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Role of insulin in carbohydrate metabolism and glycogen storage diseaseCarbohydrate metabolism and insulin sensitivity -
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HOMA-IR index was registered 1. The WC was lower by 1. Conclusion: Insufficient compensation of diabetes is noted in patients with LADA1 and LADA2 without differences in intergroup comparison, however the degree of insulin resistance and abdominal obesity as main components of metabolic syndrome prevalence in patients with LADA2 which indicates the higher cardiovascular risk in this category of patients.
Istanbul, Turkey 13 May - 16 May Endocrine Abstracts ISSN print ISSN online © Bioscientifica Privacy policy Cookie settings. Bioscientifica Abstracts is the gateway to a series of products that provide a permanent, citable record of abstracts for biomedical and life science conferences.
Searchable abstracts of presentations at key conferences in endocrinology. ISSN print ISSN online. Endocrine Abstracts. Prev Next. Obese individuals with and without type 2 diabetes show different gut microbial functional capacity and composition.
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Cell Host Microbe 29 , — Download references. We thank E. Miyauchi, T. Kanaya and T. Kato for advice; A. Ito, N. Tachibana, A. Hori and the staff at the RIKEN Yokohama animal facility for technical support; H.
Koseki, M. Furuno and H. Iwano for data discussion; and the staff at the RIKEN BioResource Research Center for providing essential materials. Kubota, 21K to H.
and 22H to H. and M. Kubota and the RIKEN Junior Research Associate Program to T. Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.
Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Division of Diabetes and Metabolism, The Institute for Medical Science Asahi Life Foundation, Tokyo, Japan.
Department of Clinical Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition NIBIOHN , Tokyo, Japan. Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science CSRS , Yokohama, Japan.
Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan. Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan.
Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.
Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Department of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Japan. Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.
Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. Institute for Advanced Biosciences, Keio University, Fujisawa, Japan. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.
Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan. Center for Epidemiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan. International University of Health and Welfare, Tokyo, Japan.
Department of Metabolism and Endocrinology, Tokyo Medical University Ibaraki Medical Center, Ami Town, Japan. Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan.
Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan. Human Biology-Microbiome-Quantum Research Center WPI-Bio2Q , Keio University, Tokyo, Japan.
Laboratory for Immune Cell Systems, RIKEN Center for Integrative Medical Sciences IMS , Yokohama, Japan. You can also search for this author in PubMed Google Scholar. Kadowaki and H. conceived the project. Kubota, Y. Mizuno, N. and T. Kadowaki contributed to the enrolment of study participants and clinical data collection.
and Y. processed faecal samples for metagenomics and metabolomic analyses. performed 16S rRNA gene sequencing and metagenomic analysis. performed metabolomic analyses for hydrophilic metabolites.
performed lipidomics analyses. and P. performed CAGE analysis. and O. performed cytokine measurement and RNA extraction from PBMCs. Mochizuki prepared fundamental information tools for the analysis. Kubota and S. performed animal experiments and analysed the data.
Kitami and K. analysed the omics data. Kubota, P. and H. provided essential materials and raised funding. Kubota and H. wrote the paper together with A. Kitami and P. Correspondence to Tetsuya Kubota or Hiroshi Ohno.
are listed as the inventors on a patent regarding the metabolic effects of gut bacteria identified by a human cohort. The other authors declare no competing interests. Nature thanks Gregory Steinberg and the other, anonymous, reviewer s for their contribution to the peer review of this work.
Insulin resistance IR and metabolic syndrome MetS were the main clinical phenotypes. To evaluate the host-microbe relationship, we collected 1 host factors: clinical, plasma metabolome, peripheral blood mononuclear cells PBMC transcriptome, and cytokine data, and 2 microbial factors: 16S rRNA pyrosequencing, shotgun metagenome, and faecal metabolome.
The numbers of elements after quality filtering are shown for each data set. b , The multi-omics analysis workflow. To identify the microbes that affect metabolic phenotypes, we first analysed the phenotype-associated metabolomic signatures by binning metabolites into co-abundance groups CAGs.
Microbial signatures were determined using the 16S and metagenomic datasets, and their associations with metabolites were analysed. We also assessed the mediation effects of plasma cytokines on the relationships between faecal metabolites and metabolic markers.
The associations between clinical phenotypes and omics markers were adjusted by age and sex wherever appropriate. a , The KEGG pathway enrichment analysis of the metabolites in hydrophilic CAGs 5, 8, 12, 15, and 18, which were associated with IR in Fig.
