Humans live in symbiosis with billions of commensal bacteria. The so-called microbiota live on different biological interfaces such as the skin, the urogenital tract and the gastrointestinal tract. Commensal bacteria replace potentially pathogenic microbes, synthesize vitamins and ferment dietary fibre. An imbalance in the bacterial composition of the intestinal microbiota has been associated with various diseases including gut-associated disorders such as inflammatory bowel diseases, colorectal cancer and nonalcoholic fatty liver disease. Furthermore, a shift in the microbiota composition appears to be of pathophysiological relevance which renders the specific modulation of the intestinal microbiota a promising approach in the treatment of the above mentioned diseases. Our intestinal microbiota composition is mainly modulated by dietary macro- and micronutrients but also by secondary plant compounds and synthetic food additives such as emulsifiers and artificial sweeteners. Nutritional interventions with the purpose to modulate the intestinal microbiota show only limited therapeutic potential in the treatment of gut-associated disorders, which may be due to individual differences in the intestinal microbiota composition and a lack of specificity. A combination of newly established technical analytic approaches involving a machine-learning algorithm may bridge the currently existing limitations by providing a personalized, highly-specific and consequently therapeutically effective microbiota modulation.
|Translated title of the contribution||Modulation of the intestinal microbiota by nutritional interventions|
|Number of pages||6|
|Publication status||Published - 01.05.2017|
Research Areas and Centers
- Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)