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      Duodenal L cell density correlates with features of metabolic syndrome and plasma metabolites


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          Enteroendocrine cells are essential for the regulation of glucose metabolism, but it is unknown whether they are associated with clinical features of metabolic syndrome (MetS) and fasting plasma metabolites.


          We aimed to identify fasting plasma metabolites that associate with duodenal L cell, K cell and delta cell densities in subjects with MetS with ranging levels of insulin resistance.

          Research design and methods

          In this cross-sectional study, we evaluated L, K and delta cell density in duodenal biopsies from treatment-naïve males with MetS using machine-learning methodology.


          We identified specific clinical biomarkers and plasma metabolites associated with L cell and delta cell density. L cell density was associated with increased plasma metabolite levels including symmetrical dimethylarginine, 3-aminoisobutyric acid, kynurenine and glycine. In turn, these L cell-linked fasting plasma metabolites correlated with clinical features of MetS.


          Our results indicate a link between duodenal L cells, plasma metabolites and clinical characteristics of MetS. We conclude that duodenal L cells associate with plasma metabolites that have been implicated in human glucose metabolism homeostasis. Disentangling the causal relation between L cells and these metabolites might help to improve the (small intestinal-driven) pathophysiology behind insulin resistance in human obesity.

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          Most cited references18

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            Improvement of Insulin Sensitivity after Lean Donor Feces in Metabolic Syndrome Is Driven by Baseline Intestinal Microbiota Composition

            The intestinal microbiota has been implicated in insulin resistance, although evidence regarding causality in humans is scarce. We therefore studied the effect of lean donor (allogenic) versus own (autologous) fecal microbiota transplantation (FMT) to male recipients with the metabolic syndrome. Whereas we did not observe metabolic changes at 18 weeks after FMT, insulin sensitivity at 6 weeks after allogenic FMT was significantly improved, accompanied by altered microbiota composition. We also observed changes in plasma metabolites such as γ-aminobutyric acid and show that metabolic response upon allogenic FMT (defined as improved insulin sensitivity 6 weeks after FMT) is dependent on decreased fecal microbial diversity at baseline. In conclusion, the beneficial effects of lean donor FMT on glucose metabolism are associated with changes in intestinal microbiota and plasma metabolites and can be predicted based on baseline fecal microbiota composition.
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              Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis

              OBJECTIVE To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite. RESULTS We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 [1.24–1.48]; I 2 = 9.5%), 36% for leucine (1.36 [1.17–1.58]; I 2 = 37.4%), 35% for valine (1.35 [1.19–1.53]; I 2 = 45.8%), 36% for tyrosine (1.36 [1.19–1.55]; I 2 = 51.6%), and 26% for phenylalanine (1.26 [1.10–1.44]; I 2 = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 [0.81–0.96] and 0.85 [0.82–0.89], respectively; both I 2 = 0.0%). CONCLUSIONS In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.

                Author and article information

                Endocr Connect
                Endocr Connect
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                May 2018
                18 April 2018
                : 7
                : 5
                : 673-680
                [1 ]Department of Gastroenterology and Hepatology Academic Medical Center, Amsterdam, the Netherlands
                [2 ]Department of Vascular Medicine Academic Medical Center, Amsterdam, the Netherlands
                [3 ]Center for Diabetes Research Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
                [4 ]Department of Biomedical Sciences Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
                [5 ]Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
                [6 ]Department of Clinical Medicine Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
                [7 ]Department of Laboratory Medicine University of Groningen, University Medical Center, Groningen, the Netherlands
                [8 ]Department of Internal Medicine VUMC Free University, Amsterdam, the Netherlands
                [9 ]Wallenberg Laboratory Sahlgrenska Hospital, University of Gothenburg, Gothenburg, Sweden
                [10 ]Horaizon BV Delft, the Netherlands
                Author notes
                Correspondence should be addressed to A C G van Baar: a.c.vanbaar@ 123456amc.nl
                © 2018 The authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                : 11 April 2018
                : 18 April 2018

                metabolic syndrome,incretins,enteroendocrine cells,plasma metabolites,machine-learning methodology


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