Padrões alimentares estimados por técnicas multivariadas: uma revisão da literatura sobre os procedimentos adotados nas etapas analíticas

Borges, Camila Aparecida; Rinaldi, Ana Elisa; Conde, Wolney Lisboa; Mainardi, Giulia Marcelino; Behar, Dora; Slater, Betzabeth

doi:10.1590/1980-5497201500040013

Resumo:

Objetivo: Descrever as soluções adotadas nas múltiplas etapas de utilização das técnicas multivariadas para obtenção de padrão alimentar (PA) no que tange: ao objetivo dos estudos, à escolha do método de aferição do consumo alimentar, aos critérios de grupamento dos alimentos, à quantidade de grupos alimentares utilizada, ao número de PA extraído e aos critérios para nomenclatura.

Métodos: Foram selecionadas publicações das bases MEDLINE e Lilacs tendo como descritores: "padrão alimentar" versus "análise fatorial"; "análise de componentes principais"; "análise de cluster " e "reduced regression rank ". A busca inicial resultou em 1.752 artigos, que após critérios de inclusão e exclusão somaram 189 publicações.

Resultados: Foram relevantes entre os estudos os seguintes aspectos: a predominância da análise de componentes principais (ACP); a predominância no uso de 4 a 5 PAs nos estudos de associação com desfechos de saúde; o uso de 30 ou mais grupos de alimentos provenientes do Questionário de Frequência Alimentar (QFA); a predominância de estudos que associaram PAs com desfechos de saúde e fatores socioeconômicos; a heterogeneidade de critérios adotados ao longo das etapas analíticas das técnicas multivariadas.

Conclusão: A heterogeneidade entre as publicações se concentra nos critérios de agrupamento dos alimentos, na nomenclatura e no número de padrões alimentares extraídos, que variou em função do número de grupos alimentares presentes nas análises. Entender, aplicar e explorar em sua totalidade as técnicas multivariadas tem se tornado necessário para melhorar a confiabilidade dos resultados e, consequentemente, aprimorar as relações com desfechos de saúde e fatores socioeconômicos.

Palavras-chave: Análise de componente principal; Análise fatorial; Análise multivariada; Análise por conglomerados; Consumo de alimentos; Comportamento alimentar.

Abstract:

Objective: To describe the solutions adopted in the multiple steps of the use of multivariate techniques to obtain a dietary pattern (DP) concerning: the objective of the studies, the selection of the method for measuring food intake, the criteria for grouping the foods, the number of food groups used, the number of DP obtained, and the nomenclature criteria.

Methods: The articles were selected from MEDLINE and Lilacs scientific databases using the following keywords: "dietary patterns" versus "factor analysis"; "principal components analysis"; "cluster analysis" and "reduced regression rank." The initial search resulted in 1,752 articles. After inclusion and exclusion criteria, 189 publications were selected.

Results: The following aspects were relevant among the studies: the prevalence of the principal component analysis (PCA); the prevalence of the use of 4 to 5 DPs in the studies of association with health outcomes; the use of 30 or more food groups from the food frequency questionnaire (FFQ); the prevalence of studies that associated DPs with health outcomes and socioeconomic factors; and the heterogeneity of criteria used throughout the analytical stages of the multivariate techniques.

Conclusion: The heterogeneity between the publications concentrates on the criteria for food grouping, the nomenclature, and the number of dietary patterns calculated, which varied depending on the number of food groups present in these analyses. To understand, apply, and explore in full, the multivariate techniques has become necessary in order to improve the reliability of the results and, consequently, to improve the relationships with health outcomes and socioeconomic factors.

Keywords: Principal component analysis; Factor analysis, statistical; Multivariate analysis; Cluster analysis; Food consumption; Feeding behavior.

INTRODUÇÃO

Técnicas de análises multivariadas são cada vez mais utilizadas para inferências sobre padrão alimentar (PA) em epidemiologia nutricional1 2 3. O PA é biologicamente importante, pois sumariza a dieta total levando em consideração que os alimentos são consumidos em combinações complexas, ocorrendo interações e sinergias entre os constituintes da dieta, e que o equilíbrio entre os componentes da alimentação de proteção e risco podem ser importantes na determinação de associações entre dieta e doenças4.

Epidemiologicamente, a expressão da alimentação como PA amplia a visão sobre o consumo alimentar da população por:

permitir a identificação de tendências e a caracterização de grupos com semelhantes comportamentos alimentares2;
identificar a introdução de novos hábitos alimentares5;
reconhecer os aspectos culturais da alimentação e a diversidade alimentar nas diferentes regiões geográficas5;
entender as relações específicas entre PAs característicos e o perfil de morbimortalidade na população6 7;
permitir o desenvolvimento de diretrizes alimentares para populações adotando como referência os alimentos, e não somente os nutrientes6 8.

Em diversos estudos o PA nomeado como western , unhealthy ou processed se caracteriza por apresentar alimentos como refrigerantes, doces e alimentos processados e tem sido associado com pior qualidade de vida das populações em diversas culturas. Por outro lado, os PA nomeados como healthy , traditional , mediterranean e prudent se caracterizam pela presença de cereais, frutas, hortaliças e produtos típicos de cada cultura e tem sido associado com melhor qualidade de vida8 9 10 11 12 13 14 15 16 17.

Quatro técnicas multivariadas são utilizadas com frequência na identificação de um PA: análise de componentes principais (ACP), análise fatorial (AF), reduced regression rank (RRR) e análise de cluster18. Na ACP e na AF, a estrutura das relações é estabelecida entre as variáveis originais que são expressas em variáveis latentes, as quais sintetizam o espectro da variância alimentar da população19. A estimativa do PA a partir de ACP e AF parte do princípio de redução da dimensionalidade do número de alimentos ou grupos de alimentos consumidos habitualmente por uma população, dados coletados por Questionário de Frequência Alimentar (QFA), recordatório alimentar de 24 horas (R24h) ou registros alimentares19. Na análise de cluster ,aestrutura das relações analisadas ocorre entre indivíduos, identificando grupos de pessoas mutuamente exclusivos (clusters ) conforme o consumo alimentar20 21. A técnica RRR analisa simultaneamente múltiplos desfechos de saúde como função do consumo de alimentos para compor os padrões alimentares3.

A estimativa de um PA por técnicas multivariadas é um procedimento complexo e que envolve múltiplas etapas22: o método de aferição do consumo alimentar, os critérios e as referências na formação dos grupos alimentares, o tipo de análise multivariada mais adequado ao conjunto de dados e aos objetivos da investigação, o número de PA extraído para representar a máxima variância explicada pelo conjunto inicial de dados, os critérios para extração do PA (eigenvalue > 1, scree plot , entre outros), o grau de correlação entre os grupos alimentares e o PA, o tipo de rotação (ortogonal, oblíqua)23 e a forma de caracterizar o PA (nomenclatura)4. Tais decisões, não harmônicas na maioria dos estudos, limitam a compreensão dos padrões, bem como dificultam sua generalização ou extrapolação para outros contextos24. O aumento de produção científica sobre PA demanda revisões da literatura estruturadas segundo aspectos conceituais e operacionais, as quais poderão contribuir para a padronização das múltiplas etapas, de maneira que propiciem ampla comparabilidade entre os estudos. Sendo assim, esta revisão teve como objetivo descrever as soluções adotadas nas múltiplas etapas de utilização das técnicas multivariadas para obtenção de PA no que tange: ao objetivo dos estudos, à escolha do método de aferição do consumo alimentar, aos critérios de grupamento dos alimentos, à quantidade de grupos alimentares utilizada, ao número de PA extraído e aos critérios para nomenclatura.

MÉTODOS

CRITÉRIO DE BUSCA E SELEÇÃO DOS ARTIGOS

A revisão da literatura teve como pergunta norteadora: como os autores organizam os procedimentos utilizados na análise multivariada para estimar o PA?

Para esta revisão, foram escolhidas quatro técnicas multivariadas: ACP, AF, RRRe cluster . Foram selecionados artigos científicos das bases de dados indexadas MEDLINE e Lilacs usando os seguintes descritores: "principal component analysis " AND "dietary pattern "; "principal component analysis " AND "food pattern "; "principal component analysis " AND "eating pattern "; "factor analysis " AND "dietary pattern "; "factor analysis " AND "food pattern "; "factor analysis " AND "eating pattern "; "RRR" AND "dietary pattern "; "RRR" AND "food pattern "; "RRR" AND "eating pattern "; "cluster " AND "dietary pattern "; "cluster " AND "food pattern "; "cluster " AND "eating pattern ". Os descritores foram selecionados na base de dados Descritores em Ciências da Saúde (DeCS: http://decs.bvs.br/).

CRITÉRIOS DE INCLUSÃO E EXCLUSÃO DE ARTIGOS

Os critérios de inclusão foram: idiomas português, inglês ou espanhol; período: 1980 a 2012; seres humanos como público-alvo; artigos originais com o objetivo de descrever o PA e/ou associá-lo com desfechos de saúde ou fatores socioeconômicos.

Os critérios de exclusão foram: publicações que extraíram PA por outras técnicas estatísticas; publicações objetivando discutir aspectos metodológicos das técnicas multivariadas; publicações que mesclavam padrões comportamentais (atividade física, sedentarismo, fumo) com PA; artigos duplicados (mesmo artigo em dois idiomas distintos ou mesmo artigo selecionado a partir de descritores diferentes); revisões sistemáticas e não sistemáticas, meta-análise, ensaios clínicos e comparação entre técnicas multivariadas; publicações sem informações suficientes sobre os aspectos abordados nesta revisão; publicações não localizadas na íntegra. Esses critérios de exclusão visavam focalizar o trabalho em estudos originais que utilizassem técnicas multivariadas na análise dos dados.

Após a primeira triagem, as publicações selecionadas foram analisadas por quatro pesquisadores, sendo que cada um era responsável por apenas um tipo de técnica multivariada (ACP, AF, RRR e cluster ). Em seguida, duas pesquisadoras reavaliaram todas as publicações comparando e selecionando cada uma de acordo com os critérios preestabelecidos. A reavaliação obedeceu esta sequência: título, resumo, texto na íntegra, seleção da bibliografia e citação dos autores, em que não foram gerados novos resultados de publicações complementares.

INFORMAÇÕES COLETADAS NAS PUBLICAÇÕES

Das 189 publicações foram extraídos os seguintes aspectos: o período de produção das publicações (organizadas decenalmente); o continente de origem (classificado em: América do Norte, América do Sul, Europa, África, Ásia, Oceania); o objetivo principal (descrever PA e/ou associar PA com desfechos de saúde e fatores socioeconômicos); os desfechos de saúde (diabetes mellitus , obesidade, doenças cardiovasculares (DCV), câncer, outros); o método de coleta de dados do consumo alimentar (QFA, R24h e registro alimentar); os critérios de formação de grupos alimentares (classificados em: provenientes do QFA, agências oficiais/recomendações dietéticas/estudos prévios, composição nutricional, uso culinário/modo de consumo, múltiplos); a quantidade de grupos alimentares incluídos nas análises (classificada em: 10 a 19; 20 a 29; 30 a 39; ≥ 40 grupos alimentares/alimentos); o número de PA extraído (classificado em: < 4, 4 a 5; 6 a 7; > 7 PA ou clusters , quando se tratava da análise de cluster ); nomenclatura do PA (rótulos qualitativos, baseados nos nomes dos alimentos, composição nutricional, múltiplos).

RESULTADOS

Das 189 publicações analisadas, 56% (n = 105) referiram a técnica ACP na obtenção do PA. Do total de publicações, 42% (n = 79) foram realizadas em países europeus, 26% (n = 49), em países da América do Norte, 32%, em países asiáticos, países da América do Sul, países africanos e países da Oceania. Em relação ao objetivo principal dos artigos, 78% (n = 147) descreveram e associaram o PA com desfechos de saúde, os demais apenas realizaram estudo descritivo/exploratório do PA e ou associaram PA com fatores socioeconômicos (Figura 1).

Figura 1:
Quadro descritivo do processo de seleção dos artigos incluídos no estudo.

As publicações encontradas no período de 2002 a 2012 representaram 94% do total das publicações. Essas informações mostram um crescimento de 15,7 vezes no número de publicações, quando comparado ao período de 1980 a 2001. O crescimento do uso das análises multivariadas em publicações de epidemiologia nutricional foi semelhante nos cinco continentes (Figura 2).

Figura 2:
Evolução das publicações envolvendo análise multivariada para formação de padrões alimentares em epidemiologia nutricional no período de 1980 a 2012.

Na Tabela 1 estão sumarizadas as principais características extraídas das publicações e sua respectiva referência na lista de referências do texto. Dentre os 147 artigos que descreveram e associaram o PA com desfechos de saúde, 64% (n = 94) utilizaram doenças crônicas não transmissíveis (DCNT) como desfecho, das quais 28% obesidade (n = 26), 27% DCV (n = 25), 29% câncer (n = 27) e 17% diabetes mellitus (n = 16) (Figura 1). Das 147 publicações que descreveram e associaram o PA com desfechos de saúde, houve a predominância na escolha da técnica de ACP em 55% dos casos (n = 81).

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Tabela 1:
Classificação dos artigos identificados na revisão de acordo com as principais características analisadas e segundo tipo de análise multivariada: análise fatorial, análise de componentes principais, cluster e reduced regression rank.

Do total de artigos analisados, 81% (n = 154) utilizaram o QFA como método de coleta de dados sobre o consumo alimentar. Em relação aos critérios utilizados para agrupar os alimentos provenientes da coleta de dados de consumo alimentar (etapa antes da entrada de dados nas técnicas multivariadas), 22% (n = 42) utilizaram grupos preexistentes provenientes do QFA, 14% (n = 26) adotaram como critério a composição nutricional, 26% (n = 46) não apresentaram nenhuma informação a respeito do critério utilizado e 38% (n = 75) relataram: agências oficiais/recomendações dietéticas, uso culinário/modo de consumo e critérios múltiplos. A respeito do total de grupos de alimentos incluídos nas análises, 70% (n = 132) relataram trabalhar com 30 ou mais grupos alimentares (Tabela 1).

Nas técnicas em que ocorre múltipla atribuição de componentes por indivíduo (ACP, AF e RRR), a extração de até 4 componentes foi predominante e alcançou 59% (n = 62) do total de artigos que abordaram essas técnicas. Na análise de cluster , quando há atribuição única de componente por indivíduo, a extração de 4 até 7 componentes por análise alcançou 73% dos artigos analisados (Figura 3).

Figura 3:
Número de padrões alimentares estimados segundo a técnica multivariada empregada: análise de componentes principais, análise fatorial, cluster e reduced regression rank no período de 1980 a 2012.

Na nomenclatura do PA, a categoria rótulos qualitativos (por exemplo: traditional , healthy , western ) foi utilizada em 44% dos estudos (n = 84); a categoria nome dos alimentos (por exemplo: frutas, carnes, cereais, pães, legumes, hortaliças), em 31% (n = 59); as categorias composição nutricional (por exemplo: fibras, gordura trans, ácidos graxos, vitaminas) ou critérios múltiplos foram utilizadas em 24% dos estudos (n = 46) (Tabela 1).

DISCUSSÃO

No período entre 1980 e a primeira metade dos anos 1990, a frequência dos estudos sobre PA se manteve relativamente baixa, ainda que com publicações de boa qualidade. Em 1998, Slattery et al.207 introduziram a AF para obtenção de PA e sua associação com câncer de cólon, lançando a provocativa questão: "podem os padrões alimentares caracterizarem o risco dieta versus doença melhor do que nutrientes e alimentos isolados?"

