The role of Ad-36 and its E4orf-1 protein in modulating glycemic control

Current treatment strategies for Type 2 Diabetes Mellitus (T2DM) include a range of anti-diabetic drugs, supplemented by lifestyle modifications to reduce dietary fat intake and body fat. However, for their anti-diabetic action, most drugs recruit insulin signaling pathways, which are already impaired in T2DM. Also, compliance and success in achieving sustained improvements in diet or obesity over the long term is marginal. Therefore, an agent that improves diabetes independent of insulin signaling or lifestyle changes may be highly useful. Human adenovirus Ad36 offers such a model. Ad36 improves glycemic control in chow-fed mice or rats and attenuates diabetes and hepatic steatosis in high fat(HF)-fed mice, despite the HF intake and without reducing adiposity. In human adults, natural Ad36 infection predicts better glycemic control and lower hepatic lipid stores. Ex-vivo cell signaling studies suggest that in mice, Ad36 activates Ras-mediated phosphatidyl- inositol 3-kinase (PI3K) pathway (Ras/PI3K) to up-regulate glucose uptake in skeletal muscle and adipose, and suppresses glucose output from the liver. This study determined if the anti-diabetic properties of Ad36 could be creatively harnessed. Objective 1 determined that Ad36 seropositivity was associated with improved glycemic control and lower hepatic lipids in Caucasian, Hispanic, and African American children and adolescents. Objective 2 determined which of the conventional contributors of insulin sensitivity are modulated by Ad36. In vitro, Ad36 increased preadipocyte differentiation, de-novo lipogenesis, and fat oxidation. Ad36 increased the proportion of small adipocytes in mice on a chow diet, whereas in HF-fed mice, Ad36 increased the proportion of large adipocytes. Adipose tissue macrophage infiltration and angiogenesis were not affected by Ad36. Objective 3 determined the E4orf1 protein of Ad36 mediates its anti-hyperglycemic property. E4orf1 is sufficient and necessary to improve glucose uptake. Mirroring the actions of Ad36, in vitro, E4orf1 also up-regulates the Ras/PI3K pathway, and adiponectin –an insulin sensitizing adipokine, and down-regulates inflammatory cytokine expression. E4orf1 increases glucose uptake in, preadipocytes and adipocytes. In hepatocytes, E4orf1 reduces glucose output and the metabolic studies indicate it favors less hepatic lipid storage. Overall, this study offers a broad foundation to further determine the potential of E4orf1 as an anti-diabetic agent

Even Modest Prediction Accuracy of Genomic Models Can Have Large Clinical Utility

Whole Genome Prediction (WGP) jointly fits thousands of SNPs into a regression model to yield estimates for the contribution of markers to the overall variance of a particular trait, and for their associations with that trait. To date, WGP has offered only modest prediction accuracy, but in some cases even modest prediction accuracy may be useful. We provide an illustration of this using a theoretical simulation that used WGP to predict weight loss after bariatric surgery with moderate accuracy (R2 = 0.07) to assess the clinical utility of WGP despite these limitations. Prevention of Type 2 Diabetes (T2DM) post surgery was considered the major outcome. Treating only patients above predefined threshold of predicted weight loss in our simulation, in the realistic context of finite resources for the surgery, significantly reduced lifetime risk of T2DM in the treatable population by selecting those most likely to succeed. Thus, our example illustrates how WGP may be clinically useful in some situations, and even with moderate accuracy, may provide a clear path for turning personalized medicine from theory to reality

Integrated genomic and BMI analysis for type 2 diabetes risk assessment.

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, and evaluated them with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5,245 subjects (4,306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 established T2D-associated SNPs. We fitted Logistic Regressions and Bayesian Regularized Neural Network and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy