Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
UCD Institute of Food and Health, University College Dublin, Dublin, Ireland.
J Nutr. 2022 Oct 6;152(10):2297-2308. doi: 10.1093/jn/nxac151.
Examination of meal intakes can elucidate the role of individual meals or meal patterns in health not evident by examining nutrient and food intakes. To date, meal-based research has been limited to focus on population rather than individual intakes, without considering portions or nutrient content when characterizing meals.
We aimed to characterize meals commonly consumed, incorporating portions and nutritional content, and to determine the accuracy of nutrient intake estimates using these meals at both population and individual levels.
The 2008-2010 Irish National Adult Nutrition Survey (NANS) data were used. A total of 1500 participants, with a mean ± SD age of 44.5 ± 17.0 y and BMI of 27.1 ± 5.0 kg/m2, recorded their intake using a 4-d weighed food diary. Food groups were identified using k-means clustering. Partitioning around the medoids clustering was used to categorize similar meals into groups (generic meals) based on their Nutrient Rich Foods Index (NRF9.3) score and the food groups that they contained. The nutrient content for each generic meal was defined as the mean content of the grouped meals. Seven standard portion sizes were defined for each generic meal. Mean daily nutrient intakes were estimated using the original and the generic data.
The 27,336 meals consumed were aggregated to 63 generic meals. Effect sizes from the comparisons of mean daily nutrient intakes (from the original compared with generic meals) were negligible or small, with P values ranging from <0.001 to 0.941. When participants were classified according to nutrient-based guidelines (high, adequate, or low), the proportion of individuals who were classified into the same category ranged from 55.3% to 91.5%.
A generic meal-based method can estimate nutrient intakes based on meal rather than food intake at the sample population and individual levels. Future work will focus on incorporating this concept into a meal-based dietary intake assessment tool.
通过检查膳食摄入量，可以阐明个体膳食或膳食模式在健康方面的作用，而这些作用通过检查营养素和食物摄入量是无法明显体现的。迄今为止，基于膳食的研究一直局限于关注人群的摄入量，而不是个体的摄入量，在描述膳食时没有考虑到份量或营养成分。
我们旨在描述通常摄入的膳食，包括份量和营养成分，并确定在人群和个体水平上使用这些膳食来估计营养素摄入量的准确性。
使用 2008-2010 年爱尔兰国家成人营养调查（NANS）的数据。共有 1500 名参与者，平均年龄为 44.5 ± 17.0 岁，BMI 为 27.1 ± 5.0 kg/m2，他们使用 4 天的称重食物日记记录了他们的摄入量。使用 K 均值聚类法识别食物组。基于中值的分区聚类法将具有相似 NRF9.3 评分和所含食物组的相似膳食分类为组（通用膳食）。为每个通用膳食定义了营养成分，定义为分组膳食的平均含量。为每个通用膳食定义了 7 种标准份量。使用原始和通用数据估算了每日平均营养素摄入量。
将 27336 餐汇总为 63 种通用膳食。从原始数据与通用数据比较的平均每日营养素摄入量（来自原始数据与通用数据）比较来看，效应大小微不足道或较小，P 值范围从<0.001 到 0.941。当根据基于营养素的指南（高、充足或低）对参与者进行分类时，归类到相同类别的个体比例范围为 55.3%至 91.5%。
基于通用膳食的方法可以根据样本人群和个体的膳食而不是食物摄入量来估计营养素摄入量。未来的工作将集中于将这一概念纳入基于膳食的膳食摄入量评估工具。