A abrangência do uso das análises multivariadas permite dar resposta parcial a esse questionamento, dado que a descrição de padrões alimentares e sua relação com desfechos de saúde se intensifica a partir da década de 2000. Essa expansão se dá em paralelo com a evolução do perfil epidemiológico, no qual a frequência das doenças multicausais relacionadas à alimentação tem participação cada vez mais relevante em epidemiologia nutricional. A revisão realizada por Hu2 contribuiu para difundir e incentivar o uso da análise do PA por técnicas multivariadas como uma abordagem alternativa e complementar no estudo de associação entre dieta e DCNT.

Do conhecimento dos autores, esta é a primeira revisão que abordou como os autores organizaram os procedimentos utilizados na análise multivariada para estimar o PA. Os principais achados desta revisão foram: uso predominante de ACP e AF; uso de 4 a 5 fatores quando associaram PA com desfechos de saúde; uso de 30 ou mais grupos alimentares provenientes do QFA; predominância de estudos de associação entre PA versus desfechos de saúde ou fatores socioeconômicos; heterogeneidade dos critérios adotados ao longo dessas etapas.

A ACP e a AF são técnicas multivariadas distintas quanto à exploração da variabilidade e ao fundamento do estimador; não obstante, são usualmente citadas sem respeitar as diferenças metodológicas que as distinguem208. Devido à natureza determinística da ACP, os padrões alimentares estimados representam o espectro do comportamento alimentar de uma dada população. Tal estimativa se baseia na premissa de que os instrumentos e o processo de coleta de dados devem apresentar erro randômico e de pequena magnitude2. A natureza probabilística da AF, por sua vez, permite o tratamento analítico do erro embutido no levantamento dos dados, um aspecto relevante quando se recorre a inquéritos alimentares para traduzir o comportamento alimentar de populações209. A RRR traz a particularidade de permitir que a seleção do PA se dê diretamente a partir da maximização da associação entre as variáveis alimentares e os marcadores do desfecho de interesse206 210. A análise de cluster se distingue metodologicamente das demais por ter como objetivo agrupar os sujeitos em clusters hierarquizados segundo o nível de dissimilaridade entre os componentes das dietas dos indivíduos211.

A escolha dos mesmos grupos alimentares encontrados no QFA predominou nas publicações levantadas. Tal seleção aumenta a consistência interna das análises, uma vez que utiliza dados de instrumentos previamente validados cujos grupos de alimentos foram formados em função do desfecho que se deseja estudar. Nos estudos em que foram utilizados R24h ou registros alimentares, os critérios predominantes para a formação de grupos alimentares foram: composição nutricional, grupos previamente classificados por agências oficiais, recomendações nutricionais governamentais e alimentos extraídos de inquéritos nacionais.

O desenho analítico e o número de variáveis selecionadas envolvem subjetividade e arbitrariedade do pesquisador e torna as análises multivariadas de difícil reprodutibilidade, com prejuízo para a comparabilidade entre estudos. A inclusão de um número grande de variáveis não correlacionadas pode ter um efeito errôneo na determinação da variância explicada por aquele PA, enquanto a eliminação de variáveis a fim de simplificar a estrutura fatorial pode levar a conclusões equivocadas24. Nas publicações estudadas, foi verificada a utilização de 30 ou mais grupos de alimentos, provavelmente em função de 76% dos artigos avaliarem o consumo alimentar por meio de QFA, os quais, na maioria das vezes, possuem um grande número de itens alimentares e/ou grupos, permitindo posteriormente, na análise multivariada, maior precisão e exatidão do PA obtido212.

O número de grupos de alimentos incluídos nas técnicas multivariadas interfere fortemente na proporção da variância explicada pelos fatores e no número de fatores a serem retidos, principalmente pelo critério da raiz latente (eigenvalue ). Quanto maior for a variância explicada, melhor será a compreensão da diversidade e da complexidade da alimentação da população estudada19.

Na presente revisão, os estudos que associaram PA com DCV e câncer utilizaram menos do que quatro PAs para explicar possíveis relações de risco ou proteção, já os que associaram obesidade com PA utilizaram de quatro a cinco fatores. Estudos realizados por Schulze et al.213 e Brennan et al.214 verificaram que a utilização de até três fatores permite fazer boas associações com desfechos de saúde. Na análise de cluster , Newby e Tucker4 encontraram boas associações com DCNT na presença de dois a oito clusters , fato também observado nesta revisão. O número de PAs obtidos está atrelado à porcentagem de variância explicada por eles, sendo que o desejado é explicar a maior variância do conjunto inicial dos dados com um número mínimo de fatores ou clusters215.

Para os estudos que designaram rótulo qualitativo ao PA, verifica-se uma base empírica no sentido de encontrar um significado nas informações científicas disponíveis. Sendo assim, foi possível encontrar semelhança no tipo de alimento que compôs esses padrões. Padrões nomeados como "traditional " continham preparações mais prevalentes da dieta do país onde o estudo foi realizado. Os rotulados como "healthy pattern " e "prudent pattern " continham hortaliças, frutas, cereais integrais, peixe, laticínios desnatados, frango, soja e derivados. O rótulo "mediterranean pattern " se caracterizou por agrupar massas, arroz, peixe, legumes, óleos vegetais, leite desnatado, saladas, frutas, vinho, pepino, enquanto o "western pattern " se caracterizou por refrigerantes, pizza, hambúrguer, carnes processadas, bebidas açucaradas, leite integral, alimentos pré-cozidos, grãos refinados, bacon, presunto.

Nesta revisão da literatura, deve-se ter em conta algumas limitações. A primeira delas decorre pelo fato de a seleção dos artigos ter sido realizada em duas bases de dados: MEDLINE e Lilacs. O fato de essas duas bases de dados compilarem vasta quantidade de artigos científicos indexados e de boa qualidade tenderia a reduzir o possível efeito causado pela restrição no número de bases consultadas. A segunda delas foi a seleção de três idiomas (inglês, português e espanhol) para o processo de busca, o que também pode ter reduzido o número de publicações encontradas. No entanto, os autores realizaram um levantamento nessas duas bases utilizando os mesmos descritores do estudo, sem restrição do idioma, e foi verificado que a perda de publicações devido ao idioma não ultrapassaria 5%. A terceira delas se relaciona ao fato de que, mesmo construída de forma ampla, a pergunta inicial não abordou aspectos tipicamente operacionais na construção de PA, como, por exemplo: tipos de rotação, métodos utilizados na extração de PA, variável qualitativa ou quantitativa, tamanho amostral, entre outros.

O uso de técnicas multivariadas tem avançado na última década, ressaltando a importância da padronização das informações publicadas. Nesta revisão, foram verificados casos em que o PA foi caracterizado e interpretado segundo os nutrientes mais prevalentes entre os grupos de alimentos que o compõem43 60 107 153 216. Essa forma de interpretação focada em um único nutriente ou conjunto de nutrientes descaracteriza o PA, na medida em que deixa de avaliar a complexidade da dieta expressa pela combinação de alimentos. Uma evidência favorável ao uso integral do PA e sua interpretação baseada nos alimentos que o compõem e contrária à tradição de selecionar nutrientes mais associados ao desfecho é a tendência de a variância explicada pelos padrões aumentar à medida que se reduz o detalhamento da informação alimentar215.

CONCLUSÃO

O uso das técnicas multivariadas em epidemiologia nutricional aumentou nas últimas décadas principalmente em função da necessidade de aprender e representar o consumo alimentar na sua forma complexa, levando em conta a interação entre os nutrientes presentes nos alimentos. Verificou-se, entre as publicações, a predominância das técnicas cuja estrutura de relações é baseada em variáveis (ACP e AF), e não em indivíduos (cluster ). Nesta revisão, a heterogeneidade dos estudos se concentrou principalmente no critério de agrupamento dos alimentos precedentes à realização das técnicas multivariadas e no critério para nomear os PAs extraídos. O agrupamento dos alimentos incide diretamente no número de padrões extraídos, assim como na interpretação e na elucidação da associação do PA com desfechos de saúde. Entender, aplicar e explorar em sua totalidade as técnicas multivariadas tem se tornado necessário para melhorar a confiabilidade dos estudos analíticos sobre PA e, consequentemente, aprimorar as relações com desfechos de saúde e fatores socioeconômicos.

AGRADECIMENTOS

Agradecemos à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), por conceder bolsa de estudos para C.A.B. para o andamento de seu doutorado. Agradecemos também o apoio fornecido pela Dra. Patrícia Hinning na sistematização e organização dos dados.

