Clinical Risk Prediction Modeling in Rheumatoid Arthritis

类风湿关节炎的临床风险预测模型

• 批准号: 7653421
• 负责人: ELIZABETH W KARLSON
• 金额: $ 45.72万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7653421
• 关键词: Air Pollution; Alleles; Anti-citrullinated peptide antibody; Area; Autoantibodies; Behavioral; Breast Feeding; Calibration; Characteristics; Cigarette; Clinical; Cohort Studies; Counseling; Data; Development; Discrimination; Disease; Environmental Exposure; Environmental Risk Factor; Epidemiologic Studies; Epidemiology; Epitopes; Etiology; Exposure to; Family history of; Female; First Degree Relative; Future; Genes; Genetic; Genetic Models; Genetic Predisposition to Disease; Genetic Risk; HLA-DR4 Antigen; HLA-DRB1; Health; Hormonal; Hormones; Immune; Individual; Inflammatory; Inherited; Intervention; Lead; Menarche; Menstruation; Meta-Analysis; Modeling; Modification; Nurses' Health Study; PTPN22 gene; Patients; Phase; Phenotype; Physical activity; Population; Predisposition; Prevention; Prevention strategy; Prolactin; Reproductive History; Research Personnel; Rheumatism; Rheumatoid Arthritis; Risk; Risk Factors; Role; STAT4 gene; Signs and Symptoms; Single Nucleotide Polymorphism; Smoking; Sushi Domain; Symptoms; TNFRSF1B gene; Target Populations; United States National Institutes of Health; Validation; Woman; Work; base; cardiovascular risk factor; cohort; cytokine; disability; effective therapy; gene environment interaction; genetic risk factor; genome wide association study; high risk; inflammatory marker; novel; pre-clinical; predictive modeling; prevent; prevention clinical trial; prospective; public health relevance; reproductive; sex

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.

描述（申请人提供）：该提案是作为NIH R 01 AR 49880 -03，女性RA的激素，细胞因子和遗传风险的竞争性延续提交的，其中我们确定了生殖因素，包括母乳喂养，初潮早期和月经不规律，炎症标志物，包括抗CCP抗体，TNFR 2水平和催乳素基因中的一个新的RA风险等位基因是RA的重要风险因素。将我们的工作扩展到开发临床风险预测模型，该提案建立在我们在护士健康研究中研究RA流行病学的良好记录之上，这是世界上最大的前瞻性风湿性疾病队列。我们的合作研究者最近在RA中的全基因组关联研究已经确定了新的风险位点。然而，尽管在了解RA的遗传基础方面取得了快速进展，但目前尚不清楚如何在临床上利用这些信息进行RA预测。在类风湿性关节炎发病前多年发现的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，关键是要了解RA风险因素的作用，以针对潜在的毒性治疗在最高风险的个人。预测建模在RA预防临床试验的进展中至关重要。我们建议建立一个RA临床风险预测模型，将RA遗传易感性等位基因和环境风险因素及其相互作用，在美国和瑞典的大队列验证。在代表预防试验目标组的独特高风险RA队列中进行进一步验证，将有助于了解模型是否预测临床前RA的发展，这是未来RA预防试验的重要信息。我们提出以下目标：1）使用经验证的RA易感性等位基因，推导遗传风险评分（GRS），并在来自护士健康研究（NHS）的700例RA病例和700例匹配对照以及来自RA流行病学调查（EIRA）队列的2000例病例和1150例匹配对照中，检查GRS与一般RA风险之间的关联，特别是与血清阳性RA风险之间的关联; 2）开发两种RA临床预测模型来预测所有RA和由性别、免疫表型和家族史定义的子集的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）使用环境因素、GRS和基因-环境相互作用项的“环境+遗传”模型;和3）检查在目标2中开发和验证的预测模型的拟合优度，用于在独特的高风险RA组群中预测中间终点、临床前RA定义的自身抗体或RA症状，类风湿性关节炎（SERA）病因研究包括2100名RA病例的一级亲属和800名富含HLA-DR 4等位基因的个体（总N=2900）。基于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体发展临床RA的5年风险的能力将是一个巨大的进步，使风险因素修改和早期引入有效的治疗方法，以消除这种疾病的破坏和残疾。公共卫生相关性：本研究将描述类风湿性关节炎（RA）患者的特点谁成功地从事体力活动和那些谁没有。这些信息将用于开发个人定制的身体活动咨询，以促进健康，降低RA患者的心血管风险。