GENETIC EPIDEMIOLOGY OF NONALCOHOLIC FATTY LIVER DISEASE

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DESCRIPTION (provided by applicant): Rohit Loomba, MD, MHSc (PI) is a hepatologist and Assistant Professor of Medicine at the University of California-San Diego. Dr. Loomba's long-term goal is to develop an independent research career in the genetic epidemiology of nonalcoholic fatty liver disease (NAFLD) by combining patient- centered research with clinical epidemiology. He is interested in underpinning genetic and environmental risk factors that are associated with NAFLD and identify novel mechanistic pathways that are linked with progressive form of NAFLD, which is termed as nonalcoholic steatohepatitis (NASH). NAFLD is the most common liver disease in the United States. It is associated with metabolic syndrome traits including hypertension (HTN), insulin resistance (IR), and hypertriglyceridemia. These metabolic traits predict increased risk of NASH, which can lead to cirrhosis. A critical barrier to progress in the field and to management of patients with NAFLD is that the mechanism underlying the association between metabolic traits and NAFLD, and those associated with progression of NAFLD, are not well understood. In order to fill this gap in knowledge, Dr. Loomba, under the mentorship of Drs. Daniel O'Connor, Elizabeth Barrett-Connor, and David Brenner, proposes to measure liver fat, quantitatively, by a novel magnetic resonance imaging (MRI) technique, in a large, previously well-characterized, existing, twin-pair cohort in humans: (specific aim 1) To examine genetic co-variance between NAFLD and metabolic risk factors including hypertension (HTN), insulin resistance (IR), and elevated triglycerides (TG), which would be assessed by using multivariate generalized estimating equations after adjustment for age and sex. Previous studies suggest that adrenergic system has a strong genetic association with HTN, IR & TG; and in-vitro and in-vivo studies suggest that norepinephrine (increased adrenergic activity) activates hepatic stellate cells & induces fibrogenesis in mice model of diet-induced fibrosis and NAFLD. Therefore, we hypothesize that adrenergic genes are associated with human NAFLD and may be responsible for the shared gene effects between NAFLD and HTN, IR, & TG. (specific aim 2) To examine if the genes in the adrenergic system (already genotyped: preliminary data suggests 2-2 adrenergic gene effect with serum gamma-glutamyl transpeptidase (GGT), a marker of NAFLD, in this twin cohort) are associated with NAFLD. This twin-pair study, which includes both mono-zygotic and di-zygotic twins, allows us to tease out the genetic versus environmental co-variance between the metabolic traits and NAFLD. Furthermore, we will be utilizing a cutting-edge MRI technique to quantify the liver fat fraction as a non-invasive biomarker of hepatic triglyceride content. These innovations would advance the knowledge in the field. In order to gain expertise in genetic epidemiology and twin studies, a rigorous career development plan is proposed that benefits from a multi-disciplinary approach designed to provide a closely mentored, patient-oriented research experience in association with a comprehensively structured didactic curriculum in genetic and advanced epidemiology. This unique twin study design would shed light on shared gene effects between NAFLD and these metabolic syndrome traits and help in identifying novel genetic variations in adrenergic genes that may explain this shared gene effect. These findings may be exploited in assessing liver disease progression and development of novel targets for treatment of NAFLD and associated metabolic complications. The long- term goal of the proposed research program is to reduce the burden of NAFLD and halt progression of NAFLD to NASH. PUBLIC HEALTH RELEVANCE: NAFLD affects one out of every three Americans. The proposed twin study will improve our understanding of disease progression in NAFLD. This study would lead to discovery of specific adrenergic genotypes that are associated with NAFLD, which may be exploited to develop new prognostic models for predicting disease progression and develop newer targets of therapy.