Journal
MICROORGANISMS
Volume 10, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/microorganisms10030668
Keywords
microbiome; aging; clocks; biological age; personalized medicine
Categories
Funding
- Nehemia Levtzion Scholarship for Outstanding Doctoral Students
- Israeli Ministry of Science and Technology Zvi Yanai Fellowship.
- Ariane de Rothschild Women Doctoral Program
- Leona M. and Harry B. Helmsley Charitable Trust
- Adelis Foundation
- Ben B. and Joyce E. Eisenberg Foundation
- Estate of Bernard Bishin
- Jeanne and Joseph Nissim Center for Life Sciences Research
- Vera Rosenberg Schwartz Research Fellow Chair
- Swiss Society Institute for Cancer Prevention Research
- Belle S. and Irving E. Meller Center for the Biology of Aging
- Sagol Institute for Longevity Research
- Sagol Weizmann-MIT Bridge Program
- Norman E Alexander Family M Foundation Coronavirus Research Fund
- Mike and Valeria Rosenbloom Foundation
- Daniel Morris Trust
- Isidore and Penny Myers Foundation
- Vainboim Family
- European Research Council
- Israel Science Foundation
- Israel Ministry of Science and Technology
- Israel Ministry of Health
- German-Israeli Helmholtz International Research School: CancerTRAX [HIRS-0003]
- Helmholtz Association's Initiative and Networking Fund
- Minerva Foundation
- Garvan Institute
- European Crohn's and Colitis Organization
- Deutsch-Israelische Projektkooperation
- IDSA Foundation
- WIS-MIT grant
- Emulate
- Charlie Teo Foundation
- Mark Foundation for Cancer Research
- Welcome Trust
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The term 'old age' generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease. Quantifying aging based on life years does not necessarily reflect how the human body ages, while characterizing biological aging based on functional parameters may better reflect a person's physiological status and disease susceptibility. The gut microbiome changes along with physiological aging and may play a pivotal role in age-related diseases, and integration of gut microbiome data and host parameters using artificial intelligence and machine learning may enable more accurate definition of aging clocks.
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.
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