Ultra-Processed Foods and Childhood Obesity

项目摘要 尽管人们对儿童肥胖症健康差异的多层次决定因素有了广泛的了解， 在低收入人群中，包括饮食和身体因素在内的风险因素往往不能充分预测日后的肥胖， 少数民族儿童。在这些人群中制定有效的儿童肥胖干预目标， 因此很难。新出现的证据表明，超加工食品消费可能部分解释了 成年人在心血管疾病和癌症方面的健康差距。然而，现有的分类方法 基于加工程度的食品是不一致和不清楚的，因此很难评估暴露于 超加工食品以及与儿童体重结果的关系。目前的建议将适用于 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。制定一个可靠和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产生证据， 描述饮食摄入的特征，并确定减少儿童肥胖症健康差异的潜在目标。 该提案建立在健康成长权（Growing Right Onto Wellness，GROW）试验的强大数据集基础上，该试验旨在 通过一项为期三年的多层次和文化定制的干预措施预防儿童肥胖。审判 随机分配了610对父母-学龄前儿童，在三年随访时保持率>90%， 数据完整率。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， BMI ≥第50百分位且<第95百分位。该数据集包括使用NDS-R收集的24小时饮食回忆数据 在基线和三个年度随访时间点使用软件。目前的建议将制定和验证一个 一种新的编码算法，将现有的超加工食品NOVA分类系统映射到这种饮食中 召回数据。该算法将生成一个分析变量，描述消耗的卡路里数 在食品加工水平的四个NOVA分类中的每一个中， 使用新开发的评估超加工食品消费的方法，我们将测试 在3年的随访中，高水平的超加工食品与儿童肥胖之间的关联。 主要的暴露变量将是超加工食品每日消耗的卡路里数量， 主要结果是儿童原始BMI。该提案的目标是：1）通过以下方式推进饮食测量： 开发一种新的方法，用于使用饮食回忆评估超加工食品消费水平 数据; 2）评估超加工食品消费水平是否可预测低- 收入，少数学龄前儿童;和3）为未来R 01资金的干预目标制定证据。

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.