Emerging Role of Soluble Suppression of Tumorigenicity 2 as Biomarker for Acute Exacerbations in COPD Patients

doi:10.21203/rs.3.rs-4708882/v1

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Emerging Role of Soluble Suppression of Tumorigenicity 2 as Biomarker for Acute Exacerbations in COPD Patients

https://doi.org/10.21203/rs.3.rs-4708882/v1

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Early and precise assessment of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is crucial for the clinical management of COPD patients. Soluble suppression of tumorigenicity 2 (sST2) has been observed to increase in inflammatory lung diseases. This study aimed to investigate whether sST2 could serve as a biomarker for AECOPD and predict disease severity. We investigated 55 AECOPD patients, 30 stable COPD patients, and 33 healthy controls. COPD patients had higher sST2 levels than healthy controls. Additionally, sST2 levels showed significant differences among AECOPD patients with different GOLD classes and levels of deterioration, and were significantly correlated with inflammatory markers and pulmonary function tests in these patients. Multivariate logistic regression analyses showed that sST2 was an independent risk factor for AECOPD patients. AECOPD patients with bacterial infection also had significantly higher sST2 levels than those with non-bacterial infection. The performance of sST2 in differentiating AECOPD patients from those with a 4-week stable condition, as well as AECOPD patients with bacterial infection, surpasses that of CRP and PCT. In conclusion, our result suggests that sST2 may serve as a biomarker to predict AECOPD and disease severity, and assist in differentiating bacterial exacerbations in COPD patients.

biomarker

COPD

classification

soluble suppression of tumorigenicity 2

Chronic obstructive pulmonary disease (COPD), primarily caused by smoking in the population, is a progressive, partially reversible, or irreversible chronic airway inflammation with significant heterogeneity, the underlying drivers of which remain poorly understood[1–4]. It is the most common chronic lung disease with a global prevalence of 11.7% in people ≥ 30 years old[1], causing 3.5–4 million deaths per year[2, 3]. In the course of COPD, the events of acute exacerbation (AECOPD) impair patients' quality of life, accelerate disease progression, and increase the risk of death[9]. There is a link between AECOPD hospitalization and poor survival, with a 5-year mortality rate of 55%[8]. Bacterial infection is a key factor of AECOPD progression, given that bacterial products impair and remodel respiratory epithelial cells, provoke inflammatory responses, disrupt elastolysis activity, and degrade end airway tissue[10]. Indeed, studies show that bacterial infection play a significant role in 50% AECOPD cases[11]. In this context, an effective biomarker capable of predicting the risk of disease progression and AECOPD may enable the optimization of early clinical management strategies and improve survival rates.

Currently, common non-specific peripheral blood biomarkers encompass C-reactive protein (CRP) and procalcitonin (PCT)[1, 2]. Additionally, interleukin 6 (IL-6), established in several studies as indicative of systemic inflammation in COPD, garners considerable attention from researchers[3, 4]. While these biomarkers provide valuable information about inflammation, their ability to assess COPD exacerbations is limited. An emerging and promising biomarker candidate is soluble suppression of tumorigenicity 2 (sST2), a member of the Toll-like/interleukin (IL)-1 receptor superfamily and a crucial receptor for proinflammatory cytokines like IL-33[5]. ST2, existing in soluble (sST2) and transmembrane (ST2L) isoforms[6, 7], is primarily secreted by hematopoietic and epithelial cells. The IL-33/ST2L signalling pathway activates mitogen-activated protein kinase (MAPK) and nuclear factor (NF-kB) signalling, resulting in the activation and regulation of Th2 cellular immune responses as well as the broader cytokine-driven inflammatory cascade [5, 8, 9]. The decoy receptor function of sST2 inhibits the activity of the IL-33/ST2L signalling pathway by competitively binding with IL-33.

sST2 has been endorsed for its predictive potential in heart disease mortality[1, 2]. It is recommended by the ACCF/AHA Guideline for the Management of Heart Failure as a biomarker for predicting in-hospital and long-term mortality in heart failure patients[3]. Moreover, sST2's potential as a biomarker for allergic and non-allergic lung diseases has been documented[4]. Our previous study has underscored the utilization of sST2 in acute pulmonary inflammation, particularly in community-acquired pneumonia[5]. Studies indicate that the IL-33/ST2L signalling pathway is more active in COPD patients, in stimulating the production of pro-inflammatory cytokines IL-6 and chemokine IL-8 production in local tissue and systemic, as well as enhancing the accumulation of neutrophils in lung tissue, resulting in worsening of local pulmonary inflammation. As a decoy receptor for the IL-33/ST2L signalling pathway, sST2 may play key roles in the inflammatory response in COPD patients[6], implying it is a superior biomarker for AECOPD compared to conventional biomarkers such as CRP, PCT, and IL-6 that are non-specific. However, the full potential of sST2 as a AECOPD biomarker remains underexplored.

Taken together, our study explored the association between sST2 and disease severity in COPD patients and assessed its efficacy as a biomarker to identify patients prone to acute exacerbations. In addition, we demonstrated the capability of sST2 in differentiating AECOPD patients with bacterial infection from those with non-bacterial infection.

