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

每年有20多万妇女被诊断为子宫内膜异位症，其中超过一半的妇女 直到症状出现8.5年后才能得到明确的诊断，而且很多时候 它们还会出现额外的合并症。虽然有几项研究表明基因组标记， 环境危险因素和炎症标志物在子宫内膜异位症的症状学中起着至关重要的作用， 目前还没有有效的工具来预测个人患子宫内膜异位症的风险或 预测其下游影响。长期目标是早期开发有效的、非侵入性的 筛查工具，以确定发生子宫内膜异位症的风险和预测长期影响。这个 这个项目的主要目标是开发模型来预测子宫内膜异位症的风险。 不同的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有发生子宫内膜异位症风险的患者和 与子宫内膜异位症(生育年龄)同时发生或之后发生的相关疾病 子宫内膜异位症的发展(长期健康结果)，使早期诊断和预防成为可能。这 一般假设将通过以下具体目标进行检验：(1)开发综合风险模型以 预测高危患者发生子宫内膜异位症；(2)建立综合风险模型 遗传和非遗传危险因素对子宫内膜异位症患者临床表现的预测； (3)建立子宫内膜异位症的终生年表，以识别有发病风险的个体 相关的合并症。在目标1中，我们将整合从 以线性和非线性方式生成EndoRisk模型的电子健康记录。在目标2中，我们 将生成与子宫内膜异位症的各种临床表现相关的其他危险因素的目录 并针对不同的表现形式建立风险模型。在目标3中，我们将评估 子宫内膜异位症与相关合并症的关系及并发症的介体风险预测模型的建立 条件和长期健康结果。在成功完成拟议的研究后， 将严格评估预期结果筛查和治疗的非侵入性计算方法 子宫内膜异位症的不同临床表现及其远期疗效 遗传因素和非遗传因素。建议的研究是创新的，因为我们的新方法 对于综合风险模型，将具有直接的转换影响。这些结果将提供一个 为未来制定改善患者预后的战略和将 通过为识别高、中、轻患者提供支持，将知识转化为临床实践 子宫内膜异位症的风险，预计这将对患有 根据子宫内膜异位症或其长期影响，根据其相对风险量身定做个性化治疗。

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.