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Abstract

Background:

Patients diagnosed with chronic obstructive pulmonary disease (COPD) have increased risks for a series of physical and mental illnesses. Tumor necrosis factor-α (TNF-α) has been reported to participate in the development of COPD and its complications. However, the values of blood TNF-α level used in the diagnosis of COPD remains controversial. In view of this, we performed a systematic review and meta-analysis to evaluate the correlation between TNF-α level and COPD.

Methods:

We searched PubMed, Web of Science, Embase and CNKI up to May 2018. The selection criteria were set according to the PICOS framework. A random-effects model was then applied to evaluate the overall effect sizes by calculating standard mean difference (SMD) and its 95% confidence intervals (CIs).

Results:

A total of 40 articles containing 4189 COPD patients and 1676 healthy controls were included in this meta-analysis. The results indicated a significant increase in TNF-α level in the COPD group compared with the control group (SMD: 1.24, 95% CI: 0.78–1.71, p < 0.00001). According to the subgroup analyses, we noted that TNF-α level was associated with predicted first second of forced expiration (FEV₁) (%) and study region. However, no association between TNF-α level and COPD was found when the participants were nonsmokers, and the mean age was less than 60 years.

Conclusions:

Our results indicated that TNF-α level was increased in COPD patients when compared with healthy controls. Illness progression and a diagnosis of COPD might contribute to higher TNF-α levels. However, the underlying mechanism still remains unknown and needs further investigation.

The reviews of this paper are available via the supplemental material section.

Keywords

biomarker chronic obstructive pulmonary disease meta-analysis tumor necrosis factor-α

Introduction

Chronic obstructive pulmonary disease (COPD) kills more than 3 million people worldwide every year.¹ Many factors have been reported to be associated with COPD, including systemic and local inflammation, air pollution and a sedentary lifestyle.^2–4 However, the exact mechanisms underlying COPD still remain unclear. Since COPD is a chronic inflammatory disease, the relationship between inflammation and COPD has been widely evaluated. Tumor necrosis factor-α (TNF-α), one of the major inflammatory factors, is implicated in the pathogenesis of many disorders, including COPD.^5,6 However, due to the small sample sizes, most studies lack adequate statistical power to clarify the relationship between TNF-α and COPD. Moreover, currently available studies have provided inconsistent, or even contrary, results. For example, Karadag and colleagues have pointed out that raised serum level of TNF-α can be used as a biomarker for the systemic inflammatory response in stable COPD patients.⁷ But Franciosi and colleagues showed that healthy people and COPD patients at different stages had no statistical difference in TNF-α concentrations.⁸ To comprehensively investigate the association between TNF-α and COPD, and evaluate the diagnostic value of TNF-α in COPD, we conducted this meta-analysis to systematically evaluate the relationship between them.

Materials and methods

Literature search

We systematically searched four electronic databases (PubMed, Web of Science, EMBASE, and Cochrane library database CENTRAL) up to May 2018. The search terms were [‘pulmonary disease, chronic obstructive’ (MeSH Terms) or ‘chronic obstructive pulmonary disease’ or ‘COPD’ or ‘COAD’ or ‘chronic obstructive airway disease’ or ‘chronic obstructive lung disease’ or ‘emphysema’ or ‘chronic bronchitis’] and [‘Tumor necrosis factor-a’ (MeSH Terms) or ‘Tumor necrosis factor-a ’or ‘TNF-α’] and (‘systemic inflammation’ or ‘biological markers’) (Supplementary Table S1). Only articles published in English were included. We also went through the references of eligible studies and review articles manually to identify possible relevant publications.

Study selection and inclusion and exclusion criteria

The inclusion criteria, set according to the PICOS framework (population, intervention, comparison, outcomes, study design), were as follows (Table 1): population, COPD patients; intervention, TNF-α; comparison, healthy control or non-COPD; outcomes, concentration of TNF-α; study design, case-control study.

Table 1.

PICOS table of included studies.

Category	Description	Search strategy terms
Population	COPD	COPD OR Chronic obstructive pulmonary disease
Intervention	TNF-α	TNF-α OR Tumor necrosis factor-alpha
Control	Healthy control or non-COPD	Healthy control or non-COPD
Outcome	Concentration of TNF-α	TNF-α concentration OR TNF-α level
Study Design	Case-control study	Case-Control study OR Case-Comparison Studies OR Case-compare study OR case-referent study OR Matched case-control study NOT animals

COPD, chronic obstructive pulmonary disease; PICOS, population, intervention, comparison, outcomes, study design; TNF-α, tumor necrosis factor-alpha

The eligible studies had to meet all of the following criteria: evaluation of the association between TNF-α and COPD was described; the specific concentration of TNF-α was provided; TNF-α level in both the control and COPD group was provided; sufficient patient data for calculating standard mean difference (SMD) and its 95% confidence intervals (CIs) were provided; COPD patients were diagnosed according to the criteria of the American Thoracic Society or Global Initiative for Chronic Obstructive Lung Disease; and healthy controls who had no medical illness or abnormalities in physical examination and laboratory date, and presented no symptoms of infection, were included. The exclusion criteria included: patients who received nutritional support with therapy; conference papers, reports, comments or review articles; studies without a control group; and patients with a history or diagnosis of asthma, allergy, or respiratory diseases other than COPD. The reasons for exclusion are shown in Table 2.

Table 2.

Exclusion criteria.

