All of the strains were cultivated in EG aCrbohydrate JCM Medium No. indistinctus -mediated improvement of IR. Article PubMed CAS Google Scholar Opie LH. Chiang GG, Abraham RT.

Carbohydrate metabolism and insulin resistance -

Clark , Jennifer J. Pointon , Keith N. Frayn; Carbohydrate Metabolism in Insulin Resistance: Glucose Uptake and Lactate Production by Adipose and Forearm Tissues in Vivo before and after a Mixed Meal.

Clin Sci Lond 1 May ; 90 5 : — To examine whether insulin resistance in vivo is manifest equally in both muscle and adipose tissues, we measured arteriovenous glucose and lactate fluxes across forearm muscle and abdominal subcutaneous adipose tissue in nine obese, glucose-intolerant subjects and 13 non-obese subjects of similar age and sex.

Compared with non-obese subjects, the forearm of the obese subjects was resistant to insulin stimulation of glucose uptake after a mixed meal. In contrast, adipose tissue showed little evidence of insulin stimulation of glucose uptake, and adipose tissue in subjects in both normal and obese groups behaved very similarly assessed per g of tissue.

For lactate flux, adipose tissue behaved very similarly per g of tissue in obese and non-obese subjects, and was a consistent lactate exporter. We conclude that insulin resistance of glucose uptake observed in the forearm of obese subjects is not evident in adipose tissue.

Adipose tissue glucose uptake in obese, insulin-resistant subjects is similar to that in lean control subjects, although it occurs at elevated circulating insulin and glucose concentrations.

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Skip Nav Destination Close navigation menu Article navigation. Volume 90, Issue 5. Previous Article Next Article. All Issues. Cover Image Cover Image. Article Navigation. Research Article May 01 Carbohydrate Metabolism in Insulin Resistance: Glucose Uptake and Lactate Production by Adipose and Forearm Tissues in Vivo before and after a Mixed Meal Simon W.

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Author affiliations. Sphingolipids from a symbiotic microbe regulate homeostasis of host intestinal natural killer T cells. Cell , — Claesson, M.

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Wang, D. Characterization of gut microbial structural variations as determinants of human bile acid metabolism. Cell Host Microbe 29 , — Download references. We thank E. Miyauchi, T. Kanaya and T. Kato for advice; A. Ito, N. Tachibana, A. Hori and the staff at the RIKEN Yokohama animal facility for technical support; H.

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

Kubota and the RIKEN Junior Research Associate Program to T. Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan.

Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Division of Diabetes and Metabolism, The Institute for Medical Science Asahi Life Foundation, Tokyo, Japan.

Department of Clinical Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition NIBIOHN , Tokyo, Japan.

Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science CSRS , Yokohama, Japan.

Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan. Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan.

Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.

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

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

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

Department of Metabolism and Endocrinology, Tokyo Medical University Ibaraki Medical Center, Ami Town, Japan. Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.

Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan. Human Biology-Microbiome-Quantum Research Center WPI-Bio2Q , Keio University, Tokyo, Japan.

Laboratory for Immune Cell Systems, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. You can also search for this author in PubMed Google Scholar.

Kadowaki and H. conceived the project. Kubota, Y. Mizuno, N. and T. Kadowaki contributed to the enrolment of study participants and clinical data collection. and Y. processed faecal samples for metagenomics and metabolomic analyses. performed 16S rRNA gene sequencing and metagenomic analysis.

performed metabolomic analyses for hydrophilic metabolites. performed lipidomics analyses. and P. performed CAGE analysis. and O. performed cytokine measurement and RNA extraction from PBMCs. Mochizuki prepared fundamental information tools for the analysis. Kubota and S. performed animal experiments and analysed the data.

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

Kitami and P. Correspondence to Tetsuya Kubota or Hiroshi Ohno. are listed as the inventors on a patent regarding the metabolic effects of gut bacteria identified by a human cohort. The other authors declare no competing interests. Nature thanks Gregory Steinberg and the other, anonymous, reviewer s for their contribution to the peer review of this work.

Insulin resistance IR and metabolic syndrome MetS were the main clinical phenotypes. To evaluate the host-microbe relationship, we collected 1 host factors: clinical, plasma metabolome, peripheral blood mononuclear cells PBMC transcriptome, and cytokine data, and 2 microbial factors: 16S rRNA pyrosequencing, shotgun metagenome, and faecal metabolome.

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

Microbial signatures were determined using the 16S and metagenomic datasets, and their associations with metabolites were analysed. We also assessed the mediation effects of plasma cytokines on the relationships between faecal metabolites and metabolic markers. The associations between clinical phenotypes and omics markers were adjusted by age and sex wherever appropriate.

a , The KEGG pathway enrichment analysis of the metabolites in hydrophilic CAGs 5, 8, 12, 15, and 18, which were associated with IR in Fig.

The size of disks shows the enrichment i. b , Partial correlations between HOMA-IR and faecal levels of short-chain fatty acids SCFA such as acetate, propionate, and butyrate left panel , and disaccharides such as maltose and sucrose right panel. Density plots indicate median and distribution. The detailed statistics are reported in Supplementary Table 5 , 6.

