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      Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories

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          Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants

          Summary Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m2 [underweight], 18·5 kg/m2 to <20 kg/m2, 20 kg/m2 to <25 kg/m2, 25 kg/m2 to <30 kg/m2, 30 kg/m2 to <35 kg/m2, 35 kg/m2 to <40 kg/m2, ≥40 kg/m2 [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m2 (95% credible interval 21·3–22·1) in 1975 to 24·2 kg/m2 (24·0–24·4) in 2014 in men, and from 22·1 kg/m2 (21·7–22·5) in 1975 to 24·4 kg/m2 (24·2–24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m2 in central Africa and south Asia to 29·2 kg/m2 (28·6–29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m2 (21·4–22·3) in south Asia to 32·2 kg/m2 (31·5–32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5–17·4) to 8·8% (7·4–10·3) in men and from 14·6% (11·6–17·9) to 9·7% (8·3–11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8–29·2) in men and 24·0% (18·9–29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4–4·1) in 1975 to 10·8% (9·7–12·0) in 2014 in men, and from 6·4% (5·1–7·8) to 14·9% (13·6–16·1) in women. 2·3% (2·0–2·7) of the world’s men and 5·0% (4·4–5·6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0·64% (0·46–0·86) in men and 1·6% (1·3–1·9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world’s poorest regions, especially in south Asia. Funding Wellcome Trust, Grand Challenges Canada.
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            Consumption of ultra-processed foods predicts diet quality in Canada.

            This study describes food consumption patterns in Canada according to the types of food processing using the Nova classification and investigates the association between consumption of ultra-processed foods and the nutrient profile of the diet. Dietary intakes of 33,694 individuals from the 2004 Canadian Community Health Survey aged 2 years and above were analyzed. Food and drinks were classified using Nova into unprocessed or minimally processed foods, processed culinary ingredients, processed foods and ultra-processed foods. Average consumption (total daily energy intake) and relative consumption (% of total energy intake) provided by each of the food groups were calculated. Consumption of ultra-processed foods according to sex, age, education, residential location and relative family revenue was assessed. Mean nutrient content of ultra-processed foods and non-ultra-processed foods were compared, and the average nutrient content of the overall diet across quintiles of dietary share of ultra-processed foods was measured. In 2004, 48% of calories consumed by Canadians came from ultra-processed foods. Consumption of such foods was high amongst all socioeconomic groups, and particularly in children and adolescents. As a group, ultra-processed foods were grossly nutritionally inferior to non-ultra-processed foods. After adjusting for covariates, a significant and positive relationship was found between the dietary share of ultra-processed foods and the content in carbohydrates, free sugars, total and saturated fats and energy density, while an inverse relationship was observed with the dietary content in protein, fiber, vitamins A, C, D, B6 and B12, niacin, thiamine, riboflavin, as well as zinc, iron, magnesium, calcium, phosphorus and potassium. Lowering the dietary share of ultra-processed foods and raising consumption of hand-made meals from unprocessed or minimally processed foods would substantially improve the diet quality of Canadian.
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              Ultra-Processed Food Products and Obesity in Brazilian Households (2008–2009)

              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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                Author and article information

                Journal
                Obesity Reviews
                Obesity Reviews
                Wiley
                1467-7881
                1467-789X
                May 17 2019
                May 17 2019
                Affiliations
                [1 ]School of Population Health, Faculty of Medical and Health SciencesThe University of Auckland Auckland New Zealand
                [2 ]Sciensano (Scientific Institute of Public Health, Department of Epidemiology and Public Health, Brussels, Belgium)
                [3 ]Harvard T.H. Chan School of Public Health, Department of Global Health and PopulationHarvard University Boston United States
                [4 ]Department of NutritionUniversity of São Paulo São Paulo Brazil
                [5 ]Department of NutritionUniversity of Montreal Montreal Canada
                [6 ]School of Public Health, Tongji Medical CollegeHuazhong University of Science and Technology Wuhan China
                [7 ]School of Mathematics, Statistics and Actuarial ScienceUniversity of Kent Canterbury UK
                Article
                10.1111/obr.12860
                31099480
                16231242-3930-4d74-b749-6a6d4f98875b
                © 2019

                http://doi.wiley.com/10.1002/tdm_license_1.1

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