Characteristics of excluded studies	Reasons
Patients who received nutritional support with therapy	Nutritional support is likely to affect the expression of TNF-α
Conference papers, reports, comments or review articles	Conference papers, reports, comments, or review articles do not have enough case-control studies. These paper cannot provide enough data about PICOS
Without control group	All included studies are case-control studies, in which patients with COPD are diagnosed as cases, and individuals who do not have the disease or non-COPD are comparable as controls
Patients with a history or diagnosis of asthma, allergy, or respiratory diseases other than COPD	The aim of this review was to investigate the relationship between TNF-α and COPD rather than other respiratory diseases

COPD, chronic obstructive pulmonary disease; TNF-α, tumor necrosis factor-alpha

Quality assessment

Two reviewers (YY and ZJ) independently evaluated the quality of included studies according to the Newcastle-Ottawa Quality Assessment Scale (NOS). The NOS is a semiquantitative scale composed of three domains: selection, comparability, and exposure. The maximum NOS score is 9: a study with a total score of ⩽3 was considered as poor quality, those scoring 4–6 were of moderate quality, and a score of 7–9 was considered high quality.

Data extraction

Two investigators (DX and YY) independently extracted the following information from the original studies: first author’s name, year of publication, country, sample size, clinical characteristics [including sex ratio, mean ages, smoking status, COPD status, body mass index (BMI), and the predicted first second of forced expiration (FEV₁)]. Disagreements between the two reviewers were resolved by consultation with a third reviewer (WSY).

Statistical analysis

The RevMan 5.3 software was used to perform the meta-analysis. The SMD and corresponding 95% CI were calculated to evaluate the relationships between TNF-α level and COPD. The Chi-squared test and I² statistics were applied to detect the heterogeneity among studies. A p < 0.05 in Chi-squared test or I² > 50% indicated the presence of significant heterogeneity. A random effect model or fixed model was then used based on the presence or absence of significant heterogeneity. A sensitivity analysis was performed to explore the origins of heterogeneity. Publication bias was assessed using funnel plots with standard error.

Results

Study selection

The initial literature search returned a total of 949 articles. We excluded 143 duplicated studies. After a careful review of the titles and abstracts of remaining studies, a further 433 articles were excluded, and another 323 articles were also excluded for various reasons. Finally, 40 studies involving 4189 COPD patients and 1676 healthy controls were included in this meta-analysis.^9–45 The flowchart for the literature search is presented in Figure 1.

Figure 1.

Flow diagram of the literature search process.

The characteristics of the included studies are summarized in Table 3. Eight studies had a NOS score of 9^{25,29,33,36,37,40,42,45}; seven studies scored 8^{13–15,17,18,22,44}; nine studies scored 7^{11,19,30,31,34,35,38,41,46}; ten studies scored 6^{9,10,12,24,27,28,32,43,47,48}; four studies scored 5^16,20,26,39; and the last two studies scored 4.^21,23 The NOS scores suggested that all included studies were of moderate or high quality. Regarding location, the majority of studies were from Europe,²⁶ two studies were from the US,^14,26 one study was from Africa,¹⁵ and eight studies were from Asia.^{22,29,32,34,35,38,43,45} Patients in 9 studies were treated with steroids, while patients in the remaining 24 studies were not treated with steroids. The mean age, smoking status, COPD status, gender, and BMI of the study participants in the included studies are also provided in Table 3.

Table 3.

Characteristics of the included studies.

