Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation
Oliver Aasmets, Kreete Lüll, Jennifer M. Lang, Calvin Pan, Johanna Kuusisto, Krista Fischer, Markku Laakso, Aldons J. Lusis, Elin Org
Abstract
Recent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider the microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes-associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using the microbiome in personalized medicine promising.