The size and colour of the disks represent the estimate and the direction of the associations. c , The associations between faecal glucose and arabinose and HOMA-IR as analysed in Fig.

The estimates of metabolites and their P values are described. The data were analysed with a generalized linear mixed-effect model with consent age and sex as fixed effects, and the sample collection site as a random effect. The estimate and P value are described.

The first faecal sampling for metabolomics was used to avoid redundancy. The detailed statistics are reported in Supplementary Table 9.

Dots represent individual data summarized into PCo1 and PCo2. Dots represent individual data summarized into PC1 and PC2. f , Co-abundance groups of genus-level microbes and their abundance in the participant clusters defined in Fig. The disk size represents the median abundance in the participants.

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

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

Only those with significant associations with metabolic markers are depicted. The disk size and colour represent absolute values of standardized coefficient and the direction of associations.

The detailed statistics are reported in Supplementary Table j , Microbe-metabolite networks of IR- or and IS-associated co-abundance microbial groups from Fig. All faecal hydrophilic metabolites and faecal microbe-related lipid metabolites were included in the analysis.

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

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

The pathways with significant associations with metabolic markers are included in the plots. The far-left column shows the type of carbohydrate metabolites that each PTS gene is involved in. The far-left column shows whether the genes were predicted to function as extracellular enzymes. g , Representative pathways in starch and sucrose metabolism KEGG pathway relating to glycosidase activities to degrade poly- and oligosaccharides into monosaccharides.

i , The presence and absence of KEGG orthologues predicted to function as extracellular enzymes in 45 strains. The strains from the top three genera positively or negatively correlated with faecal carbohydrates shown in Fig.

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

b , The cross-omics network shown in Fig. c , The number of correlations between faecal carbohydrates and other omics elements shown in Fig.

The proportion to all possible correlations is shown. d , Representative causal mediation models analysing the effects of IL and adiponectin mediating in silico relationships between faecal carbohydrates and HOMA-IR.

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

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

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

The top 10 metabolites contributing to the PCA separation left panel and 13 out of 15 IR-related carbohydrates identified in Fig.

c , d , The levels of carbohydrate fermentation products c and carbohydrates relating to IR in the human cohort d in the cell-free supernatants. e , Pie charts summarizing the consumption and production of carbohydrates shown in d. Those significantly decreased or increased compared with the vehicle control group were considered as consumption or production.

f , The top consumers of carbohydrates, which summarizes the results shown in e. Representative data of two independent experiments. c , d , Data are mean and s. a , Body mass change from the baseline. indistinctus AI groups, respectively.

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

The raw images of blotting membranes are shown in Supplementary Fig. P values for interactions between time and group are described in m.

Other metabolic measures are reported in Supplementary Table Representative data of two independent experiments c—g , k—o. a , Density plots indicate median and distribution. a , PCA plots of metabolites in caecal contents of AI-administered mice.

The top 10 metabolites contributing to the PCA separation left panel and 12 out of 15 IR-related carbohydrates identified in Fig.

b , The PC1 of PCA plots in Fig. The detailed statistics of all caecal metabolites are reported in Supplementary Table e , A schematic summary.

In this study, we combined faecal metabolome, 16S rRNA gene sequencing, and metagenome data with host metabolome, transcriptome, and cytokine data to comprehensively delineate the involvement of gut microbiota in IR upper panel.

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

Further analysis also suggests that the effects of these metabolites on host metabolic parameters such as BMI are in part mediated by specific cytokines.

Finally, our animal experiments provide evidence showing that oral administration of AI, a candidate strain selected based on human cohort findings, reduces intestinal carbohydrates and lipid accumulation, thereby leading to the amelioration of IR lower panel.

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

Raw images of blotting membranes. a,b, The blotting membranes images of p-AKT and total AKT in the liver a and epidydimal fat b. Molecular mass kDa is shown on the left. Relating to Extended Data Fig. Open Access This article is licensed under a Creative Commons Attribution 4.

Reprints and permissions. Gut microbial carbohydrate metabolism contributes to insulin resistance. Download citation. Received : 25 March Accepted : 20 July Published : 30 August Issue Date : 14 September Anyone you share the following link with will be able to read this content:.

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Thank you for visiting nature. Resistanxe are using a browser version with Ink cartridge refill Carbohydrate metabolism and insulin resistance for Abd. To obtain the best experience, metabolusm recommend you use a Carbohydratr up to Carbohydrate metabolism and insulin resistance browser or turn off resistancee mode resistznce Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes 12. Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance 3456789. Nevertheless, the underlying mechanism remains unclear.

Carbohydrate metabolism and insulin resistance -

Low magnesium status has been associated with decreased insulin sensitivity 54 , metabolic syndrome 55 , and increased risk of type 2 diabetes 30 — Clinical studies further support a role for magnesium by demonstrating that supplementation with magnesium improved insulin sensitivity in type 2 diabetic patients 56 , We previously found that the relationship between whole-grain intake and fasting insulin was mediated, in part, by magnesium Although the lack of a formal definition for the metabolic syndrome previously hampered investigation into the role of diet in the etiology of this condition, observational studies have examined the role of carbohydrate-related dietary factors and individual metabolic risk factors associated with this syndrome.

