Lifestyle interventions typically aim to improve insulin sensitivity by reducing weight through calorie restriction, improving dietary quality such as increasing fiber and antioxidant intake, and increasing physical activity, but the effect on improving insulin sensitivity varies greatly among individuals.

The authors concluded that a low calorie diet led to a reduction in body fat as well as an increase in insulin sensitivity. Therefore, researchers from various institutions from France as well as Tufts University in Boston USA sought to find a connection between how calorie restriction might improve insulin sensitivity.

They also explored the possible association with changes in the gut microbiome, host biology, such as body mass index BMI , fat tissue and genetics, and various lifestyle factors including physical activity and diet.

The authors of the study analyzed insulin sensitivity, changes in the gut microbiota and metabolism in 27 overweight or obese patients who followed a low calorie diet for 6 weeks.

The authors concluded that the low calorie diet led to a reduction in BMI and body fat as well as an increase in insulin sensitivity. Interestingly, the researchers also identified 10 species of intestinal bacteria that were associated with improved insulin sensitivity.

Additionally, there was a strong association with improved insulin sensitivity and a significant decrease in branched chain amino acids BCAAs after following the low calorie diet for 6 weeks. This is in agreement with a previous study that found an association between specific gut bacteria and insulin resistance and increased levels of BCAAs in mice.

BCAAs consist of leucine, isoleucine and valine and are among the nine essential amino acids carry out various metabolic and physiological processes in the body. Besides dietary intake, the gut microbiota may regulate BCAA levels in the blood which may affect insulin sensitivity, especially in response to a high calorie diet.

This study has identified novel associations between glucose levels, lifestyle factors and gut microbiota following a 6-week low calorie diet. This is the first study of its kind that has identified novel associations between glucose levels, lifestyle factors and gut microbiota after a 6-week low calorie diet.

Obesity is a multifactorial condition, and targeting the gut microbiota may form part of an integrative approach to treating metabolic diseases such as obesity in the future.

Reference: Dao MC, Sokolovska N, Brazeilles R, et al. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Front Physiol. Allison Clark has a master in nutrition and health from Open University in Barcelona and a master in journalism.

She is a freelance writer and nutritionist and has written various peer review papers about the role the gut microbiota plays in health, disease and endurance exercise performance.

Allison is passionate about the role diet and the gut microbiota play in health and disease. Most research on the role of gut microbiota in the gut-brain axis has focused on bacteria, while fungi living inside the gut have been overlooked. What do we know about the role of gut fungi in the communication between the gut and the brain?

The low amount of bacteria from the gut microbiota able to process bilirubin, a product of heme degradation, during the neonatal period of life suggests a strong connection between the microbiome composition and development of jaundice in infants.

In other words, the lack of certain bacteria in the gut of infants seems to be linked to the risk of developing jaundice.

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The study, published August 30 in the scientific journal Nature , involved genetic and metabolic analysis of human fecal microbiomes and then corroborating experiments in obese mice.

Insulin is a hormone released by the pancreas in response to blood sugar. Normally, it helps get the sugar into the muscles and liver so that they can use the energy.

When someone develops insulin resistance, it means that insulin is prevented from doing its job, and as a result, more sugar stays in their blood and their pancreas continues to make more insulin.

Insulin resistance can lead to obesity, pre-diabetes, and full-blown type-2 diabetes. Our guts contain trillions of bacteria, many of which break down the carbohydrates that we eat when they would otherwise remain undigested.

While many have proposed that this phenomenon is related to obesity and pre-diabetes, the facts remain unclear because there are so many different bacteria and there is a lack of metabolic data.

Ohno and his team at RIKEN IMS have addressed this lack with their comprehensive study, and in the process, discovered a type of bacteria that might help reduce insulin resistance. First, they examined as many metabolites as they could detect in the feces provided by over adults at their regular health checkups.

They compared this metabolome with the insulin resistance levels obtained from the same people. As far as the biomarker of insulin resistance Lachnospiraceae goes, Dr. They could lyse [destroy] Lachnospiraceae strains if they can be applicable for use in humans.

Ashkan Farhadi , who was not involved in the study. According to Dr. Ohno, the source of these carbohydrates is dietary fibers, or polysaccharides that are normally broken up by gut bacteria.

However, Dr. He noted that monosaccharides cannot come from inside the human body to the intestinal tract, so it is unlikely insulin is involved in the presence of high levels of monosaccharides in feces.

But this is the first study that put a little bit more detail into the evidence. Medical News Today investigates how lifestyle changes—particularly diet and exercise—can help reverse prediabetes, and shares the story of one woman's….

In this Special Feature and accompanying podcast, we look at how insulin research is developing a century after the discovery of this crucial hormone. In this edition of Medical Myths, we focus on diabetes. We discuss natural "cures," physical activity, driving, prediabetes, and more. Researchers said baricitinib, a drug used to treat rheumatoid arthritis, showed promise in a clinical trial in helping slow the progression of type 1….