Patient enrolment

This retrospective study was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (GYFYY-2016-73). A total of 85 COPD patients hospitalized and 33 non-smoking healthy controls between 2019 and 2020 were identified as suitable candidates for our study. All patients were diagnosed with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria: post-bronchodilator forced expiratory volume in 1s (FEV1) / forced vital capacity (FVC) < 0.70[27]. Exclusion criteria included: (1) inability or unwillingness to perform lung function tests; (2) suffering from the diseases other than COPD known to affect plasma sST2, including interstitial lung disease, asthma, acute respiratory distress syndrome and sepsis; (3) lack of clinical record for GOLD or the level of acute exacerbation assessment; (4) oral steroid treatment; (5) heart disease, including hypertensive heart disease, ischemic heart disease, valvular heart disease, heart failure, arrhythmia, pulmonary heart disease, myocardiopathy, and congenital heart disease, or satisfying one of the following criteria: Aspartate aminotransferase (AST) > 40U/L; Creatine Kinase (CK) > 190U/L; Creatine Kinase-MB (CK-MB) > 25U/L; Troponin (Tn) > 0.04ug/L; Myoglobin (Mb) > 70ug/L; (6) severe immunosuppression defined as meeting one of the following criteria: infection with human immunodeficiency virus and a CD4 + cell count below 350 cells per µL, immunosuppressive therapy after solid organ transplantation; (7) non-reversible airway obstruction such as bronchiectasis and previous tuberculosis. Blood samples were obtained within 24 hours of admission. Since lung function measurement is very difficult to obtain in AECOPD patients, we only performed lung function measurement in COPD patients during the 4-week stable period. We collected demographic data, including sex, age, body mass index (BMI), smoking history, and laboratory parameters of patients, using COPD Assessment Test (CAT) and modified Medical Research Council dyspnoea scale(mMRC) to measure patients’ health status and symptoms.

Grouping of COPD patients

Among the 85 COPD patients, 30 were found to be stable for 4-week duration (4-week stable)[28, 29], which included no acute exacerbation within 4 weeks, no coughing symptoms, no significant aggravation of sputum and shortness of breath, no fever or other infection symptoms, clinical symptoms not affecting normal activity level, no acute drug application, and no significant decline in short-term lung function, including fractional exhaled nitric oxide (FeNO). This period was defined as a 4-week stable period in the study to distinguish it from subjects with clinical exacerbations; 55 patients were defined as having acute exacerbation and classified according to Anthonisen criteria: Anthonisen I (mild exacerbation); Anthonisen Ⅱ (moderate exacerbation); Anthonisen Ⅲ (severe exacerbation)[30, 31]. Patients with AECOPD who satisfied the following criteria accepted therapy and achieved clinical stability[32]: no worsening cough, no increase in sputum volume, no fever or new dyspnea, no contractible movement in the chest or belly, and no new patchy exudative lesions based on lung imaging. COPD was staged following the GOLD guidelines: GOLD I (mild): FEV1 < 80% predicted; GOLD II (moderate): FEV1 < 80% and > 50% predicted; GOLD III (severe): FEV1 < 50% and > 30% predicted; and GOLD IV (very severe): FEV1 < 30% predicted. Since our study only included 1 GOLD I patient, we combined the GOLD I group with the GOLD II group. The sputum of patients was collected and cultured for the quantification of colony-forming units (CFU) to measure bacterial load. 21 AECOPD patients were defined as having AECOPD with bacterial infection according to the following criteria: (1) a high bacterial load (colony forming units > 10⁷); (2) a positive sputum culture.

Measurement of sST2 and the retrieving of laboratory data

We collected venous blood samples from all patients at the time of admission (0–24 hours) and from AECOPD patients who had reached clinical stability[33]. Whole blood samples were centrifuged at 3000g for 15 minutes at room temperature, then sera were aliquoted and stored at -80℃ upon analysis. The serum samples were prepared using the sST2 detection kit (ST22101001) and the sST2 levels were measured using the Jet-iStar 3000 (Joinstar Biomedical Technology Co., LTD, China) according to the manufacturer’s instructions. The primary antibody was an anti-sST2 mouse monoclonal antibody, and the secondary antibody was Carboxyfluorescein diacetate N-succinimidyl ester (CFSE)-labelled anti-sST2 mouse monoclonal antibody. The absorbance of the CFSE-labelled anti-sST2 mouse monoclonal antibody-sST2- anti-sST2 mouse monoclonal antibody complex was measured at 635nm. Finally, the quantified sST2 levels were obtained using a standard curve drawn from the standard concentration solutions measured on the same plate.

Statistical analysis

The Shapiro-Wilk test was used to evaluate the normality of continuous data. Parametric data are shown as mean (SD), and nonparametric data are presented as median (IQR). Median values of nonparametric variables were compared among the groups using the Mann-Whitney-Wilcoxon rank-sum test, or in case of comparison of three or more groups, the Kruskal-Wally’s test. A Chi-square test or Fisher's Exact test was used to compare categorical data. Data correlation analysis was performed using spearman’s correlation coefficient. Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curve (AUC) and the cut-off values (determined by the Youden index). The diagnostic value of sST2 for acute exacerbation, disease severity, and acute exacerbation of bacterial infection was assessed using logistic regression, and the respective odds ratios (OR) were calculated. P < 0.05 was considered statistically significant. All statistical analyses in this study were performed using SPSS Statistics version 22.0 (IBM, New York, USA) or R language (version 4.0.2).