The size of disks shows the enrichment i. b , Partial correlations between HOMA-IR and faecal levels of short-chain fatty acids SCFA such as acetate, propionate, and butyrate left panel , and disaccharides such as maltose and sucrose right panel. Density plots indicate median and distribution.
The detailed statistics are reported in Supplementary Table 5 , 6. The size and colour of the disks represent the estimate and the direction of the associations. c , The associations between faecal glucose and arabinose and HOMA-IR as analysed in Fig. The estimates of metabolites and their P values are described.
The data were analysed with a generalized linear mixed-effect model with consent age and sex as fixed effects, and the sample collection site as a random effect. The estimate and P value are described. The first faecal sampling for metabolomics was used to avoid redundancy.
The detailed statistics are reported in Supplementary Table 9. Dots represent individual data summarized into PCo1 and PCo2. Dots represent individual data summarized into PC1 and PC2.
f , Co-abundance groups of genus-level microbes and their abundance in the participant clusters defined in Fig. The disk size represents the median abundance in the participants.
g , The co-abundance groups of genus-level microbes and their abundance in the participant clusters. The size of the disks represents overabundance to the mean in four clusters of participants determined in Fig.
The far-left column shows the genera that exhibit significant differences among the four clusters. The genera forming distinct groups in f , i. The participants were clustered into three mOTU clusters A to C based on the heatmap clustering. The proportion of individuals with IS, intermediate, and IR are shown in the pie charts above the heatmap as Fig.
Only those with significant associations with metabolic markers are depicted. The disk size and colour represent absolute values of standardized coefficient and the direction of associations.
The detailed statistics are reported in Supplementary Table j , Microbe-metabolite networks of IR- or and IS-associated co-abundance microbial groups from Fig. All faecal hydrophilic metabolites and faecal microbe-related lipid metabolites were included in the analysis.
The metabolites in CAGs relating to carbohydrates shown in Fig. k , The relative abundance of IR-associated faecal carbohydrates in the participant clusters. The metabolites significantly different among these four clusters are coloured grey in the top row.
a , b , Box plots indicate the median, upper and lower quartiles, and upper and lower extremes except for outliers. Kruskal-Wallis test g , k. See the Source Data g for exact P values.
a , b , The associations between the KEGG pathways relating to amino acid metabolism a and lipid metabolism b , faecal carbohydrates, top three genera positively or negatively correlated with faecal carbohydrates in Fig.
c , The associations between representative metabolic markers and the KEGG pathways relating to carbohydrate metabolism, amino acid metabolism, lipid metabolism, and membrane transport defined in the KEGG orthology database. The pathways with significant associations with metabolic markers are included in the plots.
The far-left column shows the type of carbohydrate metabolites that each PTS gene is involved in. The far-left column shows whether the genes were predicted to function as extracellular enzymes.
g , Representative pathways in starch and sucrose metabolism KEGG pathway relating to glycosidase activities to degrade poly- and oligosaccharides into monosaccharides. i , The presence and absence of KEGG orthologues predicted to function as extracellular enzymes in 45 strains.
The strains from the top three genera positively or negatively correlated with faecal carbohydrates shown in Fig. Density plots indicate median and distribution e , h. a , Cell-type gene set enrichment analysis based on the Human Gene Atlas database using Enrichr.
Red and blue colour scales represent IR and IS-associated cell types, respectively please refer to Methods for details. b , The cross-omics network shown in Fig. c , The number of correlations between faecal carbohydrates and other omics elements shown in Fig.
The proportion to all possible correlations is shown. d , Representative causal mediation models analysing the effects of IL and adiponectin mediating in silico relationships between faecal carbohydrates and HOMA-IR.
Causal mediation analysis with multiple test corrections were used to test significance. Estimates β and P adj values of average causal mediation effects ACME , which are the indirect effects between the metabolites and host markers mediated by cytokines, and average direct effects ADE , which are the direct effects controlling for cytokines, are described.
Age and sex were adjusted in the models. The detailed information is reported in Supplementary Table a , b , PCA plots of metabolites in cell-free supernatants of 22 bacterial strains listed in a.
These strains were selected based on the findings from the genus-level co-occurrence Fig.
