Pathogenesis of chronic obstructive pulmonary disease: understanding the contributions of gene-environment interactions across the lifespan

Abstract

The traditional view of chronic obstructive pulmonary disease (COPD) as a self-inflicted disease caused by tobacco smoking in genetically susceptible individuals has been challenged by recent research findings. COPD can instead be understood as the potential end result of the accumulation of gene-environment interactions encountered by an individual over the life course. Integration of a time axis in pathogenic models of COPD is necessary because the biological responses to and clinical consequences of different exposures might vary according to both the age of an individual at which a given gene-environment interaction occurs and the cumulative history of previous gene-environment interactions. Future research should aim to understand the effects of dynamic interactions between genes (G) and the environment (E) by integrating information from basic omics (eg, genomics, epigenomics, proteomics) and clinical omics (eg, phenomics, physiomics, radiomics) with exposures (the exposome) over time (T)-an approach that we refer to as GETomics. In the context of this approach, we argue that COPD should be viewed not as a single disease, but as a clinical syndrome characterised by a recognisable pattern of chronic symptoms and structural or functional impairments due to gene-environment interactions across the lifespan that influence normal lung development and ageing.

Figure 1:. Potential lung function trajectories, opportunities for intervention, and research questions

(A) Normal, below normal, and supranormal lung function trajectories are shown, with the possibility of catch-up during development and accelerated decline leading to premature death in adulthood, at least in those with reduced peak lung function. Abnormal lung development and accelerated lung function decline can lead to COPD. Reproduced from Agustí and Faner, by permission of Elsevier. (B) Risk or protective factors associated with lung function trajectories and the development of COPD could be targeted to improve lung function and reduce the risk of COPD. Strategies for risk modification during childhood and adolescence, adulthood, and later life are shown, with questions that need to be addressed to identify new therapeutic targets and develop new strategies for improved lung health and the prevention of COPD. COPD=chronic obstructive pulmonary disease.

Figure 2:. A GETomics approach to understanding COPD and other chronic human diseases

The biological effects and clinical outcomes of different gene–environment interactions depend not only on their specific characteristics, but also on a time dimension—ie, the age of the individual at which the interaction occurs and the cumulative history of the individual’s previously encountered gene–environment interactions. We propose that future research should take a holistic approach that considers the range of interactions between genes (G) and the environment (E) that occur over an individual’s lifespan (time, T) in the context of integrated omics approaches (ie, GETomics) to better understand the pathogenesis of COPD (and probably other chronic human diseases). Examples of environmental factors (the exposome^–), from conception to death, are represented by orange shading. The positions of different exposures included in the shaded area are not necessarily related to the time axis (arrow) and might occur several times during the lifespan. At different timepoints, these environmental factors interact with the genomic background of the individual through epigenetic and other mechanisms that might be identified through various basic omics approaches. These interactions induce biological responses (endotypes)—such as innate or acquired immune responses—that modulate organ structure (development, maintenance and repair, ageing) and function. Biomarkers for these endotypes are needed to be able to characterise objectively the pathogenic mechanisms linked to altered lung structure and function. Modulation of organ structure and function, represented here by different lung function trajectories associated with development and ageing, determines long-term phenotypes associated with health and disease, which can be explored through clinical omics approaches. COPD=chronic obstructive pulmonary disease.

Figure 3:. Network of interactions between risk factors for reduced lung function in different age bins

Network of interactions between risk factors for reduced lung function—FEV₁ less than LLN (central yellow nodes)—in different age bins. Each node represents one variable (squares indicate that the variable was directly quantified, circles indicate that the variable was determined using questionnaires), node size is proportional to the prevalence of that variable in the specific age group, and node colour corresponds to the variable category (eg, biomarkers in purple, nutrition and activity in light blue). Links between nodes indicate the existence of a significant (p<0·05) relationship between variables and the line type indicates whether the odds ratio is greater than 1 (continuous; positive association) or less than 1 (dashed; negative association). The complexity of the network of interactions increases with advancing age. Modified from Breyer-Kohansal and colleagues, by permission of the American Thoracic Society. COPD=chronic obstructive pulmonary disease. CRP=C-reactive protein. FFMI=fat-free mass index. FMI=fat mass index. LLN=lower limit of normal.