Impact of common polymorphisms in candidate genes, dietary factors and their interactions on obesity risk in Cardiovascular Disease Risk Factor Two-township Study

• Main Authors: Chiao-Chi Liang, 梁喬琪
• Other Authors: 潘文涵
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/73952922694908733062

碩士 === 國立臺灣大學 === 微生物與生化學研究所 === 95 === Background: There are at least hundreds of potential obesity genes being documented. However, only a few dozens have been replicated more than five times in human association studies. The aim of this study was to find influential obesity candidate genes and those major ones interacting with dietary factors in Taiwanese population. Materials and methods: This study was within Cardiovascular Disease Risk Factor Two-township Study (CVDFACTS), using nested case-control study design. 285 obese subjects (BMI*≧27kg/m2) of the cohort were included (71%) and 285 overweight subjects (24kg/m2<BMI≦27kg/m2) were randomly selected (42%). We obtained 554 age-sex grouped matched normal BMI control (38%) and chose 15 SNPs in 12 genes: ADRB2 (Arg16Gly, Gln27Glu), ESR1 A+51193T, FABP2 Ala54Thr, LEP A-2548G, LEPR Gln223Arg, PLIN G+11842A, PPARD T-87C, PPARG (G-82362A, Pro12Ala, G+28752A), TNFA G-308A, TNFB G+252A, UCP2 Ala55Val, and UCP3 C-55T. They conformed to at least one of the following criteria: (1) it was reported to associate directly with obesity at least in five studies and was previously found to relate to morbidity obesity in our laboratory or (2) it was interacting with environmental factors in its association with obesity. Dietary information was accessed by a validated food frequency questionnaire. Association with genetic variants, nutrient parameters or gene-nutrient interactions were assessed by linear regression models with BMI as the dependent variable and potential confounders adjusted. Results: ADRB2 Arg16Gly, PPARG G-82362A and FABP2 Ala54Thr were gene variants that highly associated to BMI variation and later two only significant in men (pADRB2 Arg16Gly=0.0319, pPPARG G-82362A=0.0105 and pFABP2 Ala54Thr =0.0058). Total energy intake and fat intake (% of energy) were two dietary factors associated with elevated BMI (p =0.0187 and p=0.0011, respectively). With regard to gene-diet interactions, we found that total energy intake was associated with BMI for G allele carriers in LEP -2548 locus but not for its counterparts (p for interaction=0.0464). Furthermore, BMI was associated with dietary % fat intake for UCP2 Val55, or UCP3 T-55 variant carriers, but not for their counterparts (p for interaction=0.0004 and 0.0037, respectively). Putting all afore-mentioned significant correlates in one multivariate regression model, it could explain 6% of BMI value in our population. Conclusions: We have constructed a statistical model for predicting BMI, combining the genetic and environmental effects. With this approach, we may be able to substantially increase the predictivity of BMI or obesity, when more candidate variants are considered.