Abstract
The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR). In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits), while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar) components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.
Highlights
The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet [1,2,3]
The present findings showed that results from both approaches answered different research questions that together can be used by health professionals to improve the dietary quality of habitual eating patterns in the overall population
Hypothesis-driven analysis observed low scores for the components Whole grains and Fruits, while dietary patterns derived from data-driven analysis showed no consumption of fruits and whole grains
Summary
The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet [1,2,3]. Two major approaches have been widely used: hypothesis-oriented and data-driven analysis. Both have different purposes that are useful for deriving meaningful dietary patterns that can be associated or not to a particular health outcome. Dietary indices are the most common hypothesis-oriented approaches that evaluate the adherence of population intake to nutritional recommendations. The data-driven approach, on the other hand, provides insight into the dietary behavior of participants, since the evaluation of overall dietary pattern
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