Title

Predictive equations for estimating regional body composition: a validation study using DXA as criterion and associations with cardiometabolic risk factors

RIS ID

122976

Publication Details

Simoes, M., Severo, M., Oliveira, A., Ferreira, I. & Lopes, C. (2016). Predictive equations for estimating regional body composition: a validation study using DXA as criterion and associations with cardiometabolic risk factors. Annals of Human Biology, 43 (3), 219-228.

Abstract

Background: Regional body composition can influence disease risk and simple screening methods to identify individuals at risk are still needed. Aim: To develop equations for estimating fat and lean mass distribution, using DXA as a criterion, and to compare their performance by the association with cardiometabolic risk factors (systolic and diastolic blood pressure, high-density lipoprotein cholesterol, triacylglycerides and fasting glucose). Subject and methods: The study was conducted among 391 adults (52% female; aged 24-64 years), randomly selected from the EPIPorto cohort, with anthropometric (skin-folds and circumferences) and DXA measures. Multiple linear regression models, by sex, were used to derive predi ctive equations and to investigate their performance by the associations with cardiometabolic risk factors. Results: The best predicting models included circumferences and skin-folds. The final equations standard error (SEEs) ranged between 1.7-3.2% in men and 2.0-3.7% in women. Similar associations were found between regional body composition, measured by DXA or estimated by anthropometric equations, with traditional cardiometabolic risk factors, supporting the convergent validity of the equations. Additionally, it was found that central fat was associated adversely, whereas peripheral fat was favourably associated with cardiometabolic risk factors and their clustering. Conclusions: Equations combining both circumferences and skin-folds met the necessary standards for predicting regional body composition accurately, being an important tool to identify individuals with cardiometabolic risk.

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Link to publisher version (DOI)

http://dx.doi.org/10.3109/03014460.2015.1054427