Aging and the Microbiome

The human body is composed of many systems of different sizes and levels of complexity. A myriad of microbiota contribute to the functioning of those systems (Kim & Benayoun, 2020). 

The role of microbiota in human health and fitness has been demonstrated in a wide range of diseases including cancer, Alzheimer's, diabetes, heart disease, obesity, Parkinson’s, and Alzheimer's. For example, age-related insulin resistance has been linked to an increase in the presence of highly virulent bacteria such as E. coli and P. aeruginosa. 

BioViva Science has an array of DTC health tests that assess different molecular aspects of the aging process, are helping us understand our health. Like DNA methylation, telomeres, and virtually everything else, our microbiomes change over time. Its maintenance is a part of retaining stable microenvironments and, by extension, overall health (Kim & Jazwinski, 2018). Bacteria and other biological actors, including many viruses, are important for the body to maintain itself (Barrera-Vázquez & Gomez-Verjan, 2019). 

Exercise, environment, and nutrition can influence the composition and function of the host's microbiome as well as the microbiota of other animals and humans (Mills et al., 2019). As with other processes in the human body, even with good habits, homeostasis cannot be maintained forever. As the body ages and systems break down, changes to the microbiome accelerate the aging process (Lozupone; Turnbaugh, 2019). 

In the context of gene therapy, the microbiome can be influenced in two ways: indirectly by  altering the human genome with gene therapy or by targeting the microbiota themselves (Madhusoodanan, 2020). The genes of microscopic organisms have their own genomes, but function as a part of the human body can be altered through a process similar to the one used to deliver gene therapies to human cells.  

At some point in the development of medicine we will have to curate the foreign bodies which enter the body with a clear vision as to what different species of microbes are going to do for different people.

This is a daunting challenge, as the effects of such organisms on microenvironments, and the human body overall, are not fully understood, but with breakthroughs in data analysis, such as the creation of specific processes.

One of the most common problems that occurs in the microbiome associated with age is gut dysbiosis, which is caused by the gut losing biodiversity, resulting in digestive issues. This is one reason why senior citizens more often experience problems like constipation and  inflammation.

There are some things that can be done to maintain this diversity. A varied diet, ideally one rich in fiber, helps, but this can be a vicious cycle, at a certain point what prevents people from maintaining a varied diet are things like a damaged gut, even if that gut has generally been taken care of, it will eventually lose some of its functionality. 

While the role of the microbiome is best studied as it relates to the gut, all parts of the body are influenced by microbial organisms in ways that are not yet fully understood. The diversity of microorganisms is so numerous that isolating each one’s specific role or roles is a difficult task.

The world of microbial life forms remains uncharted territory in the realm of science and medicine, but one which will become less mysterious as machine learning allows researchers to parse through much more data (Barrera-Vázquez, Gomez-Verjan, 2019) (Cammarota et al., 2020) (Topçuoğlu, Lesniak, Ruffin, Wiens, & Schloss, 2019). The day may come when there is not much we do not understand about processes of the human body, large or small, but it will be a long road. 

 

References and Suggested Reading

Barrera-Vázquez, O. S., & Gomez-Verjan, J. C. (2019). The Unexplored World of Human Virome, Mycobiome, and Archaeome in Aging. The Journals of Gerontology: Series A, 75(10), 1834-1837. doi:10.1093/gerona/glz274

Behrouzi, A., Nafari, A. H., & Siadat, S. D. (2019). The significance of microbiome in 

personalized medicine. Clinical and translational medicine, 8(1), 16. https://doi.org/10.1186/s40169-019-0232-y

Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., . . . Tortora, G. (2020). Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nature Reviews Gastroenterology & Hepatology, 17(10), 635-648. doi:10.1038/s41575-020-0327-3

DeGruttola, A. K., Low, D., Mizoguchi, A., & Mizoguchi, E. (2016). Current 

Understanding of Dysbiosis in Disease in Human and Animal Models. Inflammatory bowel diseases, 22(5), 1137–1150. https://doi.org/10.1097/MIB.0000000000000750

Deng, F., Li, Y., & Zhao, J. (2019). The gut microbiome of healthy long-living people. 

Aging, 11(2), 289–290. https://doi.org/10.18632/aging.101771

Kim, M., & Benayoun, B. A. (2020). The microbiome: An emerging key player in 

aging and longevity. Translational Medicine of Aging, 4, 103-116. doi:10.1016/j.tma.2020.07.004

Kim, S., & Jazwinski, S. (2018). The Gut Microbiota and Healthy Aging: A Mini-Review. Gerontology, 64(6), 513-520. doi:10.1159/000490615

Lozupone, C. A., & Turnbaugh, P. (2019). Decision letter: Adjusting for age improves identification of gut microbiome alterations in multiple diseases. doi:10.7554/elife.50240.sa1

Madhusoodanan, J. (2020). News Feature: Editing the microbiome. Proceedings of the National Academy of Sciences, 117(7), 3345-3348. doi:10.1073/pnas.2000108117

Mills, J. G., Brookes, J. D., Gellie, N. J., Liddicoat, C., Lowe, A. J., Sydnor, H. R., . . . 

Breed, M. F. (2019). Relating Urban Biodiversity to Human Health With the ‘Holobiont’ Concept. Frontiers in Microbiology, 10. doi:10.3389/fmicb.2019.00550

Santoro, A., Ostan, R., Candela, M., Biagi, E., Brigidi, P., Capri, M., & Franceschi, C. 

(2018). Gut microbiota changes in the extreme decades of human life: a focus on centenarians. Cellular and molecular life sciences : CMLS, 75(1), 129–148. https://doi.org/10.1007/s00018-017-2674-y

Topçuoğlu, B. D., Lesniak, N. A., Ruffin, M., Wiens, J., & Schloss, P. D. (2019). A 

framework for effective application of machine learning to microbiome-based classification problems. doi:10.1101/81609