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Full-Text Articles in Medicine and Health Sciences
Genetic Risk Of Progression To Type 2 Diabetes And Response To Intensive Lifestyle Or Metformin In Prediabetic Women With And Without A History Of Gestational Diabetes Mellitus., Shannon D Sullivan, Kathleen A. Jablonski, Jose C Florez, Dana Dabelea, Paul W Franks, Sam Dagogo-Jack, Catherine Kim, William C Knowler, Costas A Christophi, Robert Ratner, Diabetes Prevention Program Research Group.
Genetic Risk Of Progression To Type 2 Diabetes And Response To Intensive Lifestyle Or Metformin In Prediabetic Women With And Without A History Of Gestational Diabetes Mellitus., Shannon D Sullivan, Kathleen A. Jablonski, Jose C Florez, Dana Dabelea, Paul W Franks, Sam Dagogo-Jack, Catherine Kim, William C Knowler, Costas A Christophi, Robert Ratner, Diabetes Prevention Program Research Group.
GW Biostatistics Center
OBJECTIVE The Diabetes Prevention Program (DPP) trial investigated rates of progression to diabetes among adults with prediabetes randomized to treatment with placebo, metformin, or intensive lifestyle intervention. Among women in the DPP, diabetes risk reduction with metformin was greater in women with prior gestational diabetes mellitus (GDM) compared with women without GDM but with one or more previous live births.
RESEARCH DESIGN AND METHODS We asked if genetic variability could account for these differences by comparing β-cell function and genetic risk scores (GRS), calculated from 34 diabetes-associated loci, between women with and without histories of GDM.
RESULTS β-Cell function was …
Analysis Of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model., Zhenyu Jiang, Chengan Du, Assen Jablensky, Hua Liang, Zudi Lu, Yang Ma, Kok Lay Teo
Analysis Of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model., Zhenyu Jiang, Chengan Du, Assen Jablensky, Hua Liang, Zudi Lu, Yang Ma, Kok Lay Teo
GW Biostatistics Center
Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore …