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.

Introduction

Chronic obstructive pulmonary disease (COPD) has traditionally been considered to be paradigmatic of a disease caused by gene–environment interactions, being viewed as occurring almost exclusively (with the exception of patients with α1-antitrypsin deficiency)¹ in genetically susceptible smokers.² However, several observations have challenged this conventional model.³ First, the central dogma of the gene–environment hypothesis is that a combination of gene(s) and environmental factor(s) determine disease risk.⁴ But few convincing examples of clear gene–environment interactions that have been replicated across studies have been identified for COPD,^5,6 and the genetic effects of identified loci are quite similar in smokers and never-smokers.^7,8 A recent, large genome-wide association analysis of 179 689 controls and 21 077 individuals with COPD of European ancestry from the UK Biobank⁵ confirmed the presence of interaction effects with smoking at previously reported COPD loci, but did not identify novel susceptibility loci. Two potential, non-mutually exclusive explanations can be considered here. On the one hand, molecular associations between smoking status and COPD might be detectable through other omics approaches, such as epigenomics, transcriptomics, or proteomics;⁶ as discussed below, the effect of smoking is well captured by epigenetic modifications.^9–11 On the other hand, the toxic effects caused by the thousands of different compounds and metabolites in cigarette smoke might be too complex to be captured by a reductionist approach (ie, identification of a single gene or variant),^12,13 so multilevel integrative approaches might be needed.^6,14,15 Polygenic risk scores, which are based on information about all established risk variants from well powered genome-wide association studies, might help to identify individuals with a high genetic risk who are therefore more susceptible to developing COPD when exposed to environmental factors.¹⁶

Second, a substantial proportion of patients with COPD are never-smokers.^17,18 Furthermore, epidemiological studies have shown that many different environmental exposures and host conditions other than smoking (eg, sex) are significantly associated with airflow limitation (the traditional diagnostic functional abnormality of COPD) over the lifespan.¹⁹ Of note, in a European population-based study, sex differences were found according to smoking status: 27·2% of females and 7·3% of males with COPD were never-smokers.²⁰ Finally, several studies have now shown that there is a range of lung function trajectories over the life course (ie, a trajectome; panel 1 and figure 1A), some of which can lead to COPD in late adulthood through two different but non-mutually exclusive mechanisms: abnormal lung development and accelerated lung function decline.^3,21–23

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,²² 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.

In this Series paper, we argue that to advance understanding of the contributions of gene–environment interactions to the pathogenesis of COPD, we need to consider host and environmental factors other than smoking and their biological interactions in the context of a time dimension. We review evidence for this proposed approach to COPD, with a focus on its three main components: environmental and host factors, genetics and epigenetics, and the time axis. Furthermore, we consider the clinical implications of this lifespan perspective and key questions for future, multidisciplinary research.

Gene–environment interactions: a lifespan perspective

To understand fully the roles of factors that contribute to the pathogenesis of COPD, the following must be considered. First, the series of dynamic and cumulative gene–environment interactions beyond tobacco smoking that contribute to COPD risk, including many other environmental (eg, infections, air pollutants) and host (eg, prematurity, immune fitness, senesence^24,25) factors. Second, a time dimension that takes into account the age of the individual at which a given gene–environment interaction occurs—because at different ages, the biological response(s) and clinical consequence(s) are likely to be influenced by unique sets of genes and epigenetic mechanisms that modulate the capacity of the system to respond (eg, immune function) and repair^26,27—and the cumulative history of gene–environment interactions encountered by the individual throughout life (from before birth or even before conception²⁸) that might render the lungs more fragile and less resilient to respond to new gene–environment interactions.²⁹

We propose that future research should take an integrative approach that uses multiple omics platforms to improve understanding of the pathogenesis of COPD in the context of the complex interactions between genes (G) and the environment (E) over time (T); this approach, which we refer to as GETomics (panel 1 and figure 2),^30–34 has also been proposed for other medical conditions^35,36 and could be relevant for a range of chronic human diseases. Specifically, this term refers to the integration of information from basic omics approaches (eg, genomics, epigenomics, proteomics) with that from clinical omics approaches (eg, phenomics, physiomics, radiomics) and exposures (the exposome; panel 1) over time. The time axis is crucial because both the age of the individual at which a given gene–environment interaction occurs and the previous, cumulative gene–environment interactions that an individual has encountered through life can influence the final biological and clinical outcome.³⁷

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^30–33), 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 (endotypes³⁴)—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.

