Longevity Briefs: The Observable Characteristics of Human Aging

Posted on 17 November 2020

Longevity briefs provides a short summary of novel research in biology, medicine, or biotechnology that caught the attention of our researchers in Oxford, due to its potential to improve our health, wellbeing, and longevity.

Why is this research important: Aging is a complex process, possibly one of the most complex in the world of human biology. It is for this reason, and the fact that the rate of aging is so varied between both species and individuals, that we do not yet know how to truly quantify it. To try to understand aging a little bit better, it is important to know how it tangibly manifests itself in our lives.

What did the researchers do: A trio of researchers based at the university of Copenhagen, in Denmark, used a machine learning programme to mine the biomedical publication database ‘Pubmed’ using age-associated terms, amassing a collection of research publication that delve into the science of aging and its related conditions. By analysing this spectrum of geroscience research the team built a network of associations between different concepts of aging. The aim of the investigation was to further characterise human aging.

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. Source: A defined human aging phenome

Key takeaway(s) from this research: The authors compiled the defined human aging phenome, the landscape of observable characteristics of human aging based on their prevalence in the population. This analysis is based on studies which include a combined total of over 76 million individuals.

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. Source: A defined human aging phenome

The authors conclude that this critical knowledge of the aging phenome could help determine possible outcomes for clinical trials, uncover how different pathologies arise and even identify novel biomarkers of aging.

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