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Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

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Increasing attendance to screening offers the best potential for improving the effectiveness of well-established cervical cancer screening programs. Self-sampling at home for human papillomavirus (HPV) testing as an alternative to a clinical sampling can be a useful policy to increase attendance. To determine whether self-sampling improves screening attendance for women who do not regularly attend the Norwegian Cervical Cancer Screening Programme (NCCSP), 800 women aged 25–69 years in the Oslo area who were due to receive a 2nd reminder to attend regular screening were randomly selected and invited to be part of the intervention group. Women in this group received one of two self-sampling devices, Evalyn Brush or Delphi Screener. To attend screening, women in the intervention group had the option of using the self-sampling device (self-sampling subgroup) or visiting their physician for a cervical smear. Self-sampled specimens were split and analyzed for the presence of high-risk (hr) HPV by the CLART® HPV2 test and the digene® Hybrid Capture (HC)2 test. The control group consisted of 2593 women who received a 2nd reminder letter according to the current guidelines of the NCCSP. The attendance rates were 33.4% in the intervention group and 23.2% in the control group, with similar attendance rates for both self-sampling devices. Women in the self-sampling subgroup responded favorably to both self-sampling devices and cited not remembering receiving a call for screening as the most dominant reason for previous non-attendance. Thirty-two of 34 (94.1%) hrHPV-positive women in the self-sampling subgroup attended follow-up. In conclusion, self-sampling increased attendance rates and was feasible and well received. This study lends further support to the proposal that self-sampling may be a valuable alternative for increasing cervical cancer screening coverage in Norway.

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism.

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sample sizes and a high resolution of clinical phenotypes and medication are required, while accounting for variables known to affect the gut microbiome. Finally, drug effects are often dose-dependent, yet dosage is rarely considered in microbiome studies.To overcome these limitations, we propose a general framework for separating disease from treatment associations in multi-omics cross-sectional studies and apply it to gut metagenomic, host clinical and metabolomic measurements of 2,173 European residents from the multicentre MetaCardis cohort. The MetaCardis cohort includes patients with metabolic syndrome, severe and morbid obesity, T2D, acute and chronic coronary artery disease and heart failure, and healthy control individuals. Considering cardiometabolic disease (CMD) and herein frequently prescribed medications, we investigated drug-hostmicrobiome associations for eight major indications (antidiabetic,

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.