庞庞 (2024-04-30 21:44):
#paper doi:10.1038/s41591-023-02296-6 Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality 可能我们大家比较熟悉人脑的年龄,即使用机器学习模型,基于脑指标(如功能连接、灰质体积等)预测人的年龄,预测值如果比真实年龄高,说明这个人比同龄人的脑子更加老化,反之更加年轻。预测值与真实值的差值可以衡量一个人脑的老化程度。而在这里,作者则使用UKbiobank数据集,进一步收集了除脑子以外身体的数据,构建了各个器官系统的年龄,进一步探究了器官系统的年龄是如何互相影响的、以及是如何影响脑龄的,即构建了一个Multi-organ的网络。同时他们也探究了哪些生活因素与器官老化有关,还有各种慢性病的器官年龄是怎样的异常模式。这篇文章给我们提供了一个研究个体老化的新视角,很有创新性。
IF:58.700Q1 Nature medicine, 2023-05. DOI: 10.1038/s41591-023-02296-6 PMID: 37024597
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
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Abstract:
Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.
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