Longitudinal, age-related, multi-stage dynamics in different clinical scenarios

Fig 1 presents the graphic EASI representation of a theoretical (but frequent) case of a susceptible, continuous long-life smoker who develops COPD in adult life (Table 1 presents the 15 specific input parameters used to compute this particular scenario). Of note, this theoretical individual: (1) starts to smoke at the age of 15 yrs., achieves a maximal exposure of 1.5 pack-yr. within 5 yrs., and never quit or modify the daily dose of smoking; (Fig 1, top left panel); (2) has normal lung development, as shown by an FEV₁ at the age of 20 years of 100% ref. [16] (Fig 1, heat map in bottom central panel); and, (3) begins to perceive dyspnea from a value of FEV₁ <80% ref (Fig 1, bottom right panel). The results of the relationships between the E, A, S and I modules are displayed in two age-related panels (Fig 1, central panels) that summarize the “natural history” of COPD in this particular individual. The upper central panel shows that the onset and persistence of smoking (blue line) induced a rapid and sustained inflammatory activity (red line) that is associated with a progressive loss of FEV₁ (cyan line, right Y axis), starting at the age of 30 years approximately, and reaching a FEV₁ 45% of reference at the age of 80 (see heat-map in bottom central panel), with a progressive perception of symptoms (green line) from the fifth or sixth decade of age (reaching 68% of maximal impact I at 80 years of age).

Fig 2 compares this theoretical scenario (Panel 1) with 5 other potential “natural histories” (Panels 2–6). Table 1 details the specific parameter values used to simulate each of them. Panel 2 assumes that the same individual shown in Panel 1 now quits smoking at the age of 45 yrs. (see S4 Fig for details). Note that, now, the blue (E) and red (A) lines fall to zero as a consequence of smoking cessation and inflammation remission, whereas the S (cyan line; FEV₁) and I (green line) slopes become less steep. Panel 3, presents a smoker quitting smoking later in life (at 65 yrs. (see S5 Fig for details) and shows that, in keeping with previously published data [2], improvements of S and I are less marked after quitting at older age (compare Panels 2 and 3). Panel 4 is identical to Panel 2 (see Table 1 and S6 Fig for details), except that, as suggested in the literature [17], EASI now assumes that despite quitting smoking, inflammatory activation persists at 40% of the maximal value (note that the red line now plateaus, and S and I continue to deteriorate). Panel 5 (see S7 Fig for details) is similar to Panel 1, except that now the model assumes that lung development was impaired in early life, so maximal lung function at 20 years of age was 75% of maximal normal function (see % FEV₁ values in the corresponding heat map). Finally, Panel 6 (see S8 Fig for details) is also similar to Panel 1, but now the patient perceives dyspnea poorly, as illustrated by the comparison of the green line (I) in Panels 6 vs.1.

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Fig 2. Comparison of six different, potentially relevant clinical scenarios.

Panel 1, Susceptible continuous smoker (same as Fig 1); Panel 2, Susceptible quitter at 45 years of age; Panel 3, Susceptible quitter at 65 years of age; Panel 4, Persistent inflammation after quitting; Panel 5, Abnormal lung development; and, Panel 6, Poor perceiver. In each of these five different scenarios, top panels present a longitudinal summary of age-related trajectories of Exposure (blue line), Activity (red line), Severity (FEV1, cyan line) and perceived impact of the disease by the patient (green line), whereas bottom panels show the corresponding heat-maps by decade. Table 1 details the specific parameter values to each module used to generate each of these five scenarios, and Fig 1 and S4–S7 Figs displayed each of them graphically in detail. For further explanations, see text.

https://doi.org/10.1371/journal.pone.0185502.g002

Cross-sectional patterns of trajectories

In clinical practice, patients generally consult when they perceive symptoms in late adulthood. At that time, life-long longitudinal information is lacking almost invariably, so practicing physicians need to integrate available cross-sectional information (basically E, S and I, since A is rarely, if ever, measured). It was therefore of interest to compare the cross-sectional pattern (by decade of age) of the six different scenarios described in Fig 2. Their visual comparison (see heat-maps in Fig 2) illustrates that the pattern of E, A, S and I relationships varies greatly within each clinical scenario at different ages (e.g. 30 vs. 60 yrs.), as well as between clinical scenarios at the same age (e.g., 60 years, when most COPD patients are diagnosed in the clinic).

Finally, we modeled (see Table 2 for parameter values) four different hypothetical patients consulting at the age of 50 years with a similar level of symptoms (Fig 3, arrow) but remarkably different individual life-time E, A, S and I trajectories. Fig 3 includes a non-smoker individual (Patient 4) with low lung function at early age (Table 2) who, according to S only would be likely to be diagnosed of COPD later in life [6].

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Fig 3. Age-related Exposure, Activity, Severity and Impact trajectories of four hypothetical patients.