Wirfalt et al. In the Framingham Offspring Cohort, we found that whole-grain intake was favorably associated with several metabolic risk factors of this syndrome, including central obesity, insulin sensitivity, and dyslipidemia We found no evidence for an effect of total carbohydrate intake on insulin resistance or prevalence of the metabolic syndrome.

Other observational studies have found that total carbohydrate intake is unrelated to fasting insulin 14 and the risk of developing type 2 diabetes 30 — Because total carbohydrate intake fails to take into account the glycemic effect of different carbohydrate foods, the glycemic index has been proposed to classify carbohydrate-containing foods.

A high dietary glycemic index was positively associated with both HOMA-IR and the prevalence of the metabolic syndrome. This is not unexpected given that high glycemic index foods produce higher postprandial blood glucose concentrations than those with a low glycemic index, which over the long term will generate a higher insulin demand 23 , Two intervention studies have found that after 4 weeks on a low glycemic index diet, insulin sensitivity was improved in both normal 58 patients and those with coronary heart disease More recently, a high glycemic index diet was associated with increased postprandial insulin resistance among overweight middle-aged men Although our data provide evidence that a high glycemic index diet is associated with a greater risk of metabolic syndrome, they are insufficient to examine the potential mechanisms by which a high glycemic index diet might affect risk of metabolic syndrome.

Thereby, it is likely that the inverse association between glycemic load and HOMA-IR was largely explained by the glycemic index part of the equation. In the present study, a higher prevalence of the metabolic syndrome was found with a high dietary glycemic index, but no association was found with the glycemic load.

Furthermore, no difference was found in the association between glycemic load and prevalence to the metabolic syndrome after adjustment for cereal fiber intake. Stevens et al.

The FFQ has many limitations with respect to determining carbohydrate-related dietary intakes that may have caused some misclassification of subjects, in particular with respect to fiber and whole-grain intake.

For example, the assumption that dark breads are largely made from whole-grain flour would lead to measurement error, thereby attenuating associations with cereal fiber and whole-grain intake.

Despite this potential misclassification, significant associations among these carbohydrate-related dietary factors, HOMA-IR, and the metabolic syndrome were observed. Furthermore, the FFQ reportedly underestimates refined grain intake compared with diet records, and this may explain in part the lack of association between refined grain intake, insulin resistance, and the metabolic syndrome Interpretation of the findings from the present study is subject to some additional caveats.

Although the apparent protective association with whole-grain and cereal fiber intakes persisted after adjustment for lifestyle and dietary factors associated with a healthier lifestyle, we cannot rule out residual confounding.

Another potential limitation is the use of a single measure of plasma insulin and glucose to calculated HOMA-IR. At the population level, HOMA-IR can be used as a surrogate measure of insulin resistance to identify those individuals who are most insulin resistant.

It is perhaps less useful on an individual basis, given the modest intraindividual variability in insulin and glucose levels. Furthermore, if β-cell function is failing i. However, the findings of the present study were not altered after removing those with newly diagnosed diabetes, and from the outset, we excluded individuals with established diabetes.

Finally, the cross-sectional nature of this study precludes any causal inferences, therefore, more observational and experimental studies are needed before any firm conclusions can be drawn with regard to the influence of different aspects of carbohydrate nutrition, insulin resistance, and the metabolic syndrome.

No specific dietary recommendations have been advocated by health agencies for treatment of insulin resistance or the metabolic syndrome. A high cereal fiber content and low glycemic index are inherent attributes of most whole-grain foods. Therefore, in terms of implementing dietary change, emphases should be place on increasing dietary intakes of whole-grain foods.

Given that the metabolic syndrome is an identifiable and potentially modifiable risk state for both type 2 diabetes and cardiovascular disease, increasing whole-grain cereal fiber may reduce the potential untoward effects of carbohydrate on risk of these diseases. However, more longitudinal studies are required to ascertain which aspects of carbohydrate nutrition are linked to development of the metabolic syndrome milieu.

Characteristics of subjects in the Framingham Offspring Cohort across quintile categories of HOMA-IR insulin resistance. Data are means unless otherwise indicated. Geometric means are given for levels of fasting insulin.

P values for trend for continuous variables or Mantel-Haenzel χ 2 for categorical variables across quintiles of HOMA-IR. Multivariate adjusted geometric mean HOMA-IR and prevalence OR of metabolic syndrome across quintiles of carbohydrate-related dietary factors. Geometric mean HOMA-IR adjusted for sex, age, BMI, waist-to-hip ratio, cigarette dose, total energy intake, alcohol intake, percentage saturated fat, percentage polyunsaturated fat, multivitamin use, physical activity, and treatment for blood pressure.

Results were essentially the same when the analysis was repeated using fasting insulin rather than the HOMA-IR. Adjusted for sex, age, cigarette dose, total energy intake, alcohol intake, percentage saturated fat, percentage polyunsaturated fat, multivitamin use, and physical activity.

Quintile categories are based on energy-adjusted values using the residual method, with the exception of whole and refined grains. IRS, insulin resistance syndrome. This material is based upon work supported in part with federal funds from the U. Meigs is supported by a Career Development Award from the American Diabetes Association.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U. Department of Agriculture.

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Skip Nav Destination Close navigation menu Article navigation. Volume 27, Issue 2. Previous Article Next Article. RESEARCH DESIGN AND METHODS. Article Information. Article Navigation. Carbohydrate Nutrition, Insulin Resistance, and the Prevalence of the Metabolic Syndrome in the Framingham Offspring Cohort Nicola M.

McKeown, PHD ; Nicola M. McKeown, PHD. Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts. This Site. Google Scholar. James B. Meigs, MD, MPH ; James B.

Meigs, MD, MPH. Simin Liu, MD, SCD ; Simin Liu, MD, SCD. Edward Saltzman, MD ; Edward Saltzman, MD. Peter W. Wilson, MD ; Peter W. Wilson, MD. Paul F. Jacques, SCD Paul F. Jacques, SCD. 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 [ 43 , 44 ].

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 [ 47 , 48 ].

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 [ 50 , 51 ]. 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 JNK , stress-activated protein kinases, tumor necrosis factor TNF-α , and PKC, among others, can promote serine phosphorylation of IRS, inducing a decline in insulin signaling strength along the metabolic pathway [ 49 , 52 , 53 ].

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 [ 56 , 57 , 58 ].

Visceral adipose tissue VAT has been closely linked to an increasing incidence of insulin resistance [ 56 ], T2DM, and a higher risk of cardiovascular disease [ 59 , 60 ].

VAT is associated with a high production of pro-inflammatory adipocytokines, oxidative stress, and renin—angiotensin—aldosterone system RAAS activation [ 61 , 62 ]. 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-1 , and tumor-necrosis factor-α TNFα , which 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 [ 66 , 67 , 68 ].

Elevated levels of LDL, smoking, elevated blood pressure and type 1 and type 2 diabetes, are well known risk factors for CVD, however, insulin resistance, hyperglycaemia and inflammation can also lead to and predict adverse cardiovascular events.

Furthermore, insulin resistance is related to disorders such as hypertriglyceridemia as well as low levels HDL. In , investigators in the Insulin Resistance Atherosclerosis Study IRAS , showed a direct relation between insulin resistance and atherosclerosis [ 70 ] and a follow-up prospective study in a cohort of patients reported insulin resistance as an important risk factor for CVD [ 71 ].

A meta-analysis of 65 studies, which included , participants, revealed that insulin resistance, evaluated by HOMA index, was a good predictor for CVD [ 6 ]. Even though a wealth of studies support the notion that CVD is related to insulin resistance [ 4 , 9 , 31 , 73 , 74 , 75 , 76 ], there are some controversial reports as well.

A study performed by Kozakova et al. reported the association of insulin sensitivity with risk of CVD in young to middle aged men, where as in women, atherosclerosis and plaque formation were independently associated with fasting plasma glucose levels [ 77 ].

In addition to insulin resistance, the compensatory hyperinsulinemia associated with insulin resistance can play a critical role in the formation of atherosclerotic plaques by changing the gene expression pattern associated with estrogen receptor, as reported in animal models [ 78 ].

Furthermore, hyperglycemia produces alterations in various metabolic and cellular functions [ 7 , 8 , 9 ] including dyslipidemia, hypertension, endothelial dysfunction, oxidative stress and alterations in cardiac metabolism.

Issues related to the latter alterations are discussed further along in this review. Although there seems to be a preferential use of fatty acids for the production of energy, the heart has the ability to change to another substrate for the generation of ATP, depending on availability, to ensure its energy demand.

But also the substrate transporters, GLUT4 for glucose and CD36 for fatty acids , play a role in this dynamic balance of substrate utilization [ 79 ].

During injury, the heart shifts from using fatty acids as energetic substrates toward glucose, but this metabolic flexibility is impaired under insulin resistance, leaving to fatty acid as the sole fuel source.

This shift induces an increase in the uptake and accumulation of lipid in the heart, producing lipotoxicity [ 80 ]. In this sense, the balance between lipid degradation and glucose oxidation could decrease diabetic cardiomyopathy [ 81 ].

The dyslipidemia induced by insulin resistance and type 2 diabetes diabetic dyslipidemia [ 82 ] is characterized by the lipid triad: 1 high levels of plasma triglycerides, 2 low levels of HDL, and 3 the appearance of small dense low-density lipoproteins sdLDL , as well as an excessive postprandial lipemia [ 35 , 82 , 83 , 84 ].

A study conducted in 10, people with normal blood pressure or pre-hypertension demonstrated dyslipidemia as a strong predictor of development of type 2 diabetes [ 87 ]. Frequently, diabetic dyslipidemia precedes type 2 diabetes by several years, suggesting that the abnormal lipid metabolism is an early event in the development of CVD in type 2 diabetes [ 88 ].

