Proteomic landscape of multidimensional aging phenotypes.

Abstract

Background: Proteomic signatures of aging hold promise for advancing our understanding of aging evaluation and guiding targeted therapy. Despite this potential, the proteomic landscape of multidimensional aging phenotypes remains inadequately characterized. We aimed to identify the potential proteomic biomarkers of aging process and decipher their molecular mechanisms.

Methods: We analyzed 2920 plasma proteomic biomarkers from 48,728 participants in the UK Biobank. The multidimensional aging phenotypes included Klemera and Doubal's method biological age (KDM-BA) acceleration, PhenoAge acceleration, frailty index, leukocyte telomere length (LTL), and healthspan. Two-sample Mendelian randomization (MR) analyses were performed to determine the causal effect of plasma proteome on the multidimensional aging phenotypes, and replicate the identified proteomic signatures in the FinnGen cohort. Multivariable linear regressions were used to explore the phenotypic associations between plasma proteome and multidimensional aging phenotypes. We then applied a series of bioinformatic approaches to elucidate the biological function and drug targets of the identified proteins. Multi-omics data were further leveraged to decipher the genetic mechanisms and metabolic pathways of aging process.

Results: We found that genetically determined levels of 17, 37, 12, 18, and 1 proteins were causally linked to KDM-BA acceleration, PhenoAge acceleration, frailty index, LTL, and healthspan, respectively. Replication in the FinnGen cohort confirmed a subset of these associations. We observed significant phenotypic associations for 2,186, 2,152, 1,459, 668, and 545 proteins with KDM-BA acceleration, PhenoAge acceleration, frailty index, LTL, and healthspan, respectively. Our integrative analysis identified 71 distinct plasma proteins associated with multidimensional aging phenotypes, of which 12 are promising candidates for drug targeting, primarily involved in inflammatory processes and cellular senescence. Moreover, we identified 22 genetic variants that may regulate these protein abundances in the context of aging, complemented by metabolomic profiling that highlights several metabolic pathways mediating the proteins and aging.

Conclusions: Our findings facilitate a more comprehensive understanding of the proteomic landscape of the multidimensional aging phenotypes, thereby providing an opportunity for personalized monitoring of aging and effective therapeutic strategies in aging-related diseases.