Do Genotype Patterns Predict Weight Loss Success for Low Carb vs. Low Fat Diets?

描述（由申请人提供）：基因组学研究进展迅速，基因与肥胖之间的联系不断被发现和更好地定义。越来越多的多基因单核苷酸多态性（snp）已被证明可以改变个体对饮食宏量营养素组成的反应。基于先前评估人体对膳食碳水化合物或脂肪的生理反应的遗传学研究，我们确定了来自三个基因（FABP2， PPARG和ADRB2）的多位点基因型模式：低碳水化合物反应基因型（LCG）和低脂肪反应基因型（LFG）。在一项初步的回顾性研究中（使用a TO Z减肥研究数据），我们观察到最初超重的女性在12个月的体重减轻中有3倍的差异，这些女性被确定为适当匹配低碳水化合物（low Carb）或低脂肪（low fat）饮食，基于她们的多位点基因型模式。目的：本研究的主要目的是确认和扩展初步结果，并确定如果饮食方法（低碳水化合物vs低脂肪）与个体对这些饮食的遗传倾向（LCG vs LFG）适当匹配，减肥成功率是否可以提高。这项研究将针对女性和男性（A TO Z研究仅涉及女性），并提出一系列具体目标，旨在进一步阐述这些结果的潜在机制和临床应用。在重新提交的申请中增加了一个新的次要目标，这将涉及对其他snp的严格探索性研究，这些snp已显示出与肥胖和代谢表型的全基因组显著关联，可能会改善3-SNP特征。设计：主要研究是一项采用2X2平行设计的随机试验，以检验中心假设，即当320名超重/肥胖的非糖尿病成年人根据遗传倾向（LCG vs. LFG）匹配或不匹配12个月的低碳水化合物vs.低脂肪减肥饮食（n=80/细胞）时，体重减轻会更大。在随机化之前对参与者进行基因分型，在研究期间对参与者和数据收集者的基因分型结果保持盲法。除了每月评估体重变化的主要结局外，主要数据收集将在第0、3、6和12个月进行，包括能量摄入（3天未通知的24小时召回，NDS-R）、食欲/饱腹感/饥饿感、能量消耗（静息能量消耗）、身体成分（DEXA）和血液变量（血脂、胰岛素、葡萄糖、OGTT）。影响：如果这个有趣的初步回顾性研究结果在这个全面的研究中得到证实，结果将表明，廉价的DNA测试可以帮助节食者预测他们在低碳水化合物饮食还是低脂肪饮食中减肥更成功。随着科学对干预发展的个性化医学方法的兴趣日益增加，这将提供一个潜在的重大健康影响的例子，可以通过理解从人类基因组的揭示中预期的特定基因-环境相互作用来获得。

DESCRIPTION (provided by applicant): Genomics research is advancing rapidly, and links between genes and obesity continue to be discovered and better defined. A growing number of single nucleotide polymorphisms (SNPs) in multiple genes have been shown to alter an individual's response to dietary macronutrient composition. Based on prior genetic studies evaluating the body's physiological responses to dietary carbohydrates or fats, we identified multi-locus genotype patterns with SNPs from three genes (FABP2, PPARG, and ADRB2): a low carbohydrate-responsive genotype (LCG) and a low fat-responsive genotype (LFG). In a preliminary, retrospective study (using the A TO Z weight loss study data), we observed a 3-fold difference in 12-month weight loss for initially overweight women who were determined to have been appropriately matched vs. mismatched to a low carbohydrate (Low Carb) or low fat (Low Fat) diet based on their multi-locus genotype pattern. OBJECTIVE: The primary objective of this study is to confirm and expand on the preliminary results and determine if weight loss success can be increased if the dietary approach (Low Carb vs. Low Fat) is appropriately matched to an individual's genetic predisposition (LCG vs. LFG) toward those diets. This study will target both women and men (the A TO Z study involved only women), and address a set of specific aims intended to further elaborate on potential mechanisms and the clinical utility of these results. A new secondary aim has been added to this resubmitted application that will involve a rigorous exploratory investigation of additional SNPs that have shown genome-wide significant associations with obesity and metabolic phenotypes that might improve on the 3-SNP signature. DESIGN: The main study is a randomized trial employing a 2X2 parallel design to test the central hypothesis that there will be greater weight loss when 320 overweight/obese non- diabetic adults are matched vs. mismatched by genetic predisposition (LCG vs. LFG) to a 12-month Low Carb vs. Low Fat weight loss diet (n=80/cell). Participants will be genotyped prior to randomization, and blinding will be maintained for the genotyping results for both participants and data collectors during the study. Other than the primary outcome of weight change, which will be assessed monthly, primary data collection will occur at 0, 3, 6, and 12 months and include energy intake (3-day unannounced 24-hour recalls, NDS-R), appetite/satiety/hunger, energy expenditure (resting energy expenditure), body composition (DEXA), and blood variables (lipids, insulin, glucose, OGTT). IMPACT: If the intriguing preliminary retrospective results are confirmed in this full scale study, the results will demonstrate that inexpensive DNA testing could help dieters predict whether they will have greater weight loss success on a Low Carb or a Low Fat diet. Commensurate with increasing scientific interest in personalized medicine approaches to intervention development, this would provide an example of the potentially substantial health impacts that could be obtained through understanding specific gene-environment interactions that have been anticipated from the unraveling of the human genome.