ABSTRACT

OBJECTIVES: to investigate the association between dietary patterns, physical activity, and body phenotypes in adolescents.
METHODS: this school-based cross-sectional study involved 1,022 adolescents aged ten to 19 years. Dietary patterns and body phenotypes were defined using a principal component analysis. Body phenotype was defined using anthropometry, body composition, biochemistry, sexual maturation, and dietary patterns from 19 food groups, using a food frequency questionnaire. The association between the dietary patterns and body phenotypes was assessed using a linear regression model.
RESULTS: five body phenotypes (BP1_adiposity, BP2_puberty, BP3_biochemical, BP4_muscular, BP5_{lipids_biochemical}) and five dietary patterns (DP1_{ultraprocessed_foods}, DP2_{fresh_foods}, DP3_{bread_rice_beans}, DP4_{culinary_preparations}, DP5_{cakes_rice_beans}) were identified. There were higher BP_{_adiposity} scores for obese adolescents, but energy expenditure was similar for obese and non-obese adolescents. Physical activity was positively associated with BMI, BP_{_adiposity}, and BP_{_puberty}. We observed a negative association between DP_{_ultraprocessed_foods} and BMI, and a positive association between DP_{_fresh_food. DP_fresh_foods} was positively associated with BP_{_adiposity}; DP_{_ultraprocessed_foods} and DP_{_culinary_preparations} were negatively associated with this phenotype. BP_{_biochemical} was negatively associated with DP_{_fresh_foods}.
CONCLUSION: we identified a negative association between a dietary pattern composed mainly of ultra-processed foods, fresh foods, and BP_{_adiposity}. These associations need to be better explored, especially in adolescents, as both dietary patterns and phenotypes were defined using multivariate analysis.

Keywords: Body phenotype, Dietary pattern, Adolescent, Obesity

RESUMO

OBJETIVOS: investigar associação entre padrão alimentar (PA), atividade física (AF) e fenótipos corporais (FC) em adolescentes.
MÉTODOS: estudo transversal de base escolar com 1.022 adolescentes de dez a 19 anos. Padrão alimentar e fenótipo corporal foram definidos por meio da análise de componentes principais. O fenótipo corporal foi definido usando antropometria, composição corporal, bioquímica e maturação sexual, e padrão alimentar a partir de 19 grupos de alimentos de um questionário de frequência alimentar. A associação entre padrão alimentar e fenótipo corporal foi avaliada por modelo de regressão linear.
RESULTADOS: foram identificados cinco fenótipos corporais (FC1_adiposidade, FC2puberdade, FC3_bioquímico, FC4_muscular, FC5_{lipídios_bioquímico}) e cinco padrões alimentares (PA1_{alimentos_ultraprocessados}, PA2_{alimentos_frescos}, PA3_{pão_ arroz_feijão}, PA4_{preparações_culinárias}, PA5_{bolos_arroz_feijão}). Há maiores escores de FC_{_adiposidade} para adolescentes com obesidade, mas o gasto energético foi semelhante para adolescentes com e sem diagnóstico de obesidade. Atividade física associou-se positivamente com IMC, FC_{_adiposidade} e FC_{_puberdade}. Observamos associação negativa entre PA_{_ultraprocessados} e IMC, e positiva entre PA_{_alimentos_frescos}. PA_{_alimentos_frescos} associou-se positivamente com FC_{_adiposidade}; PA_{_ultraprocessados} e PA_{_preparações_culinárias} se associaram negativamente a este fenótipo. FC_{_bioquímico} associou-se negativamente com PA_{_alimentos_frescos}.
CONCLUSÃO: identificamos associação negativa entre padrão alimentar composto principalmente por alimentos ultraprocessados e alimentos in natura e FC_{_adiposidade}. Essas associações devem ser exploradas com o mesmo público em estudos futuros, principalmente em adolescentes, pois tanto o padrão alimentar quanto o fenótipo foram definidos por meio de análise multivariada.

