Department of Pharmacotherapy, University of Utah, 30 South 2000 East Room 258, Salt Lake City, UT, 84112-5820, USA,
J Cachexia Sarcopenia Muscle. 2013 Sep;4(3):187-97. doi: 10.1007/s13539-013-0107-9. Epub 2013 May 15.
Skeletal muscle mass declines after the age of 50. Loss of skeletal muscle mass is associated with increased morbidity and mortality.
This study aims to identify predictors of low skeletal muscle mass in older adults toward development of a practical clinical assessment tool for use by clinicians to identify patients requiring dual-energy X-ray absorptiometry (DXA) screening for muscle mass.
Data were drawn from the National Health and Nutrition Examination Surveys (NHANES) from 1999 to 2004. Appendicular skeletal mass (ASM) was calculated based on DXA scans. Skeletal muscle mass index (SMI) was defined as the ratio of ASM divided by height in square centimeters. Elderly participants were classified as having low muscle mass if the SMI was 1 standard deviation (SD) below the mean SMI of young adults (20-40 years old). Logistic regression was conducted separately in males and females age ≥65 years of age to examine the relationship between patients identified as having low muscle mass and health behavior characteristics, adjusting for comorbid conditions. The model was validated on a separate sample of 200 patients.
Among the NHANES study population, 551 (39.7 %) males and 374 (27.5 %) females had a SMI below the 1 SD cutoff point. NHANES study subjects with a low SMI were older (mean age, 76.2 vs. 72.7 for male; 76.0 vs. 73.7 for female; and both p < 0.0001) and had a lower body mass index (mean BMI, 24.1 vs. 29.4 for male; 22.9 vs. 29.7 for female; p < 0.0001). In adjusted logistic regression analyses, age (for males) and BMI (for both males and females) remained statistically significant. A parsimonious logistic regression model adjusting for age and BMI only had a C statistic of 0.89 for both males and females. The discriminatory power of the parsimonious model increased to 0.93 for males and 0.95 for females when the cutoff defining low SMI was set to 2 SD below the SMI of young adults. In the validation sample, the sensitivity was 81.6 % for males and 90.6 % for females. The specificity was 66.2 % for males and females.
BMI was strongly associated with a low SMI and may be an informative predictor in the primary care setting. The predictive model worked well in a validation sample.
50 岁后骨骼肌量减少。骨骼肌量减少与发病率和死亡率增加有关。
本研究旨在确定老年人低骨骼肌量的预测因素，以便开发一种实用的临床评估工具，供临床医生用于识别需要双能 X 射线吸收法（DXA）筛查肌肉量的患者。
数据来自 1999 年至 2004 年的国家健康和营养检查调查（NHANES）。根据 DXA 扫描计算四肢骨骼肌量（ASM）。骨骼肌质量指数（SMI）定义为 ASM 除以身高的平方厘米数。如果 SMI 低于年轻成年人（20-40 岁）的平均 SMI 标准差（SD），则将老年参与者归类为肌肉量低。在年龄≥65 岁的男性和女性中分别进行 logistic 回归，以检查被确定为肌肉量低的患者与健康行为特征之间的关系，同时调整合并症。该模型在 200 名患者的独立样本上进行了验证。
在 NHANES 研究人群中，551 名男性（39.7%）和 374 名女性（27.5%）的 SMI 低于 1 SD 截止点。SMI 较低的 NHANES 研究受试者年龄较大（男性平均年龄为 76.2 岁，女性为 72.7 岁；均<0.0001），体重指数（BMI）较低（男性平均 BMI 为 24.1，女性为 29.4；均<0.0001）。在调整后的 logistic 回归分析中，年龄（男性）和 BMI（男性和女性）仍然具有统计学意义。仅调整年龄和 BMI 的简约 logistic 回归模型对男性和女性的 C 统计量分别为 0.89。当将低 SMI 的截止值设定为年轻成年人 SMI 的 2 SD 以下时，简约模型的区分能力分别增加到男性的 0.93 和女性的 0.95。在验证样本中，男性的敏感性为 81.6%，女性为 90.6%。特异性为男性 66.2%，女性 66.2%。
BMI 与低 SMI 密切相关，在初级保健环境中可能是一个有信息预测指标。预测模型在验证样本中表现良好。
