"ID";"Original Title";"Title";"Summary";"Contact";"Citation";"URL Scientific Article";"More References";"Keywords";"Field of Research";"Method/Model";"Year of Publication";"Month of Publication";"Date of Editing"; "1410";"Combinatorial, additive and dose-dependent drug–microbiome associations";"Drugs can affect the gut microbiota in different ways";"During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, the researchers show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. They quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shifts the metabolome and microbiome towards a healthier state. Different relationships between antibiotics, cardiometabolic drug dosage, improvement in clinical markers and microbiome composition are presented. Taken together, this computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using this framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.";"Karine Clément, Sorbonne Université, Paris, France, Michael Stumvoll, University of Leipzig Medical Center, Leipzig, Germany, Peer Bork, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany";"Sofia K. Forslund et al. Nature 2021";"https://www.nature.com/articles/s41586-021-04177-9";"Bionity, https://www.bionity.com/en/news/1173888/what-makes-the-heart-and-gut-inseparable.html";"gut, interactions, heart, microbiome";"Endocrinology, Metabolism, Gastroenterology, Hepatology, Cardiology, Angiology, Microbiology, Infectiology";"Human studies, Epidemiology, In silico, Artificial intelligence, OMICs, Big data";"2021";"12";"2022-04-04 16:39:21";