Fasting plasma glucose is an independent predictor for severity of H1N1 pneumonia

<p>Abstract</p> <p>Background</p> <p>The pandemic influenza A (H1N1) virus emerged during 2009 and has spread worldwide. This virus can cause injuries to the lungs, liver, and heart. However, data regarding whether this influenza virus can affect pancreatic islets are limited. We investigated the effects of influenza A (H1N1) pneumonia on fasting plasma glucose (FPG) and islet function, and evaluated possible correlations between biochemical test results and the severity of H1N1 pneumonia.</p> <p>Methods</p> <p>We performed a retrospective analysis of patients either diagnosed with or suspected of having H1N1 pneumonia who were admitted to our hospital in 2009. Possible associations between FPG levels and H1N1 virus infection were assessed by logistic regression. Correlation and regression analyses were used to assess relationships between FPG and biochemical test results. Associations between admission days and significant data were assessed by single factor linear regression. To evaluate effects of H1N1 on pancreatic β-cell function, results of a resistance index (homa-IR), insulin function index (homa-β), and insulin sensitivity index (IAI) were compared between a H1N1 group and a non-H1N1 group by t-tests.</p> <p>Results</p> <p>FPG was significantly positively associated with H1N1 virus infection (OR = 1.377, 95%CI: 1.062-1.786; p = 0.016). FPG was significantly correlated with AST (r = 0.215; p = 0.039), LDH (r = 0.400; p = 0.000), BUN (r = 0.28; p = 0.005), and arterial Oxygen Saturation (SaO₂; r = -0.416; p = 0.000) in the H1N1 group. H1N1 patients who were hypoxemic (SaO₂<93%) had higher FPG levels than those who were not hypoxic (9.82 ± 4.14 vs. 6.64 ± 1.78; p < 0.05). FPG was negatively correlated with SaO₂in the H1N1 group with hypoxia (SaO₂<93; r = -0.497; p = 0.041). SaO₂levels in patients with high FPG levels (≥7 mmol/L) were significantly lower than those of H1N1 patients with low FPG levels (<5.6 mmol/L). There were no significant differences in homa-IR, homa-β, or IAI between the H1N1 and non-H1N1 groups after adjusting for age, sex, and BMI.</p> <p>Conclusions</p> <p>FPG on admission could be an independent predictor for the severity of H1N1 pneumonia. Elevated FPG induced by H1N1 pneumonia is not a result of direct damage to pancreatic β-cells, but arises from various factors' combinations caused by H1N1 virus infection.</p

Dimensional analysis of MINMOD leads to definition of the disposition index of glucose regulation and improved simulation algorithm

BACKGROUND: Frequently Sampled Intravenous Glucose Tolerance Test (FSIVGTT) together with its mathematical model, the minimal model (MINMOD), have become important clinical tools to evaluate the metabolic control of glucose in humans. Dimensional analysis of the model is up to now not available. METHODS: A formal dimensional analysis of MINMOD was carried out and the degree of freedom of MINMOD was examined. Through re-expressing all state variable and parameters in terms of their reference scales, MINMOD was transformed into a dimensionless format. Previously defined physiological indices including insulin sensitivity, glucose effectiveness, and first and second phase insulin responses were re-examined in this new formulation. Further, the parameter estimation from FSIVGTT was implemented using both the dimensional and the dimensionless formulations of MINMOD, and the performances were compared utilizing Monte Carlo simulation as well as real human FSIVGTT data. RESULTS: The degree of freedom (DOF) of MINMOD was found to be 7. The model was maximally simplified in the dimensionless formulation that normalizes the variation in glucose and insulin during FSIVGTT. In the new formulation, the disposition index (Dl), a composite parameter known to be important in diabetes pathology, was naturally defined as one of the dimensionless parameters in the system. The numerical simulation using the dimensionless formulation led to a 1.5–5 fold gain in speed, and significantly improved accuracy and robustness in parameter estimation compared to the dimensional implementation. CONCLUSION: Dimensional analysis of MINMOD led to simplification of the model, direct identification of the important composite factors in the dynamics of glucose metabolic control, and better simulations algorithms

Selection of the appropriate method for the assessment of insulin resistance

Insulin resistance is one of the major aggravating factors for metabolic syndrome. There are many methods available for estimation of insulin resistance which range from complex techniques down to simple indices. For all methods of assessing insulin resistance it is essential that their validity and reliability is established before using them as investigations. The reference techniques of hyperinsulinaemic euglycaemic clamp and its alternative the frequently sampled intravenous glucose tolerance test are the most reliable methods available for estimating insulin resistance. However, many simple methods, from which indices can be derived, have been assessed and validated e.g. homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI). Given the increasing number of simple indices of IR it may be difficult for clinicians and researchers to select the most appropriate index for their studies. This review therefore provides guidelines and advices which must be considered before proceeding with a study

Exercise therapy in Type 2 diabetes

Structured exercise is considered an important cornerstone to achieve good glycemic control and improve cardiovascular risk profile in Type 2 diabetes. Current clinical guidelines acknowledge the therapeutic strength of exercise intervention. This paper reviews the wide pathophysiological problems associated with Type 2 diabetes and discusses the benefits of exercise therapy on phenotype characteristics, glycemic control and cardiovascular risk profile in Type 2 diabetes patients. Based on the currently available literature, it is concluded that Type 2 diabetes patients should be stimulated to participate in specifically designed exercise intervention programs. More attention should be paid to cardiovascular and musculoskeletal deconditioning as well as motivational factors to improve long-term treatment adherence and clinical efficacy. More clinical research is warranted to establish the efficacy of exercise intervention in a more differentiated approach for Type 2 diabetes subpopulations within different stages of the disease and various levels of co-morbidity