CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Trop Med Int Health. 2023 Feb;28(2):107-115. doi: 10.1111/tmi.13844. Epub 2023 Jan 2.
We aimed (1) to evaluate the agreement between two methods (equation and bio-impedance analysis [BIA]) to estimate skeletal muscle mass (SMM), and (2) to assess if SMM was associated with all-cause mortality risk in individuals across different geographical sites in Peru.
We used data from the CRONICAS Cohort Study (2010-2018), a population-based longitudinal study in Peru to assess cardiopulmonary risk factors from different geographical settings. SMM was computed as a function of weight, height, sex and age (Lee equation) and by BIA. All-cause mortality was retrieved from national vital records. Cox proportional-hazard models were developed and results presented as hazard ratios (HRs) with 95% confidence intervals (95% CIs).
At baseline, 3216 subjects, 51.5% women, mean age 55.7 years, were analysed. The mean SMM was 23.1 kg (standard deviation [SD]: 6.0) by Lee equation, and 22.7 (SD: 5.6) by BIA. Correlation between SMM estimations was strong (Pearson's ρ coefficient = 0.89, p < 0.001); whereas Bland-Altman analysis showed a small mean difference. Mean follow-up was 7.0 (SD: 1.0) years, and there were 172 deaths. In the multivariable model, each additional kg in SMM was associated with a 19% reduction in mortality risk (HR = 0.81; 95% CI: 0.75-0.88) using the Lee equation, but such estimate was not significant when using BIA (HR = 0.98; 95% CI: 0.94-1.03). Compared to the lowest tertile, subjects at the highest SMM tertile had a 56% reduction in risk of mortality using the Lee equation, but there was no such association when using BIA estimations.
There is a strong correlation and agreement between SMM estimates obtained by the Lee equation and BIA. However, an association between SMM and all-cause mortality exists only when the Lee equation is used. Our findings call for appropriate use of approaches to estimate SMM, and there should be a focus on muscle mass in promoting healthier ageing.
我们旨在（1）评估两种方法（方程和生物阻抗分析[BIA]）估计骨骼肌量（SMM）的一致性，以及（2）评估 SMM 是否与秘鲁不同地理地点的个体的全因死亡率风险相关。
我们使用来自 CRONICAS 队列研究（2010-2018 年）的数据，这是一项基于人群的秘鲁纵向研究，评估来自不同地理环境的心肺危险因素。SMM 是根据体重、身高、性别和年龄（李方程）和 BIA 计算得出的。全因死亡率是从国家生命记录中检索到的。开发了 Cox 比例风险模型，并以危险比（HR）及其 95%置信区间（95%CI）表示结果。
在基线时，分析了 3216 名受试者，其中 51.5%为女性，平均年龄为 55.7 岁。李方程的平均 SMM 为 23.1kg（标准差[SD]：6.0），BIA 为 22.7（SD：5.6）。SMM 估计值之间的相关性很强（皮尔逊 ρ系数=0.89，p<0.001）；然而，Bland-Altman 分析显示平均差异较小。平均随访时间为 7.0（SD：1.0）年，有 172 人死亡。在多变量模型中，SMM 每增加 1kg，死亡率风险降低 19%（HR=0.81；95%CI：0.75-0.88），使用李方程，但使用 BIA 时，这种估计并不显著（HR=0.98；95%CI：0.94-1.03）。与最低三分位相比，SMM 最高三分位的受试者的死亡率风险降低了 56%，但使用 BIA 估计值时则没有这种关联。
李方程和 BIA 获得的 SMM 估计值之间存在很强的相关性和一致性。然而，只有当使用李方程时，SMM 与全因死亡率之间才存在关联。我们的研究结果呼吁适当使用方法来估计 SMM，并且应该关注肌肉质量，以促进更健康的衰老。
