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Undulating changes in human plasma proteome profiles across the lifespan are linked to disease
Author(s) -
Lehallier Benoit,
Gate David,
Schaum Nicholas,
Nanasi Tibor,
Lee Song Eun,
Yousef Hanadie,
Losada Patricia Moran,
Berdnik Daniela,
Keller Andreas,
Verghese Joe,
Sathyan Sanish,
Franceschi Claudio,
Milman Sofiya,
Barzilai Nir,
WyssCoray Tony
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.043868
Subject(s) - proteome , disease , epigenetics , bioinformatics , cohort , biology , ageing , proteomics , biological age , young adult , medicine , computational biology , genetics , gerontology , evolutionary biology , gene
Background Aging is a predominant risk factor for numerous chronic diseases that limit healthspan. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues, which supports a hypothesis that age‐related molecular changes in blood could provide novel insights into age‐related disease biology. Method We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18‐95 years old) using an aptamer‐based platform. Result We identified a proteomic clock that can be used to predict chronological age. Deviations from this plasma proteomic clock are correlated with changes in clinical and functional parameters in healthy subjects and AD patients from the Addneuromed cohort have an older proteomic age than their chronologic age (n=641). We are currently expanding this analysis to 881 healthy controls, MCI and AD subjects of the EMIF‐AD consortium. Thus, this panel of proteins can be used to assess the relative health of an individual and to measure healthspan, analogous to epigenetic clocks based on DNA methylation patterns. In addition, we describe a 46‐protein aging signature that is conserved in humans and mice, allowing deeper investigation of translational aging interventions in mice. Finally, we developed a novel bioinformatics approach, which uncovered marked non‐linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh, and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age‐related diseases and phenotypic traits. Conclusion This new approach to the study of aging led to the identification of unexpected signatures and pathways, which might offer potential targets for age‐related diseases. Our results also have strong implications for the development of diagnostic and prognostic tests. Indeed, the undulating nature of the aging plasma proteome and its interactions with diseases has to be considered when developing proteomic signatures for diagnostic purposes. Such reliable tests are still urgently needed for Alzheimer’s disease.