Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level.

习惯性食用劣质饮食与许多非传染性疾病的危险因素直接相关。这导致绝大多数国家和世界卫生组织制定了健康饮食政策，以减少人口中非传染性疾病的患病率。然而，越来越多的证据表明，任何饮食干预的个体代谢反应都有差异。基于高度控制的饮食研究与深度代谢表型分析的耦合数据，我们开发了一种应用管道来了解个体间对饮食反应的差异的方法。在这项可行性研究中，我们创建了个体饮食代谢类型评分（DMS），该评分体现了个体间饮食反应的变异性，并捕获了随之而来的尿代谢物浓度的动态变化。我们发现 DMS 和血糖浓度之间存在反比关系。DMS 和尿代谢能量损失之间也存在关系。此外，我们使用代谢熵方法来可视化个人和集体对饮食干预的反应。DMS 有可能提供一种方法来瞄准和增强个人层面的饮食反应，从而减轻人口层面的非传染性疾病负担。