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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-RPRB-B (M1))
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Hawaii
United States
Zip Code
Lim, Unhee; Monroe, Kristine R; Buchthal, Steve et al. (2018) Propensity for Intra-abdominal and Hepatic Adiposity Varies Among Ethnic Groups. Gastroenterology :
Gathungu, Rose M; Larrea, Pablo; Sniatynski, Matthew J et al. (2018) Optimization of Electrospray Ionization Source Parameters for Lipidomics To Reduce Misannotation of In-Source Fragments as Precursor Ions. Anal Chem 90:13523-13532
Gathungu, Rose M; Kautz, Roger; Kristal, Bruce S et al. (2018) The integration of LC-MS and NMR for the analysis of low molecular weight trace analytes in complex matrices. Mass Spectrom Rev :
Citronberg, Jessica S; Wilkens, Lynne R; Le Marchand, Loic et al. (2018) Plasma lipopolysaccharide-binding protein and colorectal cancer risk: a nested case-control study in the Multiethnic Cohort. Cancer Causes Control 29:115-123
Randolph, Timothy W; Zhao, Sen; Copeland, Wade et al. (2018) KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA. Ann Appl Stat 12:540-566
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
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
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
Gathungu, Rose M; Stavrovskaya, Irina G; Larrea, Pablo et al. (2016) Simple LC-MS Method for Differentiation of Isobaric Phosphatidylserines and Phosphatidylcholines with Deuterated Mobile Phase Additives. Anal Chem 88:9103-10
Citronberg, Jessica S; Wilkens, Lynne R; Lim, Unhee et al. (2016) Reliability of plasma lipopolysaccharide-binding protein (LBP) from repeated measures in healthy adults. Cancer Causes Control 27:1163-6

Showing the most recent 10 out of 15 publications