Chen Jing, Cheng Zihe, Yao Yang, Wang Shengyu
Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi'an Medical University, Xi'an Medical University, No.48 Fenghao West Road, Xi'an, Shaanxi, 710077, China.
J Epidemiol Glob Health. 2024 Dec;14(4):1555-1568. doi: 10.1007/s44197-024-00307-4. Epub 2024 Sep 30.
The relationship between fat-free mass index (FFMI), fat mass index (FMI), and mortality in patients with asthma remains unknown. This study aimed to examine the associations between FFMI and FMI and all-cause mortality in a cohort of American adults diagnosed with asthma.
This study included 15,200 adults from NHANES. To assess mortality, we linked participant records to the National Death Index. FMI and FFMI were measured and evaluated using dual-energy X-ray absorptiometry (DXA). Survival differences across quintiles of FFMI and FMI were explored using Kaplan-Meier plots and log-rank tests, with the proportional hazards assumption assessed using Schoenfeld residuals. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) for mortality associated with FFMI and FMI, adjusting for potential confounders including age, sex, smoking status, physical activity, and other relevant factors. Additionally, stratified analyses based on theoretical considerations were conducted to identify subgroups of individuals exhibiting an elevated risk of mortality. This study also examined the nonlinear relationships between FFMI, FMI, and mortality using restricted cubic splines (RCS).
After a median follow-up of 184 months, 12.11% of individuals had died. Kaplan-Meier plots revealed significant differences in all-cause mortality among patients with asthma across the FFMI and FMI quintiles. Specifically, individuals in the lowest FFMI quintile (Q1, 10.4-16.0, representing the range of FFMI values) exhibited a significantly increased risk of all-cause mortality (HR: 4.63; 95% CI: 1.59, 13.5; p < 0.01). Similarly, elevated risks of all-cause mortality were observed in the upper three quintiles of FMI, with Q3 (4.8-6.1) having an HR of 2.9 (95% CI: 1.20, 7.00; p < 0.05), Q4 (6.2-8.3) having an HR of 3.37 (95% CI: 1.41, 8.03; p < 0.01), and Q5 (8.4-22.8) having an HR of 4.6 (95% CI: 1.31, 16.2; p < 0.05). Moreover, the risk of all-cause mortality increased with increasing FMI and decreasing FFMI (p for non-linearity < 0.001 in both cases). Subgroup analyses further elucidated these associations across different categories. In examining the association between FMI and all-cause mortality among asthma patients across various subgroups, a heightened mortality risk found among males, individuals with medium education levels, medium income levels, and those who consume alcohol.
The study shows that both high FMI and low FFMI are associated with increased mortality in patients with asthma. These findings underscore the critical role of FMI and FFMI in the health management of asthma patients. Therefore, it is recommended that clinicians proactively monitor and adjust these indices to improve patient prognosis and enhance health outcomes for individuals with asthma..
哮喘患者的去脂体重指数(FFMI)、脂肪量指数(FMI)与死亡率之间的关系尚不清楚。本研究旨在探讨美国成年哮喘患者队列中FFMI和FMI与全因死亡率之间的关联。
本研究纳入了来自美国国家健康与营养检查调查(NHANES)的15200名成年人。为评估死亡率,我们将参与者记录与国家死亡指数进行了关联。使用双能X线吸收法(DXA)测量和评估FMI和FFMI。使用Kaplan-Meier曲线和对数秩检验探讨FFMI和FMI五分位数间的生存差异,并使用Schoenfeld残差评估比例风险假设。采用Cox比例风险回归模型估计与FFMI和FMI相关死亡率的风险比(HR),并对年龄、性别、吸烟状况、身体活动及其他相关因素等潜在混杂因素进行调整。此外,基于理论考量进行分层分析,以识别死亡率风险升高的个体亚组。本研究还使用受限立方样条(RCS)检验FFMI、FMI与死亡率之间的非线性关系。
中位随访184个月后,12.11%的个体死亡。Kaplan-Meier曲线显示,哮喘患者中,FFMI和FMI五分位数间的全因死亡率存在显著差异。具体而言,FFMI最低五分位数(Q1,10.4 - 16.0,代表FFMI值范围)的个体全因死亡率风险显著增加(HR:4.63;95%CI:1.59,13.5;p < 0.01)。同样,FMI最高三分位数的个体全因死亡率风险升高,其中Q3(4.8 - 6.1)的HR为2.9(95%CI:1.20,7.00;p < 0.05),Q4(6.2 - 8.3)的HR为3.37(95%CI:1.41,8.03;p < 0.01),Q5(8.4 - 2