PhD Defence Emanuel Citgez | In search of markers associated with morbidity and mortality in COPD

In search of markers associated with morbidity and mortality in COPD

Emanuel Citgez is a PhD student in the department of Cognition, Data and Education. (Co)Promotors are prof.dr. J.A.M. van der Palen and dr. M.G.J. Brusse-Keizer from the faculty of Behavioural, Management and Social Sciences, University of Twente.

In this thesis, we searched for markers associated with morbidity and mortality in a large prospective single-centre chronic obstructive pulmonary disease (COPD) cohort study from Enschede, the Netherlands: the COMIC study. A total of 795 patients were included with a follow-up period of at least three years. Morbidity was defined as hospitalized (severe) Acute Exacerbations of COPD (AECOPD) and Community Acquired Pneumonia (CAP). Mortality was defined as all-cause mortality.

Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality of patients with COPD. Besides their proven effect on reducing cardiovascular disease, statins have other pleiotropic anti-inflammatory effects as well. Therefore, statins could be a potential adjunct therapy in COPD patients. In chapter 2, the use of statins, defined as having a statin for at least 90 consecutive days after inclusion, was not associated with time till first severe AECOPD and CAP in COPD patients in the COMIC study. Statin use resulted in a better overall survival (corrected hazard ratio 0.70 (95% CI, 0.51 - 0.96) in multivariate analysis), but in the sensitivity analysis, excluding immortal time bias, this association disappeared. This means that there is no role for standardized statin prescription for all our COPD patients, other than the recommendations from national and international guidelines for cardiovascular risk management.

Chapter 3-6 are about the blood biomarkers MR-proADM, Fibrinogen and the eosinophil count. Underlying chronic and systemic inflammation play an important role in the pathophysiology of COPD, its progression and its associated comorbidity. In chapter 3 we evaluated for the first time the association of stable state MR-proADM with subsequent severe AECOPD and CAP in a pooled analysis in 1285 COPD patients of two large COPD cohort studies, the COMIC study and the PROMISE-COPD study. Our study showed that 25% of the patients had a stable state MR-proADM above the frequently used cut-off level of 0.87nmol/l and that this was associated with a 30% higher risk for subsequent severe AECOPD in a population in which 34% of the patients had at least one severe AECOPD during 3 years of follow up. This was independent of other known factors associated with AECOPD, such as exacerbation history, lung function and comorbid status. For time till first CAP, only COMIC data (n=795) was available. Patients with high level stable state MR-proADM had a significantly higher risk for a CAP compared with COPD patients with low level MR-proADM in univariate analysis (HR 1.93; 95% CI, 1.24 - 3.01) but, after correction for age, lung function and previous AECOPD, the association was no longer significant (cHR 1.10; 95% CI, 0.68 - 1.80).

In chapter 4, we focused on mortality prediction in COPD and found that both stable-state MR-proADM and stable-state Fibrinogen, two validated and sensitive biomarkers, were significantly associated with mortality. When both fibrinogen and MR-proADM were included together in the survival model, a doubling in fibrinogen and MR-proADM levels gave a 2.2 (95% CI 1.3–3.7) and 2.1 (95% CI 1.5–3.0) fold increased risk of dying, respectively. Although the two biomarkers seemed correlated to each other, the actual correlation was only very weak (r=0.2; p=<0,001). This strongly suggests that both biomarkers reflect different biological processes and as such they encompass a different part of the underlying (systemic) inflammation in COPD. Despite their weak correlation, combining both biomarkers did not substantially improve predictive performance when assessed using AUC or c-statistic. When adding MR-proADM to a one-year prediction model already including Fibrinogen, this resulted in a significant increase of the predictive capacity of the model (AUC increased from 0.78 to 0.83). In contrast, adding Fibrinogen to the one-year mortality prediction model already including MR-proADM, this did not result in a significant increase of the predictive capacity of the model (AUC increased from 0.82 to 0.83). These findings suggest that MR-proADM is a better predictor for mortality than Fibrinogen.

A relevant subgroup (20-40%) of the COPD population has an eosinophilic phenotype, probably linked to underlying type 2 inflammation, similar to that observed in patients with asthma. It is suggested that this eosinophil inflammation may be associated with future AECOPD and CAP risk.

