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"This new capability will allow us to better evaluate potential therapeutic interventions that target microbiomes," said Zech Xu.
Researchers from the University of California in San Diego and the IBM company have developed a predictive tool that allows guessing a person's age from their microbiome, as published in the magazine 'mSystems'.
The microbiome, the complex communities of microbes that live in and around a person, are influenced by diet, habits, environment and genes, and are known to change with age. And its composition is recognized for its influence on our health.
Using a microbiome sample (a sample of skin or stool), researchers have shown that they can now use machine learning to predict a person's chronological age, with varying degrees of accuracy.
Skin samples provided the most accurate prediction, correctly estimating within about 3,8 years, compared to 4,5 years with an oral sample and 11,5 years with a fecal sample. The types of microbes that live in the oral cavity or within the gut of young people (ages 18 to 30) tend to be more diverse and abundant than in the comparative microbiomes of older adults (ages 60 and older).
"This new ability to correlate microbes with age will help us advance future studies into the role they play in the aging process and age-related diseases, and will allow us to better evaluate potential therapeutic interventions that target microbiomes." "says co-senior author Zhenjiang Zech Xu.
The team's ultimate goal is to create similar machine learning models to correlate the microbiome and clinical conditions, such as inflammation in autoimmune conditions. This approach could one day form the basis of a non-invasive microbiome-based test that potentially helps doctors better diagnose or assess a person's risk for a disease.
In a 2014 study, researchers at the University of Washington compared “microbial age,” age as predicted by the fecal microbiome, and actual chronological age in the context of malnourished infants during the first months of life.
The researchers noted that the difference between chronological and microbial age was associated with the degree of developmental maturity of the children. In the new study, researchers at the University of California, San Diego took this idea a step further to see if this association could apply to adults, and how easily it generalized to other sites in the body.
According to Xu, one of the most important requirements for a good statistical model is a large sample size and a representative population. To do that, the researchers pulled available microbiome sequencing data from the public databases of several citizen science projects, such as the American Gut Project, in which participants submit stool, saliva or skin samples, receive their readings personalized microbiome and contribute with their anonymity by providing data to the scientific community.
