Identification of blood biomarkers predictive of organ aging

鉴定预测器官衰老的血液生物标志物

• 批准号: 10777065
• 负责人: David Furman
• 金额: $ 38.8万
• 依托单位: BUCK INSTITUTE FOR RESEARCH ON AGING
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10777065
• 关键词: Acceleration; Activities of Daily Living; Adoption; Age; Aging; Behavioral; Biocompatible Materials; Biological; Biological Clocks; Biological Markers; Biopsy; Blood; Blood specimen; Cardiovascular Diseases; Chronic; Clinic; Clinical; Clinical Data; Clinical Trials; Cohort Studies; Complex; Cost Savings; Coupled; Cues; Data; Data Set; Databases; Deterioration; Development; Diagnostic tests; Diet; Disease; Early Diagnosis; Environment; Exercise; Framingham Heart Study; Functional disorder; Funding; Future; Gene Expression; Gene Expression Profile; General Population; Generations; Genes; Genetic; Genotype; Goals; Health; Health Personnel; Health Promotion; Healthcare Systems; Immune system; Incidence; Individual; Inflammaging; Inflammation; Inflammatory; Intervention; Libraries; Life Style; Longevity; Malignant Neoplasms; Measures; Modeling; Molecular; Network-based; Neurodegenerative Disorders; Nutritional; Organ; Outcome; Pathologic Processes; Patients; Pharmaceutical Preparations; Physiological; Predictive Value; Procedures; Process; Proteomics; Public Domains; Public Health; Rapid diagnostics; Research; Residual state; Series; Smoking; Smoking Status; Techniques; Technology; Testing; Time; Tissue Banks; Tissue Sample; Tissue-Specific Gene Expression; Tissues; Translating; United States National Institutes of Health; age related; candidate identification; clinically relevant; cohort; computer framework; diagnostic tool; disorder risk; drug repurposing; effectiveness evaluation; efficacy evaluation; epigenomics; experience; human data; human subject; improved; insight; interest; lifestyle factors; machine learning method; machine learning prediction; new technology; non-genetic; pharmacologic; predictive marker; prevent; preventive intervention; repository; technology platform; therapy development; tool; transcriptomics

Project Summary As we age, our tissues and organs experience molecular and physiological damage that prevents them from functioning properly and this ultimately leads to disease states. These changes are not only due to the aging process itself but are largely influenced by the exposome which includes all non-genetic exposures (environmental and behavioral). Depending on the complex interaction between the exposome of an individual and their genetics, different organs deteriorate over time at a different pace, resulting in tissues with different biological ages within the same individual. As the biological age of a given organ reflects its overall health and functional capacity, biologically older organs are more likely to cause health problems increasing the risk of diseases. Aging “clocks” powered by omics technologies (transcriptomics, proteomics, epigenomics, etc.) and machine learning methods have been used to approximate the biological age of specific tissues. However, tissue-specific clocks require omics data from a biopsy, making clinical adoption impractical. Therefore, there is a critical need to develop simple diagnostic tools using readily accessible biological material to measure organ- specific aging rates in an individual which can be translated into personalized actionabilities and enable accurate evaluation of the efficacy of health-promoting interventions. Using blood, the pipeline of the immune system, from aging cohorts we and others have demonstrated that accelerated aging, as evidenced by age-related chronic inflammation (inflammaging) and dysfunctional immune systems, results in organ dysfunction and an elevated risk of disease in older subjects. This is not surprising since inflammaging has been proposed to be a common denominator of most, if not all, diseases of aging. In this proposal, we hypothesize that the biological information to investigate the aging rates of a given organ is contained in the blood of the same individual and thus, can be estimated using a collection of tissue-specific gene expression signatures matched with those from blood samples. Here, we will assemble multiple public domain datasets within and outside of the NIH Common Fund to create blood-based organ-specific clocks and enable rapid diagnostics of aging rates for a given organ in an individual. To do so, we will use transcriptomic data across multiple tissues and matched blood from the Genotype-Tissue Expression (GTEx) database to construct a computational framework that calculates the rate of aging of 45 tissues in an individual using blood gene expression. We will validate the resulting models to predict organ-specific aging in disease states specific to the organ of interest, and we will assess the influence of lifestyle factors including diet, exercise and smoking on the aging of different organs using data from the Framingham Heart Study. Finally, we will use the Library of Integrated Network-based Cellular Signatures (LINCS) to identify candidate compounds that can restore the gene expression changes in the blood associated with tissue aging to optimal levels.

项目总结