One of the most interesting cellular signaling pathways involved in the development of obesity and obesity-related comorbidity has been the endocannabinoid (eCB) system. It has been known for thousands of years that cannabis extracts increase appetite, particularly for highly palatable foods and can result in weight gain. The eCB system consists of two endogenous cannabinoid receptors, CB1 and CB2. Emerging animal and human data support the hypotheses that obesity and obesity-related metabolic disorders are associated with overactive signaling specifically at the level of the CB1 receptor (gene name CNR1). The CB1 receptor has two endogenous ligands: N-arachidonylethanolamine (AEA) and 2-arachdionylglycerol (2-AG). Synthesis of these ligands is regulated by a series of transacylases and phopholipases. Degradation of AEA is catalyzed by a serine amidase, fatty acid amide hydrolase (gene name FAAH). While 2-AG is degraded by multiple enzymes, recent evidence suggests a primary role for monoacylglycerol lipase (gene name MGLL). eCB/CB1 receptor signaling is involved in the regulation of food consumption and metabolism via multiple mechanisms: it regulates the rewarding properties of food via effects on central mesolimbic pathways;it also regulates food intake at the hypothalamus;and it modulates peripheral metabolism through effects on adipose tissue, the liver, skeletal muscle and the endocrine pancreas. Over-activity of eCB/CB1 signaling at any or all of these sites can result in increased food intake and/or altered metabolism, thus promoting obesity. The goal of the studies outlined in this application will be to define the degree to which eCB/CB1 signaling pathway gene variability contributes to human obesity. We propose to test this hypothesis in two independent study cohorts. Using haplotype tagging SNPs (tagSNPs) selected from the International Human Haplotype Map (HapMap), we have begun genotyping three critical eCB/CB1 signaling genes, and testing for association between variation in these candidate genes and obesity and obesity-related metabolic disorders (changes in insulin responsiveness and derangements in lipid homeostasis) in 2209 study subjects from 507 families participating in the Take Off Pounds Sensibly (TOPS) obesity research program. We plan to test tagSNPs in each of these candidate eCB/CB1 signaling genes for association with 12 quantitative traits: body mass index (BMI), waist circumference, hip circumference, waist/hip ratio (WHR), fasting glucose, insulin, insulin/glucose ratio, homeostasis model assessment of insulin resistance (HOMA-IR), fasting triglyceride levels, total cholesterol, LDL cholesterol, and HDL cholesterol. Any tagSNPs found to be associated with BMI (as a global measure of obesity) or WHR (as a measure of abdominal obesity) will be further tested for association with total body fat by DEXA scan (as a confirmatory measure of global obesity) and the ratio of visceral fat / subcutaneous fat by CT scan (as a confirmatory measure of abdominal obesity) in 504 rigorously phenotyped individuals from the 50 TOPS families that have proven most informative in prior genetic analyses. These 504 rigorously phenotyped individuals are nested within the original cohort. Any tagSNPs found to be associated with glucose homeostasis in the original cohort will also be tested for a relationship with insulin sensitivity in these 504 more rigorously phenotyped individuals using the minimal model (MinMod), which compensates for subject-to-subject variability in insulin and glucose kinetics. The MinMod provides four well characterized glycemic control parameters: insulin sensitivity (SI), acute insulin response to intravenous glucose (AIRG), glucose effectiveness (SG), and disposition index (DI). TagSNPs found to be associated with lipid levels in the original cohort will also be tested for association with lipoprotein particle size distribution in these 504 subjects. All three candidate genes (FAAH, MGLL, and CNR1) will then be resequenced in 96 obese study subjects with insulin resistance and dyslipidemia identified from within the original TOPS cohort. All common and rare variants will be re-genotyped in the entire cohort (n = 2209), and Bayesian statistical methods will be employed to identify potentially causative alleles. For each gene, causative polymorphisms will be prioritized using a quantitative trait nucleotide (QTN) approach, and all variants with greater than 80% posterior probability of effect (based upon Bayesian information criterion) will be genotyped in a second population, the Marshfield Clinic Personalized Medicine Research Project (PMRP). The PMRP database represents one of the largest population-based Biobanks in the U.S (n = 19,573). This cohort will allow us to test the generalizability of our initial findings a population of similar ethnic composition. Each variant from the original family-based cohort will be prioritized according to pleiotropy (i.e., association with more than one obesity-related phenotypic trait). We will then use the PMRP population to test the most informative obesity alleles for association with BMI, and the most informative metabolic alleles for association with fasting glucose and routine clinical lipid data.
The current obesity epidemic represents a major international health crisis. The prevalence of obesity-related medical problems is continuing to increase, placing an unprecedented burden on health care infrastructure. We plan to quantify the role of three candidate genes in the development of obesity and obesity-related medical problems. In the future, individual patients with variation in these genes may benefit from targeted behavioral and/or pharmacological interventions designed to improve their health.
|White, C C; Feng, Q; Cupples, L A et al. (2013) CYP4A11 variant is associated with high-density lipoprotein cholesterol in women. Pharmacogenomics J 13:44-51|
|Feng, Q; Vickers, K C; Anderson, M P et al. (2013) A common functional promoter variant links CNR1 gene expression to HDL cholesterol level. Nat Commun 4:1973|
|Choi, J H; Yee, S W; Ramirez, A H et al. (2011) A common 5'-UTR variant in MATE2-K is associated with poor response to metformin. Clin Pharmacol Ther 90:674-84|
|Roden, Dan M; Johnson, Julie A; Kimmel, Stephen E et al. (2011) Cardiovascular pharmacogenomics. Circ Res 109:807-20|
|Turner, Stephen D; Berg, Richard L; Linneman, James G et al. (2011) Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS One 6:e19586|
|Roden, Dan M; Wilke, Russell A; Kroemer, Heyo K et al. (2011) Pharmacogenomics: the genetics of variable drug responses. Circulation 123:1661-70|
|Wilke, R A; Xu, H; Denny, J C et al. (2011) The emerging role of electronic medical records in pharmacogenomics. Clin Pharmacol Ther 89:379-86|
|Wilke, Russell A; Dolan, M Eileen (2011) Genetics and variable drug response. JAMA 306:306-7|
|Wilke, R A (2011) High-density lipoprotein (HDL) cholesterol: leveraging practice-based biobank cohorts to characterize clinical and genetic predictors of treatment outcome. Pharmacogenomics J 11:162-73|
|Baye, T M; Wilke, R A (2010) Mapping genes that predict treatment outcome in admixed populations. Pharmacogenomics J 10:465-77|
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