Estimating effects of aging and disease progression in current and former smokers using longitudinal models

Abstract

Objectives: 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). Study design and methods: 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 forced expiratory volume in 1 s (FEV1) for various COPD severity and smoking groups using a large cohort (COPDGene) followed for over 10 years. Results: Based on an analysis of 4967 subjects, age-related loss in FEV1 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 FEV1, but more relative loss, compared to baseline averages. Losses in FEV1 that included both aging and disease progression ranged from 1 to 3 % over severity groups, with current smokers generally exhibiting greater mean losses in FEV1 than former smokers. Discussion: 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.

Keywords

• Longitudinal
• Disease progression
• Aging
• Mixed models
• Observational study
• FEV1