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Carbohydrate metabolism and insulin sensitivity

Carbohydrate metabolism and insulin sensitivity

Nature Reviews. Sports performance supplements Natl Acad Sci USA. This disparity ijsulin associated with sex-specific factors such as differences in circulating levels of estrogen and testosterone Macotela et al.

Carbohydrate metabolism and insulin sensitivity -

Gut microbial carbohydrate metabolism contributes to insulin resistance. Article PubMed PubMed Central Google Scholar. Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Shimona Starling.

Reprints and permissions. Starling, S. Microbial metabolism linked to insulin resistance. Nat Rev Endocrinol 19 , Download citation. Published : 11 September Issue Date : November Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.

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Buy or subscribe. Change institution. Learn more. References Original article Takeuchi, T. The proportion of IR among individuals with abundant group 1 and 3 bacteria was consistently higher than those with abundant group 2 bacteria, as identified on the basis of shotgun metagenomics data Extended Data Fig.

HOMA-IR showed negative associations with the genus Alistipes in the Rikenellaceae family and several species from Bacteroides , Bifidobacterium and Ruminococcus Extended Data Fig.

Notably, different genera and species correlated with other clinical markers, suggesting that the individual association between microbial taxa and clinical manifestation is not as robust as in the co-abundance analysis. The participants were classified into four clusters, A to D, according to their taxonomic profiles.

The proportion of individuals with IR are shown. Mid, intermediate. b , HOMA-IR, BMI, triglycerides TG and HDL-C levels among the participant clusters. All faecal hydrophilic and bacteria-related lipid metabolites were included.

The metabolites in CAGs relating to carbohydrates in Fig. d , The number of significant positive and negative correlations between genera and faecal carbohydrates. The top five genera in each correlation are shown.

e , KEGG pathways relating to carbohydrate metabolism and membrane transport, faecal carbohydrates, the top three genera positively or negatively correlated with faecal carbohydrates, and the participant clusters. The median percentage of 15 faecal carbohydrates carb. The abundance was transformed by arcsine square root transformation.

The density plots in b and f indicate the median and distribution. We next constructed a microorganism—metabolite network on the basis of the significant positive or negative correlations Supplementary Table Although faecal SCFAs and lipids such as DGDG correlated with both IR- and IS-associated bacterial groups, IR-associated faecal carbohydrates predominantly correlated with genera in groups 1 and 4, the most prominent being Dorea in Lachnospiraceae Fig.

By contrast, the majority of these carbohydrates negatively correlated with IS-associated genera in group 2 bacteria such as Bacteroides , Alistipes and Flavonifractor Fig. Accordingly, the faecal carbohydrate levels were distinctly different among the participant clusters Extended Data Fig.

Previous studies have suggested that several Lachnospiraceae species are involved in polysaccharide fermentation 28 , 29 , while Alistipes is increased on an animal-based diet rather than a polysaccharide-rich diet These findings highlight a tight connection between carbohydrate-degradation products and IR- and IS-associated bacteria, suggesting that these bacteria may be involved in the aberrant faecal carbohydrate profile in IR.

The IR-associated faecal carbohydrates were also correlated with KEGG pathways relating to carbohydrate metabolism and transportation, such as the phosphotransferase system PTS , starch and sucrose metabolism, and galactose metabolism, while negatively associated with pathways relating to carbohydrate catabolism, such as glycolysis and pyruvate metabolism Fig.

These pathways were also distinctly correlated with the participant clusters defined in Fig. Amino acid metabolism was also different, particularly between clusters C and D, whereas lipid metabolism did not show distinct associations with microbiota Extended Data Fig.

Although carbohydrate pathways such as PTS and starch and sucrose metabolism showed strong positive associations with HbA1c and γ-GTP, the associations with other IR markers were generally sparse Extended Data Fig.

PTS is an essential component for bacteria to incorporate sugars into themselves as energy sources Detailed analyses of KEGG orthologues revealed that faecal carbohydrates and participant clusters mainly correlated with PTSs relating to disaccharides and amino sugars Extended Data Fig.

Glycosidases, which catalyse the breakdown of oligo- and disaccharides 32 , were also associated with faecal monosaccharides Extended Data Fig. Extracellular glucosidases such as β-fructofuranosidase K, KEGG Orthology database , amylosucrase K, KEGG Orthology database and oligo-1,6-glucosidase K, KEGG Orthology database , which were predicted to degrade sucrose and dextrin into glucose and fructose Extended Data Fig.

By contrast, glucosidases relating to starch use such as α-amylases K and K, KEGG Orthology database were negatively linked with faecal carbohydrates. Importantly, the abundance of these glycosidase genes was significantly different between participant cluster C and the other three clusters, suggesting that taxonomic profiles largely explain the variations of glucosidases Fig.

Consistently, disaccharide-breakdown genes were predominantly conserved in the genomes of Blautia and Dorea abundant in cluster D, whereas they were almost lacking in Bacteroidales abundant in cluster C Extended Data Fig.

Together, our findings reveal four distinct populations with unique taxonomic profiles and carbohydrate metabolisms characterized by sugar use and degradation, which correlate with IR and its related markers.

Consistent with previous reports 1 , 2 , the host cytokine, metabolomic and transcriptomic signatures were highly associated with IR Supplementary Tables 19 — Moreover, many of these PBMC genes were functionally involved in inflammation Extended Data Fig. Several studies have suggested that microbial components such as lipopolysaccharides have a role in facilitating inflammation of metabolic diseases 33 , However, it remains unclear whether microbial metabolism is involved in low-grade inflammation.

We therefore tried to infer possible associations between host inflammatory signatures of IR and faecal carbohydrates. First, the cross-omics correlation-based network with individual metabolites, bacteria, transcripts and cytokines associated with IR revealed that faecal carbohydrates were strongly tied with both bacteria and host IR-related signatures, especially cytokines, suggesting that these metabolites are the hubs of the host—microorganism network in IR Fig.

Differential abundance, calculated as the ratio of their abundance in IR and IS, was most pronounced in the associations between faecal carbohydrates and cytokines. Notably, IL, a plasma cytokine, showed the most prominent associations with faecal carbohydrates and modestly with PBMC-derived transcripts, supporting recent studies showing its paradoxical effect to facilitate IR 35 , 36 , Faecal carbohydrates moderately explained the variance of IL and, to a lesser extent, adiponectin, leptin and serpin E1, suggesting that faecal carbohydrates are particularly associated with these cytokines Fig.

Although the proportions of variance explained by faecal carbohydrates were lower than by plasma metabolites, they were much higher than those by genus-level abundance, highlighting the role of faecal metabolites linking gut microbiota and host inflammatory responses.

We next sought to infer whether these cytokines mediated the effects of faecal carbohydrates on host metabolism using causal mediation analyses We found that IL, serpin E1, adiponectin and leptin mediated most in silico causal relationships between faecal carbohydrates and host IR markers such as HOMA-IR Fig.

Notably, there were unique correspondences between metabolites and cytokines; for example, IL mediated the effects of fructose, mannose, xylose and rhamnose, but not other metabolites. Although the biological importance of these unique correspondences remains to be investigated, the combined analyses of faecal microbiota, metabolome and host inflammatory phenotypes in IR suggest a previously unrecognized interaction, whereby excessive monosaccharides may affect host cytokine expression.

