Strand Matthew, Bhatt Surya, Moll Matthew, Baraghoshi David
Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO, United States.
Department of Biostatistics and Informatics, Colorado School of Public Health, CU Anschutz, Aurora, CO, United States.
Glob Epidemiol. 2024 Sep 29;8:100165. doi: 10.1016/j.gloepi.2024.100165. eCollection 2024 Dec.
To separate estimates of mean change in a health outcome into components of aging and disease progression for different severity groups of chronic obstructive pulmonary disease (COPD).
A longitudinal model can be used to estimate mean change in a health outcome over time. Methods to separate this change into portions due to aging and disease progression are discussed, including conditions that allow for accurate estimation. Linear mixed models were used to estimate these changes for (FEV) for various COPD severity and smoking groups using a large cohort (COPDGene) followed for over 10 years.
Based on an analysis of 4967 subjects, age-related loss in FEV was found to be about 1 % per year, consistent with published work. Excess average losses (those beyond natural aging) were significant for all severity groups (except nonsmokers), including those with smoking history but normal lung function. Subjects in higher severity groups tended to have less loss in FEV, but more relative loss, compared to baseline averages. Losses in FEV that included both aging and disease progression ranged from 1 to 3 % over severity groups, with current smokers generally exhibiting greater mean losses in FEV than former smokers.
Effects of disease progression separate from aging can be estimated in observational studies, although care should be taken in order to make sure assumptions involving this separation are reasonable for a given study. This article demonstrates methods to estimate such effects using temporal changes in lung function for subjects in the COPDGene study.
将慢性阻塞性肺疾病（COPD）不同严重程度组健康结局的平均变化估计值分解为衰老和疾病进展的组成部分。
可使用纵向模型来估计健康结局随时间的平均变化。讨论了将这种变化分解为衰老和疾病进展部分的方法，包括允许进行准确估计的条件。使用线性混合模型，对一个大型队列（COPDGene）中随访超过10年的不同COPD严重程度和吸烟组的（第一秒用力呼气容积）（FEV）变化进行估计。
基于对4967名受试者的分析，发现FEV与年龄相关的损失约为每年1%，与已发表的研究结果一致。所有严重程度组（不包括不吸烟者）的额外平均损失（超过自然衰老的损失）都很显著，包括有吸烟史但肺功能正常的人群。与基线平均值相比，严重程度较高组的受试者FEV损失往往较少，但相对损失较大。不同严重程度组中包括衰老和疾病进展的FEV损失范围为1%至3%，当前吸烟者的FEV平均损失通常比既往吸烟者更大。
在观察性研究中可以估计疾病进展与衰老分开的影响，不过在给定研究中应谨慎确保涉及这种分离的假设是合理的。本文展示了在COPDGene研究中使用受试者肺功能的时间变化来估计此类影响的方法。
