Ultra-Processed Foods and Childhood Obesity

超加工食品和儿童肥胖

基本信息

批准号：
10063710
负责人：
William Heerman
金额：
$ 8.65万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-10 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10063710
关键词：
Acceleration Address Adult Age Algorithms Award Body mass index Calories Carbohydrates Cardiovascular Diseases Child Childhood Classification Code Cohort Analysis Computer software Consumption Control Groups Counseling Data Data Set Diet Dietary intake Eating Energy Intake Enrollment Evaluation Exposure to Family Fatty acid glycerol esters Food Food Access Food Processing Foundations Funding Future Generations Goals Growth Guidelines Health Height Hour Individual Intake Intervention Knowledge Link Logistic Regressions Low Income Population Low income Malignant Neoplasms Maps Measurement Measures Mentors Methodology Methods Minority Modeling Mothers National Heart, Lung, and Blood Institute Non obese Nursery Schools Nutritional Obesity Outcome Parents Pattern Policies Population Preschool Child Preventive Intervention Process Proteins Randomized Controlled Trials Research Risk Risk Factors Role Schools System Testing Time Underserved Population United States Weight aged arm base cohort disparity reduction early childhood effective intervention evidence base follow-up food consumption health disparity improved interest minority children novel nutrition nutritional epidemiology nutritional guideline obesity in children obesity prevention obesity risk obesity treatment prevent primary outcome programs prospective randomized trial secondary analysis sugar treatment arm

项目摘要

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.

项目概要尽管人们对儿童肥胖健康差异的多层次决定因素有广泛的了解，但已知包括饮食和身体在内的危险因素通常不能充分预测低收入人群后来的肥胖情况制定针对这些人群的儿童肥胖的有效干预目标。因此，新出现的证据表明，过度加工食品的消费可能可以部分解释这一问题。成人心血管疾病和癌症的健康差异然而，现有的分类方法。基于加工程度的食品不一致且不明确，因此很难评估其接触程度超加工食品及其与儿童体重结果的关联当前提案将适用超加工食品的 NOVA 分类与 NHLBI 资助的儿童肥胖症的饮食回忆数据预防随机对照试验。开发一种可靠有效的方法来评估超加工食品消费和测试与儿童肥胖的关联将为更好地提供证据描述膳食摄入量并确定减少儿童肥胖健康差异的潜在目标。该提案建立在 Growing Right Onto Wellness (GROW) 试验的强大数据集之上，旨在使用为期三年的多层次、针对文化的干预措施来预防儿童肥胖。对 610 名家长与学龄前儿童进行随机分组，三年随访时保留率超过 90%，且保留率很高符合条件的儿童在入学时年龄为 3-5 岁，会说英语或西班牙语，并且具有英语或西班牙语能力。 BMI ≥50% 且 <95% 的数据集包括使用 NDS-R 收集的 24 小时饮食回忆数据。当前的提案将开发并验证一个基线和三个年度后续时间点的软件。新颖的编码算法将现有的超加工食品 NOVA 分类系统映射到这种饮食中该算法将生成一个描述消耗卡路里数的分析变量。食品加工级别的四个 NOVA 分类中的每一个分类中的每天。使用新开发的方法来评估超加工食品的消费，我们将测试三年随访期间，较高水平的超加工食品与儿童肥胖之间存在关联。主要的暴露变量是每天消耗超加工食品的卡路里数和主要结果是儿童原始体重指数。该提案的目标是 1) 通过以下方式推进饮食测量。开发一种利用饮食回忆来评估超加工食品消费水平的新方法数据；2）评估超加工食品的消费水平是否可以预测低收入人群的肥胖事件收入、少数族裔学龄前儿童；以及 3) 为未来 R01 资金的干预目标提供证据。