Woolhead Clara, Gibney Michael J, Walsh Marianne C, Brennan Lorraine, Gibney Eileen R
Institute of Food and Health, University College Dublin, Belfield, Ireland.
Institute of Food and Health, University College Dublin, Belfield, Ireland
Am J Clin Nutr. 2015 Aug;102(2):316-23. doi: 10.3945/ajcn.114.106112. Epub 2015 Jun 17.
Meal pattern analysis can be complex because of the large variability in meal consumption. The use of aggregated, generic meal data may address some of these issues.
The objective was to develop a meal coding system and use it to explore meal patterns.
Dietary data were used from the National Adult Nutrition Survey (2008-2010), which collected 4-d food diary information from 1500 healthy adults. Self-recorded meal types were listed for each food item. Common food group combinations were identified to generate a number of generic meals for each meal type: breakfast, light meals, main meals, snacks, and beverages. Mean nutritional compositions of the generic meals were determined and substituted into the data set to produce a generic meal data set. Statistical comparisons were performed against the original National Adult Nutrition Survey data. Principal component analysis was carried out by using these generic meals to identify meal patterns.
A total of 21,948 individual meals were reduced to 63 generic meals. Good agreement was seen for nutritional comparisons (original compared with generic data sets mean ± SD), such as fat (75.7 ± 29.4 and 71.7 ± 12.9 g, respectively, P = 0.243) and protein (83.3 ± 26.9 and 80.1 ± 13.4 g, respectively, P = 0.525). Similarly, Bland-Altman plots demonstrated good agreement (<5% outside limits of agreement) for many nutrients, including protein, saturated fat, and polyunsaturated fat. Twelve meal types were identified from the principal component analysis ranging in meal-type inclusion/exclusion, varying in energy-dense meals, and differing in the constituents of the meals.
A novel meal coding system was developed; dietary intake data were recoded by using generic meal consumption data. Analysis revealed that the generic meal coding system may be appropriate when examining nutrient intakes in the population. Furthermore, such a coding system was shown to be suitable for use in determining meal-based dietary patterns.
由于膳食摄入存在很大差异,膳食模式分析可能很复杂。使用汇总的通用膳食数据可能会解决其中一些问题。
开发一种膳食编码系统,并使用它来探索膳食模式。
使用了来自国家成人营养调查(2008 - 2010年)的膳食数据,该调查收集了1500名健康成年人的4天食物日记信息。为每个食物项目列出了自我记录的膳食类型。确定常见的食物组组合,为每种膳食类型生成一些通用膳食:早餐、便餐、主餐、零食和饮料。确定通用膳食的平均营养成分,并代入数据集中以生成通用膳食数据集。针对原始的国家成人营养调查数据进行了统计比较。使用这些通用膳食进行主成分分析以识别膳食模式。
总共21948份个体膳食被简化为63份通用膳食。在营养比较方面(原始数据集与通用数据集的均值±标准差),如脂肪(分别为75.7±29.4克和71.7±12.9克,P = 0.243)和蛋白质(分别为83.3±26.9克和80.1±13.4克,P = 0.525),观察到良好的一致性。同样,布兰德 - 奥特曼图显示,对于许多营养素,包括蛋白质、饱和脂肪和多不饱和脂肪,一致性良好(一致性界限外<5%)。从主成分分析中确定了12种膳食类型,其膳食类型的包含/排除不同,能量密集型膳食不同,膳食成分也不同。
开发了一种新颖的膳食编码系统;使用通用膳食消费数据对膳食摄入数据进行重新编码。分析表明,通用膳食编码系统在检查人群营养摄入时可能是合适的。此外,这种编码系统被证明适用于确定基于膳食的饮食模式。