Cargando…
Plasma proteomic biomarker signature of age predicts health and life span
Older age is a strong shared risk factor for many chronic diseases, and there is increasing interest in identifying aging biomarkers. Here, a proteomic analysis of 1301 plasma proteins was conducted in 997 individuals between 21 and 102 years of age. We identified 651 proteins associated with age (5...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723412/ https://www.ncbi.nlm.nih.gov/pubmed/33210602 http://dx.doi.org/10.7554/eLife.61073 |
_version_ | 1783620332384419840 |
---|---|
author | Tanaka, Toshiko Basisty, Nathan Fantoni, Giovanna Candia, Julián Moore, Ann Z Biancotto, Angelique Schilling, Birgit Bandinelli, Stefania Ferrucci, Luigi |
author_facet | Tanaka, Toshiko Basisty, Nathan Fantoni, Giovanna Candia, Julián Moore, Ann Z Biancotto, Angelique Schilling, Birgit Bandinelli, Stefania Ferrucci, Luigi |
author_sort | Tanaka, Toshiko |
collection | PubMed |
description | Older age is a strong shared risk factor for many chronic diseases, and there is increasing interest in identifying aging biomarkers. Here, a proteomic analysis of 1301 plasma proteins was conducted in 997 individuals between 21 and 102 years of age. We identified 651 proteins associated with age (506 over-represented, 145 underrepresented with age). Mediation analysis suggested a role for partial cis-epigenetic control of protein expression with age. Of the age-associated proteins, 33.5% and 45.3%, were associated with mortality and multimorbidity, respectively. There was enrichment of proteins associated with inflammation and extracellular matrix as well as senescence-associated secretory proteins. A 76-protein proteomic age signature predicted accumulation of chronic diseases and all-cause mortality. These data support the use of proteomic biomarkers to monitor aging trajectories and to identify individuals at higher risk of disease to be targeted for in depth diagnostic procedures and early interventions. |
format | Online Article Text |
id | pubmed-7723412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-77234122020-12-09 Plasma proteomic biomarker signature of age predicts health and life span Tanaka, Toshiko Basisty, Nathan Fantoni, Giovanna Candia, Julián Moore, Ann Z Biancotto, Angelique Schilling, Birgit Bandinelli, Stefania Ferrucci, Luigi eLife Computational and Systems Biology Older age is a strong shared risk factor for many chronic diseases, and there is increasing interest in identifying aging biomarkers. Here, a proteomic analysis of 1301 plasma proteins was conducted in 997 individuals between 21 and 102 years of age. We identified 651 proteins associated with age (506 over-represented, 145 underrepresented with age). Mediation analysis suggested a role for partial cis-epigenetic control of protein expression with age. Of the age-associated proteins, 33.5% and 45.3%, were associated with mortality and multimorbidity, respectively. There was enrichment of proteins associated with inflammation and extracellular matrix as well as senescence-associated secretory proteins. A 76-protein proteomic age signature predicted accumulation of chronic diseases and all-cause mortality. These data support the use of proteomic biomarkers to monitor aging trajectories and to identify individuals at higher risk of disease to be targeted for in depth diagnostic procedures and early interventions. eLife Sciences Publications, Ltd 2020-11-19 /pmc/articles/PMC7723412/ /pubmed/33210602 http://dx.doi.org/10.7554/eLife.61073 Text en http://creativecommons.org/publicdomain/zero/1.0/ http://creativecommons.org/publicdomain/zero/1.0/This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication (http://creativecommons.org/publicdomain/zero/1.0/) . |
spellingShingle | Computational and Systems Biology Tanaka, Toshiko Basisty, Nathan Fantoni, Giovanna Candia, Julián Moore, Ann Z Biancotto, Angelique Schilling, Birgit Bandinelli, Stefania Ferrucci, Luigi Plasma proteomic biomarker signature of age predicts health and life span |
title | Plasma proteomic biomarker signature of age predicts health and life span |
title_full | Plasma proteomic biomarker signature of age predicts health and life span |
title_fullStr | Plasma proteomic biomarker signature of age predicts health and life span |
title_full_unstemmed | Plasma proteomic biomarker signature of age predicts health and life span |
title_short | Plasma proteomic biomarker signature of age predicts health and life span |
title_sort | plasma proteomic biomarker signature of age predicts health and life span |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723412/ https://www.ncbi.nlm.nih.gov/pubmed/33210602 http://dx.doi.org/10.7554/eLife.61073 |
work_keys_str_mv | AT tanakatoshiko plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT basistynathan plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT fantonigiovanna plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT candiajulian plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT mooreannz plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT biancottoangelique plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT schillingbirgit plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT bandinellistefania plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan AT ferrucciluigi plasmaproteomicbiomarkersignatureofagepredictshealthandlifespan |