Study	Year	Country	Sample size	Mean age		Sex(male/Female)		Smokingstatus	Reversibilitytest	Treat with	COPDstatus	NOS
Study	Year	Country	Sample size	Case	Control	Case	Control	Smokingstatus	Reversibilitytest	Steroids	COPDstatus	NOS
Calikoglu⁹	2004	Turkey	41	62.18 ± 2.50	54.73 ± 2.23	NR	NR	NR	No	NR	Exacerbated	6
Agusti¹⁰	2012	Spain	2409	63.5 ± 7.1	53.0 ± 8.6	1160/1004	76/169	NR	Yes	NR	NR	6
Once¹¹	2010	Turkey	73	62.8 ± 5.5	61.8 ± 7.4	38/2	31/2	Ex-smokers	No	No	NR	7
Kleniewska¹²	2016	Poland	42	59.8 ± 6.7	43.7 ± 14.4	20/0	15/7	NR	Yes	No	Stable	6
Rovina¹³	2007	Greece	30	54 ± 9	46 ± 11	NR	NR	Current-smokers	Yes	No	NR	8
Gagnon¹⁴	2014	Canada	56	65 ± 6	62 ± 8	25/12	13/6	Ex-smokers	No	NR	Mild	8
Ben Anes¹⁵	2017	Tunisia	285	61.58 ± 1.75	58.15 ± 0.7	50/6	203/26	Ex-smokers	Yes	NR	Exacerbated	8
Perez-deLiano¹⁶	2017	Spain	109	65.6 ± 10.1	59.8 ± 10.5	18/26	53/12	NR	Yes	NR	NR	5
FoschinoBarbaro¹⁷	2007	UK	42	NR	NR	24/3	12/3	Ex-smokers	Yes	No	Stable	8
Barreiro¹⁸	2013	Spain	21	59 ± 8	58 ± 14	9	12	Ex-smokers	No	No	NR	8
Beeh¹⁹	2003	Germany	26	59 ± 9.25	31 ± 8.75	8/4	8/6	Ex-smokers	Yes	No	Stable	7
Di Stefano²⁰	2018	Italy	41	NR	NR	19/4	8/10	Ex-smokers	Yes	No	Exacerbated	5
Breyer²¹	2011	Netherlands	127	NR	NR	NR	NR	Ex-smokers	No	No	Exacerbated	4
Zhang²²	2016	China	89	61.14 ± 10.21	60.92 ± 9.62	30/20	23/16	NR	Yes	No	Moderate	8
Dima²³	2010	Greece	38	58.4 ± 2.0	41.5 ± 3.5	NR	NR	Ex-smokers	Yes	No	NR	4
Kawayama²⁴	2016	UK	20	62.2 ± 6.6	64.2 ± 6.6	7/3	5/5	Ex-smokers	No	Inhaled	NR	6
Gaki²⁵	2011	Greece	354	63 ± 1.86	60 ± 1.71	169/53	97/35	Ex-smokers	No	Inhaled	Stable	9
Godoy²⁶	2003	Brazi	24	62 ± 2.25	54 ± 1.5	14/0	5/5	NR	No	No	NR	5
Hacievliyagil²⁷	2012	Turkey	40	61.2 ± 1.7	59.1 ± 5.4	17/3	14/6	NR	Yes	Oral	Stable	6
Huertas²⁸	2010	Italy	33	69 ± 8	63 ± 7	NR	NR	NR	No	No	Stable	6
Ju²⁹	2011	China	130	65.17 ± 6.79	63.98 ± 5.77	54/16	21/39	Ex-smokers	Yes	No	Stable	9
Karadag³⁰	2007	Turkey	125	63.5 ± 7.59	61.10 ± 7.68	NR	NR	NR	Yes	Inhaled	Stable	7
Karadag³¹	2008	Turkey	65	65.6 ± 7.8	63.2 ± 7.6	NR	NR	Ex-smokers	Yes	No	Stable	7
Shin³²	2007	Korea	105	63.6 ± 7.4	66.5 ± 8.9	NR	NR	NR	No	No	Stable	6
Kythreotis³³	2008	Greece	77	65.8 ± 8.3	65.9 ± 9.6	43/9	19/6	Ex-smokers	No	No	Exacerbation	9
Liu³⁴	2009	China	63	70 ± 7	70 ± 7	NR	NR	No-smoker	No	No	Stable	7
Huang³⁵	2016	China	67	60.2 ± 10.1	55.7 ± 10.3	21/11	19/16	NR	Yes	NR	NR	7
Moermans³⁶	2011	Belgium	128	62 ± 12	40 ± 12	73/21	24/10	Ex-smokers	Yes	Yes	Stable	9
Piehl-Aulin³⁷	2008	Sweden	40	64 ± 8.7	61.9 ± 7.9	11/15	7/7	Ex-smokers	No		Stable	9
Tan³⁸	2016	China	20	65 ± 3	50 ± 5	6/4	4/6	Ex-smokers	Yes	Yes	Stable	7
Guiot³⁹	2017	Belgium	62	63 ± 9	55 ± 9	24/8	11/19	NR	No	NR	NR	5
Sarioglu⁴⁰	2015	Turkey	175	64.0 ± 8.9	61.5 ± 9.2	100/10	55/10	Ex-smokers	Yes	No	Stable	9
Uzum ⁴¹	2013	Turkey	49	65.9 ± 10.0	50.2 ± 8.4	NR	NR	No-smoker	No	No	Stable	7
Kosacka⁴²	2015	Poland	210	62.2 ± 9.37	49.48 ± 13.68	121/60	18/11	Ex-smokers	No	NR	Stable	9
Cheng⁴³	2008	China	343	71.9 ± 8.0	74.7 ± 3.7	152/32	129/30	Ex-smokers	Yes	NR	NR	6
Valipour⁴⁴	2008	Austria	60	62 ± 9	59 ± 8	23/7	23/7	NR	Yes	No	Exacerbation	8
Zhang⁴⁵	2010	China	65	70.93 ± 5.58	69.16 ± 7.43	38/8	13/6	Ex-smokers	Yes	No	Stable	9
Soler⁴⁶	1999	Spain	21	68 ± 9	51 ± 11	13/0	5/3	Ex-smokers	No	No	Stable	7
Vera⁴⁷	1996	UK	30	62.5 ± 3.2	39.4 ± 3.1	NR	NR	Ex-smokers	No	No	NR	6
De Godoy⁴⁸	1996	US	23	67.0 ± 4.9	63.5 ± 5.8	6/4	11/2	NR	No	No	NR	6

COPD, chronic obstructive pulmonary disease; NOS, Newcastle-Ottawa Quality Assessment Scale; NR, not recorded

Meta-analysis

Due to the existence of significant heterogeneity (p < 0.00001, I² = 98%), this meta-analysis used a random effect model. Compared with the control group, the COPD patients had a significantly elevated level of TNF-α (SMD: 1.45, 95% CI: 0.44–2.27, p < 0.00001) (Figure 2).

Figure 2.

Comparison of tumor necrosis factor-α level between COPD patients and controls in the included studies.

