Agreement of dietary fiber and calorie intake values according to the choice of nutrient composition and household measure tables
Palabras clave:
Food analysis, Nutritional epidemiology, Tables of food compositionResumen
Objective
To analyze the variations in the daily intake of dietary fiber and calories according to the different nutrient composition and homemade measure tables.
Methods
Five different methods based on different nutrient composition and household measure tables were used to calculate daily calorie and fiber intake, measured using a food frequency questionnaire, of 633 pregnant women receiving care in primary health care units in the Southern region of Brazil; they were selected to participate in a cohort study. The agreement between the five methods was evaluated using the Kappa and weighted Kappa coefficients. The Nutritional Support Table, a Brazilian traditional food composition table and the Brazilian household expenditure survey were used in Method 1. Brazilian Food Composition Table and the Table for the Assessment of Household Measures (Pinheiro) were used in Methods 2 and 3. The average values of all subtypes
of food listed in the Brazilian Food Composition Table for each corresponding item in the food frequency questionnaire were calculated in the method 3. The United States Department of Agriculture Food CompositionTable and the table complied by Pinheiro were used in Method 4. The Brazilian Food Composition Table and the Brazilian household expenditure survey were used in Method 5.
Results
The highest agreement of calorie intake values were found between Methods 2 and 3 (Kappa=0.94; 0.92–0.95), and the lowest agreement was found between Methods 4 and 5 (Kappa=0.46; 0.42–0.50). As for the fiber intake, the highest agreement was found between Methods 2 and 5 (Kappa=0.87; 0.82–0.90), and the lowest agreement was observed between Methods 1 and 4 (Kappa=0.36; 0.3–0.43).
Conclusion
Considerable differences were found between the nutritional composition tables. Therefore, the choice of the table can influence the comparability between studies.
Citas
Elmadfa I, Meyer AL. Importance of food composition data to nutrition and public health. Eur J Clin Nutr. 2010;64(Suppl.3):S4-7. https://doi.org/10.1038/ejcn.2010.202
Summer SS, Ollberding NJ, Guy T, Setchell KD, Brown N, Kalkwarf HJ. Cross-border use of food databases: Equivalence of US and Australian databases for macronutrients. J Acad Nutr Diet. 2013;113(10):1340-5. https://doi.org/10.1016/j.jand.2013.05.021
Thompson FE, Subar AF. Dietary assessment methodology. In: Coulston AM, Boushey CJ, Ferruzzi MG. Nutrition in the prevention and treatment of disease. 3rd ed. Amsterdam: Elsevier; 2013.
Ribeiro P, Morais T, Colugnati F, Sigulem D. Tabelas de composição química de alimentos: análise comparativa com resultados laboratoriais. Rev Saúde Pública. 2003;37(2):216-25. https://doi.org/10.1590/S0034-89102003000200009
Garcia V, Rona R, Chinn S. Effect of the choice of food composition table on nutrient estimates: A comparison between the British and American (Chilean) tables. Public Health Nutr. 2004;7(4):577-83. https://doi.org/10.1079/PHN2003555
Sasaki S, Ishikawa T, Yanagihori R, Amano K. Responsiveness to a self-administered diet history questionnaire in a work-site dietary intervention trial for mildly hypercholesterolemic Japanese subjects: Correlation between change in dietary habits and serum cholesterol levels. J Cardiol. 1999;33(6):327-38.
Charrondiere U, Chevassus-Agnes S, Marroni S, Burlingame B. Impact of different macronutrient definitions and energy conversion factors on energy supply estimations. J Food Compos Anal. 2004;17:339-60. https://doi.org/10.1016/j.jfca.2004.03.011
Merchant A, Dehghan M. Food composition database development for between country comparisons. Nutr J. 2006;5(1):2. https://doi.org/10.1186/1475-2891-5-2
Menezes EW, Giuntini EB, Lajolo FM. A questão da variabilidade e qualidade de dados de composição de alimentos. J Bra Soc Food Nutr. 2003;26:63-76.
Universidade Estadual de Campinas. Tabela brasileira de composição de alimentos – TACO. 4ª ed. Campinas: Unicamp; 2011.
Mattos LL, Martins IS. Dietary fiber consumption in an adult population. Rev Saúde Publica. 2000;34(1):50-5. https://doi.org/10.1590/S0034-89102000000100010
Nunes MA, Ferri CP, Manzolli P, Soares RM, Drehmer M, Buss C, et al. Nutrition, mental health and violence: From pregnancy to postpartum Cohort of women attending primary care units in Southern Brazil – ECCAGE study. BMC Psychiatry. 2010;(10):66. https://doi.org/10.1186/1471-244X10-66
Buss C, Nunes MA, Camey S, Manzolli P, Soares RM, Drehmer M, et al. Dietary fibre intake of pregnant women attending general practices in Southern Brazil: The ECCAGE Study. Public Health Nutr. 2009;12(9):1392-8. https://doi.org/10.1017/S1368980008004096
Drehmer M, Camey SA, Nunes MA, Duncan BB, Lacerda M, Pinheiro AP, et al. Fibre intake and evolution of BMI: From pre-pregnancy to postpartum. Public Health Nutr. 2013;16(8):1403-13. https://doi.org/10.1017/S1368980012003849
Giacomello A, Schmidt MI, Nunes MAA, Duncan BB, Soares RM, Manzolli P, et al. Validação relativa de Questionário de Freqüência Alimentar em gestantes usuárias de serviços do Sistema Único de Saúde em dois municípios no Rio Grande do Sul, Brasil. Rev Bras Saúde Mater Infant. 2008;8(4):445-54. https://doi.org/10.1590/S1519-38292008000400010
Philippi ST. Tabela de composição de alimentos: suporte para decisão nutricional. 2ª ed. São Paulo: Coronário; 2002.
