Abstract

The aim of the study was to identify dietary patterns in Brazil and verify aggregation among members of the same family based on the Brazilian National Dietary Survey, a nationwide dietary survey conducted in 2008-2009 in individuals over 10 years of age. Dietary intake was estimated with a food record. Dietary patterns were identified by factor analysis, and familial aggregation was verified by linear regression. Three major dietary patterns were identified: (1) a traditional snack featuring coffee, rolls, oils and fats, and cheese; (2) traditional main meal, based on rice, beans and other legumes, and meat; and (3) fast food type snacks, namely sandwiches, processed meats, soft drinks, snacks, and pizza. Pattern 2 showed the strongest association (β = 0.37-0.64). Patterns 1 and 3 showed positive associations for all pairs of family members, with β ranging from 0.27 to 0.44 and 0.32 to 0.42, respectively. The study showed familial aggregation of dietary patterns in the Brazilian population.

Family Relations; Food Consumption; Feeding Behavior

Introduction

Unhealthy dietary practices acquired during adolescence tend to persist throughout life ¹1. Mikkilä V, Räsänen L, Raitakari OT, Pietinen P, Viikari J. Consistent dietary patterns identified from childhood to adulthood: the cardiovascular risk in Young Finns Study. Br J Nutr 2005; 93:923-31.^,22. Malik VS, Fung TT, van Dam RM, Rimm EB, Rosner B, Hu FB. Dietary patterns during adolescence and risk of type 2 diabetes in middle-aged women. Diabetes Care 2012; 35:12-8. and are associated with increased risk of chronic noncommunicable diseases such as obesity, cardiovascular diseases, and type 2 diabetes in adulthood ²2. Malik VS, Fung TT, van Dam RM, Rimm EB, Rosner B, Hu FB. Dietary patterns during adolescence and risk of type 2 diabetes in middle-aged women. Diabetes Care 2012; 35:12-8.^,33. Mikkilä V, Räsänen L, Raitakari OT, Marniemi J, Pietinen P, Rönnemaa T, et al. Major dietary patterns and cardiovascular risk factors from childhood to adulthood. The Cardiovascular Risk in Young Finns Study. Br J Nutr 2007; 98:218-25..

Effective proposals for the prevention of this group of diseases and promotion of healthy eating by adolescents require understanding the formation of eating habits and their maintenance over the course of life. Multiple socioenvironmental and personal factors interact to influence individual eating patterns, featuring interpersonal relations within the family. In addition to providing food, the family influences attitudes, preferences, and values related to food intake. Equally important are the independence and autonomy acquired during adolescence and related to food choices ⁴4. Story M, Neumark-Sztainer D, French S. Individual and environmental influences on adolescent eating behaviors. J Am Diet Assoc 2002; 102(3 Suppl):S40-51..

Studies have used various methods to evaluate food intake in order to investigate familial aggregation, that is, the resemblance of eating habits among individuals from the same family. For purposes of analysis, pairs from the nuclear family are evaluated, like fathers and children, mothers and children, spouses, and siblings ⁵5. Rankinen T, Bouchard C. Genetics of food intake and eating behavior phenotypes in humans. Annu Rev Nutr 2006; 26:413-34.. In the literature, the factors included in this research are calorie and micronutrient intake, food items and groups, and diet quality index ⁶6. Park HS, Yim KS, Cho S. Gender differences in familial aggregation of obesity-related phenotypes and dietary intake patterns in Korean families. Ann Epidemiol 2004; 14:486-91.^,77. Hasselbalch AL, Heitmann BL, Kyvik KO, Sørensen TIA. Studies of twins indicate that genetics influence dietary intake. J Nutr 2008; 138:2406-12.^,88. Beydounb MA, Wang Y. Parent-child dietary intake resemblance in the United States: evidence from a large representative survey. Soc Sci Med 2009; 68:2137-44.^,99. Shrivastava A, Murrin C, Sweeney MR, Heavey P, Kelleher CC. Familial intergenerational and maternal aggregation patterns in nutrient intakes in the Lifeways Cross-Generation Cohort Study. Public Health Nutr 2012; 16:1476-86.^,1010. Robinson LN, Rollo ME, Watson J, Burrows TL, Collins CE. Relationships between dietary intakes of children and their parents: a cross-sectional, secondary analysis of families participating in the Family Diet Quality Study. J Hum Nutr Diet 2014; 28:443-51..

