The gut microbiome is an immense microbial ecosystem with unique and diverse metabolic capabilities. In the past decade, it has been associated with multiple chronic and complex diseases, raising great hopes for novel medical advances. But are contemporary microbiome analysis methods useful in a clinical setting? I will present new tools that we developed for the analysis of the gut microbiome that utilize genomic sequencing coverage to yield biological and mechanistic insights about the microbiome in the context of health and disease. I will also present our own clinical research, in which we used microbiome analysis tools along with clinical, lifestyle and nutritional data to tackle post-meal blood glucose levels, an important risk factor for metabolic diseases such as obesity and diabetes. Our research suggests that general dietary recommendations have limited efficacy in these diseases due to high variability in the responses of different people to identical foods. Demonstrating an approach to solving this problem, we devised a machine learning algorithm based on microbiome and clinical data that accurately predicts personalized blood glucose responses to real-life, complex meals, and demonstrated that personally tailored diets based on these predictions can successfully reduce hyperglycemia.
Tal Korem is a postdoctoral fellow in the group of Prof. Eran Segal at the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science. His research focuses on designing tools for the analysis of the vast microbial ecosystem of the gut microbiome, and applying these tools in clinical settings in order to understand the relationship between nutrition, health, and gut microbes in humans. This is done by analyzing data collected on large human cohorts, with the aim of developing personalized nutrition and precision medicine.
Tal has coauthored several publications in the field of microbiome and nutritional research, linking the microbiome to the effects of artificial sweeteners (Suez et al., Nature, 2014) and host circadian rhythm (Thaiss et al., Cell 2016), inferring bacterial growth dynamics (Korem et al., Science, 2015) and predicting the glycemic responses of individuals to complex meals (Zeevi et al., Cell, 2015, Korem et al., Cell Metab. 2017).