medRxivGenetic & Genomic MedicinePreprint
Contextualizing molecular and structural aging across human organs
Juan ShuiD, Yuxin Guo, Julio A. ChirinosiD, Zirui FaniD, Yilin YangiD, Yujue Li, Xiaochen YangiD, Cameron BeecheiD, Buxin Su, Iris HorngiD, Rong ZhengiD, Peristera PaschouiD, Li‐San WangiD, Joan M. O’BrieniD, Ruben C. GuriD, Jinjie Lin, Walter R T WitscheyiD, Daniel James RaderiD, Anthony RosenzweigiD, Bingxin ZhaoiD
Abstract
Organ-specific aging clocks have shown promise as predictors of disease risk and aging trajectories; however, the underlying biological mechanisms they reflect remain largely unexplored. Here, we use large-scale proteomic and imaging data to investigate the relationships among organ-specific and modality-specific aging clocks and to uncover the biological processes they represent. By estimating paired protein-based and imaging-based aging clocks across 8 major organs, we demonstrate that these omics and structural profiles exhibit distinct phenotypic and genetic signatures, each potentially quantifying different stages and playing complementary roles within a unified biological aging process. Furthermore, context-specific aging clocks from multiple organs often converge and jointly capture established biological and disease pathways. For example, 65.7% of the KEGG Alzheimers disease pathway is enriched by at least one of 11 protein- and imaging-based aging clocks, with each clock representing different components of the pathway. These results underscore the importance of a pan-organ multi-modal perspective for quantifying the mechanisms underlying age-related diseases. Additionally, we identify modality-specific links between aging clocks and complex diseases and lifestyle factors. In summary, we uncover intricate relationships among molecular and structural aging clocks across human organs, providing novel insights into their context-specific roles in capturing consequences of aging biology and their implications for disease risk.
- organism: human
- assay: proteomics
- assay: imaging
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
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