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

项目总结/摘要： 退行性肩袖撕裂（DCT）是肩关节疼痛最常见的原因之一，但知之甚少 关于这些眼泪的遗传和身体风险因素。与之前认为DCT完全 由于反复的微创伤，新的证据已经出现内在肌腱异常， 可能导致DCT风险。肥胖症和糖尿病是与内在 肌腱变化（如肌腱脂肪和异常的微结构纤维组成，以及增加 肌腱细胞死亡和异常营养血管解剖学），并可能导致诱发条件， 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，在这方面， 大多数局限于其范围以建立因果关系，部分原因是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。的目的 拟议的工作是利用几个大型国际DNA和患者健康数据库，以及一个 正在进行的前瞻性队列研究，通过纳入 方法植根于工具变量分析（孟德尔随机化[MR]），可以克服 传统研究面临的挑战。对于我的第一个目标，我将建立，验证和比较两个 分类DCT的情况和非情况的算法。这些算法将适当匹配， 应用于各种国际生物储存库，其中遗传数据与电子健康记录相关联 （EHR）。使用从这些来源产生的DCT的全基因组关联研究（GWAS）数据， 从已发表的研究中获得的关于肥胖和糖尿病特征的GWAS数据，我将评估因果关系的证据。 糖尿病和DCT之间的关系（目的2），肥胖和DCT之间的关系（目的3）使用MR技术。 此外，我将确定糖尿病在肥胖和肥胖之间的潜在中介作用。 肩袖撕裂使用两步MR方法。DCT是一种长期发病率很高的衰弱性疾病。 随着我们人口中糖尿病和肥胖症的发病率不断增加，从这项工作中得出的结论（空 或其他）将是及时和有效的。总之，这项工作利用了未知和未充分研究的 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， 这为将来在这些人群中进行DCT的治疗和预防研究打开了大门。

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.