Abstract: The invention also provides methods, apparatuses and reagents useful for predicting future atherosclerosis based on expression levels of genes selected from the set of 68 genes with differential expression in response to pioglitazone and rosiglitazone. The invention also discloses reagent sets and biomarkers for predicting progression of atherosclerosis induced by anti-diabetic therapy in a subject. In one particular embodiment the invention provides a method for predict whether a compound will induce atherosclerosis using gene expression data from sub-acute treatments.

Abstract: The present invention relates generally to a mathematical and computer model of diabetes related disorders (e.g., human type 2 diabetes) within the framework of multiple macronutrient metabolism. The model includes a representation of complex physiological control mechanisms directing, for example, fat metabolism, protein metabolism and/or carbohydrate metabolism. In one embodiment, for example, the model can account for the interconversion between macronutrients, as well as their digestion, absorption, storage, mobilization, and adaptive utilization, as well as the endocrine control of these processes. In this embodiment, the model can simulate, for example, a heterogeneous group of diabetes related disorders, from insulin resistant to severe diabetic, and can predict the likely effects of therapeutic interventions.

Abstract: The invention relates to novel methods of informing a medical decision by determining a hormone or drug concentration in a portal vein or hepatic sinusoid of a mammal. The invention also relates to methods of determining hormone or drug concentration in the portal vein or hepatic sinusoids. Further the invention relates to methods of utilizing these concentrations to calculate infusion rates for hormone or drugs in order to achieve a desired portal vein or hepatic sinusoid concentration of such a hormone or drug.

Abstract: The present invention relates generally to a mathematical and computer model of diabetes related disorders (e.g., human type 2 diabetes) within the framework of multiple macronutrient metabolism. The model includes a representation of complex physiological control mechanisms directing, for example, fat metabolism, protein metabolism and/or carbohydrate metabolism. In one embodiment, for example, the model can account for the interconversion between macronutrients, as well as their digestion, absorption, storage, mobilization, and adaptive utilization, as well as the endocrine control of these processes. In this embodiment, the model can simulate, for example, a heterogeneous group of diabetes related disorders, from insulin resistant to severe diabetic, and can predict the likely effects of therapeutic interventions.

Abstract: In general this invention can be viewed as encompassing novel methods of treating diabetes and insulin resistance. The inventors have made the discovery that increasing secretion of endogenous glucagon-like peptide-1 (GLP-1) in combination with inhibiting the activity of dipeptidyl peptidase I (DPP-IV) can have a significant impact on hyperglycemia and insulin secretion in subjects suffering from diabetes and/or insulin resistance. Further the invention encompasses methods of identifying subjects having elevated secretion of GLP-1, methods of assessing sensitivity to a GLP-1 secretagogue, and methods of treating diabetes in these subjects by administering a GLP-1 secretagogue to alleviate at least one symptom of diabetes.

Abstract: The invention encompasses novel biomarkers and methods for assessing insulin resistance in a subject. The novel biomarkers of the invention include various plasma constituent (e.g., insulin, glucose, lactate and/or triglyceride) concentrations. The methods of the invention include measuring various plasma constituent concentrations and calculating a predicted euglycemic hyperinsulinemic clamp glucose infusion rate (GIR) based on the plasma constituent concentrations.

Abstract: The present invention relates generally to a mathematical and computer model of diabetes related disorders (e.g., human type 2 diabetes) within the framework of multiple macronutrient metabolism. The model includes a representation of complex physiological control mechanisms directing, for example, fat metabolism, protein metabolism and/or carbohydrate metabolism. In one embodiment, for example, the model can account for the interconversion between macronutrients, as well as their digestion, absorption, storage, mobilization, and adaptive utilization, as well as the endocrine control of these processes. In this embodiment, the model can simulate, for example, a heterogeneous group of diabetes related disorders, from insulin resistant to severe diabetic, and can predict the likely effects of therapeutic interventions.