iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National Trials

项目总结 十年前，美国膳食指南咨询委员会推荐了饮食模式方法 研究饮食与健康结果之间的关系。与此同时，纵向饮食数据 变得越来越可用。然而，表征动态饮食质量的方法还不够成熟 变化，并保持初步的验证纵向饮食质量模式，因此，导致不清楚 评估饮食与健康关系和制定饮食指南的证据。存在着明显的差距 在饮食模式文献和快速增长的统计学习领域之间， 人工智能算法。我们建议开发“IPAT：智能饮食质量模式分析” 协调MA--国家试验“。IPAT将利用生成的原始和新协调的饮食数据 来自NIDDK、NHLBI和NIMH资助的7项研究：4项纵向随机对照试验(RCT) 马萨诸塞州(MA)，以及3项大规模纵向多站点国家研究、一项随机对照研究和一项观察性研究 来自妇女健康倡议(WHI)的研究(OS)和来自冠状动脉风险发展的OS 青年研究(CARDIA)。我们的目标是利用20多个新协调的饮食数据集 具有高度可比性的纵向研究，跨越50个临床和健康社区，跨越35年 中心目标：1)通过采用我们新的可视化辅助弹道模式识别和验证进行创新 用于纵向饮食数据的智能简化模式分析工具(IPAT)的算法；2)支持 多视角综合认识多发性慢性病患者饮食质量轨迹模式 不同粒度的个体研究可能无法发现的疾病结果；以及3) 创建可访问和可扩展的协调饮食数据库和开放获取的饮食相关IPAT工具 学习。我们以协调数据为导向的方法将增加成功解决复杂问题的可能性 以及通过大规模饮食数据提出的微妙问题，包括但不限于文化、年龄、性别和 饮食质量模式的地理差异，以及饮食质量如何随环境和时间变化。我们的IPAT 该方法将建立在皮方的行为轨迹模式识别方法的基础上，该方法已经被 在NIDA/NCI/NHLBI资助的五个纵向OS和RCT中进行验证和复制。发展这一证据- 基于IPAT的工具将有助于饮食相关研究的基础设施，先进的模式识别方法， 帮助科学界和公众将个人饮食行为与当地和国家的饮食进行比较- 质量模式和相关的饮食健康风险。我们的工作还将有助于找到更多有效的饮食证据 指导方针。更广泛地说，该IPAT项目将有助于创建一个支持统一数据的平台 管理、近乎实时的模式分析和适应性干预。

PROJECT SUMMARY A decade ago, the U.S. Dietary Guidelines Advisory Committee recommended dietary pattern approaches to examine relationships between diet and health outcomes. Meanwhile, longitudinal dietary data have become increasingly available. However, methods are underdeveloped for characterizing dynamic diet-quality variations and remain rudimentary for validating longitudinal diet-quality patterns, thus, leading to unclear evidence for assessing diet-health relationships and formulating dietary guidelines. A noticeable gap exists between the dietary pattern literature and the fast-growing statistical learning field with explosive growth of artificial intelligence algorithms. We propose to develop “iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National Trials”. iPAT will leverage original and newly harmonized dietary data generated from 7 studies funded by NIDDK, NHLBI, and NIMH: 4 longitudinal randomized controlled trials (RCT) in Massachusetts (MA), and 3 large-scale longitudinal multi-site national studies, an RCT and one observational study (OS) from the Women’s Health Initiative (WHI), and one OS from the Coronary Artery Risk Development in Young Adults (CARDIA) study. We aim to harness over 20 newly-harmonized dietary datasets from these highly-comparable longitudinal studies that span up to 35 years and cross 50 clinical and health community centers to: 1) innovate by adapting our new visualization-aided trajectory pattern-recognition and validation algorithm to an intelligent and streamlined pattern analysis tool (iPAT) for longitudinal dietary data; 2) enable a new multi-view and comprehensive understanding of diet-quality trajectory patterns for multiple chronic disease outcomes that may not be discoverable from individual studies at different levels of granularity; and 3) create an accessible and expandable harmonized dietary database and open-access iPAT tool for diet-related studies. Our harmonized-data-driven approach will increase the likelihood of successfully addressing complex and subtle questions with large-scale dietary data, including but not limited to the cultural, age, gender and geographic variation in diet quality patterns and how diet quality may vary with context and time. Our iPAT approach will be built upon PI Fang’s behavioral trajectory pattern-recognition method which has been validated and replicated in five NIDA/NCI/NHLBI-funded longitudinal OS and RCTs. Developing this evidence- based iPAT tool will contribute to the infrastructure for diet-related studies, advance pattern-recognition methods, help scientific communities and the public to compare individual dietary behavior with local and national diet- quality patterns and associated dietary health risks. Our work will also help grow more valid evidence for dietary guidelines. More broadly, this iPAT project will contribute to creating a platform that supports harmonized data management, near-real-time pattern analyses and adaptive interventions.