Host-derived markers significantly associated with IR Supplementary Tables 19 — 21 , 15 faecal carbohydrates and 20 genera identified in Fig. The line widths show the absolute values of coefficients, and the red and grey lines show positive and negative correlations, respectively.

Detailed information with complete annotations is shown in Extended Data Fig. b , The explained variance of ten plasma cytokines predicted by each omics dataset using random-forest classifiers. c , An alluvial plot showing the plasma cytokines significantly mediated the in silico effects of faecal carbohydrates on host metabolic markers.

The lines show the mediation effects and the colours represent the associations mediated by individual cytokines. Details are provided in Supplementary Table The above findings from human multi-omics analyses revealed an association between carbohydrate metabolites and IR pathology.

To address the causal relationship between gut microbiota, faecal carbohydrates and metabolic diseases, we first analysed metabolites in the bacterial culture of 22 human faecal IS- and IR-associated bacteria. These bacteria were selected on the basis of the findings from the genus-level co-occurrence Fig.

Principal component analysis plots of metabolites indicated that Bacteroidales, a representative IS-associated bacterial order, showed a distinct metabolic profile along PC1 Extended Data Fig. The top 10 metabolites contributing to the group separation included several amino acids and fermentation products such as succinate and fumarate, and the majority of these metabolites were preferentially produced by Bacteroidales Extended Data Fig.

We detected 13 out of 15 carbohydrates associated with IR Fig. Most of these carbohydrates were plotted negatively along PC1, suggesting that these metabolites were negatively associated with Bacteroidales.

Glucose, mannose and glucosamine were preferentially consumed by Bacteroidales compared with the other orders, whereas lactulose was mainly produced by Eubacteriales Extended Data Fig. Alistipes indistinctus was the most potent in consuming a wide variety of carbohydrates Extended Data Fig.

These findings show that Bacteroidales species are potent consumers of several carbohydrates, driving the production of their fermentation products.

We next tested the potential therapeutic effects of seven candidate bacteria shown to be associated with IS in human cohort findings. Postprandial blood glucose levels were particularly reduced in mice administered with A.

indistinctus , Alistipes finegoldii and Bacteroides thetaiotaomicron that were fed a high-fat diet Fig. Insulin tolerance tests also revealed that these strains ameliorated IR, most prominently by A. indistinctus administration Fig.

indistinctus administration ameliorated body mass gain, ectopic triglyceride accumulation in the liver and glucose intolerance Extended Data Fig. Serum levels of HDL-C, adiponectin and, to a lesser extent, triglycerides, were also improved in mice that were treated with A.

indistinctus Extended Data Fig. The findings of the hyperinsulinaemic—euglycaemic clamp analysis indicated that A. indistinctus administration significantly improved IR and, particularly, whole-body glucose disposal Extended Data Fig.

Phosphorylation of AKT in the liver and epididymal fat was increased in mice treated with A. indistinctus and A. finegoldii mice Extended Data Fig. These findings reveal a potency of A. indistinctus administration in ameliorating diet-induced obesity and IR.

The abbreviations are defined in Extended Data Fig. d , The correlations between the AUC of the insulin tolerance test and caecal levels of fructose, glucose and mannose in the A.

indistinctus sky blue or vehicle grey groups. ITT, insulin tolerance test. Representative data of two a and d or three b and c independent experiments. Exact P values for a and c are provided in the Source Data.

Mechanistically, metabolic measurement revealed that carbohydrate oxidation was significantly reduced in mice that were treated with A. indistinctus , implying that carbohydrate use is limited Extended Data Fig. As dietary intake and locomotor activity remained unchanged Extended Data Fig.

In this regard, A. indistinctus administration substantially altered caecal metabolites, characterized by a reduction in several carbohydrates including fructose, a lipogenic monosaccharide 39 Extended Data Fig. Fructose was similarly reduced in the serum Extended Data Fig.

Importantly, the AUC of insulin tolerance test was positively correlated with the caecal monosaccharides fructose, glucose and mannose Fig. Collectively, these findings reveal that A. indistinctus ameliorates IR and affects intestinal carbohydrate metabolites in mice, supporting our observations in the human cohort.

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.

Although carbohydrate metabolism by the gut microorganisms has been suggested to influence the pathogenesis of obesity 3 , 4 , 25 and prediabetes 6 , 8 , the actual mechanistic linkage has been elusive in humans owing to the lack of detailed metabolomic information.

In this regard, the major strength of our approach is that we combine faecal metabolomics cataloguing more than 2, annotated metabolites with both microbiome and host pathology. This metabolome-based approach enabled us to identify the faecal metabolites related to IR, identify an association between faecal carbohydrates and low-grade inflammation of IR, and efficiently select candidate strains for functional validations in experimental settings Extended Data Fig.

Together, our study highlights the advantage of comprehensive omics strategy in exploring the involvement of microbial metabolism and their products in the pathogenesis of IR.

Excessive monosaccharides have the potential to promote ectopic lipid accumulation while also activating immune cells, leading to low-grade inflammation and IR 40 , 41 , Fructose is a widely recognized risk factor for inflammation and IR due to its role in lipid accumulation 39 , whereas galactose has been shown to participate in the energy metabolism of activated immune cells Our in vivo studies confirm that A.

indistinctus administration improves lipid accumulation and thereby IR, while simultaneously reducing intestinal monosaccharide levels Fig.

Nevertheless, we are aware that further mechanistic studies are needed to examine the kinetics of absorption and their effects on host metabolism. In particular, how Alistipes strains suppress carbohydrate metabolism is an intriguing question for example, whether these bacteria per se inhibit carbohydrate metabolism, or whether they interact with other commensals , as it would directly open the possibility of a new therapeutic strategy.

Given that A. indistinctus improved whole-body IS Extended Data Fig. Such investigations hold the potential to shed light on the underlying mechanisms that contribute to A. indistinctus -mediated improvement of IR. Finally, two participants in the human study were unable to collect their faeces in the morning, which could potentially influence the outcomes due to the lack of stringent control over time-of-day and fasting conditions.

We therefore believe that longitudinal studies incorporating a timely documentation of dietary habits are warranted to dissect the intricate impacts of microbial metabolism on the trajectory of diabetes and its complications while accounting for potential confounding factors. The study participants were recruited from to during their annual health check-ups at the University of Tokyo Hospital.

The individuals included both male and female Japanese individuals aged from 20 to 75 years. Written consent was obtained from the participants after a thorough explanation of the nature of the study at their health-checkups. The sample size was determined on the basis of previous metagenomics studies showing microbial signatures of diabetic patients 5 , 6.

We enrolled , and individuals for the normal, obese and prediabetic groups, respectively. The participants were provided with instructions to fast overnight before their visits, and all clinical information and blood samples were collected in the morning during their hospital visit.

The participants were also instructed to collect faecal samples in the morning and were provided with guidance on how to collect and preserve faecal samples, along with a kit comprising a sampling tube and an ice pack.

The faecal samples were then transported to the hospital either by refrigerated shipping or by the participants themselves. Consequently, participants collected their faeces in the morning on the day of their hospital visit. As for the remaining participants, they collected their faeces in the morning between 2 days before and 7 days after their hospital visit, with the exception of 5 individuals who collected their faeces in the morning more than 7 days after their hospital visit, 2 individuals who reported collecting their faeces in the evening 1 day before their hospital visit, and 5 individuals who did not provide faecal samples.

