AIMS: The Environmental Determinants of Diabetes in the Young planned biomarker discovery studies on longitudinal samples for persistent confirmed islet cell autoantibodies and type 1 diabetes using dietary biomarkers, metabolomics, microbiome/viral metagenomics and gene expression. METHODS: This article describes the details of planning The Environmental Determinants of Diabetes in the Young biomarker discovery studies using a nested case-control design that was chosen as an alternative to the full cohort analysis. In the frame of a nested case-control design, it guides the choice of matching factors, selection of controls, preparation of external quality control samples and reduction of batch effects along with proper sample allocation. RESULTS AND CONCLUSION: Our design is to reduce potential bias and retain study power while reducing the costs by limiting the numbers of samples requiring laboratory analyses. It also covers two primary end points (the occurrence of diabetes-related autoantibodies and the diagnosis of type 1 diabetes). The resulting list of case-control matched samples for each laboratory was augmented with external quality control samples.
AIMS: The Environmental Determinants of Diabetes in the Young planned biomarker discovery studies on longitudinal samples for persistent confirmed islet cell autoantibodies and type 1 diabetes using dietary biomarkers, metabolomics, microbiome/viral metagenomics and gene expression. METHODS: This article describes the details of planning The Environmental Determinants of Diabetes in the Young biomarker discovery studies using a nested case-control design that was chosen as an alternative to the full cohort analysis. In the frame of a nested case-control design, it guides the choice of matching factors, selection of controls, preparation of external quality control samples and reduction of batch effects along with proper sample allocation. RESULTS AND CONCLUSION: Our design is to reduce potential bias and retain study power while reducing the costs by limiting the numbers of samples requiring laboratory analyses. It also covers two primary end points (the occurrence of diabetes-related autoantibodies and the diagnosis of type 1 diabetes). The resulting list of case-control matched samples for each laboratory was augmented with external quality control samples.
Authors: Monica Benito; Joel Parker; Quan Du; Junyuan Wu; Dong Xiang; Charles M Perou; J S Marron Journal: Bioinformatics Date: 2004-01-01 Impact factor: 6.937
Authors: Jeffrey T Leek; Robert B Scharpf; Héctor Corrada Bravo; David Simcha; Benjamin Langmead; W Evan Johnson; Donald Geman; Keith Baggerly; Rafael A Irizarry Journal: Nat Rev Genet Date: 2010-09-14 Impact factor: 53.242
Authors: John M Wentworth; Naiara G Bediaga; Megan A S Penno; Esther Bandala-Sanchez; Komal N Kanojia; Konstantinos A Kouremenos; Jennifer J Couper; Leonard C Harrison Journal: Metabolomics Date: 2018-09-25 Impact factor: 4.290