一种更精确的肌肉量估计方法：一个新的估计方程。
A more accurate method to estimate muscle mass: A new estimation equation.
机构信息
Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
出版信息
J Cachexia Sarcopenia Muscle. 2023 Aug;14(4):1753-1761. doi: 10.1002/jcsm.13254. Epub 2023 May 18.
BACKGROUND
Measurement of muscle mass is important in the diagnosis of sarcopenia. Current measurement equipment are neither cost-effective nor standardized and cannot be used in a variety of medical settings. Some simple measurement tools have been proposed that are subjective and unvalidated. We aimed to develop and validate a new estimation equation in a more objective and standardized way, based on current proven variables that accurately reflect muscle mass.
METHODS
Cross-sectional analysis with The National Health and Nutrition Examination Survey database for equation development and validation. Overall, 9875 participants were included for development (6913 participants) and validation (2962 participants), for whom the database included demographic data, physical measurements, and main biochemical indicators. Appendicular skeletal muscle mass (ASM) was estimated by dual-energy x-ray absorptiometry (DXA) and low muscle mass was defined by reference to five international diagnostic criteria. Linear regression was used to estimate the logarithm of the actual ASM from demographic data, physical measurements, and biochemical indicators.
RESULTS
This study of 9875 participants comprised 4492 females (49.0%), with a weighted mean (SE) age of 41.83 (0.36) years and range of 12 to 85 years. The estimated ASM equations performed well in the validation data set. The variability in estimated ASM was low compared with the actual ASM (R : Equation 1 = 0.91, Equation 4 = 0.89), with low bias (median difference: Equation 1 = -0.64, Equation 4 = 0.07; root mean square error: Equation 1 = 1.70 [1.69-1.70], Equation 4 = 1.85 [1.84-1.86]), high precision (interquartile range of the differences: Equation 1 = 1.87, Equation 4 = 2.17), and high efficacy in diagnosing low muscle mass (area under the curve: Equation 1 = 0.91 to 0.95, Equation 4 = 0.90 to 0.94).
CONCLUSIONS
The estimated ASM equations are accurate and simple and can be routinely applied clinically to estimate ASM and thus assess sarcopenia.
背景
肌肉量的测量在肌少症的诊断中很重要。目前的测量设备既不经济实惠，也不标准化，无法在各种医疗环境中使用。一些简单的测量工具已经被提出，但它们是主观的，未经验证的。我们旨在以更客观和标准化的方式开发和验证一种新的估算方程，该方程基于当前能够准确反映肌肉量的已证实变量。
方法
使用全国健康和营养检查调查数据库进行横断面分析，以开发和验证方程。共有 9875 名参与者被纳入开发（6913 名参与者）和验证队列（2962 名参与者），数据库中包含了参与者的人口统计学数据、身体测量值和主要生化指标。通过双能 X 射线吸收法（DXA）估计四肢骨骼肌量（ASM），并根据五项国际诊断标准来定义低肌肉量。线性回归用于从人口统计学数据、身体测量值和生化指标估算出实际 ASM 的对数。
结果
这项包含 9875 名参与者的研究包括 4492 名女性（49.0%），加权平均（SE）年龄为 41.83（0.36）岁，年龄范围为 12 岁至 85 岁。在验证数据集中，估算的 ASM 方程表现良好。与实际 ASM 相比，估算的 ASM 变异性较低（R：方程 1=0.91，方程 4=0.89），偏差较小（中位数差异：方程 1=-0.64，方程 4=0.07；均方根误差：方程 1=1.70[1.69-1.70]，方程 4=1.85[1.84-1.86]），精度较高（差值的四分位间距：方程 1=1.87，方程 4=2.17），诊断低肌肉量的效能较高（曲线下面积：方程 1=0.91 至 0.95，方程 4=0.90 至 0.94）。
结论
估算的 ASM 方程准确且简单，可以常规应用于临床，以估算 ASM，从而评估肌少症。
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