However less is known about its prognostic accuracy to predict future morbidity and mortality when measured at AECOPD. In chapter 5 we studied the association of the blood eosinophil count measured at severe AECOPD with subsequent morbidity and mortality. We analysed this for different frequently used cut-off levels and for eosinophil count as a continuous variable. Our multivariate analyses and sensitivity analyses (excluding patients with recent systemic steroid use), revealed that none of the four eosinophil classifications nor eosinophil count as continuous variable were associated with time till all-cause mortality, next severe AECOPD or next CAP. Despite the high rates of morbidity and mortality observed in COPD patients following a severe AECOPD—where nearly 25% developed at least one subsequent episode of CAP, almost 50% experienced another severe AECOPD, and 43% died within three years—the blood eosinophil count measured during the initial exacerbation did not prove to be a reliable prognostic marker for these outcomes. This highlights that, although eosinophilic inflammation may play a role in the underlying disease mechanisms and can inform treatment decisions, eosinophil levels measured during severe AECOPD lack predictive value for future disease burden or mortality.

Despite this, elevated blood eosinophil count during an AECOPD remain clinically relevant as they are associated with steroid responsiveness. Due to this association it has been shown to be a promising biomarker for establishing personalised treatment strategies to reduce corticosteroid use, either inhaled or systemic, in COPD. Eosinophil levels seem relatively stable over time in stable state, but little is known whether this is also true in subsequent severe AECOPD. In chapter 6 we studied the stability of eosinophil categorisation across 2 subsequent severe AECOPDs for different frequently used cut-off levels. Depending upon the used cut-off and expression of the eosinophil, the overall stability in eosinophil categorisation varied between 70%-85% during two subsequent AECOPDs. From patients who were eosinophilic at the first AECOPD only 34%-45% remained eosinophilic at the subsequent AECOPD while 9%-21% of patients being non-eosinophilic at the first AECOPD became eosinophilic at the subsequent AECOPD. There was only a weak correlation between the blood eosinophil counts measured at 2 subsequent severe AECOPDs (r=0.19, p=0.003 for the absolute counts, r=0.22, p=0.001 for the relative counts). Recent steroid use did not influence the eosinophil categorisation stability. Although it did lower the eosinophil numbers, it did not normalize them (10-22% were still labelled as eosinophilic depending on cut-off used). Our study concludes that the eosinophil variability in subsequent AECOPDs leads to category changes. Therefore, if eosinophil guided strategies for steroid use for AECOPD will be implemented, one cannot rely on the eosinophil count measured during an earlier severe AECOPD. The eosinophil count has to be determined at every new severe AECOPD. If our results can be extrapolated also to moderate AECOPD then we recognize that this potentially will have major implications for COPD self-management interventions, as this should then lead to measuring eosinophils at the moment of each AECOPD. Therefore, in chapter 7 we proposed the potential for the introduction of personalised and biomarker-guided COPD self-treatment approaches, while addressing the hurdles to be overcome. We emphasized that the integration of necessary refinements and tailoring of self-treatment approaches will be an ongoing and evolving process, with practical and logistical challenges to overcome. Recent advances in biomarker-guided antibiotic therapy for AECOPD and blood eosinophil-guided prednisolone treatment highlight the need for COPD patients within a self-management program to have rapid, easy access to point-of-care tools establishing their biomarker(s) at AECOPD onset, without delaying treatment. Embedding these personalised and biomarker-guided COPD self-treatment approaches in self-management interventions, will contribute to lower the use of systemic steroids. Frequent use of systemic steroids can have side effects and can contribute to the comorbid status of the COPD patient and impair the immune status. Specific immune deviations in COPD have been suggested previously. We hypothesized that an impaired immune status could be associated with a higher risk for CAP, AECOPD and mortality. However, there is no clear definition or test to determine and define a COPD patient’s overall immune status. In the study presented in chapter 8 we studied the hypothesis that COPD patients who do not achieve seroprotective levels after influenza vaccination, are a less immune-competent group with a higher risk of morbidity and mortality. In total 578 patients included in the COMIC cohort had pre- and post-vaccination stable state blood samples drawn, in which influenza-vaccine specific antibodies were measured. Our results show that 42% of the patients achieved seroprotective levels to both H1N1 and H3N2 after vaccination. Seroprotective levels to H3N2 were markedly higher (96%) than to H1N1(43%). Having seroprotective levels to both H1N1 and H3N2 was not associated with less morbidity (severe AECOPD HR 0.91 (95% 0.66-1.25; p=0.56), CAP HR 1.23 (95% 0.75-2.00; p=0.41)) or lower mortality (HR 1.10 (95% 0.87-1.38; p=0.43)). While achieving seroprotection after vaccination can be considered a surrogate marker of being immunocompetent, this was not associated with lower morbidity and mortality. Whether this means that the immune status is not a relevant pheno/endotype in COPD patients for the course of their disease or that seroprotection is not an adequate (surrogate) marker to define the immune status in COPD needs to be further studied.

In chapter 9 we discuss the results presented in the chapters 2-8 and place them in a broader context and suggestions for future research are provided.