Instituto Brasileiro de Geografia e Estatística. Estudo Nacional de Despesa Familiar 6˜ ENDEF. Rio de Janeiro: IBGE; 1976.
Pinheiro ABV, Lacerda EMA, Benzecry EH, Gomes MCS, Costa VM. Tabela para avaliação de consumo alimentar em medidas caseiras. 4ª ed. Rio de Janeiro: Atheneu; 2000.
Hoffmann JF, Nunes MA, Schmidt MI, Olinto MT, Melere C, Ozcariz SG, et al. Dietary patterns during pregnancy and the association with sociodemographic characteristics among women attending general practices in Southern Brazil: The ECCAGe Study. Cad Saúde Pública. 2013;29(5):970-80. https://doi.org/10.1590/S0102-311X2013000500014
Melere C, Hoffmann JF, Nunes MA, Drehmer M, Buss C, Ozcariz SG, et al. Healthy eating index for pregnancy: Adaptation for use in pregnant women in Brazil. Rev Saúde Pública. 2013;47(1):20-8. https://doi.org/10.1590/S0034-89102013000100004
United States Department of Agriculture. Nutrient Data Laboratory. Washington (DC): USDA; 2012 [cited 6 Dec 2016]. Available from: http//:www.ars.usda.gov/main/site_main.htm?modecode=12-35-45-00.
Camey AS, Nunes LN, Cruz LN. Beanplot uma nova ferramenta gráfica. Rev HCPA Fac Med Univ Fed Rio G Sul. 2010;30:185-91.
Kampstra P. Beanplot: A boxplot alternative for visual comparison of distributions. J Statistical Software. 2008;28(1):1-9. https://doi.org/10.18 637/jss.v028.c01
Kaiser L, Allen LH. American Dietetic Association. Position of the American Dietetic Association: Nutrition and lifestyle for a healthy pregnancy outcome. J Am Diet Assoc. 2008;108(3):553-61. https://doi.org/10.1016/j.jada.2008.01.030
Davison AC, Hinkley DV. Bootstrap methods and their applications. Cambridge: University Press; 1997.
R Core Team. R: A language and environment for statistical computing. Vienna: Foundation for Statistical Computing; 2015 [cited 6 Dec 2016]. Available from: https://www.R-project.org
Kampstra P. Beanplot: A Boxplot alternative for visual comparison of distributions. J Statistical Software. Netherlands: Faculty of Exact Sciences; 2008 [cited 6 Dec 2016];28(1):1-9. Available from: http://www.jstatsoft.org/v28/c01
Falissard B. Psy: Various procedures used in psychometry. R package version 1.1; 2012 [cited 6 Dec 2016]. Available from: http://CRAN.R-project. org/package=psy
Canty A, Ripley B. Boot: Bootstrap R (S-Plus) Functions. Version 1.3-17. Cambridge: Cambridge University Press; 2015.
Castro MBT, Kac G, de Leon AP, Sichieri R. Highprotein promotes a moderate postpartum weight loss in a prospective cohort of Brazilian women. Nutrition. 2009;25(10-11):1120-8.
Menezes EW, Gonçalves FAR, Giuntini EB, Lajolo FM. Brazilian food composition database: Internet dissemination and others recent developments. J Food Compost Anal. 2002;15(4):453-64. https://doi.org/10.1006/jfca.2002.1083
Uusitalo U, Kronberg-Kippila C, Aronsson CA, Schakel S, Schoen S, Mattisson I, et al. The TEDDY Study Group. Food composition database harmonization for between-country comparisons of nutrient data in the TEDDY Study. J Food Compos Anal. 2011;24(4-5):494-505. https://doi.org/10.1016/j.jfca.2011.01.012
Galeazzi MAM, Lima DM, Colugnati FAB, Padovani RM, Rodriguez-Amaya DB. Sampling Plan for the Brazilian TACO Project. J Food Compos Anal. 2002;15(4):499-505. https://doi.org/10.1006/jfca.2002.1092
Instituto Brasileiro de Geografia e Estatística. Pesquisa de orçamentos familiares 2008-2009. Rio de Janeiro: IBGE; 2010.
Bloch KV, Klein CH, Szklo M, Kuschnir MC, Abreu Gde A, Barufaldi LA. ERICA: Prevalences of hypertension and obesity in Brazilian adolescents. Rev Saúde Pública. 2016;50(Suppl.1):1-13. https://doi.org/10.1590/S01518-8787.2016050006685
Slimani N, Deharveng G, Unwin I, Southgate DA, Vignat J, Skeie G, et al. The EPIC nutrient database project (ENDB): A first attempt to standardize nutrient databases across the 10 European countries participating in the EPIC study. Eur J Clin Nutr. 2007;61(9):1037-56. https://doi.org/10.1038/sj.ejcn.1602679
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2023 Michele DREHMER, Cristiane MELERE, Shaline Modena REINHEIMER, Suzi Alves CAMEY
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
