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Modulation of IKK[beta] with AMPK improves insulin sensitivity in skeletal muscleBikman, Benjamin Thomas. Dohm, G. Lynis. January 2008 (has links)
Thesis (Ph.D.)--East Carolina University, 2008. / Presented to the faculty of the Department of Exercise and Sport Science. Advisor: G. Lynis Dohm. Title from PDF t.p. (viewed Apr. 23, 2010). Includes bibliographical references.
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Muscle morphology and the insulin resistance syndrome : a population-based study of 70 year-old-men in Uppsala /Hedman, Anu, January 1900 (has links)
Diss. (sammanfattning) Uppsala : Univ., 2001. / Härtill 4 uppsatser.
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Hemodynamic and metabolic changes in muscle in relation to insulin action /Mahajan, Hema. January 2005 (has links)
Thesis (Ph.D.)--University of Tasmania, 2005. / Includes 2 articles in back pocket. Includes bibliographical references.
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Effects of perioperative nutrition on insulin action in postoperative metabolism /Soop, Mattias, January 2003 (has links)
Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 4 uppsatser.
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Metabolické účinky chronického podávání metforminu u obézních myší v závislosti na složení vysokotukové diety / Metabolic effects of chronic metformin administration in obese mice depending on the composition of high-fat dietRoubalová, Jana January 2011 (has links)
Obesity leads to many severe metabolic disorders, e.g. dyslipidemia, insulin resistance, ectopic fat accumulation in the liver and skeletal muscles, non-alcoholic fatty liver disease and finally diabetes mellitus type 2. Metformin (1,1-dimethylbiguanide) is the most favored medicament for the treatment and prevention of these disorders. It stimulates cellular glucose uptake and normalizes blood levels of lipid metabolites without triggering insulin secretion. Research on insulin resistance and diabetes is often realized through developing diet- induced obesity in laboratory animals. The aim of this project is to compare metabolic effects of two different high-fat diets named HFD and HSD. The HFD diet consists chiefly of n-6 polyunsaturated fatty acids (corn oil) and starch (100% glucose). The HSD diet contains mainly saturated fatty acids (lard) and sucrose (50% glucose and 50% fructose). I also studied metabolic effects of metformin by adding it continuously to the drinking water given to obese mice fed with the HFD or the HSD diet. Methods: Intraperitoneal glucose tolerance test (IPGTT), blood and tissue levels of lipid metabolites assessment, radio-immunological assessment of blood levels of insulin, assessment of AMPK activity in liver by western blotting. Results: Increased consumption of the...
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Insulin signalling in human adipocytes : mechanisms of insulin resistance in type 2 diabetes /Danielsson, Anna, January 2007 (has links)
Diss. (sammanfattning) Linköping : Linköpings universitet, 2007. / Härtill 4 uppsatser.
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Insulin dynamics in African Americans and European Americans mechanistic aspects, and association with inflammation /Phadke, Radhika P. January 2007 (has links) (PDF)
Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Title from first page of PDF file (viewed June 23, 2008). Includes bibliographical references.