Nutritional Epidemiology has used the identification of dietary patterns to overcome the limitations of studies based on computation of nutrients and foods, given the complex combination of nutrients and anti-nutritional factors in the human diet. Statistical methods are used for this purpose that allow analyzing correlations between a large number of variables (food groups in this case), defining a set of common latent dimensions that can provide a more objective basis for the elaboration of dietary recommendations and guidelines ¹¹11. Ocké MC. Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proc Nutr Soc 2013; 72:191-9..

Despite much discussion in this regard, to our knowledge there are no studies aimed at verifying the association between food intake patterns among individuals from the same family. Such an approach allows identifying family influence on eating habits that are consistent with disease risk or protection. The aim of the current study is to investigate familial aggregation of dietary patterns in a representative sample of the Brazilian population.

Material and methods

Study design and population

The study was based on data from the Brazilian National Dietary Survey (INA) ¹²12. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares, 2008-2009. Análise do consumo alimentar no Brasil. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2010., included as a module of the Brazilian Household Budget Survey (POF) 2008-2009 conducted by the Brazilian Institute of Geography and Statistics (IBGE) ¹³13. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares, 2008-2009. Despesas, rendimentos e condições de vida. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2010.. This was a cross-sectional study with a population consisting of individuals of both sexes, over 10 years of age, considering all of Brazil’s major geographic regions and urban and rural areas.

Data were collected in the POF from May 2008 to May 2009. Following geographic and statistical stratification of the primary sampling units, namely the census tracts from the 2000 Population Census, a two-stage cluster sampling plan was adopted. The first stage was the choice of census tracts, selected by proportional probability in relation to the number of households in each tract. A subsample of tracts was selected by simple random sampling in each stratum.

The secondary units sampled in the second stage were the permanent private households within each of the selected tracts, selected by simple random sampling without replacement. Tracts were evaluated over the 12 months of study, thus allowing the geographic and socioeconomic strata to be represented by the selected households during every quarter.

Individual food intake was assessed by simple random sampling of a subsample of 25% of the units from the second stage, the households in each tract. All the tracts selected in the POF 2008-2009 were surveyed, as were all residents over ten years of age in the households from the INA subsample. Of the 55,970 households selected for the POF 2008-2009, 34,003 individuals over ten years of age participated in the evaluation of individual food intake.

For analysis of familial aggregation, we selected households where at least one adolescent child lived. We thus identified each household resident based on information on the degree of kinship or nature of household subordination in relation to the reference person in the consumption unit, with the following options: (01) reference person; (02) spouse; (03) child; (04) other relative; (05) tenant; (06) pensioner; (07) domestic servant; or (08) domestic servant’s relative. Further details on the sampling and data collection have been published by IBGE ¹³13. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares, 2008-2009. Despesas, rendimentos e condições de vida. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2010..

“Reference persons”, “spouses”, and “children” were grouped separately. The NODUPKEY procedure (software SAS, version 9.3; SAS Inst., Cary, USA) was used to randomly select a child from each household, and a second child was chosen, independently of gender and also randomly, as the sibling. Through the child, the mother was identified as the female reference person and the father as the male reference person. Of the total INA sample, 5,927 families had at least one child, who added to a sibling, father, and/or mother, when they existed, totaled 17,918 individuals in the sample. Figure 1 shows the flowchart with the formation of the sample for analysis of familial aggregation.

Evaluation and analysis of food intake data

Food intake was estimated by recording all foods and beverages consumed on predetermined days, besides reporting the times, amounts consumed (in household measuring units), and preparation. Food intake records were completed by the informants themselves except when they presented some impediment, in which case the assistance of another household resident or relative was suggested. For the current article, the analyses are based on the first day of the food record due to the lack of data (for 1,103 individuals) on the second day. Missing data in studies with complex samples affect important information such as those on strata and clusters due to the study design’s properties, which can skew the estimates ¹⁴14. Gorrell P. Survey analysis: options for missing data. Silver Spring: Social & Scientific Systems, Inc.; 2009..