In our view, the integration of a time axis in our understanding of disease pathogenesis could uncover novel opportunities for prevention or early treatment of COPD, and possibly also for prevention and treatment of the multimorbid conditions that frequently accompany COPD because they share similar risk factors and biological mechanisms.³⁸

Environmental and host factors

Tobacco smoking remains a key, preventable environmental risk factor for COPD,² and its impact on lung function can begin even before conception—eg, with exposures of future parents during adolescence.^28,39 The effects of maternal tobacco smoking during pregnancy have been well described, with an estimated lower birthweight of about 19 g for each cigarette smoked per day by the mother.³⁶ Stopping smoking during pregnancy at any time up to 30 weeks results in increased birthweight, although the greatest effects are seen when the mother quits before 16 weeks of pregnancy.⁴⁰ Needless to say, the effects on lung function of passive smoking, both during childhood and in adulthood, as well as those of active smoking, are well established.²

However, smoking exposure is not the only risk factor for low lung function through life, as discussed in the second paper in this Series.¹⁸ A cross-sectional study of 11 423 participants in the LEAD (Lung, hEart, sociAl, body) cohort, a general population study in Austria that enrolled individuals with an age range of 6–82 years,⁴¹ showed that conditions associated with low lung function are different at different ages, increase in number with time and, importantly, interact with each other,¹⁹ so that the network of interactions becomes much more complex with advancing age (figure 3). In the younger age bins, an asthma signature (including the presence of an asthma diagnosis, wheezing, and elevated eosinophils) emerges as a risk factor for low lung function, in combination with other early-life factors, such as prematurity, no breastfeeding, hospital admission in infancy due to respiratory problems, systemic inflammation, poor nutrition, low fruit and vegetable intake, alcohol consumption in early life, low physical activity, and heavy smoking exposure at an age at which the lungs are still growing (figure 3).¹⁹ Importantly, some of these factors are preventable. In adulthood, the association network includes respiratory symptoms, allergies, exposure to environmental pollutants other than smoking, low socioeconomic status, and multimorbidity. The increasing complexity (density) of the network with increasing age suggests that any preventive or therapeutic intervention might be more effective in younger individuals.³⁸ The relative individual effect of each of these factors is difficult to quantify, but their co-occurrence in the same individual significantly increases the risk of low lung function at any age.¹⁹ Importantly, reduced lung function might not always translate into clinical COPD.^3,19

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).¹⁹ The complexity of the network of interactions increases with advancing age. Modified from Breyer-Kohansal and colleagues,¹⁹ 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.

Prematurity as a host risk factor for COPD deserves specific discussion. Premature birth (birth at <37 weeks of gestation), especially if it occurs before 32 weeks of gestation, results in smaller airways, fewer alveoli, and an impaired vascular system.^42,43 As a result, children born prematurely do not often reach expected peak lung function in early adulthood and typically follow a low lung function trajectory that can potentially lead to a COPD-like phenotype later in life (figure 1A).^44–46 The term dysanapsis, originally described by Mead in 1980,⁴⁷ refers to the mismatch of airway tree calibre to lung size. Once believed to be a physiological curiosity,⁴⁸ it is now considered to be clinically relevant in several conditions, including prematurity,⁴⁹ obesity and asthma in children,⁵⁰ and smoking in older adults.⁵¹ Recent studies showed that adults with evidence of dysanapsis on CT had more severe airflow limitation⁵² and a differential spirometric response to a standardised dose of inhaled bronchodilator when compared with those with no dysanapsis.⁵³ Furthermore, microCT studies have shown that the number of patent terminal and transitional bronchioles is significantly reduced in adult patients with COPD.^54,55 However, whether this is due to smoking-induced injury⁵⁶ or abnormal lung development in early life (dysanapsis⁵²) is unclear, although these mechanisms can coexist.³ Nevertheless, given that the incidence of prematurity has increased, and mortality of babies born prematurely has decreased significantly over the past three decades,⁵⁷ a marked increase in adult survivors of premature birth with potential lung development limitations is expected in the near future.

