Identifying molecular traits associated with extreme human longevity using an AI based integrative approach

项目摘要/摘要 预期寿命在增加，因此，与年龄相关的慢性疾病的负担也在增加。 针对人类衰老的基本生物过程的干预和治疗有可能 以减轻我们老龄化人口面临的多种疾病的风险。开发针对老龄化的干预措施 在这个过程中，必须确定与老化的临床终点相关的预测因素和生物标记物。通过 定义，基于衰老的状况和疾病的发展需要时间，并需要大量的 跟进时间。加快人类衰老、人类衰老生物标记物和预后生物标记物的研究 人类的健康老龄化是迫切需要的。没有可靠的生物标志物，早期药物开发 严重受限。在这一应用中，我们提出了一个框架来识别健康人类衰老的生物标记物 使用先进的人工智能(AI)方法应用于广泛的深表型研究， 从人类和非人类那里收集数据。我们组建了一支在临床上拥有深厚专业知识的团队 老龄化研究、遗传流行病学、衰老生物学和人工智能。通过研究发现衰老的生物标记物 为了将组学数据与人工智能相结合，我们提出了以下具体目标：目标1(R21，第一阶段)。 将Framingham心脏研究(FHS)和长寿联盟(LC)的数据集与多个 测试人工智能方法并识别与人类衰老相关的生物标记物的组学。目标2(R21，第一阶段)。 使用FHS和LC数据测试生物信息AI深度神经网络(DNN)模型，以集成OMIC 数据，预测结果，并确定预测性的基因组特征。目标3(R33，第二阶段)。应用模型来源 将公共数据添加到非凡长寿(EL)数据中。目标4(R33，第二阶段)。建立因果关系 生物标志物与长寿表型之间的孟德尔随机化(MR)分析与细胞 培养实验。

PROJECT SUMMARY/ABSTRACT Life expectancy is increasing, and consequently, the burden of chronic age-related disease is also increasing. Interventions and treatments that target the fundamental biological process of human aging have the potential to mitigate risk of multiple diseases faced by our aging population. To develop interventions targeting the aging process, one must identify predictive factors and biomarkers associated with the aging clinical endpoints. By definition, the development of aging-based conditions and diseases takes time and requires a great deal of follow-up time. To accelerate research in human aging, biomarkers of human aging and prognostic biomarkers of healthy human aging are desperately needed. Without reliable biomarkers, early-stage drug development is severely limited. In this application, we propose a framework to identify biomarkers of healthy human aging using advanced Artificial Intelligence (AI) methods applied to a wide range of deeply phenotyped studies that collected data from humans and non-humans. We have assembled a team with deep expertise in clinical research of aging, genetic epidemiology, biology of aging, and AI. To identify biomarkers of aging through the integrative analysis of omic data with AI, we propose the following specific aims: Aim 1 (R21, first stage). Assemble datasets from the Framingham Heart Study (FHS) and the Longevity Consortium (LC) with multiple omics to test AI methods and to identify biomarkers associated with human aging. Aim 2 (R21, first stage). Test biologically informed AI Deep Neural Network (DNN) models with FHS and LC data to integrate omic data, predict outcomes, and identify predictive omic features. Aim 3 (R33, second stage). Apply models from public data onto exceptional longevity (EL) data. Aim 4 (R33, second stage). Establishing causal relationship between biomarkers and longevity phenotypes through Mendelian Randomization (MR) analysis and cell culture experiments.