Moreover, two individuals withdrew from the study after enrolment. Thus, individuals who underwent physical examination, laboratory tests, faecal sampling for faecal 16S rRNA pyrosequencing and metabolomic analyses, and plasma sampling for plasma metabolomic analyses were included for the analysis.

Owing to the limited samples, faecal metagenomics data were available for individuals; CAGE analysis data for individuals; and plasma cytokine and insulin data for individuals. The number of samples included in each analysis is described in the figure legends.

Although we determined the criteria for enrolment, these criteria were not necessarily appropriate for subsequent analyses. For example, those in the prediabetes group were significantly leaner than those in the obese group We therefore reasoned that, in these subclinical conditions of diabetes, many metabolic traits may be overlapping between prediabetes and obesity groups and they do not necessarily capture their distinct features in metabolic and clinical continuums.

This hinders us from distinguishing microbial and metabolomic characteristics directly related to human metabolic dysfunctions. Nevertheless, we observed consistent results even with the clinical criteria of obesity and prediabetes Extended Data Fig.

HOMA-IR values could be calculated for individuals only, owing to the limited data of plasma insulin in some participants. Individuals who meet the criteria of abdominal circumference but only one clinical abnormality were defined as pre-MetS, as reported previously Measurements below the lower detection limits were considered to be zero, and those above the upper detection limits were considered to be the highest values of analysed cytokines.

DNA of the faecal microbiome was extracted from the pellet. The analysis was performed using a Shimadzu GCMS-TQ triple quadrupole mass spectrometer with a capillary column BPX5.

The data were processed and concentration was calculated by LabSolutions Insight Shimadzu. The values below the limit of detection were replaced with zero.

The lipidomics analysis was performed according to a previously reported study Methanol, isopropanol, chloroform and acetonitrile of liquid chromatography LC —MS grade were purchased from Wako. Ammonium acetate and EDTA were purchased from Wako and Dojindo, respectively.

Milli-Q water was purchased from Millipore Merck. EquiSPLASH was purchased from Avanti Polar Lipids. Palmitic acid-d 3 and stearic acid-d 3 were purchased from Olbracht Serdary Research Laboratories.

All of the samples were divided into four batches for plasma analyses and five batches for faecal analyses, with 70—80 and 55—60 samples per batch after randomization, respectively. A quality control sample was prepared by mixing the same volume of plasma from the first batch subjects.

A procedure blank was prepared by using the same volume of water instead of a biological sample. The blank sample was analysed at the beginning and the end of each analysis batch, and the quality-control sample was injected every ten study samples.

The LC system consisted of a Waters Acquity UPLC system. A sample volume of 0. All analyses were performed in high-resolution mode in MS1 ~35, full width at half-maximum and the high sensitivity mode ~20, full width at half-maximum in MS2. MS-DIAL v. The default values were used for other parameters.

In faecal lipidomics, a total of 48, and 20, chromatographic peaks were detected in positive- and negative-ion mode data, respectively. Of these, 2, unique lipid molecules were annotated and semi-quantified in the MS-DIAL software program and used for further statistical analyses.

Likewise, in plasma lipidomics, 1, and 2, chromatographic peaks were detected in positive- and negative-ion mode data, respectively, and unique lipid molecules were annotated and semi-quantified.

The semi-quantitative value of lipids was calculated by the internal standards according to the previous study The abbreviations of lipids are listed in Supplementary Table Details of lipid subclass characterization follow the previous study These metabolites were clustered based on their co-abundance using the R package WGCNA 49 v.

The following parameters were used for the analysis. As soft thresholding of WGCNA was not able to cluster all of the metabolites, the remaining metabolites that did not fit the criteria were subsequently clustered on the basis of biweight midcorrelation. The following parameters were used for the secondary clustering.

The clusters with biweight midcorrelation above 0. The first principal component PC1 of each cluster was calculated using the moduleEigengenes command of WGCNA and used as the representative value of the cluster for further analyses. The representative classes of the clusters were described in Supplementary Tables 2 and 3.

KEGG pathway enrichment analysis of CAGs was performed on MetaboAnalyst v. Hypergeometric test and false-discovery rate FDR -adjusted P values were used to test significance. The enrichment ratio was calculated as the ratio of actual metabolite number to the expected value in each pathway.

To validate the associations between clinical markers and faecal metabolites, we used the metabolomic data of TwinsUK 17 and HMP2 ref. The metabolome data of the TwinsUK cohort included 1, metabolites including 36 carbohydrates.

As reported previously 17 , the metabolite levels were scaled by run-day medians. The data were then log-transformed and scaled. The record of BMI and HOMA-IR were used for phenotypic outcomes. For BMI, we retrieved samples measured on the same day of faecal collection. We identified individuals who underwent both faecal collection and glucose and insulin measurement in the same year and included them in the analysis.

Among the 26 out of samples from non-IBD control, BMI data were available for 20 samples. As HMP2 is a longitudinal study, only the first faecal sampling for metabolomics was used for the current analysis to avoid redundancy. DNA extraction was performed according to a protocol described previously 47 with slight modifications.

Lysozyme Sigma-Aldrich , achromopeptidase Wako and proteinase K Merck were subsequently added to the samples for cell lysis. DNA was recovered by a phenol—chloroform extraction method. To purify the extracted DNA, RNA was digested with RNase Nippon Gene.

DNA was then precipitated in a solution containing polyethylene glycol Hampton Research. The DNA concentration was quantified using Quant-iT PicoGreen Thermo Fisher Scientific. The hypervariable V1—V2 region of the 16S rRNA gene was amplified by PCR using barcoded primers.

PCR amplicons were purified using AMPure XP magnetic purification beads Beckman Coulter , and quantified using the Quant-iT PicoGreen dsDNA Assay Kit Life Technologies Japan. Equal amounts of each PCR amplicon were mixed and then sequenced using the MiSeq Illumina system. On the basis of sample-specific barcodes, reads were assigned to each sample using bcl2fastq.

Next, the reads lacking both forward and reverse primer sequences were removed using BLAST and parasail followed by trimming of both primer sequences. The filter-passed reads were used for further analysis.

The 16S database was constructed from three publicly available databases: the Ribosomal Database Project RDP; v. Operational taxonomic unit OTU clustering and UniFrac analysis from the filter-passed reads, 3, high-quality reads per sample were randomly chosen. The representative sequences of the generated OTUs were processed for homology search against the databases mentioned above using the GLSEARCH program for taxonomic assignments.

After quality filtering, reads mapped to the human genome HG19 or the phiX bacteriophage genome were removed. For each individual, the filter-passed NovaSeq reads were assembled using MEGAHIT v.

