Multi-omics for obesity-associated liver disease in a high-risk population cohort

We submitted 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 advances the science related to use of multi-omics technologies to study health and disease in a variety of populations. CCHC-Liver builds on the extant infrastructure of the Cameron County Hispanic Cohort (CCHC)—a large, randomly ascertained, well-characterized longitudinal cohort representative of South Texas, with participants consented for future contact. As part of this project, we are implementing a longitudinal study of liver disease progression, collecting serial specimens and measures of cardiometabolic risk factors (CMRF), non-medical determinants of health (NMDoH), 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 elevated in south Texas, yet, longitudinal omics in accessible tissues for liver disease, whole blood (WB) and abdominal subcutaneous adipose (SAT), are scarce, particularly in populations with high rates of cardiometabolic disease. To address this gap, we proposed 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 are working collaboratively to complete three operational sub aims 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 population living in south Texas. In this aim we will 1) enroll 300 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 NMDoH 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.