Identifying Nutrient Patterns in South African Foods to Support National Nutrition Guidelines and Policies

Abstract

Food composition databases (FCDBs) provide the nutritional content of foods and are essential for developing nutrition guidance and effective intervention programs to improve nutrition of a population. In public and nutritional health research studies, FCDBs are used in the estimation of nutrient intake profiles at the population levels. However, such studies investigating nutrient co-occurrence and profile patterns within the African context are very rare. This study aimed to identify nutrient co-occurrence patterns within the South African FCDB (SAFCDB). A principal component analysis (PCA) was applied to 28 nutrients and 971 foods in the South African FCDB to determine compositionally similar food items. A second principal component analysis was applied to the food items for validation. Eight nutrient patterns (NPs) explaining 73.4% of the nutrient variation among foods were identified: (1) high magnesium and manganese; (2) high copper and vitamin B12; (3) high animal protein, niacin, and vitamin B6; (4) high fatty acids and vitamin E; (5) high calcium, phosphorous and sodium; (6) low moisture and high available carbohydrate; (7) high cholesterol and vitamin D; and (8) low zinc and high vitamin C. Similar food patterns (FPs) were identified from a PCA on food items, yielding subgroups such as dark-green, leafy vegetables and, orange-coloured fruit and vegetables. One food pattern was associated with high sodium levels and contained bread, processed meat and seafood, canned vegetables, and sauces. The data-driven nutrient and food patterns found in this study were consistent with and support the South African food-based dietary guidelines and the national salt regulations.

Statistical Methods for the Analysis of Food Composition Databases: A Review

“…PCA [ 41 ] and factor analysis [ 42 ] are data reduction techniques that combine correlated variables into a smaller number of components. Two studies [ 17 , 33 ] investigated the nutrient co-occurrence patterns in foods. Similä et al [ 33 ] used the food composition database of Finland, which consisted of 530 food items, used as ingredients (fresh, uncooked, and edible), and 106 nutrients and non-nutrients (for instance, phytosterols and heavy metals).…”

“…Balakrishna et al [ 17 ] also investigated nutrient co-occurrence patterns. PCA with varimax rotation was applied to 971 food items and 28 nutrients from the South African FCDB.…”

“…However, users need to be aware of poor data quality and collinearity among the predictors as these issues can affect the validity and predictive power of the model. As shown in [ 9 , 17 , 24 ], particular nutrients can be highly correlated and could contribute to collinearity. The reliability of the model can also decrease with an increasing number of predictors.…”

“…PCA and factor analysis were used to investigate nutrient co-occurrence patterns amongst food items. One study was able to relate the nutrient patterns found to existing national guidelines and policies [ 17 ]. The advantages of using factor analysis and PCA to analyse FCD are that several nutrients are considered at once and the components found can be used as covariates in models when the use of the original larger dataset is not feasible.…”

Is There a Convergence between the Food Classification Adopted by Food-Based Dietary Guidelines and Food Science and Technology?

“…Food consumption has been recognized as an essential predictor of health or behavior that strongly influences health and future disease risk. Food composition and classification data are used in health studies, from which results are the basis for formulating public policies [1][2][3][4][5][6]. However, in epidemiological studies, comparing food and nutrient consumption in different countries indicates difficulties in finding a solid foundation for classifying food [7][8][9][10][11][12].…”