Statistics from Altmetric.com
In the last decade, disease-association studies have dominated gut microbiome research. In many cases, the set-up of these studies has been explorative, screening faecal microbiomes for diagnostic signals with potential predictive or therapeutic perspectives. While such data-driven approaches contributed substantially to tool development and the establishment of metagenome research as a stand-alone field within microbial sciences, they also demonstrated that the untargeted characterisation of microbiome variation in the function of disease pathology is far from trivial.1 Researchers able to successfully establish microbiota–disease associations beyond microbiome background variation awaited the long and painful task to discriminate between causes and consequences, taking into account the continuously expanding list of microbiome covariates. Top-ranked among those covariates is the use of medication,2 emphasising a need for careful study design and cautious statistics when attempting to differentiate between drug and disease associations.3
Aside from data-driven gut microbiome research, far fewer studies have applied metagenome analyses to investigate predefined hypotheses. For some pathologies, a (co-)causative role of the gut microbial community in disease development had been hypothesised long before advance in sequencing technology revolutionised gut microbiota research. One such long-standing hypothesis concerns the potential implication of gut bacteria in idiopathic kidney stone …
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.