205 resultados para metabolic profiling


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OBJECTIVE—To assess change in health-related quality of life (HRQOL) in children with diabetes over 2 years and determine its relationship to change in metabolic control.

RESEARCH DESIGN AND METHODS—In 1998, parents of children aged 5–18 years attending a tertiary diabetes clinic reported their child’s HRQOL using the Child Health Questionnaire PF-50. Those aged 12–18 years also self-reported their HRQOL using the analogous Child Health Questionnaire CF-80. HbA1c levels were recorded. In 2000, identical measures were collected for those who were aged ≤18 years and still attending the clinic.

RESULTS
—Of 117 eligible subjects, 83 (71%) participated. Parents reported no significant difference in children’s HRQOL at baseline and follow-up. However, adolescents reported significant improvements on the Family Activities (P < 0.001), Bodily Pain (P = 0.04), and General Health Perceptions (P = 0.001) scales and worsening on the Behavior (P = 0.04) scale. HbA1c at baseline and follow-up were strongly correlated (r = 0.57). HbA1c increased significantly (mean 7.8% in 1998 vs. 8.5% in 2000; P < 0.001), with lower baseline HbA1c strongly predicting an increase in HbA1c over the 2 years (r2 = 0.25, P < 0.001). Lower parent-reported Physical Summary and adolescent-reported Physical Functioning scores at baseline also predicted increasing HbA1c. Poorer parent-reported Psychosocial Summary scores were related to higher HbA1c at both times but did not predict change in HbA1c.

CONCLUSIONS—Changes in parent and adolescent reports of HRQOL differ. Better physical functioning may protect against deteriorating HbA1c, at least in the medium term. While the HRQOL of children with diabetes does not appear to deteriorate over time, we should not be complacent, as it is consistently poorer than that of their healthy peers.


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Endurance exercise improves insulin sensitivity and increases fat oxidation, which are partly facilitated by the induction of metabolic transcription factors. Next to exercise, increased levels of FFA's also increase the gene expression of transcription factors, hence making it difficult to discern the effects from contractile signals produced during exercise, from those produced by increased circulatory FFA's. We aimed to investigate, in human skeletal muscle, whether acute exercise affects gene expression of metabolic transcriptional co-activators and transcription factors, including PGC-1α, PRC, PPARα, β/δ, and γ and RXR, SREBP-1c and FKHR, and to discern the effect of exercise per se from those of elevated levels of FFA. Two hours of endurance exercise was performed either in the fasted state, or following carbohydrate ingestion prior to and during exercise, thereby blunting the fasting-induced increase in FA availability and oxidation. Of the genes measured, PGC-1α and PRC mRNA increased immediately after, while PPARβ/δ and FKHR mRNA increased 1–4 h after exercise, irrespective of the increases in FFA's. Our results suggest that the induction in vivo of metabolic transcription factors implicated in mitochondrial biogenesis are under the control of inherent signals, (PGC-1α, PRC), while those implicated in substrate selection are under the control of associated signals (PPARβ/δ, FKHR) stimulated from the contracting skeletal muscle that are independent of circulating FFA levels.

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Soluble protein hormones are key regulators of a number of metabolic processes, including food intake and insulin sensitivity. We have used a signal sequence trap to identify genes that encode secreted or membrane-bound proteins in Psammomys obesus, an animal model of obesity and type 2 diabetes (T2D). Using this signal sequence trap, we identified the chemokine chemerin as being a novel adipokine. Gene expression of chemerin and its receptor, chemokine-like receptor 1 (CMKLR1), was significantly higher in adipose tissue of obese and type 2 diabetic P. obesus compared with lean, normoglycemic P. obesus. Fractionation of P. obesus adipose tissue confirmed that chemerin was predominantly expressed in adipocytes, whereas CMKLR1 was expressed in both adipocytes and stromal-vascular cells of adipose tissue. In 3T3-L1 adipocytes, chemerin was markedly induced during differentiation, whereas CMKLR1 was down-regulated during differentiation. Serum chemerin levels were measured by ELISA in human plasma samples from 114 subjects with T2D and 142 normal glucose tolerant controls. Plasma chemerin levels were not significantly different between subjects with T2D and normal controls. However, in normal glucose tolerant subjects, plasma chemerin levels were significantly associated with body mass index, circulating triglycerides, and blood pressure. Here we report, for the first time, that chemerin is an adipokine, and circulating levels of chemerin are associated with several key aspects of metabolic syndrome.

