Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables.
Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables.
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DOI：
10.1371/journal.pone.0177175
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Hind K
中科院分区：
文献类型：
--
作者：
Swainson MG;Batterham AM;Tsakirides C;Rutherford ZH;Hind K
肥胖的常规测量利用身体质量指数（BMI）标准。虽然这种方法有好处，但令人担忧的是，并不是所有有肥胖相关疾病风险的人都能被识别出来。全身脂肪百分比（%FM），特别是内脏脂肪组织（VAT）质量，与疾病轨迹相关并可能涉及疾病轨迹，但不能通过BMI评估完全解释。本研究的目的是（a）比较五个人体测量预测%FM和VAT质量，和（B）探索新的临界点，这些预测最好的，以改善肥胖的特点。测量并计算了81名成年人（40名女性，41名男性;平均（SD）年龄：38.4（17.5）岁; 94%白人）的BMI、腰围（WC）、腰臀比（WHR）、腰高比（WHtR）和腰高0.5（WHT.5R）。还使用Corescan（GE Lunar iDXA，Encore版本15.0）进行全身双能X射线吸收测定，以定量%FM和VAT质量。采用线性回归分析，按性别分层，预测每个人体测量变量的%FM和VAT质量。在每种性别中，我们使用信息理论方法（Akaike信息标准; AIC）来比较模型。对于最佳的人体测量预测，我们得出了将个体分类为肥胖的暂定分界点（男性> 25%FM或女性> 35%FM，或VAT质量>最高三分位数）。男性和女性的%FM和VAT质量的最佳预测因子是WHtR。预测全身肥胖的临界点男性为0.53，女性为0.54。预测男性和女性内脏型肥胖的临界点均为0.59。在缺乏更客观的测量中心性肥胖和肥胖的情况下，WHtR是女性和男性的合适替代指标。建议的DXA-%FM和VAT质量临界值需要在更大规模的研究中进行验证，但提供了改善肥胖特征和识别最受益于治疗干预的个体的潜力。
The conventional measurement of obesity utilises the body mass index (BMI) criterion. Although there are benefits to this method, there is concern that not all individuals at risk of obesity-associated medical conditions are being identified. Whole-body fat percentage (%FM), and specifically visceral adipose tissue (VAT) mass, are correlated with and potentially implicated in disease trajectories, but are not fully accounted for through BMI evaluation. The aims of this study were (a) to compare five anthropometric predictors of %FM and VAT mass, and (b) to explore new cut-points for the best of these predictors to improve the characterisation of obesity. BMI, waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and waist/height0.5 (WHT.5R) were measured and calculated for 81 adults (40 women, 41 men; mean (SD) age: 38.4 (17.5) years; 94% Caucasian). Total body dual energy X-ray absorptiometry with Corescan (GE Lunar iDXA, Encore version 15.0) was also performed to quantify %FM and VAT mass. Linear regression analysis, stratified by sex, was applied to predict both %FM and VAT mass for each anthropometric variable. Within each sex, we used information theoretic methods (Akaike Information Criterion; AIC) to compare models. For the best anthropometric predictor, we derived tentative cut-points for classifying individuals as obese (>25% FM for men or >35% FM for women, or > highest tertile for VAT mass). The best predictor of both %FM and VAT mass in men and women was WHtR. Derived cut-points for predicting whole body obesity were 0.53 in men and 0.54 in women. The cut-point for predicting visceral obesity was 0.59 in both sexes. In the absence of more objective measures of central obesity and adiposity, WHtR is a suitable proxy measure in both women and men. The proposed DXA-%FM and VAT mass cut-offs require validation in larger studies, but offer potential for improvement of obesity characterisation and the identification of individuals who would most benefit from therapeutic intervention.