Beschreibung:
<jats:p>The intestinal microbiome plays a central role in human health and disease. While its composition is relatively stable throughout adulthood, the microbial balance starts to decrease in later life stages. Thus, in order to maintain a good quality of life, including the prevention of age-associated diseases in the elderly, it is important to understand the dynamics of the intestinal microbiome. In this study, stool samples of 278 participants were sequenced by whole metagenome shotgun sequencing and their taxonomic and functional profiles characterized. The two age groups, <jats:italic>below65</jats:italic> and <jats:italic>above65</jats:italic>, could be separated based on taxonomic and associated functional features using Multivariate Association of Linear Models. In a second approach, through machine learning, biomarkers connecting the intestinal microbiome with age were identified. These results reflect the importance to select age-matched study groups for unbiased metagenomic data analysis and the possibility to generate robust data by applying independent algorithms for data analysis. Furthermore, since the intestinal microbiome can be modulated by antibiotics and probiotics, the data of this study may have implications on preventive strategies of age-associated degradation processes and diseases by microbiome-altering interventions.</jats:p>