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

使用基于人工智能的综合方法识别与人类极端长寿相关的分子特征

• 批准号: 10745015
• 负责人: Daniel Spencer Evans
• 金额: $ 23.81万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10745015
• 关键词: Acceleration; Aging; Artificial Intelligence; Biological Assay; Biological Factors; Biological Markers; Biological Process; Biological Testing; Biology; Biology of Aging; Cell Culture Techniques; Centenarian; Chronic; Clinical; Clinical Research; Cohort Studies; Collection; Complex; Computational Biology; Data; Data Set; Development; Disease; Epidemiology; Family; Foundations; Framingham Heart Study; Funding; Genomics; Human; Intervention; Investments; Knowledge; Life Expectancy; Longevity; Machine Learning; Mendelian randomization; Modeling; Molecular; Multiomic Data; Mus; Neural Network Simulation; Outcome; Pathway interactions; Performance; Phenotype; Phylogenetic Analysis; Physiology; Predictive Factor; Process; Prognostic Marker; Proteins; Research; Research Design; Testing; Time; age related; aging population; artificial intelligence method; biomarker identification; biomarker validation; cohort; data harmonization; data integration; data management; deep neural network; design; drug development; experimental study; follow-up; genetic epidemiology; healthy aging; high dimensionality; human data; molecular marker; multiple omics; neural network architecture; novel; novel marker; outcome prediction; predictive marker; risk mitigation; trait

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.

项目总结/文摘