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 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 encompasses methods, including computer-implemented methods, of defining a virtual patient population and mapping the virtual patient population to a population of real patients. The invention utilizes virtual measures from one or more virtual patients, and data representative of multiple real subjects in a sample population, such as data collected from patients in a clinical trial or epidemiological study of a real population. The invention includes evaluating the similarity between the virtual patients and the real subjects, and assigning prevalences to the virtual patients based on the evaluation. The similarity can be assessed using some or all of the virtual measures of the virtual patients and some or all of the data obtained for the real subjects. Any of various goodness-of-fit measures can be used to evaluate the similarity or to help identify prevalences. The virtual patient population is defined as the virtual patients according to their respective prevalences.

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.