Prodigal v. The genes with the best hit correlating to eukaryotic genes were excluded from further analysis. Multi-mapped reads, that is, the reads that mapped to multiple genes with identical scores, were normalized to the proportion of the number of other reads that uniquely mapped to these genes, according to a strategy outlined in a previous report 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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Senxitivity Poster Presentations Diabetes, Obesity, Carbohydrate metabolism and insulin sensitivity and Nutrition abstracts. Insjlin State Carbohydrate metabolism and insulin sensitivity University, Clinical Injury rehabilitation through nutrition, Allergology and Metwbolism, Chernivtsi, Ukraine. Inuslin Chronic pyelonephritis CP is a common kidney infection complication in patients with diabetes insulij and one of the main reasons of chronic kidney disease in diabetic patients. The objective: Of the study was to determine the features of the carbohydrate metabolism and insulin resistance in patients with CP depending on the phenotype of latent autoimmune diabetes in adults LADA. Methods: 28 patients with LADA and CP were examined, as well as 25 representatives of the control group. The patients were divided into two groups by the phenotype of LADA: 19 patients with LADA1 and 9 with LADA2. This wensitivity forms part of a themed Sports performance supplements on Insulin Resistance Sports performance supplements Type 2 Diabetes Autophagy and GTPases. The Guest Insluin for this collection Carbohyxrate Matthias Blüher, Stefan Bornstein and Martin Haluzík. Metabolic tests are vital to determine Carbohydrate metabolism and insulin sensitivity vivo insulin sensitivity and glucose metabolism in preclinical models, usually rodents. Such tests include glucose tolerance tests, insulin tolerance tests, and glucose clamps. Although these tests are not standardized, there are general guidelines for their completion and analysis that are constantly being refined. In this review, we describe metabolic tests in rodents as well as factors to consider when designing and performing these tests. Breakthroughs in metabolic research rely upon in vivo studies using animal models, usually rodents.

Cardiovascular Diabetology volume 17Article number: Cite this article. Metrics details. For many years, cardiovascular disease CVD has aensitivity the znd cause of death around the world. Often associated with CVD are comorbidities such as obesity, Cagbohydrate lipid Carbohydrste and insulin resistance.

Insulin is a key an that functions Brain health for children a metabolosm of cellular metabolism Body detoxification and stress relief many tissues in the human body.

Jetabolism resistance is Effective hunger control as a sensitivkty in tissue response metabolissm insulin stimulation thus insulin resistance is characterized by defects in uptake and oxidation of glucose, a decrease in glycogen synthesis, and, to a lesser ineulin, the ability to suppress lipid oxidation.

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New therapies focused on decreasing insulin resistance may metabolksm to a decrease in both CVD inxulin atherosclerotic plaque mstabolism. The pathological processes and risk factors associated with CVD Metformin and hormonal regulation as early as during childhood [ 1 ].

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Insluin physiological conditions, insulin stimulates the use of metabolic sensiticity in multiple tissues Carbohydrate metabolism and insulin sensitivity heart, skeletal muscle, liver, and adipose tissue.

Catbohydrate the cardiomyocytes, insulin promotes glucose and fatty acid aensitivity, but inhibits the use of fatty acids as an energy insulni.

As a result metabolims insulin resistance, the abd attempts to compensate by secreting Carbohycrate amounts of insulin, resulting in hyperinsulinemia indulin 4 ]. Interestingly, a strong correlation between sensitivit resistance and risk insulim develop CVD has been established [ 6 nisulin.

Several molecular mechanisms contribute abd the association insulni insulin resistance and CVD [ meabolism7 jnsulin, 8 insjlin, 9 ]. These mechanisms Cabrohydrate the role of insulin resistance in atherosclerosis development, vascular function, hypertension and macrophage accumulation [ 9 ].

This review will be focused on the interactions among insulin resistance, and vascular disease, and metabllism molecular mechanism involved.

Specifically, the focus will be on the metabollism changes in glucose imsulin lipid Stimulant-free weight loss pills induced by insulin and their impact on the CVD development. Insulin Overcoming sports setbacks and adversity a potent anabolic hormone that ijsulin a variety of effects on many types of cells.

Some of the main metabolic actions of insulin are stimulating glucose uptake in skeletal muscles and adipocytes, promoting glycogen synthesis in metabokism muscles, suppressing hepatic glucose production, and inhibiting lipolysis in adipocytes [ 10 ].

After ingestion, insulin is secreted from the pancreas and induces the uptake of circulating glucose Carbohydrafe its Carbkhydrate tissues by binding to an insulin Carbohydratf. This binding activates receptor autophosphorylation, which senitivity a downstream signaling cascade through the phosphorylation Carbohydrxte tyrosine residues of the insulin receptor substrates, IRS IRS-1 or IRS-2followed znd phosphorylation of phosphatidylinositol 3-kinase PI3Kphosphoinositide dependent kinase-1, Akt Akt1 and Akt2 Vitamin and mineral essentials, protein kinase Carboyydrate PKC and mammalian target Crabohydrate rapamycin mTORas well as ribosomal protein S6 kinase beta 1 S6K1 Carbohydraate 1011 ].

These events result in an increased translocation of the glucose transporter 4 GLUT4 to Carbohydratee membrane, thus facilitating Carbohydrate metabolism and insulin sensitivity uptake Carboyhdrate 12 ].

After Carrbohydrate, free glucose is rapidly Increase metabolic efficiency to glucose 6-phosphate G6Pwhich subsequently enters different metabolic pathways [ 13 Fermented foods and balance gut bacteria. On the other hand, insulin signaling enhances lipid storage in adipocytes by two mechanisms, by stimulating triacylglycerol Carbohydrate metabolism and insulin sensitivity and by inhibiting lipolysis.

Triglycerides are stored in lipid droplets, which contain lipid droplet proteins, including perilipin [ 14 ]. Sensitiviy inhibition of lipolysis occurs through the reduction of cAMP levels and the inhibition of protein kinase Carbohyvrate PKA activity, hence attenuating HSL insylin lipase metaboliem and Cabrohydrate, causing a decline Xnd the lipolysis rate [ 15 ].

Nutritional needs change during exercise and starvation; triglycerides within the adipocyte lipid droplets are hydrolyzed to fatty acids, acylglycerides and glycerol by activating HSL [ 16 ].

In the liver, insulin inhibits glucose production and release, by blocking gluconeogenesis and glycogenolysis through the regulation of expression of phosphoenolpyruvate carboxylase PEPCK [ 17 ].

Furthermore, insulin can stimulate glycogen synthesis through Akt2 activation, glycogen synthase kinase 3 GSK3 inhibition, and glycogen synthase GS activation via desphosphorylation of serine residues at both the NH 2 and COOH-terminals of these proteins [ 18 ].

On the other hand, the vascular actions of insulin are complex, which may have either protective or deleterious effects on the vasculature. The deleterious effects involve the induction of vascular smooth muscle cell VSMC proliferation, vasoconstriction and proinflammatory activity.

These vascular effects are mediated through the mitogen-activated protein kinase MAPK pathway, which is involved only in the mitogenic effects of insulin, but not in its metabolic effects [ 19 ].

A simplified model of insulin resistance. The loss of suppressive effects of insulin on lipolysis in adipocytes increases free fatty acids.

Increased free fatty acids flux to the liver stimulates the assembly and secretion of VLDL resulting in hypertriglyceridemia.

Triglycerides TG in VLDL are transferred to both HDL and LDL through the action of cholesteryl ester transfer protein CETP. This process results in a triglyceride-enriched HDL and LDL particle. Triglyceride-enriched HDL is more rapidly cleared from the circulation by the kidney, leaving fewer HDL particles to accept cholesterol from the vasculature.

