Integrative risk modeling for early prediction of endometriosis and its long-term health outcomes

子宫内膜异位症早期预测及其长期健康结果的综合风险模型

基本信息

批准号：
10567234
负责人：
SHEFALI Setia VERMA
金额：
$ 71.31万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-15 至 2028-02-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10567234
关键词：
Address Affect Age Anxiety Case Study Cataloging Catalogs Chronic Chronic Disease Chronology Clinical Computing Methodologies Data Development Diagnosis Diagnostic Disease Distant Dysmenorrhea Early Diagnosis Electronic Health Record Environmental Risk Factor Event Female Genital Diseases Future Genetic Goals Gynecologic Health Heterogeneity High Risk Woman Hormonal Incidence Individual Infertility Inflammation Knowledge Life Life Cycle Stages Life Style Link Long-Term Effects Machine Learning Malignant Female Reproductive System Neoplasm Malignant neoplasm of ovary Maps Mediating Mediator Medicine Mental Depression Methodology Methods Mission Modeling Morbidity - disease rate Myocardial National Institute of Child Health and Human Development Organ Outcome Patient risk Patient-Focused Outcomes Patients Pelvis Phenotype Play Population Prevalence Prevention Prevention strategy Preventive measure Public Health Quality of life Relative Risks Reporting Reproduction Research Risk Risk Assessment Risk Estimate Risk Factors Role Screening procedure Sum Symptoms Systemic disease Testing Translating United States Validation Variant Woman Work abstracting biobank chronic pelvic pain clinical practice clinical predictors clinical risk comorbidity disease heterogeneity disorder risk early screening electronic health record system endometriosis epigenomics genetic variant genome wide association study genomic biomarker heart disease risk high risk improved inflammatory marker innovation lifetime risk long-term sequelae modifiable risk multimodality non-genetic novel personalized medicine polygenic risk score prevent reproductive risk prediction risk prediction model risk sharing screening symptomatology timeline tool

项目摘要

More than 200,000 women are diagnosed with endometriosis every year and over half of those women do not receive a definitive diagnosis until 8.5 years after the onset of symptoms and many times when they present with additional comorbidities. While several studies have suggested that genomic markers, environmental risk factors and inflammatory markers play crucial roles in endometriosis symptomatology, there are no effective tools available to predict an individual's risk of developing endometriosis or to predict its downstream effects. The long-term goal is to develop effective and non-invasive early screening tools to identify patients at risk of developing endometriosis and predict long-term effects. The main objective of this project is the development of models to predict the risk of endometriosis across varied clinical manifestations and associated long-term health outcomes. Our central hypothesis is that integrative risk models will successfully identify patients at risk of developing endometriosis and associated diseases that occur either concurrently with endometriosis (reproductive age) or after endometriosis development (long-term health outcomes), enabling early diagnosis and prevention. This general hypothesis will be tested via the following specific aims:(1) Develop an integrative risk model to predict patients at high risk of developing endometriosis; (2) Develop an integrative risk model combining genetic and nongenetic risk factors to predict clinical manifestations among women with endometriosis ; (3) Create a lifelong chronological map of endometriosis to identify individuals at risk of developing associated comorbidities. In aim 1, we will integrate genetic and non-genetic risk factors extracted from Electronic Health Records in linear and non-linear fashion to generate an EndoRisk model. In aim 2, we will generate a catalog of additional risk factors linked to various clinical manifestations of endometriosis and develop risk model for varied manifestations. In aim 3, we will evaluate mediating risk of endometriosis on associated comorbidities and develop a mediator risk prediction model for concomitant conditions and long-term health outcomes. At the successful completion of the proposed research, the expected outcomes will be rigorously evaluated non-invasive computational methods for screening and diagnosing endometriosis across various clinical manifestations and its long-term effects based on genetic and non-genetic factors. The proposed research is innovative because our novel methodology for integrated risk models will have immediate translational implications. These results will provide a strong basis for future development of strategies for improving patient outcomes and translating the knowledge to clinical practice by providing support for identifying patients at high, moderate, and mild risk of endometriosis, which is expected to have a significant impact on women suffering from endometriosis or its long-term effects by tailoring personalized treatments based on their relative risk.

每年有超过200,000名妇女被诊断出患有子宫内膜异位症，其中一半以上直到症状发作后的8.5年，多次他们提出了其他合并症。虽然几项研究表明基因组标记，但环境危险因素和炎症标志物在子宫内膜异位症症状学中起着至关重要的作用，没有可用的有效工具可以预测个人患子宫内膜异位症或预测其下游效果。长期目标是早期发展有效和无创筛查工具，以识别患有子宫内膜异位症风险并预测长期影响的患者。这该项目的主要目的是开发模型，以预测整个子宫内膜异位症的风险各种临床表现和相关的长期健康结果。我们的中心假设是综合风险模型将成功识别出患有子宫内膜异位症风险的患者与子宫内膜异位症（生殖年龄）或之后同时发生的相关疾病子宫内膜异位发育（长期健康结果），可早期诊断和预防。这一般假设将通过以下特定目的进行检验：（1）开发一个综合风险模型预测患有子宫内膜异位症的高风险患者；（2）开发组合的综合风险模型预测子宫内膜异位症女性临床表现的遗传和非遗传危险因素；（3）创建终身子宫内膜异位症的年代表，以识别有发展风险的个体相关的合并症。在AIM 1中，我们将整合从中提取的遗传和非遗传风险因素线性和非线性方式的电子健康记录生成内虫模型。在AIM 2中，我们将产生与子宫内膜异位症各种临床表现有关的其他风险因素的目录并为各种表现形式开发风险模型。在AIM 3中，我们将评估中介的风险对相关合并症的子宫内膜异位症并为伴随的中介风险预测模型开发条件和长期健康结果。在成功完成拟议的研究时，预期的结果将经过严格评估，用于筛选和根据各种临床表现及其长期作用诊断子宫内膜异位症遗传和非遗传因素。拟议的研究具有创新性，因为我们的新方法对于综合风险模型，将具有立即的翻译含义。这些结果将提供强大的基础，以发展改善患者预后和翻译的策略的发展依据通过为识别高，中和温和的患者提供支持，了解临床实践的知识子宫内膜异位症的风险，预计将对患有患者的妇女产生重大影响子宫内膜异位症或其长期影响通过根据其相对风险来量身定制个性化治疗方法。