Abstract
The development of minimally invasive multi-organ aging clocks, established through the deconvolution of plasma proteomics, has provided a convenient tool to assess the organ heterogeneity of aging. However, prior studies relied on bulk transcriptomic data for organ marker identification, which may lead to the potential misidentification of protein markers, and their research scope was largely confined to a few common diseases. To address these limitations, this study integrated multi-dimensional data to refine organ-enriched marker panels by incorporating organ-specific proteome information, and developed Proteome-Aware Organ Proxy Proteome Aging Clock (PAOPAC). PAOPAC exhibited decelerated biological age corresponding to improved physiological phenotypes across two independent external datasets, demonstrating its generalizability. We then leveraged PAOPAC to generate a comprehensive disease-aging landscape and to investigate the process of chronological and biological aging. Our analyses revealed that the majority of diseases are associated with an accelerated aging phenotype.
Full text
1,182 characters
· extracted from
oa-doi-fallback
· click to expand
Abstract
The development of minimally invasive multi-organ aging clocks, established through the deconvolution of plasma proteomics, has provided a convenient tool to assess the organ heterogeneity of aging. However, prior studies relied on bulk transcriptomic data for organ marker identification, which may lead to the potential misidentification of protein markers, and their research scope was largely confined to a few common diseases. To address these limitations, this study integrated multi-dimensional data to refine organ-enriched marker panels by incorporating organ-specific proteome information, and developed Proteome-Aware Organ Proxy Proteome Aging Clock (PAOPAC). PAOPAC exhibited decelerated biological age corresponding to improved physiological phenotypes across two independent external datasets, demonstrating its generalizability. We then leveraged PAOPAC to generate a comprehensive disease-aging landscape and to investigate the process of chronological and biological aging. Our analyses revealed that the majority of diseases are associated with an accelerated aging phenotype.
Competing Interest Statement
The authors have declared no competing interest.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.