Literature DB >> 26435103

Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method.

Yasmin Y Al-Gindan1, Catherine R Hankey1, Lindsay Govan2, Dympna Gallagher3, Steven B Heymsfield4, Michael E J Lean1.   

Abstract

The reference organ-level body composition measurement method is MRI. Practical estimations of total adipose tissue mass (TATM), total adipose tissue fat mass (TATFM) and total body fat are valuable for epidemiology, but validated prediction equations based on MRI are not currently available. We aimed to derive and validate new anthropometric equations to estimate MRI-measured TATM/TATFM/total body fat and compare them with existing prediction equations using older methods. The derivation sample included 416 participants (222 women), aged between 18 and 88 years with BMI between 15·9 and 40·8 (kg/m2). The validation sample included 204 participants (110 women), aged between 18 and 86 years with BMI between 15·7 and 36·4 (kg/m2). Both samples included mixed ethnic/racial groups. All the participants underwent whole-body MRI to quantify TATM (dependent variable) and anthropometry (independent variables). Prediction equations developed using stepwise multiple regression were further investigated for agreement and bias before validation in separate data sets. Simplest equations with optimal R (2) and Bland-Altman plots demonstrated good agreement without bias in the validation analyses: men: TATM (kg)=0·198 weight (kg)+0·478 waist (cm)-0·147 height (cm)-12·8 (validation: R 2 0·79, CV=20 %, standard error of the estimate (SEE)=3·8 kg) and women: TATM (kg)=0·789 weight (kg)+0·0786 age (years)-0·342 height (cm)+24·5 (validation: R (2) 0·84, CV=13 %, SEE=3·0 kg). Published anthropometric prediction equations, based on MRI and computed tomographic scans, correlated strongly with MRI-measured TATM: (R (2) 0·70-0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R (2) 0·70-0·80), with mean bias of 2·5-4·9 kg, correctable with log-transformation in most equations. In conclusion, new equations, using simple anthropometric measurements, estimated MRI-measured TATM with correlations and agreements suitable for use in groups and populations across a wide range of fatness.

Entities:  

Keywords:  Adipose tissue; Anthropometry; CT computed tomography; Epidemiology; MRI; PI prediction interval; Prediction equations; SEE standard error of the estimate; TATFM total adipose tissue fat mass; TATM total adipose tissue mass; Total body fat; UWW underwater weighing

Mesh:

Year:  2015        PMID: 26435103      PMCID: PMC5276707          DOI: 10.1017/S0007114515003670

Source DB:  PubMed          Journal:  Br J Nutr        ISSN: 0007-1145            Impact factor:   3.718


  27 in total

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Authors:  Wei Shen; ZiMian Wang; Haiying Tang; Stanley Heshka; Mark Punyanitya; Shankuan Zhu; Jianbo Lei; Steven B Heymsfield
Journal:  Obes Res       Date:  2003-02

2.  Density of body fat in man and other mammals.

Authors:  F FIDANZA; A KEYS; J T ANDERSON
Journal:  J Appl Physiol       Date:  1953-10       Impact factor: 3.531

3.  Body mass index as a measure of body fatness: age- and sex-specific prediction formulas.

Authors:  P Deurenberg; J A Weststrate; J C Seidell
Journal:  Br J Nutr       Date:  1991-03       Impact factor: 3.718

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Authors:  A Bosy-Westphal; B Schautz; W Later; J J Kehayias; D Gallagher; M J Müller
Journal:  Eur J Clin Nutr       Date:  2013-01       Impact factor: 4.016

5.  Body composition from fluid spaces and density: analysis of methods. 1961.

Authors:  W E Siri
Journal:  Nutrition       Date:  1993 Sep-Oct       Impact factor: 4.008

6.  Association of anthropometric obesity measures with chronic kidney disease risk in a non-diabetic patient population.

Authors:  James O Burton; Laura J Gray; David R Webb; Melanie J Davies; Kamlesh Khunti; Winston Crasto; Sue J Carr; Nigel J Brunskill
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Review 7.  Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies.

Authors:  Abel Romero-Corral; Victor M Montori; Virend K Somers; Josef Korinek; Randal J Thomas; Thomas G Allison; Farouk Mookadam; Francisco Lopez-Jimenez
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8.  Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample.

Authors:  T S Han; E M van Leer; J C Seidell; M E Lean
Journal:  BMJ       Date:  1995-11-25

9.  How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups?

Authors:  D Gallagher; M Visser; D Sepúlveda; R N Pierson; T Harris; S B Heymsfield
Journal:  Am J Epidemiol       Date:  1996-02-01       Impact factor: 4.897

10.  Derivation and validation of simple equations to predict total muscle mass from simple anthropometric and demographic data.

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