Participants on in the INA survey cited a total of 1,120 food items on the first day, all of which were grouped into 27 groups according to nutritional similarity and frequency of consumption, in order to subsequently identify the dietary patterns by exploratory factor analysis. Table 1 lists the 27 food groups.

Identification of dietary patterns by exploratory factor analysis

Identification of food intake patterns used factor analysis, with principal components analysis as the extraction method, aimed at reducing the data (food groups) into factors (dietary patterns) based on the correlations between these variables. This procedure was employed due to the complexity of the POF sample. We began by establishing the estimated correlations matrix considering the sample’s complex design, using the GLM procedure from the SAS statistical package; factor analysis was performed using as “input” the estimated correlations matrix considering the sample’s complex design according to the methodology proposed by Skinner et al. ¹⁵15. Skinner CJ, Holt D, Smith TMF. Analysis of complex surveys. New York: Wiley; 1989. and employed in Nutritional Epidemiology by Kerver et al. ¹⁶16. Kerver JM, Yang EJ, Bianchi L, Song WO. Dietary patterns associated with risk factors for cardiovascular disease in healthy US adults. Am J Clin Nutr 2003; 78:1103-10..

To determine the adequacy of using factor analysis in the sample, we used Kaiser-Meyer-Olkin (KMO) values greater than 0.50 as acceptable and significant results on the Bartlett sphericity test ¹⁷17. Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis with readings. 6ª Ed. Upper Sadle River: Prentice Hall; 1995.. Scree plot was used to determine the number of factors needed to represent the data, where the values situated before the curve began to flatten determined the number of factors to be retained ¹⁸18. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 2004; 62:177-203.^,1919. Olinto MTA. Padrões alimentares: análise de componentes principais. In: Kac G, Sichieri R, Gigante DP, organizadores. Epidemiologia nutricional. Rio de Janeiro: Editora Fiocruz/Editora Atheneu; 2007. p. 213-25..

Varimax was used for rotation of the factors, aimed at obtaining a structure of independence between the factors and greater interpretability. Food items with factor load greater than or equal to 0.30 were retained in the patterns¹⁷17. Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis with readings. 6ª Ed. Upper Sadle River: Prentice Hall; 1995.^,1919. Olinto MTA. Padrões alimentares: análise de componentes principais. In: Kac G, Sichieri R, Gigante DP, organizadores. Epidemiologia nutricional. Rio de Janeiro: Editora Fiocruz/Editora Atheneu; 2007. p. 213-25.^,2020. Northstone K, Ness AR, Emmet PR, Rogers IS. Adjusting for energy intake in dietary pattern investigations using principal components analysis. Eur J Clin Nutr 2008; 62:931-8..

The analysis generated factor scores that represent the sum of the loads of each factor weighted by the factor’s eigenvalue and multiplied by each individual’s standardized food group intake. The scores represent standardized variables, with mean equal to zero and standard deviation equal to one.

Familial aggregation

Familial aggregation of dietary patterns was verified by linear regression. The PROC SURVEYREG procedure was used, which allows analysis of data from complex samples, considering the expansion factors, by means of the SAS version 9.3 statistical package. In order to test the respective correlations between the pairs: “father and son”, mother and son”, “father and daughter”, “mother and daughter”, “siblings”, and “father and mother”, each linear regression model was constructed using the normalized factor scores for each pair’s component, while in the linear regression analyses between mothers and children and between fathers and children, the child’s factor scores were used as the dependent variable, and for the father-mother pairs the father’s factor scores were used as the dependent variable. It was assumed that children are extensively influenced by the parents’ eating habits ²¹21. Wardle J. Eating behaviour and obesity. Obes Rev 2007; 8 Suppl 1:73-5.^,2222. Skinner J, Carruth B, Moran J, Houch K, Schmidhammer J, Reed A, et al. Toddler’s food preferences: concordance with family member’s preferences. J Nutr Educ 1998; 30:17-22.^,2323. Branen L, Fletcher J. Comparison of college students’ current eating habits and recollections of their childhood food practices. J Nutr Educ 1999; 31:304-10.^,2424. Larson NI, Neumark-Sztainer D, Hannan PJ, Story M. Family meals during adolescence are associated with higher diet quality and healthful meal patterns during young adulthood. J Am Diet Assoc 2007; 107:1502-10.^,2525. Rossi A, Moreira EA, Rauen MS. Determinants of eating behavior: a review focusing on the family. Rev Nutr 2008; 21:739-48., that the woman exerts influence on the man’s eating, since she historically has greater control over and management of the family’s eating as a whole ²⁶26. Canesqui AM, Garcia RWD, organizadores. Antropologia e nutrição: um diálogo possível. Rio de Janeiro: Editora Fiocruz; 2005., and that for the pairs of siblings, the adolescents were selected randomly in the household and the first one selected was used as the dependent variable. Linear regression coefficients and their respective 95% confidence intervals were estimated, which allowed comparison of the relations between family pairs for each of the three factors.

