Addressing racial disparities in monoclonal gammopathy of undetermined significance and progression to multiple myeloma from a prevention perspective

项目总结/摘要 多发性骨髓瘤（MM）是一种致命的肿瘤和常见的血液恶性肿瘤。MM是一致的 之前是意义不明的单克隆丙种球蛋白病（MGUS）。与MM不同，MGUS患者 都没有症状目前对MGUS的管理是观察等待疾病进展。明显 在这种疾病领域的种族差异是长期建立的，非洲裔美国人的风险增加2至3倍 (AAs)与高加索人相比。此外，肥胖是MM的独立于种族的危险因素。肥胖是 在美国成年人中普遍存在，特别是在AA中比高加索人更普遍。因此，没有任何 如果不采取干预措施，这种疾病的种族差异将继续恶化。Metastasis，一种广泛使用的，安全的，好- 耐受性和廉价的药物，诱导体重减轻，并已被发现更有效， 与高加索人相比，AA的血糖控制。它也被用于非糖尿病的前瞻性试验 适应症和恶性实体瘤。因此，我们假设二甲双胍在MGUS患者中的使用将 预防MM和减少MM差异。本项目计划重点研究MM-MGUS的前驱条件。 拟议项目的结果将为生物机制研究和MGUS/MM预防提供信息 审判长期目标是确定预防MGUS进展为MM的干预策略， 减少MM的总体负担，并减少MM差异。我们计划确定高体重 体重指数（BMI）和/或BMI在生命过程中的显著变化是不同种族MGUS的风险因素（目标1）， 利用链接的数据库，对BMI和其他健康指标进行几乎终身的随访， 人工智能，即，机器学习方法来执行大数据分析。我们将评估 二甲双胍与非二甲双胍使用者MGUS诊断后M蛋白轨迹的种族差异， 诊断为糖尿病的MGUS患者亚组（目的2.1）和二甲双胍使用的相关性 随着MGUS进展为MM（目标2.2）。最后，我们将评估M蛋白的种族差异， 无糖尿病的MGUS患者亚组的轨迹（目的3）。该项目在其 1）识别可能是唯一可改变的风险因素（高BMI），为预防MM的干预措施提供信息; 2)通过种族（M蛋白浓度）鉴定疾病进展的动态标志物，其中这些 生物标志物可以在未来的预防试验中替代MM诊断;和3）减少MM健康 a）鉴定MGUS和MGUS进展的种族特异性生物标志物，可通过 临床接触（与昂贵的基因检测相反）;和B）探索二甲双胍作为 化学预防措施它的创新之处在于：1）专注于MM的预防而不是治疗; 2） 利用人工智能分析大数据。成功完成这项研究将提供证据， 对于MGUS管理的当前临床实践的范式转变，并帮助预防MM，一种不可治愈的， 昂贵的疾病更重要的是，它将提供证据，指导干预措施，以减少MM差距。

PROJECT SUMMARY/ABSTRACT Multiple myeloma (MM) is a lethal neoplasm and a common hematologic malignancy. MM is uniformly preceded by monoclonal gammopathy of undetermined significance (MGUS). Unlike MM, patients with MGUS are asymptomatic. The current management for MGUS is watchful waiting for disease progression. A marked racial disparity in this disease area is long-established with a 2 to 3-fold increased risk in African Americans (AAs) compared to Caucasians. Moreover, obesity is a risk factor for MM independent of race. Obesity is prevalent in U.S. adults, and particularly more prevalent among AAs than Caucasians. As a result, without any intervention, racial disparities in this disease will continue to worsen. Metformin, a widely-used, safe, well- tolerated, and inexpensive medication, induces weight loss and has been found to be more effective in glycemic control in AAs compared to Caucasians. It has also been used in prospective trials for non-diabetes indications and solid tumor malignancies. We therefore hypothesize that metformin use in MGUS patients will prevent MM and reduce MM disparities. This project plans to focus on the precursor condition of MM – MGUS. The findings from the proposed project will inform biological mechanism studies and MGUS/MM prevention trials. The long-term goal is to identify intervention strategies to prevent the progression of MGUS to MM, reduce the overall burden of MM, and reduce MM disparities. We plan to identify whether high body mass index (BMI) and/or significant change in BMI over the life course are risk factors for MGUS by race (Aim 1), utilizing linked databases with nearly lifelong follow-up of BMI and other health measures ,as well as utilizing artificial intelligence, i.e., machine learning approaches, to perform big data analyses. We will then assess racial differences in the M-protein trajectory after MGUS diagnosis in metformin versus non-metformin users in a subgroup of MGUS patients diagnosed with diabetes mellitus (Aim 2.1) and the association of metformin use with the progression of MGUS to MM (Aim 2.2). Last, we will assess racial differences in the M-protein trajectory in the subgroup of MGUS patients without diabetes mellitus (Aim 3). This project is significant in its capability to 1) identify perhaps the only modifiable risk factor (high BMI) to inform interventions to prevent MM; 2) identify a dynamic marker for disease progression by race (M-protein concentration), where these biomarkers can be a surrogate for MM diagnosis in future prevention trials; and 3) reduce MM health disparities by a) identifying race-specific biomarkers for MGUS and MGUS progression, available through clinical encounters (as opposed to expensive genetic testing); and b) exploring metformin use as a chemopreventive measure. It is innovative in its 1) focus on MM prevention rather than treatment and 2) utilization of artificial intelligence to analyze big data. Successful completion of this study will provide evidence for a paradigm shift in current clinical practice of MGUS management and help prevent MM, an incurable and costly disease. More importantly, it will provide evidence to guide interventions to reduce MM disparities.