REFERÊNCIAS

1. Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004; 159(10): 935-44.
2. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002; 13(1): 3-9.
3. Van Dam RM. New approaches to the study of dietary patterns. Br J Nutr 2005; 93(5): 573-4.
4. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: A Review. Nutr Rev 2004; 62(5): 177-203
5. Tucker KL. Dietary patterns, approaches, and multicultural perspective. Appl Physiol Nutr Metab Physiol Appliquée Nutr Métabolisme 2010; 35(2): 211-8.
6. Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc. 2004;104(4):615-35.
7. Michels KB, Schulze MB. Can dietary patterns help us detect diet? Disease associations? Nutr Res Rev 2005; 18(2): 241-8.
8. Katz DL, Meller S. Can we say what diet is best for health? Annu Rev Public Health 2014; 35: 83-103.
9. Liese AD, Weis KE, Schulz M, Tooze JA. Food intake patterns associated with incident type 2 diabetes. Diabetes Care 2009; 32(2): 263-8.
10. Berg CM, Lappas G, Strandhagen E, Wolk A, Torén K, Rosengren A, et al. Food patterns and cardiovascular disease risk factors: the Swedish INTERGENE research program. Am J Clin Nutr 2008; 88(2): 289-97.
11. Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr 2008; 138(2): 358-63.
12. Uglem S, Stea TH, Frølich W, Wandel M. Body weight, weight perceptions and food intake patterns. A cross-sectional study among male recruits in the Norwegian National Guard. BMC Public Health 2011; 11: 343.
13. Yannakoulia M, Ntalla I, Papoutsakis C, Farmaki AE, Dedoussis GV. Consumption of vegetables, cooked meals, and eating dinner is negatively associated with overweight status in children. J Pediatr 2010; 157(5): 815-20.
14. Panagiotakos D, Pitsavos C, Chrysohoou C, Palliou K, Lentzas I, Skoumas I, et al. Dietary patterns and 5-year incidence of cardiovascular disease: a multivariate analysis of the ATTICA study. Nutr Metab Cardiovasc Dis NMCD 2009; 19(4): 253-63.
15. Fung TT, Willett WC, Stampfer MJ, Manson JAE, Hu FB. Dietary patterns and the risk of coronary heart disease in women. Arch Intern Med 2001; 161(15): 1857.
16. Demetriou CA, Hadjisavvas A, Loizidou MA, Loucaides G, Neophytou I, Sieri S, et al. The mediterranean dietary pattern and breast cancer risk in Greek-Cypriot women: a case-control study. BMC Cancer 2012; 12: 113.
17. Sichieri R. Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obesity 2002; 10(1): 42-8.
18. Moeller SM, Reedy J, Millen AE, Dixon LB, Newby PK, Tucker KL, et al. Dietary patterns: challenges and opportunities in dietary patterns research an Experimental Biology workshop, April 1, 2006. J Am Diet Assoc 2007; 107(7): 1233-9.
19. Hair JF Jr, Black WC, Babin BJ. Multivariate Data Analysis. 7th ed. Paperback; 2009. ISBN-10: 0138132631
20. Wirfält AE, Jeffery RW. Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J Am Diet Assoc 1997; 97(3): 272-9.
21. Akin JS, Guilkey DK, Popkin BM, Fanelli MT. Cluster analysis of food consumption patterns of older Americans. J Am Diet Assoc 1986; 86(5): 616.
22. Costello AB, Osborne J. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation 2014; 10(7): 9.
23. Castro MA de, Baltar VT, Selem SS de C, Marchioni DML, Fisberg RM, Castro MA de, et al. Padrões alimentares empiricamente derivados: interpretabilidade e validade de construto segundo diferentes métodos de rotação fatorial. Cad Saúde Pública 2015; 31(2): 298-310.
24. Martínez ME, Marshall JR, Sechrest L. The Arbitrary Nature Of The Factor Analytical Process. Am J Epidemiol 1998; 148(1): 17-9
25. Slattery ML, Boucher KM, Caan BJ, Potter JD, Ma KN. Eating patterns and risk of colon cancer. Am J Epidemiol 1998; 148(1): 4-16.
26. Hardle W, Simar L. Applied Multivariate Statistical Analysis. 2th ed. Springer; 2007. ISBN 978-3-540-72243-4.
27. Hu FB, Rimm EB, Stampfer MJ, Ascherio A, Spiegelman D, Willett WC. Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 2000; 72(4): 912-21.
28. Cunha DB, Almeida RMVR de, Pereira RA. A comparison of three statistical methods applied in the identification of eating patterns. Cad Saúde Pública 2010; 26(11): 2138-48.
29. Manios Y, Kourlaba G, Grammatikaki E, Androutsos O, Ioannou E, Roma-Giannikou E. Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the GENESIS study. Eur J Clin Nutr 2010; 64(12): 1407-14.
30. Funtikova AN, Benítez-Arciniega AA, Fitó M, Schröder H. Modest validity and fair reproducibility of dietary patterns derived by cluster analysis. Nutr Res 2015; 35(3): 265-8
31. Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A, et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 1999; 69(2): 243-9.
32. Schulze MB, Hoffmann K. Methodological approaches to study dietary patterns in relation to risk of coronary heart disease and stroke. Br J Nutr 2006; 95(5): 860-9.
33. Brennan SF, Cantwell MM, Cardwell CR, Velentzis LS, Woodside JV. Dietary patterns and breast cancer risk: a systematic review and meta-analysis. Am J Clin Nutr 2010; 91(5): 1294-302.
34. Martínez ME, Marshall JR, Sechrest L. Invited commentary: Factor analysis and the search for objectivity. Am J Epidemiol 1998; 148(1): 17-9.
35. Van den Berg L, Henneman P, Willems van Dijk K, Delemarre van de Waal H, Oostra B, van Duijn C, et al. Heritability of dietary food intake patterns. Acta Diabetol 2013; (50): 721-26
36. Togo P, Osler M, Sørensen TI, Heitmann BL. Food intake patterns and body mass index in observational studies. Int J Obes Relat Metab Disord 2001; 25(12): 1741.
37. Schulze MB, Hoffmann K, Kroke A, Boeing H. Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br J Nutr 2001; 85(3): 363-73.
38. McNaughton SA, Wattanapenpaiboon N, Wark JD, Nowson CA. An energy-dense, nutrient-poor dietary pattern is inversely associated with bone health in women. J Nutr 2011; 141(8): 1516-23.
39. Johnson L, Mander AP, Jones LR, Emmett PM, Jebb SA. Energy-dense, low-fiber, high-fat dietary pattern is associated with increased fatness in childhood. Am J Clin Nutr 2008; 87(4): 846-54.
40. Fung TT SM. DIetary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med 2004; 164(20): 2235-40.
41. Schulze MB, Hoffmann K, Manson JE, Willett WC, Meigs JB, Weikert C, et al. Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am J Clin Nutr 2005; 82(3): 675-84; quiz 714-5.
42. Heidemann C, Hoffmann K, Spranger J, Klipstein-Grobusch K, Möhlig M, Pfeiffer AFH, et al. A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)--Potsdam Study cohort. Diabetologia. 2005; 48(6): 1126-34.
43. Mizoue T, Yamaji T, Tabata S, Yamaguchi K, Ogawa S, Mineshita M, et al. Dietary patterns and glucose tolerance abnormalities in Japanese men. J Nutr 2006; 136(5): 1352-8.
44. McNaughton SA, Mishra GD, Brunner EJ. Dietary patterns, insulin resistance, and incidence of type 2 diabetes in the Whitehall II Study. Diabetes Care 2008; 31(7): 1343-8.
45. Kim H-S, Park S-Y, Grandinetti A, Holck PS, Waslien C. Major dietary patterns, ethnicity, and prevalence of type 2 diabetes in rural Hawaii. Nutr Burbank Los Angel Cty Calif 2008; 24(11-12): 1065-72.
46. Lau C, Toft U, Tetens I, Carstensen B, Jørgensen T, Pedersen O, et al. Dietary patterns predict changes in two-hour post-oral glucose tolerance test plasma glucose concentrations in middle-aged adults. J Nutr 2009; 139(3): 588-93.
47. Liese AD, Weis KE, Schulz M, Tooze JA. Food intake patterns associated with incident type 2 diabetes. Diabetes Care 2009; 32(2): 263-8.
48. Qi L, Cornelis MC, Zhang C, van Dam RM, Hu FB. Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men. Am J Clin Nutr 2009; 89(5): 1453-8.
49. Villegas R, Yang G, Gao Y-T, Cai H, Li H, Zheng W, et al. Dietary patterns are associated with lower incidence of type 2 diabetes in middle-aged women: the Shanghai Women's Health Study. Int J Epidemiol 2010; 39(3): 889-99.
50. Odegaard AO, Koh W-P, Butler LM, Duval S, Gross MD, Yu MC, et al. Dietary patterns and incident type 2 diabetes in chinese men and women: the singapore chinese health study. Diabetes Care 2011; 34(4): 880-5.
51. Iimuro S, Yoshimura Y, Umegaki H, Sakurai T, Araki A, Ohashi Y, et al. Dietary pattern and mortality in Japanese elderly patients with type 2 diabetes mellitus: does a vegetable- and fish-rich diet improve mortality? An explanatory study. Geriatr Gerontol Int 2012; 12 Suppl 1: 59-67.
52. 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(1): 12-8.
53. Newby PK, Muller D, Hallfrisch J, Andres R, Tucker KL. Food patterns measured by factor analysis and anthropometric changes in adults. Am J Clin Nutr 2004; 80(2): 504-13.
54. Schulz M, Nöthlings U, Hoffmann K, Bergmann MM, Boeing H. Identification of a Food Pattern Characterized by High-Fiber and Low-Fat Food Choices Associated with Low Prospective Weight Change in the EPIC-Potsdam Cohort. J Nutr 2005; 135(5): 1183-9.
55. Schulze MB, Fung TT, Manson JE, Willett WC, Hu FB. Dietary patterns and changes in body weight in women. Obes Silver Spring Md 2006; 14(8): 1444-53.
56. Newby PK, Weismayer C, Akesson A, Tucker KL, Wolk A. Longitudinal changes in food patterns predict changes in weight and body mass index and the effects are greatest in obese women. J Nutr 2006; 136(10): 2580-7.
57. 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(8): 1311-21.
58. Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr 2008; 138(2): 358-63.
59. Shi Z, Hu X, Yuan B, Hu G, Pan X, Dai Y, et al. Vegetable-rich food pattern is related to obesity in China. Int J Obes (Lond) 2008; 32(6): 975-84.
60. Uusitalo U, Arkkola T, Ovaskainen ML, Kronberg-Kippilä C, Kenward MG, Veijola R, et al. Unhealthy dietary patterns are associated with weight gain during pregnancy among Finnish women. Public Health Nutr 2009; 12(12): 2392-9.
61. Kjøllesdal MR, Holmboe-Ottesen G, Wandel M. Frequent use of staff canteens is associated with unhealthy dietary habits and obesity in a Norwegian adult population. Public Health Nutr 2011; 14(1): 133-41.
62. Oellingrath IM, Svendsen MV, Brantsæter AL. Eating patterns and overweight in 9- to 10-year-old children in Telemark County, Norway: a cross-sectional study. Eur J Clin Nutr 2010; 64(11): 1272-9.
63. Manios Y, Kourlaba G, Grammatikaki E, Androutsos O, Ioannou E, Roma-Giannikou E. Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the GENESIS study. Eur J Clin Nutr 2010; 64(12): 1407-14.
64. Wosje KS, Khoury PR, Claytor RP, Copeland KA, Hornung RW, Daniels SR, et al. Dietary patterns associated with fat and bone mass in young children. Am J Clin Nutr 2010; 92(2): 294-303.
65. Cunha DB, de Almeida RMVR, 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(06): 908-13.
66. Cho YA, Shin A, Kim J. Dietary patterns are associated with body mass index in a Korean population. J Am Diet Assoc 2011; 111(8): 1182-6.
67. Shi Z, Yuan B, Hu G, Dai Y, Zuo H, Holmboe-Ottesen G. Dietary pattern and weight change in a 5-year follow-up among Chinese adults: results from the Jiangsu Nutrition Study. Br J Nutr 2011; 105(7): 1047-54.
68. Boggs DA, Palmer JR, Spiegelman D, Stampfer MJ, Adams-Campbell LL, Rosenberg L. Dietary patterns and 14-y weight gain in African American women. Am J Clin Nutr 2011; 94(1): 86-94.
69. Cutler GJ, Flood A, Hannan PJ, Slavin JL, Neumark-Sztainer D. Association between major patterns of dietary intake and weight status in adolescents. Br J Nutr 2012; 108(2): 349-56.
70. Lin H, Bermudez OI, Tucker KL. Dietary patterns of Hispanic elders are associated with acculturation and obesity. J Nutr 2003; 133(11): 3651-7.
71. Ambrosini GL, Emmett PM, Northstone K, Howe LD, Tilling K, Jebb SA. Identification of a dietary pattern prospectively associated with increased adiposity during childhood and adolescence. Int J Obes 2012; 36(10): 1299-305.
72. Fialkowski MK, McCrory MA, Roberts SM, Tracy JK, Grattan LM, Boushey CJ. Dietary patterns are associated with dietary recommendations but have limited relationship to BMI in the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort. Public Health Nutr 2012; 15(10): 1948-58.
73. Kim J-H, Lee JE, Jung I-K. Dietary pattern classifications and the association with general obesity and abdominal obesity in Korean women. J Acad Nutr Diet 2012; 112(10): 1550-9.
74. Kim J, Jo I, Joung H. A rice-based traditional dietary pattern is associated with obesity in Korean adults. J Acad Nutr Diet 2012; 112(2): 246-53.
75. Maskarinec G, Novotny R, Tasaki K. Dietary Patterns Are Associated with Body Mass Index in Multiethnic Women. J Nutr 2000; 130(12): 3068-72.
76. Osler M, Helms Andreasen A, Heitmann B, Høidrup S, Gerdes U, Mørch Jørgensen L, et al. Food intake patterns and risk of coronary heart disease: a prospective cohort study examining the use of traditional scoring techniques. Eur J Clin Nutr 2002; 56(7): 568-74.
77. Quatromoni PA, Copenhafer DL, Demissie S, D'Agostino RB, O'Horo CE, Nam B, et al. The internal validity of a dietary pattern analysis. The Framingham Nutrition Studies. J Epidemiol Community Health 2002; 56(5): 381-8.
78. 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(6): 1103-10.
79. Villegas R, Salim A, Collins MM, Flynn A, Perry IJ. Dietary patterns in middle-aged Irish men and women defined by cluster analysis. Public Health Nutr 2004; 7(8): 1017-24.
80. Hoffmann K, Zyriax B-C, Boeing H, Windler E. A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease. Am J Clin Nutr 2004; 80(3): 633-40.
81. Nettleton JA, Schulze MB, Jiang R, Jenny NS, Burke GL, Jacobs DR. A priori-defined dietary patterns and markers of cardiovascular disease risk in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2008; 88(1): 185-94.
82. Akesson A, Weismayer C, Newby PK, Wolk A. Combined effect of low-risk dietary and lifestyle behaviors in primary prevention of myocardial infarction in women. Arch Intern Med 2007; 167(19): 2122-7.
83. Shimazu T, Kuriyama S, Hozawa A, Ohmori K, Sato Y, Nakaya N, et al. Dietary patterns and cardiovascular disease mortality in Japan: a prospective cohort study. Int J Epidemiol 2007; 36(3): 600-9.
84. DiBello JR, Kraft P, McGarvey ST, Goldberg R, Campos H, Baylin A. Comparison of 3 methods for identifying dietary patterns associated with risk of disease. Am J Epidemiol 2008; 168(12): 1433-43.
85. Panagiotakos D, Pitsavos C, Chrysohoou C, Palliou K, Lentzas I, Skoumas I, et al. Dietary patterns and 5-year incidence of cardiovascular disease: a multivariate analysis of the ATTICA study. Nutr Metab Cardiovasc Dis NMCD 2009; 19(4): 253-63.
86. Liu L, Nettleton JA, Bertoni AG, Bluemke DA, Lima JA, Szklo M. Dietary pattern, the metabolic syndrome, and left ventricular mass and systolic function: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr 2009; 90(2): 362-8.
87. Nettleton JA, Matijevic N, Follis JL, Folsom AR, Boerwinkle E. Associations between dietary patterns and flow cytometry-measured biomarkers of inflammation and cellular activation in the Atherosclerosis Risk in Communities (ARIC) Carotid Artery MRI Study. Atherosclerosis 2010; 212(1): 260-7.
88. Kesse-Guyot E, Vergnaud A-C, Fezeu L, Zureik M, Blacher J, Péneau S, et al. Associations between dietary patterns and arterial stiffness, carotid artery intima-media thickness and atherosclerosis. Eur J Cardiovasc Prev Rehabil 2010; 17(6): 718-24.
89. Liese AD, Nichols M, Hodo D, Mellen PB, Schulz M, Goff DC, et al. Food intake patterns associated with carotid artery atherosclerosis in the Insulin Resistance Atherosclerosis Study. Br J Nutr 2010; 103(10): 1471-9.
90. Hamer M, Mishra GD. Dietary patterns and cardiovascular risk markers in the UK Low Income Diet and Nutrition Survey. Nutr Metab Cardiovasc Dis NMCD 2010; 20(7): 491-7.
91. Fung TT, Willett WC, Stampfer MJ, Manson JAE, Hu FB. Dietary patterns and the risk of coronary heart disease in women. Arch Intern Med 2001; 161(15): 1857.
92. Meyer J, Döring A, Herder C, Roden M, Koenig W, Thorand B. Dietary patterns, subclinical inflammation, incident coronary heart disease and mortality in middle-aged men from the MONICA/KORA Augsburg cohort study. Eur J Clin Nutr 2011; 65(7): 800-7.
93. Daniel CR, Prabhakaran D, Kapur K, Graubard BI, Devasenapathy N, Ramakrishnan L, et al. A cross-sectional investigation of regional patterns of diet and cardio-metabolic risk in India. Nutr J 2011; 10: 12.
94. Guo H, Niu K, Monma H, Kobayashi Y, Guan L, Sato M, et al. Association of Japanese dietary pattern with serum adiponectin concentration in Japanese adult men. Nutr Metab Cardiovasc Dis NMCD 2012; 22(3): 277-84.
95. Yap RWK, Shidoji Y, Hon WM, Masaki M. Association and interaction between dietary pattern and VEGF receptor-2 (VEGFR2) gene polymorphisms on blood lipids in Chinese Malaysian and Japanese adults. Asia Pac J Clin Nutr 2012; 21(2): 302-11.
96. Osler M, Heitmann BL, Gerdes LU, Jorgensen LM, Schroll M. Dietary patterns and mortality in Danish men and women: a prospective observational study. Br J Nutr 2001; 85(2): 219-25.
97. Nicklas TA, Webber LS, Thompson B, Berenson GS. A multivariate model for assessing eating patterns and their relationship to cardiovascular risk factors: the Bogalusa Heart Study. Am J Clin Nutr 1989; 49(6): 1320-7.
98. Markaki I, Linos D, Linos A. The influence of dietary patterns on the development of thyroid cancer. Eur J Cancer Oxf Engl 2003; 39(13): 1912-9.
99. Fung T, Hu FB, Fuchs C, Giovannucci E, Hunter DJ, Stampfer MJ, et al. Major dietary patterns and the risk of colorectal cancer in women. Arch Intern Med 2003; 163(3): 309-14.
100. Kim MK, Sasaki S, Sasazuki S, Tsugane S; Japan Public Health Center-based Prospective Study Group. Prospective study of three major dietary patterns and risk of gastric cancer in Japan. Int J Cancer 2004; 110(3): 435-42.
101. Mizoue T, Yamaji T, Tabata S, Yamaguchi K, Shimizu E, Mineshita M, et al. Dietary patterns and colorectal adenomas in Japanese men: the Self-Defense Forces Health Study. Am J Epidemiol 2005; 161(4): 338-45.
102. Balder HF, Goldbohm RA, van den Brandt PA. Dietary patterns associated with male lung cancer risk in the Netherlands Cohort Study. Cancer Epidemiol Biomarkers Prev 2005;14(2):483-90.
103. Meyerhardt JA, Niedzwiecki D, Hollis D, Saltz LB, Hu FB, Mayer RJ, et al. Association of dietary patterns with cancer recurrence and survival in patients with stage III colon cancer. JAMA 2007; 298(7): 754-64.
104. Campbell PT, Sloan M, Kreiger N. Dietary patterns and risk of incident gastric adenocarcinoma. Am J Epidemiol 2008; 167(3): 295-304.
105. Sant M, Allemani C, Sieri S, Krogh V, Menard S, Tagliabue E, et al. Salad vegetables dietary pattern protects against HER-2-positive breast cancer: a prospective Italian study. Int J Cancer 2007; 121(4): 911-4.
106. Hirose K, Matsuo K, Iwata H, Tajima K. Dietary patterns and the risk of breast cancer in Japanese women. Cancer Sci 2007; 98(9): 1431-8.
107. Schulz M, Hoffmann K, Weikert C, Nöthlings U, Schulze MB, Boeing H. Identification of a dietary pattern characterized by high-fat food choices associated with increased risk of breast cancer: the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Br J Nutr 2008; 100(5): 942-6.
108. De Stefani E, Boffetta P, Fagundes RB, Deneo-Pellegrini H, Ronco AL, Acosta G, et al. Nutrient patterns and risk of squamous cell carcinoma of the esophagus: a factor analysis in uruguay. Anticancer Res 2008; 28(4C): 2499-506.
109. Cottet V, Touvier M, Fournier A, Touillaud MS, Lafay L, Clavel-Chapelon F, et al. Postmenopausal breast cancer risk and dietary patterns in the E3N-EPIC prospective cohort study. Am J Epidemiol 2009; 170(10): 1257-67.
110. Agurs-Collins T, Rosenberg L, Makambi K, Palmer JR, Adams-Campbell L. Dietary patterns and breast cancer risk in women participating in the Black Women's Health Study. Am J Clin Nutr 2009; 90(3): 621-8.
111. Pham T-M, Fujino Y, Kikuchi S, Tamakoshi A, Matsuda S, Yoshimura T. Dietary patterns and risk of stomach cancer mortality: the Japan collaborative cohort study. Ann Epidemiol 2010; 20(5): 356-63.
112. Edefonti V, Bravi F, Garavello W, La Vecchia C, Parpinel M, Franceschi S, et al. Nutrient-based dietary patterns and laryngeal cancer: evidence from an exploratory factor analysis. Cancer Epidemiol Biomarkers Prev 2010; 19(1): 18-27.
113. Mishra GD, dos Santos Silva I, McNaughton SA, Stephen A, Kuh D. Energy intake and dietary patterns in childhood and throughout adulthood and mammographic density: results from a British prospective cohort. Cancer Causes Control 2011; 22(2): 227-35.
