BGI-Shenzhen, Shenzhen, China.
Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China.
Clin Nutr. 2023 Apr;42(4):511-518. doi: 10.1016/j.clnu.2023.02.005. Epub 2023 Feb 16.
BACKGROUND & AIMS: Body mass index and waist circumference are simple measures of obesity. However, they do not distinguish between visceral and subcutaneous fat, or muscle, potentially leading to biased relationships between individual body composition parameters and adverse health outcomes. The purpose of this study was to develop and validate prediction models for volumetric adipose and muscle.
Based on cross-sectional data of 18,457, 18,260, and 17,052 White adults from the UK Biobank, we developed sex-specific equations to estimate visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and total thigh fat-free muscle (FFM) volumes, respectively. Volumetric magnetic resonance imaging served as the reference. We used the least absolute shrinkage and selection operator and the extreme gradient boosting methods separately to fit three sequential models, the inputs of which included demographics and anthropometrics and, in some, bioelectrical impedance analysis parameters. We applied comprehensive metrics to assess model performance in the temporal validation set.
The equations that included more predictors generally performed better. Accuracy of the equations was moderate for VAT (percentage of estimates that differed <30% from the measured values, 70 to 78 in males, 64 to 69 in females) and good for ASAT (85 to 91 in males, 90 to 95 in females) and FFM (99 to 100 in both sexes). All the equations appeared precise (interquartile range of the difference, 0.89 to 1.76 L for VAT, 1.16 to 1.61 L for ASAT, 0.81 to 1.39 L for FFM). Bias of all the equations was negligible (-0.17 to 0.05 L for VAT, -0.10 to 0.12 L for ASAT, -0.07 to 0.09 L for FFM). The equations achieved superior cardiometabolic correlations compared with body mass index and waist circumference.
The developed equations to estimate VAT, ASAT, and FFM volumes achieved moderate to good performance. They may be cost-effective tools to revisit the implications of diverse body components.
体重指数和腰围是衡量肥胖的简单指标。然而，它们不能区分内脏脂肪和皮下脂肪或肌肉，这可能导致个体身体成分参数与不良健康结果之间存在有偏差的关系。本研究的目的是开发和验证用于评估容量脂肪和肌肉的预测模型。
基于来自英国生物库的 18457、18260 和 17052 名白人成年人的横断面数据，我们分别开发了用于估计内脏脂肪组织（VAT）、腹部皮下脂肪组织（ASAT）和大腿总脂肪自由肌肉（FFM）体积的性别特异性方程。磁共振成像被用作参考。我们分别使用最小绝对值收缩和选择算子和极端梯度提升方法拟合三个连续的模型，这些模型的输入包括人口统计学和人体测量学，以及某些生物电阻抗分析参数。我们应用综合指标在时间验证集中评估模型性能。
通常，包含更多预测因子的方程表现更好。VAT 方程的准确性中等（估计值与实测值相差<30%的百分比为 70%至 78%，男性；64%至 69%，女性），而 ASAT 和 FFM 方程的准确性较好（男性为 85%至 91%，女性为 90%至 95%）。所有方程似乎都很精确（VAT 的差异四分位距为 0.89 至 1.76 L，ASAT 为 1.16 至 1.61 L，FFM 为 0.81 至 1.39 L）。所有方程的偏差都可以忽略不计（VAT 为-0.17 至 0.05 L，ASAT 为-0.10 至 0.12 L，FFM 为-0.07 至 0.09 L）。与体重指数和腰围相比，所有方程的心血管代谢相关性都更好。
开发的用于估计 VAT、ASAT 和 FFM 体积的方程表现出中等至良好的性能。它们可能是重新审视不同身体成分影响的经济有效的工具。
