Abstract
Elevated blood glucose levels are rapidly increasing in the general population, resulting in a sharp incline in the prevalence of pre-diabetes and impaired glucose tolerance, and eventual development of type II diabetes mellitus. Dietary intake is considered a central determinant of glucose levels, with high post-meal glucose levels affecting weight gain, obesity, hunger, energy dips, and being associated with increased risk of cardiovascular disease, cancer, and overall mortality. However, despite their importance, existing dietary methods for controlling post-meal glucose levels have limited efficacy.
By continuously monitoring week-long glucose levels in over 1,000 people, we found high variability in the response of different people to identical meals, suggesting that generic population-wide dietary recommendations have limited utility and are ineffective in achieving proper glycemic control. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glucose responses to real-life meals. Moreover, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses in a cohort of pre-diabetics and consistent alterations to gut microbiota configuration.
Finally, I will also present our studies of the mechanisms driving recurrent post-dieting obesity in which we identified an intestinal microbiome signature that persists after successful dieting of obese mice. This microbiome signature contributes to faster weight regain and metabolic aberrations upon re-exposure to obesity-promoting conditions and transmits the accelerated weight regain phenotype upon inter-animal transfer. We further find that the microbiome contributes to diminished post-dieting flavonoid levels and reduced energy expenditure, and demonstrate that flavonoid- based ‘post-biotic’ intervention ameliorates excessive secondary weight gain. These results suggest that microbiome-targeting approaches may help to diagnose and treat this common disorder.