114. Biel RK, Friedenreich CM, Csizmadi I, Robson PJ, McLaren L, Faris P, et al. Case-control study of dietary patterns and endometrial cancer risk. Nutr Cancer 2011; 63(5): 673-86.
115. Zhang CX, Ho SC, Fu JH, Cheng SZ, Chen YM, Lin FY. Dietary patterns and breast cancer risk among Chinese women. Cancer Causes Control 2011; 22(1): 115-24.
116. Buck K, Vrieling A, Flesch-Janys D, Chang-Claude J. Dietary patterns and the risk of postmenopausal breast cancer in a German case-control study. Cancer Causes Control 2011; 22(2): 273-82.
117. Demetriou CA, Hadjisavvas A, Loizidou MA, Loucaides G, Neophytou I, Sieri S, et al. The mediterranean dietary pattern and breast cancer risk in Greek-Cypriot women: a case-control study. BMC Cancer 2012; 12: 113.
118. Piyathilake CJ, Badiga S, Kabagambe EK, Azuero A, Alvarez RD, Johanning GL, et al. A dietary pattern associated with LINE-1 methylation alters the risk of developing cervical intraepithelial neoplasia. Cancer Prev Res 2012; 5(3): 385-92.
119. Bradshaw PT, Siega-Riz AM, Campbell M, Weissler MC, Funkhouser WK, Olshan AF. Associations between dietary patterns and head and neck cancer: the Carolina head and neck cancer epidemiology study. Am J Epidemiol 2012; 175(12): 1225-33.
120. Terry P, Hu FB, Hansen H, Wolk A. Prospective study of major dietary patterns and colorectal cancer risk in women. Am J Epidemiol 2001; 154(12): 1143-9.
121. Randall E, Marshall JR, Graham S, Brasure J. Patterns in food use and their associations with nutrient intakes. Am J Clin Nutr 1990; 52(4): 739-45.
122. Zhuo XG, Watanabe S. Factor analysis of digestive cancer mortality and food consumption in 65 Chinese counties. J Epidemiol 1999; 9(4): 275-84.
123. Terry P, Suzuki R, Hu FB, Wolk A. A prospective study of major dietary patterns and the risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2001; 10(12): 1281-5.
124. Millen BE, Quatromoni PA, Nam BH, O'Horo CE, Polak JF, D'Agostino RB. Dietary patterns and the odds of carotid atherosclerosis in women: the Framingham Nutrition Studies. Prev Med 2002; 35(6): 540-7.
125. Newby PK, Muller D, Hallfrisch J, Qiao N, Andres R, Tucker KL. Dietary patterns and changes in body mass index and waist circumference in adults. Am J Clin Nutr 2003; 77(6): 1417-25.
126. Ledikwe JH, Smiciklas-Wright H, Mitchell DC, Miller CK, Jensen GL. Dietary patterns of rural older adults are associated with weight and nutritional status. J Am Geriatr Soc 2004; 52(4): 589-95.
127. Fung TT, Stampfer MJ, Manson JE, Rexrode KM, Willett WC, Hu FB. Prospective study of major dietary patterns and stroke risk in women. Stroke J Cereb Circ 2004; 35(9): 2014-9.
128. Weikert C, Hoffmann K, Dierkes J, Zyriax B-C, Klipstein-Grobusch K, Schulze MB, et al. A homocysteine metabolism-related dietary pattern and the risk of coronary heart disease in two independent German study populations. J Nutr 2005; 135(8): 1981-8.
129. Moreira P, de Almeida MDV, Sampaio D. Cognitive restraint is associated with higher intake of vegetables in a sample of university students. Eat Behav 2005; 6(3): 229-37.
130. Waijers PMCM, Ocké MC, van Rossum CTM, Peeters PHM, Bamia C, Chloptsios Y, et al. Dietary patterns and survival in older Dutch women. Am J Clin Nutr 2006; 83(5): 1170-6.
131. Panagiotakos DB, Pitsavos C, Skoumas Y, Stefanadis C. The association between food patterns and the metabolic syndrome using principal components analysis: The ATTICA Study. J Am Diet Assoc 2007; 107(6): 979-87.
132. McCann SE, McCann WE, Hong CC, Marshall JR, Edge SB, Trevisan M, et al. Dietary patterns related to glycemic index and load and risk of premenopausal and postmenopausal breast cancer in the Western New York Exposure and Breast Cancer Study. Am J Clin Nutr 2007; 86(2): 465-71.
133. Okubo H, Sasaki S, Murakami K, Kim MK, Takahashi Y, Hosoi Y, et al. Dietary patterns associated with functional constipation among Japanese women aged 18 to 20 years: a cross-sectional study. J Nutr Sci Vitaminol (Tokyo) 2007; 53(3): 232-8.
134. McNaughton SA, Mishra GD, Stephen AM, Wadsworth MEJ. Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate. J Nutr 2007; 137(1): 99-105.
135. Okubo H, Sasaki S, Horiguchi H, Oguma E, Miyamoto K, Hosoi Y, et al. Dietary patterns associated with bone mineral density in premenopausal Japanese farmwomen. Am J Clin Nutr 2006; 83(5): 1185-92.
136. Masala G, Ceroti M, Pala V, Krogh V, Vineis P, Sacerdote C, et al. A dietary pattern rich in olive oil and raw vegetables is associated with lower mortality in Italian elderly subjects. Br J Nutr 2007; 98(2): 406-15.
137. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Dietary patterns, insulin resistance, and prevalence of the metabolic syndrome in women. Am J Clin Nutr 2007; 85(3): 910-8.
138. Yannakoulia M, Yiannakouris N, Melistas L, Kontogianni MD, Malagaris I, Mantzoros CS. A dietary pattern characterized by high consumption of whole-grain cereals and low-fat dairy products and low consumption of refined cereals is positively associated with plasma adiponectin levels in healthy women. Metabolism 2008; 57(6): 824-30.
139. Schröder H, Vila J, Marrugat J, Covas MI. Low energy density diets are associated with favorable nutrient intake profile and adequacy in free-living elderly men and women. J Nutr 2008; 138(8): 1476-81.
140. McNaughton SA, Ball K, Mishra GD, Crawford DA. Dietary patterns of adolescents and risk of obesity and hypertension. J Nutr 2008; 138(2): 364-70.
141. Lutsey PL, Steffen LM, Stevens J. Dietary intake and the development of the metabolic syndrome: the Atherosclerosis Risk in Communities study. Circulation 2008; 117(6): 754-61.
142. Nanri A, Yoshida D, Yamaji T, Mizoue T, Takayanagi R, Kono S. Dietary patterns and C-reactive protein in Japanese men and women. Am J Clin Nutr 2008; 87(5): 1488-96.
143. Konstantinova SV, Tell GS, Vollset SE, Ulvik A, Drevon CA, Ueland PM. Dietary patterns, food groups, and nutrients as predictors of plasma choline and betaine in middle-aged and elderly men and women. Am J Clin Nutr 2008; 88(6): 1663-9.
144. McNaughton SA, Mishra GD, Brunner EJ. Food patterns associated with blood lipids are predictive of coronary heart disease: the Whitehall II study. Br J Nutr 2009; 102(4): 619-24.
145. Vujkovic M, Steegers EA, Looman CW, Ocké MC, van der Spek PJ, Steegers-Theunissen RP. The maternal Mediterranean dietary pattern is associated with a reduced risk of spina bifida in the offspring. BJOG Int J Obstet Gynaecol 2009; 116(3): 408-15.
146. Brantsaeter AL, Haugen M, Samuelsen SO, Torjusen H, Trogstad L, Alexander J, et al. A dietary pattern characterized by high intake of vegetables, fruits, and vegetable oils is associated with reduced risk of preeclampsia in nulliparous pregnant Norwegian women. J Nutr 2009; 139(6): 1162-8.
147. Oddy WH, Robinson M, Ambrosini GL, O'Sullivan TA, de Klerk NH, Beilin LJ, et al. The association between dietary patterns and mental health in early adolescence. Prev Med 2009; 49(1): 39-44.
148. Nafar M, Noori N, Jalali-Farahani S, Hosseinpanah F, Poorrezagholi F, Ahmadpoor P, et al. Mediterranean diets are associated with a lower incidence of metabolic syndrome one year following renal transplantation. Kidney Int 2009; 76(11): 1199-206.
149. Gustaw-Rothenberg K. Dietary patterns associated with Alzheimer's disease: population based study. Int J Environ Res Public Health 2009; 6(4): 1335-40.
150. Vujkovic M, de Vries JH, Dohle GR, Bonsel GJ, Lindemans J, Macklon NS, et al. Associations between dietary patterns and semen quality in men undergoing IVF/ICSI treatment. Hum Reprod Oxf Engl 2009; 24(6): 1304-12.
151. Nanri A, Kimura Y, Matsushita Y, Ohta M, Sato M, Mishima N, et al. Dietary patterns and depressive symptoms among Japanese men and women. Eur J Clin Nutr 2010; 64(8): 832-9.
152. Bakolis I, Hooper R, Thompson RL, Shaheen SO. Dietary patterns and adult asthma: population-based case-control study. Allergy 2010; 65(5): 606-15.
153. Gu Y, Nieves JW, Stern Y, Luchsinger JA, Scarmeas N. Food combination and Alzheimer disease risk: a protective diet. Arch Neurol 2010; 67(6): 699-706.
154. Shi Z, Hu X, Yuan B, Pan X, Dai Y, Holmboe-Ottesen G. Association between dietary patterns and anaemia in adults from Jiangsu Province in Eastern China. Br J Nutr 2006; 96(5): 906-12.
155. Hamer M, McNaughton SA, Bates CJ, Mishra GD. Dietary patterns, assessed from a weighed food record, and survival among elderly participants from the United Kingdom. Eur J Clin Nutr 2010; 64(8): 853-61.
156. Yakub M, Iqbal MP, Iqbal R. Dietary patterns are associated with hyperhomocysteinemia in an urban Pakistani population. J Nutr 2010; 140(7): 1261-6.
157. Jacka FN, Pasco JA, Mykletun A, Williams LJ, Hodge AM, O'Reilly SL, et al. Association of Western and traditional diets with depression and anxiety in women. Am J Psychiatry 2010; 167(3): 305-11.
158. Hardcastle AC, Aucott L, Fraser WD, Reid DM, Macdonald HM. Dietary patterns, bone resorption and bone mineral density in early post-menopausal Scottish women. Eur J Clin Nutr 2011; 65(3): 378-85.
159. Chatzi L, Melaki V, Sarri K, Apostolaki I, Roumeliotaki T, Georgiou V, et al. Dietary patterns during pregnancy and the risk of postpartum depression: the mother-child "Rhea" cohort in Crete, Greece. Public Health Nutr 2011; 14(9): 1663-70.
160. Keding GB, Msuya JM, Maass BL, Krawinkel MB. Dietary patterns and nutritional health of women: the nutrition transition in rural Tanzania. Food Nutr Bull 2011; 32(3): 218-26.
161. Noh HY, Song YJ, Lee JE, Joung H, Park MK, Li SJ, et al. Dietary patterns are associated with physical growth among school girls aged 9-11 years. Nutr Res Pract 2011; 5(6): 569-77.
162. Timmermans S, Steegers-Theunissen RPM, Vujkovic M, Bakker R, den Breeijen H, Raat H, et al. Major dietary patterns and blood pressure patterns during pregnancy: the Generation R Study. Am J Obstet Gynecol 2011; 205(4): 337.e1-12.
163. Zhang FF, Morabia A, Carroll J, Gonzalez K, Fulda K, Kaur M, et al. Dietary patterns are associated with levels of global genomic DNA methylation in a cancer-free population. J Nutr 2011; 141(6): 1165-71.
164. Howard AL, Robinson M, Smith GJ, Ambrosini GL, Piek JP, Oddy WH. ADHD is associated with a "Western" dietary pattern in adolescents. J Atten Disord 2011; 15(5): 403-11.
165. Pierce BL, Argos M, Chen Y, Melkonian S, Parvez F, Islam T, et al. Arsenic exposure, dietary patterns, and skin lesion risk in Bangladesh: a prospective study. Am J Epidemiol 2011; 173(3): 345-54.
166. Jacka FN, Pasco JA, Mykletun A, Williams LJ, Nicholson GC, Kotowicz MA, et al. Diet quality in bipolar disorder in a population-based sample of women. J Affect Disord 2011; 129(1-3): 332-7.
167. Grieger JA, Scott J, Cobiac L. Dietary patterns and breast-feeding in Australian children. Public Health Nutr 2011; 14(11): 1939-47.
168. Cho YA, Kim J, Cho ER, Shin A. Dietary patterns and the prevalence of metabolic syndrome in Korean women. Nutr Metab Cardiovasc Dis NMCD 2011; 21(11): 893-900.
169. Smithers LG, Brazionis L, Golley RK, Mittinty MN, Northstone K, Emmett P, et al. Associations between dietary patterns at 6 and 15 months of age and sociodemographic factors. Eur J Clin Nutr 2012; 66(6): 658-66.
170. Hodge A, Almeida OP, English DR, Giles GG, Flicker L. Patterns of dietary intake and psychological distress in older Australians: benefits not just from a Mediterranean diet. Int Psychogeriatr IPA 2013; 25(3): 456-66.
171. Weng TT, Hao JH, Qian QW, Cao H, Fu JL, Sun Y, et al. Is there any relationship between dietary patterns and depression and anxiety in Chinese adolescents? Public Health Nutr 2012; 15(4): 673-82.
172. Yu FJ, Huang MC, Chang WT, Chung HF, Wu CY, Shin SJ, et al. Increased ferritin concentrations correlate with insulin resistance in female type 2 diabetic patients. Ann Nutr Metab 2012; 61(1): 32-40.
173. Hong S, Song Y, Lee KH, Lee HS, Lee M, Jee SH, et al. A fruit and dairy dietary pattern is associated with a reduced risk of metabolic syndrome. Metabolism 2012; 61(6): 883-90.
174. Azadbakht L, Esmaillzadeh A. Dietary patterns and attention deficit hyperactivity disorder among Iranian children. Nutrition 2012; 28(3): 242-9.
175. Sichieri R, Castro JFG, Moura AS. Fatores associados ao padrão de consumo alimentar da população brasileira urbana. Cad Saúde Pública 2003; 19: S47-53.
176. Martikainen P, Brunner E, Marmot M. Socioeconomic differences in dietary patterns among middle-aged men and women. Soc Sci Med 2003; 56(7): 1397-410.
177. Sánchez-Villegas A, Delgado-Rodríguez M, Martínez-González MA, De Irala-Estévez J; Seguimiento Universidad de Navarra Group. Gender, age, socio-demographic and lifestyle factors associated with major dietary patterns in the Spanish Project SUN (Seguimiento Universidad de Navarra). Eur J Clin Nutr 2003; 57(2): 285-92.
178. Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004; 159(10): 935-44.
179. Northstone K, Emmett P. Multivariate analysis of diet in children at four and seven years of age and associations with socio-demographic characteristics. Eur J Clin Nutr 2005; 59(6): 751-60.
180. Yang EJ, Kerver JM, Song WO. Dietary patterns of Korean Americans described by factor analysis. J Am Coll Nutr 2005; 24(2): 115-21.
181. Alves ALS, Olinto MTA, Costa JSD da, Bairros FS de, Balbinotti MAA. [Dietary patterns of adult women living in an urban area of Southern Brazil]. Rev Saúde Pública 2006; 40(5): 865-73.
182. Pala V, Sieri S, Masala G, Palli D, Panico S, Vineis P, et al. Associations between dietary pattern and lifestyle, anthropometry and other health indicators in the elderly participants of the EPIC-Italy cohort. Nutr Metab Cardiovasc Dis 2006; 16(3): 186-201.
183. Weismayer C, Anderson JG, Wolk A. Changes in the stability of dietary patterns in a study of middle-aged Swedish women. J Nutr 2006; 136(6): 1582-7.
184. Nettleton JA, Steffen LM, Schulze MB, Jenny NS, Barr RG, Bertoni AG, et al. Associations between markers of subclinical atherosclerosis and dietary patterns derived by principal components analysis and reduced rank regression in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2007; 85(6): 1615-25.
185. Northstone K, Emmett PM, Rogers I. Dietary patterns in pregnancy and associations with nutrient intakes. Br J Nutr 2008; 99(2): 406-15.
186. Sánchez-Villegas A, Toledo E, Bes-Rastrollo M, Martín-Moreno JM, Tortosa A, Martínez-González MA. Association between dietary and beverage consumption patterns in the SUN (Seguimiento Universidad de Navarra) cohort study. Public Health Nutr 2009; 12(3): 351-8.
187. Serra-Majem L, Bes-Rastrollo M, Román-Viñas B, Pfrimer K, Sánchez-Villegas A, Martínez-González MA. Dietary patterns and nutritional adequacy in a Mediterranean country. Br J Nutr 2009; 101 Suppl 2: S21-8.
188. Cutler GJ, Flood A, Hannan P, Neumark-Sztainer D. Major patterns of dietary intake in adolescents and their stability over time. J Nutr 2009; 139(2): 323-8.
189. Ambrosini GL, Oddy WH, Robinson M, O'Sullivan TA, Hands BP, de Klerk NH, et al. Adolescent dietary patterns are associated with lifestyle and family psycho-social factors. Public Health Nutr 2009; 12(10): 1807-15.
190. Northstone K, Emmett PM. Dietary patterns of men in ALSPAC: associations with socio-demographic and lifestyle characteristics, nutrient intake and comparison with women's dietary patterns. Eur J Clin Nutr 2010; 64(9): 978-86.
191. Chen GW, Ding WH, Ku HY, Chao HR, Chen HY, Huang MC, et al. Alkylphenols in human milk and their relations to dietary habits in central Taiwan. Food Chem Toxicol 2010; 48(7): 1939-44.
192. Kjøllesdal MR, Holmboe-Ottesen G, Mosdøl A, Wandel M. The relative importance of socioeconomic indicators in explaining differences in BMI and waist:hip ratio, and the mediating effect of work control, dietary patterns and physical activity. Br J Nutr 2010; 104(8): 1230-40.
193. Mishra GD, McNaughton SA, Ball K, Brown WJ, Giles GG, Dobson AJ. Major dietary patterns of young and middle aged women: results from a prospective Australian cohort study. Eur J Clin Nutr 2010; 64(10): 1125-33.
194. Langsetmo L, Poliquin S, Hanley DA, Prior JC, Barr S, Anastassiades T, et al. Dietary patterns in Canadian men and women ages 25 and older: relationship to demographics, body mass index, and bone mineral density. BMC Musculoskelet Disord 2010; 11: 20.
195. Rezazadeh A, Rashidkhani B, Omidvar N. Association of major dietary patterns with socioeconomic and lifestyle factors of adult women living in Tehran, Iran. Nutrition 2010; 26(3): 337-41.
196. Olinto MTA, Willett WC, Gigante DP, Victora CG. Sociodemographic and lifestyle characteristics in relation to dietary patterns among young Brazilian adults. Public Health Nutr 2011; 14(1): 150-9.
197. Hare-Bruun H, Togo P, Andersen LB, Heitmann BL. Adult food intake patterns are related to adult and childhood socioeconomic status. J Nutr 2011; 141(5): 928-34.
198. Bibiloni M del M, Martínez E, Llull R, Pons A, Tur JA. Western and Mediterranean dietary patterns among Balearic Islands' adolescents: socio-economic and lifestyle determinants. Public Health Nutr 2012; 15(4): 683-92.
199. Charreire H, Kesse-Guyot E, Bertrais S, Simon C, Chaix B, Weber C, et al. Associations between dietary patterns, physical activity (leisure-time and occupational) and television viewing in middle-aged French adults. Br J Nutr 2011; 105(6): 902-10.
200. Lee JE, Kim JH, Son SJ, Ahn Y, Lee J, Park C, et al. Dietary pattern classifications with nutrient intake and health-risk factors in Korean men. Nutrition 2011; 27(1): 26-33.
201. Cho ER, Shin A, Lim SY, Kim J. Dietary patterns and their associations with health behaviours in Korea. Public Health Nutr 2011; 14(2): 356-64.
202. Nobre LN, Lamounier JA, Franceschini SCC. Padrão alimentar de pré-escolares e fatores associados. J Pediatr (Rio J) 2012; 88(2): 129-36.
203. Mullie P, Aerenhouts D, Clarys P. Demographic, socioeconomic and nutritional determinants of daily versus non-daily sugar-sweetened and artificially sweetened beverage consumption. Eur J Clin Nutr 2012; 66(2): 150-5.
204. Mohammadifard N, Sarrafzadegan N, Nouri F, Sajjadi F, Alikhasi H, Maghroun M, et al. Using factor analysis to identify dietary patterns in Iranian adults: Isfahan Healthy Heart Program. Int J Public Health 2012; 57(1): 235-41.
205. Elstgeest LEM, Mishra GD, Dobson AJ. Transitions in living arrangements are associated with changes in dietary patterns in young women. J Nutr 2012; 142(8): 1561-7.
206. Villegas R, Xiang YB, Cai H, Elasy T, Cai Q, Zhang X, et al. Lifestyle determinants of C-reactive protein in middle-aged, urban Chinese men. Nutr Metab Cardiovasc Dis 2012; 22(3): 223-30.
207. Wirfält AK, Jeffery RW. Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J Am Diet Assoc 1997; 97(3): 272-9.
208. Van den Bree MB, Eaves LJ, Dwyer JT. Genetic and environmental influences on eating patterns of twins aged >/=50 y. Am J Clin Nutr 1999; 70(4): 456-65.
209. Knol LL, Haughton B, Fitzhugh EC. Dietary patterns of young, low-income US children. J Am Diet Assoc 2005; 105(11): 1765-73.
210. Sofianou A, Fung TT, Tucker KL. Differences in diet pattern adherence by nativity and duration of US residence in the Mexican-American population. J Am Diet Assoc 2011; 111(10): 1563-9.e2.
211. Schwerin HS, Stanton JL, Riley AM, Schaefer AE, Leveille GA, Elliott JG, et al. Food eating patterns and health: a reexamination of the Ten-State and HANES I surveys. Am J Clin Nutr 1981; 34(4): 568-80.
212. Perrin AE, Dallongeville J, Ducimetière P, Ruidavets JB, Schlienger JL, Arveiler D, et al. Interactions between traditional regional determinants and socio-economic status on dietary patterns in a sample of French men. Br J Nutr 2005; 93(1): 109-14.
213. Mishra GD, McNaughton SA, Bramwell GD, Wadsworth MEJ. Longitudinal changes in dietary patterns during adult life. Br J Nutr 2006; 96(4): 735-44.
214. Cucó G, Fernández-Ballart J, Sala J, Viladrich C, Iranzo R, Vila J, et al. Dietary patterns and associated lifestyles in preconception, pregnancy and postpartum. Eur J Clin Nutr 2006; 60(3): 364-71.
215. Pryer JA, Nichols R, Elliott P, Thakrar B, Brunner E, Marmot M. Dietary patterns among a national random sample of British adults. J Epidemiol Community Health 2001; 55(1): 29-37.
216. Perrin AE, Dallongeville J, Ducimetière P, Ruidavets JB, Schlienger JL, Arveiler D, et al. Interactions between traditional regional determinants and socio-economic status on dietary patterns in a sample of French men. Br J Nutr 2005; 93(1): 109-14.