Subgroup analysis

Subsequently, subgroup analyses stratified for FEV₁%, smoking history, COPD status, country, mean age, and BMI were performed to further understand the association between TNF-α level and COPD, and discover the source of heterogeneity (Table 4). A total of 36 studies were included in the subgroup analysis based on FEV₁%; the TNF-α level was 1.49 higher in COPD group compared with the control group (95% CI: 0.89–2.00, p < 0.00001) (Figure 3). The heterogeneity was still significant (>50%: p < 0.00001, I² = 94%; <50%: p < 0.0001, I² = 98%). In the subgroup analysis based on smoking status (Figure 4), the TNF-α level in the ex-smokers/current smoker group was higher than those in the control and case groups (SMD: 1.63, 95% CI: 0.77–2.49, p = 0.0002), but was not different for smoking patients (SMD: 0.70, 95% CI: –1.36 to 2.76, p = 0.51). The heterogeneity in both groups was still significant (ex-smokers/current smoker: p < 0.00001, I² = 98%; No: p < 0.00001, I² = 95%). A subgroup analysis was then performed according to COPD status (Figure 5). Patients with stable COPD and exacerbated COPD had higher TNF-α levels than the control group (stable: SMD: 1.33, 95% CI: 0.46–2.21, p = 0.003; exacerbated: SMD: 2.43, 95% CI: 0.29–4.57, p = 0.03), but the heterogeneity was still significant regardless of COPD status (stable: p < 0.00001, I² = 98%; exacerbated: p < 0.00001, I² = 99%). Moreover, a subgroup analysis was carried out based on mean age (Figure 6). The TNF-α level in age >60 groups was 0.98 higher than that of the control group (SMD: 0.98 95% CI: 0.29–1.68, p = 0.006). The heterogeneity was still significant in both groups (>60: p < 0.00001, I² = 97%; <60: p < 0.00001, I² = 91%) (Figure 6). In addition, in the country and BMI subgroup, the TNF-α level in the case group was significantly higher than that of the control group (Europe: SMD: 1.58, 95% CI: 0.94–2.23, p < 0.00001; others: SMD: 1.76, 95% CI: 0.78–2.74, p = 0.0004) (BMI ⩾20: SMD: 8.53, 95% CI: 7.43–9.62, p < 0.00001; BMI <20: SMD: 4.85, 95% CI: –3.22 to 12.93, p = 0.24). The heterogeneity was still obvious (Europe: p < 0.00001, I² = 98%; others: p < 0.00001, I² = 98%) (BMI ⩾ 20: p < 0.00001, I² = 100%; BMI < 20: p < 0.00001, I² = 95%). (Figures 7 and 8). Finally, subgroup analysis was performed based on sample source. The TNF-α level in the case group was significantly higher than that of the control group in serum and BAL; the difference has statistical significance (serum: p < 0.00001, I² = 100%; BAL: p < 0.00001, I² = 100%) (Figure 9).

Table 4.

Subgroup analysis of TNF-α level in COPD.

Subgroups	N	SMD (95%CI)	p	Test of heterogeneity
Subgroups	N	SMD (95%CI)	p	I ²	p
COPD Status
Stable	1654	1.33 (0.46–2.21)	p = 0.003	98	p < 0.00001
Exacerbated	590	2.43 (0.29–4.57)	p = 0.03	99	p < 0.00001
FEV1 %
>50%	1046	1.49 (0.88–2.10)	p < 0.00001	94	p < 0.00001
<50%	4154	1.39 (0.56–2.22)	p = 0.0010	98	p < 0.00001
Current smoking statusEx-smokers/current smokers	2352	1.63 (0.77–2.49)	p = 0.0002	98	p < 0.00001
No	98	0.70 (–1.36 to 2.76)	p = 0.51	95	p < 0.00001
Country
Europe	4461	1.58 (0.94–2.23)	p < 0.00001	98	p < 0.00001
Others	1190	1.76 (0.78–2.74)	p = 0.0004	98	p < 0.00001
Mean age
>60	1585	0.98 (0.29–1.68)	p = 0.006	97	p < 0.00001
<60	157	0.58 (–0.59 to 1.74)	p = 0.33	91	p < 0.00001
BMI
>20	2146	0.72 (0.69–0.76)	p < 0.00001	100	p < 0.00001
<20	228	2.61 (1.67–3.55)	p < 0.00001	95	p < 0.00001

BMI, Body mass index; COPD, chronic obstructive pulmonary disease; FEV1, first second of forced expiration; TNF-α, tumor necrosis factor-alpha

Figure 3.

Subgroup analyses of the relationship between tumor necrosis factor-α and chronic obstructive pulmonary disease according to first second of forced expiration (%).

Figure 4.

Subgroup analyses of the relationship between tumor necrosis factor-α and chronic obstructive pulmonary disease according to smoking status.

Figure 5.

Subgroup analyses of the relationship between TNF-α and chronic obstructive pulmonary disease (COPD) according to COPD status.

Figure 6.

Subgroup analyses of the relationship between tumor necrosis factor-α and chronic obstructive pulmonary disease according to age.

Figure 7.

Subgroup analyses of the relationship between tumor necrosis factor-α and chronic obstructive pulmonary disease according to country.

Figure 8.

Subgroup analyses of the relationship between tumor necrosis factor-α and chronic obstructive pulmonary disease according to body mass index.

Figure 9.

Subgroup analyses of the relationship between tumor necrosis factor-α and chronic obstructive pulmonary disease according to sample source.