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The relationship between glycemic intake and insulin resistance in older womenO'Sullivan, Therese Anne January 2008 (has links)
Glycemic intake influences the rise in blood glucose concentration following consumption of a carbohydrate containing meal, known as the postprandial glycemic response. The glycemic response is a result of both the type and amount of carbohydrate foods consumed and is commonly measured as the glycemic index (GI) or glycemic load (GL), where the GI is a ranking in comparison to glucose and the GL is an absolute value encompassing both the GI and amount of carbohydrate consumed. Evidence from controlled trials in rat models suggests that glycemic intake has a role in development of insulin resistance, however trials and observational studies of humans have produced conflicting results. As insulin resistance is a precursor to type 2 diabetes mellitus, lifestyle factors that could prevent development of this condition have important public health implications. Previous observational studies have used food frequency questionnaires to assess usual diet, which could have resulted in a lack of precision in assessment of individual serve sizes, and have been limited to daily measures of glycemic intake. Daily measures do not take fluctuations in glycemic intake on a per meal basis into account, which may be a more relevant measure for investigation in relation to disease outcomes. This PhD research was conducted in a group of Brisbane women aged 42 to 81 years participating in the multidisciplinary Brisbane Longitudinal Assessment of Ageing in Women (LAW study). Older women may be at particular risk of insulin resistance due to age, hormonal changes, and increases in abdominal obesity associated with menopause, and the LAW study provided an ideal opportunity to study the relationship between diet and insulin resistance. Using the diet history tool, we aimed to assess the glycemic intake of the population and hypothesised that daily GI and daily GL would be significantly positively associated with increased odds of insulin resistant status. We also hypothesised that a new glycemic measure representing peaks in GL at different meals would be a stronger predictor of insulin resistant status than daily measures, and that a specially designed questionnaire would be an accurate and repeatable dietary tool for assessment of glycemic intake. To address these hypotheses, we conducted a series of studies. To assess glycemic intake, information on usual diet was obtained by detailed diet history interview and analysed using Foodworks and the Australian Food and Nutrient (AUSNUT) database, combined with a customised GI database. Mean ± SD intakes were 55.6 ± 4.4% for daily GI and 115 ± 25 for daily GL (n=470), with intake higher amoung younger participants. Bread was the largest contributor to intakes of daily GI and GL (17.1% and 20.8%, respectively), followed by fruit (15.5% and 14.2%, respectively). To determine whether daily GI and GL were significantly associated with insulin resistance, the homeostasis model assessment of insulin resistance (HOMA) was used to assess insulin resistant status. Daily GL was significantly higher in subjects who were insulin resistant compared to those who were not (134 ± 33 versus 114 ± 24 respectively, P<0.001) (n=329); the odds of subjects in the highest tertile of GL intake being insulin resistant were 12.7 times higher when compared with the lowest tertile of GL (95% CI 1.6-100.1, P=0.02). Daily GI was not significantly different in subjects who were insulin resistant compared to those who were not (56.0 ± 3.3% versus 55.7 ± 4.5%, P=0.69). To evaluate whether a new glycemic measure representing fluctuations in daily glycemic intake would be a stronger predictor of insulin resistant status than other glycemic intake measures, the GL peak score was developed to express in a single value the magnitude of GL peaks during an average day. Although a significant relationship was seen between insulin resistant status and GL peak score (Nagelkerke’s R2=0.568, P=0.039), other glycemic intake measures of daily GL (R2=0.671, P<0.001) and daily GL per megajoule (R2=0.674, P<0.001) were stronger predictors of insulin resistant status. To develop an accurate and repeatable self-administered tool for assessment of glycemic intake, two sub-samples of women (n=44 for the validation study and n=52 for the reproducibility study) completed a semi-quantitative questionnaire that contained 23 food groupings selected to include the top 100 carbohydrate foods consumed by the study population. While there were significant correlations between the glycemic intake questionnaire and the diet history for GL (r=0.54, P<0.01), carbohydrate (r=0.57, P<0.01) and GI (r=0.40, P<0.01), Bland-Altman plots showed an unacceptable difference between individual intakes in 34% of subjects for daily GL and carbohydrate, and 41% for daily GI. Reproducibility results showed significant correlations for daily GL (r=0.73, P<0.001), carbohydrate (r=0.76, P<0.001) and daily GI (r=0.64, P<0.001), but an unacceptable difference between individual intakes in 25% of subjects for daily GL and carbohydrate, and 27% for daily GI. In summary, our findings show that a significant association was observed between daily glycemic load and insulin resistant status in a group of older women, using a diet history interview to obtain precise estimation of individual carbohydrate intake. Both the type and quantity of carbohydrate are important to consider when investigating relationships between diet and insulin resistance, although our results suggest the association is more closely related to overall daily glycemic intake than individual meal intake variations. A dietary tool that permits precise estimation of carbohydrate intake is essential when evaluating possible associations between glycemic intake and individual risk of chronic diseases such as insulin resistance. Our results also suggest that studies using questionnaires to estimate glycemic intake should state degree of agreement as well as correlation coefficients when evaluating validity, as imprecise estimates of carbohydrate at an individual level may have contributed to the conflicting findings reported in previous studies.
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