Intestine microbiome can predict modifications in Kind 2 diabetes associated glucose regulation


A study conducted by researchers from the Institute of Genomics at the University of Tartu found that the human gut microbiome can be used to predict changes in glucose regulation associated with type 2 diabetes for up to four years.

Type 2 diabetes is a metabolic disease characterized by high blood sugar levels that causes millions of deaths worldwide each year. Their prevalence is increasing rapidly. Type 2 diabetes precedes “prediabetes” – a condition in which glucose levels have started to rise but the progression of the disease can still be stopped and reversed. Therefore, early detection of disease progression is needed, and previous research suggests the gut microbiome could be used for this purpose, said Elin Org, last author of the paper and associate professor of genomics and microbiomics.

The aim of this study was to investigate whether the gut microbiome can be used to predict changes in metabolic parameters such as plasma insulin and glucose levels in the early stages of the disease. “This is one of the first studies to examine the role of the gut microbiome in type 2 diabetes over time,” said Oliver Aasmets, the paper’s lead author.

The results showed that the gut microbiome can predict changes in glucose regulation, mainly related to insulin levels and secretion. “Our study design allowed us to compare predictions made a year and a half and four years ago. These showed significant differences and provided input for further study,” said Aasmets. In addition, the study showed which microbes are most useful for predicting changes in metabolic parameters.

The use of the gut microbiome as a risk factor in predicting various diseases is a promising area of ​​research. However, further studies in different populations and with larger sample sets are needed to validate the results and further develop the predictive models. “

Elin Org, last author


Estonian Research Council

Journal reference:

O. Aasmets et al. (2021) Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation. American Society for Microbiology Journals.