Meta-regression analysis

To further determine the source of heterogeneity, meta-regression analyses were conducted. The results indicated that publication year, region, BMI, NOS, study sample size, and smoking status were not potential sources of heterogeneity (Table 5).

Table 5.

Meta-regression analysis coefficients for TNF-α levels.

Covariates	Coefficient	p	95% confidence interval
Year	−0.44053	0.593	(–0.21028 to 0.12217)
Region	−0.20124	0.843	(–2.25307 to 1.85058)
BMI	−0.33315	0.724	(–2.23490 to 1.56859)
Sample size	−0.000526	0.659	(–0.00293 to 0.00187)
Smoking status	−0.203374	0.810	(–1.91399 to 1.50724)
NOS	0.556458	0.886	(–7245434 to 0.83583)

BMI, Body mass index; NOS, Newcastle-Ottawa Quality Assessment Scale; TNF-α, tumor necrosis factor-alpha

Sensitivity analysis and publication bias

The sensitivity analysis showed that removing each of the 40 included studies did not result in significant change in the pooled effect size, indicating that the results of the present meta-analysis were stable (Table 6). Potential publication bias in this meta-analysis was evaluated with a funnel plot. The result showed that the included studies were symmetrically distributed, excluding the presence of significant publication bias (Figure 10).

Table 6.

Sensitivity analysis.

Study	SMD (95% CI)	p Heterogeneity	I ²
Calikoglu 2004⁹	0.69 (0.62–0.76)	p < 0.00001	97
Agusti 2012¹⁰	1.00 (0.91–1.08)	p < 0.00001	97
Once 2010¹¹	0.70 (0.63–0.77)	p < 0.00001	98
Kleniewska 2016¹²	0.70 (0.63–0.77)	p < 0.00001	98
Rovina 2007¹³	0.69 (0.62–0.77)	p < 0.00001	97
Gagnon 2014¹⁴	0.71 (0.64–0.79)	p < 0.00001	98
Ben Anes 2017¹⁵	0.72 (0.64–0.79)	p < 0.00001	98
Perez-de-Liano 2017¹⁶	0.68 (0.61–0.75)	p < 0.00001	97
Foschino Barbaro 2007¹⁷	0.69 (0.62–0.77)	p < 0.00001	97
Barreiro 2013¹⁸	0.71 (0.64–0.78)	p < 0.00001	98
Beeh 2003¹⁹	0.71 (0.63–0.78)	p < 0.00001	98
Di Stefano 2018²⁰	0.71 (0.64–0.78)	p < 0.00001	98
Breyer 2011²¹	0.69 (0.62–0.77)	p < 0.00001	98
Zhang 2016²²	0.72 (0.65–0.80)	p < 0.00001	97
Dima 2010²³	0.70 (0.62–0.77)	p < 0.00001	98
Kawayama 2016²⁴	0.71 (0.63–0.78)	p < 0.00001	98
Gaki 2011²⁵	0.58 (0.50–0.65)	p < 0.00001	97
Godoy 2003²⁶	0.71 (0.64–0.78)	p < 0.00001	98
Hacievliyagil 2012²⁷	0.69 (0.62–0.76)	p < 0.00001	97
Huertas 2010²⁸	0.69 (0.62–0.76)	p < 0.00001	97
Ju 2011²⁹	0.70 (0.63–0.77)	p < 0.00001	98
Karadag 2007³⁰	0.72 (0.64–0.79)	p < 0.00001	98
Karadag 2008³¹	0.72 (0.64–0.79)	p < 0.00001	98
Shin 2007³²	0.71 (0.64–0.79)	p < 0.00001	98
Kythreotis 2008³³	0.69 (0.62–0.76)	p < 0.00001	97
Liu 2009³⁴	0.69 (0.62–0.76)	p < 0.00001	97
Huang 20162³⁵	0.68 (0.61–0.75)	p < 0.00001	97
Moermans 2011³⁶	0.76 (0.69–0.83)	p < 0.00001	97
Piehl-Aulin 2008³⁷	0.70 (0.62–0.77)	p < 0.00001	98
Tan 2016³⁸	0.70 (0.63–0.77)	p < 0.00001	97
Guiot 2017³⁹	0.72 (0.64–0.79)	p < 0.00001	98
Sarioglu 2015⁴⁰	0.65 (0.58–0.73)	p < 0.00001	97
Uzum 2013⁴¹	0.71 (0.64–0.79)	p < 0.00001	97
Kosacka 2015⁴²	0.72 (0.64–0.80)	p < 0.00001	97
Cheng 2008⁴³	0.59 (0.51–0.67)	p < 0.00001	97
Valipour 2008⁴⁴	0.74 (0.64–0.81)	p < 0.00001	97
Zhang 2010⁴⁵	0.70 (0.62–0.77)	p < 0.00001	98
Vera 1996⁴⁶	1.19 (0.72–1.66)	p < 0.00001	97
Soler 1999⁴⁷	1.25 (0.78–1.73)	p < 0.00001	97
De Godoy 1996⁴⁸	1.25 (0.78–1.73)	p < 0.00001	97

SMD, Standard mean difference; CI, 95% confidence intervals

Figure 10.

A funnel plot analysis of publication bias.

Discussion

COPDs induced by chronic bronchitis and emphysema are characterized by not fully reversible and progressive airflow limitation, and represent one of the most serious public health concerns in the world.^49,50 As an inflammatory disease, inflammation of airways and lung parenchyma have been identified as one of the major pathogenic mechanisms of COPD.⁵¹ Inflammation is a complex process, in which a variety of cells and molecules are involved and a series of inflammatory signaling pathways are activated.