In the glucose metabolism, the insulin resistance results in decreased hepatic glycogen synthesis, owing to decreased activation of glycogen synthase, increased hepatic gluconeogenesis, and glucose delivery by the liver. Insulin resistance is defined as an experimental or clinical condition in which insulin exerts a biological effect lower than expected.

This phenomenon is due to marked defects in the insulin-stimulated glucose uptake, particularly, in glycogen synthesis and, to a lesser extent, glucose oxidation.

The effects of insulin resistance in different tissues depend on the physiological as well as metabolic function of the tissues. Due to their high metabolic demand insulin resistance has significant effects on skeletal muscle, adipocytes and liver tissue, which are the main targets of intracellular glucose transport as well as glucose and lipid metabolism [ 20 ].

Insulin resistance cause impaired glycogen synthesis and protein catabolism in skeletal muscles and inhibit lipoprotein lipase activity in adipocytes leading to an increased release of free fatty acids and inflammatory cytokines such as IL-6, TNFα, and leptin.

Insulin resistance causes endothelial cell dysfunction by decreasing the production of nitric oxide from endothelial cells and increasing the release of pro-coagulant factors leading to platelet aggregation.

In an insulin resistant state, the PI3K pathway is affected whereas the MAP kinase pathway is intact, which causes mitogenic effect of insulin in endothelial cells leading to atherosclerosis [ 2223 ].

Interestingly, low levels of circulating insulin and insulin resistance have significant physiological roles in regulating metabolic adaptation during starvation and pregnancy. During starvation, low glucose levels leads to decreased secretion of insulin which facilitates the mobilization of glucose from liver, fatty acids and glycerol from adipocytes and amino acids from muscle tissue.

These compensatory mechanisms help maintain blood glucose levels and utilization by vital systems like the brain and red blood cells [ 24 ]. Insulin resistance is increased in pregnancy particularly from the second to third trimester. This ensures the adequate supply of metabolic substrates and nutrients to the fetus for its proper growth and development [ 25 ].

On the contrary, insulin resistance is a key player in the pathogenesis of metabolic diseases like type 2 diabetes [ 26 ] and can be observed in several clinical conditions such as breast cancer [ 27 ], rheumatoid arthritis [ 28 ], polycystic ovary syndrome [ 29 ], non-alcoholic fatty liver disease [ 30 ], and CVD [ 31 ].

The excess of lipids in the cardiomyocyte shunted into non-oxidative pathways results in the accumulation of toxic lipid species lipotoxicitywhich alters cellular signaling and cardiac structure.

Disruptions in several cellular signaling pathways such as in mitochondrial dysfunction and endoplasmic reticulum stress have been associated with lipotoxicity. Mediators such as reactive oxygen species ROSnitric oxide NOceramide, phosphatidylinositolkinase, diacylglycerol DAGligands of PPAR nuclear receptors, leptin have been proposed to promote these lipotoxic effects and enhances rates of apoptosis [ 32 ].

Insulin works on multiple processes, essentially providing an integrated set of signals that allows the correct balance between nutrient supply and demand [ 33 ].

In insulin resistance, the target cells fail to respond to ordinary levels of circulating insulin thus higher concentrations of insulin are required for a normal response [ 34 ]. In this vein, an insulin resistant state is defined as the impairment of glucose uptake in muscle and an increased gluconeogenesis by the liver resulting in hyperglycemia, both in fasting and postprandial states [ 35 ].

A number of theories have been suggested to understand the mechanisms associated with insulin resistance, including genetic defects. Nonetheless, the pathogenesis of insulin resistance can be grouped into: genetic defects, fat derived signal ectopic lipid accumulationphysical inactivity, obesity, and inflammation [ 363738 ].

One approach to analyze the genetic defect is to define candidate genes based on the present knowledge of the insulin signaling chain.

In this regard, some alterations in the genes associated with insulin signaling have been found in insulin resistance and type 2 diabetes. Disruption of IRS-1 and IRS-2 genes in mice showed that IRS-1 knockout mice are insulin resistant but not hyperglycemic [ 39 ].

On the other hand, IRSdeficient mice are severely hyperglycemic due to abnormalities of peripheral insulin action and failure of β cell secretion [ 40 ]. The disruption of Akt1 in mice causes no significant perturbations in metabolism, whereas mice knocked-out for Akt2 show insulin resistance, with a phenotype closely resembling type 2 diabetes of humans [ 41 ].

Other mutations that have been identified and studied as possibly responsible for type 2 diabetes are mutations in the insulin receptor, in PI3K, in the liver glucokinase promoter, GLUT4, in the glycogen synthase, and in the protein phosphatase Despite having identified different mutations that may be responsible for the onset of type 2 diabetes, only a few number of individuals are diabetic due to genetic mutations [ 42 ].

There may be several other genetic defects, which are not yet identified, that may contribute to the development of insulin resistance or to type 2 diabetes. In relation to external factors, the increase in free fatty acids FFA induced by obesity can trigger insulin resistance through lipid accumulation ectopic lipids.

This may activate atypical PKC that inhibits insulin signaling and insulin-stimulated glucose uptake in skeletal muscles, as well as decreases the insulin-stimulated hepatic glycogen synthesis [ 4344 ]. This can lead to insulin resistance and increased glucose delivery by the liver [ 45 ].

Additionally, FFA triggers insulin resistance by direct activation of Toll-like Receptor 4 TLR4 and the innate immune response [ 46 ]. Furthermore, obesity is associated with inflammatory factors characterized by an increase in the accumulation of ATMs adipose tissue macrophages.

The inflammatory factors increase lipolysis and promote hepatic triglyceride synthesis, and hyperlipidemia due to increased fatty acid esterification.

ATM also stimulates inflammatory cytokines that inhibit insulin signaling and expedites hepatic gluconeogenesis, and postprandial hyperglycemia [ 4748 ].

Other mechanisms that explain insulin resistance are the activation of both mTOR and S6K1 pathways [ 49 ].

These activations cause serine phosphorylation of IRS-1, with a subsequent decline in the IRS-1—associated PI3K activity [ 49 ]. It has been suggested that under nutrient saturation conditions, S6K1 may negatively regulate insulin signaling and sensitivity [ 5051 ].

In addition, serine phosphorylation of IRS-1 has been examined under different circumstances. It seems that in addition to the mTOR-S6K1—dependent mechanism, various serine kinases, such as c-Jun NH 2 -terminal kinase JNKstress-activated protein kinases, tumor necrosis factor TNF-αand PKC, among others, can promote serine phosphorylation of IRS, inducing a decline in insulin signaling strength along the metabolic pathway [ 495253 ].

Moreover, central obesity is linked to insulin resistance. However, the molecular mechanism by which fat causes insulin resistance is unclear; inflammation due to lipid accumulation, the inhibitory effect of fatty acid oxidation on glucose oxidation, and the secretion of adipocytokines have all been linked to the development of local and systemic insulin resistance [ 55 ].

Increasing evidence suggests that the heterogeneity of fat composition and the distribution of adipose tissue can be crucial in the development of insulin resistance and cardiometabolic disruptions [ 565758 ].

Visceral adipose tissue VAT has been closely linked to an increasing incidence of insulin resistance [ 56 ], T2DM, and a higher risk of cardiovascular disease [ 5960 ].