Fonte de financiamento: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Datas de Publicação

Publicação nesta coleção
Out-Dec 2015

Histórico

Recebido
10 Fev 2015
Aceito
14 Maio 2015

This is an open-access article distributed under the terms of the Creative Commons Attribution License

[1] 1. Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004; 159(10): 935-44.

[2] 2. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002; 13(1): 3-9.

[3] 3. Van Dam RM. New approaches to the study of dietary patterns. Br J Nutr 2005; 93(5): 573-4.

[4] 4. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: A Review. Nutr Rev 2004; 62(5): 177-203

[5] 5. Tucker KL. Dietary patterns, approaches, and multicultural perspective. Appl Physiol Nutr Metab Physiol Appliquée Nutr Métabolisme 2010; 35(2): 211-8.

[6] 6. Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc. 2004;104(4):615-35.

[7] 7. Michels KB, Schulze MB. Can dietary patterns help us detect diet? Disease associations? Nutr Res Rev 2005; 18(2): 241-8.

[8] 8. Katz DL, Meller S. Can we say what diet is best for health? Annu Rev Public Health 2014; 35: 83-103.

[9] 9. Liese AD, Weis KE, Schulz M, Tooze JA. Food intake patterns associated with incident type 2 diabetes. Diabetes Care 2009; 32(2): 263-8.

[10] 10. Berg CM, Lappas G, Strandhagen E, Wolk A, Torén K, Rosengren A, et al. Food patterns and cardiovascular disease risk factors: the Swedish INTERGENE research program. Am J Clin Nutr 2008; 88(2): 289-97.

[11] 11. Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr 2008; 138(2): 358-63.

[12] 12. Uglem S, Stea TH, Frølich W, Wandel M. Body weight, weight perceptions and food intake patterns. A cross-sectional study among male recruits in the Norwegian National Guard. BMC Public Health 2011; 11: 343.

[13] 13. Yannakoulia M, Ntalla I, Papoutsakis C, Farmaki AE, Dedoussis GV. Consumption of vegetables, cooked meals, and eating dinner is negatively associated with overweight status in children. J Pediatr 2010; 157(5): 815-20.

[14] 14. Panagiotakos D, Pitsavos C, Chrysohoou C, Palliou K, Lentzas I, Skoumas I, et al. Dietary patterns and 5-year incidence of cardiovascular disease: a multivariate analysis of the ATTICA study. Nutr Metab Cardiovasc Dis NMCD 2009; 19(4): 253-63.

[15] 15. Fung TT, Willett WC, Stampfer MJ, Manson JAE, Hu FB. Dietary patterns and the risk of coronary heart disease in women. Arch Intern Med 2001; 161(15): 1857.

[16] 16. Demetriou CA, Hadjisavvas A, Loizidou MA, Loucaides G, Neophytou I, Sieri S, et al. The mediterranean dietary pattern and breast cancer risk in Greek-Cypriot women: a case-control study. BMC Cancer 2012; 12: 113.

[17] 17. Sichieri R. Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obesity 2002; 10(1): 42-8.

[18] 18. Moeller SM, Reedy J, Millen AE, Dixon LB, Newby PK, Tucker KL, et al. Dietary patterns: challenges and opportunities in dietary patterns research an Experimental Biology workshop, April 1, 2006. J Am Diet Assoc 2007; 107(7): 1233-9.

[19] 19. Hair JF Jr, Black WC, Babin BJ. Multivariate Data Analysis. 7th ed. Paperback; 2009. ISBN-10: 0138132631

[20] 20. Wirfält AE, Jeffery RW. Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J Am Diet Assoc 1997; 97(3): 272-9.

[21] 21. Akin JS, Guilkey DK, Popkin BM, Fanelli MT. Cluster analysis of food consumption patterns of older Americans. J Am Diet Assoc 1986; 86(5): 616.

[22] 22. Costello AB, Osborne J. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation 2014; 10(7): 9.

[23] 23. Castro MA de, Baltar VT, Selem SS de C, Marchioni DML, Fisberg RM, Castro MA de, et al. Padrões alimentares empiricamente derivados: interpretabilidade e validade de construto segundo diferentes métodos de rotação fatorial. Cad Saúde Pública 2015; 31(2): 298-310.

[24] 24. Martínez ME, Marshall JR, Sechrest L. The Arbitrary Nature Of The Factor Analytical Process. Am J Epidemiol 1998; 148(1): 17-9

[25] 25. Slattery ML, Boucher KM, Caan BJ, Potter JD, Ma KN. Eating patterns and risk of colon cancer. Am J Epidemiol 1998; 148(1): 4-16.

[26] 26. Hardle W, Simar L. Applied Multivariate Statistical Analysis. 2th ed. Springer; 2007. ISBN 978-3-540-72243-4.

[27] 27. Hu FB, Rimm EB, Stampfer MJ, Ascherio A, Spiegelman D, Willett WC. Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 2000; 72(4): 912-21.

[28] 28. Cunha DB, Almeida RMVR de, Pereira RA. A comparison of three statistical methods applied in the identification of eating patterns. Cad Saúde Pública 2010; 26(11): 2138-48.

[29] 29. Manios Y, Kourlaba G, Grammatikaki E, Androutsos O, Ioannou E, Roma-Giannikou E. Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the GENESIS study. Eur J Clin Nutr 2010; 64(12): 1407-14.

[30] 30. Funtikova AN, Benítez-Arciniega AA, Fitó M, Schröder H. Modest validity and fair reproducibility of dietary patterns derived by cluster analysis. Nutr Res 2015; 35(3): 265-8

[31] 31. Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A, et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 1999; 69(2): 243-9.

[32] 32. Schulze MB, Hoffmann K. Methodological approaches to study dietary patterns in relation to risk of coronary heart disease and stroke. Br J Nutr 2006; 95(5): 860-9.

[33] 33. Brennan SF, Cantwell MM, Cardwell CR, Velentzis LS, Woodside JV. Dietary patterns and breast cancer risk: a systematic review and meta-analysis. Am J Clin Nutr 2010; 91(5): 1294-302.

[34] 34. Martínez ME, Marshall JR, Sechrest L. Invited commentary: Factor analysis and the search for objectivity. Am J Epidemiol 1998; 148(1): 17-9.

[35] 35. Van den Berg L, Henneman P, Willems van Dijk K, Delemarre van de Waal H, Oostra B, van Duijn C, et al. Heritability of dietary food intake patterns. Acta Diabetol 2013; (50): 721-26

[36] 36. Togo P, Osler M, Sørensen TI, Heitmann BL. Food intake patterns and body mass index in observational studies. Int J Obes Relat Metab Disord 2001; 25(12): 1741.

[37] 37. Schulze MB, Hoffmann K, Kroke A, Boeing H. Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br J Nutr 2001; 85(3): 363-73.

[38] 38. McNaughton SA, Wattanapenpaiboon N, Wark JD, Nowson CA. An energy-dense, nutrient-poor dietary pattern is inversely associated with bone health in women. J Nutr 2011; 141(8): 1516-23.

[39] 39. Johnson L, Mander AP, Jones LR, Emmett PM, Jebb SA. Energy-dense, low-fiber, high-fat dietary pattern is associated with increased fatness in childhood. Am J Clin Nutr 2008; 87(4): 846-54.

[40] 40. Fung TT SM. DIetary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med 2004; 164(20): 2235-40.

[41] 41. Schulze MB, Hoffmann K, Manson JE, Willett WC, Meigs JB, Weikert C, et al. Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am J Clin Nutr 2005; 82(3): 675-84; quiz 714-5.

[42] 42. Heidemann C, Hoffmann K, Spranger J, Klipstein-Grobusch K, Möhlig M, Pfeiffer AFH, et al. A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)--Potsdam Study cohort. Diabetologia. 2005; 48(6): 1126-34.

[43] 43. Mizoue T, Yamaji T, Tabata S, Yamaguchi K, Ogawa S, Mineshita M, et al. Dietary patterns and glucose tolerance abnormalities in Japanese men. J Nutr 2006; 136(5): 1352-8.

[44] 44. McNaughton SA, Mishra GD, Brunner EJ. Dietary patterns, insulin resistance, and incidence of type 2 diabetes in the Whitehall II Study. Diabetes Care 2008; 31(7): 1343-8.

[45] 45. Kim H-S, Park S-Y, Grandinetti A, Holck PS, Waslien C. Major dietary patterns, ethnicity, and prevalence of type 2 diabetes in rural Hawaii. Nutr Burbank Los Angel Cty Calif 2008; 24(11-12): 1065-72.

[46] 46. Lau C, Toft U, Tetens I, Carstensen B, Jørgensen T, Pedersen O, et al. Dietary patterns predict changes in two-hour post-oral glucose tolerance test plasma glucose concentrations in middle-aged adults. J Nutr 2009; 139(3): 588-93.