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To determine whether preexercise muscle glycogen content influences the transcription of several early-response genes involved in the regulation of muscle growth, seven male strength-trained subjects performed one-legged cycling exercise to exhaustion to lower muscle glycogen levels (Low) in one leg compared with the leg with normal muscle glycogen (Norm) and then the following day completed a unilateral bout of resistance training (RT). Muscle biopsies from both legs were taken at rest, immediately after RT, and after 3 h of recovery. Resting glycogen content was higher in the control leg (Norm leg) than in the Low leg (435 ± 87 vs. 193 ± 29 mmol/kg dry wt; P < 0.01). RT decreased glycogen content in both legs (P < 0.05), but postexercise values remained significantly higher in the Norm than the Low leg (312 ± 129 vs. 102 ± 34 mmol/kg dry wt; P < 0.01). GLUT4 (3-fold; P < 0.01) and glycogenin mRNA abundance (2.5-fold; not significant) were elevated at rest in the Norm leg, but such differences were abolished after exercise. Preexercise mRNA abundance of atrogenes was also higher in the Norm compared with the Low leg [atrogin: 14-fold, P < 0.01; RING (really interesting novel gene) finger: 3-fold, P < 0.05] but decreased for atrogin in Norm following RT (P < 0.05). There were no differences in the mRNA abundance of myogenic regulatory factors and IGF-I in the Norm compared with the Low leg. Our results demonstrate that 1) low muscle glycogen content has variable effects on the basal transcription of select metabolic and myogenic genes at rest, and 2) any differences in basal transcription are completely abolished after a single bout of heavy resistance training. We conclude that commencing resistance exercise with low muscle glycogen does not enhance the activity of genes implicated in promoting hypertrophy.

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OBJECTIVE: To measure the prevalence of overweight, obesity and the metabolic syndrome (MetS) in rural Australia.

DESIGN, SETTING AND PARTICIPANTS: Cross-sectional surveys were conducted in two rural areas in Victoria and South Australia in 2004-2005. A stratified random sample of men and women aged 25-74 years was selected from the electoral roll. Data were collected by a self-administered questionnaire, physical measurements and laboratory tests.

MAIN OUTCOME MEASURES: Prevalence of overweight and obesity, as defined by body mass index (BMI) and waist circumference; prevalence of MetS and its components.

RESULTS: Data on 806 participants (383 men and 423 women) were analysed. Based on BMI, the prevalence of overweight and obesity combined was 74.1% (95% CI, 69.7%-78.5%) in men and 64.1% (95% CI, 59.5%-68.7%) in women. Based on waist circumference, the prevalence of overweight and obesity was higher in women (72.4%; 95% CI, 68.1%-76.7%) than men (61.9%; 95% CI, 57.0%-66.8%). The overall prevalence of obesity was 30.0% (95% CI, 26.8%-33.2%) based on BMI (> or = 30.0 kg/m(2)) and 44.7% (95% CI, 41.2%-48.1%) based on waist circumference (> or = 102 cm [men] and > or= 88 cm [women]). The prevalence of MetS as defined by the US National Cholesterol Education Program Adult Treatment Panel III 2005 criteria was 27.1% (95% CI, 22.7%-31.6%) in men and 28.3% (95% CI, 24.0%-32.6%) in women; based on International Diabetes Federation criteria, prevalences for men and women were 33.7% (95% CI, 29.0%-38.5%) and 30.1% (95% CI, 25.7%-34.5%), respectively. Prevalences of MetS, central (abdominal) obesity, hyperglycaemia, hypertension and hypertriglyceridaemia increased with age.

CONCLUSIONS: In rural Australia, prevalences of MetS, overweight and obesity are very high. Urgent population-wide action is required to tackle the problem.

