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      Hyperglycemia and a Common Variant of GCKR Are Associated With the Levels of Eight Amino Acids in 9,369 Finnish Men

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          Abstract

          We investigated the association of glycemia and 43 genetic risk variants for hyperglycemia/type 2 diabetes with amino acid levels in the population-based Metabolic Syndrome in Men (METSIM) Study, including 9,369 nondiabetic or newly diagnosed type 2 diabetic Finnish men. Plasma levels of eight amino acids were measured with proton nuclear magnetic resonance spectroscopy. Increasing fasting and 2-h plasma glucose levels were associated with increasing levels of several amino acids and decreasing levels of histidine and glutamine. Alanine, leucine, isoleucine, tyrosine, and glutamine predicted incident type 2 diabetes in a 4.7-year follow-up of the METSIM Study, and their effects were largely mediated by insulin resistance (except for glutamine). We also found significant correlations between insulin sensitivity (Matsuda insulin sensitivity index) and mRNA expression of genes regulating amino acid degradation in 200 subcutaneous adipose tissue samples. Only 1 of 43 risk single nucleotide polymorphisms for type 2 diabetes or hyperglycemia, the glucose-increasing major C allele of rs780094 of GCKR, was significantly associated with decreased levels of alanine and isoleucine and elevated levels of glutamine. In conclusion, the levels of branched-chain, aromatic amino acids and alanine increased and the levels of glutamine and histidine decreased with increasing glycemia, reflecting, at least in part, insulin resistance. Only one single nucleotide polymorphism regulating hyperglycemia was significantly associated with amino acid levels.

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          Most cited references 37

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          Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp.

          Several methods have been proposed to evaluate insulin sensitivity from the data obtained from the oral glucose tolerance test (OGTT). However, the validity of these indices has not been rigorously evaluated by comparing them with the direct measurement of insulin sensitivity obtained with the euglycemic insulin clamp technique. In this study, we compare various insulin sensitivity indices derived from the OGTT with whole-body insulin sensitivity measured by the euglycemic insulin clamp technique. In this study, 153 subjects (66 men and 87 women, aged 18-71 years, BMI 20-65 kg/m2) with varying degrees of glucose tolerance (62 subjects with normal glucose tolerance, 31 subjects with impaired glucose tolerance, and 60 subjects with type 2 diabetes) were studied. After a 10-h overnight fast, all subjects underwent, in random order, a 75-g OGTT and a euglycemic insulin clamp, which was performed with the infusion of [3-3H]glucose. The indices of insulin sensitivity derived from OGTT data and the euglycemic insulin clamp were compared by correlation analysis. The mean plasma glucose concentration divided by the mean plasma insulin concentration during the OGTT displayed no correlation with the rate of whole-body glucose disposal during the euglycemic insulin clamp (r = -0.02, NS). From the OGTT, we developed an index of whole-body insulin sensitivity (10,000/square root of [fasting glucose x fasting insulin] x [mean glucose x mean insulin during OGTT]), which is highly correlated (r = 0.73, P < 0.0001) with the rate of whole-body glucose disposal during the euglycemic insulin clamp. Previous methods used to derive an index of insulin sensitivity from the OGTT have relied on the ratio of plasma glucose to insulin concentration during the OGTT. Our results demonstrate the limitations of such an approach. We have derived a novel estimate of insulin sensitivity that is simple to calculate and provides a reasonable approximation of whole-body insulin sensitivity from the OGTT.
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            Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

            By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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              Relationships Between Circulating Metabolic Intermediates and Insulin Action in Overweight to Obese, Inactive Men and Women

              OBJECTIVE To determine whether circulating metabolic intermediates are related to insulin resistance and β-cell dysfunction in individuals at risk for type 2 diabetes. RESEARCH DESIGN AND METHODS In 73 sedentary, overweight to obese, dyslipidemic individuals, insulin action was derived from a frequently sampled intravenous glucose tolerance test. Plasma concentrations of 75 amino acids, acylcarnitines, free fatty acids, and conventional metabolites were measured with a targeted, mass spectrometry–based platform. Principal components analysis followed by backward stepwise linear regression was used to explore relationships between measures of insulin action and metabolic intermediates. RESULTS The 75 metabolic intermediates clustered into 19 factors comprising biologically related intermediates. A factor containing large neutral amino acids was inversely related to insulin sensitivity (S I) (R 2 = 0.26). A factor containing fatty acids was inversely related to the acute insulin response to glucose (R 2 = 0.12). Both of these factors, age, and a factor containing medium-chain acylcarnitines and glucose were inversely and independently related to the disposition index (DI) (R 2 = 0.39). Sex differences were found for metabolic predictors of S I and DI. CONCLUSIONS In addition to the well-recognized risks for insulin resistance, elevated concentrations of large, neutral amino acids were independently associated with insulin resistance. Fatty acids were inversely related to the pancreatic response to glucose. Both large neutral amino acids and fatty acids were related to an appropriate pancreatic response, suggesting that these metabolic intermediates might play a role in the progression to type 2 diabetes, one by contributing to insulin resistance and the other to pancreatic failure. These intermediates might exert sex-specific effects on insulin action.
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                Author and article information

                Journal
                Diabetes
                Diabetes
                diabetes
                diabetes
                Diabetes
                Diabetes
                American Diabetes Association
                0012-1797
                1939-327X
                July 2012
                15 June 2012
                : 61
                : 7
                : 1895-1902
                Affiliations
                1Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
                2Department of Human Genetics, the Department of Microbiology, Immunology and Molecular Genetics, and the Department of Medicine, University of California, Los Angeles, Los Angeles, California
                3Computational Medicine Research Group, Institute of Clinical Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
                4Nuclear Magnetic Resonance Metabonomics Laboratory, Laboratory of Chemistry, Department of Biosciences, University of Eastern Finland, Kuopio, Finland
                5Departments of Medicine and Clinical Nutrition, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
                6National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
                7Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
                8Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
                9Department of Internal Medicine and Biocenter Oulu, Clinical Research Center, University of Oulu, Oulu, Finland.
                Author notes
                Corresponding author: Markku Laakso, markku.laakso@ 123456kuh.fi .
                Article
                1378
                10.2337/db11-1378
                3379649
                22553379
                © 2012 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

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                Categories
                Genetics/Genomes/Proteomics/Metabolomics

                Endocrinology & Diabetes

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