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. 2019 Aug 12;11(15):5786-5806.
doi: 10.18632/aging.102166. Epub 2019 Aug 12.

A defined human aging phenome

Søren Norge Andreassen  1 , Michael Ben Ezra  1 , Morten Scheibye-Knudsen  1
Affiliations

A defined human aging phenome

Søren Norge Andreassen et al. Aging (Albany NY). .

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.

Keywords: aging; data mining; phenome; phenotype.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
An approach to identifying age-related features. (A) Workflow-diagram of the project. (B) Top and bottom clinical terms that are enriched in the aging dataset (see Figure S1 for the expanded list). (C) Mean enrichment of the terms (Mean ± SEM, n = 44, p-value determine by Chi-square test, see Figure S2 for individual terms).
Figure 2
Figure 2
Age-associated clinical terms show distinct pathological clusters. T-distributed Stochastic Neighbor Embedding (t-SNE) clustering of z-score normalized data.
Figure 3
Figure 3
A defined aging phenome shows functional clustering. Agglomerative hierarchical clustering of 105 clinical terms describing human aging based on z-score normalized representation in the literature. Colors represent different clusters. The approximately unbiased value is shown in red while the bootstrap probability value is shown in blue.
Figure 4
Figure 4
The hallmarks of aging are associated with certain human features. Heatmap and cluster analysis of the association between age-associated clinical terms and hallmarks of aging.
Figure 5
Figure 5
The aging phenome. (A) The prevalence of features in the elderly (manually curated literature describing 76,928,696 individuals). HDL: High density lipoprotein, IGF-1: Insulin like growth factor-1, LDL: Low density lipoprotein (B) Agglomerative hierarchical clustering using uncentered similarity and average linkage of aging and genetic diseases (red: primary mitochondrial disorders, green: non-mitochondrial disorders, purple: segmental progerias). The approximately unbiased value is shown in red while the bootstrap probability value is shown in blue. ADOA: Autosomal dominant optic atrophy, MELAS: Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes, MERRF: Myoclonic epilepsy with ragged-red fibers, XPA: Xeroderma pigmentosum complementation group A.
See this image and copyright information in PMC

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