Biomarker-based clustering of patients with chronic obstructive pulmonary disease

Discussion

This is the first study to cluster a sample of well-characterised patients with COPD based on a set of biomarkers. This resulted in four clusters with distinct biomarker patterns. Although the clusters had a similar degree of airflow limitation, distinct patient profiles could, to a certain extent, be attributed to the different biomarker clusters. Additionally, those clusters appeared connected and enriched for numerous signalling pathways.

A large heterogeneity in the levels of the different biomarkers was observed in this study, which corroborates previous findings [3, 25–27]. The current clustering resulted in two lower-level clusters (1 and 2) and two higher-level clusters (3 and 4). Interestingly, only three biomarkers in clusters 1 and 2 and only four biomarkers in clusters 3 and 4 overlapped in the same direction. In contrast, four biomarkers in the low-level and three in the high-level clusters exhibited opposing expression patterns. These findings illustrate that different biological processes can be involved in patients with a high inflammatory state, as well as in patients with a low inflammatory state. 15 biomarkers were not significantly altered in any of the four clusters; however, multiple biomarkers have been found to be altered in COPD and/or shown to play a role in the pathogenesis, and could thus point to common pathogenic mechanisms. (e.g. α2-macroglobulin [42], factor VII [21], ferritin [32, 43, 44], fetuin A [23], IL-1β [6], IL-8 [1], monocyte chemotactic protein-4 [7], macrophage migration inhibitory factor [8], matrix metalloproteinase-9 [17], thymus and activation-regulated chemokine [9] and vitamin D-binding protein [24]).

The degree of airflow limitation was comparable between clusters, indicating the limited value of the degree of airflow limitation in predicting systemic levels of the biomarkers and vice versa. Interestingly, there were some significant differences in clinical characteristics between the clusters. Overall, the lower-level biomarker clusters have less disease manifestations compared to the high-level clusters. This is in line with Pinto-Plata et al. [26], who reported that patients in the highest quartile of a broader set of inflammatory biomarkers were more clinically compromised and had higher mortality. Cluster 1 could be considered as metabolically healthier with fewer comorbidities compared to the whole study population. Moreover, this cluster showed one of the smallest protein interaction networks, suggesting limited systemic alterations. Clinically, cluster 2 relates in some way to the cachectic or implosive cluster or phenotype [29]. This finding might be in contrast with previous data suggesting that cachectic patients have increased levels of systemic inflammation [45], as the majority of the systemic biomarkers were decreased.

The clear differences in body composition between clusters 1 and 2 may help to explain some biomarker differences between the groups, such as the low leptin, high adiponectin and brain-derived neurotrophic factor (BDNF) levels in cluster 2. The higher levels of the selective competitive inhibitor of IL-1, IL-1Ra, in cluster 1 confirms a robust low inflammatory status. Indeed, the lack of upregulation of IL-1Ra in cluster 2 may be underlying the implosive phenotype rather than upregulation of other pro-inflammatory mediators as seen in the high clusters. Conversely, cluster 2 exhibited decreased levels of IL-23, which is involved in the proliferation and maintenance of T-helper 17 cells and, thus, proper mucosal defences via, for example, the induction and production of secretory immunoglobulin (sIg)A. Importantly, sIgA levels were also lower in cluster 2, which could indicate a specific defect in mucosal immunity [46].

Cluster 4 is clearly the most inflamed cluster in terms of the number of significantly altered biomarkers (27 out of 57 increased). Perhaps this high level of inflammation is unsurprising due to the clinical characteristics of this cluster (table 2), as age, smoking and cardiovascular risk have indeed been repeatedly associated with increased inflammatory status [1].

Cluster 3, the other “high-level” biomarker cluster is clearly different from cluster 4 in terms of number (12 versus 27) and type of increased markers. On the clinical level, this cluster was mainly characterised by metabolic dysregulation. It is likely that the biomarker pattern in this cluster is partly driven by increased fat mass. The adipokine leptin was slightly increased in this cluster. The adipokines BDNF and insulin-like growth factor-binding protein 2, which have been implicated in the pathogenesis of metabolic syndrome [47], were bidirectionally different between clusters 3 and 4. Increased levels of IL12p40 have been found in the serum of obese adolescents compared to normal-weight controls [48]. Additionally, plasminogen activator inhibitor (PAI)-1, a major inhibitor of fibrinolysis, which was elevated in cluster 3, but not in cluster 4, has been shown to significantly correlate with components of metabolic syndrome [22].

