will be appropriately matched and applied to a variety of international biorepositories with genetic data that linked to electronic health records (EHR). Using genome-wide association study (GWAS) data for DCT generated from these sources, and GWAS data on obesity and diabetes traits from published studies, I will evaluate evidence for causal relationships between diabetes and DCT (Aim 2), and obesity and DCT (Aim 3) using MR techniques. Additionally, I will determine the potential mediating role of diabetes on the association between obesity and rotator cuff tear using two-step MR methods. DCT is a debilitating condition with great long-term morbidity. With the ever-increasing rates of diabetes and obesity in our population, conclusions drawn from this work (null or otherwise) will be timely and impactful. Together, this work taps into unknown and understudied musculoskeletal consequences of diabetes and obesity, and will inform approaches to mitigating risk of injury, opening the door for future studies on treatment and prevention of DCT in these populations.