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      Estimating body mass and composition from proximal femur dimensions using dual energy x-ray absorptiometry

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          Abstract

          Body mass prediction from the skeleton most commonly employs femoral head diameter (FHD). However, theoretical predictions and empirical data suggest the relationship between mass and FHD is strongest in young adults, that bone dimensions reflect lean mass better than body or fat mass and that other femoral measurements may be superior. Here, we generate prediction equations for body mass and its components using femoral head, neck and proximal shaft diameters and body composition data derived from dual-energy x-ray absorptiometry (DXA) scans of young adults ( n = 155, 77 females and 78 males, mean age 22.7 ± 1.3 years) from the Andhra Pradesh Children and Parents Study, Hyderabad, India. Sex-specific regression of log-transformed data on femoral measurements predicted lean mass with smaller standard errors of estimate (SEEs) than body mass (12–14% and 16–17% respectively), while none of the femoral measurements were significant predictors of fat mass. Subtrochanteric mediolateral shaft diameter gave lower SEEs for lean mass in both sexes and for body mass in males than FHD, while FHD was a better predictor of body mass in women. Our results provide further evidence that lean mass is more closely related to proximal femur dimensions than body or fat mass and that proximal shaft diameter is a better predictor than FHD of lean but not always body mass. The mechanisms underlying these relationships have implications for selecting the most appropriate measurement and reference sample for estimating body or lean mass, which also depend on the question under investigation.

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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 reduced major axis for line-fitting.

            Many investigators use the reduced major axis (RMA) instead of ordinary least squares (OLS) to define a line of best fit for a bivariate relationship when the variable represented on the X-axis is measured with error. OLS frequently is described as requiring the assumption that X is measured without error while RMA incorporates an assumption that there is error in X. Although an RMA fit actually involves a very specific pattern of error variance, investigators have prioritized the presence versus the absence of error rather than the pattern of error in selecting between the two methods. Another difference between RMA and OLS is that RMA is symmetric, meaning that a single line defines the bivariate relationship, regardless of which variable is X and which is Y, while OLS is asymmetric, so that the slope and resulting interpretation of the data are changed when the variables assigned to X and Y are reversed. The concept of error is reviewed and expanded from previous discussions, and it is argued that the symmetry-asymmetry issue should be the criterion by which investigators choose between RMA and OLS. This is a biological question about the relationship between variables. It is determined by the investigator, not dictated by the pattern of error in the data. If X is measured with error but OLS should be used because the biological question is asymmetric, there are several methods available for adjusting the OLS slope to reflect the bias due to error. RMA is being used in many analyses for which OLS would be more appropriate.
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              Body mass and encephalization in Pleistocene Homo.

              Many dramatic changes in morphology within the genus Homo have occurred over the past 2 million years or more, including large increases in absolute brain size and decreases in postcanine dental size and skeletal robusticity. Body mass, as the 'size' variable against which other morphological features are usually judged, has been important for assessing these changes. Yet past body mass estimates for Pleistocene Homo have varied greatly, sometimes by as much as 50% for the same individuals. Here we show that two independent methods of body-mass estimation yield concordant results when applied to Pleistocene Homo specimens. On the basis of an analysis of 163 individuals, body mass in Pleistocene Homo averaged significantly (about 10%) larger than a representative sample of living humans. Relative to body mass, brain mass in late archaic H. sapiens (Neanderthals) was slightly smaller than in early 'anatomically modern' humans, but the major increase in encephalization within Homo occurred earlier during the Middle Pleistocene (600-150 thousand years before present (kyr BP)), preceded by a long period of stasis extending through the Early Pleistocene (1,800 kyr BP).
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                Author and article information

                Contributors
                +44 (0) 151 231 2815 , e.e.pomeroy@ljmu.ac.uk
                Journal
                Archaeol Anthropol Sci
                Archaeol Anthropol Sci
                Archaeological and Anthropological Sciences
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1866-9557
                1866-9565
                18 June 2018
                18 June 2018
                2019
                : 11
                : 5
                : 2167-2179
                Affiliations
                [1 ]ISNI 0000 0004 0368 0654, GRID grid.4425.7, School of Natural Sciences and Psychology, , Liverpool John Moores University, ; Byrom Street, Liverpool, L3 3AF UK
                [2 ]GRID grid.444673.6, Deccan College Postgraduate and Research Institute, ; Pune, India
                [3 ]ISNI 0000 0004 0496 9898, GRID grid.419610.b, National Institute of Nutrition, ; Hyderabad, India
                [4 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, ; London, UK
                [5 ]ISNI 0000000121885934, GRID grid.5335.0, ADaPt Project, PAVE Research Group, Department of Archaeology and Anthropology, , University of Cambridge, ; Cambridge, UK
                [6 ]ISNI 0000000121901201, GRID grid.83440.3b, UCL Great Ormond Street Institute of Child Health, UCL, ; London, UK
                Author information
                http://orcid.org/0000-0001-6251-2165
                Article
                665
                10.1007/s12520-018-0665-z
                6743672
                31565085
                6b71117f-ed1c-4e91-93f7-1d6d220564e3
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.

                History
                : 15 February 2018
                : 4 June 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000275, Leverhulme Trust;
                Award ID: ECF-2015-520
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000286, British Academy;
                Award ID: PM140125
                Award ID: PM140125
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/M012069/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 084774
                Award Recipient :
                Categories
                Original Paper
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                lean mass estimation,fat mass estimation,india,archaeology,forensics,dxa

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