Chronic diseases and conditions such as obesity, diabetes and cardiovascular disease are among the most common, costly, and preventable of all health problems in the United States. A healthy dietary pattern is paramount in disease risk reduction. Since 2010, the dietary pattern approach has been recommended to examine the relation of the totality of diet and health outcomes by U.S. Dietary Guidelines Advisory Committees; meanwhile longitudinal dietary data have become increasingly available. Yet, methods are underdeveloped for characterizing longitudinal diet-quality variations and even rudimentary for validating diet- quality patterns that describe these dynamic variations, therefore, leading to unclear evidence for assessing diet-health/disease relationships and formulating dietary guidelines. A noticeable gap exists between dietary pattern literature and the fast-growing statistical learning field. We propose to develop an innovative statistical learning tool for diet-quality trajectory pattern-recognition based on rich and highly-comparable longitudinal dietary datasets from randomized controlled trials (RCT) and observational studies (OS) pertaining to a variety of individuals, race/ethnicities, and geographical locations, and spanning up to 30 years, collected across 4 NIH- funded RCTs in Massachusetts, and 2 large-scale multi-site national RCT and OS studies as well as simulated dietary data based on these trials. Our project builds on PI Fang?s NIH-funded behavioral trajectory pattern-recognition tool (Multiple-Imputation based Fuzzy Clustering, MIFuzzy) which processes longitudinal trial data with missing and zero-inflated values, and identifies latent trajectory patterns that characterize patients? complex engagement and cognitive response variations during multi-component RCTs and better explains the heterogeneity of treatment effects. This project will enhance and expand MIFuzzy to a Visual- Valid Dietary Behavior Pattern Recognition tool (VIP), adapted to diet-quality trajectory pattern analyses and chronic disease risk assessment. Our goal is to provide a new multi-view of diet-quality trajectory patterns and associated outcomes from longitudinal studies. Based upon high-quality and comparable RCT and OS longitudinal dietary data from NIDDK-, NHLBI-, and NIMH-funded studies, this VIP project will help grow more valid evidence for developing dietary guidelines and clarify our understanding of diet-disease relationships for a range of patient/individual types, potentially enabling better personalized, adaptive dietary strategies. Developing this evidence-based VIP tool will also contribute to the infrastructure for diet-related studies, advance pattern- recognition methods, help scientific communities and the lay public compare with local and national diet-quality guidelines, and assess dietary health risks. In the long run, this VIP project will contribute to creating a data management platform that support near-real-time pattern analyses and adaptive interventions.
/ PUBLIC HEALTH RELEVANCE Evaluating diet quality and encouraging healthful dietary change is paramount for preventing, or managing, many chronic conditions and diseases, including obesity, diabetes and cardiovascular disease. Our innovative Visual-Valid Dietary Behavior Pattern-recognition (VIP) method will identify clusters of distinct diet-quality patterns in multiple longitudinal dietary datasets from local and national NIH-funded randomized controlled trials and observational studies. This work will help formulate stronger evidence for national dietary guidelines, deepen our understanding of diet quality trajectory patterns, and elucidate relationships between these patterns and chronic diseases for a range of patient/individual types; it can potentially enable personalized, adaptive dietary strategies. Open-access dissemination of our diet quality pattern recognition tool will contribute to the infrastructure of dietary-intervention studies, advance pattern recognition methodology, and help the scientific community and the public to compare individual dietary behaviors with local and national diet-quality patterns and associated health risks.
