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      Ambient particulate air pollution (PM2.5) is associated with the ratio of type 2 diabetes to obesity

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      Scientific Reports
      Nature Publishing Group UK

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          Abstract

          We used county level data for T2D prevalence across the mainland USA and matched this to county level ambient PM2.5. Multiple linear regression was used to determine the relation between prevalence of T2D with PM2.5 after adjustment for confounding factors. PM2.5 explained 6.3% of the spatial variation in obesity, and 17.9% of the spatial variation in T2D. After correcting the T2D prevalence for obesity, race, poverty, education and temperature, PM2.5 still explained 8.3% of the residual variation in males (P < 0.0001) and 11.5% in females (P < 0.0001). The effect on obesity prevalence corrected for poverty, race education and temperature was much lower and hence the ratio of T2D to obesity prevalence was significantly associated with PM2.5 in males (R 2 = 11.1%, P < 0.0001) and females (R 2 = 16.8%, P < 0.0001). This association was repeated across non-African countries (R 2 = 14.9%, P < 0.0001). High levels of PM2.5 probably contribute to increased T2D prevalence in the USA, but have a more minor effect on the obesity. Exposure to high environmental levels of PM2.5 (relative to the USA) may explain the disproportional risk of T2D in relation to obesity in Asian populations.

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          Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013.

          In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013. We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19,244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m(2) or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4-29·3) to 36·9% (36·3-37·4) in men, and from 29·8% (29·3-30·2) to 38·0% (37·5-38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9-24·7) of boys and 22·6% (21·7-23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7-8·6) to 12·9% (12·3-13·5) in 2013 for boys and from 8·4% (8·1-8·8) to 13·4% (13·0-13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Bill & Melinda Gates Foundation. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

            The Lancet, 380(9859), 2224-2260
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              Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure.

              Questionnaires are used to estimate disease burden. Agreement between questionnaire responses and a criterion standard is important for optimal disease prevalence estimates. We measured the agreement between self-reported disease and medical record diagnosis of disease. A total of 2,037 Olmsted County, Minnesota residents > or =45 years of age were randomly selected. Questionnaires asked if subjects had ever had heart failure, diabetes, hypertension, myocardial infarction (MI), or stroke. Medical records were abstracted. Self-report of disease showed >90% specificity for all these diseases, but sensitivity was low for heart failure (69%) and diabetes (66%). Agreement between self-report and medical record was substantial (kappa 0.71-0.80) for diabetes, hypertension, MI, and stroke but not for heart failure (kappa 0.46). Factors associated with high total agreement by multivariate analysis were age 12 years, and zero Charlson Index score (P < .05). Questionnaire data are of greatest value in life-threatening, acute-onset diseases (e.g., MI and stroke) and chronic disorders requiring ongoing management (e.g.,diabetes and hypertension). They are more accurate in young women and better-educated subjects.
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                Author and article information

                Contributors
                j.speakman@abdn.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 August 2017
                22 August 2017
                2017
                : 7
                : 9144
                Affiliations
                [1 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, , Chinese Academy of Sciences, ; Chaoyang Beijing, China
                [2 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, , University of the Chinese Academy of Sciences, ; Beijing, China
                [3 ]ISNI 0000 0004 1936 7291, GRID grid.7107.1, Institute of Biological and Environmental Science, , University of Aberdeen, ; Scotland, UK
                Article
                8287
                10.1038/s41598-017-08287-1
                5567252
                28831041
                daead67d-bc14-489f-b4be-dcec32180bc1
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2017
                : 10 July 2017
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