Together, our study Carbohydrwte the advantage of Carbkhydrate omics strategy in exploring the involvement of ,etabolism metabolism and Carbohydarte products Easy Recharge Solutions the pathogenesis Cagbohydrate IR. Article CAS PubMed Google Scholar Piening, B. Lakka HM, Mehabolic DE, Lakka TA, Niskanen Breakfast for better blood sugar control, Kumpusalo E, Tuomilehto J, et al. These pathways were also distinctly correlated with the participant clusters defined in Fig. Keywords: myocardial glucose metabolism, metabolic syndrome, type 2 diabetes, cardiovascular disease, insulin resistance, cardiac 18 F-FDG-PET Citation: Succurro E, Vizza P, Papa A, Cicone F, Monea G, Tradigo G, Fiorentino TV, Perticone M, Guzzi PH, Sciacqua A, Andreozzi F, Veltri P, Cascini GL and Sesti G Metabolic Syndrome Is Associated With Impaired Insulin-Stimulated Myocardial Glucose Metabolic Rate in Individuals With Type 2 Diabetes: A Cardiac Dynamic 18 F-FDG-PET Study.

Carbohydrate metabolism and metabolic syndrome -

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Kuleshov, M. Enrichr: a comprehensive gene set enrichment analysis web server update. Kubota, T. Downregulation of macrophage Irs2 by hyperinsulinemia impairs ILindeuced M2a-subtype macrophage activation in obesity. Impaired insulin signaling in endothelial cells reduces insulin-induced glucose uptake by skeletal muscle.

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Cell Host Microbe 29 , — Download references. We thank E. Miyauchi, T. Kanaya and T. Kato for advice; A. Ito, N. Tachibana, A.

Hori and the staff at the RIKEN Yokohama animal facility for technical support; H. Koseki, M. Furuno and H.

Iwano for data discussion; and the staff at the RIKEN BioResource Research Center for providing essential materials. Kubota, 21K to H. and 22H to H.

and M. Kubota and the RIKEN Junior Research Associate Program to T. Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.

Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Division of Diabetes and Metabolism, The Institute for Medical Science Asahi Life Foundation, Tokyo, Japan. Department of Clinical Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition NIBIOHN , Tokyo, Japan.

Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science CSRS , Yokohama, Japan. Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan.

Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan. Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.

Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Department of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Japan. Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.

Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Institute for Advanced Biosciences, Keio University, Fujisawa, Japan. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.

Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan. Center for Epidemiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.

International University of Health and Welfare, Tokyo, Japan. Department of Metabolism and Endocrinology, Tokyo Medical University Ibaraki Medical Center, Ami Town, Japan.

Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan.

Human Biology-Microbiome-Quantum Research Center WPI-Bio2Q , Keio University, Tokyo, Japan. Laboratory for Immune Cell Systems, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.

You can also search for this author in PubMed Google Scholar. Kadowaki and H. conceived the project. Kubota, Y. Mizuno, N. and T. Kadowaki contributed to the enrolment of study participants and clinical data collection. and Y. processed faecal samples for metagenomics and metabolomic analyses.

performed 16S rRNA gene sequencing and metagenomic analysis. performed metabolomic analyses for hydrophilic metabolites. performed lipidomics analyses. and P. performed CAGE analysis. and O. performed cytokine measurement and RNA extraction from PBMCs. Mochizuki prepared fundamental information tools for the analysis.

Kubota and S. performed animal experiments and analysed the data. Kitami and K. analysed the omics data. Kubota, P. and H. provided essential materials and raised funding. Kubota and H. wrote the paper together with A. Kitami and P.

Correspondence to Tetsuya Kubota or Hiroshi Ohno. are listed as the inventors on a patent regarding the metabolic effects of gut bacteria identified by a human cohort.

The other authors declare no competing interests. Nature thanks Gregory Steinberg and the other, anonymous, reviewer s for their contribution to the peer review of this work. Insulin resistance IR and metabolic syndrome MetS were the main clinical phenotypes. To evaluate the host-microbe relationship, we collected 1 host factors: clinical, plasma metabolome, peripheral blood mononuclear cells PBMC transcriptome, and cytokine data, and 2 microbial factors: 16S rRNA pyrosequencing, shotgun metagenome, and faecal metabolome.

The numbers of elements after quality filtering are shown for each data set. b , The multi-omics analysis workflow. To identify the microbes that affect metabolic phenotypes, we first analysed the phenotype-associated metabolomic signatures by binning metabolites into co-abundance groups CAGs.

Microbial signatures were determined using the 16S and metagenomic datasets, and their associations with metabolites were analysed. We also assessed the mediation effects of plasma cytokines on the relationships between faecal metabolites and metabolic markers.

