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Grant Details

Grant Number: 1U01CA288325-01 Interpret this number
Primary Investigator: Mccormick, Joseph
Organization: University Of Texas Hlth Sci Ctr Houston
Project Title: Multi-Omics for Obesity-Associated Liver Disease Discovery in Hispanics/Latinos: the Cameron County Hispanic Cohort
Fiscal Year: 2023


ABSTRACT We submit “Multi-omics for obesity-associated liver disease discovery in Hispanics/Latinos: the Cameron County Hispanic Cohort” (hereafter CCHC-Liver) in response to RFA-HG-22-008, as a disease study site (DSS) that will participate in the Multi-omics for Health and Disease Consortium (hereafter, “the Consortium”). Collectively, this Consortium will advance the science related to use of multi-omics technologies to study health and disease in ancestrally diverse populations. CCHC-Liver will leverage the extant infrastructure of the CCHC, a large, randomly ascertained, exquisitely phenotyped, longitudinal cohort of Hispanic/Latino (HL) participants that have been consented for future contact. We will implement a longitudinal study of liver disease progression, collecting serial specimens and measures of cardiometabolic risk factors (CMRF), social determinants of health (SDH), and of metabolic-associated fatty liver disease (MAFLD), assessed with serial transient elastography (TE) and biomarkers (FIB-4, APRI)] .The umbrella term “MAFLD” encompasses a range of chronic liver diseases, including non-alcoholic fatty liver (NAFLD), non-alcoholic steatohepatitis (NASH), and fibrosis, and has no approved drug therapy. Its prevalence is highest in HL populations, yet, longitudinal omics in accessible tissues for liver disease, namely, whole blood (WB) and abdominal subcutaneous adipose (SAT), are scarce, particularly inthe most vulnerable populations.To address this gap, we propose two Aims. 1) Consortium participation as a disease study site (DSS). Together with the teams from RFA-HG-22-009 [Omics Production Centers (OPCs)] and RFA-HG-22-010 [Data Analysis and Coordination Center (DACC)], we will work collaboratively to complete three operational subaims to: develop best practices for the collection, harmonization, and integration of longitudinal multi-omic, phenotypic, and environmental exposure data; develop best practices for data analysis to detect and assess molecular “profiles” associated with healthy and disease states; and create a multi- dimensional dataset that is available to the research community. 2) Design and implement a study of liver disease progression in an understudied, high-risk HL population. In this aim we will 1) enroll 300 HL participants, 200 with MAFLD and 100 without disease, consented for collection of data, future research use, and broad data sharing from an extant population-based study; 2) collect phenotypic data and biospecimens suitably preserved for omics data generation by OPCs across three time points; 3) submit biospecimens to the OPCs for data production; 4) integrate data to identify changes in WB and SAT multi-omics associated with liver disease progression; 5) perform causal inference in multi-omics to determine associations using Mendelian randomization (MR); 6) combine MAFLD-associated genetic factors with multi-omics measures to evaluate mechanistic frameworks via colocalization; 7) combine SDH and CMRF with multi-omics and MAFLD to assess causal mediation; 8) characterize novel multi-omics signals via structural equation modeling; and 9) determine global and local ancestry effects on multi-omics associated with liver disease progression.



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