Utilization of Host and Microbiome Features in Determination of Biological Aging

Abstract

The term 'old age' generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person's life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person's temporal physiological status and associated disease susceptibility state. As such, differentiating 'chronological aging' from 'biological aging' holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person's physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.

Keywords: aging; biological age; clocks; microbiome; personalized medicine.

Figure 1

Four microbiome-based aging clocks. Age-related decline in gut microbiome biodiversity can be used to determine biological age. Certain species are enriched in different individuals and relate to the biological clock; for example, A. muciniphila is enriched in centenarians and is associated with longevity, while C. jejuni is enriched in individuals with higher biological age. Metagenomic and metatranscriptomic analysis gives insights into microbiome functions that affect host aging. Metametabolomics of blood, urine, or stool can identify microbiome-derived metabolites associated with biological age. SCFA: short-chain fatty acids. Figure created with BioRender (biorender.com).