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

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2021 · $224,806

## Abstract

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.

## Key facts

- **NIH application ID:** 10053329
- **Project number:** 5R01CA226937-03
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Yikyung Park
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $224,806
- **Award type:** 5
- **Project period:** 2018-12-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10053329

## Citation

> US National Institutes of Health, RePORTER application 10053329, TEMPORAL DIETARY PATTERNS: DEVELOPMENT AND EVALUATION AGAINST ADIPOSITY AND METABOLIC BIOMARKERS (5R01CA226937-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10053329. Licensed CC0.

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