Ethical considerations

The study protocol for individual food intake in the POF 2008-2009 was approved by the Ethics Research Committee of Instituto of Social Medicine in the Rio de Janeiro State University (CAAE 0011.0.259.000-11).

Results

The sample consisted of 5,545 mothers, 3,917 fathers, and 8,456 children, of which 52.6% were boys and 47.4% girls. Mean age was 48 ± 12 years for fathers, 46 ± 12 for mothers, and 18 ± 8 for children. KMO (0.534) and Bartlett’s sphericity test (p < 0.01) indicated that the correlations between the items were sufficient and adequate for factor analysis. Scree plot indicated that three factors should be retained, as shown in Figure 2.

The first pattern, characterized as the “traditional snack”, included breads, cheeses, oils and fats, and coffee and did not include sweets, salted snacks, or dairy products. The second pattern, characterized as the “traditional main meal”, included rice and rice dishes, beans and other legumes, and meats, and did not include soups, broths, macaroni, or pasta. The third pattern, “fast food type snacks”, included sandwiches, processed meats, salted snacks, pizzas, and soft drinks and not fruit or breakfast cereals.

The three dietary patterns jointly explained 65.9% of total variance in food intake. Table 2 shows the rotated factor matrix.

The highest beta values occurred in the “traditional main meal” pattern for all the target pairs, varying from 0.37 (father-daughter) to 0.64 (father-son). Father-daughter and mother-daughter showed the lowest beta values, while father-son and mother-son showed the highest. In the “traditional snack” pattern, beta varied from 0.27 (father-daughter) to 0.44 (mother-father). In the “fast food type snacks” pattern, the lowest beta was also for father-daughter (0.32), statistically different from all the other pairs. The highest beta values were for mother-son (0.42), siblings (0.41), and mother-father (0.40), similar to each other (Table 3).

Discussion

The current study identified three dietary patterns in a representative sample of the Brazilian population, called “traditional snack”, “traditional main meal”, and “fast food type snacks”.

To our knowledge this study was the first attempt to evaluate familial aggregation of food intake using nationally representative data for the Brazilian population. Although previous studies tested associations between groups of foods and nutrients between family members ¹⁰10. Robinson LN, Rollo ME, Watson J, Burrows TL, Collins CE. Relationships between dietary intakes of children and their parents: a cross-sectional, secondary analysis of families participating in the Family Diet Quality Study. J Hum Nutr Diet 2014; 28:443-51.^,2727. Da Veiga GV, Sichieri R. Correlation in food intake between parents and adolescents depends on socioeconomic level. Nutr Res 2006; 26:517-23.^,2828. Mitchell BD, Rainwater DL, Hsueh WC, Kennedy AJ, Stern MP, Maccluer JW. Familial aggregation of nutrient intake and physical activity: results from the San Antonio Family Heart Study. Ann Epidemiol 2003; 13:128-35.^,2929. Oliveria SA, Ellison RC, Moore LL, Gillman MW, Garrahie EJ, Singer MR. Parent-child relationships in nutrient intake: the Framingham Children’s Study. Am J Clin Nutr 1992; 56:593-8.^,3030. Faith MS, Keller KL, Johnson SL, Pietrobelli A, Matz PE, Must S, et al. Familial aggregation of energy intake in children. Am J Clin Nutr 2004; 79:844-50., none evaluated familial aggregation in dietary patterns.

