Clinical Risk Prediction Modeling in Rheumatoid Arthritis

描述(由申请人提供)：本提案是NIH R01 AR49880-03，激素、细胞因子和遗传风险对女性类风湿性关节炎的竞争性延续，在该提案中，我们确定包括母乳喂养、月经初潮和月经不调在内的生殖因素、包括抗CCP抗体在内的炎症标记物、以及催乳素基因中的TNFR2水平和新的RA风险等位基因是类风湿性关节炎的重要风险因素。这项建议扩展了我们开发临床风险预测模型的工作，建立在我们在护士健康研究中研究类风湿性关节炎流行病学的良好记录的基础上，护士健康研究是世界上最大的预期风湿病队列。我们的合作研究人员最近在RA中进行的全基因组关联研究已经确定了新的风险基因。然而，尽管在了解类风湿性关节炎的遗传基础方面取得了快速进展，但如何在临床上利用这些信息来预测类风湿性关节炎仍不清楚。在RA发病前多年发现自身抗体和细胞因子，为临床前阶段的干预提供了一个令人兴奋的机会。然而，了解RA危险因素在针对高危个体的潜在毒性治疗中的作用是至关重要的。预测性建模在RA预防临床试验的进展中至关重要。我们建议建立一个包含RA遗传易感等位基因和环境风险因素及其相互作用的RA临床风险预测模型，并在美国和瑞典的大型队列中进行验证。在代表预防试验的目标群体的独特的高风险RA队列中的进一步验证将导致理解这些模型是否预测临床前RA的发展，这是未来RA预防试验的基本信息。我们提出了以下目标：1)使用确认的RA易感等位基因，推导出遗传风险分数(GRS)，并检验GRS与RA总体风险以及与血清阳性RA风险之间的关系，具体而言，来自护士健康研究(NHS)的700例RA患者和700名匹配对照，以及来自RA流行病学调查(EIRA)队列的2000例患者和1150名匹配对照；2)建立两个RA临床预测模型，以预测所有RA和由性别、免疫表型和家族史定义的亚组的5年RA风险：(A)使用行为因素、环境暴露和临床因素的“环境”模型，和(B)包括环境因素、GRS和基因-环境相互作用项的“环境遗传”模型；以及3)检验在目标2中开发和验证的预测模型的拟合优度，该目标2用于在独特的高危RA队列中预测中间终点、临床前RA定义的自身抗体或RA症状，该队列包括2100名RA病例的一级亲属和800名富含HLA-DR4等位基因的个体(总N=2900)。能够根据简单的遗传风险评分、行为、环境和临床风险因素准确预测个人5年内发展为临床RA的风险将是一个巨大的进步，使风险因素修改和更早引入有效的治疗方法能够消除这种疾病的破坏和残疾。公共卫生相关性：这项研究将描述成功从事体力活动的类风湿性关节炎(RA)患者和不进行体力活动的患者的特征。这些信息将被用于开发个人量身定制的体力活动咨询，以促进RA患者的健康并降低心血管风险。

DESCRIPTION (provided by applicant): This proposal is submitted as a competing continuation of NIH R01 AR49880-03, Hormone, Cytokine and Genetic Risks for RA in Women, in which we identified reproductive factors including breastfeeding, early menarche, and irregular menses, inflammatory markers including anti-CCP antibodies, and TNFR2 levels and a novel RA risk allele in the prolactin gene as significant risk factors for RA. Extending our work to develop clinical risk prediction models, this proposal builds on our strong track record of studying RA epidemiology in the Nurses' Health Studies, the largest prospective rheumatic disease cohorts in the world. Recent whole genome association studies in RA from our co-investigators have identified novel risk loci. However, despite rapid advances in understanding the genetic basis of RA, it is unclear how to utilize this information clinically for RA prediction. Identification of autoantibodies and cytokines present many years prior to RA onset provides an exciting opportunity to intervene during the pre-clinical phase. However, it is critical to understand the role of RA risk factors for the targeting of potentially toxic therapies at highest risk individuals. Predictive modeling is critical in the progress towards an RA prevention clinical trial. We propose to build a RA clinical risk prediction models incorporating RA genetic susceptibility alleles and environmental risk factors and their interactions, with validation in large U.S. and Swedish cohorts. Further validation in a unique high risk RA cohort, representing a target group for prevention trials, will lead to understanding of whether the models predict development of pre-clinical RA, essential information for future RA prevention trials. We propose the following aims: 1) Using validated RA susceptibility alleles, derive a Genetic Risk Score (GRS) and examine associations between GRS and RA risk in general, and with seropositive RA risk specifically, in 700 RA cases and 700 matched controls from the Nurses' Health Study (NHS) and in 2000 cases and 1150 matched controls from the Epidemiologic Investigation of RA (EIRA) cohort; 2) Develop two RA clinical prediction models to predict 5-year RA risk for all RA and for subsets defined by sex, immune phenotype, and family history: (a) an "environmental" model using behavioral factors, environmental exposures, and clinical factors , and (b) an "environmental + genetic" model with environmental factors, GRS, and gene-environment interaction terms; and 3) Examine the goodness of fit of the prediction models developed and validated in Aim 2 for predicting an intermediate endpoint, pre-clinical RA defined autoantibodies, or RA symptoms, in a unique high risk RA cohort, the Studies of the Etiologies of Rheumatoid Arthritis (SERA) comprised of 2100 first-degree relatives of RA cases and of 800 individuals enriched with HLA-DR4 alleles (total N=2900). The ability to accurately predict an individual's 5-year risk of developing clinical RA based on a simple genetic risk score, behavioral, environmental and clinical risk factors would be an enormous advance, enabling risk factor modification and earlier introduction of effective therapies to abrogate the destruction and disability of this disease. PUBLIC HEALTH RELEVANCE: This study will describe the characteristics of patients with rheumatoid arthritis (RA) who successfully engage in physical activity and those who do not. This information will be used to develop personally tailored physical activity counseling to promote health and reduce cardiovascular risk of patients with RA.