# Ultra-Processed Foods and Childhood Obesity

> **NIH NIH R03** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $86,500

## Abstract

PROJECT SUMMARY
Despite a broad understanding of the multi-level determinants of health disparities in childhood obesity, known
risk factors including diet and physical often do not adequately predict later obesity among low-income and
minority children. Developing effective intervention targets for childhood obesity in these populations is
therefore difficult. Emerging evidence suggests that ultra-processed food consumption may partially explain
health disparities in cardiovascular disease and cancer among adults. However, existing methods to classify
foods based on the extent of processing are inconsistent and unclear, making it difficult to assess exposure to
ultra-processed foods and associations with childhood weight outcomes. The current proposal will apply the
NOVA classification for ultra-processed foods to dietary recall data from an NHLBI-funded childhood obesity
prevention randomized controlled trial. Developing a reliable and valid methodology for assessing ultra-
processed food consumption and testing associations with childhood obesity will generate evidence to better
characterize dietary intake and to identify potential targets for reducing health disparities in childhood obesity.
This proposal builds on a robust dataset from the Growing Right Onto Wellness (GROW) trial, which aimed to
prevent childhood obesity using a three-year multi-level and culturally-tailored intervention. The trial
randomized 610 parent-preschool child pairs and achieved >90% retention at three-year follow-up, with high
rates of data completeness. Eligible children were ages 3-5 at enrollment, spoke English or Spanish, and had
BMI ≥50th percentile and <95th percentile. The dataset includes 24-hour diet recall data collected using NDS-R
software at baseline and three annual follow-up timepoints. The current proposal will develop and validate a
novel coding algorithm to map the existing NOVA classification system for ultra-processed foods onto this diet
recall data. This algorithm will generate an analytic variable that describes the number of calories consumed
per day in each of the four NOVA classifications for food processing level.
Using the newly developed approach to assessing ultra-processed food consumption, we will test the
association between higher levels of ultra-processed foods and childhood obesity across 3 years of follow up.
The main exposure variable will be the number of daily calories consumed for ultra-processed foods and the
primary outcome will be child raw BMI. The goals of this proposal are to 1) advance dietary measurement by
developing a novel methodology for evaluating levels of ultra-processed food consumption using diet recall
data; 2) assess whether ultra-processed food consumption level is predictive of incident obesity among low-
income, minority preschoolers; and 3) develop evidence for intervention targets for future R01 funding.

## Key facts

- **NIH application ID:** 10063710
- **Project number:** 1R03HL154243-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** William Heerman
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $86,500
- **Award type:** 1
- **Project period:** 2020-08-10 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10063710

## Citation

> US National Institutes of Health, RePORTER application 10063710, Ultra-Processed Foods and Childhood Obesity (1R03HL154243-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10063710. Licensed CC0.

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