Analysis of the Correlation between Human Gut Microbiome and Age
The human intestinal microbiome consists of a large collection of symbiotic bacteria living in the human body. The commensal microbes are responsible for breaking down certain types of fiber and synthesize beneficial compounds that can be carried to other body parts. With the advancement of sequencing technology, we now know that the composition of the gut microbiome is correlated with and affected by factors such as age, diet and disease. In this work we hope to identify whether the microbiome composition is correlated with human age and can serve as age predictor using machine learning approach. In this study, we incorporated multiple 16S amplicon sequencing datasets from Japan. The amplicon datasets were processed using Qiime2 to extract the OTU feature tables, which was trained and cross-validated using a variety of machine learning methods. Our preliminary results demonstrate that the OTU composition can indeed serve as a good predictor for age (R-square 0.86), suggesting that microbiome is correlated with the aging process. We also found that certain age range (20-60) can be better predicted compared to very old or very young people, indicating unstable microbiome (and thus poorer predictor) for the development or aging-related dysbiosis conditions.
Keywords - Age prediction, Gut microbiome, QIIME2, R-square.