Publication date: Feb 12, 2020
Given a microbiome sample (skin, mouth or fecal swab), researchers have demonstrated they can now use machine learning to predict a person’s chronological age, with a varying degree of accuracy.
“This new ability to correlate microbes with age will help us advance future studies of the roles microbes play in the aging process and age-related diseases, and allow us to better test potential therapeutic interventions that target microbiomes,” said co-senior author Zhenjiang Zech Xu, PhD, who was at the time of the study a postdoctoral researcher in the UC San Diego School of Medicine lab of co-senior author Rob Knight, PhD, professor and director of the UC San Diego Center for Microbiome Innovation.
In a 2014 study, Washington University researchers 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.
“This was the most comprehensive investigation of microbiome and age to date,” said first author Shi Huang, PhD, a postdoctoral researcher in Knight’s lab and the UC San Diego Center for Microbiome Innovation.
“The accuracy of our results demonstrate the potential for applying machine learning and artificial intelligence techniques to better understand human microbiomes,” said co-author Ho-Cheol Kim, PhD, program director of the Artificial Intelligence for Healthy Living Program, a collaboration between IBM Research and UC San Diego under the IBM AI Horizons Network.
According to co-author Yoshiki VcE1zquez-Baeza, PhD, associate director of bioinformatic integration at the UC San Diego Center for Microbiome Innovation, age prediction is a particularly attractive method for training predictive models because participants don’t need to meet special criteria in order to become a sample donor, and assessing age typically does not require a visit to a hospital.
|disease||MESH||inflammatory bowel disease|