Clinical characteristics of participants

Baseline characteristics of the cases and controls are shown in Table 1. Compared to the healthy controls, neutrophil count and ratio, CRP, IL-6 and sST2 are all higher in the COPD patients (all with P < 0.05). Age, sex, BMI, leukocyte count, PCT, AST, CK, and CK-MB were comparable between the groups. The levels of Tn and Mb in COPD patients were significantly higher than those in healthy controls, but remained within the range of normal reference values (Table 1). Compared to healthy controls, the sST2 levels in patients with 4-week stable disease were higher, but the FVC, FEV1, and FEV1/FVC levels were lower (Fig. 1a). Nonetheless, in patients with a 4-week stable condition, there were no significant correlation between sST2 and systemic inflammation, as indicated by its correlations with CRP (R = 0.048, P > 0.05), PCT (R=-0.143, P > 0.05), IL-6 (R=-0.184, P > 0.05), leukocyte count (R=-0.381, P < 0.05), neutrophil count (R=-0.058, P > 0.05). Additionally, there were no significant correlation with disease severity, as indicated by sST2’s correlations with CAT (R=-0.220, P > 0.05), MMRC (R=-0.427, P < 0.05), GOLD (R = 0.346, P > 0.05) (Fig. 2). Overall, for the 4-week stable condition, the correlations between sST2 and the above clinical indexes were weak, which aligns with clinical expectation.

Table 1

Characteristics of healthy controls and COPD patients
Parameter	Healthy controls	COPD patients	P-value
Number	33	85	-
Age, mean (SD)	53.2 (12.5)	67.9 (9.4)	< 0.001
Male, n (%)	20 (60.6)	77 (90.6)	< 0.001
BMI, median (IQR)	24.7 (22.4–26.6)	22.3 (18.9–24.0)	< 0.001
Smoker, n (%)	-	60 (70.6%)	-
Smoking index, median (IQR)	-	600 (0-1000)	-
GOLD, n			-
GOLD 1	-	1
GOLD 2	-	32
GOLD 3	-	22
GOLD 4	-	30
CAT, median (IQR)	-	20.0 (17.0-23.5)	-
mMRC, median (IQR)	-	3.0 (2.0–4.0)	-
Lung function test
FVC, % predicted, median (IQR)	83.7 (71.4-102.9)	67.4 (54.1–75.5)	< 0.001
FEV1, % predicted, median (IQR)	84.9 (70.6–96.8)	42.5 (25.2–54.0)	< 0.001
FEV1/FVC, median (IQR)	101.4 (92.1-107.7)	61.7 (42.5–67.8)	< 0.001
Laboratory tests
Leukocyte (*109/L), median (IQR)	7.7 (6.0-8.8)	7.6 (5.8–10.4)	0.365
Neutrophils(*109/L), median (IQR)	4.4 (3.3–5.8)	5.7 (4.0–8.0)	0.007
Neutrophils (%), median (IQR)	59.1 (52.1–70.0)	77.0 (63.9–88.4)	< 0.001
sST2(ng/ml), median (IQR)	7.1 (4.2–9.8)	32.3 (13.4–80.4)	< 0.001
CRP (ng/ml), median (IQR)	1.4 (0.5–2.7)	12.2 (3.7–30.5)	< 0.001
PCT (ng/ml), median (IQR)	0.1 (0.1–0.13)	0.1 (0.1–0.16)	0.255
IL-6(pg/ml), median (IQR)	12.7 (8.3–26.4)	27.1 (13.2–35.1)	0.008
AST	19.5 (17.2–24.5)	20.6 (17.3–24.8)	0.524
CK	65.2 (36.2–93.1)	71.3 (38.5-100.4)	0.760
CK-MB	10.0 (9.0–13.0)	12.0 (10.0–15.0)	0.066
Tn	0 (0–0)	0 (0–0)	< 0.05
Mb	17.7 (15.1–25.9)	27.8 (19.9–38.3)	< 0.05
P-value, P-value between healthy controls and COPD patients. BMI, Body Mass Index; GOLD, Global Initiative for Chronic Obstructive Lung Disease; CAT, COPD Assessment Test TM; mMRC, modified Medical Research Council dyspnea scale; FVC, Forced Vital Capacity; FEV1, Forced Expiratory Volume in the first second; FEV1/FVC, Forced Expiratory Volume in the first second/ Forced Vital Capacity; sST2, Soluble suppression of tumorigenicity 2; CRP, C-reactive protein; PCT, Procalcitonin; IL-6, Interleukin-6; AST, Aspartate aminotransferase; CK, Creatine Kinase; CK-MB, Creatine Kinase-MB; Tn, Troponin; Mb, Myoglobin.

sST2 elevated in AECOPD and correlated with clinical parameters

Compared to patients with 4-week stable condition, AECOPD patients were more likely to receive oxygen therapy, and had higher CAT, mMRC scores, and laboratory parameters, such as leukocyte count, neutrophil count, neutrophil proportion, CRP, PCT, and IL-6 (all P < 0.05) (Table 2). The sST2 level in AECOPD patients was significantly higher compared to levels in patients with 4-week stable condition (68.3ng/ml vs. 12.1ng/ml, P < 0.05) (Fig. 1a). Using the GOLD criteria to categorize disease severity in AECOPD patients, we observed higher sST2 levels in GOLD IV compared to GOLD II (99.25 ng/ml vs. 44.60 ng/ml, P < 0.05, Fig. 1b). Furthermore, sST2 levels increased in higher degrees of AECOPD (P < 0.05, Fig. 1c).

