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

项目总结/摘要 预期寿命在增加，因此，与年龄有关的慢性疾病的负担也在增加。 针对人类衰老的基本生物学过程的干预和治疗具有潜力 以减轻我们老龄化人口所面临的多种疾病的风险。制定针对老龄化的干预措施， 在这一过程中，必须确定与衰老临床终点相关的预测因素和生物标志物。通过 根据定义，基于衰老的病症和疾病的发展需要时间，需要大量的 后续时间。加速人类衰老、人类衰老生物标志物和预后生物标志物的研究 健康的人类衰老是迫切需要的。如果没有可靠的生物标志物，早期的药物开发 严格限制。在这个应用中，我们提出了一个框架，以确定健康的人类衰老的生物标志物 使用先进的人工智能（AI）方法，应用于广泛的深度表型研究， 收集人类和非人类的数据。我们组建了一个在临床方面具有深厚专业知识的团队， 衰老研究、遗传流行病学、衰老生物学和人工智能。为了确定衰老的生物标志物， 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）。 将来自心脏病研究（FHS）和长寿联盟（LC）的数据集与多个 组学来测试人工智能方法，并确定与人类衰老相关的生物标志物。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，以整合组学 数据，预测结果，并识别预测性组学特征。目标3（R33，第二阶段）。应用模型 将公共数据转换为异常寿命（EL）数据。目标4（R33，第二阶段）。建立因果关系 通过孟德尔随机化（MR）分析和细胞计数， 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.