A new review indicates that insulin—used to manage diabetes—can be kept at room temperature for months without losing its potency. My podcast changed me Can 'biological race' explain disparities in health?

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Medical News Today. Health Conditions Health Products Discover Tools Connect. How gut bacteria could improve insulin resistance and lower diabetes risk. The researchers found that while most bacteria that produce butyrate were associated with better insulin sensitivity, a few were associated with insulin resistance.

Goodarzi explained. For the study, investigators analyzed data from people who had not previously been diagnosed with diabetes. Of the participants, were non-Hispanic whites, and were African-American. None of the participants had recently experienced severe gastrointestinal illness or used medicines like antibiotics that could impact the microbiome.

Researchers found 28 of the participants had diabetes, and an additional were classified as having prediabetes. Participants with diabetes and prediabetes were combined into a single group and were compared with the participants with healthy glucose tolerance. Participants were asked to collect a stool sample 1—2 days before coming to the clinic.

Researchers found that participants with abnormalities in blood glucose levels were older, more often male, and had higher BMI. They discovered that Coprococcus and related bacteria had beneficial effects on insulin sensitivity. But Flavonifractor , despite producing butyrate, was associated with insulin resistance.

The analyses found 10 bacteria associated with a lower rate of blood sugar levels fluctuating abnormally and two bacteria associated with adverse associations on blood sugar levels.

Goodarzi told MNT. If so proven, clinical trials will be the next step to determine whether modulating these bacteria via prebiotics, probiotics, or antibiotics, depending on the bacterial targets are a viable option to prevent or treat diabetes.

For individuals looking to promote their gut health in general, Dr. Kristin Kirkpatrick , R. Sources of prebiotics include:. Roxana Ehsani , R. Ehsani suggested kefir for people looking to improve their gut health. Researchers think that gut bacteria may trigger inflammation, which in turn prevents insulin from working correctly, thus causing type 2 diabetes.

New research in young mice and zebrafish has uncovered the role of a bacterial protein in the development of diabetes.

The proportion of KEGG orthologues was calculated from the number of reads mapped to them. We defined intracellular glucosidase by their substrate described in the KEGG pathway map; those cleave phosphorylated carbohydrates were recognized as intracellular, and the rest of the genes were recognized to possess extracellular enzymatic activities.

The pathways were further summarized into carbohydrate metabolism , amino acid metabolism , lipid metabolism and membrane transport on the basis of the KEGG Orthology database. The list of KEGG organisms used for this genome analysis is listed in Supplementary Table All KEGG organisms from genera Alistipes , Bacteroides , Flavonifractor , Blautia , Dorea and Coprococcus , which showed the top three positive or negative correlations with faecal carbohydrates in Fig.

The presence of KEGG orthologues relating to extracellular glycoside hydrolases in starch and sucrose metabolism pathways shown in grey in Extended Data Fig. Blood samples were collected in Vacutainer CPT tubes Becton Dickinson and mixed with the anticoagulant by gently inverting the tubes 8 to 10 times.

The quality of the RNA was assessed using Bioanalyzer Agilent , as recommended by the manufacturer. The RNAs were quantified using the GloMax plate reader Promega and Quant-iT RiboGreen RNA Assay Kit Thermo Fisher Scientific.

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 , The sequenced reads were processed with the MOIRAI pipeline 57 : low quality and rDNA reads were first removed, then the remaining reads were mapped to the human genome version hg38 patch 1 using BWA v.

The mapped reads falling under each FANTOM5 CAGE cluster were summed to produce the raw expression counts. Expression counts were converted to counts per million CPM , and CAGE clusters expressed in less than samples with at least 1 CPM and greater than 1 sample with at least 10 CPM were removed from further analysis.

Cell type specificities of promoters of interest were determined using the FANTOM5 hg38 human promoterome view. Top-hit cells for analysed promoters were described. The following strains were used for this culture analysis: A. indistinctus JCM , A. finegoldii JCM , Alistipes putredinis JCM , B.

thetaiotaomicron JCM , Bacteroides xylanisolvens JCM , Bacteroides ovatus JCM , Bacteroides caccae JCM , Parabacteroides merdae JCM , Parabacteroides distasonis JCM , D.

formicigenerans JCM , D. longicatena JCM , B. hydrogenotrophica JCM , Blautia producta BP, JCM , Coprococcus comes JCM , Faecalibacterium prausnitzii JCM , Flavonifractor plautii JCM , Clostridium spiroforme JCM , Coriobacterium glomerans JCM , Roseburia hominis JCM , Adlercreutzia equolifaciens subsp.

equolifaciens JCM , Eggerthella lenta JCM and Collinsella aerofaciens JCM All strains were obtained from RIKEN BioResource Research Center. All of the strains were cultivated in EG medium JCM Medium No.