Previously, several meta-analyses have evaluated the association between TNF-α levels and COPD; however, the conclusions were conflicting. Gan and colleagues performed a meta-analysis including 14 studies and reported a significant correlation between systemic inflammatory markers, including TNF-α, and lung function.⁵² Bin and colleagues, however, indicated that there was no significant correlation between COPD and TNF-α level in a meta-analysis of 24 studies.⁵³ The main limitation of the previous meta-analyses is the relatively small number of the included studies, which leads to a small size of participant cohort. To overcome this limitation, we conducted the updated meta-analysis presented here, which includes 40 articles with 4152 COPD patients and 1639 healthy controls, to better evaluate the potential associations between TNF-α level and COPD. We found that COPD patients had significantly higher TNF-α levels than healthy controls. To explain this result, the following factors need to be taken into account. First, common genetic or constitutional differences between COPD patients and controls probably exist, and these differences predispose COPD patients to both systemic and pulmonary inflammation.⁵⁴ Second, during inflammation processes, activated inflammatory cells and a variety of released inflammatory mediators, such as IL-8, IL-6, and TNF-α,can destroy lung structure and promote the inflammatory response of neutrophils.⁵⁵ Third, the elevated blood inflammatory factors might be explained by several previously proposed mechanisms, such as local pulmonary inflammation due to smoking, oxidative stress, and tissue hypoxia.⁵⁶

Due to the high heterogeneity, a subgroup analysis was then performed to minimize heterogeneity among the included studies. FEV₁ is the most widely used parameter for diagnosis and evaluation of treatment effect in severe COPD, and the current COPD staging system is based mainly on this parameter.⁸ Therefore, we subclassified the patients into two subgroups: FEV₁% >50% and FEV₁% <50% to perform subgroup analysis. The results showed that TNF-α level was elevated in COPD patients with both FEV₁% >50% and FEV₁% <50% compared with controls. Smoking is known to be one of the main causes of COPD; thus, a subgroup analysis based on smoking status was performed. We found a significant association between TNF-α level and COPD in participants with smoking history, but we failed to find this association in nonsmoking participants. This result was consistent with that of a study by Mosran and colleagues, who showed that, compared with non-COPD smokers, smokers with COPD had markedly higher levels of TNF-α,⁵⁷ suggesting that smoking can further increase TNF-α levels. In addition, the level of TNF-α was also associated with COPD status, region of study, and BMI of participants. However, no association was found between TNF-α level and COPD if the mean age was less than 60 years.

Although our results reached the same conclusion as many studies, some other studies report different results. Schmidt-loanas and colleagues suggested there were no significant differences in the correlation between TNF-α levels and COPD exacerbation.⁵⁸ Monika and colleagues also did not observe any obvious difference in serum TNF-α levels between COPD patients and controls.⁴² This inconsistency among studies could be explained by differences in study design; different COPD status of enrolled patients across the included studies, since early-stage COPD are insensitive to TNF-α; and the inclusion of studies with different baseline characteristics.

Before we draw any firm conclusions, there are several limitations of this study that need to be considered. First, the significant heterogeneity in the present meta-analysis may limit generalization of the pooled results. Second, the methods for measuring TNF-α level were inconsistent. Third, since we limited the language of publication to English, we may have missed other related studies published in other languages. For example, the literature search for CNKI found several related studies in Chinese, but we excluded them from this study according to the exclusion criteria. Finally, the association between TNF-α level and patient quality of life was not evaluated due to the limited information available.

Conclusion

In this meta-analysis, a significant association between COPD and elevated TNF-α level was identified. These results encourage further exploration of the roles of TNF-α in COPD formation and development, and the potential of TNF-α as a novel biomarker and therapeutic target for COPD.

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Footnotes

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and publication of this article: This study was supported by funds from the Respiratory Prevention and Treatment Center of Shaanxi Provincial Government (2016HXKF09), Shaanxi Province Key Program (2017SF-256).

Conflict of interest statement

The authors declare that there is no conflict of interest.

ORCID iD

Yang Yao

Supplemental material

Supplemental material for this article is available online.

References

Rabe

Watz

Chronic obstructive pulmonary disease. Lancet 2017; 389: 1931–1940.

Dransfield

Stolz

Kleinert

et al

Towards eradication of chronic obstructive pulmonary disease: a lancet commission. Lancet 2019; 393: 1786–1788.

Cai

Jin

et al

Combined effects of chronic obstructive pulmonary disease and depression on spatial memory in old rats. Chin Med Sci J 2018; 30: 260–266.

Yazdani

Marefati

Shahesmaeili

et al

Effect of aerobic exercises on serum levels of apolipoprotein A1 and apolipoprotein B, and their ratio in patients with chronic obstructive pulmonary disease. Tanaffos 2018; 17: 82–89.

Emami Ardestani

Zaerin

. Role of serum interleukin 6, albumin and C-reactive protein in COPD patients. Tanaffos 2015; 14: 134–140.

Crisafulli

Menendez

Huerta

et al

Systemic inflammatory pattern of patients with community-acquired pneumonia with and without COPD. Chest 2013; 143: 1009–1017.

Karadag

Karul

Cildag

et al

Biomarkers of systemic inflammation in stable and exacerbation phases of COPD. Lung 2008; 186: 403–409.

