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

项目摘要/摘要 多发性骨髓瘤(MM)是一种致命性肿瘤，也是常见的血液系统恶性肿瘤。Mm是一致的 在此之前，出现意义不明的单克隆性伽马病(MGUS)。与MM不同，MGUS患者 是没有症状的。目前对MGUS的管理是警惕的，等待疾病的进展。有记号的 这种疾病领域的种族差异由来已久，非洲裔美国人的风险增加了2到3倍。 (AAS)与高加索人相比。此外，肥胖是MM的一个独立于种族的危险因素。肥胖是 在美国成年人中流行，尤其是在AA中比高加索人更普遍。因此，在没有任何 如果采取干预措施，这种疾病的种族差异将继续恶化。二甲双胍是一种应用广泛、安全、良好的药物。 耐受性和廉价的药物可导致体重减轻，并已被发现在 与高加索人相比，AAS患者的血糖控制。它还被用于非糖尿病的前瞻性试验。 适应症和实体肿瘤恶性肿瘤。因此，我们假设在MGUS患者中使用二甲双胍将 预防多发性骨髓瘤，减少多发性骨髓瘤的差异。本项目计划重点研究MM-MGUS的前驱条件。 拟议项目的发现将为生物机制研究和MGUS/MM预防提供信息 审判。长期目标是确定干预策略，以防止MGUS进展为MM， 减轻MM总体负担，缩小MM差距。我们计划确定高体重是否 指数(BMI)和/或生命过程中BMI的显著变化是按种族划分的MGUS的风险因素(目标1)， 利用关联的数据库，对BMI和其他健康措施进行几乎终身的跟踪，以及利用 人工智能，即机器学习方法，以执行大数据分析。然后我们将评估 在MGUS诊断为二甲双胍与非二甲双胍使用者后M-蛋白轨迹的种族差异 诊断为糖尿病的MGUS患者亚组(AIM 2.1)与二甲双胍使用的相关性 随着MGUS向MM的进展(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患者亚组的运动轨迹(目标3)。这个项目的意义在于它的 有能力1)确定可能是唯一可修改的危险因素(高BMI)，以便为预防多发性骨髓瘤的干预措施提供信息； 2)根据种族(M蛋白浓度)确定疾病进展的动态标记物，其中 在未来的预防试验中，生物标志物可以替代多发性骨髓瘤的诊断；以及3)减少多发性骨髓瘤的健康 差异通过a)确定MGUS和MGUS进展的种族特异性生物标记物，可通过 临床接触(而不是昂贵的基因测试)；和b)探索二甲双胍作为一种 化学预防措施。它的创新之处在于：1)专注于多发性骨髓瘤的预防，而不是治疗；2) 利用人工智能分析大数据。这项研究的成功完成将提供证据 目前MGUS治疗的临床实践中的范式转变，并帮助预防MM，一种不治之症 昂贵的疾病。更重要的是，它将为指导干预措施减少MM差异提供证据。

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.