Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes
Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes
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DOI：
10.1001/jamanetworkopen.2018.8102
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发表时间：
2019-02-01
影响因子：
13.8
通讯作者：
Nelson, Heidi
中科院分区：
文献类型：
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作者：
Mendes-Soares, Helena;Raveh-Sadka, Tali;Nelson, Heidi
重要性新出现的证据表明，对食物的餐后血糖反应（PPGR）可能受到每个人独特特征的影响，并根据这些特征进行预测，包括人体测量和微生物组变量。PPGR对食物的个体间差异需要一种个性化的方法来维持健康的血糖水平。假设使用一种模型来描述和预测个体对各种食物的血糖反应，该模型除了考虑所消耗食物的特征外，还考虑了个体的生理学和微生物组。这项使用个性化预测模型的队列研究从2016年10月11日至2017年12月13日在明尼苏达州和佛罗里达招募了327名无糖尿病的个体，作为持续6天研究的一部分。该研究测量了人体测量变量，描述了肠道微生物组成，并使用连续葡萄糖监测仪每5分钟评估一次血糖水平。参与者在研究期间记录了他们的食物和活动信息。一个预测模型的个性化PPGRs的各种各样的foods.Main结果和措施的预测模型的训练和applications. Glycosides反应的食物消耗超过6天，每个参与者。个性化PPGR的预测模型除了考虑所食用食物的特征外，还考虑了个体特征，包括微生物组。结果327名个体对相同食物的餐后反应各不相同（平均[SD]年龄，45 [12]岁; 78.0%女性）。预测每个人对食物的反应的模型，除了食物特征外，还考虑了几个单独的因素，（R = 0.62）比目前仅使用营养成分的标准治疗方法（卡路里R = 0.34，碳水化合物R = 0.40）控制餐后血糖水平。结论和相关性在接受检查的无糖尿病成人队列中，除了营养成分外，考虑个体独特特征（如临床特征，生理变量和微生物组）的个性化预测模型比目前仅关注食物卡路里或碳水化合物含量的饮食方法更具预测性。为个体提供基于他们的PPGR的个性化预测来管理他们对食物的血糖反应的工具可以允许他们将他们的血糖水平维持在与良好健康相关联的限度内。
IMPORTANCE Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels.OBJECTIVES To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed.DESIGN, SETTING, AND PARTICIPANTS This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied.MAIN OUTCOMES AND MEASURES Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed.RESULTS Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels.CONCLUSIONS AND RELEVANCE Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.