Literature DB >> 31408848

A defined human aging phenome.

Søren Norge Andreassen1, Michael Ben Ezra1, Morten Scheibye-Knudsen1.   

Abstract

Aging is among the most complex phenotypes that occur in humans. Identifying the interplay between different age-associated features is undoubtedly critical to our understanding of aging and thus age-associated diseases. Nevertheless, what constitutes human aging is not well characterized. Towards this end, we mined millions of PubMed abstracts for age-associated terms, enabling us to generate a detailed description of the human aging phenotype. We discovered age-associated features in clusters that can be broadly associated with previously defined hallmarks of aging, consequently identifying areas where interventions could be pursued. Importantly, we validated the newly discovered features by manually verifying the prevalence of these features in combined cohorts describing 76 million individuals, allowing us to stratify features in aging that appear to be the most prominent. In conclusion, we propose a comprehensive landscape of human aging: the human aging phenome.

Entities:  

Keywords:  aging; data mining; phenome; phenotype

Mesh:

Year:  2019        PMID: 31408848     DOI: 10.18632/aging.102166

Source DB:  PubMed          Journal:  Aging (Albany NY)        ISSN: 1945-4589            Impact factor:   5.682


  3 in total

1.  Latest advances in aging research and drug discovery.

Authors:  Daniela Bakula; Andrea Ablasser; Adriano Aguzzi; Adam Antebi; Nir Barzilai; Martin-Immanuel Bittner; Martin Borch Jensen; Cornelis F Calkhoven; Danica Chen; Aubrey D N J de Grey; Jerome N Feige; Anastasia Georgievskaya; Vadim N Gladyshev; Tyler Golato; Andrei V Gudkov; Thorsten Hoppe; Matt Kaeberlein; Pekka Katajisto; Brian K Kennedy; Unmesh Lal; Ana Martin-Villalba; Alexey A Moskalev; Ivan Ozerov; Michael A Petr; David C Rubinsztein; Alexander Tyshkovskiy; Quentin Vanhaelen; Alex Zhavoronkov; Morten Scheibye-Knudsen
Journal:  Aging (Albany NY)       Date:  2019-11-21       Impact factor: 5.682

2.  Bibliometric Analysis on Geriatric Nursing Research in Web of Science (1900-2020).

Authors:  Arezoo Ghamgosar; Maryam Zarghani; Leila Nemati-Anaraki
Journal:  Biomed Res Int       Date:  2021-09-28       Impact factor: 3.411

3.  Biological mechanisms of aging predict age-related disease co-occurrence in patients.

Authors:  Helen C Fraser; Valerie Kuan; Ronja Johnen; Magdalena Zwierzyna; Aroon D Hingorani; Andreas Beyer; Linda Partridge
Journal:  Aging Cell       Date:  2022-03-08       Impact factor: 11.005

  3 in total

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