VAT is associated with a high production of pro-inflammatory adipocytokines, oxidative stress, and renin—angiotensin—aldosterone system RAAS activation [ 6162 ].

Chronic caloric excess causes increased visceral fat mass due to hypertrophy of individual adipocytes and hyperplasia of adipocyte precursors [ 63 ].

As adiposity increases, the adipocytes release chemotactic factors such as monocyte chemoattractant protein-1 MCP-1and tumor-necrosis factor-α TNFαwhich modulates an inflammatory response in adipose tissue.

MCP-1 initiates the migration of monocytes into VAT and promotes their differentiation into macrophages. Macrophages then secrete large amounts of TNFα, increasing lipolysis and reducing insulin-stimulated glucose transporter 4, triglyceride biosynthesis, and adipocyte storage in the VAT, thus resulting in an increase in circulating triglyceride levels [ 64 ].

This event could result in ectopic lipid deposition of toxic fatty acid species i. The increase in EAT leads to cardiac steatosis and to an increase in mass in both ventricles, resulting in ventricular hypertrophy, contractile dysfunction, apoptosis, fibrosis, and impaired left ventricular diastolic function [ 666768 ].

: Carbohydrate metabolism and insulin sensitivity

Microbial metabolism linked to insulin resistance | Nature Reviews Endocrinology Hori and the staff at the RIKEN Yokohama animal facility for technical support; H. The characteristics of the study population across quintile categories of HOMA-IR are presented in Table 1. Latest Most Read Most Cited Magnesium Depletion Score and Metabolic Syndrome in US Adults: Analysis of NHANES Alternatively, the insulin infusion rate can be altered in one of the groups in order to match the clamp insulin concentrations across groups Pereira et al. Ketogenic diet reduces midlife mortality and improves memory in aging mice. In the present study, fiber from cereals was inversely related with the prevalence of the metabolic syndrome, whereas fiber from fruit, vegetable, and legumes was not.
Hormonal interactions in carbohydrate metabolism Sports performance supplements PubMed Carbohydrate metabolism and insulin sensitivity Central CAS Google Scholar Srnsitivity AC, Smith SR, Eckel RH, Hokanson Cadbohydrate, Burkhardt BR, Sudini Quercetin and muscle recovery, Wu Y, Schauer IE, Pereira RI, Snell-Bergeon JK. Journal of the American Senditivity for Laboratory Animal Carbohydrate metabolism and insulin sensitivity 50 Carbohyrate This review will be focused on the interactions among insulin resistance, and vascular disease, and the molecular mechanism involved. No drug references linked in this topic. We selected 5 to 50 variables in 5 increments as the maximum number of genera was Consequently, our study included relatively healthy individuals compared with most of the previous metagenomic studies of diabetes and obesity 56781112 ; the median interquartile range IQR body mass index BMI and glycated haemoglobin HbA1c were
Gut microbial carbohydrate metabolism contributes to insulin resistance | Nature The hormone resistin links obesity to diabetes. Samuel Klein. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4. Verges B. Article ADS PubMed Google Scholar Turnbaugh, P. Reproducibility of glucose, fatty acid and VLDL kinetics and multi-organ insulin sensitivity in obese subjects with non-alcoholic fatty liver disease.
Microbial metabolism linked to insulin resistance

In addition, the percentage of total plasma FFAs as palmitate was lower at dinner than breakfast, and the percentage of total plasma FFAs as oleate was higher at dinner than breakfast, suggesting an increased contribution of FFAs from ingested meals, which contained predominantly oleate and little palmitate.

These data demonstrate a plausible mechanism for a decrease in insulin sensitivity in the evening in healthy people because an increase in circulating FFAs can cause insulin resistance 6.

We also found a diurnal variation in skeletal muscle expression of genes involved in regulating fatty acid metabolism; the expression of genes that regulate fatty acid oxidation was lower, whereas the expression of genes involved in de novo lipogenesis was higher, at pm before dinner than at am before breakfast.

These data suggest a shift from muscle fatty acid oxidation toward lipogenesis in the evening, which could lead to insulin resistance by producing specific fatty acid metabolites that impair insulin action The mechanism s responsible for this diurnal variability is not clear but could be related to the expression of core clock genes, which oscillate in adipose tissue and muscle in people 16 — 18 and regulate fatty acid metabolic pathways 1 , It is also possible that the differences in the duration of fasting before breakfast 12 h fast and dinner 6.

Nonetheless, our data represent the normal diurnal variations in metabolic pathways in people consuming a typical daily meal pattern. However, the morning-to-evening direction of the variation in muscle clock gene expression in people is opposite from the direction observed in nocturnal mice 17 , 19 , Taken together, the data from our study and previous studies conducted in people and rodents support the notion that the core molecular clock machinery is involved in regulating both diurnal variations in fatty acid metabolism and insulin action.

In conclusion, the present study demonstrates that insulin resistance in the evening is associated with both an increase in circulating FFAs and alterations in cellular metabolic pathways associated with skeletal muscle fatty acid metabolism and core clock genes in metabolically normal women.

However, our study is not able to prove a direct cause-and-effect relationship between diurnal variations in fatty acid metabolism and insulin resistance. Further studies are needed to evaluate the complex mechanistic relationships among clock genes and metabolic pathways in people.

We thank Martha Hessler for help with subject recruitment; Janine Kampelman, Jennifer Shew, Freida Custodio, Anna C. Moseley, Kelly L.

Stromsdorfer, and Ioana Gruchevska for technical assistance; the staff of the Clinical Research Unit for their help in performing the studies; and the study subjects for their participation. This study was registered at clinicaltrials.

gov as trial number NCT This study was supported by National Institutes of Health Grants DK and DK to the Washington University School of Medicine Nutrition Obesity Research Center , Grant DK to the Washington University School of Medicine Diabetes Research Center , Grant RR to the Washington University Biomedical Mass Spectrometry Resource , Grant UL1 TR to the Washington University School of Medicine Clinical Translational Science Award including KL2 Subaward TR, and the Central Society for Clinical and Translational Research Early Career Development Award.

Disclosure Summary: S. is a shareholder and consultant for Aspire Bariatrics and serves on the Scientific Advisory Boards for NovoNordisk, Takeda Pharmaceuticals, the Egg Nutrition Council, and NuSi. The other authors have nothing to declare. Maury E , Ramsey KM , Bass J.

Circadian rhythms and metabolic syndrome: from experimental genetics to human disease. Circ Res. Google Scholar. Morgan LM , Aspostolakou F , Wright J , Gama R. Diurnal variations in peripheral insulin resistance and plasma non-esterified fatty acid concentrations: a possible link?

Ann Clin Biochem. Saad A , Dalla Man C , et al. Diurnal pattern to insulin secretion and insulin action in healthy individuals.

Lee A , Ader M , Bray GA , Bergman RN. Diurnal variation in glucose tolerance. Cyclic suppression of insulin action and insulin secretion in normal-weight, but not obese, subjects.

Van Cauter E , Shapiro ET , Tillil H , Polonsky KS. Circadian modulation of glucose and insulin responses to meals: relationship to cortisol rhythm. Am J Physiol. Roden M , Price TB , Perseghin G , et al. Mechanism of free fatty acid-induced insulin resistance in humans. J Clin Invest. Third Report of the National Cholesterol Education Program NCEP Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Adult Treatment Panel III final report.