The samples were centrifuged, and the cell-free supernatant was collected for analysis. GC—MS was performed to measure hydrophilic metabolites as described above.

They were randomly assigned to either the control or treatment group and housed in a conventional animal facility of Yokohama City University. finegoldii JCM , B. thetaiotaomicron JCM , B. xylanisolvens JCM , P. merdae JCM , F. prausnitzii JCM and C. spiroforme JCM were used to broadly compare the efficacy of bacterial administration on the animal model.

These strains were cultivated in EG medium overnight, and the concentration was adjusted to 2. Body mass was measured before oral gavage. In both experiments, the blood glucose was collected from the tail vein and serially measured using GLUCOCARD G Black Arkray.

For the necropsy, the mice were euthanized by isoflurane MSD , and the fat mass of perigonadal and mesenteric fats was measured. Blood was drawn through cardiac puncture after the anaesthesia.

We gave three oral gavages of A. indistinctus or PBS vehicle control every other day and then placed the mice individually in acrylic cages. Their metabolic activity, dietary intake and physical activity were subsequently monitored. of body mass were indistinctus groups, respectively.

Oxygen and carbon dioxide concentration was measured using the ARCO system, an open-circuit metabolic gas analysis system with a mass spectrometer Arco Systems. VO 2 , VCO 2 , energy expenditure, fat oxidation rate, carbohydrate oxidation rate and respiratory quotient were calculated within the system.

Dietary intake and physical activity were simultaneously monitored through ACTIMOM and MFDM Shinfactory. The differences in diurnal variation were tested using two-way mixed ANOVA, and P values for interactions between time and group were reported.

The sample size was determined on the basis of our preliminary experiments. Bacterial administration and body mass measurements were performed by an independent researcher who was not involved in the grouping and outcome assessments.

To analyse phosphorylation of AKT p-AKT at Ser, the mice administered with A. indistinctus , A. Phosphorylated or total protein of AKT was isolated by immunoblotting using specific antibodies after the tissue lysates were resolved by SDS—PAGE and transferred to a Hybond-P PVDF transfer membrane Amersham Biosciences.

Bound antibodies were detected with HRP-conjugated secondary antibodies using ECL detection reagents Amersham Biosciences. Rabbit polyclonal antibodies directed against AKT and p-AKT Ser were purchased from Cell Signaling Technology.

Precision Plus Protein All Blue Standards Bio-Rad were used for the molecular mass markers. The protocol has been published elsewhere 62 , Mice administered with A.

In brief, a mouse was anaesthetized with isoflurane MSD , and the right jugular vein was exposed. A double-channel catheter was subsequently inserted to the vein. Human regular insulin Eli Lilly was intravenously administered at 7. The plasma levels of glucose and 6,6-d 2- glucose were measured using GC—MS.

The rate of glucose disappearance was determined on the basis of the plasma levels of 6,6-d 2 -glucose and total glucose using a non-steady-state equation as described previously 63 , 64 and considered as the whole-body glucose disposal after insulin stimulation. Hepatic glucose production was determined as the subtraction of glucose disappearance rate and glucose infusion rate.

For the necropsy, the mice were anesthetized using isoflurane MSD , and the left half of liver was dissected, weighed and frozen in liquid nitrogen. The extraction of triglyceride contents from the liver tissue has been reported elsewhere 62 , The extraction step was repeated three times.

test of the R package psych v. test of the R package ppcor v. To predict the metabolite levels and their CAGs Fig. For the ordinal independent variables that is, IR, MetS, and original categories with obese and prediabetes , IS, no MetS, and healthy categories were considered as the references, respectively, and the coefficients and P values for other categories were calculated against these reference categories.

For the analyses involving generalized linear models GLM such as Fig. To enhance comparability, the standardized coefficient was also calculated by standard deviations of dependent and independent variables using the function lm.

beta of the R package QuantPsyc v. In the reanalysis of TwinsUK data, we fitted generalized linear mixed-effects models with age, sex, zygosity and BMI as fixed effects and sample collection year as a random effect using the function glmer of R package lme4 v.

To calculate the KEGG pathway enrichment associated with the participant clusters Fig. For comparison of metabolites in bacterial cultures Extended Data Fig.

For comparisons of time-series data such as insulin tolerance test, two-way repeated-measures ANOVA was used and the between-group difference was analysed by estimated marginal means.

We also validated the assumption of this ANCOVA model, that is, homogeneity of regression slopes, homogeneity of variances and normality of residuals.

For multiple-testing corrections, P values were corrected using the Benjamini—Hochberg procedure using the R function p. All data were collected using Microsoft Excel All statistical and graphical analyses were conducted using R v. 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.

