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，这种模式可以捕捉饮食的整体质量及其组成部分， 食物和营养素。通常使用的饮食模式是基于一组饮食特征的先验饮食评分/指数。 健康饮食的建议（例如，地中海饮食，健康饮食指数）或数据驱动的饮食 图案（例如，谨慎饮食，西方饮食）。许多研究表明，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 饮食模式包括进食行为例如当我们进食时（即，我们吃的时间（也就是我们吃的时间） 一天中的进食频率。因为一次进食所消耗的食物和营养 影响随后进食时的食物消耗量和一天的总摄入量，进食时间 和频率是饮食模式的组成部分。此外，一些证据表明， 表明进食时间和频率以及膳食组成在体重调节中起作用， 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， 最后是慢性病。鉴于显然需要扩大饮食模式框架， 饮食模式的方法学工作，我们建议1）制定一个“时间”的饮食模式的基础上， 在一天中进食时间和频率的时间分布;以及2）评估所识别的时间分布是否 饮食模式与i）总体饮食质量和营养摄入，ii）肥胖（例如，BMI，腰围 周长），和iii）代谢生物标志物（例如，胰岛素、HOMA-IR、LDL-胆固醇、C-反应蛋白）。到 克服了传统统计方法不能捕捉到 时间饮食模式（例如，24-多维特征向量，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。交互式 美国退休人员协会（IDATA）的饮食和活动跟踪研究，重复收集饮食，人体测量和血液 将使用来自1，021名50-74岁男性和女性的样本。在一年的时间里， 每隔一个月收集一次每次进食的24小时回忆和时钟时间（共6次24小时回忆）; 测量人体测量三次（基线和第6个月和第12个月）;并采集血液两次，6个月 apart.成功完成我们提出的研究将确定时间饮食模式， 饮食质量和代谢健康，并验证了时间饮食模式作为未来新工具的效用 研究饮食与健康的关系和预防慢性病。

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.