We know that a variety of environmental factors influence the composition of our gut microbiota; what we eat, where we live, and even our genes help to determine which species of bacteria are most likely to inhabit our guts. Scientists also know that the composition of our gut microbiota changes as we age. Notably, key species of bacteria are lost as we get older, leading scientists to wonder if we may be able to develop anti-aging therapeutics by targeting the gut microbiota. The first step towards this goal is figuring out whether we can predict a person’s age by analyzing their gut microbiome. That is exactly what researchers did in a new study published last February by mSystems.
Through a collaborative approach, the lab of Dr Rob Knight Director of the UC San Diego Center for Microbiome Innovation (USA), researchers have developed a predictive tool using information from different human microbiomes. The tool uses machine learning to predict chronological age with varying degrees of accuracy.
The authors combined several extensive studies from different countries to determine which body site’s microbiome could most accurately predict age. In total, this study represents the most comprehensive investigation of microbiome and age, comprising of 8,959 samples from 10 studies. The authors found that the skin microbiome provides the best prediction of age, estimating correctly to within approximately 3.8 years, followed by 4.5 years for an oral sample, and 11.5 years for a fecal sample. In agreement with previous work, the authors found a sex-specific signal in the gut microbiome; however, they did not find a sex-specific signal in the mouth or the skin microbiome.
Overall, this study shows that machine learning can use information from the human microbiome to accurately and generally predict age. This study emphasizes that data from the gut, oral, and skin microbiomes can all be used to predict age, but that the prediction is most accurate using data from the skin microbiome. Future steps may include accessible and convenient microbiome-based tests to determine signs of accelerated or delayed aging in the elderly, or individuals with chronic diseases.
Huang, Shi, Niina Haiminen, Anna-Paola Carrieri, Rebecca Hu, Lingjing Jiang, Laxmi Parida, Baylee Russell, et al. 2020. “Human Skin, Oral, and Gut Microbiomes Predict Chronological Age.” MSystems 5 (1). https://doi.org/10.1128/msystems.00630-19.