Metabolusm topic will review the changes in carbohydrate and insulin metabolism that occur in CKD and the clinical implications of Sensitivit abnormalities Liver detox after chemotherapy Citrus oil as natural insect repellent without diabetes. Mdtabolism impact metaboliem these changes on the management of hyperglycemia in patients with diabetes and metabo,ism kidney disease insylin Citrus oil as natural insect repellent separately. See "Management of hyperglycemia in patients with type 2 diabetes and advanced chronic kidney disease or end-stage kidney disease". NORMAL RENAL HANDLING OF INSULIN. From this rate of renal clearance, it can be calculated that 6 to 8 units of insulin are degraded by the kidney each day, which accounts for approximately 25 percent of the daily production of insulin by the pancreas. The contribution of kidney metabolism is enhanced in diabetic subjects receiving exogenous insulin since injected insulin enters the systemic circulation directly, without first passing through the liver. Why UpToDate? Jun Yoshino, Paloma Almeda-Valdes, Bruce W. Patterson, Metanolism L. The mechanism s responsible Citrus oil as natural insect repellent diurnal variations in insulin sensitivlty of glucose metabolism ajd healthy people are unclear. The objective of Carbohydrahe study was Carbohydrate metabolism and insulin sensitivity Enhance blood circulation whether diurnal variations in whole-body and cellular fatty acid metabolism could contribute to evening insulin resistance in metabolically normal people. We measured plasma the free fatty acid FFA concentration, palmitate kinetics, and skeletal muscle expression of genes involved in fatty acid metabolism at breakfast am and dinner pm in 13 overweight body mass index However, adipose tissue lipolytic activity was not different in the evening and in the morning.Carbohydrate metabolism and insulin sensitivity -
Metabolically normal women demonstrate diurnal variations in fatty acid metabolism, manifested by an increase in circulating FFAs, presumably derived from previous meal consumption rather than lipolysis of adipose tissue triglycerides, and a shift in muscle fatty acid metabolism from oxidation to lipogenesis.
These metabolic alterations could be responsible for the known evening decline in insulin sensitivity. Many metabolic pathways and functions vary according to the time of day 1. In healthy people, insulin sensitivity with respect to glucose metabolism is lower in the evening than in the morning 2 — 4.
Consequently, glucose or meal ingestion results in a greater increase in plasma glucose concentration in the evening than in the morning 3 — 5. The mechanism s responsible for diurnal variation in insulin action is not known but could be related to alterations in systemic free fatty acid FFA availability and muscle fatty acid metabolism.
Increased FFA availability from plasma can cause insulin resistance in skeletal muscle 6. Furthermore, clock genes, which regulate circadian rhythm, could contribute to diurnal variation in muscle insulin action because they have been shown to regulate insulin sensitivity and fatty acid metabolic pathways in rodent models 1.
The purpose of the present study was to test the hypothesis that diurnal variations in clock gene expression, plasma FFA availability, and muscle fatty acid metabolism are associated with diurnal variation in insulin-mediated glucose metabolism.
Accordingly, we evaluated the effect of consuming identical breakfast and dinner meals on plasma glucose, insulin, and FFA concentrations, adipose tissue lipolytic rate, and diurnal variation in expression of genes associated with adipose tissue lipolytic activity and skeletal muscle fatty acid metabolism in metabolically normal women.
Thirteen women participated in this study Supplemental Table 1. All subjects were considered to be metabolically normal, based on a history and physical examination, an oral glucose tolerance test, and one or fewer metabolic syndrome criteria 7. Written informed consent was obtained before the subjects participated in the study, which was approved by the Institutional Review Board of Washington University School of Medicine.
Total body fat mass and fat-free mass FFM were determined by using dual-energy X-ray absorptiometry. Intraabdominal adipose tissue volume and intrahepatic triglyceride content were quantified by using magnetic resonance imaging and magnetic resonance spectroscopy 8. Resting energy expenditure was determined by measuring the expiratory gas exchange TrueOne ; ParvoMedics , and the total daily energy requirement was calculated as 1.
At pm , catheters were inserted into an antecubital vein for palmitate tracer infusion and a contralateral radial artery for blood sampling. Subjects consumed three identical liquid mixed meals at am breakfast , pm lunch , and pm dinner.
Subjects rested in bed to avoid the influence of physical activity on our outcome measures. Blood samples were obtained 10 minutes and immediately before and at 20, 40, 60, 90, , , , and minutes after starting breakfast and dinner.
Subcutaneous abdominal adipose tissue and skeletal muscle vastus lateralis biopsies were obtained at am and pm , as described previously The second fat biopsy was obtained from the opposite side of the abdomen of the first biopsy in all 13 subjects.
The second muscle biopsy was obtained from the opposite leg of the first biopsy data were available for six subjects. Plasma glucose, insulin, and FFA concentrations and palmitate tracer to tracee ratio were determined as previously described Plasma cortisol was measured by using an immunoassay Elecsys; Roche Diagnostics GmbH.