Obesity is a world-wide epidemic and intimately associated with the development of type 2 diabetes and CVDs. Visceral and epicardial adiposity related to obesity are the major drivers for cardiac disease in these individuals [ 60 ].

Obesity has a major effect in modifying the lipoprotein profile and factors associated with systemic and vascular inflammation, and endothelial dysfunction [ 89 ].

Abnormal concentrations of lipids and apolipoproteins can produce changes in the production, conversion, or catabolism of lipoprotein particles. These changes may contribute to increased basal lipolysis in obesity and the release of fatty acids into the circulation that consequences a proatherogenic phenotype [ 19 , 90 ].

VLDL, very low-density lipoprotein, is assembled and produced in the liver, which depends on the availability of substrates and is tightly regulated by insulin [ 91 ].

Hepatic VLDL production is induced in the fasting state, which results in increased levels of VLDL in the blood. The increase of lipids from different sources, such as circulating FFA, endocytosis of triglyceride-rich lipoproteins, and de novo lipogenesis, allows for the posttranslational stabilization of apoB and enhances the assembly and secretion of VLDL particles.

This leads to VLDL and FFA production, which carries energy between the liver and the adipose tissue [ 92 ]. In response to insulin secretion, VLDL synthesis is inhibited to limit the level of plasma triglycerides [ 83 , 93 ].

Normally, insulin, through PI3K activation, promotes the degradation of apoB, but under insulin resistance this degradation is impaired [ 92 , 94 ].

Thus, facing a combination of: 1 an excess of fatty acids available, 2 a limited degradation of apoB, and 3 greater stabilization of apoB; an increase in VLDL synthesis is produced, which explains the hypertriglyceridemia observed under insulin resistance [ 95 ].

Insulin resistance also decreases lipoprotein lipase activity, a major mediator of VLDL clearance. This effect has a minor contribution in the plasmatic triglycerides level, though it is a mechanism that is also altered. In subjects with type 2 diabetes, hepatic uptake of VLDL, IDL, and LDL is decreased, resulting in increased residence time of these lipoproteins in the plasma [ 96 ].

The formation of sdLDL and decreased HDL levels are closely related to insulin resistance. In a prospective study among Atherosclerosis Risk in Communities ARIC , the plasma levels of sdLDL were associated with risk for incident coronary heart disease CHD [ 97 ].

Besides, VLDL levels is the major predictor of LDL size [ 98 ]. The formation of sdLDL depends on the participation of both, cholesteryl ester transfer protein CETP and hepatic lipase. CETP facilitates the transfer of triglycerides from VLDL to LDL and HDL, generating triglyceride-rich LDL and leading to low HDL-C [ 99 ].

Triglyceride-rich LDL is a substrate for hepatic lipase, increasing lipolysis of triglyceride-rich LDL, resulting in the formation of sdLDL [ ]. Various mechanisms have been suggested to explain the enhanced atherogenic activity of sdLDL, these mechanisms include: 1 lower affinity for the LDL receptor, 2 facilitated entry into the arterial wall, 3 major arterial retention, 4 major susceptibility to oxidation, 5 longer half-time [ 97 ].

Increased sdLDL levels represent an increased number of atherogenic particles, which may not be reflected by the levels of LDL, as the sdLDL particles contain less cholesterol Fig.

The triglyceride enrichment of HDL particles by CETP, combined with the lipolytic action of hepatic lipase, leads to a reduction of plasma HDL-C and apoA-I, which impacts the formation of small dense HDL and leads to an increased catabolism of these particles [ ].

A retrospective study conducted in non-diabetic individuals reported that the ratio of triglyceride to HDL cholesterol ratio can predict insulin resistance and likelihood of metabolic diseases [ ]. Additionally, correlation of lipid accumulation products and triglyceride glucose index with insulin resistance and CVD has been demonstrated [ , ].

Insulin resistance leads to increased release of FFA from adipocytes and the product of fasting plasma FFA by insulin concentration is called adipose tissue insulin resistance. Adipose tissue insulin resistance has been reported as a risk factor for aortic valve calcification, thereby predicting cardiovascular outcomes [ ].

The coexistence of hypertension in diabetic patients greatly enhances the likelihood of these patients developing CVD. It has been suggested that abnormalities in vasodilatation, blood flow, and the renin—angiotensin—aldosterone system RAAS can be a linked to hypertension and insulin resistance [ , ].

An additional cause of hypertension in insulin-resistant patients is over-activity of the sympathetic nervous system, which promotes myocyte hypertrophy, interstitial fibrosis and reduced contractile function, accompanied by increased myocyte apoptosis [ ].

In the RAAS, angiotensinogen is converted to angiotensin I by renin, which is then converted to angiotensin II Ang II by ACE angiotensin converting enzyme. Finally, Ang II acts on both AT1 and AT2 receptors. The AT1 receptor mediates all the classic effects of Ang II, such as blood pressure elevation, vasoconstriction, increased cardiac contractility, renal sodium retention, water reabsorption and aldosterone release from by the zona glomerulosa of the adrenal cortex in the adrenal gland [ ].

