Energy-Dense and Low-Fiber Dietary Pattern May Be a Key Contributor to the Rising Obesity Rates in Brazil

Author:

Alves Iuna Arruda12ORCID,Jessri Mahsa23ORCID,Monteiro Luana Silva4ORCID,Gomes Luiz Eduardo da Silva56ORCID,Lopes Taís de Souza7ORCID,Yokoo Edna Massae8ORCID,Sichieri Rosely9ORCID,Pereira Rosangela Alves7ORCID

Affiliation:

1. Graduate Program in Nutrition, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-590, RJ, Brazil

2. Food, Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada

3. Centre for Health Services and Policy Research (CHSPR), Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada

4. Institute of Food and Nutrition, Federal University of Rio de Janeiro (UFRJ), Macaé 27930-560, RJ, Brazil

5. Graduate Program in Statistics, Institute of Mathematics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-909, RJ, Brazil

6. Department of Quantitative Methods, Center of Exact Sciences and Technology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro 22290-240, RJ, Brazil

7. Department of Social and Applied Nutrition, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-590, RJ, Brazil

8. Department of Epidemiology and Biostatistics, Institute of Collective Health, Fluminense Federal University (UFF), Niterói 24030-210, RJ, Brazil

9. Institute of Social Medicine, Rio de Janeiro State University (UERJ), Rio de Janeiro 20550-013, RJ, Brazil

Abstract

Hybrid methods are a suitable option for extracting dietary patterns associated with health outcomes. This study aimed to identify the dietary patterns of Brazilian adults (20–59 years old; n = 28,153) related to dietary components associated with the risk of obesity. Data from the 2017–2018 Brazilian National Dietary Survey were analyzed. Food consumption was obtained through 24 h recall. Dietary patterns were extracted using partial least squares regression, using energy density (ED), percentage of total fat (%TF), and fiber density (FD) as response variables. In addition, 32 food groups were established as predictor variables in the model. The first dietary pattern, named as energy-dense and low-fiber (ED-LF), included with the positive factor loadings solid fats, breads, added-sugar beverages, fast foods, sauces, pasta, and cheeses, and negative factor loadings rice, beans, vegetables, water, and fruits (≥|0.15|). Higher adherence to the ED-LF dietary pattern was observed for individuals >40 years old from urban areas, in the highest income level, who were not on a diet, reported away-from-home food consumption, and having ≥1 snack/day. The dietary pattern characterized by a low intake of fruits, vegetables, and staple foods and a high intake of fast foods and sugar-sweetened beverages may contribute to the obesity scenario in Brazil.

Funder

Brazilian Federal Agency for Support and Evaluation of Graduate Education

Publisher

MDPI AG

Reference67 articles.

1. World Health Organization (2024, January 14). Draft Recommendations for the Prevention and Management of Obesity over the Life Course, Including Potential Targets, Available online: https://www.who.int/publications/m/item/who-discussion-paper-draft-recommendations-for-the-prevention-and-management-of-obesity-over-the-life-course-including-potential-targets.

2. NCD Risk Factor Collaboration (NCD-RisC) (2024). Worldwide trends in underweight and obesity from 1990 to 2022: A pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet, 403, 1027–1050.

3. Estivaleti, J.M., Guzman-Habinger, J., Lobos, J., Azeredo, C.M., Ferrari, R.C.G., Adami, F., and Rezende, L.F.M. (2022). Time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030. Sci. Rep., 12.

4. Increasing trends in obesity prevalence from 2013 to 2019 and associated factors in Brazil;Ferreira;Rev. Bras. Epidemiol.,2021

5. Brasil. Ministério da Saúde, Secretaria de Vigilância em Saúde e Ambiente & Departamento de Análise Epidemiológica e Vigilância de Doenças Não Transmissíveis (2024, January 21). Vigitel Brasil 2023: Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico: Estimativas Sobre Frequência e Distribuição Sociodemográfica de Fatores de Risco e Proteção para Doenças Crônicas nas Capitais dos 26 Estados Brasileiros e no Distrito Federal em 2023, Available online: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/vigitel/vigitel-brasil-2023-vigilancia-de-fatores-de-risco-e-protecao-para-doencas-cronicas-por-inquerito-telefonico/view.

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