Table 2

Characteristics of COPD patients with different state
Parameter	COPD at 4-weeks stable	AECOPD	P-value^&	AECOPD with bacterial infection	AECOPD with non-bacterial infection	P-value^#
number	30	55	-	21	34	-
Age, mean (SD)	65.1 (8.7)	69.4 (9.6)	0.068	71.5 (10.4)	68.1 (9.0)	0.207
Male, n (%)	26 (86.7)	51 (92.7)	0.599	19 (90.5)	32 (94.1)	1.000
BMI, median (IQR)	22.4 (19.5–24.2)	22.3 (18.6–24.0)	0.579	23.1 (18.7–24.9)	21.1 (18.2–23.8)	0.139
Smoker, n (%)	19 (63.3)	41 (74.5)	0.278	15 (71.4)	26 (76.5)	0.677
Smoking index, median (IQR)	600 (0-1212.5)	600 (0-1000)	0.837	800 (0-1900)	600 (75-1000)	0.340
Long-term oxygen therapy, n (%)	10 (33.3)	34 (61.8)	0.012	17 (81.0)	17 (50.0)	0.022
GOLD, n			0.370			0.664
GOLD 1	1	0		0	0
GOLD 2	11	21		7	14
GOLD 3	10	12		4	8
GOLD 4	8	22		10	12
Exacerbation, n			-			0.03
Mild exacerbation	-	22		4	18
Moderate exacerbation	-	16		7	9
Severe exacerbation	-	17		10	7
CAT, median (IQR)	17.5 (12.8–20.0)	21.0 (19.0–26.0)	< 0.001	25.0 (20.0–32.0)	21.0 (17.8–23.0)	0.007
MMRC, median (IQR)	3 (2.0-3.3)	3.0 (3.0–4.0)	0.004	4.0 (3.0–5.0)	3.0 (2.8-4.0)	0.01
Lung function test
FVC, % predicted, median (IQR)	70.2 (64.7–83.1)	65.7 (52.5–72.0)	0.035	65.0 (40.3–68.6)	66.1 (55.3–75.1)	0.112
FEV1, % predicted, median (IQR)	46.3 (29.6–55.5)	38.6 (24.4–53.7)	0.175	31.4 (22.0-50.7)	41.1 (25.1–54.2)	0.149
FEV1/FVC, median (IQR)	61.1 (49.5–64.6)	63.1 (37.6–68.3)	0.948	61.4 (39.1–63.8)	64.7 (36.3–69.2)	0.204
Laboratory tests
Leukocyte (*109/L), median (IQR)	5.9 (4.9–8.3)	8.4 (7.0-11.6)	< 0.001	9.6 (6.8–12.6)	7.9 (7.1–11.1)	0.588
Neutrophils(*109/L), median (IQR)	3.9 (3.0–5.0)	6.9 (5.4–10.5)	< 0.001	8.4 (6.2–10.5)	6.4 (5.1–9.5)	0.086
Neutrophils (%), median (IQR)	63.3 (55.3–70.9)	81.2 (76.3–90.8)	< 0.001	89.6 (82.9–92.1)	77.6 (70.9–86.4)	< 0.001
sST2 (ng/ml), median (IQR)	12.1 (7.3–19.2)	68.3 (32.6-119.2)	< 0.001	119.2 (70.1–201)	43.0 (26.5–76.4)	< 0.001
CRP (ng/ml), median (IQR)	6.6 (1.4–11.4)	28.7 (10.1–67.8)	< 0.001	55.0 (29.1-108.5)	17.3 (5.9–39.4)	0.007
PCT (ng/ml), median (IQR)	0.09 (0.09–0.09)	0.11 (0.90 − 0.30)	< 0.001	0.1 (0.1–0.14)	0.3 (0.1–1.5)	0.003
IL-6 (pg/ml), median (IQR)	20.2 (8.2–28.5)	28.9 (21.7-127.1)	0.014	126.2 (27.1-256.8)	27.2 (11.3–33.2)	0.012
^&P-value, P-value between COPD patients at the stable state and COPD exacerbation; ^#P-value, P-value between COPD patients at exacerbation with bacterial infection and COPD at exacerbation with non-bacterial infection.
AECOPD, Chronic Obstructive Pulmonary Disease with acute exacerbation; BMI, Body Mass Index; GOLD, Global Initiative for Chronic Obstructive Lung Disease; CAT, COPD Assessment Test TM; mMRC, modified Medical Research Council dyspnea scale; FVC, Forced Vital Capacity; FEV1, Forced Expiratory Volume in the first second; FEV1/FVC, Forced Expiratory Volume in the first second/ Forced Vital Capacity; sST2, Soluble suppression of tumorigenicity 2; CRP, C-reactive protein; PCT, Procalcitonin; IL-6, Interleukin-6.

sST2 correlated with disease severity

We used logistics regression analysis to evaluate factors associated with acute exacerbation and disease severity in COPD patients. As shown by univariate logistics regression analysis, sST2 was significantly correlated with acute exacerbation, exacerbation degree (mild, moderate, severe exacerbation), GOLD classification (GOLD Ⅱ, GOLD Ⅲ, and GOLD Ⅳ), and bacterial infection in COPD patients, with OR values of 1.142 (1.067–1.223, P < 0.05), 1.074 (1.050–1.095, P < 0.05), 1.014 (1.006–1.022, P < 0.05), and 1.031 (1.014–1.048, P < 0.05), respectively. In multivariate regression analysis adjusted with oxygen therapy, PCT, mMRC, and CAT, sST2 was an independent predictor of acute exacerbation, exacerbation degree, and bacterial infection of COPD patients, with OR values of 1.235 (1.071–1.425, P < 0.05), 1.065 (1.041–1.090, P < 0.05), 1.027 (1.005–1.051, P < 0.05), while sST2 was not an independent predictor for GOLD classification (P > 0.05) (Table 3).