Another potential host factor to consider is obesity. A recent mendelian randomisation analysis of the SAPALDIA (Swiss Cohort Study on Air Pollution and Lung Diseases in Adults) cohort,⁵⁸ a general population study in Switzerland that included 9651 participants aged 18–60 years at baseline (51% female) in whom lung function and body-mass index were measured three times over 20 years of follow-up, showed that gene scores linked to childhood body-mass index were associated with lung function later in life. This finding highlights the importance of a life-course perspective in studies of the causal role of body-mass index (or other environmental and host factors) in respiratory health.

Finally, it is important to highlight that many previous studies have shown that childhood asthma can (in some individuals) track impaired lung function into adulthood, so an early-life diagnosis of asthma is often considered to be an important host risk factor for COPD later in life.⁵⁹ For instance, early-life predictors of COPD in adulthood in the Tasmanian Longitudinal Health Study⁶⁰ included childhood asthma, bronchitis, pneumonia, allergic rhinitis, eczema, parental asthma, and maternal smoking. However, what is often missed is that asthma is a clinical diagnosis based on a pattern of respiratory symptoms such as wheezing, dyspnoea, chest tightness or cough, and variable expiratory airflow limitation.⁶¹ This same pattern of symptoms can occur in children who, for whatever reason, have impaired lung development and who, most likely, will be diagnosed with (and treated for) asthma. A careful interpretation of the evidence indicates that it is a diagnosis of asthma in childhood that tracks with poor lung function in adulthood (and perhaps with clinical COPD), and this diagnosis might correspond to the disease that we call asthma or, alternatively, to deficient lung development, the symptoms of which might clinically resemble asthma and lead to COPD later in life.⁶² Of note, a recent report from the ECRHS (European Community Respiratory Health Survey) indicated that the asthma–COPD overlap syndrome in adults aged 40–68 years seems to have its origins earlier in life than does COPD alone.⁶³

To advance understanding of the complex set of host risk factors and the exposome, future research will have to encompass the dynamic integration of multilevel basic, clinical, and epidemiological data through the lifespan of individuals in diverse populations. This approach might eventually allow the identification and validation of biomarkers representative of relevant endotypes as early as possible during the life course of an individual, facilitating prevention or early therapeutic intervention (figure 2 and panel 2).

Genetics and epigenetics

The genetic basis of COPD is reviewed in the first paper in this Series⁶ and will not be discussed in detail here. Many gene variants associated with risk of COPD, lung function (eg, FEV₁, forced vital capacity), and other pulmonary and non-pulmonary traits of the disease have been identified (thus helping to explain the clinical heterogeneity of the disease), although the largest contribution to the phenotype (panel 1) arises from the combined effect of many common variants of small effect size and not from rare variants with large effect sizes.⁶ Of particular relevance, large-scale genome-wide association studies have found a clear and consistent overlap between genes related to lung function in children and in adults,^64–66 and some of the genes associated with COPD are related to lung development.⁶ The use of polygenic risk scores supports findings of overlapping genetics for lung function traits in adults and children.⁶⁷

Conversely, environmental exposures might interact with the genome via epigenetic modifications, which primarily refer to chemical changes to the DNA molecule that affect gene expression but do not alter the genetic sequence.^26,68,69 DNA methylation, the addition of a methyl group to cytosines next to guanines (CpG sites), is the most widely studied epigenetic modification in humans, but other epigenetic mechanisms such as histone modifications (acetylation or methylation) and microRNA also modify gene expression. Epigenetic changes can be inherited across cell generations, and recent data indicate that these changes could be transmitted across human generations (ie, transgenerational changes).⁷⁰ Of note, even in the absence of disease, age-associated epigenetic changes can occur. In fact, chronological age is one of the strongest determinants of DNA methylation levels.⁷¹

Central to the concept of epigenetics is that gene expression activity is affected. If detrimental exposures and associated epigenetic changes occur early in life, perhaps starting in utero (eg, through maternal smoking), lifelong cellular trajectories could be affected, which might be followed by tissue and organ effects that have pathophysiological consequences. For instance, maternal smoking during pregnancy is associated with widespread changes in offspring DNA methylation across the genome at birth; importantly, these changes appear to persist at least until adolescence.⁷² Changes in the DNA methylome as a consequence of maternal smoking are associated with diseases such as childhood asthma, but differential methylation as a causal mechanism in the relationship between maternal smoking and asthma has not been proven.¹³ Similarly, epigenetic marks identified in fetal lung tissue might signal risk for COPD in later life.⁷³ Finally, the epigenetic effects of smoking relate to both the intensity and the duration of exposure and can have long-lasting effects, from years to decades, further emphasising the relevance of the time axis in this gene–environment interaction.¹¹

