A new study published in mSystems, a journal from the American Society for Microbiology, shows that the skin and mouth microbiomes are better predictors of age than the gut microbiome.
A very broad study
The authors used a very large population that is highly impressive among studies of this kind. Previously, a team containing some of the same researchers had done a gut microbiome study of over four thousand people from multiple countries . This time, the researchers took skin, saliva, and fecal samples from roughly 2,000, 2,500, and 4,500 people, respectively; this study was done with nearly 9,000 people in total, and the team stated that it was the most comprehensive microbiome study done to date. The team used a “random forest” machine learning approach to determine what microbiota were and were not predictive of age .
Country and sex differences in gut – but not skin or mouth
There are differences between the sexes, and between countries, in the gut microbiome. The team offers the example of a bacterium in the Bifidobacterium genus, which is present in Chinese cohorts but not in American ones. The skin and saliva samples did not have this issue.
The researchers were able to use skin from the forehead and hand, and they found that a model trained on the forehead was able to correctly predict age based on hand bacteria, and vice versa, despite the different environments. These changes are due to decreased sebum (oil) production and increasing dryness.
Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.
As the abstract makes clear, while the skin and mouth tests are not perfectly accurate at four and four and a half years, they are substantially better than the gut tests, which have an average error of eleven and a half years. This is good news for medical professionals, as it is obviously much more convenient to take skin and saliva samples than fecal samples.
This is also good news for people who want to live longer lives! We have published previous articles highlighting the role of the gut microbiome, including its relationship with the brain. The inaccuracy of gut results means that it is possible to keep a substantially more youthful gut microbiome than average, even without next-generation rejuvenative interventions.
These results also mean that rejuvenative interventions that affect the skin or mouth should be able to be accurately tested; if sebum production and moisture are restored to the skin, for example, then it is logical to conclude that the youthful skin microbiome will be restored accordingly.
 de la Cuesta-Zuluaga, J., Kelley, S. T., Chen, Y., Escobar, J. S., Mueller, N. T., Ley, R. E., … & Thackray, V. G. (2019). Age-and sex-dependent patterns of gut microbial diversity in human adults. mSystems, 4(4), e00261-19.
 Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.