Table 3

Effects of sST2 levels on predicting clinical outcome for COPD patients.
		Univariate analysis		Multivariate analysis
Clinical outcome	Number	Odds Ratio (95%CI)	P value	Odds Ratio (95%CI)	P value
Exacerbation	85	1.142 (1.067–1.223)	< 0.001	1.235 (1.071–1.425)	0.004
Exacerbation level	85	1.074 (1.050–1.095)	< 0.001	1.065 (1.041–1.090)	< 0.001
GOLD	85	1.014 (1.006–1.022)	< 0.001	1.005 (0.990–1.020)	0.536
Exacerbation with bacterial infection	55	1.031 (1.014–1.048)	< 0.001	1.027 (1.005–1.051)	0.017
Multivariate analysis, adjusted for clinical variables, procalcitonin, Soluble form of suppression of tumorigenicity-2, Forced Vital Capacity, modified Medical Research Council dyspnea scale, and COPD Assessment Test TM. P values were calculated using logistic regression analysis. Exacerbation level was determined by the Anthonisen criteria.
95% CI, 95% confidence interval; GOLD, Global Initiative for Chronic Obstructive Lung Disease.

Performance of sST2 in differentiating AECOPD patients

Using ROC curves to assess the performance of sST2 to differentiate AECOPD patients from those with 4-week stable condition, the AUC (95% CI) of sST2 was 0.94 (0.89–0.99), significantly higher than that of CRP and PCT, with the AUCs of 0.81 (0.71–0.91) and 0.74 (0.63–0.84), respectively (Fig. 1d). According to the Youden index, the cut-off value of sST2 to assign AECOPD was 30.1ng/ml, with a sensitivity of 80.0% and a specificity of 96.7%. In AECOPD patients, significant correlations were observed between sST2 and systemic inflammation, as indicated by correlations with CRP (R = 0.513, P < 0.05), PCT (R = 0.490, P < 0.05), IL-6 (R = 0.492, P < 0.05), leukocyte count ((R = 0.147, P > 0.05), neutrophil count (R = 0.320, P < 0.05), and disease severity [indicated by correlations with CAT (R = 0.620, P < 0.05), MMRC(R = 0.497, P < 0.05), GOLD (R = 0.451, P < 0.05)] (Fig. 2).

sST2 to distinguish AECOPD with bacterial infection from those with non-bacterial infection

The sST2 levels in AECOPD patients with bacterial infection were significantly higher than those with non-bacterial infection (119.20ng/ml vs. 43.0ng/ml, P < 0.05) (Fig. 3a). The ROC analysis for the performance of sST2 to differentiate AECOPD patients with bacterial infection from those with non-bacterial infection showed that the AUC (95% CI) was 0.85(0.75–0.96), surpassing that of CRP and PCT with the AUCs of 0.75 (0.60–0.90) and 0.73 (0.58–0.88), respectively (Fig. 3b). According to the Youden index, the cut-off value of sST2 to differentiate AECOPD patients with bacterial infection was 99.25 ng/ml, with a sensitivity of 66.7% and a specificity of 91.2%. The pathogen information of 21 AECOPD patients with bacterial infection is shown in supplementary Table 1. The differences in sST2 levels were not significant among AECOPD patients with various bacterial infections (P > 0.05).

sST2 levels declined after AECOPD patients reached clinical stability

We investigated the dynamic sST2 levels in AECOPD patients from acute exacerbation to clinical stability. The sST2 levels in the clinical stable stage were significantly lower than those in the acute exacerbation phase (68.30ng/ml vs. 22.00ng/ml, P < 0.05). Furthermore, the sST2 levels during clinical stability were lower than the cut-off value of AECOPD (22.00ng/ml vs. 30.1ng/ml, P < 0.05) (Fig. 4). The sST2 levels tended to decrease as the condition of AECOPD patients became stable.

sST2 as a biomarker for AECOPD

Our results demonstrate that sST2 levels were significantly higher in COPD patients compared to healthy controls, and higher in AECOPD patients compared to patients with a 4-week stable condition. Moreover, sST2 levels in AECOPD patients correlated with FVC, FEV1, FEV1/FVC, CRP, PCT, IL-6, neutrophil count, CAT, and MMRC scores. sST2 declined after AECOPD patients returned to a stable state. Multivariate logistics regression analysis showed that the sST2 was an independent predictor for acute exacerbation, exacerbation severity degree, and bacterial infection in COPD patients. This is the first study showing that sST2 is a sensitive and specific predictor for bacterial exacerbation of COPD.

COPD is associated with chronic pulmonary inflammation mainly involving neutrophils[34–36]. AECOPD is a serious adverse event of COPD patients, which is correlated with poor survival outcomes[9]. Suissa et al. found that the risk of subsequent severe exacerbation increased three-fold after the second severe exacerbation and 24-fold after the tenth, relative to the first. In addition, the median time between hospitalization because of AECOPD decreases with the number of previous exacerbations[37]. Moreover, understanding COPD exacerbations is essential for unravelling the underlying biological pathways involved in COPD pathogenesis[38]. Consequently, it is crucial to find appropriate tools (e.g., biomarkers) for risk classification and prognostic evaluation of AECOPD patients. Among COPD biomarkers currently under clinical study, blood samples are more available, with more standardized detection methods, greater quality control, and lower cost, compared to pulmonary samples (such as sputum, alveolar lavage fluid, lung biopsy), making them more popular among clinicians and researchers. In our study, we found that sST2 levels increased significantly in AECOPD patients and correlated with disease severity, supporting sST2 as a biomarker of AECOPD and with high levels predictive of pulmonary exacerbation. To the best of our knowledge, this study represents the first evidence showing the potential of sST2 as a biomarker for both disease severity and exacerbation risk in COPD patients.

