TEMPORAL DIETARY PATTERNS: DEVELOPMENT AND EVALUATION AGAINST ADIPOSITY AND METABOLIC BIOMARKERS

摘要 人们对饮食模式越来越感兴趣，这种模式反映了饮食的整体质量及其构成 食物和营养素。常用的饮食模式是基于一组饮食的先验饮食分数/指数 健康饮食的建议(如地中海饮食、健康饮食指数)或数据驱动的饮食 模式(例如，谨慎饮食、西式饮食)。大量研究表明，这些饮食模式 与心脏病、糖尿病和癌症等慢性疾病的风险有关。然而，这些都不是 饮食模式包括饮食行为，如我们何时进食(即进食时间)和我们进食的频率(即 进食频率)。因为一次进食所消耗的食物和营养素的量 影响随后进食场合的食物摄入量和一天的总摄入量，进食时间 和频率是饮食模式不可分割的一部分。此外，有几条证据一致 建议进食时间和频率以及膳食成分在体重调节和饮食结构中发挥作用 代谢健康，也调节昼夜节律，所有这些都可能导致代谢功能障碍和 最终导致慢性疾病。鉴于显然需要扩大饮食模式框架并弥合#年的差距 饮食模式的方法论工作，我们建议1)开发一种基于以下因素的“时间”饮食模式 一天中进食时间和频率的时间分布；以及2)评估所识别的时间 饮食模式与i)总体饮食质量和营养摄入量有关，ii)肥胖(例如，BMI、腰围 周长)，以及iii)代谢生物标志物(例如，胰岛素、HOMA-IR、低密度脂蛋白、C-反应蛋白)。至 克服了传统统计方法不能捕捉多维方面的限制 时间饮食模式(例如，24维特征向量、多变量饮食摄入量时间序列数据)，我们 将使用一种结合营养学和系统科学的新方法--机器学习方法。互动型 美国儿科学会(IDATA)反复收集饮食、人体测量和血液的研究中的饮食和活动跟踪 样本来自1021名年龄在50-74岁之间的男性和女性。在一年时间里，IDATA的研究 每隔一个月，每隔一个月收集一次24小时的回忆，包括每个进餐场合的时钟时间(总共6次24小时回忆)； 测量了三次人体尺寸(基线和6个月和12个月)，并采集了两次血，6个月 分开。我们建议的研究的成功完成将确定与以下方面相关的时间饮食模式 饮食质量和代谢健康，并验证时间饮食模式作为未来新工具的效用 饮食与健康关系与慢性病防治的研究。

ABSTRACT There is a growing interest in dietary patterns that capture the overall quality of diet as well as its constituent foods and nutrients. Commonly used dietary patterns are a priori diet score/index based on a set of dietary recommendations for a healthy diet (e.g., Mediterranean diet, Healthy Eating Index) or data-driven dietary patterns (e.g., prudent diet, western diet). Numerous studies have shown that those dietary patterns were related to the risk of chronic diseases such as heart disease, diabetes, and cancer. However, none of these dietary patterns incorporates eating behavior such as when we eat (i.e., eating time) and how often we eat (i.e. eating frequency) during a day. Since the amount of foods and nutrients consumed at one eating occasion influences the food consumption at the subsequent eating occasion and overall intake of the day, eating time and frequency are integral parts of dietary patterns. Furthermore, several lines of evidence consistently suggest that eating time and frequency as well as a meal composition play roles in body weight regulation and metabolic health and also regulate circadian rhythms, all of which may lead to metabolic dysfunctions and ultimately chronic diseases. Given a clear need to expand the dietary patterns framework and close a gap in dietary patterns methodological work, we propose to 1) develop a “temporal” dietary patterns based on temporal distribution of eating time and frequency during a day; and 2) evaluate if the identified temporal dietary patterns are associated with i) overall diet quality and nutrient intakes, ii) adiposity (e.g., BMI, waist circumference), and iii) metabolic biomarkers (e.g., insulin, HOMA-IR, LDL-cholesterol, c-reactive protein). To overcome a limitation that a conventional statistical method cannot capture multidimensional aspects of temporal dietary patterns (e.g., 24-dimensional feature vectors, multivariate dietary intake time-series data), we will use a novel approach combining nutrition and systems science—machine learning method. The Interactive Diet and Activity Tracking in AARP (IDATA) study that repeatedly collected diet, anthropometry, and blood samples from 1,021 men and women, 50-74 years old will be used. During one year, the IDATA study collected 24-hour recalls with clock time for each eating occasion, every other month (total six 24-hour recalls); measured anthropometry three times (baseline and at month 6 and 12); and collected blood twice, 6-month apart. Successful completion of our proposed study will identify temporal dietary patterns that are related to diet quality and metabolic health and validate the utility of temporal dietary patterns as a new tool for future research on diet-health relations and prevention of chronic diseases.