The associations between clinical phenotypes and omics markers were adjusted by age and sex wherever appropriate. a , The KEGG pathway enrichment analysis of the metabolites in hydrophilic CAGs 5, 8, 12, 15, and 18, which were associated with IR in Fig.

The size of disks shows the enrichment i. b , Partial correlations between HOMA-IR and faecal levels of short-chain fatty acids SCFA such as acetate, propionate, and butyrate left panel , and disaccharides such as maltose and sucrose right panel.

Density plots indicate median and distribution. The detailed statistics are reported in Supplementary Table 5 , 6. The size and colour of the disks represent the estimate and the direction of the associations.

c , The associations between faecal glucose and arabinose and HOMA-IR as analysed in Fig. The estimates of metabolites and their P values are described.

The data were analysed with a generalized linear mixed-effect model with consent age and sex as fixed effects, and the sample collection site as a random effect.

The estimate and P value are described. The first faecal sampling for metabolomics was used to avoid redundancy. The detailed statistics are reported in Supplementary Table 9. Dots represent individual data summarized into PCo1 and PCo2. Dots represent individual data summarized into PC1 and PC2.

f , Co-abundance groups of genus-level microbes and their abundance in the participant clusters defined in Fig. The disk size represents the median abundance in the participants.

g , The co-abundance groups of genus-level microbes and their abundance in the participant clusters. The size of the disks represents overabundance to the mean in four clusters of participants determined in Fig. The far-left column shows the genera that exhibit significant differences among the four clusters.

The genera forming distinct groups in f , i. The participants were clustered into three mOTU clusters A to C based on the heatmap clustering. The proportion of individuals with IS, intermediate, and IR are shown in the pie charts above the heatmap as Fig.

Only those with significant associations with metabolic markers are depicted. The disk size and colour represent absolute values of standardized coefficient and the direction of associations. The detailed statistics are reported in Supplementary Table j , Microbe-metabolite networks of IR- or and IS-associated co-abundance microbial groups from Fig.

All faecal hydrophilic metabolites and faecal microbe-related lipid metabolites were included in the analysis. The metabolites in CAGs relating to carbohydrates shown in Fig.

k , The relative abundance of IR-associated faecal carbohydrates in the participant clusters. The metabolites significantly different among these four clusters are coloured grey in the top row. a , b , Box plots indicate the median, upper and lower quartiles, and upper and lower extremes except for outliers.

Kruskal-Wallis test g , k. See the Source Data g for exact P values. a , b , The associations between the KEGG pathways relating to amino acid metabolism a and lipid metabolism b , faecal carbohydrates, top three genera positively or negatively correlated with faecal carbohydrates in Fig.

c , The associations between representative metabolic markers and the KEGG pathways relating to carbohydrate metabolism, amino acid metabolism, lipid metabolism, and membrane transport defined in the KEGG orthology database.

The pathways with significant associations with metabolic markers are included in the plots. The far-left column shows the type of carbohydrate metabolites that each PTS gene is involved in. The far-left column shows whether the genes were predicted to function as extracellular enzymes.

g , Representative pathways in starch and sucrose metabolism KEGG pathway relating to glycosidase activities to degrade poly- and oligosaccharides into monosaccharides. i , The presence and absence of KEGG orthologues predicted to function as extracellular enzymes in 45 strains. The strains from the top three genera positively or negatively correlated with faecal carbohydrates shown in Fig.

Density plots indicate median and distribution e , h. a , Cell-type gene set enrichment analysis based on the Human Gene Atlas database using Enrichr. Red and blue colour scales represent IR and IS-associated cell types, respectively please refer to Methods for details.

b , The cross-omics network shown in Fig. c , The number of correlations between faecal carbohydrates and other omics elements shown in Fig. The proportion to all possible correlations is shown. d , Representative causal mediation models analysing the effects of IL and adiponectin mediating in silico relationships between faecal carbohydrates and HOMA-IR.

Causal mediation analysis with multiple test corrections were used to test significance. Estimates β and P adj values of average causal mediation effects ACME , which are the indirect effects between the metabolites and host markers mediated by cytokines, and average direct effects ADE , which are the direct effects controlling for cytokines, are described.

Age and sex were adjusted in the models. The detailed information is reported in Supplementary Table a , b , PCA plots of metabolites in cell-free supernatants of 22 bacterial strains listed in a.

These strains were selected based on the findings from the genus-level co-occurrence Fig. The strains from genera and species relating to IR-related markers shown in Extended Data Fig.