The IL-33/ST2 signalling pathway in COPD

This finding not only introduces a new COPD biomarker but also highlights a new avenue for studying biological pathways of COPD pathogenesis. Previous research has demonstrated the key role of the IL-33/ST2 signalling pathway in COPD pathogenesis[26, 39, 40]. For instance, in mouse models of cigarette-induced COPD, the expression of IL-33 and ST2L in pulmonary tissues increased significantly [41]. In addition, administering IL-33 to these models led to characteristic histopathological changes associated with COPD, including hypertrophy of airway linings and goblet cells, and mucus hypersecretion[17]. These histopathological changes were inhibited in mouse models treated with anti-IL-33 antibodies [42].

IL-33/ST2 signalling activates various downstream signalling proteins involved in the nuclear factor KB signalling (NF-κB) and the mitogen-activated protein kinase (MAPK) pathways. These effects are mediated by the activation of MAPK extracellular signal-regulated kinase (ERK), p38 and Jun N-terminal kinase (JNK), and leading to increased expression of target genes, e.g., IL-1[1, 2]. The study by Wu et al. demonstrated that the IL-33/ST2L signalling pathway induces the expression of proinflammatory cytokines (e.g., IL-6) and chemokines (e.g., IL-8) through the P38-MAPK signalling[3]. As typical biomarkers of systemic inflammation, IL-6 and IL-8 have been shown of association with the pathological mechanism and complication of COPD[47–49]. IL-33, IL-6, and IL-8 can induce neutrophils to migrate to the lung, injuring lung tissue by secreting elastases and proteases, further exacerbating pulmonary inflammatory responses, pulmonary fibrosis, and lung function decline[50].

The biological response mediated by IL-33/ST2L was not limited to lung tissue. Studies have shown that pulmonary stimulating factors not only induce pulmonary endothelial cells and epithelial cells to produce IL-33 but also induce the expression of IL-33 in mononuclear cells and lymphocytes in peripheral blood[40, 45]. Lin et al. reported that in human corneal epithelial cells, IL-33/ST2L signalling was enhanced after exposure to IL-33, promoting the expression of IL-6 and IL-8 at both mRNA and protein levels[46]. This systemic response may further exacerbate the development of COPD by activating the immune system. In our study, we demonstrated that the levels of sST2 in serum increased as the status of AECOPD patients deteriorated. Additionally, we observed a positive correlation of sST2 with IL-6. sST2 and ST2L are produced by the same mRNA, and their differences are mainly due to the different binding promoters[51]. Based on these findings, we propose that the inflammatory reaction is activated by the IL-33/ST2L signalling when COPD patients are stimulated by various stimuli (such as cigarette smoke and bacterial products), along with an increase in sST2 as a negative feedback factor in peripheral blood.

Differentiating bacterial exacerbations with sST2

Studies have shown that the precipitating factors in 70%-80% of AECOPD cases are respiratory infections (such as bacteria and viruses)[32], which increase antibiotic resistance in patients[52]. Finding effective biomarkers for guiding antibiotic therapy for AECOPD is critical in clinical practice, especially for AECOPD with bacterial infection. Many studies have sought to use nonspecific biomarkers like CRP and PCT to guide antibiotic therapy for AECOPD patients in the hospital. Prins et al. showed that antibiotic therapy guided by CRP significantly minimized the use of antibiotics, while the increase in acute adverse events was not significant[53]. The study by Corti et al. confirmed that the PCT-guided algorithm substantially reduced the exposure of antibiotics to AECOPD hospitalized patients without causing remarkable injury, facilitating clinicians in determining the appropriate antibiotic prescription[54]. Our study not only demonstrated the significant correlation between sST2 and CRP or PCT but also evidenced that the performance of sST2 in distinguishing AECOPD with/without bacterial infection was better than CRP and PCT, suggesting that the potential of sST2 for guiding antibiotic therapy for AECOPD is not inferior to that of than CRP and PCT. However, this conclusion still needs clinical trials with larger sample sizes to verify.

Perspective

Our findings show that sST2 can reflect the risk of acute exacerbation and the disease severity of COPD patients. Its performance in predicting acute exacerbation events of COPD is better than that of PCT and CRP. It has the potential to serve as a biomarker for bacterial deterioration and to guide antibiotic therapy in COPD patients. Further studies are warranted to develop in several aspects. Firstly, expanding research to multiple centers would allow for a more diverse patient population and increase the generalizability of the findings. Secondly, investigating the long-term prognostic events of COPD patients would provide valuable insights into the disease progression and prognosis. Lastly, efforts should be made to detect sST2 levels in lung tissue, which will confirm its role in the pathophysiology of COPD exacerbations. By addressing these areas in future research, we will establish sST2 as a biomarker in AECOPD.

Acknowledgments

We appreciate the technical support provided by Mr. Baowei and Guangzhou Yuanguji Technology Co., Ltd.

Authors’ Contributions

Conception and design of the research: Baoqing Sun, Hakon Hakonarson and Yuanlin Song; Drafting the manuscript: Qiurong Hu, Mingshan Xue and Huiqi Qu. Data collection: Yifeng Zeng, Runpei Lin, Lijun Su, Xiaowen Wang. Reviewing the manuscript: Jie Liu, Yi Mo, Zhi Xiang.

Ethical approval

This study was approved by the ethics committee of The First Affiliated Hospital of Guangzhou Medical University (Reference number: GYFYY-2016-73).