Similar to maternal smoking during pregnancy, gestational age has been associated with DNA methylation levels in thousands of genes in newborns, some of which might persist through childhood.⁷⁴ A similar effect on methylation profiles has been observed for birthweight.⁷⁵ During the fetal period, epigenetic processes are important for controlling development and growth.⁷⁶ The examples of the associations of maternal smoking during pregnancy and gestational age at birth with persistent epigenetic changes in newborns provide compelling evidence of early and long-lasting effects of certain environmental exposures and highlight the potential role of such early-life events and related mechanisms in development, growth, and health and disease in later life.

Extending the notion that trajectories of the epigenome can be set in utero, network-based approaches have been applied to capture the complexity of gene–environment interactions on a genome-wide scale. For example, a co-methylation analysis revealed network features that were highly preserved between fetal and adult lung data associated with in-utero smoke exposure, COPD, and reduced adult lung function, with enrichment in developmental and inflammatory pathways, including Hippo, Wnt, transforming growth factor-β, and PI3K/AKT (phosphatidylinositol 3-kinase/protein kinase B) pathways.⁷³ A recent study has shown that lung methylation profiles are more closely related to smoking in patients with mild-to-moderate COPD than in patients with severe disease.⁷⁷ In this study, however, patients with severe disease were significantly younger, suggesting that different epigenetic patterns are associated with different lung function trajectories, some of which can lead to severe COPD at younger ages.⁷⁷

Finally, investigation of DNA methylation in lung tissue has shown differences between men and women.⁷⁸ A recent study showed that sex-specific methylation patterns were associated with trajectories of lung function measured at ages 10, 18, and 26 years, and that pathways enriched for methylation were associated with poorer lung function trajectories.⁷⁹ Sex-specific differences in COPD symptoms later in life might therefore begin in early life with methylation patterns that track with specific lung function trajectories. Indeed, sex-specific associations with COPD have been observed with different omics levels, including genetics, epigenetics, transcriptomics, metabolomics, and proteomics,⁸⁰ with sex and gender emerging as important endogenous (eg, by way of hormones or sex chromosomes) and exogenous (eg, through smoking behaviours or occupational roles) determinants of the final outcomes of similar gene–environment interactions over the life course, with varying and complex effects on health and disease.

Future research will need to integrate the findings of a range of clinical and basic omics analyses to unravel the many gene–environment interactions, including a range of genetic and epigenetic factors, that contribute to different lung function trajectories and, eventually, to the development of COPD (panel 2). A better understanding of these factors could also help to identify those at risk of COPD.

The time axis

Normal lung development starts in utero during the embryonic phase (gestational weeks 4–7) with lung bud differentiation and trachea and bronchi formation.⁸¹ It continues throughout the fetal and postnatal periods, with lung growth and cellular proliferation occurring until maximally attained lung function peaks at around age 20–25 years for men (earlier in women).^82,83 This developmental phase is followed by a relatively short plateau phase of about a decade, with mild lung function decline thereafter (about 20 mL/year for women, more in men⁸²) due to physiological lung ageing²² (figure 1A).

Any insult to the airways or lung parenchyma during the very long (>20 years) lung developmental period could have long-term consequences for health and disease,²⁷ a fact now recognised in the concept of the developmental origins of health and disease (DOHaD).^84,85 Indeed, as we have discussed, several asthma, COPD, and lung function genes identified in studies of adults also seem to have relevant functional roles in fetal developmental processes.^6,86 Conversely, adult lung diseases might be the consequence of an acceleration of physiological age-associated changes.⁵⁶ Ageing-related DNA methylation pathways have also been linked to pathways of lung development;⁸⁷ therefore, biological age, starting in early life, might reflect linear and non-linear cumulative environmental pressures on the epigenome that could increase the risk of COPD later in life. Given that lung ageing seems to recapitulate key developmental epigenetic signatures of transcription factors, and that differential methylation of transcription factors has been associated with COPD,⁸⁸ close inspection of the molecular endotypes (ie, the biological mechanisms that underlie particular phenotypes) that might emerge from investigations of gene–environment interactions over time could reveal novel opportunities for the early prevention of COPD through targeting of gene regulation, among other potential molecular strategies.