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Sixteen female cross-bred (Large White × Landrace) pigs (initial weight 65 kg) with venous catheters were randomly allocated to four treatment groups in a 2×2 factorial design. The respective factors were dietary fat (25 or 100 g/kg) and dietary conjugated linoleic acid (CLA; 0 or 10 g CLA-55/kg). Pigs were fed every 3 h (close to ad libitum digestible energy intake) for 8 d and were bled frequently. Plasma glucose and non-esterified fatty acid (NEFA) responses to insulin and adrenaline challenges were determined on day 8. Plasma concentrations of NEFA were significantly increased (10·5 and 5·4 % for low- and high-fat diets respectively, P=0·015) throughout the experiment, suggesting that there was a possible increase in fat mobilisation. The increase in lipolysis, an indicator of ß-adrenergic stimulated lipolysis, was also evident in the NEFA response to adrenaline. However, the increase in plasma triacylglycerol (11·0 and 7·1 % for low- and high-fat diets respectively, P=0·008) indicated that CLA could have reduced fat accretion via decreased adipose tissue triacylglycerol synthesis from preformed fatty acids, possibly through reduced lipoprotein lipase activity. Plasma glucose, the primary substrate for de novo lipid synthesis, and plasma insulin levels were unaffected by dietary CLA suggesting that de novo lipid synthesis was largely unaffected (P=0·24 and P=0·30 respectively). In addition, the dietary CLA had no effect upon the ability of insulin to stimulate glucose removal.

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The BEACON gene (also known as UBL5) was identified as differentially expressed between lean and obese Psammomys obesus, a polygenic animal model of obesity, type 2 diabetes, and dyslipidemia. The human homologue of BEACON is located on chromosome 19p, a region likely to contain genes affecting metabolic syndrome–related quantitative traits as established by linkage studies. To assess whether the human BEACON gene may be involved in influencing these traits, we exhaustively analyzed the complete gene for genetic variation in 40 unrelated individuals and identified four variants (three novel). The two more common variants were tested for association with a number of quantitative metabolic syndrome–related traits in two large cohorts of unrelated individuals. Significant associations were found between these variants and fat mass (P = 0.026), percentage of fat (P = 0.001), and waist-to-hip ratio (P = 0.031). The same variants were also associated with total cholesterol (P = 0.024), LDL cholesterol (P = 0.019), triglycerides (P = 0.006), and postglucose load insulin levels (P = 0.018). Multivariate analysis of these correlated phenotypes also yielded a highly significant association (P = 0.0004), suggesting that BEACON may influence phenotypic variation in metabolic syndrome–related traits.

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Changes in dietary macronutrient intake alter muscle and blood substrate availability and are important for regulating gene expression. However, few studies have examined the effects of diet manipulation on gene expression in human skeletal muscle. The aim of this study was to quantify the extent to which altering substrate availability impacts on subsequent mRNA abundance of a subset of carbohydrate (CHO)- and fat-related genes. Seven subjects consumed either a low- (LOW; 0.7 g/kg body mass CHO) or high- (HIGH; 10 g/kg body mass CHO) CHO diet for 48 h after performing an exhaustive exercise bout to deplete muscle glycogen stores. After intervention, resting muscle and blood samples were taken. Muscle was analyzed for the gene abundances of GLUT4, glycogenin, pyruvate dehydrogenase kinase-4 (PDK-4), fatty acid translocase (FAT/CD36), carnitine palmitoyltransferase I (CPT I), hormone-sensitive lipase (HSL), β-hydroxyacyl-CoA dehydrogenase (΄β-HAD), and uncoupling binding protein-3 (UCP3), and blood samples for glucose, insulin, and free fatty acid (FFA) concentrations. Glycogen-depleting exercise and HIGH-CHO resulted in a 300% increase in muscle glycogen content (P < 0.001) relative to the LOW-CHO condition. FFA concentrations were twofold higher after LOW- vs. HIGH-CHO (P < 0.05). The exercise-diet manipulation exerted a significant effect on transcription of all carbohydrate-related genes, with an increase in GLUT4 and glycogenin mRNA abundance and a reduction in PDK-4 transcription after HIGH-CHO (all P < 0.05). FAT/CD36 (P < 0.05) and UCP3 (P < 0.01) gene transcriptions were increased following LOW-CHO. We conclude that 1) there was a rapid capacity for a short-term exercise and diet intervention to exert coordinated changes in the mRNA transcription of metabolic related genes, and 2) genes involved in glucose regulation are increased following a high-carbohydrate diet.

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Aims/hypothesis We analysed a sample of Australian adults to determine the strength of associations of TV viewing and participation in physical activity with the metabolic syndrome.