Aldosterone, however, also exerts effects on the kidney, blood vessels and the myocardium, which can have pathophysiological consequences [ ]. Literature has shown that hyperglycemia increases transcription of angiotensinogen, ACE and Ang II [ , ].

On a different matter, an up regulation of RAAS in their cardiovascular system has been found in individuals with type 2 diabetes. An up regulated RAAS may contribute to the development of many diabetic complications, including microvascular and macrovascular diseases [ , ], in addition, it has been shown that the up regulation of Ang II and the activation of mineralocorticoid receptor by aldosterone might promote insulin resistance through activation of the mTOR—S6K1 signal transduction pathway by inducing phosphorylation in serine residues of IRS [ ] Fig.

Mechanisms implicated in the development of diabetic cardiomyopathy. Normally, the insulin signaling regulates the glucose and lipids metabolism in heart. Insulin resistance produces a metabolic derangement that results in high lipid oxidation and low of glucose oxidation. The activation of the renin—angiotensin—aldosterone system RAAS can cause mitochondrial dysfunction, endoplasmic reticulum stress and oxidative stress.

ER endoplasmic reticulum, FFA free fatty acids. Moreover, it has been shown that the activation of RAAS and hyperinsulinemia may synergistically stimulate the MAPK pathway, which exerts an effect damaging to the vascular wall by inducing endothelial dysfunction and promoting atherosclerosis [ ].

Additionally, new studies have suggested that the signal transduction pathways of insulin and Ang II share a number of downstream effectors and cross talk at multiple levels [ ].

In a related matter, the activation of RAAS Ang II and aldosterone and over nutrition contributes to endothelial dysfunction through an increase in the ROS production mediated by nicotinamide adenine dinucleotide phosphate NADPH -oxidase, a mechanism that also contributes to hypertension and other CVDs [ ].

Indeed ROS leads, in turn, to activation of redox-sensitive kinases such as S6K1 and mTOR, causing an inhibition insulin-PI3K signaling pathway, through phosphorylation at serine residues of IRS-1 [ 53 ].

The latter mechanism results in inhibition of downstream signaling of Akt phosphorylation, Glut-4 translocation to the sarcolemma, and Nitric Oxide NO production in endothelium [ ]. Additionally, hypertension and type 2 diabetes are also associated with a decreased number and impaired function of endothelial progenitor cells, which are circulating bone marrow-derived stem cells that play an important role in the endothelial repair of vascular wall [ ].

In some clinical and experimental studies, it has been shown that RAAS inhibition improved insulin signaling and insulin sensitivity [ ], however, in others, no beneficial effect has been shown [ ]. This discrepancy may be explained by either differences in experimental design or in study populations.

It also leads to impaired myocardial glucose utilization and to a decrease in diastolic relaxation. The integrity of the functional endothelium is a fundamental vascular health element.

NO is considered to be the most potent endogenous vasodilator in the body, and the reduction in the NO bioavailability is a hallmark of endothelial dysfunction.

The endothelial dysfunction contributes to CVD, including hypertension, atherosclerosis and coronary artery disease, which are also caused by insulin resistance [ ]. NO participates in vascular wall homeostasis by platelet aggregation, leukocyte adhesion inhibition and anti-inflammatory properties [ ].

In physiological conditions, constitutive stimulation of NO production by insulin may play an important role in vascular health maintenance by virtue of its ability to relax vascular smooth muscle. However, in insulin resistance state, the NO synthesis stimulated by insulin is selectively impaired and the compensatory hyperinsulinemia may activate the MAPK pathway, resulting in a vasoconstriction enhancement, inflammation, increased sodium and water retention, resulting in the elevation of blood pressure [ ].

In addition, insulin resistance in endothelial cells causes an increased level of prothrombotic factors, proinflammatory markers, and ROS, that lead to an increase in the intracellular levels of adhesion molecule 1 ICAM-1 and vascular cell adhesion molecule 1 VCAM-1 [ ].

The relation between endothelial function and insulin metabolism is very important. This is because, the association between insulin resistance and endothelial signaling disturbances contributes to inflammation, disrupting the balance between endothelial vasodilator and vasoconstrictor mechanisms and increases cardiovascular risk [ 10 ].

A study conducted in non-diabetic patients with suspected myocardial defects reported that insulin resistance measured by HOMA-IR is strongly correlated with endothelial dysfunction with prognostic value [ ].

The increased CVD risk in patients with type 2 diabetes has been known for many years [ ]. Patients with diabetes have increased vascular morbidity and mortality, which lowers their life expectancy by approximately 5—15 years.

In addition, it has been shown that the CVD incidence is two- to eightfold higher in subjects with type 2 diabetes than in those without diabetes, and this disease accounts for the majority of deaths [ ]. To support the latter, epidemiological and pathophysiological studies suggest that hyperglycemia may be largely responsible for CVD.

Long-term follow up data from patients with type 1 and type 2 diabetes suggest that hyperglycemia is a risk factor for diabetes related diseases and CVDMoreover, it has been suggested by Salvin et al.