As discussed, there is a range of lung function trajectories through life,^3,22 both above and below the normal trajectory (figure 1A). Lung function trajectories below the expected normal range can be due to suboptimal growth and development in childhood and adolescence, leading to reduced peak lung function, or to shortened plateau or accelerated lung function decline caused by accelerated lung ageing or other factors that damage the lungs,⁸⁹ and are known to be associated with increased risk of developing COPD in adulthood.^21,60,90 Results from the population-based CARDIA (Coronary Artery Risk Development in Young Adults) study showed that both lower peak and accelerated decline in FEV₁ are risk factors for future emphysema identified on CT scans, independent of smoking status.⁹¹ Moreover, low peak lung function in early adulthood is associated with increased prevalence and earlier incidence of cardiovascular and metabolic conditions, as well as premature death,⁹² suggesting that abnormal lung development could be associated with altered development of other organ systems. If this is the case, reduced lung function in infancy, adolescence, or early adulthood could be a marker of a more systemic deficit.^3,22 Conversely, recent research has shown that supranormal trajectories (figure 1A) are associated with healthier ageing⁹³ and reduced risk of developing COPD.⁹⁴ Therefore, a lifespan approach to understanding the gene–environment interactions that underlie these different lung function trajectories is of paramount importance to prevent and treat the disease-associated conditions as early and efficiently as possible, and to promote healthier ageing (figure 1B).³⁸

Finally, like any organ, the immune system develops, matures, and ages.⁹⁵ As a result, there are marked differences in immune function and immune phenotypes between children and adults.⁹⁵ Therefore, the immune responses elicited by gene–environment interactions might differ depending on the age (time) at which they occur.^25,96 Studies of the asthma locus on chromosome 17q12–21 have taught us important lessons about the effects of gene–environment interactions over time, because single-nucleotide polymorphisms at this major locus have been repeatedly associated with childhood asthma.⁹⁷ Data from seven US birth cohorts (n=3786) suggest that the association with this locus primarily reflects early-life susceptibility to respiratory viruses,⁹⁸ which might have long-term consequences for lung health.⁹⁹ Functional interactions between one of the key 17q12–21 genes, GSDMB (gasdermin B), and COPD-associated genes such as IL27 (interleukin 27) and HHIP (hedgehog-interacting protein) have now been identified, thus linking this asthma locus to COPD.¹⁰⁰

In experimental models, neutrophilic inflammation during development disrupts the extracellular matrix and predisposes adult mice to a COPD-like phenotype,¹⁰¹ whereas aged T cells in young mice (with mitochondrial dysfunction) induce premature ageing and multimorbidity.¹⁰² Furthermore, infection with γ-herpesvirus in aged mice causes a more intense inflammatory response than that elicited in young animals, suggesting that the same environmental insult can have different consequences depending on the age at which it occurs.³⁷ Finally, cell senescence, a hallmark of cellular ageing,¹⁰³ also has a key role in organ development and disease, in which senescence cells accumulate and secrete a specific set of immune mediators collectively known as the senescence-associated secretory phenotype.¹⁰⁴ Thus, an excessively aged immune system for a given chronological age might also increase the risk of COPD.

In summary, future research will have to address the role of the time axis by combining longitudinal studies with different omic platforms to better understand the determinants of lung health in adulthood, lung ageing, and COPD (panel 2).

Clinical implications of integrating the time axis

Understanding the pathogenesis of COPD in terms of the occurrence and accumulation of gene–environment interactions along the time axis has implications for disease prevention and management. We propose that COPD should no longer be viewed as a single disease caused almost exclusively by tobacco smoking, but as a complex clinical syndrome (panel 1).¹⁰⁵ A syndrome is a recognisable pattern of symptoms and clinical signs that can be caused by different mechanisms (eg, febrile syndrome).^106,107 According to this definition, we propose that COPD should be considered as a syndrome characterised by a recognisable pattern of chronic symptoms (one or more of dyspnoea, cough, or expectoration) and structural (one or more of bronchitis, bronchiolitis, or emphysema) or functional (airflow limitation or abnormal gas exchange) impairments, or both, due to mechanisms (endotypes³⁴) that are the end result of different and cumulative gene–environment interactions through the lifetime of the individual. Currently, the presence of non-fully reversible airflow limitation is a mandatory requirement for the diagnosis of COPD,¹⁰⁸ but there is increasing recognition of the existence of a pre-COPD stage, with symptoms, structural abnormalities, and functional abnormalities other than airflow limitation,¹⁰⁹ although its natural history and response to treatment is still unclear.¹¹⁰