Wang et al. ³¹31. Wang Y, Beydoun MA, Li J, Liu Y, Moreno LA. Do children and their parents eat a similar diet? Resemblance in child and parental dietary intake: systematic review and meta-analysis. J Epidemiol Community Health 2011; 65:177-89., in a recent systematic review and meta-analysis on resemblance of food intake by parents and children, observed weak to moderate associations in studies published since 1980. However, the authors highlighted that many of these studies were based on small samples, and that few had been performed in developing countries, where eating away from home by children and adolescents is not as common as in developed countries.

Studies have also indicated that family influence on adolescents’ food choices is decreasing, given the increasing autonomy and opportunities for choices in this stage of life ³¹31. Wang Y, Beydoun MA, Li J, Liu Y, Moreno LA. Do children and their parents eat a similar diet? Resemblance in child and parental dietary intake: systematic review and meta-analysis. J Epidemiol Community Health 2011; 65:177-89.^,3232. Popkin BM. Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr 2006; 84:289-98.^,3333. Vereecken CA, Inchley J, Subramanian SV, Hublet A, Maes L. The relative influence of individual and contextual socio-economic status on consumption of fruit and soft drinks among adolescents in Europe. Eur J Public Health 2005; 15:224-32.. The current study showed moderate associations between factor scores of dietary patterns between parents and adolescent children, indicating familial aggregation of food choices, even in this age bracket. Our values were similar to those observed by other authors that examined the correlation in food intake between parents and children ¹⁰10. Robinson LN, Rollo ME, Watson J, Burrows TL, Collins CE. Relationships between dietary intakes of children and their parents: a cross-sectional, secondary analysis of families participating in the Family Diet Quality Study. J Hum Nutr Diet 2014; 28:443-51.^,2727. Da Veiga GV, Sichieri R. Correlation in food intake between parents and adolescents depends on socioeconomic level. Nutr Res 2006; 26:517-23.^,3131. Wang Y, Beydoun MA, Li J, Liu Y, Moreno LA. Do children and their parents eat a similar diet? Resemblance in child and parental dietary intake: systematic review and meta-analysis. J Epidemiol Community Health 2011; 65:177-89.^,3434. Philips N, Sioen I, Michels N, Sleddens E, De Henauw S. The influence of parenting style on health related behavior of children: findings from the ChiBS study. Int J Behav Nutr Phys Act 2014; 11:95..

Factors that may contribute to familial aggregation include family meals, transmission of dietary information to children, and parents’ initiatives and efforts to encourage children to consume healthy foods, including through household food purchases ³⁵35. Patrick H, Hennessy E, McSpadden K, Oh A. Parenting styles and practices in children’s obesogenic behaviors: scientific gaps and future research directions. Child Obes 2013; 9 Suppl:S73-86.^,3636. Cooke LJ, Wardle J, Gibson EL, Sapochnik M, Sheiham A, Lawson M. Demographic, familial and trait predictors of fruit and vegetable consumption by pre-school children. Public Health Nutr 2004; 7:251-2.^,3737. Pearson N, Biddle SJ, Gorely T. Family correlates of fruit and vegetable consumption in children and adolescents: a systematic review. Public Health Nutr 2009; 12:267-83.^,3838. Birch LL, Fisher JO. Mothers’ child-feeding practices influence daughters’ eating and weight. Am J Clin Nutr 2000; 71:1054-61..

