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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Associação de padrões alimentares com obesidade

Perozzo, Gabriela 09 July 2007 (has links)
Made available in DSpace on 2015-03-05T20:05:12Z (GMT). No. of bitstreams: 0 Previous issue date: 9 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Com objetivo de estudar a associação de padrões alimentares com obesidade, realizou-se estudo transversal de base populacional com amostra representativa de 1.026 mulheres (20 a 60 anos) em São Leopoldo, RS, Brasil. A obesidade geral foi avaliada utilizando-se Índice de Massa Corporal (IMC) e a adiposidade abdominal, Circunferência da Cintura (CC). Os padrões alimentares foram identificados por análise fatorial. Para análise multiva¬riada foi utilizada regressão de Poisson. Entre o total de mulheres, 18,0% (IC95% 15,66–20,53) tinham obesidade geral e 23,3% (IC95% 20,72–26,06) abdominal. Após controle para fatores de confusão, o baixo consumo do PA-Frutas associou-se positivamente com o IMC (RP=2,18; IC95% 1,35–3,53; p=0,001). Já o baixo consumo do PA-Vegetais apresentou efeito protetor para o aumento nos níveis de IMC (RP=0,64; IC95% 0,47–0,86; p=0,004) e o do PA-Nozes/Oleaginosas para o aumento na medida da CC (RP=0,93; IC95% 0,89–0,98; p=0,008). O estudo aponta para a complexidade envolvida na relação entre / The objective of this study was to study the association between dietary patterns and obesity. A cross sectional population based study was carried out in 1026 adult women from Southern Brazil. Waist circumference (WC), height and weight were measured with a standardized protocol and Body Mass Index (BMI) calculated. Obesity was defined as a BMI≥30Kg/m2 and WC≥88cm. Principal component analysis was used to identify the dietary patterns. The multivariable analysis used a Poisson Regression model to estimate the prevalence ratios and their respective confidence intervals. The prevalence of obesity was 18,0% (CI95% 15,66–20,53) and abdominal obesity, 23,3% (CI95% 20,72–26,06). After controlling for possible confounders, low consumption of the “fruits” pattern was a risk factor for high BMI (PR=2,18; CI95% 1,35–3,53; p=0,001). Low consumption of the “vegetables” pattern protected against increased BMI (PR = 0,64; CI95% 0,47–0,86; p=0,004) while low consumption of “nuts” pattern protected against incre

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