Franciosi

Page

Celli

et al

Markers of disease severity in chronic obstructive pulmonary disease. Pulm Pharmacol Ther 2006; 19: 189–199.

Calikoglu

Sahin

Unlu

et al

Leptin and TNF-alpha levels in patients with chronic obstructive pulmonary disease and their relationship to nutritional parameters. Respiration 2004; 71: 45–50.

10.

Agusti

Edwards

Rennard

et al

Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype. PLos One 2012; 7: e37483.

11.

Oncel

Baser

Cam

et al

Peripheral neuropathy in chronic obstructive pulmonary disease. COPD 2010; 7: 11–16.

12.

Kleniewska

Walusiak-Skorupa

Piotrowski

et al

Comparison of biomarkers in serum and induced sputum of patients with occupational asthma and chronic obstructive pulmonary disease. J Occup Health 2016; 58: 333–339.

13.

Rovina

Papapetropoulos

Kollintza

et al

Vascular endothelial growth factor: an angiogenic factor reflecting airway inflammation in healthy smilers and in patients with bronchitis type of chronic obstructive pulmonary disease?

Respir Res 2007; 8: 53–61.

14.

Gagonon

Lemire

Dube

et al

Preserved function and reduced angiogenesis potential of the quadriceps in patients with mild COPD. Respir Res 2014; 15: 4

15.

Ben Anes

Ben Nasr

Garrouch

et al

Alterations in acetylcholinesterase and butyrylcholinesterase activities in chronic obstructive pulmonary disease: relationships with oxidative and inflammatory markers. Mol Cell Biochem 2018; 445: 1–11.

16.

Pérez-de-Llano

Cosio

and CHACOS Study Group. Asthma-COPD overlap is not a homogeneous disorder: further supporting data. Respir Res 2017; 18: 183–187.

17.

Foschino Barbaro

Carpagnano

Spanevello

et al

Inflammation, oxidative stress and systemic effects in mild chronic obstructive pulmonary disease. Int J Immunopathol Pharmacol 2007; 20: 753–763.

18.

Barreiro

Fermoselle

Mateu-Jimenez

et al

Oxidative stress and inflammation in the normal airways and blood of patients with lung cancer and COPD. Free Radic Biol Med 2013; 65: 859–871.

19.

Beeh

Beier

Kornmann

et al

Sputum matrix metalloproteinase-9, tissue inhibitor of metalloprotinease-1, and their molar ratio in patients with chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis and healthy subjects. Respir Med 2003; 97: 634–639.

20.

Di Stefano

Coccini

Roda

et al

Blood MCP-1 levels are increased in chronic obstructive pulmonary disease patients with prevalent emphysema. Int J Chron Obstruct Pulmon Dis 2018; 24: 1691–1700.

21.

Breyer

Rutten

Vernooy

et al

Gender differences in the adipose secretome system in chronic obstructive pulmonary disease (COPD): a pivotal role of leptin. Respir Med 2011; 105: 1046–1053.

22.

Zhang

Wang

et al

Gender difference in plasma fatty-acid-binding protein 4 levels in patients with chronic obstructive pulmonary disease. Biosci Rep 2016; 36: e00302.

23.

Dima

Rovina

Gerassimou

et al

Pulmonary function tests, sputum induction, and bronchial provocation tests: diagnostic tools in the challenge of distinguishing asthma and COPD phenotypes in clinical practice. Int J Chron Obstruct Pulmon Dis 2010; 7: 287–296.

24.

Kawayama

Kinoshita

Matsunaga

et al

Responsiveness of blood and sputum inflammatory cells in Japanese COPD patients, non-COPD smoking controls, and non-COPD nonsmoking controls. Int J Chron Obstruct Pulmon Dis 2016; 10: 295–303.

25.

Gaki

Kontogianni

Papaioannou

et al

Associations between BODE index and systemic inflammatory biomarkers in COPD. COPD 2011; 8: 408–413.

26.

Godoy

Campana

Geraldo

et al

Cytokines and dietary energy restriction in stable chronic obstructive pulmonary disease patients. Eur Respir J 2003; 22: 920–925.

27.

Hacievliyaqil

Mutlu

Temel

et al

Airway inflammatory markers in chronic obstructive pulmonary disease patients and healthy smokers. Niger J Clin Pract 2013; 16: 76–81.

28.

Huertas

Testa

Riccioni

et al

Bone marrow-derived progenitors are greatly reduced in patients with severe COPD and low-BMI. Respir Physiol Neurobiol 2010; 31: 23–31.

29.

Chen

RC.

Serum myostatin levels and skeletal muscle wasting in chronic obstructive pulmonary disease. Respir Med 2012; 106: 102–108.

30.

Karadag

Ozcan

Karul

et al

Correlates of erectile dysfunction in moderate-to-severe chronic obstructive pulmonary disease patients. Respirology 2007; 12: 248–253.

31.

Karadag

Kirdar

Karul

et al

The value of C-reactive protein as a marker of systemic inflammation in stable chronic obstructive pulmonary disease. Eur J Intern Med 2008; 19: 104–108.

32.

Shin

Chung

Lee

KH.

Effects of TNF-αlpha and leptin on weight loss in patients with stable chronic obstructive pulmonary disease. Korean J Intern Med 2007; 22: 249–255.

33.