Frimel TN , Deivanayagam S , Bashir A , O'Connor R , Klein S. Assessment of intrahepatic triglyceride content using magnetic resonance spectroscopy. J Cardiometab Syndr. James WP , McNeill G , Ralph A. Metabolism and nutritional adaptation to altered intakes of energy substrates. Am J Clin Nutr.

Mittendorfer B , Liem O , Patterson BW , Miles JM , Klein S. What does the measurement of whole-body fatty acid rate of appearance in plasma by using a fatty acid tracer really mean? Yoshino J , Conte C , Fontana L , et al. Resveratrol supplementation does not improve metabolic function in nonobese women with normal glucose tolerance.

Cell Metab. Patterson BW , Zhao G , Elias N , Hachey DL , Klein S. Validation of a new procedure to determine plasma fatty acid concentration and isotopic enrichment. J Lipid Res.

Magkos F , Patterson BW , Mittendorfer B. Reproducibility of stable isotope-labeled tracer measures of VLDL-triglyceride and VLDL-apolipoprotein B kinetics.

Magkos F , Fabbrini E , Korenblat K , Okunade AL , Patterson BW , Klein S. Reproducibility of glucose, fatty acid and VLDL kinetics and multi-organ insulin sensitivity in obese subjects with non-alcoholic fatty liver disease.

Int J Obes Lond. Schenk S , Saberi M , Olefsky JM. Insulin sensitivity: modulation by nutrients and inflammation. Gomez-Abellan P , Diez-Noguera A , Madrid JA , Lujan JA , Ordovas JM , Garaulet M.

Glucocorticoids affect 24 h clock genes expression in human adipose tissue explant cultures. PLoS One. Zambon AC , McDearmon EL , Salomonis N , et al. Time- and exercise-dependent gene regulation in human skeletal muscle. Genome Biol. Otway DT , Mantele S , Bretschneider S , et al.

Rhythmic diurnal gene expression in human adipose tissue from individuals who are lean, overweight, and type 2 diabetic. McCarthy JJ , Andrews JL , McDearmon EL , et al.

Identification of the circadian transcriptome in adult mouse skeletal muscle. Physiol Genomics. Dyar KA , Ciciliot S , Wright LE , et al. Muscle insulin sensitivity and glucose metabolism are controlled by the intrinsic muscle clock. Mol Metab. Oxford University Press is a department of the University of Oxford.

It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Endocrine Society Journals. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation.

Volume Article Contents Methods and Materials. Journal Article. Diurnal Variation in Insulin Sensitivity of Glucose Metabolism Is Associated With Diurnal Variations in Whole-Body and Cellular Fatty Acid Metabolism in Metabolically Normal Women.

Jun Yoshino , Jun Yoshino. Oxford Academic. Paloma Almeda-Valdes. Bruce W. Adewole L. Shin-ichiro Imai. Bettina Mittendorfer. Samuel Klein. PDF Split View Views. Cite Cite Jun Yoshino, Paloma Almeda-Valdes, Bruce W. Select Format Select format.

ris Mendeley, Papers, Zotero. enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation. If two experimental groups have different plasma glucose concentrations, the following approaches have been used: i divide glucose kinetics results by plasma glucose concentrations or ii make the average plasma glucose concentration of the control group the target plasma glucose concentration for all groups during the clamp Pereira et al.

In addition to being technically challenging, the clamp is laborious, expensive especially if tracer methodology is used , and usually terminal. However, it is a powerful technique in metabolic research. Important clamp-specific factors to address in the design, performance, and reporting of this technique have been published recently Ayala et al.

The pancreatic euglycemic clamp is used when an investigator wants to test the effect of a treatment without the confounding effect of alterations in endogenous insulin secretion.

The pancreatic euglycemic clamp has also been extensively used to study how hormones and nutrients in the brain affect peripheral glucose metabolism; in such studies intracerebroventricular cannulation surgery is performed before vessel cannulation surgery Lam et al.

The experimental flow and many aspects of the pancreatic euglycemic clamp are similar to those of the hyperinsulinemic euglycemic clamp Lam et al.

Similar to the hyperinsulinemic euglycemic clamp, the pancreatic euglycemic clamp requires chronic blood vessel cannulation. Moreover, the pancreatic euglycemic clamp also has two steady states basal and clamp , usually lasts 2 h, and can be combined with tracer methodology.

During the pancreatic euglycemic clamp, however, somatostatin is infused to inhibit endogenous insulin and glucagon secretion by the pancreas and exogenous insulin is infused at rate so that basal insulin concentrations can be achieved during the clamp steady state. Plasma glucose is measured throughout the clamp, and the rate of infusion of a glucose solution Ginf is altered as needed to achieve euglycemia Fig.

Techniques that require steady states, like the hyperinsulinemic or pancreatic euglycemic clamp, are essentially reductionist approaches to assess glucose metabolism Meneses et al. HOMA-IR and QUICKI also assume a steady state during fasting. In contrast, GTTs, ITTs, and PTTs are dynamic tests because, in addition to the main variable, namely, circulating glucose concentration, other variables such as circulating insulin may be changing.

Another factor to consider is when, in the feed—fast cycle, the metabolic tests are performed. Clamps, GTTs, ITTs, and PTTs, are usually done in the fasting state to minimize the confounding effect of changes in nutrients and hormones associated with feeding.

Moreover, circulating levels of nutrients and hormones as well as glucose metabolism have a circadian rhythm Ando et al. Therefore, the time of day when metabolic tests are performed should be consistent in a given experiment and reported.

All in vivo glucose metabolism techniques described in this review require blood sampling. When obtaining a blood sample, ideally the rodent should be free-moving and under minimal stress, especially from handling and restraint. Chronic cannulation of blood vessels, such as jugular vein and carotid artery, in rodents allow blood sampling to occur largely under such conditions Ayala et al.

The disadvantage of chronic vessel cannulation is that rodents cannot be kept for long periods of time. In contrast, GTTs, ITTs, and PTTs can be done in longitudinal studies of rodents that do not have vessel cannulation, where a given rodent can be studied multiple times throughout its life as long as blood volume limitations are respected.

Therefore, approaches other than chronic vessel cannulation are used to obtain blood in conscious rodents. If larger amounts of blood are needed for other measurements, two common sources of blood are used: the saphenous vein and the tail via tail clipping.

Obtaining blood from the saphenous vein involves rapid restraint and can be performed repeatedly during a test Abatan et al. When tail clipping is used, restraint may not be necessary if the tail is briefly held Abatan et al.

The extent of stress induced by sampling from the saphenous vein or via tail clip is approximately the same Abatan et al. However, tail clip is not recommended when larger blood samples are required, and tail clip commonly causes hemolysis Christensen et al.

Moreover, glucometers usually have a maximal reading of ~33 mM for blood Pereira et al. The accuracy of glucometers when measuring glucose in rodent blood has been compared to results obtained with a glucose assay kit or glucose analyzer Togashi et al.

The difference in plasma glucose concentrations between various models of glucometers and a glucose assay kit increases as plasma glucose concentration rises Togashi et al.

Furthermore, the direction of this error changes depending on whether mice have been fasted or not Togashi et al. Therefore, a glucometer type should be used consistently in a given experiment Ayala et al.