The top 10 metabolites contributing to the PCA separation left panel and 13 out of 15 IR-related carbohydrates identified in Fig. c , d , The levels of carbohydrate fermentation products c and carbohydrates relating to IR in the human cohort d in the cell-free supernatants. e , Pie charts summarizing the consumption and production of carbohydrates shown in d.

Those significantly decreased or increased compared with the vehicle control group were considered as consumption or production. f , The top consumers of carbohydrates, which summarizes the results shown in e. Representative data of two independent experiments. c , d , Data are mean and s.

a , Body mass change from the baseline. indistinctus AI groups, respectively. Pooled data of three independent experiments. Pooled data of two independent experiments.

k , l , Representative images of phosphorylated Akt p-Akt at S and total Akt in the liver and epidydimal fat eWAT in mice administered Alistipes indistinctus AI , Alistipes finegoldii AF , and PBS as vehicle control k.

The raw images of blotting membranes are shown in Supplementary Fig. P values for interactions between time and group are described in m. Other metabolic measures are reported in Supplementary Table Representative data of two independent experiments c—g , k—o.

a , Density plots indicate median and distribution. a , PCA plots of metabolites in caecal contents of AI-administered mice. The top 10 metabolites contributing to the PCA separation left panel and 12 out of 15 IR-related carbohydrates identified in Fig.

b , The PC1 of PCA plots in Fig. The detailed statistics of all caecal metabolites are reported in Supplementary Table e , A schematic summary. In this study, we combined faecal metabolome, 16S rRNA gene sequencing, and metagenome data with host metabolome, transcriptome, and cytokine data to comprehensively delineate the involvement of gut microbiota in IR upper panel.

Carbohydrate degradation products such as monosaccharides are prominently increased in IR middle panel. Metagenomic findings show that the degradation and utilization of poly- and disaccharides are facilitated in IR and that these microbial functions are strongly associated with faecal monosaccharides.

Further analysis also suggests that the effects of these metabolites on host metabolic parameters such as BMI are in part mediated by specific cytokines. Finally, our animal experiments provide evidence showing that oral administration of AI, a candidate strain selected based on human cohort findings, reduces intestinal carbohydrates and lipid accumulation, thereby leading to the amelioration of IR lower panel.

Taken together, our study provides novel insights into the mechanisms of host-microbe interplays in IR. b , Box plots indicate the median, upper and lower quartiles, and upper and lower extremes except for outliers. Two-sided Wilcoxon rank-sum test b—d.

Raw images of blotting membranes. a,b, The blotting membranes images of p-AKT and total AKT in the liver a and epidydimal fat b.

Molecular mass kDa is shown on the left. Relating to Extended Data Fig. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Gut microbial carbohydrate metabolism contributes to insulin resistance. Download citation. Received : 25 March Accepted : 20 July Published : 30 August Issue Date : 14 September Anyone you share the following link with will be able to read this content:.

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Skip to main content Thank you for visiting nature. nature articles article. Download PDF. Subjects Metabolomics Metagenomics Microbiome Next-generation sequencing Pre-diabetes. Abstract Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes 1 , 2.

Full size image. Faecal carbohydrates are increased in IR We next searched for the associations between clinical phenotypes and faecal metabolite CAGs Fig.

Microorganism—metabolite relationships in IR We next investigated the alteration in gut microbiota and the functions of gut microbiota that are associated with IR. Faecal carbohydrates and inflammation in IR Consistent with previous reports 1 , 2 , the host cytokine, metabolomic and transcriptomic signatures were highly associated with IR Supplementary Tables 19 — IS-associated bacteria in experimental models The above findings from human multi-omics analyses revealed an association between carbohydrate metabolites and IR pathology.

Discussion To deepen our understanding of the host—microorganism relationship in IR, we used multimodal techniques to conduct a comprehensive and extensive study investigating the interactions between the gut microbiome and metabolic diseases in humans. Methods Study participants and data collection The study participants were recruited from to during their annual health check-ups at the University of Tokyo Hospital.

Lipidomics of faecal and plasma samples The lipidomics analysis was performed according to a previously reported study Reanalysis of publicly available metabolomic data To validate the associations between clinical markers and faecal metabolites, we used the metabolomic data of TwinsUK 17 and HMP2 ref.

DNA extraction from faecal samples DNA extraction was performed according to a protocol described previously 47 with slight modifications. Comparison of KEGG organism genomes The list of KEGG organisms used for this genome analysis is listed in Supplementary Table RNA extraction from PBMC Blood samples were collected in Vacutainer CPT tubes Becton Dickinson and mixed with the anticoagulant by gently inverting the tubes 8 to 10 times.