Palavras-chave: Fenótipo corporal, Padrão alimentar, Adolescente, Obesidade

Discussion
In this study, we applied a multivariate analysis to estimate the outcomes (BPs) and smain predictors (DPs). We identified five profiles of BP namely as BP1_adiposity, BP2_puberty, BP3_biochemical, BP4_muscule, and BP5_{lipids_biochemical}. We highlighted the first two BPs that explain the major data variability, express adiposity and body volume, and explain linear growth (chronological axis of adolescence). Five DPs were identified. The first was composed mainly of ultra-processed foods, and in three of them rice and beans were identified. DP__{ultraprocessed} and DP__{fresh_foods} were negatively associated with BP__adiposity, and DP_{_culinary_preparation} was positively associated with BP__adiposity in girls, and adolescents aged 15 - 19 years. DP__{fresh_foods} was negatively associated with BP__biochemical for girls and wealth score.
The multivariate analysis applied in our study may be considered an innovative approach to assess nutritional status. This proposal based on the multidimensionality of parameters of nutritional status (BPs) allowed us to explore multiple interactions among anthropometric, body composition, and biochemical variables.^12,13 Also, a positive aspect of multivariate analysis is the absence of cut-off point for anthropometric measurements, body composition, and biochemical data. These biological measurements were used in our analysis without assumptions, and BP analysis is reproducible in other adolescent populations.¹⁸
The perspective of analysis to investigate the relationship between DPs and nutritional status indicators presented in our study is still recent and unprecedented in the type of proposal presented here. The use of DPs to examine the association between diet and health outcomes is innovative. Studies linking specific DPs to chronic diseases, including obesity and related phenotypes such as body composition and cardiometabolic markers^19,20 are growing. However, most studies still prioritize the use of BMI or obesity phenotypes (weight, waist, and lipid levels) in isolation to assess nutritional status.
A study carried out in China in 2009, including 5267 children and adolescents (six to 13 years old), found a positive association between the western dietary pattern and higher levels of triglycerides and glucose. However, mean triglycerides and glucose values are similar between healthy and western dietary patterns. There was no association between DPs and the presence of hypertriglyceridemia or elevated glucose.²¹ In another study, carried out in England in 2014, using data from Avon Longitudinal Study of Parents and Children (ALSPAC), higher scores for healthy eating patterns were associated with lower glucose levels.²²
Several studies have already investigated the relationship between eating behaviors and nutritional status indicators in both young people^23-24 and adults.^25,26 The results of these studies showed in general that unhealthy eating practices, characterized by the presence of ultra-processed foods, high in free sugar, saturated and trans fat, depleted in protein, fiber, and most micronutrients; these refined products increase the risk of elevated body weight. In contrast, the presence of fruits, vegetables, whole grains, and nuts are protective against gains in body fat. It is important to note that the relationship between DPs and the indicators of nutritional status is better established among adults. In children and adolescents, some reviews^27,28 have highlighted that the results are inconsistent in cross-sectional studies.
Contrary to expectations, our study identified a negative association between DP marked by the presence of ultra-processed foods and culinary preparations, and a positive association between DP marked by fresh food and the adiposity phenotype. DP__{ultraprocessed} was the first principal component in our study, and we observed a high percentage of adolescents who consumed ultra-processed foods identified in this DP such as processed meat, fast foods, and sugary drinks. In a study conducted in 2010 with adolescents in the city of São Paulo, Brazil, the ‘healthy' DP was also associated with the obesity profile.²⁹ In a study carried out in the United States of America in 2012 using data from Project EAT – Eating Among Teens, a positive association was observed between the "sweet and salty snacks pattern" and the risk of overweight / obesity in boys, and higher scores for the "fruit pattern" were positively associated with risk of overweight / obesity in younger boys (mean age = 12.9 years); for girls, these associations were the opposite. The authors hypothesized that the food frequency questionnaire might not reflect all the foods consumed by adolescents included in the study; therefore, DPs did not show a clear association with weight status. Perhaps the use of multiple 24-hour recalls, considered the standard tool for assessing dietary intake, could better reflect the eating habits of the adolescents under study. Another hypothesis raised was that food consumption was not the main determinant of weight status in adolescents.³⁰
In a systematic review and meta-analysis study, the authors reported higher scores for unhealthy DPs, and higher values for cardiometabolic risk factors (body weight, waist circumference, lipid profile, and blood glucose). However, healthy patterns were also associated with higher values of BMI and waist circumference. The authors highlighted publication bias in their study, and due to an unexpected or implausible association between healthy patterns and higher cardiometabolic risk, and unhealthy patterns and lower cardiometabolic risk, it may not be published.³⁰ Also, the protective effect of healthy DPs in adolescents may be unclear and more studies are necessary to understand the relationship of diet and nutritional status outcomes.
The main strength of our study was its originality. To the best of our knowledge, this is possibly the first study to address both the domain of food consumption and nutritional status in a multidimensional manner and the first to analyze the association between BPs as latent variables in the model. In addition, the food frequency questionnaire used to assess food consumption was validated t the study population. We also highlighted the adjusted regression analysis by energy expenditure, sex, age, and wealth status.
Among the limitations of the present study, we highlight a cross-sectional design that prevents the attribution of causality between variables and presents the possibility of the occurrence of reverse causality, as can be seen in this study and; b) the absence of some foods in the list of the food frequency questionnaire and the presence of ultra-processed and processed foods in the same food groups (i.e., pasta and noodles, and homemade cake and industrialized cake). It is important to note that when the questionnaire was developed, there was no classification based on the extent and purpose of industrial food processing, this is a recent proposal for food classification; and c) the overestimation of physical activity by adolescents. Our questionnaire included a list of all types of physical activity, and adolescents may have been overestimated. In contrast to our study, the prevalence of physically active Brazilian adolescents (with 300 min or more of exercise per a week) in 2012 and 2015 was low (21% and 20.7%, respectively). Furthermore, overweight and obese adolescents could practice physical activity to lose their body weight. Finally, we highlighted possible residual confounding effects, even after adjusting for the main factors (age, sex, wealth, and energy expenditure).
We identified a negative association between dietary patterns composed mainly of ultra-processed foods, fresh foods, and BP_{_adiposity}. These associations need to be better explored, especially in adolescents, as both dietary patterns and phenotypes were defined using a multivariate analysis. Little is known about the association of DPs with relation to obesity, metabolic risk, or both among young people in emerging economies such as Brazil. Thus, multidimensional analysis of the parameters of nutritional status and its relationship with DPs should be further explored for a better understanding of this association, as it is an innovative approach.

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Author's contribution: Rinaldi AEM contributed to the study design, analysis and interpretation of data, writing and critical review of the article. Enes CC contributed to the interpretation of data, writing and critical review of the article. Conde WL contributed to the writing and critical review of the article. All authors approved the final version and declare no conflict of the interest.

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Received on January 24, 2023
Final version presented on November 27, 2023
Approved on December 11, 2023

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Associated Editor: Pricila Mullachery