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Title: | 探討重要肥胖候選基因、飲食因素及其交互作用對肥胖風險之影響:竹東朴子社區研究 Impact of common polymorphisms in candidate genes, dietary factors and their interactions on obesity risk in Cardiovascular Disease Risk Factor Two-township Study |
Authors: | Chiao-Chi Liang 梁喬琪 |
Advisor: | 潘文涵 |
Keyword: | 肥胖,身體質量指數,飲食因子,單核苷,酸多型性,基因—環境交互作用, Obesity,Body mass index,Dietary factors,Single nucleotide polymorphism,gene-environment interaction, |
Publication Year : | 2007 |
Degree: | 碩士 |
Abstract: | 背景及目的 過去已有眾多與人類肥胖風險相關的基因被發現,然而當中具重覆性結果的研究卻有限。本研究目的旨在找尋與台灣族群肥胖相關的基因及探討這些重要基因與飲食環境交互作用對肥胖所產生的影響。
材料與方法 採用巢式病例對照研究設計,自「竹東及朴子地區心臟血管疾病長 期追蹤研究」中挑選符合條件的病例與對照。病例組共分為二:肥胖組(BMI≧27kg/m2)及過重組(24 kg/m2<BMI≦27kg/m2)。肥胖組納入可供挑選樣本之71%,計有285人,而過重組則納入42%為285人;以性別及年齡變項利用群組配對方式選出各組別之對照樣本共554人(挑選比例38%),總計1124人。研究涵蓋12個肥胖候選基因,挑選了15個基因位點: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,UCP3 C-55T。以上位點挑選準則為至少符合下列一項條件:(1)與肥胖的直接相關性:此基因在肥胖相關性上的報告多於五篇或此位點過去在本實驗室的結果發現與病態性肥胖相關,(2)過去此基因位點曾被發現與環境因子產生交互作用而影響肥胖相關表現型。分析中使用之飲食資料以飲食頻率問卷方式評估求得。各基因型、飲食因子及其交互作用對於肥胖風險影響,利用應變項為BMI之線性迴歸模型進行分析。 結果 ADRB2 Arg16Gly、FABP2 Ala54Thr及PPARG G-82362A為台灣族群中影響BMI之重要肥胖相關基因,惟後兩者僅在男性中有統計上之顯著性。飲食因子部分觀察到總熱量攝取及脂肪攝取(熱量百分比)與肥胖有顯著相關。另外,基因—飲食交互作用分析上則觀察到,攜帶LEP -2548 G對偶基因之族群BMI值在總熱量攝取較高時有較高的平均值,而在AA同型合子者身上則無看到此現象(p for interaction=0.0464)。另外,肥胖風險隨著飲食中脂肪攝取增加而提高可在UCP2 Val55及UCP3 T-55的攜帶者身上觀察到,而此二基因位點的其他基因型則無此現象(pUCP2=0.0004,pUCP3=0.0037)。若將以上結果中所有具統計顯著性之變項放入同一個迴歸模型中(包含基因及營養參數主效應及其交互作用項),可發現此統計模型對於族群中之BMI值有6%的解釋度。 結論 本研究不但再次強調了在肥胖基因研究中,環境因子影響的重要性,未來也或許能夠將此結果應用於某些基因型攜帶者特殊的飲食策略上。重要的是,我們建立了一個概念上較為完整的生物統計模型,未來當模型中與肥胖相關之基因或飲食因子增加時,應可有效地對BMI或肥胖風險作預測。 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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29393 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 微生物學科所 |
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