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      Adiposity assessments: agreement between dual-energy X-ray absorptiometry and anthropometric measures in US children 12-3

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          Abstract

          Objectives

          To evaluate performance of anthropometric measures relative to percentage body fat (%BF) measured by dual-energy X-ray absorptiometry (DXA) in children.

          Design and Methods

          We used data from 8-19-y-old US children enrolled in a nationally representative cross-sectional survey in 2001-2004 (n=5,355) with measured %BF, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and triceps skinfold thickness (TSF). Agreement and prediction were evaluated based on standardized regression coefficients ( β), kappa, and the area under the receiver-operating characteristic curves (AUC).

          Results

          The association between Z scores for %BF and anthropometric measures was strong ( β of ~0.75-0.90, kappa of ~0.60-0.75, and AUC of ~0.87-0.98; P<0.001 for all), with only some variations by race-ethnicity, mostly in girls. In boys, TSF and WHtR Z-scores had stronger agreement with %BF than BMI ( β of 0.91 and 0.86 vs. 0.79, kappa of 0.75 and 0.71 vs. 0.59, and AUC of 0.97 and 0.97 vs. 0.91; P<0.05 for all). In boys with BMI < median but %BF ≥ median, β value of TSF Z score was higher than those from BMI. In girls, TSF also provided a higher agreement than BMI, but was only statistically higher for kappa.

          Conclusions

          High agreement and small racial-ethnic variations in the association between percentage body fat and anthropometric measures support the use of anthropometric measures, especially waist-to-height ratio and triceps skinfold thickness, as proxy indicators for adiposity.

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          Most cited references24

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          Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

          The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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            Smoothing reference centile curves: the LMS method and penalized likelihood.

            Refence centile curves show the distribution of a measurement as it changes according to some covariate, often age. The LMS method summarizes the changing distribution by three curves representing the median, coefficient of variation and skewness, the latter expressed as a Box-Cox power. Using penalized likelihood the three curves can be fitted as cubic splines by non-linear regression, and the extent of smoothing required can be expressed in terms of smoothing parameters or equivalent degrees of freedom. The method is illustrated with data on triceps skinfold in Gambian girls and women, and body weight in U.S.A. girls.
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              Anthropometric measurement error and the assessment of nutritional status.

              Anthropometry involves the external measurement of morphological traits of human beings. It has a widespread and important place in nutritional assessment, and while the literature on anthropometric measurement and its interpretation is enormous, the extent to which measurement error can influence both measurement and interpretation of nutritional status is little considered. In this article, different types of anthropometric measurement error are reviewed, ways of estimating measurement error are critically evaluated, guidelines for acceptable error presented, and ways in which measures of error can be used to improve the interpretation of anthropometric nutritional status discussed. Possible errors are of two sorts; those that are associated with: (1) repeated measures giving the same value (unreliability, imprecision, undependability); and (2) measurements departing from true values (inaccuracy, bias). Imprecision is due largely to observer error, and is the most commonly used measure of anthropometric measurement error. This can be estimated by carrying out repeated anthropometric measures on the same subjects and calculating one or more of the following: technical error of measurement (TEM); percentage TEM, coefficient of reliability (R), and intraclass correlation coefficient. The first three of these measures are mathematically interrelated. Targets for training in anthropometry are at present far from perfect, and further work is needed in developing appropriate protocols for nutritional anthropometry training. Acceptable levels of measurement error are difficult to ascertain because TEM is age dependent, and the value is also related to the anthropometric characteristics of the group of population under investigation. R > 0.95 should be sought where possible, and reference values of maximum acceptable TEM at set levels of R using published data from the combined National Health and Nutrition Examination Surveys I and II (Frisancho, 1990) are given. There is a clear hierarchy in the precision of different nutritional anthropometric measures, with weight and height being most precise. Waist and hip circumference show strong between-observer differences, and should, where possible, be carried out by one observer. Skinfolds can be associated with such large measurement error that interpretation is problematic. Ways are described in which measurement error can be used to assess the probability that differences in anthropometric measures across time within individuals are due to factors other than imprecision. Anthropometry is an important tool for nutritional assessment, and the techniques reported here should allow increased precision of measurement, and improved interpretation of anthropometric data.
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                Author and article information

                Journal
                101264860
                32902
                Obesity (Silver Spring)
                Obesity (Silver Spring)
                Obesity (Silver Spring, Md.)
                1930-7381
                1930-739X
                14 April 2014
                18 February 2014
                June 2014
                01 December 2014
                : 22
                : 6
                : 1495-1504
                Affiliations
                Johns Hopkins Global Center on Childhood Obesity, Department of International Health Human Nutrition Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA (NTT and YW); Alive & Thrive project and Strategic Information, Family Health International (FHI) 360, Hanoi, Vietnam (NTT); and Department of Social and Preventive Medicine, School of Public Health and Health Professions, University at Buffalo, State University of New York, USA (YW)
                Author notes
                [3 ] Address correspondence and reprint requests: Youfa Wang, MD, MS, PhD Department of International Health Human Nutrition Program Johns Hopkins Bloomberg School of Public Health 615 North Wolfe Street, Baltimore, MD 21287, USA Tel: 410-502-3102, ywang@ 123456jhsph.edu
                Article
                NIHMS554598
                10.1002/oby.20689
                4037470
                24415710
                ffe0efb4-967b-4551-89fd-68db346d70f2
                History
                Categories
                Article

                Medicine
                adiposity,anthropometry,children,dual-energy x-ray absorptiometry (dxa),obesity
                Medicine
                adiposity, anthropometry, children, dual-energy x-ray absorptiometry (dxa), obesity

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