Suppr超能文献

开发和验证一种简单的人体测量方程,以预测四肢骨骼肌量。

Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass.

机构信息

Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan.

Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan; Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo, 162-8636, Japan.

出版信息

Clin Nutr. 2021 Nov;40(11):5523-5530. doi: 10.1016/j.clnu.2021.09.032. Epub 2021 Sep 24.

Abstract

BACKGROUND & AIMS: A limited number of studies have developed simple anthropometric equations that can be implemented for predicting muscle mass in the local community. Several studies have suggested calf circumference as a simple and accurate surrogate maker for muscle mass. We aimed to develop and cross-validate a simple anthropometric equation, which incorporates calf circumference, to predict appendicular skeletal muscle mass (ASM) using dual-energy X-ray absorptiometry (DXA). Furthermore, we conducted a comparative validity assessment of our equation with bioelectrical impedance analysis (BIA) and two previously reported equations using similar variables.

METHODS

ASM measurements were recorded for 1262 participants (837 men, 425 women) aged 40 years or older. Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop the DXA-measured ASM prediction equation. Parameters including age, sex, height, weight, waist circumference, and calf circumference were incorporated as predictor variables. Total error was calculated as the square root of the sum of the square of the difference between DXA-measured and predicted ASMs divided by the total number of individuals.

RESULTS

The most optimal ASM prediction equation developed was: ASM (kg) = 2.955 × sex (men = 1, women = 0) + 0.255 × weight (kg) - 0.130 × waist circumference (cm) + 0.308 × calf circumference (cm) + 0.081 × height (cm) - 11.897 (adjusted R = 0.94, standard error of the estimate = 1.2 kg). Our equation had smaller total error and higher intraclass correlation coefficient (ICC) values than those for BIA and two previously reported equations, for both men and women (men, total error = 1.2 kg, ICC = 0.91; women, total error = 1.1 kg, ICC = 0.80). The correlation between DXA-measured ASM and predicted ASM by the present equation was not significantly different from the correlation between DXA-measured ASM and BIA-measured ASM.

CONCLUSIONS

The equation developed in this study can predict ASM more accurately as compared to equations where calf circumference is used as the sole variable and previously reported equations; it holds potential as a reliable and an effective substitute for estimating ASM.

摘要

背景与目的

已有少量研究开发了简单的人体测量学方程,可用于预测当地社区的肌肉量。 一些研究表明小腿围度是肌肉量的简单准确替代标志物。 我们旨在开发并交叉验证一种简单的人体测量学方程,该方程结合小腿围度,使用双能 X 射线吸收法(DXA)预测四肢骨骼肌量(ASM)。 此外,我们还使用类似的变量对我们的方程与生物电阻抗分析(BIA)和两个先前报道的方程进行了比较有效性评估。

方法

记录了 1262 名年龄在 40 岁或以上的参与者(837 名男性,425 名女性)的 ASM 测量值。 参与者被随机分为发展或验证组。 逐步多元线性回归用于开发 DXA 测量的 ASM 预测方程。 将年龄、性别、身高、体重、腰围和小腿围度等参数作为预测变量。 总误差计算为 DXA 测量的 ASM 与预测的 ASM 之间的平方差的总和除以个体总数的平方根。

结果

开发的最佳 ASM 预测方程为:ASM(kg)=2.955×性别(男性=1,女性=0)+0.255×体重(kg)-0.130×腰围(cm)+0.308×小腿围度(cm)+0.081×身高(cm)-11.897(调整后的 R=0.94,估计的标准误差=1.2kg)。 我们的方程在男性和女性中均具有较小的总误差和较高的组内相关系数(ICC)值,优于 BIA 和两个先前报道的方程(男性,总误差=1.2kg,ICC=0.91;女性,总误差=1.1kg,ICC=0.80)。 本研究中开发的方程预测的 DXA 测量的 ASM 与通过该方程预测的 ASM 之间的相关性与 DXA 测量的 ASM 与 BIA 测量的 ASM 之间的相关性无显著差异。

结论

与仅使用小腿围度作为单一变量的方程和先前报道的方程相比,本研究中开发的方程可以更准确地预测 ASM,具有作为估计 ASM 的可靠且有效的替代方法的潜力。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验