Kythreotis

Kokkini

Avgeropoulou

et al

Plasma leptin and insulin-like growth factor I levels during acute exacerbations of chronic obstructive pulmonary disease. BMC Pulm Med 2009; 5: 11–21.

34.

Liu

Chen

et al

Circulating visfatin in chronic obstructive pulmonary disease. Nutrition 2009; 25: 373–378.

35.

Huang

Liu

et al

Plasma Inflammatory Cytokine IL-4, IL-8, IL-10, and TNF-α levels correlate with pulmonary function in patients with asthma-chronic obstructive pulmonary disease (COPD) overlap syndrome. Med Sci Monit 2016; 9: 2800–2808.

36.

Moermans

Heinen

Nguyen

et al

Local and systemic cellular inflammation and cytokine release in chronic obstructive pulmonary disease. Cytokine 2011; 56: 298–304.

37.

Piehl-Aulin

Jones

Lindvall

et al

Increased serum inflammatory markers in the absence of clinical and skeletal muscle inflammation in patients with chronic obstructive pulmonary disease. Respiration 2009; 78: 191–196.

38.

Tan

Xuan

Cao

et al

Decreased histone deacetylase 2 (HDAC2) in peripheral blood monocytes (PBMCs) of COPD patients. PLoS One 2016; 11: e0147380.

39.

Guiot

Henket

Corhay

et al

Sputum biomarkers in IPF: evidence for raised gene expression and protein level of IGFBP-2, IL-8 and MMP-7. PLoS One 2017; 12: e0171344.

40.

Sarioglu

Hismiogullari

Bilen

et al

Is the COPD assessment test (CAT) effective in demonstrating the systemic inflammation and other components in COPD?

Rev Port Pneumol 2016; 22: 11–17.

41.

Uzum

Aydin

Tutuncu

et al

Serum ghrelin and adiponectin levels are increased but serum leptin level is unchanged in low weight chronic obstructive pulmonary disease patients. Eur J Intern Med 2014; 25: 363–369.

42.

Kosacka

Porebska

Korzeniewska

et al

Serum levels of apoptosis-related markers (sFasL, TNF-α, p53 and bcl-2) in COPD patients. Pneumonol Alergol Pol 2016; 84: 11–15.

43.

Cheng

Wang

et al

Increased expression of placenta growth factor in COPD. Thorax 2008; 63: 500–506.

44.

Valipour

Schreder

Wolzt

et al

Circulating vascular endothelial growth factor and systemic inflammatory markers in patients with stable and exacerbated chronic obstructive pulmonary disease. Clin Sci (Lond) 2008; 115: 225–232.

45.

Zhang

et al

Relevance of lower airway bacterial colonization, airway inflammation, and pulmonary function in the stable stage of chronic obstructive pulmonary disease. Eur J Clin Microbiol Infec Dis 2010; 29: 1487–1493.

46.

Soler

Ewig

Torres

et al

Airway inflammation and bronchial microbial patterns in patients with stable chronic obstructive pulmonary disease. Eur Respir J 1999; 14: 1015–1022.

47.

Keatings

Collins

Scott

et al

Differences in interleukin-8 and tumor necrosis factor-alpha in induced sputum from patients with chronic obstructive pulmonary disease or asthma. Am J Respir Crit Care Med 1996; 153: 530–534.

48.

De Godoy

Donahoe

Calhoun

et al

Elevated TNF-alpha production by peripheral blood monocytes of weight-losing COPD patients. Am J Respir Crit Care Med 1996; 153: 633–637.

49.

Ostojic

Pintaric

Chronic obstructive pulmonary disease and heart failure: closer than close. Acta Clin Croat 2017; 56: 269–276.

50.

Chen

Zhang

et al

Vitamin D binding protein gene polymorphisms and chronic obstructive pulmonary disease: a meta-analysis. J Thorac Dis 2015; 7: 1423–1440.

51.

Jiang

Wang

Liu

et al

The effect of ventilator mask atomization inhalation of ipratropium bromide and budesonide suspension liquid in the treatment of COPD in acute exacerbation period on circulating levels of inflammation and prognosis. Eur Rev Med Pharmacol Sci 2017; 21: 5211–5216.

52.

Gan

Man

SFP

Senthilselvan

et al

Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a metaanalysis. Thorax 2004; 59: 547–580.

53.

Liu

Fan

et al

Inflammatory markers and the risk of chronic obstructive pulmonary disease: a systematic review and meta-analysis. PLos One 2016; 11: e0150586.

54.

Pauwels

Buist

Calverley

et al

Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO global initiative for chronic obstructive lung disease (GOLD) workshop summary. Am J Respir Crit Care Med 2001; 163: 1256–1276.

55.

Emami Ardestani

Zaerin

. Role of serum interleukin 6, albumin and C-reactive protein in COPD patients. Tanaffos 2015; 14: 134–140.

56.

Vaguliene

Zemaitis

Lavinskiene

et al

Local and systemic inflammation in chronic obstructive pulmonarydisease. BMC Immunol 2013; 14: 36.

57.

Mosrane

Bougrida

Alloui

et al

Systemic inflammatory profile of smokers with and without COPD. Rev Pneumol Clin 2017; 73: 188–198.

58.

Schmidt-loanas

Pletz

de Roux

et al

Apoptosis of peripheral blood neutrophils in COPD exacerbation does not correlate with serum cytokines. Respir Med 2006; 100: 639–647.

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