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2+2(+2)=4:一种通过超声评估附肢肌肉量的新方法。

2 + 2 (+ 2) = 4: A new approach for appendicular muscle mass assessment by ultrasound.

机构信息

Post-Graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil.

Post-Graduate Program in Health and Behavior, Catholic University of Pelotas, Pelotas, RS, Brazil; Pennington Biomedical Research Center, Louisiana State University. Baton Rouge, Louisiana, United States.

出版信息

Nutrition. 2021 Mar;83:111056. doi: 10.1016/j.nut.2020.111056. Epub 2020 Nov 11.

Abstract

OBJECTIVE

The aim of this study was to develop new appendicular lean mass (ALM) prediction models based on ultrasound and anthropometric measurements.

METHODS

This was a cross-sectional assessment of a subsample from a population-based study (COMO VAI?), conducted with community-dwelling individuals ≥60 y of age living in a southern Brazilian city. ALM was measured by dual-energy x-ray absorptiometry (DXA). Muscle thickness (MT) from supine participants was assessed by ultrasound on the anterior aspect of both upper and lower limbs. Such measures, along with anthropometric data, were used to develop prediction models (multivariable linear regression) through the backward stepwise method.

RESULTS

The study included 190 participants composed mainly of women, white, and middle-class individuals. The best ALM predictive performance was achieved by a model based on two "lengths" (height and arm length), two circumferences (dominant arm and thigh), and two ultrasound-measured MTs (dominant arm and thigh): R = 0.90, limits of agreement: ±2.36 kg. Closely satisfactory results were also achieved by an "abbreviated" model composed by the two aforementioned "lengths" and MTs (R = 0.89, limits of agreement: ±2.51 kg). ALM estimates from both equations were unbiased and similar to DXA measurements (P = 0.13 and 0.09, respectively). Bootstrap analysis favorably suggested the validity of the models.

CONCLUSIONS

Based on two ultrasound assessments and a few anthropometric measurements, the developed equations produced accurate and unbiased ALM estimates in the sample. Hence: 2 MTs + 2 lengths (+ 2 circumferences) = 4 limbs' muscle mass. Such models might represent promising alternatives for muscle assessment among older individuals.

摘要

目的

本研究旨在基于超声和人体测量学指标开发新的四肢瘦体重(ALM)预测模型。

方法

这是一项基于人群的研究(COMO VAI?)的横断面评估,研究对象为居住在巴西南部城市的社区居民中≥60 岁的个体。ALM 通过双能 X 射线吸收法(DXA)进行测量。仰卧位参与者的肌肉厚度(MT)通过超声在四肢的前侧进行评估。通过逐步向后法,使用这些测量值以及人体测量数据来开发预测模型(多变量线性回归)。

结果

该研究纳入了 190 名参与者,主要由女性、白人和中产阶级组成。基于两个“长度”(身高和臂长)、两个周长(优势臂和大腿)以及两个超声测量的 MT(优势臂和大腿)的模型具有最佳的 ALM 预测性能:R=0.90,一致性界限:±2.36kg。由上述两个“长度”和 MT 组成的“简化”模型也取得了非常接近的满意结果(R=0.89,一致性界限:±2.51kg)。两个方程得出的 ALM 估计值无偏且与 DXA 测量值相似(分别为 P=0.13 和 0.09)。自举分析有利地表明了模型的有效性。

结论

基于两项超声评估和一些人体测量学测量,所开发的方程在样本中产生了准确且无偏的 ALM 估计值。因此:2 个 MT+2 个长度(+2 个周长)=4 个肢体的肌肉质量。这些模型可能是评估老年人肌肉的有前途的替代方法。

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