Funding

This work was supported by the [Zhong Nanshan Medical Foundation of Guangdong Province #1] under Grant [ZNSA-2020001; ZNSA-2021005; ZNSA-2020012; ZNSA-20200016]; [Guangzhou Laboratory Emergency Research Project #2] under Grant [EKPG21-302]; [Cultivation Project of the First Affiliated Hospital of Guangzhou Medical University #3] under Grant [ZH202105] and [SKLRD #4] under Grant [GHMJLRID-Z-202102].

Declaration of interest statement:

The author(s) declared no potential conflicts of interest for the research, authorship, and/or publication of this article.

Dicker AJ, Crichton ML, Pumphrey EG, et al. Neutrophil extracellular traps are associated with disease severity and microbiota diversity in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2018 Jan;141(1):117-127.
Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012;380(9859):2095-2128.
Celli BR, Decramer M, Wedzicha JA, et al. An Official American Thoracic Society/European Respiratory Society Statement: Research questions in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2015 Apr 1;191(7):e4-e27.
Lange P, Celli B, Agustí A, et al. Lung-Function Trajectories Leading to Chronic Obstructive Pulmonary Disease. N Engl J Med. 2015 Jul 9;373(2):111-22.
Huang X, Tan X, Liang Y, et al. Differential DAMP release was observed in the sputum of COPD, asthma and asthma-COPD overlap (ACO) patients. Sci Rep. 2019 Dec 17;9(1):19241.
Singh D, Agusti A, Anzueto A, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: the GOLD science committee report 2019. The European respiratory journal. 2019 May;53(5).
Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2015;385(9963):117-171.
Leitao Filho FS, Ra SW, Mattman A, et al. Serum IgG subclass levels and risk of exacerbations and hospitalizations in patients with COPD. Respir Res. 2018 Feb 14;19(1):30.
Viniol C, Vogelmeier CF. Exacerbations of COPD. Eur Respir Rev. 2018 Mar 31;27(147).
Sethi S. Bacterial infection and the pathogenesis of COPD. Chest. 2000 May;117(5 Suppl 1):286s-91s.
Bafadhel M, McKenna S, Terry S, et al. Acute exacerbations of chronic obstructive pulmonary disease: identification of biologic clusters and their biomarkers. Am J Respir Crit Care Med. 2011 Sep 15;184(6):662-71.
Agusti A, Edwards LD, Rennard SI, et al. Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype. PLoS One. 2012;7(5):e37483.
Duvoix A, Dickens J, Haq I, et al. Blood fibrinogen as a biomarker of chronic obstructive pulmonary disease. Thorax. 2013 Jul;68(7):670-6.
Garcia-Rio F, Miravitlles M, Soriano JB, et al. Systemic inflammation in chronic obstructive pulmonary disease: a population-based study. Respir Res. 2010 May 25;11:63.
Al-shair K, Kolsum U, Dockry R, et al. Biomarkers of systemic inflammation and depression and fatigue in moderate clinically stable COPD. Respir Res. 2011 Jan 5;12:3.
Oshikawa K, Yanagisawa K, Tominaga S, et al. ST2 protein induced by inflammatory stimuli can modulate acute lung inflammation. Biochem Biophys Res Commun. 2002 Nov 22;299(1):18-24.
Schmitz J, Owyang A, Oldham E, et al. IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines. Immunity. 2005 Nov;23(5):479-90.
Ho JE, Sritara P, deFilippi CR, et al. Soluble ST2 testing in the general population. Am J Cardiol. 2015 Apr 2;115(7 Suppl):22b-5b.
Gaggin HK, Szymonifka J, Bhardwaj A, et al. Head-to-head comparison of serial soluble ST2, growth differentiation factor-15, and highly-sensitive troponin T measurements in patients with chronic heart failure. JACC Heart failure. 2014 Feb;2(1):65-72.
Homsak E, Gruson D. Soluble ST2: A complex and diverse role in several diseases. Clin Chim Acta. 2020 Aug;507:75-87.
Wang J, Tan GJ, Han LN, et al. Novel biomarkers for cardiovascular risk prediction. J Geriatr Cardiol. 2017 Feb;14(2):135-150.
Ghashghaei R, Arbit B, Maisel AS. Current and novel biomarkers in heart failure: bench to bedside. Current opinion in cardiology. 2016 Mar;31(2):191-5.
Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013 Oct 15;62(16):e147-239.
Jonsdottir B, Ziebell Severinsen M, von Wowern F, et al. ST2 Predicts Mortality In Patients With Acute Hypercapnic Respiratory Failure Treated With Noninvasive Positive Pressure Ventilation. Int J Chron Obstruct Pulmon Dis. 2019;14:2385-2393.
Zeng Y, Xue M, Zhang T, et al. Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia. Exp Biol Med (Maywood). 2021 Jul 5:15353702211027116.
Gabryelska A, Kuna P, Antczak A, et al. IL-33 Mediated Inflammation in Chronic Respiratory Diseases-Understanding the Role of the Member of IL-1 Superfamily. Front Immunol. 2019;10:692.
Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med. 2017 Mar 1;195(5):557-582.
Thulborn SJ, Mistry V, Brightling CE, et al. Neutrophil elastase as a biomarker for bacterial infection in COPD. Respir Res. 2019 Jul 30;20(1):170.
Contoli M, Baraldo S, Conti V, et al. Airway inflammatory profile is correlated with symptoms in stable COPD: A longitudinal proof-of-concept cohort study. Respirology (Carlton, Vic). 2020 Jan;25(1):80-88.
Stolk J, Tov N, Chapman KR, et al. Efficacy and safety of inhaled alpha1-antitrypsin in patients with severe alpha1-antitrypsin deficiency and frequent exacerbations of COPD. The European respiratory journal. 2019 Nov;54(5).