We first selected feature variables in each dataset, that is, the best explaining variables in the given dataset, using the minimum redundancy maximum relevance mRMR algorithm The function mRMR.

classic of the R package mRMRe v. The datasets were square-root-transformed before mRMR calculation. We selected 5 to 50 variables in 5 increments as the maximum number of genera was Using the selected variables, we next established random-forest models using the R package caret v.

Specifically, the results of mRMR were split into train and test datasets in a ratio. The generated random-forest models were evaluated using a tenfold cross-validation method and applied to the test datasets to obtain probability scores.

The accuracy of each classification model was described by the AUC of ROC curves using the R package pROC v. Bacteria that exhibited a positive correlation with one another were determined to be members of an independent co-abundance microbial group, except for the interaction between Bacteroides and Robinsoniella.

We decided to categorize Robinsoniella into the Blautia and Dorea group owing to its stronger correlation with Blautia in comparison to Bacteroides , both of which showed the highest centrality within their respective networks. Those weakly associated with each other or negatively associated with the members of other CAGs were classified as miscellaneous Extended Data Fig.

To characterize the microbial profiles of the study participants, the individuals were clustered on the basis of the abundance of 28 genera, which includes 20 genera in co-abundance microbial groups identified with CCREPE and 8 unclustered genera, using the ward.

D function of the R package pheatmap v. Bacteria-related metabolites were defined according to previous reports 20 , The following classes were selected: DGDG, PE-Cer, MGDG O, FAHFA, Cer-AS, Cer-BDS, NAGly, NAGlySer, PI-Cer, SL, AcylCer, bile acids, DGDG O and AAHFA.

The networks were visualized using Cytoscape v. 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.

We finally identified 6, 21 and 36 significant associations from plasma cytokines, plasma metabolites and CAGE promoter expression data, respectively Supplementary Tables 19 — In terms of bacteria, 20 genera with significant interactions between each other, which were identified with CCREPE as shown in Extended Data Fig.

In terms of faecal metabolites, 15 carbohydrates associated with IR in the CAG analysis as shown in Fig. The size of nodes was determined as the ratio of median abundance in IR over IS. As the median values of genera Robinsoniella and Rothia were zero, these elements were removed from the visualization.

as in the microorganism—metabolite networks described above. To assess the explained variance of ten plasma cytokines, we established random-forest models using the R package caret v.

Plasma cytokines were log 10 -transformed and scaled before the regression analyses. The data were split into train and test datasets at a ratio. The generated random-forest models were evaluated using a tenfold cross-validation method and applied to the test datasets to obtain predictions.

The negative values were considered as zero. 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.

Age and sex were included as independent variables in both models. In both models, faecal carbohydrate and plasma cytokine values were scaled before the analyses, and GLM with Gaussian distribution was used. A nonparametric bootstrap procedure was used to calculate the significance, followed by multiple testing corrections using the R function p.

Average causal mediation effects and average direct effects with P adj values from representative models are reported in Extended Data Fig. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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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. In the last several years, multiple studies, including this o ne from , have found that individuals with type 2 diabetes have lower levels of a certain type of bacteria that produces a type of short-chain fatty acid called butyrate.

Goodarzi said. The researchers found that while most bacteria that produce butyrate were associated with better insulin sensitivity, a few were associated with insulin resistance. Goodarzi explained. For the study, investigators analyzed data from people who had not previously been diagnosed with diabetes.

Of the participants, were non-Hispanic whites, and were African-American. None of the participants had recently experienced severe gastrointestinal illness or used medicines like antibiotics that could impact the microbiome.

Researchers found 28 of the participants had diabetes, and an additional were classified as having prediabetes. Participants with diabetes and prediabetes were combined into a single group and were compared with the participants with healthy glucose tolerance. Participants were asked to collect a stool sample 1—2 days before coming to the clinic.

Researchers found that participants with abnormalities in blood glucose levels were older, more often male, and had higher BMI.

They discovered that Coprococcus and related bacteria had beneficial effects on insulin sensitivity.

But Flavonifractor , despite producing butyrate, was associated with insulin resistance. The analyses found 10 bacteria associated with a lower rate of blood sugar levels fluctuating abnormally and two bacteria associated with adverse associations on blood sugar levels.

Goodarzi told MNT. If so proven, clinical trials will be the next step to determine whether modulating these bacteria via prebiotics, probiotics, or antibiotics, depending on the bacterial targets are a viable option to prevent or treat diabetes. For individuals looking to promote their gut health in general, Dr.

Kristin Kirkpatrick , R. Sources of prebiotics include:. Roxana Ehsani , R. Ehsani suggested kefir for people looking to improve their gut health. Researchers think that gut bacteria may trigger inflammation, which in turn prevents insulin from working correctly, thus causing type 2 diabetes.

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