Regarding the prevention of COPD, there is no question that avoiding smoke or inhaled exposures at all times (in utero and during infancy through to adolescence and adulthood) and favouring early quitting of active smoking⁸² are key preventive measures that must continue to be fully endorsed. However, the study of gene–environment interactions in the context of a lifetime perspective opens opportunities for the prevention of COPD that go well beyond smoking (figure 1B).²⁷ For instance, in a chronically undernourished population in Nepal, maternal supplementation of diet with vitamin A before, during, and after pregnancy improved lung function in the offspring in preadolescent years.¹¹¹ Similarly, supplemental vitamin C taken by pregnant American smokers improved newborn lung function and decreased wheezing in the offspring up to the age of 1 year.¹¹² Whether other preventive strategies can contribute to improved lung health in other populations requires further research.¹¹³ There is a knowledge gap regarding lung function during the transition from infancy to adulthood, sometimes referred to as a black box; however, this phase might hold promise for prevention and early intervention.¹¹⁴ For instance, studies have shown that the presence of chronic bronchitis in children¹¹⁵ and young adults¹¹⁶ is predictive of the development of airflow limitation later in life. In this context, spirometry should be viewed as a test with global application that, if used routinely for screening in young populations (eg, during school, university, army, driving licence testing), might help to identify individuals at risk of respiratory and non-respiratory multimorbidity and premature death who might benefit from intervention.^38,92,117

Finally, regarding disease management, an understanding of lung function impairment and the development of COPD in terms of gene–environment interactions along the time axis requires translation of the concept of lifelong lung function (the trajectome) to clinical practice. Unfortunately, lung function is not currently measured routinely in infancy, adolescence, or early adulthood, so identification in the clinic of the lung function trajectory that an individual patient has followed so far (or will follow in the future) would be challenging. This strategy would clearly require that the attending physician specifically questions the patient on potentially relevant early-life factors through careful clinical history taking (eg, prematurity, maternal smoking, allergies and respiratory infections in infancy). Validation of biomarkers associated with different lung function trajectories is also needed to aid early detection of those at risk of disease and to ascertain which lung function trajectory a given patient might have followed before being seen in the clinic, because it is likely that different lung function trajectories are associated with different prognosis and might require different treatment strategies. Several examples of such biomarkers already exist. First, serum concentrations of the pneumoprotein CC16 (club cell secretory protein) appear to be consistently associated with low lung function in different age groups,¹¹⁸ have been associated with accelerated lung function decline,^119,120 and are influenced by smoking.^121–123 CC16 concentrations are also related to persistence of asthma and reduced lung function, both in childhood and adulthood.¹¹⁸ Furthermore, several genetic variants have been associated with serum CC16 concentrations.¹²⁴ Second, CHI3L1 (chitinase-3-like protein 1, also known as YKL-40) is a secreted human glycoprotein involved in airway remodelling that has been associated with early-life inflammation in children with a history of prematurity, and is now considered to be a biomarker of altered lung development.¹²⁵ Third, plasma concentrations of the microRNA miR-145-5p in children with asthma appear to be associated with abnormal lung growth leading to COPD.¹²⁶ Fourth, eosinophil concentrations seem to be associated with the development of persistent airflow limitation in children with a diagnosis of asthma.¹²⁷ More recent research has confirmed that higher circulating eosinophil concentrations were associated with increased risk of developing COPD,¹²⁸ and that FEV₁ decline was faster in patients with mild-to-moderate COPD and higher blood eosinophil counts.¹²⁹ Finally, some biomarkers of ageing, such as telomere shortening, have also been associated with structural remodelling of the lung in children with bronchopulmonary dysplasia, and in young patients with COPD,¹³⁰ supporting another potential link between altered lung development and ageing.¹²⁵ Therefore, current and future therapeutic interventions need to be tested in younger individuals,¹¹⁰ in whom they might be more effective because of the reduced complexity of the network of interacting risk factors (figure 3).^38,131 For instance, treatment with tiotropium in the UPLIFT (Understanding Potential Long-Term Impacts on Function with Tiotropium) study reduced the decline of lung function in young (≤50 years)¹³² but not in older patients with COPD.¹³³