Arrow (bottom right panel) highlights that all four of them had the same I at 50 years of age (when they consult with the physician). Yet, their E, A, and S trajectories are quite different. Note that one patient (#4) is a never-smoker individual with low lung function at early age, who may be diagnosed of COPD later in life (60 years). For further explanations, see text.

https://doi.org/10.1371/journal.pone.0185502.g003

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Table 2. Parameter values used to generate the EASI relationships presented in the four different hypothetical patients shown in Fig 3.

https://doi.org/10.1371/journal.pone.0185502.t002

Variability of COPD trajectories (i.e., different “natural histories”)

EASI displays the variability and sensitivity to changes of different individual COPD trajectories by showing that the variation of a single parameter (Fig 2) can change them markedly. Needless to say that this is a direct consequence of the way EASI was built, having one module feeding the necessary information to the next one (feed-forward). This kind of relationship is likely to occur also in real life, but the situation in vivo is much more complex and includes the possibility of feed-back, redundancy and a range of potential adaptations [12] that are not considered in the current version of EASI. For example, patients can adapt their daily life activities to minimize symptoms (i.e., I) when airflow limitation (i.e., S) is severe. Likewise, rehabilitation can have beneficial effects on I and, perhaps on A, without changing S. EASI can be scaled in the future to accommodate all these potential modifiers and others, such as exacerbations of the disease [18]. In this context, it is noteworthy that the single change with the greatest effect on lung function later in life was poor lung function in early adulthood (Fig 2), in line with several recent observations [5, 6, 19].

Likewise, EASI also shows that the relationships between E, A, S and I not only vary markedly longitudinally (different scenarios through life) but also cross-sectionally (same scenario across different ages) (Fig 2). In fact, four different hypothetical patients can present in this “virtual clinic” with the same I (i.e., symptoms) after having had very different longitudinal trajectories (Fig 3). In essence, this highlights the need of longitudinal studies in well characterized cohorts of patients to understand in vivo these different trajectories and, in turn, confirm (or refute) the initial and simple EASI model presented here.

Disease progression

COPD progression is often assessed by the rate of FEV₁ decline [1] but recent evidence has demonstrated that only a proportion of patients have enhanced FEV₁ decline [5, 6]. The EASI model goes further and indicates that (Fig 2) disease progression in COPD can either indicate that the disease continues to be biologically “active” (A), with or without continuous exposure (E), that it has become more “severe” (S) and/or that it has a greater “impact” on the patient (I). These different disease components require careful and independent consideration when assessing “disease progression” in COPD.

Susceptibility to COPD

It is well established that not all smokers develop COPD [1, 2], probably because different genetic backgrounds [20]. Our analysis suggests that this assumption is likely to be much more complex since differences in “susceptibility” may occur for each of the relevant disease stages. For instance, the relationship between E (i.e. smoking) and S (i.e. FEV₁) is modulated by A, so it is possible that some individuals may be highly susceptible in terms of mounting an inflammatory response that does not translate into clinical disease because their repair mechanisms prevent lung function deterioration. Such individuals would have been traditionally considered a “resistant” smoker despite being highly “susceptible” to the biological effects (A) of smoking. This will have to be considered in future studies of COPD “susceptibility”.

Symptom perception

The clinical impact of any disease, including COPD, depends on how physiologic perturbations are perceived by the individual, and how much these perturbations modify the activities of daily living [21, 22]. In COPD, it has been generally assumed that mild disease (as assessed by the FEV₁ value) has a minor impact on the patient, whereas the impact is much greater in severe disease [23]. Yet, recent evidence has revealed a more complex picture, since the relationship between the severity of airflow limitation and the level of symptoms reported is poor, and individual variability is enormous [10, 24, 25]. All in all, these observations suggest that there may be poor symptom perceivers among patients with COPD [11], as it is well described in asthma [26, 27]. In fact, EASI predicts that different patients with a similar level of symptoms (I) at a given age might have had and will continue to have very different E, A, S and I trajectories (Fig 3).

Limitations and opportunities

Currently, EASI has several important limitations: (1) it is an oversimplification of the natural history of COPD and, for the most part, it lacks experimental validation; (2) it includes only four modules and accounts deterministically for their sequential relationships. Each of these modules is susceptible to accommodate other potentially relevant parameters, such as other environmental factors (e.g. occupational exposures or diet), and future EASI iterations could also include other COPD characteristics that can influence the natural history of COPD too, such as the frequency and severity of COPD exacerbations and/or the occurrence of comorbidities; (3) the sequential relationships between modules in EASI do not include effect modifiers of module-to-module relationships, such as genetic variants or epigenetic modifications, whose interactions with exposures are likely to influence the individual trajectories of COPD. As a disclaimer, though, the precise genes involved in the pathogenesis of COPD at different time points are unclear, so this first version of EASI considers the genetic background of the individual as a whole. EASI, however, does indeed consider the possibility of modifying lung function achieved at early adulthood [6, 19]; (4) EASI modeled A pragmatically, without detailing any specific innate and/or acquired mechanism involved [17], because the dynamic interactions of these mechanisms are mostly unknown [28–30]; (5) S was modeled on the basis of FEV₁ only, despite that it is now well established that COPD can associate multiple pulmonary and extra-pulmonary co-morbidities that can contribute to S independently [31]; and, (6) the sequential relationships between modules could also be influenced by the effects of drugs used to treat COPD, such as those of inhaled bronchodilators (potentially modifying S) or corticosteroids (potentially modifying A), so future evolutions of the model can include them. No doubt, therefore, EASI requires experimental validation and refinement through an iterative research strategy that combines experimental and modeling data.

Yet, EASI also opens some opportunities: (1) it is a first step forward towards the computational modeling (hence better understanding [12, 32, 33]) of the variable “natural histories” that can occur in different COPD patients; and, (2) by doing so, it can open novel approaches for research, teaching and, eventually, practice (precision medicine [34]) of COPD.

Conclusion

EASI is a conceptual, individualized, multi-stage, first-pass, computational model that allows the investigation of the individual relationships between E, A, S and I and, as a result, a better understanding of the complexity and heterogeneity of COPD. The current EASI model cannot be directly applied to clinical practice because it requires validation and further refinement, but it points toward a potential path towards COPD precision medicine.