CAGE analysis The CAGE libraries were constructed according to the dual-index nanoCAGE protocol, a template-switching-based variation of the standard CAGE protocol designed for low quantities of RNA 55 , Metabolite measurement in bacterial culture The following strains were used for this culture analysis: A.

Western blot analysis of phosphorylated AKT To analyse phosphorylation of AKT p-AKT at Ser, the mice administered with A. Hyperinsulinaemic—euglycaemic clamp test The protocol has been published elsewhere 62 , Analysis of triglyceride contents in the liver For the necropsy, the mice were anesthetized using isoflurane MSD , and the left half of liver was dissected, weighed and frozen in liquid nitrogen.

ROC curve analysis of omics datasets To analyse ROC curves of omics datasets, the datasets of faecal metabolomics, including hydrophilic and lipid metabolites, faecal 16S rRNA gene sequencing at the genus level, faecal metagenome consisting of KEGG orthologues and clinical metadata, were included.

Construction of cross-omics networks To construct and visualize a correlation-based network of omics data, we first analysed IR-associated host signatures using plasma cytokines, plasma metabolites and CAGE promoter expression data.

Explained variance of plasma cytokines by omics data To assess the explained variance of ten plasma cytokines, we established random-forest models using the R package caret v.

Causal mediation analysis To infer the effects of plasma cytokines on in silico causal relationships between faecal carbohydrates and IR markers HOMA-IR, BMI, triglycerides and HDL-C , we performed causal mediation analysis using the R package mediation v.

Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. References Moller, D.

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The information on this site should not be used as a substitute for professional medical care or advice. Contact a health care provider if you have questions about your health. Carbohydrate Metabolism Disorders. On this page Basics Summary. Learn More Specifics Genetics. See, Play and Learn No links available.

Research Clinical Trials Journal Articles. Resources No links available. For You Children. Diabetes: MedlinePlus Health Topic National Library of Medicine Also in Spanish Galactosemia American Liver Foundation Glycogen Storage Disease Type 1 von Gierke American Liver Foundation Hurler Syndrome National Marrow Donor Program MPS Diseases National MPS Society Mucopolysaccharidoses National Institute of Neurological Disorders and Stroke Pompe Disease National Institute of Neurological Disorders and Stroke.

Clinical Trials. gov: Carbohydrate Metabolism, Inborn Errors National Institutes of Health ClinicalTrials. gov: Mucopolysaccharidoses National Institutes of Health. Some hereditary metabolic disorders are X-linked X-Linked Recessive Disorders , which means only one copy of the abnormal gene can cause the disorder in boys.

See also Overview of Hereditary Metabolic Disorders Overview of Hereditary Metabolic Disorders Hereditary metabolic disorders are inherited genetic conditions that cause metabolism problems.

Carbohydrates Carbohydrates Carbohydrates, proteins, and fats are the main types of macronutrients in food nutrients that are required daily in large quantities. read more are sugars.

Some sugars are simple, and others are more complex. Sucrose table sugar is made of two simpler sugars called glucose and fructose. Lactose milk sugar is made of glucose and galactose.

Both sucrose and lactose must be broken down into their component sugars by enzymes before the body can absorb and use them. The carbohydrates in bread, pasta, rice, and other carbohydrate-containing foods are long chains of simple sugar molecules. These longer molecules must also be broken down by the body.

If an enzyme that is needed to process a certain sugar is missing, that sugar can accumulate in the body, causing problems. Galactosemia Galactosemia Galactosemia a high blood level of galactose is a carbohydrate metabolism disorder that is caused by a lack of one of the enzymes necessary for metabolizing galactose, a sugar that is part Glycogen storage diseases Glycogen Storage Diseases Glycogen storage diseases are carbohydrate metabolism disorders that occur when there is a defect in the enzymes that are involved in the metabolism of glycogen, often resulting in growth abnormalities

The National Institutes Cargohydrate Health Fat burner pills define metabolic syndrome as having three or more Anv the following traits, including traits for which metzbolism Enzymes for enzyme deficiency be taking medication to control:. If aggressive lifestyle changes such zyndrome diet and exercise aren't enough, your doctor might suggest medications to help control your blood pressure, cholesterol and blood sugar levels. Explore Mayo Clinic studies testing new treatments, interventions and tests as a means to prevent, detect, treat or manage this condition. If you've been diagnosed with metabolic syndrome or any of its components, making healthy lifestyle changes can help prevent or delay serious health problems, such as a heart attack or stroke. A healthy lifestyle includes:.

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Carbohydrate Structure and Metabolism, an Overview, Animation.