If the GETomics approach proposed here, in which COPD is viewed as a syndrome attributable to one or several mechanisms (endotypes), is adopted in research and applied in practice, it could allow us to develop novel preventive strategies and to consider much earlier intervention (even in the absence of poorly reversible airflow limitation, the current functional defining characteristic of COPD) to promote respiratory health. Moving upstream to younger individuals holds the promise of more effective prevention and treatment of COPD; a recent publication presents the rationale for this approach and discusses the design of appropriate clinical trials in young patients with COPD and those with pre-COPD.¹¹⁰

Conclusions and future directions

Life starts as a tabula rasa that begins to be written from conception (and even before^39,134). As discussed here, integration of the time axis in our understanding of the origins of COPD—with improved understanding of the range of contributing gene–environment interactions and their biological effects—has the potential to enable more effective prevention and management of COPD and, potentially, other chronic non-communicable diseases of the elderly that often share genetic and environmental risk factors with COPD.¹¹⁴ Furthermore, taking a gene–environment–time approach to COPD research could provide relevant information not only on the determinants of lung function, but also on the timeframe for COPD development and timing of diagnosis, severity of disease, and heterogeneity of clinical presentation, including exacerbations, chronic bronchitis versus emphysema, systemic manifestations, associated multimorbidity, and response to therapy.

To achieve this potential, however, we need to improve understanding of the paradigm proposed here. Key questions for future research are presented in panel 2. The ideal study would involve a birth cohort that is followed prospectively until death with repeated and detailed pheno-endotypic characterisation (including biomarker measurement), ample biological profiling assessment, and comprehensive monitoring of the exposome (perhaps through the blood exposome, which includes all biologically active chemicals¹³⁵), as well as tissue probes, if possible. As this ideal study is not currently a realistic option, a potential alternative is to use different cohorts that span different age groups to understand the biological mechanisms and clinical effects of the range of lung function trajectories and their gene–environment determinants over the lifespan. This is precisely the goal of the CADSET (Chronic Airway DiSeases Early sTratification) network, a European Respiratory Society Clinical Research Collaboration.¹³⁶ In this setting, investigations of the gene–environment interactions that underpin the range of lung function trajectories over time will have to consider not only the specific nature of a given gene–environment interaction but also the following: first, the phase of the lung function trajectory in which the specific gene–environment interaction occurs (development vs ageing); second, the specific lung function trajectory that the individual was following when the gene–environment interaction took place; third, the duration, intensity, and repetitiveness of the exposure; and fourth, the previous, cumulative exposome history of the individual (figure 2). Comprehensive analytical, non-reductionistic, and agnostic approaches (network analysis, systems biology) that integrate different omics levels and environmental and clinical data will be required to provide a more integrated view of gene–environment interactions over time (a GETomics approach) by showing both their relationships with the outcomes of interest and between the components of the network.^30,137–140

Finally, we also need to deploy this gene–environment–time perspective in daily clinical practice when considering the potential need for preventive or therapeutic interventions. This would require a considerable educational effort directed to health-care professionals. For instance, asking about early-life events and exposures should become routine in clinics for adults with chronic airway diseases, as well as considering the role of potential biomarkers. It is time to implement a looking-back-to-look-forward concept in addressing COPD in particular and, importantly, respiratory and human health in general.

Key messages.

• COPD has traditionally been understood as a self-inflicted disease caused by tobacco smoking in genetically susceptible individuals, but recent research has challenged this view; many genetic variants and a range of different environmental exposures and host factors are now known to be associated with risk for COPD over the lifespan

• COPD might have its origins in early life, with impaired lung development (low peak lung function) or lung ageing (accelerated lung function decline) affecting an individual’s lung function trajectory and risk of developing COPD; improved understanding of gene–environment interactions over the lifespan in the pathogenesis of COPD is needed

• To understand the origins and pathobiology of COPD, the time axis needs to be considered because the age of an individual at which a given gene–environment interaction occurs, as well as the cumulative history of previous gene–environment interactions, might affect the biological response(s) and clinical consequence(s) of a given exposure

• Comprehensive investigation of the interactions between genes (G) and the environment (E) over time (T) is needed to attain a complete picture of COPD pathogenesis; to achieve this aim, we propose an approach that integrates information from basic omics and clinical omics approaches with exposures (the exposome) over the lifespan—a strategy that we refer to as GETomics

• Rather than being viewed as a single disease caused mainly by tobacco smoking, we argue that COPD might instead be regarded as a clinical syndrome that is characterised by a recognisable pattern of chronic respiratory symptoms and structural lung abnormalities or functional lung impairments, or both, and due to one or more mechanisms (endotypes) that reflect different and cumulative gene–environment interactions over the life course

• A GETomics approach—leading to improved understanding of underlying pathogenic mechanisms over the lifespan—could enable identification of novel targets for the prevention and early treatment of COPD

COPD=chronic obstructive pulmonary disease.