Even in the absence of overt diabetes, impairment in the glucose homeostasis can affect the cardiac autonomic function leading to high risk of cardiac diseases [ ]. The detrimental effects of hyperglycemia on cardiomyocytes can be explained by a phenomenon called hyperglycemic memory , which is known as a long-term persistence of hyperglycemic stress even after blood glucose normalization [ , ].

Glucose fluctuations and hyperglycemia trigger inflammatory responses via mitochondrial dysfunction and endoplasmic reticulum stress. This promotes ROS accumulation, which in turn generates cellular damage [ ] Fig.

Hyperglycemia may also increase pro-inflammatory and pro-coagulant factors expression, promoting leukocyte adhesion to endothelial cells. It also induces apoptosis and impairs NO release, leading to endothelial dysfunction [ 7 , ].

For this reason, inflammation leads to insulin resistance and β-cell dysfunction, which further aggravates hyperglycemia, the latter help perpetuate this deregulation. Moreover, changes produced by glucose fluctuations and hyperglycemia can induce long-lasting epigenetic modifications in the promoter of the NF-κB, which appears to be mediated by increased oxidative stress [ ].

Another harmful effect of persistent hyperglycemia is the advanced glycation end products AGEs generation, which are non-enzymatic glycation products of proteins and lipids as a result of exposure to sugars [ ]. In general, the AGEs accumulate in the vessel wall, affecting the structural integrity of the extracellular matrix ECM also known as matrix cell interactions.

The latter induces endothelium damage and decreases NO activity. Overall, AGEs contributes to the progression of diabetic complications such as retinopathy, nephropathy and CVD [ ].

The thickest layer of the heart wall is the myocardium, composed of cardiac muscle cells, thus, the knowledge provided by skeletal muscle cell physiology helps explain the cardiac metabolic function [ ].

The mammalian heart must contract incessantly; which means the energy requirement for an optimal function is immense and this is an interesting phenomenon because there is no ATP reserve in heart muscle.

Instead, energy is stored in cardiac muscle cells in three forms:. The first is Phosphocreatine PCr , which can rapidly donate its high-energy phosphates to produce ATP from ADP [ ]. The energy available from PCr is relatively modest, used only during very rapid bursts of exercise [ ].

The second is glycogen, which forms the endogenous form of energy in the cell. However, its advantage is that it consumes much less oxygen compared to fatty acids and is readily available for use as fuel in muscle [ ]. The third form is triglycerides and FFA. Their oxidation is less efficient compared to glycogen, though it has greater energy input.

It is widely accepted that FFAs are the predominant substrates used in the adult myocardium for ATP production in the mitochondrion [ ]. The levels of circulating FFAs determines largely FFA uptake in the heart [ ]. Once the FFA is absorbed, its metabolism is regulated predominantly at the transcriptional level by a family of ligand-activated transcriptional factors namely peroxisome proliferator activator receptor α PPAR-α [ ].

Depending on their availability or energy requirement feeding, fasting, and intense exercise , the cardiac metabolic network is highly flexible in using other substrates [ ]. Glucose uptake is mediated via glucose transporters. GLUT1 and GLUT4 are the major players for glucose transport in the heart.

GLUT4 represents the major mechanism that regulates glucose entry in the beating heart, with GLUT1 playing a lesser role as it is primarily localized on plasma membranes and is responsible for basal cardiac glucose uptake. GLUT4 is mostly present in the intracellular vesicles at resting stages and is translocated to the plasma membrane upon insulin stimulation [ ].

After uptake, free glucose is rapidly phosphorylated to glucose 6-phosphate G6P , which subsequently enters many metabolic pathways [ 13 ]. Glycolysis represents the major pathway in glucose and yields pyruvate for subsequent oxidation.

Beside glycolysis, G6P also may be channeled into glycogen synthesis or the pentose phosphate pathway PPP. The PPP is an important source of NADPH, which plays a critical role in regulating cellular oxidative stress and is required for lipid synthesis [ ].

In response to an increased energy demand, heart muscle cells initially rely on carbohydrate oxidation. For example, under stress such as exercise, ischemia and pathological hypertrophy, the substrate preference of glucose can be changed [ ].

Under stress, a rapid increase in GLUT4 expression is an early adaptive response that suggests the physiological role of this adaptation is to enhance the replenishment of muscle glycogen stores. When glycogen content is high, the heart preferentially uses glycogen as a source, but when glycogen stores are low, it changes to fatty acid oxidation.

This induction can be prevented by a high carbohydrate diet during recovery. The control of metabolism in recovery by glycogen levels underlines its importance as the metabolic muscles reserve [ ].

In insulin resistance, the heart is embedded in a rich fatty acid and glucose environment [ , , ]. An excess of insulin promotes increased uptake of FFA in the heart due to up regulation of the cluster differentiation protein 36 CD36 [ ], which is a potent FFA transporter; this increases intracellular fatty acids levels and PPAR-α expression.

