School of Medicine, Epi-Centre for Healthy Ageing, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Deakin University, PO Box 281 Barwon Health, Geelong, VIC, 3220, Australia.
Faculty of Health, Biostatistics Unit, Deakin University, Geelong, VIC, Australia.
Calcif Tissue Int. 2021 Nov;109(5):525-533. doi: 10.1007/s00223-021-00865-w. Epub 2021 May 20.
We investigated and quantified the predictability of frailty associated with musculoskeletal parameters. This longitudinal study included 287 men aged ≥ 50 yr at baseline (2001-2006) from the Geelong Osteoporosis Study. Baseline musculoskeletal measures included femoral neck bone mineral density (BMD), appendicular lean mass index (ALMI, kg/m) and whole-body fat mass index (FMI, kg/m) and lower-limb strength. Frailty at the 15 yr-follow-up (2016-2019) was defined as ≥ 3 and non-frail as < 3, of the following: unintentional weight loss, weakness, low physical activity, exhaustion, and slowness. Binary regression models and AUROC curves quantified the attributable risk of musculoskeletal factors to frailty and their predictive ability. Potential confounders included anthropometry, smoking, alcohol, FMI, socioeconomic status and comorbidities. Forty-eight (16.7%) men were frail at 15 yr-follow-up. Musculoskeletal models were better predictors of frailty compared to the referent (confounders only) model (AUROC for musculoskeletal factors 0.74 vs 0.67 for the referent model). The model with the highest AUROC (0.74; 95% CI 0.66-0.82) included BMD, ALMI and muscle strength (hip abductors) and was better than the referent model that included only lifestyle factors (p = 0.046). Musculoskeletal parameters improved the predictability model as measured by AUROC for frailty after 15 years. In general, muscle models performed better compared to bone models. Musculoskeletal parameters improved the predictability of frailty of the referent model that included lifestyle factors. Muscle deficits accounted for a greater proportion of the risk for frailty than did bone deficits. Targeting musculoskeletal health could be a possible avenue of intervention in regards to frailty.
我们研究并量化了与肌肉骨骼参数相关的虚弱的可预测性。这项纵向研究包括 287 名基线时（2001-2006 年）年龄≥50 岁的男性，他们来自 Geelong 骨质疏松研究。基线肌肉骨骼测量包括股骨颈骨密度（BMD）、四肢瘦体重指数（ALMI，kg/m）和全身脂肪量指数（FMI，kg/m）以及下肢力量。在 15 年随访（2016-2019 年）时，定义为≥3 分为虚弱，<3 分为非虚弱：非故意体重减轻、虚弱、低体力活动、疲惫和缓慢。二元回归模型和 AUROC 曲线量化了肌肉骨骼因素对虚弱的归因风险及其预测能力。潜在的混杂因素包括人体测量学、吸烟、饮酒、FMI、社会经济地位和合并症。48 名（16.7%）男性在 15 年随访时虚弱。与参照（仅混杂因素）模型相比，肌肉骨骼模型对虚弱的预测能力更好（肌肉骨骼因素的 AUROC 为 0.74，参照模型为 0.67）。AUROC 最高的模型（0.74；95%CI 0.66-0.82）包括 BMD、ALMI 和肌肉力量（髋关节外展肌），优于仅包含生活方式因素的参照模型（p=0.046）。肌肉骨骼参数通过 AUROC 改善了 15 年后虚弱的预测模型。一般来说，肌肉模型比骨骼模型表现更好。肌肉骨骼参数改善了包括生活方式因素的参照模型对虚弱的预测能力。肌肉缺陷比骨骼缺陷导致虚弱的风险更大。针对肌肉骨骼健康可能是干预虚弱的一种可能途径。