Panel 1: Key terms and definitions.

The following terms and definitions are used in this Series paper to describe and propose approaches to understanding the nature and origins of chronic obstructive pulmonary disease.

Basic omics

The range of methods that can be used to comprehensively characterise and quantify pools of biological molecules and potentially identify molecular mechanisms of disease (endotypes); examples include genomics, transcriptomics, epigenomics, proteomics, lipidomics, and metabolomics

Chronic obstructive pulmonary disease (COPD)

A clinical syndrome characterised by a recognisable pattern of chronic symptoms and structural or functional abnormalities (including, but not limited to, airflow limitation), or both, and attributable to one or several mechanisms (endotypes) that reflect different, cumulative gene–environment interactions occurring through the lifetime of the individual

Clinical omics

The range of methods that can be used to comprehensively characterise and quantify the clinical features of health and disease; examples include phenomics (study of the phenome, encompassing the phenotypes of cells, tissues, organs, and organisms), physiomics (study of the physiological function of cells, tissues, organs, and organisms), radiomics (study of the range of qualitative and quantitative features on medical imaging), and microbiomics (study of the microbiome of organs and systems, including the bacteriome, mycobiome, and virome)

Exposome

The full set of (qualitative and quantitative) environmental exposures of an individual in their lifetime

GETomics

A holistic strategy that considers all gene–environment interactions that an individual might encounter through the lifespan (the time axis, T) to better understand the roles of genes (G) and environmental exposures (E) in disease pathogenesis; the approach aims to integrate basic omics, clinical omics, and exposures (the exposome) over time, accounting for both the age of the individual at which a given gene–environment interaction occurs and the previous, cumulative gene–environment interactions that the individual might have encountered through life

Clinical phenotype

A single disease attribute or combination of attributes that describe differences between patients as they relate to clinically meaningful outcomes (eg, symptoms, exacerbations, response to therapy, rate of disease progression, or death)

Trajectome

The range of possible lung function trajectories across the lifespan; lung function trajectories below the expected normal range—due to suboptimal growth and development in childhood and adolescence, or early or accelerated lung function decline in adulthood—can be associated with an increased risk of COPD

Panel 2: Questions for future research.

• How do genetic, clinical, epidemiological, and biological factors associated with lung function trajectories and airflow limitation differ across the lifespan?

• How do responses to the same environmental exposure differ across the lifespan, and what can omics analyses teach us about detrimental responses after exposure?

• Are differences in the response to specific environmental factors at a particular timepoint determined by regenerative capacity or ageing effects on different pathways, or by the history of previous exposures?

• Which early-life exposures result in a lower threshold for developing lung disease, including COPD, later in life?

• Which biological and clinical thresholds determine health and disease outcomes in different age groups, and how are these thresholds affected by exposures at different ages and by the accumulation of gene–environment interactions over time?

• How can these thresholds be measured in a clinically meaningfully way (eg, through assessments of lung function, breathomics, blood biomarkers)?

• How might a GETomics understanding of the origins of disease be harnessed to prevent or manage COPD?

COPD=chronic obstructive pulmonary disease.

Search strategy and selection criteria.

This narrative review is based mainly on the knowledge and judgement of the authors, supported by selected references from a PubMed search for papers published from database inception to Oct 27, 2021, using three sets of search terms: (“gene–environment” OR “early life”) AND (“lung function” OR “FEV1” OR “FVC” OR “COPD”); “lifelong” AND “exposure” AND “COPD”; and “genetic” AND (“environmental” OR “environment”) AND “exposure” AND “COPD”. For the three searches, we retrieved 188, 22, and 153 studies, respectively, reporting gene–environment interactions occurring in early life with different lung function indices both in childhood and early adulthood and evidence of risk factors associated with lung function trajectories. Key papers from the authors’ own files and online searches were also considered. Papers were selected, without language restrictions, on the basis of relevance to our review and proposal.