The latter, increases the gene expression in the three stages of fatty acid oxidation by increasing the synthesis of 1 FFA transporters in the cell, 2 proteins that imports FFA to the mitochondrium, and 3 enzymes in the fatty acid oxidation [ ].

On the other hand, due to the inhibition of glucose utilization, a glycolytic intermediate accumulates in the cardiomyocytes, which induces glucotoxicity. Furthermore, when diabetes progresses or when additional stresses are posed on the heart; metabolic mal-adaptation can occur and there is a great loss of metabolic flexibility [ ].

The heart decreases its ability to use fatty acids, increasing FFA delivery, and leading to intramyocardial lipid accumulation ceramides, diacylglycerols, long-chain acyl-CoAs, and acylcarnitines [ ].

This lipid accumulation may contribute to apoptosis, impairing mitochondrial function, cardiac hypertrophy, and contractile dysfunction [ , ] Fig. For example, diacylglycerol and fatty acyl-coenzyme CoA induce activation of atypical PKC, which results in impaired insulin signal transduction [ ].

Ceramides act as key components of lipotoxic signaling pathways linking lipid-induced inflammation with insulin signaling inhibition [ ]. On other hand, high lipid contents can induce contractile dysfunction independently of insulin resistance [ ].

Therefore, the resultant defect in myocardial energy production impairs myocyte contraction and diastolic function [ 93 , ] Fig. These alterations produce functional changes that lead to cardiomyopathy and heart failure [ , , , ].

In uncontrolled diabetes, the body goes from the fed to the fasted state and the liver switches from carbohydrate or lipid utilization to ketone production in response to low insulin levels and high levels of counter-regulatory hormones [ ].

The ketone bodies generated in the liver enter in the blood stream and are used by other organs, such as the brain, kidneys, skeletal muscle, and heart.

Disruptions in myocardial fuel metabolism and bioenergetics contribute to cardiovascular disease as the adult heart requires high energy for contractile function [ ].

In this situation, the heart uses alternative pathways such as ketone bodies as fuel for oxidative ATP production [ ]. However, there is still controversy around whether this fuel shift is adaptive or maladaptive. The ketogenic diet effect can be mediated by suppressing longevity-related insulin signaling and mTOR pathway, and activation of peroxisome proliferator activated receptor α PPARα , the master regulator that switches on genes involved in ketogenesis [ ].

Several reports suggest that ketogenic diet may be associated with a decreased incidence of risk factors of cardiovascular disease such obesity, diabetes, arterial blood pressure and cholesterol levels, but these effects are usually limited in time [ ].

However other reports indicated that cardiac risk factor reductions corresponded with weight loss regardless of a type of diet used [ ].

Excessive production of ROS leads to protein, DNA, and membrane damage. In addition, ROS exerts deleterious effects on the endoplasmic reticulum.

This also contributes to diabetic cardiomyopathy pathogenesis [ , ]. Insulin essentially provides an integrated set of signals allowing the balance between nutrient demand and availability. Impaired nutrition contributes to hyperlipidemia and insulin resistance causing hyperglycemia.

This condition alters cellular metabolism and intracellular signaling that negatively impact cells. In the cardiomyocyte, this damage can be summarized into three actions: 1 alteration in insulin signaling.

All these effects induce cellular events including: 1 gene expression modifications, 2 hyperglycemia and dyslipidemia, 3 activation of oxidative stress and inflammatory response, 4 endothelial dysfunction, and 5 ectopic lipid accumulation, which, favored by obesity, perpetuates the metabolic deregulation.

Overall, insulin resistance contributes to generate CVD via two independent pathways: 1 atheroma plaque formation and 2 ventricular hypertrophy and diastolic abnormality. Both effects lead to heart failure. Future research is needed to understand the precise mechanism between insulin resistance and its progression to heart failure with a focus on new therapy development.

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Simon W. CoppackMetabilism M. FisherSandy M. HumphreysMo L. ClarkJennifer J. PointonKeith N. When Cagbohydrate eat a food containing carbohydrates, the rdsistance system breaks down the digestible ones Promote metabolic wellness sugar, which enters insu,in blood. These carbohydrates resisrance composed resistancce sugars redistance as fructose Carbohydrate metabolism and insulin resistance resostance which have simple chemical structures composed of Herbal antidepressant supplement one sugar monosaccharides or two sugars disaccharides. Simple carbohydrates are easily and quickly utilized for energy by the body Carbohydrate metabolism and insulin resistance of their simple chemical structure, often leading to a faster rise in blood sugar and insulin secretion from the pancreas — which can have negative health effects. These carbohydrates have more complex chemical structures, with three or more sugars linked together known as oligosaccharides and polysaccharides. Many complex carbohydrate foods contain fiber, vitamins and minerals, and they take longer to digest — which means they have less of an immediate impact on blood sugar, causing it to rise more slowly. But other so called complex carbohydrate foods such as white bread and white potatoes contain mostly starch but little fiber or other beneficial nutrients. Dividing carbohydrates into simple and complex, however, does not account for the effect of carbohydrates on blood sugar and chronic diseases.

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