6 resultados para oral fat tolerance test

em Université de Lausanne, Switzerland


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Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretins secreted in response to oral glucose ingestion by intestinal L and K cells, respectively. The molecular mechanisms responsible for intestinal cell glucose sensing are unknown but could be related to those described for beta-cells, brain and hepatoportal sensors. We determined the role of GLUT2, GLP-1 or GIP receptors in glucose-induced incretins secretion, in the corresponding knockout mice. GLP-1 secretion was reduced in all mutant mice, while GIP secretion did not require GLUT2. Intestinal GLP-1 content was reduced only in GIP and GLUT2 receptors knockout mice suggesting that this impairment could contribute to the phenotype. Intestinal GIP content was similar in all mice studied. Furthermore, the impaired incretins secretion was associated with a reduced glucose-stimulated insulin secretion and an impaired glucose tolerance in all mice. In conclusion, both incretins secretion depends on mechanisms involving their own receptors and GLP-1 further requires GLUT2.

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Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10(-3), n = 22,044), increased triglycerides (p = 2.6×10(-14), n = 93,440), increased waist-to-hip ratio (p = 1.8×10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10(-13), n = 96,748) and decreased BMI (p = 1.4×10(-4), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.

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PURPOSE: The purpose of this study was to develop a mathematical model (sine model, SIN) to describe fat oxidation kinetics as a function of the relative exercise intensity [% of maximal oxygen uptake (%VO2max)] during graded exercise and to determine the exercise intensity (Fatmax) that elicits maximal fat oxidation (MFO) and the intensity at which the fat oxidation becomes negligible (Fatmin). This model included three independent variables (dilatation, symmetry, and translation) that incorporated primary expected modulations of the curve because of training level or body composition. METHODS: Thirty-two healthy volunteers (17 women and 15 men) performed a graded exercise test on a cycle ergometer, with 3-min stages and 20-W increments. Substrate oxidation rates were determined using indirect calorimetry. SIN was compared with measured values (MV) and with other methods currently used [i.e., the RER method (MRER) and third polynomial curves (P3)]. RESULTS: There was no significant difference in the fitting accuracy between SIN and P3 (P = 0.157), whereas MRER was less precise than SIN (P < 0.001). Fatmax (44 +/- 10% VO2max) and MFO (0.37 +/- 0.16 g x min(-1)) determined using SIN were significantly correlated with MV, P3, and MRER (P < 0.001). The variable of dilatation was correlated with Fatmax, Fatmin, and MFO (r = 0.79, r = 0.67, and r = 0.60, respectively, P < 0.001). CONCLUSIONS: The SIN model presents the same precision as other methods currently used in the determination of Fatmax and MFO but in addition allows calculation of Fatmin. Moreover, the three independent variables are directly related to the main expected modulations of the fat oxidation curve. SIN, therefore, seems to be an appropriate tool in analyzing fat oxidation kinetics obtained during graded exercise.

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The plasma glucose excursion may influence the metabolic responses after oral glucose ingestion. Although previous studies addressed the effects of hyperglycemia in conditions of hyperinsulinemia, it has not been evaluated whether the route of glucose administration (oral vs. intravenous) plays a role. Our aim was to determine the effects of moderately controlled hyperglycemia on glucose metabolism before and after oral glucose ingestion. Eight normal men underwent two oral glucose clamps at 6 and 10 mmol/l plasma glucose. Glucose turnover and cycling rates were measured by infusion of [2H7]glucose. The oral glucose load was labeled by D-[6,6-2H2]glucose to monitor exogenous glucose appearance, and respiratory exchanges were measured by indirect calorimetry. Sixty percent of the oral glucose load appeared in the systemic circulation during both the 6 and 10 mmol/l plasma glucose tests, although less endogenous glucose appeared during the 10 mmol/l tests before glucose ingestion (P &lt; 0.05). This inhibitory effect of hyperglycemia was not detectable after oral glucose ingestion, although glucose utilization was increased (+28%, P &lt; 0.05) due to increased nonoxidative glucose disposal [10 vs. 6 mmol/l: +20%, not significant (NS) before oral glucose ingestion; +40%, P &lt; 0.05 after oral glucose ingestion]. Glucose cycling rates were increased by hyperglycemia (+13% before oral glucose ingestion, P &lt; 0.001; +31% after oral glucose ingestion, P &lt; 0.05) and oral glucose ingestion during both the 6 (+10%, P &lt; 0.05) and 10 mmol/l (+26%, P &lt; 0.005) tests. A moderate hyperglycemia inhibits endogenous glucose production and contributes to glucose tolerance by enhancing nonoxidative glucose disposal. Hyperglycemia and oral glucose ingestion both stimulate glucose cycling.

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Body mass index (BMI) is related with cardiorespiratory fitness (CRF), but less is known regarding the combined relationships between BMI and body fat (BF) on CRF. Cross-sectional study included 2361 girls and 2328 boys aged 10–18 years living in the area of Lisbon, Portugal. BMI was calculated by measuring height and weight, and obesity was assessed by international criteria. BF was assessed by bioimpedance. CRF was assessed by the 20-m shuttle run and the participants were classified as normal-to-high or low-CRF level according to Fitness gram criterion-referenced standards. The prevalence of low CRF was 47 and 39% in girls and boys, respectively. The corresponding values for the prevalence of obesity were 4.8 and 5.6% (not significant) and of excess BF of 12.1 and 25.1% (P <0.001), respectively. In both sexes, BMI and BF were inversely related with CRF: r = – 0.53 and – 0.45 for BMI and % BF, respectively, in boys and the corresponding values in girls were – 0.50 and – 0.33 (all P <0.01). When compared with a participant with normal BMI and BF, the odds ratios (95% confidence interval) for low CRF were 1.94 (1.46–2.58) for a participant with normal BMI and high BF, and 6.19 (5.02–7.63) for a participant with high BMI and high BF. The prevalence of low-CRF levels is high in Portuguese youths. BF negatively influences CRF levels among children/adolescents with normal BMI.

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To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.