Gene expression was determined by using real-time PCR primer sequences in Supplemental Table 3 based on their cycle threshold CT values relative to glyceraldehydephosphate dehydrogenase GAPDH , as previously described Plasma substrate and hormone concentration total areas under the curve AUCs and incremental AUC iAUC from baseline values before and for 4 hours after breakfast and dinner were calculated by using the trapezoid method.
The palmitate rate of appearance Ra of total FFA Ra in plasma was calculated as previously described The differences between single values obtained at breakfast and dinner were evaluated by using the paired Student's t test. Repeated-measures ANOVA was used to compare changes in substrate kinetics and concentrations induced by breakfast and dinner.
Results are presented as means ± SD, unless otherwise stated. Plasma glucose iAUC Figure 1 A and total AUC These findings suggest that insulin sensitivity with respect to glucose metabolism was lower in the evening than in the morning, which is consistent with results from previous studies conducted in healthy people 2 — 4.
Plasma glucose iAUC after breakfast am and dinner pm A. Plasma FFA concentration after breakfast and dinner B , the percentage of total plasma FFA as C palmitate and C oleate right before breakfast and dinner C , the FFA Ra D , and palmitate Ra E after breakfast and dinner, and adipose tissue gene expression of ATGL and HSL at am before breakfast and pm before dinner F are shown.
Expression of ATGL and HSL was normalized to GAPDH expression. Data are means ± SEM. The contribution of C palmitate to total FFA concentration decreased from Meal ingestion rapidly reduced plasma FFA concentration by approximately fold after both breakfast and dinner, but plasma FFA concentrations Figure 1 B and the FFA AUC 0.
Adipose tissue triglyceride lipase ATGL was the same, and hormone sensitive lipase HSL was slightly lower at pm before dinner than at am before breakfast Figure 1 F. Expression of genes involved in muscle fatty acid oxidation [pyruvate dehydrogenase kinase 4 PDK4 , uncoupling protein-3 UCP3 , and carnitine palmitoyltransferase 1A CPT1A ] were lower at pm than at am Figure 2 A.
In contrast, the expressions of genes involved in de novo lipogenesis [sterol regulatory element binding protein-1c SREBP-1c and fatty acid synthase FAS ] were greater at pm than at am Figure 2 B.
We also found diurnal variations in the core clock genes circadian locomotor output cycles kaput CLOCK , brain, and muscle Arnt-like protein 1 BMAL1 , period 1 and 2 PER1 and PER2 , cryptochrome 1 CRY1 , and D site of albumin promoter albumin D-box binding protein DBP in skeletal muscle Figure 2 C.
CRY2 expression in the morning was not different from that in the evening data not shown. The expression of genes of interest was normalized to GAPDH expression. Although insulin sensitivity and glucose tolerance are often worse in the evening than in the morning in healthy people 2 — 5 , the mechanism s responsible for diurnal variation in glucose homeostasis are not clear.
We investigated whether there are diurnal variations in fatty acid metabolism that could contribute to this phenomenon in metabolically normal women and found that insulin resistance with respect to glucose metabolism in the evening was accompanied by increased plasma FFA availability.
The increased FFA availability was likely derived from the hydrolysis of chylomicron triglycerides from previous meals, not from an increase in adipose tissue lipolytic activity, because palmitate Ra and adipose tissue gene expression of lipolytic enzymes were the same or lower in the evening than in the morning.
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.
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As blood sugar levels rise, the pancreas produces insulin, a hormone that prompts cells to absorb blood sugar for energy or storage. As cells absorb blood sugar, levels in the bloodstream begin to fall. When this happens, the pancreas start making glucagon, a hormone that signals the liver to start releasing stored sugar.
This interplay of insulin and glucagon ensure that cells throughout the body, and especially in the brain, have a steady supply of blood sugar. Type 2 diabetes usually develops gradually over a number of years, beginning when muscle and other cells stop responding to insulin.
This condition, known as insulin resistance, causes blood sugar and insulin levels to stay high long after eating. Over time, the heavy demands made on the insulin-making cells wears them out, and insulin production eventually stops.
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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Nicola M. SensitkvityJames B. MeigsMetanolism LiuEdward Carbohydrate metabolism and insulin sensitivityPeter W. WilsonPaul F. Jacques; Carbohydrate Nutrition, Insulin Resistance, and the Prevalence of the Metabolic Syndrome in the Framingham Offspring Cohort.
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