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      Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology.

      1 , 2 , 3 , 4 , 1 , 1 , 3 , 3 , 5 , 6 , 7 , 1 , 3 , 3 , 3 , 3 , 8 , 3 , 9 , 10 , 11 , 12 , 13 , 11 , 5 , 14 , 11 , 11 , 12 , 13 , 7 , 7 , 7 , 7 , 7 , 7 , 7 , 14 , 15 , 16 , 17 , 11 , 12 , 4 , 18 , 19 , 20 , 21 , 11 , 6 , 7 , 3 , 4 , 22 , 23 , 24 , 25 , 26
      Nature communications
      Springer Science and Business Media LLC

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          Abstract

          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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          Most cited references64

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          Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

          The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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            From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites.

            A compelling set of links between the composition of the gut microbiota, the host diet, and host physiology has emerged. Do these links reflect cause-and-effect relationships, and what might be their mechanistic basis? A growing body of work implicates microbially produced metabolites as crucial executors of diet-based microbial influence on the host. Here, we will review data supporting the diverse functional roles carried out by a major class of bacterial metabolites, the short-chain fatty acids (SCFAs). SCFAs can directly activate G-coupled-receptors, inhibit histone deacetylases, and serve as energy substrates. They thus affect various physiological processes and may contribute to health and disease.
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              A metagenome-wide association study of gut microbiota in type 2 diabetes.

              Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.
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                Author and article information

                Journal
                Nat Commun
                Nature communications
                Springer Science and Business Media LLC
                2041-1723
                2041-1723
                Nov 18 2020
                : 11
                : 1
                Affiliations
                [1 ] Wallenberg Laboratory, Department of Molecular and Clinical Medicine and Sahlgrenska Center for Cardiovascular and Metabolic Research, University of Gothenburg, 413 45, Gothenburg, Sweden.
                [2 ] Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden.
                [3 ] INSERM, Nutrition and Obesities; Systemic Approaches (NutriOmics), Sorbonne Université, Paris, France.
                [4 ] Assistance Publique Hôpitaux de Paris, Pitie-Salpêtrière Hospital, Nutrition department, CRNH Ile de France, Paris, France.
                [5 ] Integromics Unit, Institute of Cardiometabolism and Nutrition, 75013, Paris, France.
                [6 ] Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.
                [7 ] Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
                [8 ] Assistance Publique Hôpitaux de Paris, Clinical Investigation Center Paris East, 75013, Paris, France.
                [9 ] Assistance Publique Hôpitaux de Paris, Biochemistry and Hormonology Department, Tenon Hospital, 75020, Paris, France.
                [10 ] Experimental and Clinical Research Center, A Cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany.
                [11 ] Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Paris, France.
                [12 ] Laboratory of Molecular Bacteriology, Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
                [13 ] Center for Microbiology, VIB, Leuven, Belgium.
                [14 ] Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, 93143, Bondy, France.
                [15 ] Sorbonne Paris Cité Epidemiology and Statistics Research Centre (CRESS), U1153 Inserm, U1125, Inra, Cnam, University of Paris 13, Nutritional Epidemiology Research Team (EREN), 93017, Bobigny, France.
                [16 ] Computational and Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK.
                [17 ] Genomic and Environmental Medicine, National Heart & Lung Institute, Faculty of Medicine, Imperial College London, London, SW3 6KY, UK.
                [18 ] Biobyte Solutions GmbH, Bothestr. 142, 69117, Heidelberg, Germany.
                [19 ] Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128, Gothenburg, Sweden.
                [20 ] Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany.
                [21 ] Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany.
                [22 ] INSERM, Nutrition and Obesities; Systemic Approaches (NutriOmics), Sorbonne Université, Paris, France. karine.clement@inserm.fr.
                [23 ] Assistance Publique Hôpitaux de Paris, Pitie-Salpêtrière Hospital, Nutrition department, CRNH Ile de France, Paris, France. karine.clement@inserm.fr.
                [24 ] Wallenberg Laboratory, Department of Molecular and Clinical Medicine and Sahlgrenska Center for Cardiovascular and Metabolic Research, University of Gothenburg, 413 45, Gothenburg, Sweden. fredrik.backhed@wlab.gu.se.
                [25 ] Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark. fredrik.backhed@wlab.gu.se.
                [26 ] Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden. fredrik.backhed@wlab.gu.se.
                Article
                10.1038/s41467-020-19589-w
                10.1038/s41467-020-19589-w
                7676231
                33208748
                d869d27a-0ddc-4215-b9b5-c7b9429cb810
                History

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