School of Physical Education, Sport and Exercise Sciences, University of Otago, Dunedin, New Zealand.
Department of Medicine-Western Health, Melbourne Medical School, The University of Melbourne, St Albans, Melbourne, VIC, Australia; Australian Institute for Musculoskeletal Science (AIMSS), Geroscience & Osteosarcopenia Research Program, The University of Melbourne and Western Health, St Albans, Melbourne, VIC, Australia.
Exp Gerontol. 2023 Mar;173:112106. doi: 10.1016/j.exger.2023.112106. Epub 2023 Jan 26.
BACKGROUND/OBJECTIVE: By having a better understanding of transitions in osteosarcopenia, interventions to reduce morbidity and mortality can be better targeted. The aim of this study was to show the rationale and method of using minimal clinically important differences (MCID's) to classify transitions, and the effects of demographic variables on transitions in a 9-year follow-up data from the New Mexico Aging Process Study (NMAPS).
Transitions were identified in four aspects of osteosarcopenia: bone mineral density (BMD), appendicular skeletal muscle mass/body mass index ratio (ASM/BMI), grip strength and gait speed. Transitions were identified using a MCID score. As there is currently no available MCID for BMD and ASM/BMI, those were determined using a distribution-based and an anchor-based method. Total transitions were calculated for all four measures of osteosarcopenia in all transition categories (maintaining a health status, beneficial transition, harmful transitions). Poisson regression was used to test for effects of demographic variables, including age, sex, physical activity, medication, and health status, on transitions.
Over the 9-year follow-up, a total of 2163 MCID-derived BMD transitions were reported, 1689 ASM/BMI transitions, 2339 grip strength transitions, and 2151 gait speed transitions. Additionally, some MCID-derived transition categories were associated with sex, age, and health status.
Use of MCID-derived transitions reflected the fluctuation and the dynamic nature of health in older adults. Future research should focus on transitions of modifiable markers in osteosarcopenia to design intervention trials.
背景/目的:通过更好地了解骨质疏松-肌少症的转变,可以更有针对性地进行减少发病率和死亡率的干预。本研究旨在展示使用最小临床重要差异(MCID)对转变进行分类的原理和方法,并展示人口统计学变量对新墨西哥老化过程研究(NMAPS) 9 年随访数据中骨质疏松-肌少症转变的影响。
在骨密度(BMD)、四肢骨骼肌质量/体重指数比(ASM/BMI)、握力和步态速度四个方面确定了骨质疏松-肌少症的转变。使用 MCID 评分确定转变。由于目前尚无 BMD 和 ASM/BMI 的可用 MCID,因此使用基于分布和基于锚定的方法确定。对于所有四种骨质疏松-肌少症措施的所有转变类别(维持健康状态、有益转变、有害转变),计算了总转变数。使用泊松回归检验年龄、性别、身体活动、药物和健康状况等人口统计学变量对转变的影响。
在 9 年的随访中,共报告了 2163 例基于 MCID 的 BMD 转变、1689 例 ASM/BMI 转变、2339 例握力转变和 2151 例步态速度转变。此外,一些基于 MCID 的转变类别与性别、年龄和健康状况有关。
使用 MCID 衍生的转变反映了老年人健康的波动和动态性质。未来的研究应重点关注骨质疏松-肌少症中可改变标志物的转变,以设计干预试验。