Biomarkers of aging are urgently needed to identify individuals at high risk of developing age-associated disease or disability. Growing evidence from population-based studies points to whole-body magnetic resonance imaging’s (MRI) enormous potential for quantifying subclinical disease burden and for assessing changes that occur with aging in all organ systems. The Aging Imageomics Study aims to identify biomarkers of human aging by analyzing imaging, biopsychosocial, cardiovascular, metabolomic, lipidomic, and microbiome variables. This study recruited 1030 participants aged ≥50 years (mean 67, range 50–96 years) that underwent structural and functional MRI to evaluate the brain, large blood vessels, heart, abdominal organs, fat, spine, musculoskeletal system and ultrasonography to assess carotid intima-media thickness and plaques. Patients were notified of incidental findings detected by a certified radiologist when necessary. Extensive data were also collected on anthropometrics, demographics, health history, neuropsychology, employment, income, family status, exposure to air pollution and cardiovascular status. In addition, several types of samples were gathered to allow for microbiome, metabolomic and lipidomic profiling. Using big data techniques to analyze all the data points from biological phenotyping together with health records and lifestyle measures, we aim to cultivate a deeper understanding about various biological factors (and combinations thereof) that underlie healthy and unhealthy aging.

迫切需要衰老的生物标志物，以鉴定处于发展与年龄有关的疾病或残疾的高风险中的个体。基于人群的研究的越来越多的证据表明，全身磁共振成像（MRI）在量化亚临床疾病负担以及评估所有器官系统随年龄增长而发生的变化方面具有巨大潜力。老化图像组学研究旨在通过分析影像学，生物心理，心血管，代谢组学，脂质组学和微生物组变量来识别人类衰老的生物标志物。这项研究招募了1030名年龄≥50岁（平均67岁，范围50-96岁）的参与者，他们接受了结构和功能MRI评估大脑，大血管，心脏，腹腔器官，脂肪，脊柱，肌肉骨骼系统和超声检查以评估颈动脉内膜中层厚度和斑块。必要时，将由认证放射科医生发现的偶然发现通知患者。还收集了有关人体测量学，人口统计学，健康史，神经心理学，就业，收入，家庭状况，空气污染暴露和心血管状况的大量数据。另外，收集了几种类型的样品以进行微生物组，代谢组学和脂质组学分析。我们使用大数据技术来分析来自生物表型的所有数据点，以及健康记录和生活方式测量，我们旨在对构成健康和不健康衰老的各种生物学因素（及其组合）有更深入的了解。神经心理学，就业，收入，家庭状况，空气污染暴露和心血管状况。另外，收集了几种类型的样品以进行微生物组，代谢组学和脂质组学分析。我们使用大数据技术来分析来自生物表型的所有数据点，以及健康记录和生活方式测量，我们旨在对构成健康和不健康衰老的各种生物因素（及其组合）有更深入的了解。神经心理学，就业，收入，家庭状况，空气污染暴露和心血管状况。另外，收集了几种类型的样品以进行微生物组，代谢组学和脂质组学分析。我们使用大数据技术来分析来自生物表型的所有数据点，以及健康记录和生活方式测量，我们旨在对构成健康和不健康衰老的各种生物因素（及其组合）有更深入的了解。