Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil; Center for Epidemiological Studies in Health and Nutrition, University of São Paulo, São Paulo, Brazil.
Risk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
J Nutr. 2023 Jan;153(1):225-241. doi: 10.1016/j.tjnut.2022.09.001. Epub 2022 Dec 15.
The degree of food processing may be an important dimension of diet in how it relates to health outcomes. A major challenge is standardizing food processing classification systems for commonly used datasets.
To standardize and increase transparency in its application, we describe the approach used to classify foods and beverages according to the Nova food processing classification in the 24-h dietary recalls from the 2001-2018 cycles of What We Eat in America (WWEIA), NHANES, and investigate variability and potential for Nova misclassification within WWEIA, NHANES 2017-2018 data via various sensitivity analyses.
First, we described how the Nova classification system was applied to the 2001-2018 WWEIA, NHANES data using the reference approach. Second, we calculated the percentage energy from Nova groups [1: unprocessed or minimally processed foods, 2: processed culinary ingredients, 3: processed foods, and 4: ultraprocessed foods (UPFs)] for the reference approach using day 1 dietary recall data from non-breastfed participants aged ≥1 y from the 2017-2018 WWEIA, NHANES. We then conducted 4 sensitivity analyses comparing potential alternative approaches (e.g., opting for more vs. less degree of processing for ambiguous items) to the reference approach, to assess how estimates differed.
The energy contribution of UPFs using the reference approach was 58.2% ± 0.9% of the total energy; unprocessed or minimally processed foods contributed 27.6% ± 0.7%, processed culinary ingredients contributed 5.2% ± 0.1%, and processed foods contributed 9.0% ± 0.3%. In sensitivity analyses, the dietary energy contribution of UPFs ranged from 53.4% ± 0.8% to 60.1% ± 0.8% across alternative approaches.
We present a reference approach for applying the Nova classification system to WWEIA, NHANES 2001-2018 data to promote standardization and comparability of future research. Alternative approaches are also described, with total energy from UPFs differing by ∼6% between approaches for 2017-2018 WWEIA, NHANES.
食物加工程度可能是饮食与健康结果相关的一个重要维度。一个主要的挑战是为常用数据集标准化食物加工分类系统。
为了使其应用标准化并提高透明度,我们描述了一种方法,该方法用于根据 Nova 食物加工分类系统对 2001-2018 年《美国人饮食》(WWEIA)、NHANES 中 24 小时膳食回忆中的食物和饮料进行分类,并通过各种敏感性分析调查 WWEIA、2017-2018 年 NHANES 数据中 Nova 分类的可变性和潜在错误分类。
首先,我们描述了如何使用参考方法将 Nova 分类系统应用于 2001-2018 年 WWEIA、NHANES 数据。其次,我们使用 2017-2018 年 WWEIA、NHANES 中≥1 岁非母乳喂养参与者的第 1 天膳食回忆数据,计算 Nova 组(1:未加工或轻度加工食品、2:加工烹饪原料、3:加工食品和 4:超加工食品(UPF))的百分比能量,对于参考方法。然后,我们进行了 4 项敏感性分析,比较了潜在的替代方法(例如,对于模棱两可的项目选择更多或更少的加工程度)与参考方法的差异,以评估估计值的差异。
使用参考方法,UPF 的能量贡献占总能量的 58.2%±0.9%;未加工或轻度加工食品贡献 27.6%±0.7%,加工烹饪原料贡献 5.2%±0.1%,加工食品贡献 9.0%±0.3%。在敏感性分析中,替代方法中 UPF 的膳食能量贡献范围为 53.4%±0.8%至 60.1%±0.8%。
我们提出了一种将 Nova 分类系统应用于 WWEIA、NHANES 2001-2018 年数据的参考方法,以促进未来研究的标准化和可比性。还描述了替代方法,对于 2017-2018 年 WWEIA、NHANES,不同方法之间 UPF 的总能量差异约为 6%。