Metabolomics from a Controlled Feeding Study and Large Cohorts to Identify Diet-Associated Biomarkers, Augmented by Genetics to Infer Causality for Cardiometabolic Outcomes

饮食与健康和疾病有着密切的关系。许多研究已经将饮食结构的变化与心脏代谢结果相关，如肥胖、2型糖尿病(T2D)或心血管疾病(CVD)。然而，相关性并不能证明因果关系。一个关键的挑战是确定饮食的哪些影响(如果有的话)与长期健康结果存在因果关系。传统上，随机对照试验(RCT)被用来证明因果关系。然而，这些试验在评估饮食对长期健康结果的机制和因果影响方面基本上是不可行的。饮食的改变改变了许多变量，对生理产生了无数的影响。此外，依从性的下降通常比结果的增加快得多，需要强大的生物标记物来表明参与者已经遵守了饮食，并且饮食已经达到了预期的效果。幸运的是，全面的代谢物分析，包括在大规模、良好控制的饮食RCT和人群队列的背景下，加上人类遗传学，提供了一条潜在的前进路线，可以识别强大的生物标记物，并评估饮食模式、生物标记物和长期心脏代谢结果之间的预测性和因果关系。我们建议利用新的代谢组学和遗传学方法来确定一个关键的饮食模式，碳水化合物/脂肪比率的强大生物标志物，并使用这些生物标志物来检查这种饮食模式和心脏代谢结果(肥胖、T2D和心血管疾病)之间的关系。我们将实现三个具体目标。在SA#1中，我们将在令人兴奋的初步结果的基础上，从大量仔细控制的饮食RCT中生成全面的非靶向代谢物图谱数据，以确定与体重稳定受试者的饮食碳水化合物/脂肪比率相关的代谢物(已知和未知)。在SA#2中，我们将利用基于现有人群的大型队列和饮食和代谢数据来验证这些代谢物是否为可靠的生物标记物。在SA#3中，我们将使用纵向数据来测试这些生物标记物是否可以预测心脏代谢结果，并将使用一种称为孟德尔随机化(RCT的遗传学等价物)的方法来评估与饮食相关的代谢物是否导致肥胖、T2D或CVD。我们将采用多种创新方法，包括饮食随机对照试验中的代谢物分析、非靶向代谢物分析数据的大型基因研究、避免孟德尔随机化的已知困难的方法，以及协调跨研究的未识别代谢物和非靶向分析的方法。这些目标的成功完成将开发关键饮食模式(碳水化合物与脂肪的比率)的强大生物标志物，测试这些生物标志物是否有助于预测长期健康结果，并测试对这种饮食模式的生理反应与心脏代谢结果的因果关系。这种方法将为改进营养干预研究提供一个范例，以便更好地了解饮食与健康之间的关系，并最终加强饮食治疗以预防和治疗慢性病。

Diet has a strong relationship with health and illness. Many studies have correlated variation in dietary composition with cardiometabolic outcomes such as obesity, type 2 diabetes (T2D) or cardiovascular disease (CVD. However, correlation does not prove causation. A key challenge is to determine which (if any) of the effects of diet are causally linked to long-term health outcomes. Classically, randomized controlled trials (RCTs) are used to prove causality. However, such trials are essentially infeasible for assessing mechanism and causal effects of diet on long-term health outcomes. Dietary modifications alter many variables, with myriad effects on physiology. Furthermore, compliance typically wanes much more quickly than outcomes accrue, and robust biomarkers are needed that indicate that a participant has complied with a diet and that the diet has had the desired effect. Fortunately, comprehensive metabolite profiling, including in the setting of a large, well-controlled dietary RCT and population-based cohorts, augmented by human genetics, offers a potential route forward to identify robust biomarkers and to assess the predictive and causal relationships between dietary patterns, biomarkers, and long term cardiometabolic outcomes. We propose to take advantage of new metabolomics and genetic methods to identify robust biomarkers of a key dietary pattern, carbohydrate-to-fat ratio, and to use these biomarkers to examine the relationship between this dietary pattern and cardiometabolic outcomes (obesity, T2D, and CVD). We will achieve three specific aims. In SA#1, we will build on exciting preliminary results and generate comprehensive untargeted metabolite profiling data from a large carefully controlled dietary RCT, to identify metabolites (known and unknown) that are associated with dietary carbohydrate-to-fat ratio in weight-stable subjects. In SA#2, we will leverage large existing population-based cohorts with dietary and metabolomic data to validate these metabolites as robust biomarkers. In SA#3, we will use longitudinal data to test whether these biomarkers predict cardiometabolic outcomes, and will employ an approach called Mendelian randomization (the genetic equivalent of a RCT) to assess whether the diet-associated metabolites are causal for obesity, T2D or CVD. We will deploy multiple innovative approaches, including metabolite profiling in a dietary RCT, large genetic studies of untargeted metabolite profiling data, methods to avoid known difficulties with Mendelian randomization, and also methods to harmonize unidentified metabolites from untargeted profiling across studies. Successful completion of these aims will develop robust biomarkers of a key dietary pattern (carbohydrate-to- fat ratio), test whether these biomarkers are useful predictors of long term health outcomes, and test causality of the physiological responses to this dietary pattern for cardiometabolic outcomes. This approach will provide a paradigm for improving nutritional interventional studies, for better understanding the relationship between diet and health, and ultimately enhance dietary treatments to prevent and treat chronic diseases.