Jean Mayer-United States Department of Agriculture Human Nutrition Research Center on Aging, Boston, MA, USA.
Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA.
Am J Clin Nutr. 2020 Apr 1;111(4):893-902. doi: 10.1093/ajcn/nqaa037.
Although diet response prediction for cardiometabolic risk factors (CRFs) has been demonstrated using single genetic variants and main-effect genetic risk scores, little investigation has gone into the development of genome-wide diet response scores.
We sought to leverage the multistudy setup of the Women's Health Initiative cohort to generate and test genetic scores for the response of 6 CRFs (BMI, systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting glucose) to dietary fat.
A genome-wide interaction study was undertaken for each CRF in women (n ∼ 9000) not participating in the dietary modification (DM) trial, which focused on the reduction of dietary fat. Genetic scores based on these analyses were developed using a pruning-and-thresholding approach and tested for the prediction of 1-y CRF changes as well as long-term chronic disease development in DM trial participants (n ∼ 5000).
Only 1 of these genetic scores, for LDL cholesterol, predicted changes in the associated CRF. This 1760-variant score explained 3.7% (95% CI: 0.09, 11.9) of the variance in 1-y LDL cholesterol changes in the intervention arm but was unassociated with changes in the control arm. In contrast, a main-effect genetic risk score for LDL cholesterol was not useful for predicting dietary fat response. Further investigation of this score with respect to downstream disease outcomes revealed suggestive differential associations across DM trial arms, especially with respect to coronary heart disease and stroke subtypes.
These results lay the foundation for the combination of many genome-wide gene-diet interactions for diet response prediction while highlighting the need for further research and larger samples in order to achieve robust biomarkers for use in personalized nutrition.
虽然已经有研究利用单一遗传变异和主要遗传风险评分来预测心血管代谢风险因素（CRFs）的饮食反应，但对于全基因组饮食反应评分的开发研究却很少。
我们试图利用妇女健康倡议队列的多研究设计，生成和测试针对 6 个 CRFs（BMI、收缩压、LDL 胆固醇、HDL 胆固醇、甘油三酯和空腹血糖）对饮食脂肪反应的遗传评分。
在未参与饮食干预试验（该试验侧重于减少饮食脂肪）的女性（n ∼ 9000）中，对每个 CRF 进行全基因组交互研究。基于这些分析的遗传评分是使用修剪和阈值方法开发的，并在参与饮食干预试验的（n ∼ 5000）参与者中测试了其对 1 年 CRF 变化以及长期慢性疾病发展的预测能力。
只有一个 LDL 胆固醇的遗传评分可以预测相关 CRF 的变化。这个由 1760 个变体组成的评分解释了干预组中 1 年 LDL 胆固醇变化的 3.7%（95%CI：0.09，11.9），但与对照组的变化无关。相比之下，LDL 胆固醇的主要遗传风险评分对于预测饮食脂肪反应则没有用。进一步研究该评分与下游疾病结局的关系表明，在饮食干预试验中，该评分存在跨臂的差异关联，特别是在冠心病和中风亚型方面。
这些结果为结合许多全基因组基因-饮食相互作用进行饮食反应预测奠定了基础，同时强调需要进一步的研究和更大的样本量，以实现个性化营养中使用的稳健生物标志物。