Through examination of protein–protein interactions and pathway analysis we can begin to shed light on the underlying molecular mechanisms that may be partially responsible for aspects of disease in the different clusters. Interestingly, we found that three out of the four clusters were enriched in the OSM and RAGE pathways. Ample evidence has already implicated the RAGE pathway in the pathogenesis of COPD. This evidence includes protection from experimental emphysema in RAGE-deficient mice and the identification of the AGER, the gene encoding RAGE protein, as a susceptibility gene in genome-wide association studies [49]. Due to the direction of alterations of key molecules associated with the RAGE pathway (supplementary tables S2, S4 and S5), we would predict that the RAGE pathway is downregulated in cluster 1 and upregulated in clusters 3 and 4, which fits the role of RAGE as a major contributor to inflammation and the data obtained in animal studies. Additionally, RAGE-deficient mice were not prone to weight gain, even when fed a high-fat diet [50]. We see that our COPD patients in cluster 1 show clinical characteristics of lower cholesterol and normal body weight, suggesting that these clinical manifestations may be due to systemic downregulation of the RAGE pathway. Conversely, we find that where the RAGE pathway is predicted to be increased, significantly higher body weight and higher cholesterol are observed (cluster 3). Furthermore, an increase in RAGE is reported to be associated with platelet activation and, in agreement, we find a significant increase in thrombocytes and PAI-1 in cluster 3 and a significant decrease in cluster 1 [51]. The enrichment of the RAGE pathway in cluster 4 only partially overlaps with cluster 3, with additional effects on inflammatory mediators such as IL-6 and soluble vascular adhesion molecule 1. Important to note is that our dataset included mostly inflammatory mediators downstream of RAGE, whereas ligands activating the pathway are underrepresented. It is likely that levels of the various ligands identified for RAGE also differ between clusters, which could be a reason for the observed discriminatory downstream effects between clusters 3 and 4 in particular. As the RAGE signalling pathway is furthermore inhibited by soluble RAGE (of which levels are in general lower in COPD patients), this is yet another unexplored mechanism through which the pathway could be differentially regulated between clusters and contributing to the pathogenesis and clinical characteristics between patients and clusters. Due to the prevalence of RAGE components and enrichment of the pathway, it would be interesting to determine these additional aspects, as well as genetic variations of AGER in our identified COPD clusters in future studies.

OSM pathways were highly significant in the same direction and in the same clusters as RAGE. This is not entirely surprising, since a number of biomarkers are indeed shared between both pathways (supplementary tables S1, S2 and S4). OSM is a pleiotropic cytokine of the IL-6 family of cytokines, which like RAGE, contains multiple positive feedback loops to enhance its pro-inflammatory actions. Moreover, it has previously been shown to be increased in COPD and is involved in airway remodelling [52, 53]. Interestingly, it was also demonstrated using RAGE-deficient mice that RAGE mediates hypomethylation in the promoter of OSM and that the resulting elevated expression of OSM is likely to contribute to the observed effects of RAGE in cigarette-induced airway inflammation and emphysema development [54].

Previously, a subanalysis of the TESRA study was the first to use proteomics to subtype stable ex-smoking COPD patients [28]. The authors included 87 biomarkers measured in 396 COPD patients and identified three different clusters. They report that 18 biomarkers were different between the three clusters, but do not report the distribution of markers among the three clusters. Similar to our results, BDNF, fibrinogen, osteoprotegerin, stem cell factor and vascular endothelial growth factor had discriminating power in differentiating the clusters. The authors specifically pointed out a smaller cluster of participants with increased inflammatory biomarkers, impaired quality of life yet less emphysema, which could be reminiscent of cluster 4 in the current study, as this cluster was also characterised by increased markers of α1-antitrypsin, C-reactive protein, haptoglobin and tumour necrosis factor receptor I. Although diffusion capacity was lower in cluster 4, which commonly relates to the degree of emphysema. In TESRA, subjects were characterised with less detail; for example, no information on comorbidities and functional performance is provided. Most COPD patients have multiple chronic conditions which are strongly interconnected. Previously, five different comorbidity clusters have been identified in COPD patients of the CIROCO cohort [29]. The present study highlights the complexity of the pathophysiology, as the clinical characteristics of patients in the four observed clusters of circulating biomarkers did not seem to correspond well with the previously described comorbidity clusters [29]. This probably reflects the heterogeneity of patients with COPD, where different clinical traits and diseases coexist to varying degrees within the same individual, and there is an inability to delineate mutually exclusive clusters of (mechanisms of) disease(s) [55].

The current results are hypothesis-generating rather than definitive and there are several limitations to the current study. Indeed, our findings need to be corroborated, using additional cohorts of individuals to determine the robustness of our clustering approach. We have thus far only examined patients with moderate-to-severe COPD, but not those in early stages of the disease. Including early-stage individuals and following them over time might gain additional insight into systemic changes in inflammation and how they may mirror the development of clinical characteristics. Although a large set of biomarkers was used, this still does not reflect the complexity of biological interactions in the human body. Moreover, there is considerable debate over how reflective systemic inflammation is of local inflammation in the airways. However, using a relatively noninvasive sampling method, such as whole-blood sampling, numerous biomarkers can be examined relatively easily without causing unnecessary stress to the patient. Recognising the fact that the discussion is valid only for the biomarkers we have explored, our findings may help to shed light on the underlying pathogenic processes involved in COPD.

Biomarker-based clustering in patients with COPD resulted in four clusters, illustrating the heterogeneity of biomarkers in patients with COPD. A dichotomy in clusters was seen based on mainly lower/higher levels of biomarkers, which in turn resulted in a unique biomarker composition for each cluster. Although the degree of airflow limitation was similar among the clusters, differences in clinical characterisation were seen. Pathway analysis revealed components of the RAGE pathway to be enriched in three of the four clusters, suggesting a systemic role for this pathway in COPD. These new insights may help to understand the functionally relevant inflammatory interactions in the pathophysiology of COPD as a heterogeneous disease.