The Data Management and Analysis Core (Core D) will provide support for the projects and other cores in all aspects of study design and data management and analysis. This POl focuses on the characterization of specific adiposity phenotypes by imaging techniques, the identification of predictors of the phenotypes among lifestyle and biomarker variables derived from measurements in various biological dimensions (genefics, metabolomics, microbiome), and testing the association of the predictors, as proxy measures, with the risk of several cancers. The study subjects are participants in the well-established Multiethnic Cohort (MEC) study. The experienced biostatisticians and bioinformaticians of Core D collectively have extensive experience with the MEC data and design, and the types of data and analyses needed to support the aims of the P01.
Specific aims i nclude (1) to enhance the complex MEC informatics system to identify cohort members to approach for recruitment, to track their progress through the study, and to monitor receipt, storage and shipment of all biologic materials, (2) to pre-process high through-put data from genetic variation, metabolomics and gut microbiome in order to reduce the dimensionality and provide the most appropriate data for analysis, and (3) to develop and execute analysis plans for each of the research projects of this P01 application, in conjunction with the Project Leaders. In particular, appropriate statistical tools and software will be used to find the most predictive models of the adiposity phenotype under study and for the association studies of these predictors with cancer risk. Heterogeneity will be examined and tested across the ethnic groups in the prediction and association models.
The focus of this P01 is on the identification of factors that predict obesity patterns that are linked to cancer incidence. This information could be used to distiniguish individuals at high-risk for obesity-related cancers and to identify possible targets for intervention. The Data Management and Analysis Core will help ensure that the science is of the highest quality by providing sound data management and analysis support.
|Maskarinec, Gertraud; Lim, Unhee; Jacobs, Simone et al. (2017) Diet Quality in Midadulthood Predicts Visceral Adiposity and Liver Fatness in Older Ages: The Multiethnic Cohort Study. Obesity (Silver Spring) 25:1442-1450|
|Utzschneider, Kristina M; Kratz, Mario; Damman, Chris J et al. (2016) Mechanisms Linking the Gut Microbiome and Glucose Metabolism. J Clin Endocrinol Metab 101:1445-54|
|Fu, Benjamin C; Randolph, Timothy W; Lim, Unhee et al. (2016) Characterization of the gut microbiome in epidemiologic studies: the multiethnic cohort experience. Ann Epidemiol 26:373-9|
|Setiawan, Veronica Wendy; Lim, Unhee; Lipworth, Loren et al. (2016) Sex and Ethnic Differences in the Association of Obesity With Risk of Hepatocellular Carcinoma. Clin Gastroenterol Hepatol 14:309-16|
|Gathungu, Rose M; Bird, Susan S; Sheldon, Diane P et al. (2014) Identification of metabolites from liquid chromatography-coulometric array detection profiling: gas chromatography-mass spectrometry and refractionation provide essential information orthogonal to LC-MS/microNMR. Anal Biochem 454:23-32|
|Hullar, Meredith A J; Fu, Benjamin C (2014) Diet, the gut microbiome, and epigenetics. Cancer J 20:170-5|
|Sedaghat, Nafiseh; Saegusa, Takumi; Randolph, Timothy et al. (2014) Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways. Cancer Inform 13:55-66|
|Lim, Unhee; Wilkens, Lynne R; Albright, Cheryl L et al. (2013) University of Hawai'i Cancer Center Connection: bias in self-reported anthropometry in relation to adiposity and adulthood weight gain among postmenopausal Caucasian and Japanese American Women. Hawaii J Med Public Health 72:445-9|