Growing evidence from metagenome-wide association studies link multiple common disorders to microbial dysbiosis but effects of drug treatment are often not accounted for; here, the authors re-analyse two previous metagenomic studies of type 2 diabetes mellitus patients together with a novel cohort to determine the effects of the widely prescribed antidiabetic drug metformin and highlight the need to distinguish the effects of a disease from the effects of treatment on the gut microbiota. There is growing evidence from metagenome-wide association studies that several common human disorders, such as type 2 diabetes mellitus (T2D), are associated with intestinal dysbiosis, an unhealthy imbalance of the gut microbiota. However, the contribution of antidiabetic drug treatment to dysbiosis is often not accounted for. Oluf Pedersen and colleagues analysed two previous metagenomic studies of T2D patients that yielded divergent conclusions regarding the association of the disease with dysbiosis, together with a novel cohort, to determine the effects of the widely prescribed antidiabetic drug metformin. They find that metformin is indeed a confounding factor, but that a unified signature of gut microbiome shifts in T2D is still apparent. In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported1,2. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis3,4. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa3,4. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. ClinicalTrials.gov NCT03814915.

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal.

Purpose: Cachexia is a multifactorial syndrome, associated with poor survival in patients with cancer, and is influenced by the gut microbiota. We investigated the effects of fecal microbiota transplantation (FMT) on cachexia and treatment response in patients with advanced gastroesophageal cancer. Experimental Design: In a double-blind randomized placebo-controlled trial performed in the Amsterdam University Medical Center, we assigned 24 cachectic patients with metastatic HER2-negative gastroesophageal cancer to either allogenic FMT (healthy obese donor) or autologous FMT, prior to palliative chemotherapy (capecitabine and oxaliplatin). Primary objective was to assess the effect of allogenic FMT on satiety. Secondary outcomes were other features of cachexia, along with disease control rate (DCR), overall survival (OS), progression-free survival (PFS), and toxicity. Finally, exploratory analyses were performed on the effect of FMT on gut microbiota composition (metagenomic sequencing) and metabolites (untargeted metabolomics). Results: Allogenic FMT did not improve any of the cachexia outcomes. Patients in the allogenic group (n = 12) had a higher DCR at 12 weeks (P = 0.035) compared with the autologous group (n = 12), longer median OS of 365 versus 227 days [HR = 0.38; 95% confidence interval (CI), 0.14–1.05; P = 0.057] and PFS of 204 versus 93 days (HR = 0.50; 95% CI, 0.21–1.20; P = 0.092). Patients in the allogenic group showed a significant shift in fecal microbiota composition after FMT (P = 0.010) indicating proper engraftment of the donor microbiota. Conclusions: FMT from a healthy obese donor prior to first-line chemotherapy did not affect cachexia, but may have improved response and survival in patients with metastatic gastroesophageal cancer. These results provide a rational for larger FMT trials.