When doing GTTs and PTTs in models of diabetes, plasma glucose concentrations can be determined after completion of the tests using a glucose assay kit Pereira et al. The HemoCue glucose analyzer, which requires ~5 μL of blood to measure plasma glucose concentration in humans, has been used to measure plasma glucose concentration during clamps in mice Nahle et al.

For clamps in rats, glucose concentrations in 5—10 μL plasma samples can be determined using glucose analyzers such as GM9 from Analox Castellani et al. Continuous glucose monitoring CGM involves invasive surgery, namely, surgical implantation of a telemetry probe in the aorta Evers et al.

Assuming surgical expertise and funds exist in a laboratory, CGM is a great way to determine circulating glucose concentrations in rats and mice over weeks without handling them Evers et al.

Unless glucose metabolism is being specifically assessed in the feeding and postprandial states, all in vivo glucose metabolism tests are done in the fasting postabsorptive state to avoid the confounding effect of altered concentrations of hormones and nutrients associated with food consumption.

The length of fast depends on the species, rodent model, and metabolic test. Glucose production is the sum of glycogenolysis and gluconeogenesis, and as fasting increases, glycogen stores become depleted and the contribution of gluconeogenesis to glucose production increases Landau et al.

Overnight fasting, which typically lasts 16 h, is considered stressful in mice. An important species difference is that while a 24 h fast decreases insulin sensitivity in humans Salgin et al.

Thus, shorter fasting times are usually advised in mice also for translatability Ayala et al. The ideal length of fasting for GTTs and ITTs is an active area of investigation. Four to six hours of fasting is commonly used and advised Andrikopoulos et al. Recently, it was reported that shorter fasting 2 h is ideal when performing ITTs because hepatic glycogen content is similar to the nonfasted state Carper et al.

Genetic background of mice affects metabolic parameters such as insulin sensitivity and counterregulatory response to hypoglycemia Berglund et al.

Among healthy mice, females are more insulin sensitive and have better glucose tolerance than males Macotela et al. Similarly, women are more insulin sensitive than men Tramunt et al. This disparity is associated with sex-specific factors such as differences in circulating levels of estrogen and testosterone Macotela et al.

Sex hormones also affect body composition amount and distribution of fat tissue , which is an important determinant of insulin sensitivity Elbers et al. The enhanced insulin sensitivity associated with being female often deteriorates in insulin-resistant states Tramunt et al.

Although a shift is occurring, preclinical research often only uses male rodents Willingham From a metabolic perspective, one of the reasons may be a combination of the often-increased probability of finding metabolic disturbances in males and the publication bias toward positive findings Joober et al.

Results for males and females should not be pooled Willingham and flowcharts to design experiments that examine how sex affects metabolism have been published Mauvais-Jarvis et al.

Another reason why females are less studied than males is the fact that females have cyclic alterations in circulating gonadal hormones, namely, estrogen and progesterone.

Blood glucose concentrations have been found to change throughout the menstrual cycle Lin et al. It could be argued that the estrous cycle increases variance of metabolic studies; therefore, data from female rodents should be presented by estrous phase Della Torre et al.

However, the requirement to present metabolic data by estrous phase may depend on the primary parameter being investigated Mauvais-Jarvis et al. Indeed, there is evidence that in cases where estrous phases are not tracked, but sample size is sufficient, females do not show increased variance in various parameters of glucose metabolism Berglund et al.

Ultimately, it is up to each investigator to determine if, in addition to studying females, data should be presented by estrous phase. Key factors underlying this decision include the research question and cost. Estrous cycle is not the only sex-specific factor that can modulate glucose metabolism and potentially increase variance.

For example, aggression, which is associated with stress, is more prevalent in male mice Lidster et al. Moreover, the stress hormone corticosterone causes greater insulin resistance in male vs female mice Kaikaew et al.

The housing of female mice for metabolic studies usually involves nonpregnant or nonnursing females and occurs in the absence of males; such conditions favor low levels of aggression in female mice Newman et al.

Doses for GTTs, ITTs, PTTs, and clamps in rodents are usually expressed per kg of body weight. Glucose kinetics in rodents are also usually expressed per kg of body weight Berglund et al.

Such approaches are appropriate when comparing experimental groups that have similar body composition, especially the amount of fat and lean fat-free mass. Thus, the age of the rodents should be reported and matched across experimental groups. From a clinical perspective, there is a need to study metabolism throughout the life span, including menopause and andropause Kautzky-Willer et al.

There are many excellent published papers that use in vivo techniques to assess glucose metabolism in rodent models of obesity and type 2 diabetes. We will highlight some of the key findings from three of these papers in the current review.

First, hyperinsulinemic euglycemic clamps were utilized to conclude that knocking down pyruvate carboxylase in adipose tissue and liver with antisense oligonucleotides ameliorates hepatic insulin sensitivity in male HFD-fed rats, which are a model of obesity, and male Zucker diabetic fatty rats, which are a model of type 2 diabetes Kumashiro et al.

Second, tamoxifen-inducible adipocyte-specific insulin receptor and insulin growth factor 1 knockout mice were generated and characterized Sakaguchi et al.

Two days following tamoxifen administration, male double knockout mice were hyperglycemic, glucose intolerant based on OGTT results and insulin resistant based on ITT as well as HOMA-IR results. Moreover, HOMA-IR was used to track insulin sensitivity over time, and it was found that insulin sensitivity was similar between male double knockout and control mice by 30 days post tamoxifen administration.

Third, male and female mice lacking complement factor 5, which is part of the innate immune system, were placed on an HFD and studied Winn et al. Using IPGTTs, it was concluded that in the context of HFD-induced obesity, knocking out complement factor 5 only affects deteriorates glucose tolerance in male mice.

Hence, these examples support the importance of in vivo techniques for assessment of glucose metabolism in understanding the mechanisms of obesity, type 2 diabetes, and insulin resistance as well as new treatments for metabolic disorders.

Methods of in vivo tests of glucose metabolism should be detailed for readers to repeat experiments and to understand the context of results.

Preclinical research is valuable if it is translatable to humans Drucker ; therefore, one must frequently question and answer how metabolic tests in rodents relate to human physiology.

In this review, we have described the commonly used techniques for the assessment of insulin sensitivity and glucose metabolism in rodents. We have also highlighted the pros and cons of each technique. Furthermore, we have discussed key factors that can affect glucose metabolism, such as fasting duration and sex of the rodents.

Other factors that may cause stress and alter glucose metabolism have begun to be described in the literature, such as temperature, method of cage change, and even the sex of the scientist Sorge et al.

Regarding the latter, stress responses in mice vary depending on the sex of the investigator handling the mice and merely because men and women give off different scents Sorge et al.

It will be interesting to determine how additional factors such as these can be utilized to further optimize metabolic tests in rodents in the future. The guest editors for this collection were Matthias Blüher, Stefan Bornstein, and Martin Haluzík. Grants from the Banting and Best Diabetes Centre BBDC , Canadian Institutes of Health Research CIHR , and PSI Foundation were awarded to MKH.

MKH also has support from an Academic Scholars Award from the Department of Psychiatry, University of Toronto, and holds the Kelly and Michael Meighen Chair in Psychosis Prevention.

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Carbohydrate metabolism and insulin sensitivity

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