title: Systems analysis and the prediction and prevention of Type 2 diabetes mellitus.
abstract: Prevalence of Type 2 diabetes has increased at an alarming rate, highlighting the need to correctly predict the development of this disease in order to allow intervention and thus, slow progression of the disease and resulting metabolic derangement. There have been many recent 'advances' geared toward the detection of pre-diabetes, including genome wide association studies and metabolomics. Although these approaches generate a large amount of data with a single blood sample, studies have indicated limited success using genetic and metabolomics information alone for identification of disease risk. Clinical assessment of the disposition index (DI), based on the hyperbolic law of glucose tolerance, is a powerful predictor of Type 2 diabetes, but is not easily assessed in the clinical setting. Thus, it is evident that combining genetic or metabolomic approaches for a more simple assessment of DI may provide a useful tool to identify those at highest risk for Type 2 diabetes, allowing for intervention and prevention.
authors: Cedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, CA 90048, USA. Electronic address: Richard.Bergman@cshs.org.
authors: Richard N
authors: RN
authors: Bergman
authors: Cedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, CA 90048, USA.
authors: Darko
authors: D
authors: Stefanovski
authors: Cedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, CA 90048, USA.
authors: Stella P
authors: SP
authors: Kim
mesh_terms: D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000378:metabolism / Q000517:prevention & control*; D055106:Genome-Wide Association Study; D006801:Humans; D007328:Insulin / Q000378:metabolism; D055432:Metabolomics; D011237:Predictive Value of Tests; D049490:Systems Biology
chemical_list: D007328:Insulin