[47] 47. Liese AD, Weis KE, Schulz M, Tooze JA. Food intake patterns associated with incident type 2 diabetes. Diabetes Care 2009; 32(2): 263-8.

[48] 48. Qi L, Cornelis MC, Zhang C, van Dam RM, Hu FB. Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men. Am J Clin Nutr 2009; 89(5): 1453-8.

[49] 49. Villegas R, Yang G, Gao Y-T, Cai H, Li H, Zheng W, et al. Dietary patterns are associated with lower incidence of type 2 diabetes in middle-aged women: the Shanghai Women's Health Study. Int J Epidemiol 2010; 39(3): 889-99.

[50] 50. Odegaard AO, Koh W-P, Butler LM, Duval S, Gross MD, Yu MC, et al. Dietary patterns and incident type 2 diabetes in chinese men and women: the singapore chinese health study. Diabetes Care 2011; 34(4): 880-5.

[51] 51. Iimuro S, Yoshimura Y, Umegaki H, Sakurai T, Araki A, Ohashi Y, et al. Dietary pattern and mortality in Japanese elderly patients with type 2 diabetes mellitus: does a vegetable- and fish-rich diet improve mortality? An explanatory study. Geriatr Gerontol Int 2012; 12 Suppl 1: 59-67.

[52] 52. 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(1): 12-8.

[53] 53. Newby PK, Muller D, Hallfrisch J, Andres R, Tucker KL. Food patterns measured by factor analysis and anthropometric changes in adults. Am J Clin Nutr 2004; 80(2): 504-13.

[54] 54. Schulz M, Nöthlings U, Hoffmann K, Bergmann MM, Boeing H. Identification of a Food Pattern Characterized by High-Fiber and Low-Fat Food Choices Associated with Low Prospective Weight Change in the EPIC-Potsdam Cohort. J Nutr 2005; 135(5): 1183-9.

[55] 55. Schulze MB, Fung TT, Manson JE, Willett WC, Hu FB. Dietary patterns and changes in body weight in women. Obes Silver Spring Md 2006; 14(8): 1444-53.

[56] 56. Newby PK, Weismayer C, Akesson A, Tucker KL, Wolk A. Longitudinal changes in food patterns predict changes in weight and body mass index and the effects are greatest in obese women. J Nutr 2006; 136(10): 2580-7.

[57] 57. 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(8): 1311-21.

[58] 58. Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr 2008; 138(2): 358-63.

[59] 59. Shi Z, Hu X, Yuan B, Hu G, Pan X, Dai Y, et al. Vegetable-rich food pattern is related to obesity in China. Int J Obes (Lond) 2008; 32(6): 975-84.

[60] 60. Uusitalo U, Arkkola T, Ovaskainen ML, Kronberg-Kippilä C, Kenward MG, Veijola R, et al. Unhealthy dietary patterns are associated with weight gain during pregnancy among Finnish women. Public Health Nutr 2009; 12(12): 2392-9.

[61] 61. Kjøllesdal MR, Holmboe-Ottesen G, Wandel M. Frequent use of staff canteens is associated with unhealthy dietary habits and obesity in a Norwegian adult population. Public Health Nutr 2011; 14(1): 133-41.

[62] 62. Oellingrath IM, Svendsen MV, Brantsæter AL. Eating patterns and overweight in 9- to 10-year-old children in Telemark County, Norway: a cross-sectional study. Eur J Clin Nutr 2010; 64(11): 1272-9.

[63] 63. Manios Y, Kourlaba G, Grammatikaki E, Androutsos O, Ioannou E, Roma-Giannikou E. Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the GENESIS study. Eur J Clin Nutr 2010; 64(12): 1407-14.

[64] 64. Wosje KS, Khoury PR, Claytor RP, Copeland KA, Hornung RW, Daniels SR, et al. Dietary patterns associated with fat and bone mass in young children. Am J Clin Nutr 2010; 92(2): 294-303.

[65] 65. Cunha DB, de Almeida RMVR, 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(06): 908-13.

[66] 66. Cho YA, Shin A, Kim J. Dietary patterns are associated with body mass index in a Korean population. J Am Diet Assoc 2011; 111(8): 1182-6.

[67] 67. Shi Z, Yuan B, Hu G, Dai Y, Zuo H, Holmboe-Ottesen G. Dietary pattern and weight change in a 5-year follow-up among Chinese adults: results from the Jiangsu Nutrition Study. Br J Nutr 2011; 105(7): 1047-54.

[68] 68. Boggs DA, Palmer JR, Spiegelman D, Stampfer MJ, Adams-Campbell LL, Rosenberg L. Dietary patterns and 14-y weight gain in African American women. Am J Clin Nutr 2011; 94(1): 86-94.

[69] 69. Cutler GJ, Flood A, Hannan PJ, Slavin JL, Neumark-Sztainer D. Association between major patterns of dietary intake and weight status in adolescents. Br J Nutr 2012; 108(2): 349-56.

[70] 70. Lin H, Bermudez OI, Tucker KL. Dietary patterns of Hispanic elders are associated with acculturation and obesity. J Nutr 2003; 133(11): 3651-7.

[71] 71. Ambrosini GL, Emmett PM, Northstone K, Howe LD, Tilling K, Jebb SA. Identification of a dietary pattern prospectively associated with increased adiposity during childhood and adolescence. Int J Obes 2012; 36(10): 1299-305.

[72] 72. Fialkowski MK, McCrory MA, Roberts SM, Tracy JK, Grattan LM, Boushey CJ. Dietary patterns are associated with dietary recommendations but have limited relationship to BMI in the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort. Public Health Nutr 2012; 15(10): 1948-58.

[73] 73. Kim J-H, Lee JE, Jung I-K. Dietary pattern classifications and the association with general obesity and abdominal obesity in Korean women. J Acad Nutr Diet 2012; 112(10): 1550-9.

[74] 74. Kim J, Jo I, Joung H. A rice-based traditional dietary pattern is associated with obesity in Korean adults. J Acad Nutr Diet 2012; 112(2): 246-53.

[75] 75. Maskarinec G, Novotny R, Tasaki K. Dietary Patterns Are Associated with Body Mass Index in Multiethnic Women. J Nutr 2000; 130(12): 3068-72.

[76] 76. Osler M, Helms Andreasen A, Heitmann B, Høidrup S, Gerdes U, Mørch Jørgensen L, et al. Food intake patterns and risk of coronary heart disease: a prospective cohort study examining the use of traditional scoring techniques. Eur J Clin Nutr 2002; 56(7): 568-74.

[77] 77. Quatromoni PA, Copenhafer DL, Demissie S, D'Agostino RB, O'Horo CE, Nam B, et al. The internal validity of a dietary pattern analysis. The Framingham Nutrition Studies. J Epidemiol Community Health 2002; 56(5): 381-8.

[78] 78. 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(6): 1103-10.

[79] 79. Villegas R, Salim A, Collins MM, Flynn A, Perry IJ. Dietary patterns in middle-aged Irish men and women defined by cluster analysis. Public Health Nutr 2004; 7(8): 1017-24.

[80] 80. Hoffmann K, Zyriax B-C, Boeing H, Windler E. A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease. Am J Clin Nutr 2004; 80(3): 633-40.

[81] 81. Nettleton JA, Schulze MB, Jiang R, Jenny NS, Burke GL, Jacobs DR. A priori-defined dietary patterns and markers of cardiovascular disease risk in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2008; 88(1): 185-94.

[82] 82. Akesson A, Weismayer C, Newby PK, Wolk A. Combined effect of low-risk dietary and lifestyle behaviors in primary prevention of myocardial infarction in women. Arch Intern Med 2007; 167(19): 2122-7.

[83] 83. Shimazu T, Kuriyama S, Hozawa A, Ohmori K, Sato Y, Nakaya N, et al. Dietary patterns and cardiovascular disease mortality in Japan: a prospective cohort study. Int J Epidemiol 2007; 36(3): 600-9.

[84] 84. DiBello JR, Kraft P, McGarvey ST, Goldberg R, Campos H, Baylin A. Comparison of 3 methods for identifying dietary patterns associated with risk of disease. Am J Epidemiol 2008; 168(12): 1433-43.

[85] 85. Panagiotakos D, Pitsavos C, Chrysohoou C, Palliou K, Lentzas I, Skoumas I, et al. Dietary patterns and 5-year incidence of cardiovascular disease: a multivariate analysis of the ATTICA study. Nutr Metab Cardiovasc Dis NMCD 2009; 19(4): 253-63.

[86] 86. Liu L, Nettleton JA, Bertoni AG, Bluemke DA, Lima JA, Szklo M. Dietary pattern, the metabolic syndrome, and left ventricular mass and systolic function: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr 2009; 90(2): 362-8.

[87] 87. Nettleton JA, Matijevic N, Follis JL, Folsom AR, Boerwinkle E. Associations between dietary patterns and flow cytometry-measured biomarkers of inflammation and cellular activation in the Atherosclerosis Risk in Communities (ARIC) Carotid Artery MRI Study. Atherosclerosis 2010; 212(1): 260-7.

[88] 88. Kesse-Guyot E, Vergnaud A-C, Fezeu L, Zureik M, Blacher J, Péneau S, et al. Associations between dietary patterns and arterial stiffness, carotid artery intima-media thickness and atherosclerosis. Eur J Cardiovasc Prev Rehabil 2010; 17(6): 718-24.

[89] 89. Liese AD, Nichols M, Hodo D, Mellen PB, Schulz M, Goff DC, et al. Food intake patterns associated with carotid artery atherosclerosis in the Insulin Resistance Atherosclerosis Study. Br J Nutr 2010; 103(10): 1471-9.

[90] 90. Hamer M, Mishra GD. Dietary patterns and cardiovascular risk markers in the UK Low Income Diet and Nutrition Survey. Nutr Metab Cardiovasc Dis NMCD 2010; 20(7): 491-7.

[91] 91. Fung TT, Willett WC, Stampfer MJ, Manson JAE, Hu FB. Dietary patterns and the risk of coronary heart disease in women. Arch Intern Med 2001; 161(15): 1857.

[92] 92. Meyer J, Döring A, Herder C, Roden M, Koenig W, Thorand B. Dietary patterns, subclinical inflammation, incident coronary heart disease and mortality in middle-aged men from the MONICA/KORA Augsburg cohort study. Eur J Clin Nutr 2011; 65(7): 800-7.

[93] 93. Daniel CR, Prabhakaran D, Kapur K, Graubard BI, Devasenapathy N, Ramakrishnan L, et al. A cross-sectional investigation of regional patterns of diet and cardio-metabolic risk in India. Nutr J 2011; 10: 12.

[94] 94. Guo H, Niu K, Monma H, Kobayashi Y, Guan L, Sato M, et al. Association of Japanese dietary pattern with serum adiponectin concentration in Japanese adult men. Nutr Metab Cardiovasc Dis NMCD 2012; 22(3): 277-84.

[95] 95. Yap RWK, Shidoji Y, Hon WM, Masaki M. Association and interaction between dietary pattern and VEGF receptor-2 (VEGFR2) gene polymorphisms on blood lipids in Chinese Malaysian and Japanese adults. Asia Pac J Clin Nutr 2012; 21(2): 302-11.

[96] 96. Osler M, Heitmann BL, Gerdes LU, Jorgensen LM, Schroll M. Dietary patterns and mortality in Danish men and women: a prospective observational study. Br J Nutr 2001; 85(2): 219-25.

[97] 97. Nicklas TA, Webber LS, Thompson B, Berenson GS. A multivariate model for assessing eating patterns and their relationship to cardiovascular risk factors: the Bogalusa Heart Study. Am J Clin Nutr 1989; 49(6): 1320-7.

[98] 98. Markaki I, Linos D, Linos A. The influence of dietary patterns on the development of thyroid cancer. Eur J Cancer Oxf Engl 2003; 39(13): 1912-9.

[99] 99. Fung T, Hu FB, Fuchs C, Giovannucci E, Hunter DJ, Stampfer MJ, et al. Major dietary patterns and the risk of colorectal cancer in women. Arch Intern Med 2003; 163(3): 309-14.

[100] 100. Kim MK, Sasaki S, Sasazuki S, Tsugane S; Japan Public Health Center-based Prospective Study Group. Prospective study of three major dietary patterns and risk of gastric cancer in Japan. Int J Cancer 2004; 110(3): 435-42.

[101] 101. Mizoue T, Yamaji T, Tabata S, Yamaguchi K, Shimizu E, Mineshita M, et al. Dietary patterns and colorectal adenomas in Japanese men: the Self-Defense Forces Health Study. Am J Epidemiol 2005; 161(4): 338-45.

[102] 102. Balder HF, Goldbohm RA, van den Brandt PA. Dietary patterns associated with male lung cancer risk in the Netherlands Cohort Study. Cancer Epidemiol Biomarkers Prev 2005;14(2):483-90.

[103] 103. Meyerhardt JA, Niedzwiecki D, Hollis D, Saltz LB, Hu FB, Mayer RJ, et al. Association of dietary patterns with cancer recurrence and survival in patients with stage III colon cancer. JAMA 2007; 298(7): 754-64.

[104] 104. Campbell PT, Sloan M, Kreiger N. Dietary patterns and risk of incident gastric adenocarcinoma. Am J Epidemiol 2008; 167(3): 295-304.

[105] 105. Sant M, Allemani C, Sieri S, Krogh V, Menard S, Tagliabue E, et al. Salad vegetables dietary pattern protects against HER-2-positive breast cancer: a prospective Italian study. Int J Cancer 2007; 121(4): 911-4.

[106] 106. Hirose K, Matsuo K, Iwata H, Tajima K. Dietary patterns and the risk of breast cancer in Japanese women. Cancer Sci 2007; 98(9): 1431-8.

[107] 107. Schulz M, Hoffmann K, Weikert C, Nöthlings U, Schulze MB, Boeing H. Identification of a dietary pattern characterized by high-fat food choices associated with increased risk of breast cancer: the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Br J Nutr 2008; 100(5): 942-6.

[108] 108. De Stefani E, Boffetta P, Fagundes RB, Deneo-Pellegrini H, Ronco AL, Acosta G, et al. Nutrient patterns and risk of squamous cell carcinoma of the esophagus: a factor analysis in uruguay. Anticancer Res 2008; 28(4C): 2499-506.

[109] 109. Cottet V, Touvier M, Fournier A, Touillaud MS, Lafay L, Clavel-Chapelon F, et al. Postmenopausal breast cancer risk and dietary patterns in the E3N-EPIC prospective cohort study. Am J Epidemiol 2009; 170(10): 1257-67.

[110] 110. Agurs-Collins T, Rosenberg L, Makambi K, Palmer JR, Adams-Campbell L. Dietary patterns and breast cancer risk in women participating in the Black Women's Health Study. Am J Clin Nutr 2009; 90(3): 621-8.

[111] 111. Pham T-M, Fujino Y, Kikuchi S, Tamakoshi A, Matsuda S, Yoshimura T. Dietary patterns and risk of stomach cancer mortality: the Japan collaborative cohort study. Ann Epidemiol 2010; 20(5): 356-63.

[112] 112. Edefonti V, Bravi F, Garavello W, La Vecchia C, Parpinel M, Franceschi S, et al. Nutrient-based dietary patterns and laryngeal cancer: evidence from an exploratory factor analysis. Cancer Epidemiol Biomarkers Prev 2010; 19(1): 18-27.

[113] 113. Mishra GD, dos Santos Silva I, McNaughton SA, Stephen A, Kuh D. Energy intake and dietary patterns in childhood and throughout adulthood and mammographic density: results from a British prospective cohort. Cancer Causes Control 2011; 22(2): 227-35.

[114] 114. Biel RK, Friedenreich CM, Csizmadi I, Robson PJ, McLaren L, Faris P, et al. Case-control study of dietary patterns and endometrial cancer risk. Nutr Cancer 2011; 63(5): 673-86.

