Boquet-Pujadas Aleix, Anagnostakis Filippos, Duggan Michael R, Joynes Cassandra M, Toga Arthur W, Yang Zhijian, Walker Keenan A, Davatzikos Christos, Wen Junhao
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.
medRxiv. 2025 Jun 9:2025.01.04.25319995. doi: 10.1101/2025.01.04.25319995.
Multi-organ research investigates interconnections among multiple human organ systems, enhancing our understanding of human aging and disease mechanisms. Here, we used multi-organ imaging (=105,433), individual- and summary-level genetics, and proteomics (=53,940) from the UK Biobank, Baltimore Longitudinal Study of Aging, FinnGen, and Psychiatric Genomics Consortium to delineate a brain-heart-eye axis via 2003 brain patterns of structural covariance (PSC), 82 heart imaging-derived phenotypes (IDP) and 84 eye IDPs. Cross-organ phenotypic associations highlight the central autonomic network between the brain and heart and the central visual pathway between the brain and eye. Proteome-wide associations of the PSCs and IDPs show both within-organ specificity and cross-organ interconnections, verified by the RNA and protein expression profiles of the 2923 plasma proteins. Pleiotropic effects of common genetic variants are observed across multiple organs, and key genetic parameters, such as SNP-based heritability, polygenicity, and selection signatures, are comparatively evaluated among the three organs. A gene-drug-disease network shows the potential of drug repurposing for cross-organ diseases. Colocalization and causal analyses reveal cross-organ causal relationships between PSC/IDP and chronic diseases, such as Alzheimer's disease, heart failure, and glaucoma. Finally, integrating multi-organ/omics features improves prediction for systemic disease categories and cognition compared to single-organ/omics features. This study depicts a detailed brain-heart-eye axis and highlights future avenues for modeling human aging and disease across multiple scales. All results are publicly available at https://labs-laboratory.com/medicine/.
多器官研究调查了多个人体器官系统之间的相互联系,增进了我们对人类衰老和疾病机制的理解。在这里,我们使用了来自英国生物银行、巴尔的摩老龄化纵向研究、芬兰基因库和精神基因组学联盟的多器官成像(=105,433)、个体水平和汇总水平的遗传学以及蛋白质组学(=53,940),通过2003种结构协方差(PSC)脑模式、82种心脏成像衍生表型(IDP)和84种眼部IDP来描绘脑-心-眼轴。跨器官表型关联突出了大脑和心脏之间的中枢自主网络以及大脑和眼睛之间的中枢视觉通路。PSC和IDP的全蛋白质组关联显示了器官内特异性和跨器官相互联系,这在2923种血浆蛋白的RNA和蛋白质表达谱中得到了验证。在多个器官中观察到常见基因变异的多效性,并对三个器官之间的关键遗传参数,如基于单核苷酸多态性的遗传力、多基因性和选择特征进行了比较评估。一个基因-药物-疾病网络显示了药物重新用于治疗跨器官疾病的潜力。共定位和因果分析揭示了PSC/IDP与慢性疾病(如阿尔茨海默病、心力衰竭和青光眼)之间的跨器官因果关系。最后,与单器官/组学特征相比,整合多器官/组学特征可提高对全身性疾病类别和认知的预测。本研究描绘了详细的脑-心-眼轴,并突出了跨多个尺度对人类衰老和疾病进行建模的未来途径。所有结果均可在https://labs-laboratory.com/medicine/上公开获取。