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多器官磁共振成像在蛋白质组学、代谢组学和遗传学领域将生物衰老时钟数字化。

Multi-organ MRI digitizes biological aging clocks across proteomics, metabolomics, and genetics.

作者信息

Cao Huizi, Song Zhiyuan, Duggan Michael R, Erus Guray, Srinivasan Dhivya, Tian Ye Ella, Bai Wenjia, Rafii Michael S, Aisen Paul, Belsky Daniel W, Walker Keenan A, Zalesky Andrew, Ferrucci Luigi, Davatzikos Christos, Wen Junhao

机构信息

Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.

Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

出版信息

medRxiv. 2025 Jul 11:2025.07.10.25331263. doi: 10.1101/2025.07.10.25331263.

Abstract

Leveraging clinical phenotypes, neuroimaging, proteomics, metabolomics, and epigenetics, biological aging clocks across organ systems and tissues have advanced our understanding of human aging and disease. In this study, we expand this biological aging clock framework to multi-organ magnetic resonance imaging (MRI) by developing 7 organ-specific MRI-based biological age gaps (MRIBAGs), including the brain, heart, liver, adipose tissue, spleen, kidney, and pancreas. Leveraging imaging, genetic, proteomic, and metabolomic data from 313,645 individuals curated by the MULTI consortium, we link the 7 MRIBAGs to 2,923 plasma proteins, 327 metabolites, and 6,477,810 common genetic variants. These associations reveal organ-specific and cross-organ interconnection landscapes, identifying distinct molecular signatures related to organ aging. Genome-wide associations identify 53 MRIBAG-locus pairs (P<5×10). Genetic correlation and Mendelian randomization analyses further support organ-specific and cross-organ interconnections with 9 phenotype-based, 11 proteome-based, and 5 metabolome-based aging clocks, as well as 525 disease endpoints. Through functional gene mapping and Bayesian colocalization analysis linking evidence from genetics, proteomics, and metabolomics, we prioritize 9 druggable genes as targets for future anti-aging treatments. Finally, we demonstrate the clinical relevance of the 7 MRIBAGs in predicting disease endpoints (e.g., diabetes mellitus), all-cause mortality, and capturing differential and heterogeneous cognitive decline trajectories over 240 weeks of treatment with the Alzheimer's disease drug (Solanezumab). Sex differences are evident across multiple organ systems, manifesting at structural, molecular, and genetic levels. In summary, we developed 7 MRI-based aging clocks that enhance the existing multi-organ biological aging framework, offer multi-scale insights into aging biology, and demonstrate clinical potential to advance future aging research.

摘要

利用临床表型、神经影像学、蛋白质组学、代谢组学和表观遗传学,跨器官系统和组织的生物衰老时钟增进了我们对人类衰老和疾病的理解。在本研究中,我们通过开发7种基于多器官磁共振成像(MRI)的生物年龄差距(MRIBAGs),将这种生物衰老时钟框架扩展到多器官磁共振成像,包括大脑、心脏、肝脏、脂肪组织、脾脏、肾脏和胰腺。利用来自MULTI联盟整理的313,645个人的影像、遗传、蛋白质组学和代谢组学数据,我们将7种MRIBAGs与2,923种血浆蛋白、327种代谢物和6,477,810种常见基因变异联系起来。这些关联揭示了器官特异性和跨器官的相互连接景观,识别出与器官衰老相关的独特分子特征。全基因组关联分析确定了53对MRIBAG-基因座对(P<5×10)。遗传相关性和孟德尔随机化分析进一步支持了器官特异性和跨器官与9种基于表型、11种基于蛋白质组和5种基于代谢组的衰老时钟以及525种疾病终点的相互连接。通过功能基因图谱绘制和贝叶斯共定位分析,将遗传学、蛋白质组学和代谢组学的证据联系起来,我们将9个可药物化基因列为未来抗衰老治疗的靶点。最后,我们证明了7种MRIBAGs在预测疾病终点(如糖尿病)、全因死亡率以及捕捉阿尔茨海默病药物(索拉苏单抗)治疗240周期间的差异性和异质性认知衰退轨迹方面的临床相关性。性别差异在多个器官系统中很明显,在结构、分子和遗传水平上都有体现。总之,我们开发了7种基于MRI的衰老时钟,增强了现有的多器官生物衰老框架,为衰老生物学提供了多尺度见解,并展示了推进未来衰老研究的临床潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a44/12265779/729ca8f0665a/nihpp-2025.07.10.25331263v1-f0007.jpg

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