Agyapong-Badu Sandra, Warner Martin B, Samuel Dinesh, Koutra Vasiliki, Stokes Maria
School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham B15 2TT, UK.
School of Health Sciences, University of Southampton, Southampton SO17 1BJ, UK.
J Clin Med. 2021 Mar 25;10(7):1352. doi: 10.3390/jcm10071352.
A novel approach to ageing studies assessed the discriminatory ability of a combination of routine physical function tests and novel measures, notably muscle mechanical properties and thigh composition (ultrasound imaging) to classify healthy individuals according to age and gender. The cross-sectional study included 138 community-dwelling, self-reported healthy males and females (65 young, mean age ± SD = 25.7 ± 4.8 years; 73 older, 74.9 ± 5.9 years). Handgrip strength; quadriceps strength; respiratory peak flow; timed up and go; stair climbing time; anterior thigh tissue thickness; muscle stiffness, tone, elasticity (Myoton technology), and self-reported health related quality of life (SF36) were assessed. Stepwise feature selection using cross-validation with linear discriminant analysis was used to classify cases based on criterion variable derived from known effects of age on physical function. A model was trained and features selected using 126 cases with 0.92 accuracy (95% CI = 0.86-0.96; Kappa = 0.89). The final model included five features (peak flow, timed up and go, biceps brachii elasticity, anterior thigh muscle thickness, and percentage thigh muscle) with high sensitivity (0.82-0.96) and specificity (0.94-0.99). The most sensitive novel biomarkers require no volition, highlighting potentially useful tests for screening and monitoring effects of interventions on musculoskeletal health for vulnerable older people with pain or cognitive impairment.
一种新的衰老研究方法评估了常规身体功能测试与新指标（特别是肌肉力学特性和大腿成分（超声成像））相结合，根据年龄和性别对健康个体进行分类的判别能力。这项横断面研究纳入了138名居住在社区、自我报告健康的男性和女性（65名年轻人，平均年龄±标准差=25.7±4.8岁；73名老年人，74.9±5.9岁）。评估了握力、股四头肌力量、呼吸峰值流量、计时起立行走测试、爬楼梯时间、大腿前部组织厚度、肌肉僵硬度、张力、弹性（Myoton技术）以及自我报告的与健康相关的生活质量（SF - 36）。使用线性判别分析的交叉验证进行逐步特征选择，以根据年龄对身体功能的已知影响得出的标准变量对病例进行分类。使用126个病例训练了一个模型并选择了特征，准确率为0.92（95%置信区间=0.86 - 0.96；Kappa = 0.89）。最终模型包括五个特征（峰值流量、计时起立行走测试、肱二头肌弹性、大腿前部肌肉厚度和大腿肌肉百分比），具有高敏感性（0.82 - 0.96）和特异性（0.94 - 0.99）。最敏感的新型生物标志物不需要意志努力，这突出了对于有疼痛或认知障碍的弱势老年人筛查和监测干预措施对肌肉骨骼健康影响的潜在有用测试。