The strongest associations identified in the current study were in the “traditional main meal” pattern in all the family pairs analyzed, which suggests parental influence in maintaining traditional Brazilian eating habits among children, encouraging consumption of the traditional Brazilian diet based on rice and beans ²⁶26. Canesqui AM, Garcia RWD, organizadores. Antropologia e nutrição: um diálogo possível. Rio de Janeiro: Editora Fiocruz; 2005.^,3939. Sichieri R. Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obes Res 2002; 10:42-8.. Various studies have shown that this pattern exerts a protective effect against overweight and obesity in both adults and children and adolescents ³⁹39. Sichieri R. Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obes Res 2002; 10:42-8.^,4040. Sichieri R, Castro JFG, Moura AS. Fatores associados ao padrão de consumo alimentar da população brasileira urbana. Cad Saúde Pública 2003; 19 Suppl 1:S47-53.^,4141. Marchioni DML, Latorre MRDO, Eluf-Neto J, Wünsch-Filho V, Fisberg RM. Identification of dietary patterns using factor analisys in na epidemiological study in São Paulo. São Paulo Med J 2005; 123:124-7.^,4242. Neumann AICP, Martins IS, Marcopito LF, Araujo EAC. Padrões alimentares associados a fatores de risco para doenças cardiovasculares entre residentes de um município brasileiro. Rev Panam Salud Pública 2007; 22:329-39.^,4343. Cunha DB, de Almeida RM, Sichieri R, Pereira RA. Association of dietary patterns with BMI and waist circumference in a low-income neighbourhood in Brazil. Br J Nutr 2010; 104:908-13.^,4444. Marchioni DML, Claro RM. Patterns of food acquisition in Brazilian households and associated factors: a population-based survey. Public Health Nutr 2011; 14:1586-92.^,4545. Nascimento S, Barbosa FS, Sichieri R, Pereira RA. Dietary availabillity patterns of the Brazilian macro-regions. Nutr J 2011; 10:79.^,4646. Velasquez-Melendez G. Tendências da frequência do consumo de feijão por meio de inquérito telefônico nas capitais brasileiras, 2006 a 2009. Ciênc Saúde Coletiva 2012; 17:3363-70.^,4747. Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, et al. Fatores associados a padrões alimentares em adolescentes: um estudo de base escolar em Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15:662-74.. In the current study, in addition to the traditional main meal pattern, a traditional snack pattern was also observed, characterized by a traditional Brazilian light meal: bread, butter or margarine, cheese, and coffee, which showed positive and moderate associations in all the family pairs, especially mother-father. According to De Moura Souza et al. ⁴⁸48. De Moura Souza A, Pereira RA, Yokoo EM, Levy RB, Sichieri R. Alimentos mais consumidos no Brasil: Inquérito Nacional de Alimentação 2008-2009. Rev Saúde Pública 2013; 47:190s-9., in Brazil these foods are mainly consumed by women.

