Epi-Centre for Healthy Ageing, IMPACT - The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Deakin University, PO Box 281, Geelong, VIC, 3220, Australia.
Faculty of Health, Biostatistics Unit, Deakin University, Geelong, VIC, Australia.
BMC Musculoskelet Disord. 2021 Nov 1;22(1):921. doi: 10.1186/s12891-021-04795-4.
Musculoskeletal conditions and physical frailty have overlapping constructs. We aimed to quantify individual contributions of musculoskeletal factors to frailty.
Participants included 347 men and 360 women aged ≥60 yr (median ages; 70.8 (66.1-78.6) and 71.0 (65.2-77.5), respectively) from the Geelong Osteoporosis Study. Frailty was defined as ≥3, pre-frail 1-2, and robust 0, of the following; unintentional weight loss, weakness, low physical activity, exhaustion, and slowness. Measures were made of femoral neck BMD, appendicular lean mass index (ALMI, kg/m) and whole-body fat mass index (FMI, kg/m) by DXA (Lunar), SOS, BUA and SI at the calcaneus (Lunar Achilles Insight) and handgrip strength by dynamometers. Binary and ordinal logistic regression models and AUROC curves were used to quantify the contribution of musculoskeletal parameters to frailty. Potential confounders included anthropometry, smoking, alcohol, prior fracture, FMI, SES and comorbidities.
Overall, 54(15.6%) men and 62(17.2%) women were frail. In adjusted-binary logistic models, SI, ALMI and HGS were associated with frailty in men (OR = 0.73, 95%CI 0.53-1.01; OR=0.48, 0.34-0.68; and OR = 0.11, 0.06-0.22; respectively). Muscle measures (ALMI and HGS) contributed more to this association than did bone (SI) (AUROCs 0.77, 0.85 vs 0.71, respectively). In women, only HGS was associated with frailty in adjusted models (OR = 0.30 95%CI 0.20-0.45, AUROC = 0.83). In adjusted ordinal models, similar results were observed in men; for women, HGS and ALMI were associated with frailty (ordered OR = 0.30 95%CI 0.20-0.45; OR = 0.56, 0.40-0.80, respectively).
Muscle deficits appeared to contribute more than bone deficits to frailty. This may have implications for identifying potential musculoskeletal targets for preventing or managing the progression of frailty.
肌肉骨骼疾病和身体虚弱具有重叠的结构。我们旨在量化肌肉骨骼因素对虚弱的个体贡献。
参与者包括 347 名男性和 360 名年龄≥60 岁的女性（中位数年龄分别为 70.8（66.1-78.6）和 71.0（65.2-77.5）），来自 Geelong 骨质疏松症研究。虚弱定义为以下任意 3 项或以上的总和：非故意体重减轻、虚弱、低体力活动、疲劳和缓慢；通过 DXA（Lunar）测量股骨颈 BMD、四肢瘦体重指数（ALMI，kg/m）和全身脂肪量指数（FMI，kg/m）、跟骨的 SOS、BUA 和 SI（Lunar Achilles Insight）以及测力计的握力。使用二元逻辑回归模型和 AUROC 曲线来量化肌肉骨骼参数对虚弱的贡献。潜在的混杂因素包括人体测量学、吸烟、饮酒、既往骨折、FMI、SES 和合并症。
总体而言，54 名男性（15.6%）和 62 名女性（17.2%）虚弱。在调整后的二元逻辑模型中，SI、ALMI 和 HGS 与男性的虚弱有关（OR=0.73，95%CI 0.53-1.01；OR=0.48，0.34-0.68；OR=0.11，0.06-0.22）。肌肉指标（ALMI 和 HGS）比骨骼（SI）对这种关联的贡献更大（AUROCs 分别为 0.77、0.85 和 0.71）。在女性中，仅 HGS 在调整模型中与虚弱有关（OR=0.30，95%CI 0.20-0.45，AUROC=0.83）。在调整后的有序模型中，男性也观察到类似的结果；对于女性，HGS 和 ALMI 与虚弱有关（有序 OR=0.30，95%CI 0.20-0.45；OR=0.56，0.40-0.80）。
肌肉缺陷似乎比骨骼缺陷对虚弱的贡献更大。这可能对确定预防或管理虚弱进展的潜在肌肉骨骼目标具有重要意义。