[115] 115. Zhang CX, Ho SC, Fu JH, Cheng SZ, Chen YM, Lin FY. Dietary patterns and breast cancer risk among Chinese women. Cancer Causes Control 2011; 22(1): 115-24.

[116] 116. Buck K, Vrieling A, Flesch-Janys D, Chang-Claude J. Dietary patterns and the risk of postmenopausal breast cancer in a German case-control study. Cancer Causes Control 2011; 22(2): 273-82.

[117] 117. Demetriou CA, Hadjisavvas A, Loizidou MA, Loucaides G, Neophytou I, Sieri S, et al. The mediterranean dietary pattern and breast cancer risk in Greek-Cypriot women: a case-control study. BMC Cancer 2012; 12: 113.

[118] 118. Piyathilake CJ, Badiga S, Kabagambe EK, Azuero A, Alvarez RD, Johanning GL, et al. A dietary pattern associated with LINE-1 methylation alters the risk of developing cervical intraepithelial neoplasia. Cancer Prev Res 2012; 5(3): 385-92.

[119] 119. Bradshaw PT, Siega-Riz AM, Campbell M, Weissler MC, Funkhouser WK, Olshan AF. Associations between dietary patterns and head and neck cancer: the Carolina head and neck cancer epidemiology study. Am J Epidemiol 2012; 175(12): 1225-33.

[120] 120. Terry P, Hu FB, Hansen H, Wolk A. Prospective study of major dietary patterns and colorectal cancer risk in women. Am J Epidemiol 2001; 154(12): 1143-9.

[121] 121. Randall E, Marshall JR, Graham S, Brasure J. Patterns in food use and their associations with nutrient intakes. Am J Clin Nutr 1990; 52(4): 739-45.

[122] 122. Zhuo XG, Watanabe S. Factor analysis of digestive cancer mortality and food consumption in 65 Chinese counties. J Epidemiol 1999; 9(4): 275-84.

[123] 123. Terry P, Suzuki R, Hu FB, Wolk A. A prospective study of major dietary patterns and the risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2001; 10(12): 1281-5.

[124] 124. Millen BE, Quatromoni PA, Nam BH, O'Horo CE, Polak JF, D'Agostino RB. Dietary patterns and the odds of carotid atherosclerosis in women: the Framingham Nutrition Studies. Prev Med 2002; 35(6): 540-7.

[125] 125. Newby PK, Muller D, Hallfrisch J, Qiao N, Andres R, Tucker KL. Dietary patterns and changes in body mass index and waist circumference in adults. Am J Clin Nutr 2003; 77(6): 1417-25.

[126] 126. Ledikwe JH, Smiciklas-Wright H, Mitchell DC, Miller CK, Jensen GL. Dietary patterns of rural older adults are associated with weight and nutritional status. J Am Geriatr Soc 2004; 52(4): 589-95.

[127] 127. Fung TT, Stampfer MJ, Manson JE, Rexrode KM, Willett WC, Hu FB. Prospective study of major dietary patterns and stroke risk in women. Stroke J Cereb Circ 2004; 35(9): 2014-9.

[128] 128. Weikert C, Hoffmann K, Dierkes J, Zyriax B-C, Klipstein-Grobusch K, Schulze MB, et al. A homocysteine metabolism-related dietary pattern and the risk of coronary heart disease in two independent German study populations. J Nutr 2005; 135(8): 1981-8.

[129] 129. Moreira P, de Almeida MDV, Sampaio D. Cognitive restraint is associated with higher intake of vegetables in a sample of university students. Eat Behav 2005; 6(3): 229-37.

[130] 130. Waijers PMCM, Ocké MC, van Rossum CTM, Peeters PHM, Bamia C, Chloptsios Y, et al. Dietary patterns and survival in older Dutch women. Am J Clin Nutr 2006; 83(5): 1170-6.

[131] 131. Panagiotakos DB, Pitsavos C, Skoumas Y, Stefanadis C. The association between food patterns and the metabolic syndrome using principal components analysis: The ATTICA Study. J Am Diet Assoc 2007; 107(6): 979-87.

[132] 132. McCann SE, McCann WE, Hong CC, Marshall JR, Edge SB, Trevisan M, et al. Dietary patterns related to glycemic index and load and risk of premenopausal and postmenopausal breast cancer in the Western New York Exposure and Breast Cancer Study. Am J Clin Nutr 2007; 86(2): 465-71.

[133] 133. Okubo H, Sasaki S, Murakami K, Kim MK, Takahashi Y, Hosoi Y, et al. Dietary patterns associated with functional constipation among Japanese women aged 18 to 20 years: a cross-sectional study. J Nutr Sci Vitaminol (Tokyo) 2007; 53(3): 232-8.

[134] 134. McNaughton SA, Mishra GD, Stephen AM, Wadsworth MEJ. Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate. J Nutr 2007; 137(1): 99-105.

[135] 135. Okubo H, Sasaki S, Horiguchi H, Oguma E, Miyamoto K, Hosoi Y, et al. Dietary patterns associated with bone mineral density in premenopausal Japanese farmwomen. Am J Clin Nutr 2006; 83(5): 1185-92.

[136] 136. Masala G, Ceroti M, Pala V, Krogh V, Vineis P, Sacerdote C, et al. A dietary pattern rich in olive oil and raw vegetables is associated with lower mortality in Italian elderly subjects. Br J Nutr 2007; 98(2): 406-15.

[137] 137. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Dietary patterns, insulin resistance, and prevalence of the metabolic syndrome in women. Am J Clin Nutr 2007; 85(3): 910-8.

[138] 138. Yannakoulia M, Yiannakouris N, Melistas L, Kontogianni MD, Malagaris I, Mantzoros CS. A dietary pattern characterized by high consumption of whole-grain cereals and low-fat dairy products and low consumption of refined cereals is positively associated with plasma adiponectin levels in healthy women. Metabolism 2008; 57(6): 824-30.

[139] 139. Schröder H, Vila J, Marrugat J, Covas MI. Low energy density diets are associated with favorable nutrient intake profile and adequacy in free-living elderly men and women. J Nutr 2008; 138(8): 1476-81.

[140] 140. McNaughton SA, Ball K, Mishra GD, Crawford DA. Dietary patterns of adolescents and risk of obesity and hypertension. J Nutr 2008; 138(2): 364-70.

[141] 141. Lutsey PL, Steffen LM, Stevens J. Dietary intake and the development of the metabolic syndrome: the Atherosclerosis Risk in Communities study. Circulation 2008; 117(6): 754-61.

[142] 142. Nanri A, Yoshida D, Yamaji T, Mizoue T, Takayanagi R, Kono S. Dietary patterns and C-reactive protein in Japanese men and women. Am J Clin Nutr 2008; 87(5): 1488-96.

[143] 143. Konstantinova SV, Tell GS, Vollset SE, Ulvik A, Drevon CA, Ueland PM. Dietary patterns, food groups, and nutrients as predictors of plasma choline and betaine in middle-aged and elderly men and women. Am J Clin Nutr 2008; 88(6): 1663-9.

[144] 144. McNaughton SA, Mishra GD, Brunner EJ. Food patterns associated with blood lipids are predictive of coronary heart disease: the Whitehall II study. Br J Nutr 2009; 102(4): 619-24.

[145] 145. Vujkovic M, Steegers EA, Looman CW, Ocké MC, van der Spek PJ, Steegers-Theunissen RP. The maternal Mediterranean dietary pattern is associated with a reduced risk of spina bifida in the offspring. BJOG Int J Obstet Gynaecol 2009; 116(3): 408-15.

[146] 146. Brantsaeter AL, Haugen M, Samuelsen SO, Torjusen H, Trogstad L, Alexander J, et al. A dietary pattern characterized by high intake of vegetables, fruits, and vegetable oils is associated with reduced risk of preeclampsia in nulliparous pregnant Norwegian women. J Nutr 2009; 139(6): 1162-8.

[147] 147. Oddy WH, Robinson M, Ambrosini GL, O'Sullivan TA, de Klerk NH, Beilin LJ, et al. The association between dietary patterns and mental health in early adolescence. Prev Med 2009; 49(1): 39-44.

[148] 148. Nafar M, Noori N, Jalali-Farahani S, Hosseinpanah F, Poorrezagholi F, Ahmadpoor P, et al. Mediterranean diets are associated with a lower incidence of metabolic syndrome one year following renal transplantation. Kidney Int 2009; 76(11): 1199-206.

[149] 149. Gustaw-Rothenberg K. Dietary patterns associated with Alzheimer's disease: population based study. Int J Environ Res Public Health 2009; 6(4): 1335-40.

[150] 150. Vujkovic M, de Vries JH, Dohle GR, Bonsel GJ, Lindemans J, Macklon NS, et al. Associations between dietary patterns and semen quality in men undergoing IVF/ICSI treatment. Hum Reprod Oxf Engl 2009; 24(6): 1304-12.

[151] 151. Nanri A, Kimura Y, Matsushita Y, Ohta M, Sato M, Mishima N, et al. Dietary patterns and depressive symptoms among Japanese men and women. Eur J Clin Nutr 2010; 64(8): 832-9.

[152] 152. Bakolis I, Hooper R, Thompson RL, Shaheen SO. Dietary patterns and adult asthma: population-based case-control study. Allergy 2010; 65(5): 606-15.

[153] 153. Gu Y, Nieves JW, Stern Y, Luchsinger JA, Scarmeas N. Food combination and Alzheimer disease risk: a protective diet. Arch Neurol 2010; 67(6): 699-706.

[154] 154. Shi Z, Hu X, Yuan B, Pan X, Dai Y, Holmboe-Ottesen G. Association between dietary patterns and anaemia in adults from Jiangsu Province in Eastern China. Br J Nutr 2006; 96(5): 906-12.

[155] 155. Hamer M, McNaughton SA, Bates CJ, Mishra GD. Dietary patterns, assessed from a weighed food record, and survival among elderly participants from the United Kingdom. Eur J Clin Nutr 2010; 64(8): 853-61.

[156] 156. Yakub M, Iqbal MP, Iqbal R. Dietary patterns are associated with hyperhomocysteinemia in an urban Pakistani population. J Nutr 2010; 140(7): 1261-6.

[157] 157. Jacka FN, Pasco JA, Mykletun A, Williams LJ, Hodge AM, O'Reilly SL, et al. Association of Western and traditional diets with depression and anxiety in women. Am J Psychiatry 2010; 167(3): 305-11.

[158] 158. Hardcastle AC, Aucott L, Fraser WD, Reid DM, Macdonald HM. Dietary patterns, bone resorption and bone mineral density in early post-menopausal Scottish women. Eur J Clin Nutr 2011; 65(3): 378-85.

[159] 159. Chatzi L, Melaki V, Sarri K, Apostolaki I, Roumeliotaki T, Georgiou V, et al. Dietary patterns during pregnancy and the risk of postpartum depression: the mother-child "Rhea" cohort in Crete, Greece. Public Health Nutr 2011; 14(9): 1663-70.

[160] 160. Keding GB, Msuya JM, Maass BL, Krawinkel MB. Dietary patterns and nutritional health of women: the nutrition transition in rural Tanzania. Food Nutr Bull 2011; 32(3): 218-26.

[161] 161. Noh HY, Song YJ, Lee JE, Joung H, Park MK, Li SJ, et al. Dietary patterns are associated with physical growth among school girls aged 9-11 years. Nutr Res Pract 2011; 5(6): 569-77.

[162] 162. Timmermans S, Steegers-Theunissen RPM, Vujkovic M, Bakker R, den Breeijen H, Raat H, et al. Major dietary patterns and blood pressure patterns during pregnancy: the Generation R Study. Am J Obstet Gynecol 2011; 205(4): 337.e1-12.

[163] 163. Zhang FF, Morabia A, Carroll J, Gonzalez K, Fulda K, Kaur M, et al. Dietary patterns are associated with levels of global genomic DNA methylation in a cancer-free population. J Nutr 2011; 141(6): 1165-71.

[164] 164. Howard AL, Robinson M, Smith GJ, Ambrosini GL, Piek JP, Oddy WH. ADHD is associated with a "Western" dietary pattern in adolescents. J Atten Disord 2011; 15(5): 403-11.

[165] 165. Pierce BL, Argos M, Chen Y, Melkonian S, Parvez F, Islam T, et al. Arsenic exposure, dietary patterns, and skin lesion risk in Bangladesh: a prospective study. Am J Epidemiol 2011; 173(3): 345-54.

[166] 166. Jacka FN, Pasco JA, Mykletun A, Williams LJ, Nicholson GC, Kotowicz MA, et al. Diet quality in bipolar disorder in a population-based sample of women. J Affect Disord 2011; 129(1-3): 332-7.

[167] 167. Grieger JA, Scott J, Cobiac L. Dietary patterns and breast-feeding in Australian children. Public Health Nutr 2011; 14(11): 1939-47.

[168] 168. Cho YA, Kim J, Cho ER, Shin A. Dietary patterns and the prevalence of metabolic syndrome in Korean women. Nutr Metab Cardiovasc Dis NMCD 2011; 21(11): 893-900.

[169] 169. Smithers LG, Brazionis L, Golley RK, Mittinty MN, Northstone K, Emmett P, et al. Associations between dietary patterns at 6 and 15 months of age and sociodemographic factors. Eur J Clin Nutr 2012; 66(6): 658-66.

[170] 170. Hodge A, Almeida OP, English DR, Giles GG, Flicker L. Patterns of dietary intake and psychological distress in older Australians: benefits not just from a Mediterranean diet. Int Psychogeriatr IPA 2013; 25(3): 456-66.

[171] 171. Weng TT, Hao JH, Qian QW, Cao H, Fu JL, Sun Y, et al. Is there any relationship between dietary patterns and depression and anxiety in Chinese adolescents? Public Health Nutr 2012; 15(4): 673-82.

[172] 172. Yu FJ, Huang MC, Chang WT, Chung HF, Wu CY, Shin SJ, et al. Increased ferritin concentrations correlate with insulin resistance in female type 2 diabetic patients. Ann Nutr Metab 2012; 61(1): 32-40.

[173] 173. Hong S, Song Y, Lee KH, Lee HS, Lee M, Jee SH, et al. A fruit and dairy dietary pattern is associated with a reduced risk of metabolic syndrome. Metabolism 2012; 61(6): 883-90.

[174] 174. Azadbakht L, Esmaillzadeh A. Dietary patterns and attention deficit hyperactivity disorder among Iranian children. Nutrition 2012; 28(3): 242-9.

[175] 175. Sichieri R, Castro JFG, Moura AS. Fatores associados ao padrão de consumo alimentar da população brasileira urbana. Cad Saúde Pública 2003; 19: S47-53.

[176] 176. Martikainen P, Brunner E, Marmot M. Socioeconomic differences in dietary patterns among middle-aged men and women. Soc Sci Med 2003; 56(7): 1397-410.

[177] 177. Sánchez-Villegas A, Delgado-Rodríguez M, Martínez-González MA, De Irala-Estévez J; Seguimiento Universidad de Navarra Group. Gender, age, socio-demographic and lifestyle factors associated with major dietary patterns in the Spanish Project SUN (Seguimiento Universidad de Navarra). Eur J Clin Nutr 2003; 57(2): 285-92.

[178] 178. Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004; 159(10): 935-44.

[179] 179. Northstone K, Emmett P. Multivariate analysis of diet in children at four and seven years of age and associations with socio-demographic characteristics. Eur J Clin Nutr 2005; 59(6): 751-60.

[180] 180. Yang EJ, Kerver JM, Song WO. Dietary patterns of Korean Americans described by factor analysis. J Am Coll Nutr 2005; 24(2): 115-21.