The “fast food snacks” pattern, including soft drinks, sweets, cakes, cookies, salted snacks, processed meats, and other high-fat products, also identified in other studies ⁴⁷47. Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, et al. Fatores associados a padrões alimentares em adolescentes: um estudo de base escolar em Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15:662-74.^,4949. Dishchekenian VRM, Escrivão MAMS, Palma D, Ancona-Lopez F, Araújo EAC, Taddei JAAC. Padrões alimentares de adolescentes obesos e diferentes repercussões metabólicas. Rev Nutr 2011; 24:17-29.^,5050. Salvatti AG, Escrivão MAMS, Taddei JAAC, Bracco MM. Padrões alimentares de adolescentes na Cidade de São Paulo. Rev Nutr 2011; 24:703-13.^,5151. Pinho LD, Silveira MF, Botelho AC, Caldeira AP. Identification of dietary patterns of adolescents attending public schools. J Pediatr (Rio J.) 2014; 90:267-72., showed positive and moderate associations in all the target pairs. This finding may indicate the Westernization of eating habits in the family, since this pattern consists of foods that are high in fat, sugar, and salt, reflecting the incorporation of unhealthy habits and customs related to the Western lifestyle ⁵²52. Tanabe FH, Drehmer M, Neutzling MB. Consumo alimentar e fatores dietéticos envolvidos no processo saúde e doença de Nikkeis: revisão sistemática. Rev Saúde Pública 2013; 47:634-46.^,5353. Gimeno SGA, Andreoni S, Ferreira SRG, Franco LJ, Cardoso MA. Assessing food dietary intakes in Japanese-Brazilians using factor analysis. Cad Saúde Pública 2010; 26:2157-67.^,5454. Pierce BL, Austin MA, Crane PK, Retzlaff BM, Fish B, Hutter CM, et al. Measuring dietary acculturation in Japanese-Americans with the use of confirmatory factor analysis of food frequency data. Am J Clin Nutr 2007; 86:496-503.^,5555. Morinaka T, Wozniewicz M, Jeszka J, Bajerska J, Nowaczyk P, Sone Y. Westernization of dietary patterns among young Japanese and Polish females: a comparison study. Ann Agric Environ Med 2013; 20:122-30., especially in Brazilian families with adolescent children, given that consumption of such foods is more prevalent in this age bracket ⁴⁸48. De Moura Souza A, Pereira RA, Yokoo EM, Levy RB, Sichieri R. Alimentos mais consumidos no Brasil: Inquérito Nacional de Alimentação 2008-2009. Rev Saúde Pública 2013; 47:190s-9.. This dietary pattern has been associated with an increase in metabolic disorders and weight gain in both adolescence ⁴⁷47. Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, et al. Fatores associados a padrões alimentares em adolescentes: um estudo de base escolar em Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15:662-74.^,5656. Joung H, Hong S, Song Y, Ahn BC, Park MJ. Dietary patterns and metabolic syndrome risk factors among adolescents. Korean J Pediatr 2012; 55:128-35.^,5757. Ambrosini GL, Huang RC, Mori TA, Hands BP, O’Sullivan TA, de Klerk NH, et al. Dietary patterns and markers for the metabolic syndrome in Australian adolescents. Nutr Metab Cardiovasc Dis 2010; 20:274-83. and adulthood ⁵⁵55. Morinaka T, Wozniewicz M, Jeszka J, Bajerska J, Nowaczyk P, Sone Y. Westernization of dietary patterns among young Japanese and Polish females: a comparison study. Ann Agric Environ Med 2013; 20:122-30.^,5858. Schulze MB, Fung TT, Manson JE, Willett WC, Hu FB. Dietary patterns and changes in body weight in women. Obesity (Silver Spring) 2006; 14:1444-53.^,5959. Murtaugh MA, Herrick JS, Sweeney C, Baumgartner KB, Guiliano AR, Byers T, et al. Diet composition and risk of overweight and obesity in women living in the southwestern United States. J Am Diet Assoc 2007; 107:1311-21.^,6060. Martinez-Gonzalez MA, Martin-Calvo N. The major European dietary patterns and metabolic syndrome. Rev Endocr Metab Disord 2013; 14:265-71.^,6161. Mu M, Wang SF, Sheng J, Zhao Y, Wang GX, Liu KY, et al. Dietary patterns are associated with body mass index and bone mineral density in Chinese freshmen. J Am Coll Nutr 2014; 33:120-8..

Studies in various areas have shown beneficial effects of family meals, such as better quality of the foods consumed and good nutritional status, especially in children. Family meals have been associated with the consumption of traditional staples and sources of fiber and inversely associated with consumption of unhealthy foods such as fried salted snacks and soft drinks ⁶²62. Gillman MW, Rifas-Shiman SL, Frazier AL, Rockett HR, Camargo Jr. CA, Field AE, et al. Family dinner and diet quality among older children and adolescents. Arch Fam Med 2000; 9:235-40.^,6363. Fonseca AB, Souza TSN, Frozi DS, Pereira RA. Modernidade alimentar e consumo de alimentos: contribuições sócio-antropológicas para a pesquisa em nutrição. Ciênc Saúde Coletiva 2011; 16:3853-62.^,6464. Teixeira AS, Philippi ST, Leal GVS, Araki EL, Estima CCP, Guerreiro RER. Substituição de refeições por lanches em adolescentes. Rev Paul Pediatr 2012; 30:330-3..

Lower beta values were observed for pairs with daughters when compared to pairs with sons, which may indicate gender influence on dietary aggregation, with boys more influenced by their parents’ food intake than girls.

The study’s possible methodological limitations include the use of factor analysis for deriving dietary patterns, since this method involves some arbitrary decisions such as grouping of food items and retention and naming of factors. Still, the dietary patterns identified here were comparable to those of other studies.

The study identified familial aggregation of food intake patterns in the Brazilian population, suggesting family influence on individual eating habits. This context corroborates the importance of developing strategies to encourage eating meals in the family and exploring the contribution of family life to healthy dietary practices, providing evidence for elaborating dietary recommendations and public health policies.

Acknowledgments

The authors wish to thank the General Coordination of Food and Nutrition of the Brazilian Ministry of Health for the funding and the Brazilian Graduate Studies Coordinating Board (Capes) research funding agency for the grant to F. A. Massarani.

References

Publication Dates

History