Methods
This population-based cross-sectional study included 6,241 adults aged ge35 years who were free from diagnosed diabetes mellitus and self-reported ischaemic disease and were not taking lipid-lowering or antihypertensive drugs. The metabolic syndrome was defined according to the 1999 World Health Organization criteria. Participants self-reported TV viewing time and physical activity time for the previous week.

Results The adjusted odds ratio of having the metabolic syndrome was 2.07 (95% CI 1.49–2.88) in women and 1.48 (95% CI 0.95–2.31) in men who watched TV for >14 h per week compared with those who watched le7.0 h per week. Compared with those who were less active (<2.5 h per week), the odds ratio for the metabolic syndrome was 0.72 (95% CI 0.58–0.90) in men and 0.53 (95% CI 0.38–0.74) in women who were active (ge2.5 h per week). Longer TV viewing (>14 h per week) was associated with an increased risk of insulin resistance, obesity and dyslipidaemia in both men and women. A total physical activity time of ge2.5 h per week was associated with a reduced prevalence of both insulin resistance and dyslipidaemia in both sexes and reduced prevalence of both obesity and hypertension in women.

Conclusions/interpretation Increased TV viewing time was associated with an increased prevalence of the metabolic syndrome, while physical activity was associated with a reduced prevalence. Population strategies addressing the metabolic syndrome should focus on reducing sedentary behaviours such as TV viewing, as well as increasing physical activity.

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Catch-up growth, a risk factor for later obesity, type 2 diabetes, and cardiovascular diseases, is characterized by hyperinsulinemia and an accelerated rate for recovering fat mass, i.e., catch-up fat. To identify potential mechanisms in the link between hyperinsulinemia and catch-up fat during catch-up growth, we studied the in vivo action of insulin on glucose utilization in skeletal muscle and adipose tissue in a previously described rat model of weight recovery exhibiting catch-up fat caused by suppressed thermogenesis per se. To do this, we used euglycemic-hyperinsulinemic clamps associated with the labeled 2-deoxy-glucose technique. After 1 week of isocaloric refeeding, when body fat, circulating free fatty acids, or intramyocellular lipids in refed animals had not yet exceeded those of controls, insulin-stimulated glucose utilization in refed animals was lower in skeletal muscles (by 20–43%) but higher in white adipose tissues (by two- to threefold). Furthermore, fatty acid synthase activity was higher in adipose tissues from refed animals than from fed controls. These results suggest that suppressed thermogenesis for the purpose of sparing glucose for catch-up fat, via the coordinated induction of skeletal muscle insulin resistance and adipose tissue insulin hyperresponsiveness, might be a central event in the link between catch-up growth, hyperinsulinemia and risks for later metabolic syndrome.

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The original DIGAMI protocol recommended using intravenous insulin to manage myocardial infarction from first presentation followed by subcutaneous insulin for 3 months in patients with diabetes. This paper describes the metabolic and cardiac outcomes and barriers to implementing a protocol designed to match the DIGAMI principles across our emergency and cardiology departments. Patients managed using the revised DIGAMI protocol achieved better blood glucose control and had fewer reinfarcts than those managed without insulin. The major barrier to using the protocol appeared to be staff fear of causing hypoglycaemia.

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Objective. To investigate lipid profiles in Psammomys obesus and relationships between lipid profile and other components of the Metabolic Syndrome.

Methods.
A total number of 49 adults with a wide range of body weight and glucose tolerance were studied in a cross-sectional analysis. Plasma cholesterol distribution profiles were measured by size exclusion lipid chromatography. Blood glucose was measured using an enzymatic glucose analyser, and plasma insulin was determined by radioimmunossay.

Results.
Obese diabetic Psammomys obesus had elevated plasma cholesterol (P=0.003) and triglyceride levels (p>0.001) compared to their lean littermates. The hypercholesterolemia was mainly due to increased circulating levels of VLDL-cholesterol (P=0.003) and LDL-cholesterol (P=0.003) in these animals. Multiple linear regression analyses revealed that body weight was independently associated with plasma cholesterol (P=0.011) and LDL concentration (P=0.009), while plasma insulin was associated with VLDL-cholesterol concentration (P=0.005). All of the variables measured exhibited continuous distributions across a wide range of phenotypes, from a normal rodent lipid profile to profound dyslipidemia.

Conclusions.
These data suggest that the dyslipidemia in obese, diabetic Psammomys obesus is closely associated with other components of the Metabolic Syndrome, including obesity and insulin resistance.