[181] 181. Alves ALS, Olinto MTA, Costa JSD da, Bairros FS de, Balbinotti MAA. [Dietary patterns of adult women living in an urban area of Southern Brazil]. Rev Saúde Pública 2006; 40(5): 865-73.

[182] 182. Pala V, Sieri S, Masala G, Palli D, Panico S, Vineis P, et al. Associations between dietary pattern and lifestyle, anthropometry and other health indicators in the elderly participants of the EPIC-Italy cohort. Nutr Metab Cardiovasc Dis 2006; 16(3): 186-201.

[183] 183. Weismayer C, Anderson JG, Wolk A. Changes in the stability of dietary patterns in a study of middle-aged Swedish women. J Nutr 2006; 136(6): 1582-7.

[184] 184. Nettleton JA, Steffen LM, Schulze MB, Jenny NS, Barr RG, Bertoni AG, et al. Associations between markers of subclinical atherosclerosis and dietary patterns derived by principal components analysis and reduced rank regression in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2007; 85(6): 1615-25.

[185] 185. Northstone K, Emmett PM, Rogers I. Dietary patterns in pregnancy and associations with nutrient intakes. Br J Nutr 2008; 99(2): 406-15.

[186] 186. Sánchez-Villegas A, Toledo E, Bes-Rastrollo M, Martín-Moreno JM, Tortosa A, Martínez-González MA. Association between dietary and beverage consumption patterns in the SUN (Seguimiento Universidad de Navarra) cohort study. Public Health Nutr 2009; 12(3): 351-8.

[187] 187. Serra-Majem L, Bes-Rastrollo M, Román-Viñas B, Pfrimer K, Sánchez-Villegas A, Martínez-González MA. Dietary patterns and nutritional adequacy in a Mediterranean country. Br J Nutr 2009; 101 Suppl 2: S21-8.

[188] 188. Cutler GJ, Flood A, Hannan P, Neumark-Sztainer D. Major patterns of dietary intake in adolescents and their stability over time. J Nutr 2009; 139(2): 323-8.

[189] 189. Ambrosini GL, Oddy WH, Robinson M, O'Sullivan TA, Hands BP, de Klerk NH, et al. Adolescent dietary patterns are associated with lifestyle and family psycho-social factors. Public Health Nutr 2009; 12(10): 1807-15.

[190] 190. Northstone K, Emmett PM. Dietary patterns of men in ALSPAC: associations with socio-demographic and lifestyle characteristics, nutrient intake and comparison with women's dietary patterns. Eur J Clin Nutr 2010; 64(9): 978-86.

[191] 191. Chen GW, Ding WH, Ku HY, Chao HR, Chen HY, Huang MC, et al. Alkylphenols in human milk and their relations to dietary habits in central Taiwan. Food Chem Toxicol 2010; 48(7): 1939-44.

[192] 192. Kjøllesdal MR, Holmboe-Ottesen G, Mosdøl A, Wandel M. The relative importance of socioeconomic indicators in explaining differences in BMI and waist:hip ratio, and the mediating effect of work control, dietary patterns and physical activity. Br J Nutr 2010; 104(8): 1230-40.

[193] 193. Mishra GD, McNaughton SA, Ball K, Brown WJ, Giles GG, Dobson AJ. Major dietary patterns of young and middle aged women: results from a prospective Australian cohort study. Eur J Clin Nutr 2010; 64(10): 1125-33.

[194] 194. Langsetmo L, Poliquin S, Hanley DA, Prior JC, Barr S, Anastassiades T, et al. Dietary patterns in Canadian men and women ages 25 and older: relationship to demographics, body mass index, and bone mineral density. BMC Musculoskelet Disord 2010; 11: 20.

[195] 195. Rezazadeh A, Rashidkhani B, Omidvar N. Association of major dietary patterns with socioeconomic and lifestyle factors of adult women living in Tehran, Iran. Nutrition 2010; 26(3): 337-41.

[196] 196. Olinto MTA, Willett WC, Gigante DP, Victora CG. Sociodemographic and lifestyle characteristics in relation to dietary patterns among young Brazilian adults. Public Health Nutr 2011; 14(1): 150-9.

[197] 197. Hare-Bruun H, Togo P, Andersen LB, Heitmann BL. Adult food intake patterns are related to adult and childhood socioeconomic status. J Nutr 2011; 141(5): 928-34.

[198] 198. Bibiloni M del M, Martínez E, Llull R, Pons A, Tur JA. Western and Mediterranean dietary patterns among Balearic Islands' adolescents: socio-economic and lifestyle determinants. Public Health Nutr 2012; 15(4): 683-92.

[199] 199. Charreire H, Kesse-Guyot E, Bertrais S, Simon C, Chaix B, Weber C, et al. Associations between dietary patterns, physical activity (leisure-time and occupational) and television viewing in middle-aged French adults. Br J Nutr 2011; 105(6): 902-10.

[200] 200. Lee JE, Kim JH, Son SJ, Ahn Y, Lee J, Park C, et al. Dietary pattern classifications with nutrient intake and health-risk factors in Korean men. Nutrition 2011; 27(1): 26-33.

[201] 201. Cho ER, Shin A, Lim SY, Kim J. Dietary patterns and their associations with health behaviours in Korea. Public Health Nutr 2011; 14(2): 356-64.

[202] 202. Nobre LN, Lamounier JA, Franceschini SCC. Padrão alimentar de pré-escolares e fatores associados. J Pediatr (Rio J) 2012; 88(2): 129-36.

[203] 203. Mullie P, Aerenhouts D, Clarys P. Demographic, socioeconomic and nutritional determinants of daily versus non-daily sugar-sweetened and artificially sweetened beverage consumption. Eur J Clin Nutr 2012; 66(2): 150-5.

[204] 204. Mohammadifard N, Sarrafzadegan N, Nouri F, Sajjadi F, Alikhasi H, Maghroun M, et al. Using factor analysis to identify dietary patterns in Iranian adults: Isfahan Healthy Heart Program. Int J Public Health 2012; 57(1): 235-41.

[205] 205. Elstgeest LEM, Mishra GD, Dobson AJ. Transitions in living arrangements are associated with changes in dietary patterns in young women. J Nutr 2012; 142(8): 1561-7.

[206] 206. Villegas R, Xiang YB, Cai H, Elasy T, Cai Q, Zhang X, et al. Lifestyle determinants of C-reactive protein in middle-aged, urban Chinese men. Nutr Metab Cardiovasc Dis 2012; 22(3): 223-30.

[207] 207. Wirfält AK, Jeffery RW. Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J Am Diet Assoc 1997; 97(3): 272-9.

[208] 208. Van den Bree MB, Eaves LJ, Dwyer JT. Genetic and environmental influences on eating patterns of twins aged >/=50 y. Am J Clin Nutr 1999; 70(4): 456-65.

[209] 209. Knol LL, Haughton B, Fitzhugh EC. Dietary patterns of young, low-income US children. J Am Diet Assoc 2005; 105(11): 1765-73.

[210] 210. Sofianou A, Fung TT, Tucker KL. Differences in diet pattern adherence by nativity and duration of US residence in the Mexican-American population. J Am Diet Assoc 2011; 111(10): 1563-9.e2.

[211] 211. Schwerin HS, Stanton JL, Riley AM, Schaefer AE, Leveille GA, Elliott JG, et al. Food eating patterns and health: a reexamination of the Ten-State and HANES I surveys. Am J Clin Nutr 1981; 34(4): 568-80.

[212] 212. Perrin AE, Dallongeville J, Ducimetière P, Ruidavets JB, Schlienger JL, Arveiler D, et al. Interactions between traditional regional determinants and socio-economic status on dietary patterns in a sample of French men. Br J Nutr 2005; 93(1): 109-14.

[213] 213. Mishra GD, McNaughton SA, Bramwell GD, Wadsworth MEJ. Longitudinal changes in dietary patterns during adult life. Br J Nutr 2006; 96(4): 735-44.

[214] 214. Cucó G, Fernández-Ballart J, Sala J, Viladrich C, Iranzo R, Vila J, et al. Dietary patterns and associated lifestyles in preconception, pregnancy and postpartum. Eur J Clin Nutr 2006; 60(3): 364-71.

[215] 215. Pryer JA, Nichols R, Elliott P, Thakrar B, Brunner E, Marmot M. Dietary patterns among a national random sample of British adults. J Epidemiol Community Health 2001; 55(1): 29-37.

[216] 216. Perrin AE, Dallongeville J, Ducimetière P, Ruidavets JB, Schlienger JL, Arveiler D, et al. Interactions between traditional regional determinants and socio-economic status on dietary patterns in a sample of French men. Br J Nutr 2005; 93(1): 109-14.

Características destacadas nos estudos	ACP n (%)	AF n (%)	RRR n (%)	Cluster n (%)	Total (n)	Número de referência dos artigos na lista de referências
Desfechos de saúde					Total (n)	Número de referência dos artigos na lista de referências
Diabetes	7 (44)	3 (19)	5 (31)	1 (6)	16	(1,25,26,27,28,29,30,31,32,33,34,35,36,37)
Obesidade	15 (58)	5 (19)	5 (19)	1 (4)	26	(38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62)
DCV	12 (48)	5 (20)	6 (24)	2 (8)	25	(63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84)
Câncer	18 (67)	8 (30)	1 (4)	0 (0)	27	(85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111)
Outras doenças**	29 (55)	13 (24)	8 (15)	3 (6)	53	(112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136, 137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163)
Método de coleta de dados do consumo alimentar					53
QFA	87 (56)	40 (26)	20 (13)	7 (4)	154	(25,26,27,28,29,30,31,32,33,34,35,36,37,39,40,41,44,45,46,47,48,51,52,53,54,55,56,59,60, 62, 63,64,65,66,67,68, 69,70,71,72,73,74,76, 78,80,81,82,83,84, 85,86,87,88,89,90,91,92,93,94,95,97,98,99,101,102,103,104,105,106,107,108,109, 111,112,115,116,117,118,119,120, 124,125, 127,129,130,131,132,133,134,135,136, 137,138,139,140,141,142, 144,145,146,147,150,151,152, 154,155,157,158,159,160,161,163,164,165,166,167,168,170,171,172,173,174,175,176,177, 178,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197)
R24h	9 (60)	3 (20)	1 (7)	2 (13)	15	(38,39, 61, 75, 77, 114, 126, 128, 148,149, 156, 188,198,199,200)
Registro alimentar	9 (45)	4 (20)	5 (26)	2 (10)	20	(42,43, 50, 57,58,79,96,100,110,113, 121,122,123,143,153,162,201,202,203,204)
Critérios de agrupamento dos alimentos					20
Grupos provenientes do QFA	24 (57)	11 (26)	5 (12)	2 (5)	42	(32,36,53,55,56,59, 65,66,67, 69,70,73,78,81,84,85,91,93,95,104,106,109,130,135,139,140,142, 145,146,147, 154,155, 159,160,170, 181,182,183,187,193,194)
Agências oficiais/recomendações dietéticas/estudos prévios	10 (50)	2 (10)	5 (25)	3 (15)	20	(27,39,46,57,61,94,100,114,116,137,150,153,162,164,165,174,198,199,200,202)
Composição nutricional	17 (65)	6 (23)	1 (4)	2 (8)	26	(34, 43,44,47,54,62,72,75,90,97,99,102,119,125, 129,136,138,144,184,188,191,192,196,205)
Uso culinário/modo de consumo	13 (76)	0 (0,0)	1 (6)	3 (18)	17	(50, 63,64, 74, 77, 83, 87,89, 98, 112,113, 118, 123, 131, 148, 158, 185)
Múltiplos*	18 (51)	12 (34)	5 (14)	0 (0)	35	(25,26,37,40,42,51,52,59,71,86,88,92,101,103,105,107,108,111,115,120,121,124,126,134,167,168,171,172,180,186,189,190)
Quantidade de grupos alimentares na análise					35
10 a 19	16 (55)	7 (24)	3 (10)	3 (10)	29	(34,36,47,60,64,82,102,110,112,118,126,127,133,137,144, 147,148,151, 157,158,175,180,190,200, 205,206)
20 a 29	20 (71)	3 (11)	2 (7)	3 (11)	28	(45,51,53, 61,62, 63,72,83,85, 99,108,111,114,119,129,136,138,142, 149,150, 164,172,187,191,193,198, 203,204)
30 a 39	30 (52)	19 (33)	7 (12)	2 (3)	58	(25,26,32, 40,41,48, 50, 52,54,56,59,65,69,74, 75,76,77,78,79,81,86,88,90, 92,93,94,98,101, 103,104,105,121,123,128,135,141, 146,152,154, 160,161,162,163,166,176,178, 183,184,185,186, 188,189,192, 196,197,199)
≥ 40	39 (53)	18 (24)	14 (19)	3 (4)	74	(27,28,29,30,31,33,35, 37, 38,39, 42,43,44,46,55, 57,58, 66,67,68, 70,71,73,77,80,84,87,89,91, 95,96,97,100, 106,107,109,113, 115,116,117,120,122,124,125, 130,131,132,134, 139,140,143,145,153, 155,156,159,165, 167,168,170,171, 173,174,177,179, 181,182, 194,195,202)
Número de padrões alimentares extraídos					74
< 4	62 (55)	29 (26)	18 (16)	3 (3)	112	(25,26,27,28,29,30,31,32,33,34,35,36,37, 39,40, 43,44, 50,51,52,54,57,60,63,64, 66,67,68, 70, 74,75,76, 78,79, 81,82,83, 86,87,88, 90,91, 94,95,97,98, 101,102,103,105,106,108,111, 115,116,118,122,125, 127,128, 131,132,133, 135,136, 137,138,139, 144,145,147, 149,150,151,152, 154,155,156,157, 159,160,161,164,166, 168,169, 172,173, 175,176,178, 183,184, 186,187,188, 190,191,192,195,197,202,203,205,206)
4 a 5	34 (58)	14 (24)	6 (10)	5 (8)	59	(41,42,45, 47,48,53, 55,56,59, 61,62,69,71,73,77, 85,89, 92,93, 99,100,104, 112,113,117,119,120,121, 123,124, 129,130,134,140, 142,143,146,148,153, 158, 162,163,165,167, 170,171,174,177,179, 180,181,185, 193,194,199,204)
6 a 7	6 (50)	2 (17)	2 (17)	2 (17)	12	(38,46,58,65,107,110,114,141,182,196,200)
> 7	3 (50)	2 (33)	0 (0)	1 (17)	6	(72,80,84,109,126,198)
Nomenclatura padrão					6	(72,80,84,109,126,198)
Rótulos qualitativos	55 (65)	18 (21)	5 (6)	6 (7)	84	(31,33, 36,37,40,42,44,45,46,47,51,55,57,60,63,64,65,66,68,74,78, 81,82,83, 86,87,90,91,96,97,103,105,108,111, 12,115,118,120,125, 131,132,133,135,138,139,140,142,145,146,147,150,151,152,155, 157,158,159,161, 164,165,166, 168,170, 174,175,176, 178,179, 183,184, 186,187,188, 190,191,192,193, 198,199, 204,205)
Baseados nos nomes dos alimentos	25 (42)	14 (24)	17 (29)	3 (5)	59	(26,27, 29,30,32, 34,35, 38,39,43,50,52,54,56, 58,59, 61,62,67,72, 76,79,80, 84, 85,89,94,98, 100,101,102,104, 106,107,110, 116,117,122, 126,127,128,129,137,149, 153,154,173,177,180, 188,189, 195,196,200, 202,203,206)
Composição nutricional	3 (43)	2 (29)	2 (27)	0 (0)	7	(75,95,99,141,167,197)
Múltiplos*	22 (56)	13 (33)	2 (5)	2 (5)	39	(28,36,41,48,53, 69,70,71,73,77,88,92,93,109, 113,114,119,121, 123,124, 130,134,136, 143,144,148,156,160, 162,163,169, 171,172, 181,182,185,192,194)