Anthonisen NR, Manfreda J, Warren CP, et al. Antibiotic therapy in exacerbations of chronic obstructive pulmonary disease. Annals of internal medicine. 1987 Feb;106(2):196-204.
Reis AJ, Alves C, Furtado S, et al. COPD exacerbations: management and hospital discharge. Pulmonology. 2018 Nov - Dec;24(6):345-350.
Nguyen HQ, Rondinelli J, Harrington A, et al. Functional status at discharge and 30-day readmission risk in COPD. Respir Med. 2015 Feb;109(2):238-46.
Mannino DM, Buist AS. Global burden of COPD: risk factors, prevalence, and future trends. Lancet (London, England). 2007 Sep 1;370(9589):765-73.
Aldonyte R, Jansson L, Piitulainen E, et al. Circulating monocytes from healthy individuals and COPD patients. Respir Res. 2003;4(1):11.
Oh JY, Lee YS, Min KH, et al. Difference in systemic inflammation and predictors of acute exacerbation between smoking-associated COPD and tuberculosis-associated COPD. Int J Chron Obstruct Pulmon Dis. 2018;13:3381-3387.
Suissa S, Dell'Aniello S, Ernst P. Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality. Thorax. 2012 Nov;67(11):957-63.
Wells JM, Xing D, Viera L, et al. The matrikine acetyl-proline-glycine-proline and clinical features of COPD: findings from SPIROMICS. Respir Res. 2019 Nov 12;20(1):254.
Byers DE, Alexander-Brett J, Patel AC, et al. Long-term IL-33-producing epithelial progenitor cells in chronic obstructive lung disease. The Journal of clinical investigation. 2013 Sep;123(9):3967-82.
Xia J, Zhao J, Shang J, et al. Increased IL-33 expression in chronic obstructive pulmonary disease. Am J Physiol Lung Cell Mol Physiol. 2015 Apr 1;308(7):L619-27.
Gimenes JA, Jr., Srivastava V, ReddyVari H, et al. Rhinovirus-induces progression of lung disease in a mouse model of COPD via IL-33/ST2 signaling axis. Clin Sci (Lond). 2019 Apr 30;133(8):983-996.
Qiu C, Li Y, Li M, et al. Anti-interleukin-33 inhibits cigarette smoke-induced lung inflammation in mice. Immunology. 2013 Jan;138(1):76-82.
Smith DE. IL-33: a tissue derived cytokine pathway involved in allergic inflammation and asthma. Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology. 2010 Feb;40(2):200-8.
Weber A, Wasiliew P, Kracht M. Interleukin-1 (IL-1) pathway. Science signaling. 2010 Jan 19;3(105):cm1.
Wu H, Yang S, Wu X, et al. Interleukin-33/ST2 signaling promotes production of interleukin-6 and interleukin-8 in systemic inflammation in cigarette smoke-induced chronic obstructive pulmonary disease mice. Biochem Biophys Res Commun. 2014 Jul 18;450(1):110-6.
Lin J, Zhang L, Zhao G, et al. A novel interleukin 33/ST2 signaling regulates inflammatory response in human corneal epithelium. PLoS One. 2013;8(4):e60963.
Bradford E, Jacobson S, Varasteh J, et al. The value of blood cytokines and chemokines in assessing COPD. Respir Res. 2017 Oct 24;18(1):180.
Luo Y, Zheng SG. Hall of Fame among Pro-inflammatory Cytokines: Interleukin-6 Gene and Its Transcriptional Regulation Mechanisms. Front Immunol. 2016;7:604.
Ruwanpura SM, McLeod L, Miller A, et al. Interleukin-6 promotes pulmonary emphysema associated with apoptosis in mice. American journal of respiratory cell and molecular biology. 2011 Oct;45(4):720-30.
Hoenderdos K, Condliffe A. The neutrophil in chronic obstructive pulmonary disease. American journal of respiratory cell and molecular biology. 2013 May;48(5):531-9.
Lott JM, Sumpter TL, Turnquist HR. New dog and new tricks: evolving roles for IL-33 in type 2 immunity. Journal of leukocyte biology. 2015 Jun;97(6):1037-48.
Sager R, Kutz A, Mueller B, et al. Procalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med. 2017 Jan 24;15(1):15.
Prins HJ, Duijkers R, van der Valk P, et al. CRP-guided antibiotic treatment in acute exacerbations of COPD in hospital admissions. The European respiratory journal. 2019 May;53(5).
Corti C, Fally M, Fabricius-Bjerre A, et al. Point-of-care procalcitonin test to reduce antibiotic exposure in patients hospitalized with acute exacerbation of COPD. Int J Chron Obstruct Pulmon Dis. 2016;11:1381-9.

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Emerging Role of Soluble Suppression of Tumorigenicity 2 as Biomarker for Acute Exacerbations in COPD Patients

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Abstract

Figures

Introduction

Materials and methods

Patient enrolment

Grouping of COPD patients

Measurement of sST2 and the retrieving of laboratory data

Statistical analysis

Results

Clinical characteristics of participants

sST2 elevated in AECOPD and correlated with clinical parameters

sST2 correlated with disease severity

Performance of sST2 in differentiating AECOPD patients

sST2 to distinguish AECOPD with bacterial infection from those with non-bacterial infection

sST2 levels declined after AECOPD patients reached clinical stability

Discussion

sST2 as a biomarker for AECOPD

The IL-33/ST2 signalling pathway in COPD

Differentiating bacterial exacerbations with sST2

Perspective

Declarations

References

Additional Declarations

Supplementary Files

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