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      Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity

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          Abstract

          Despite decades of unequivocal evidence that waist circumference provides both independent and additive information to BMI for predicting morbidity and risk of death, this measurement is not routinely obtained in clinical practice. This Consensus Statement proposes that measurements of waist circumference afford practitioners with an important opportunity to improve the management and health of patients. We argue that BMI alone is not sufficient to properly assess or manage the cardiometabolic risk associated with increased adiposity in adults and provide a thorough review of the evidence that will empower health practitioners and professional societies to routinely include waist circumference in the evaluation and management of patients with overweight or obesity. We recommend that decreases in waist circumference are a critically important treatment target for reducing adverse health risks for both men and women. Moreover, we describe evidence that clinically relevant reductions in waist circumference can be achieved by routine, moderate-intensity exercise and/or dietary interventions. We identify gaps in the knowledge, including the refinement of waist circumference threshold values for a given BMI category, to optimize obesity risk stratification across age, sex and ethnicity. We recommend that health professionals are trained to properly perform this simple measurement and consider it as an important ‘vital sign’ in clinical practice.

          Abstract

          In this Consensus Statement, the International Atherosclerosis Society and International Chair on Cardiometabolic Risk Working Group on Visceral Obesity recommend that waist circumference be included routinely as a measurement in clinical practice. They summarize the evidence that waist circumference and BMI together can provide improved assessments of cardiometabolic risk compared with either measurement alone.

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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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            Use and misuse of the receiver operating characteristic curve in risk prediction.

            The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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              Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women.

              Accumulating evidence indicates that abdominal adiposity is positively related to cardiovascular disease (CVD) risk and some other diseases independently of overall adiposity. However, the association of premature death resulting from these diseases with abdominal adiposity has not been widely studied, and findings are inconsistent. In a prospective cohort study of 44,636 women in the Nurses' Health Study, associations of abdominal adiposity with all-cause and cause-specific mortality were examined. During 16 years of follow-up, 3507 deaths were identified, including 751 cardiovascular deaths and 1748 cancer deaths. After adjustment for body mass index and potential confounders, the relative risks across the lowest to the highest waist circumference quintiles were 1.00, 1.11, 1.17, 1.31, and 1.79 (95% confidence interval [CI], 1.47 to 1.98) for all-cause mortality; 1.00, 1.04, 1.04, 1.28, and 1.99 (95% CI, 1.44 to 2.73) for CVD mortality; and 1.00, 1.18, 1.20, 1.34, and 1.63 (95% CI, 1.32 to 2.01) for cancer mortality (all P or = 88 cm was 3.02 (95% CI, 1.31 to 6.99) and for waist-to-hip ratio > 0.88 was 3.45 (95% CI, 2.02 to 6.92). After adjustment for waist circumference, hip circumference was significantly and inversely associated with CVD mortality. Anthropometric measures of abdominal adiposity were strongly and positively associated with all-cause, CVD, and cancer mortality independently of body mass index. Elevated waist circumference was associated with significantly increased CVD mortality even among normal-weight women.
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                Author and article information

                Contributors
                rossr@queensu.ca
                Journal
                Nat Rev Endocrinol
                Nat Rev Endocrinol
                Nature Reviews. Endocrinology
                Nature Publishing Group UK (London )
                1759-5029
                1759-5037
                4 February 2020
                4 February 2020
                2020
                : 16
                : 3
                : 177-189
                Affiliations
                [1 ]ISNI 0000 0004 1936 8331, GRID grid.410356.5, School of Kinesiology and Health Studies, School of Medicine, Department of Endocrinology and Metabolism, , Queen’s University, ; Kingston, ON Canada
                [2 ]ISNI 0000 0000 9482 7121, GRID grid.267313.2, Department of Internal Medicine, Division of Cardiology, , University of Texas Southwestern Medical Center, ; Dallas, TX USA
                [3 ]ISNI 0000 0004 0373 3971, GRID grid.136593.b, Departments of Cardiovascular Medicine and Community Medicine, , Osaka University Graduate School of Medicine, ; Osaka, Japan
                [4 ]ISNI 0000 0004 1937 0511, GRID grid.7489.2, Faculty of Health Sciences, , Ben-Gurion University of the Negev, ; Beer-Sheva, Israel
                [5 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Health Sciences and the EMGO Institute for Health and Care Research, , VU University Amsterdam, ; Amsterdam, Netherlands
                [6 ]ISNI 0000 0004 1757 2822, GRID grid.4708.b, Department of Pharmacological and Biomolecular Sciences, , Universita’ degli Studi di Milano, ; Milan, Italy
                [7 ]ISNI 0000 0004 1784 7240, GRID grid.420421.1, Scientific Institute for Research, Hospitalization and Health Care (IRCCS) MultiMedica, ; Sesto San Giovanni, Italy
                [8 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Lipid Clinic Heart Institute (InCor), , University of São Paulo, Medical School Hospital, ; São Paulo, Brazil
                [9 ]ISNI 0000 0001 0385 1941, GRID grid.413562.7, Hospital Israelita Albert Einstein, ; Sao Paulo, Brazil
                [10 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Department of Kinesiology, Faculty of Medicine, , Université Laval, ; Québec, QC Canada
                [11 ]ISNI 0000 0004 0604 1831, GRID grid.477064.6, Department of Clinical Nutrition and Metabolism, , Clínica Las Condes, ; Santiago, Chile
                [12 ]ISNI 000000041936754X, GRID grid.38142.3c, Departments of Nutrition and Epidemiology, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [13 ]ISNI 0000 0004 0407 4824, GRID grid.5475.3, Department of Nutritional Sciences, , University of Surrey, ; Guildford, UK
                [14 ]ISNI 0000 0004 1757 3470, GRID grid.5608.b, Department of Medicine - DIMED, , University of Padua, ; Padova, Italy
                [15 ]ISNI 0000 0004 4902 0432, GRID grid.1005.4, School of Medical Sciences, , University of New South Wales Australia, ; Sydney, NSW Australia
                [16 ]Fondation Cœur et Artères, Lille, France
                [17 ]ISNI 0000 0001 0703 675X, GRID grid.430503.1, Division of Endocrinology, Metabolism and Diabetes, and Division of Cardiology, , Anschutz University of Colorado School of Medicine, ; Aurora, CO USA
                [18 ]ISNI 0000 0004 0378 1308, GRID grid.416709.d, Department of Endocrinology and Metabolism, , Sumitomo Hospital, ; Osaka, Japan
                [19 ]ISNI 0000 0004 1936 8390, GRID grid.23856.3a, Department of Medicine, Faculty of Medicine, , Université Laval, ; Québec, QC Canada
                Author information
                http://orcid.org/0000-0001-6875-785X
                http://orcid.org/0000-0003-2240-8456
                http://orcid.org/0000-0002-5193-583X
                Article
                310
                10.1038/s41574-019-0310-7
                7027970
                32020062
                d03bd4bb-b367-4894-ba03-b411bb0934fc
                © The Author(s) 2020

                Open Access This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 December 2019
                Categories
                Consensus Statement
                Custom metadata
                © Springer Nature Limited 2020

                disease prevention,obesity,metabolic syndrome,predictive markers

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