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      Ultra-Processed Food Products and Obesity in Brazilian Households (2008–2009)

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          Abstract

          Background

          Production and consumption of industrially processed food and drink products have risen in parallel with the global increase in overweight and obesity and related chronic non-communicable diseases. The objective of this study was to analyze the relationship between household availability of processed and ultra-processed products and the prevalence of excess weight (overweight plus obesity) and obesity in Brazil.

          Methods

          The study was based on data from the 2008–2009 Household Budget Survey involving a probabilistic sample of 55,970 Brazilian households. The units of study were household aggregates (strata), geographically and socioeconomically homogeneous. Multiple linear regression models were used to assess the relationship between the availability of processed and ultra-processed products and the average of Body Mass Index (BMI) and the percentage of individuals with excess weight and obesity in the strata, controlling for potential confounders (socio-demographic characteristics, percentage of expenditure on eating out of home, and dietary energy other than that provided by processed and ultra-processed products). Predictive values for prevalence of excess weight and obesity were estimated according to quartiles of the household availability of dietary energy from processed and ultra-processed products.

          Results

          The mean contribution of processed and ultra-processed products to total dietary energy availability ranged from 15.4% (lower quartile) to 39.4% (upper quartile). Adjusted linear regression coefficients indicated that household availability of ultra-processed products was positively associated with both the average BMI and the prevalence of excess weight and obesity, whereas processed products were not associated with these outcomes. In addition, people in the upper quartile of household consumption of ultra-processed products, compared with those in the lower quartile, were 37% more likely to be obese.

          Conclusion

          Greater household availability of ultra-processed food products in Brazil is positively and independently associated with higher prevalence of excess weight and obesity in all age groups in this cross-sectional study.

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          Most cited references 34

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          Development of a WHO growth reference for school-aged children and adolescents

          OBJECTIVE: To construct growth curves for school-aged children and adolescents that accord with the WHO Child Growth Standards for preschool children and the body mass index (BMI) cut-offs for adults. METHODS: Data from the 1977 National Center for Health Statistics (NCHS)/WHO growth reference (1-24 years) were merged with data from the under-fives growth standards' cross-sectional sample (18-71 months) to smooth the transition between the two samples. State-of-the-art statistical methods used to construct the WHO Child Growth Standards (0-5 years), i.e. the Box-Cox power exponential (BCPE) method with appropriate diagnostic tools for the selection of best models, were applied to this combined sample. FINDINGS: The merged data sets resulted in a smooth transition at 5 years for height-for-age, weight-for-age and BMI-for-age. For BMI-for-age across all centiles the magnitude of the difference between the two curves at age 5 years is mostly 0.0 kg/m² to 0.1 kg/m². At 19 years, the new BMI values at +1 standard deviation (SD) are 25.4 kg/m² for boys and 25.0 kg/m² for girls. These values are equivalent to the overweight cut-off for adults (> 25.0 kg/m²). Similarly, the +2 SD value (29.7 kg/m² for both sexes) compares closely with the cut-off for obesity (> 30.0 kg/m²). CONCLUSION: The new curves are closely aligned with the WHO Child Growth Standards at 5 years, and the recommended adult cut-offs for overweight and obesity at 19 years. They fill the gap in growth curves and provide an appropriate reference for the 5 to 19 years age group.
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            The global obesity pandemic: shaped by global drivers and local environments

            The Lancet, 378(9793), 804-814
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              Sweetened beverage consumption and risk of coronary heart disease in women.

              Previous studies have linked full-calorie sugar-sweetened beverages (SSBs) with greater weight gain and an increased risk of type 2 diabetes. We prospectively examined the association between consumption of SSBs and the risk of coronary heart disease (CHD) in women. Women (n = 88,520) from the Nurses' Health Study aged 34-59 y, without previously diagnosed coronary heart disease (CHD), stroke, or diabetes in 1980, were followed from 1980 to 2004. Consumption of SSBs was derived from 7 repeated food-frequency questionnaires administered between 1980 and 2002. Relative risks (RRs) for CHD were calculated by using Cox proportional hazards models and adjusted for known cardiovascular disease risk factors. During 24 y of follow-up, we ascertained 3105 incident cases of CHD (nonfatal myocardial infarction and fatal CHD). After standard and dietary risk factors were adjusted for, the RRs (and 95% CIs) of CHD according to categories of cumulative average of SSB consumption ( or =2 servings/d) were 1.0, 0.96 (0.87, 1.06), 1.04 (0.95, 1.14), 1.23 (1.06, 1.43), and 1.35 (1.07, 1.69) (P for trend < 0.001). Additional adjustment for body mass index, energy intake, and incident diabetes attenuated the associations, but they remained significant. Artificially sweetened beverages were not associated with CHD. Regular consumption of SSBs is associated with a higher risk of CHD in women, even after other unhealthful lifestyle or dietary factors are accounted for.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                25 March 2014
                : 9
                : 3
                Affiliations
                [1 ]Departamento de Nutrição, Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brazil
                [2 ]Núcleo de Pesquisas Epidemiológicas em Nutrição e Saúde, Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brazil
                [3 ]Departamento de Medicina Preventiva, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
                [4 ]Departamento de Nutrição, Escola de Enfermagem, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
                NIDDK/NIH, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DSC RBL CAM. Analyzed the data: DSC RBL. Wrote the paper: DSC RBL APBM RMC JCM LGB GC CAM.

                Article
                PONE-D-13-16464
                10.1371/journal.pone.0092752
                3965451

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 6
                Funding
                This study was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - process number 472162/2011-0). DSC received a doctoral scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). APBM received a doctoral scholarship and JCM received a postdoctoral fellowship from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Nutrition
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Medicine and Health Sciences
                Epidemiology
                Epidemiological Methods and Statistics
                Health Care
                Health Care Policy
                Public and Occupational Health
                Research and Analysis Methods
                Research Design
                Survey Research
                Survey Methods

                Uncategorized

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