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. 2023 Feb 3;102(5):e32846. doi: 10.1097/MD.0000000000032846

Investigation of the efficacy and pharmacological mechanism of Danhong injections for treating chronic obstructive pulmonary disease: A PRISMA-compliant meta-analysis and network pharmacology analysis

Xiaoyu Gao a, Jinsong Gao b,*
PMCID: PMC9901954  PMID: 36749263

Background:

Accumulating evidence supported the clinical efficacy of Danhong injection (DHI) on chronic obstructive pulmonary disease (COPD). It is urgent to summarize the effects of DHI on various outcomes in COPD patients and to elucidate the molecular mechanisms of DHI in treating COPD.

Methods:

Eligible studies were retrieved from 6 databases including China national knowledge infrastructure, Wangfang, VIP, web of science, PubMed, and Embase. The heterogeneity across studies was tested using the I2 statistic and the quality of studies was assessed. The pooled evaluation of outcomes was calculated using a fix- or random-effect model according to the heterogeneity. The underlying mechanism of DHI in treating COPD was analyzed using network pharmacology.

Results:

A total of 34 eligible studies with a general medium quality were included in the meta-analysis. The pooled data showed that DHI intervention significantly increased clinical efficacy as compared to routine treatment. Meanwhile, our data also revealed that the addition of DHI markedly improved hemorheological indicators, lung function index, arterial blood gas index, and as well as blood coagulation functions. However, the current meta-analysis lacked sufficient data to support the significant effect of DHI on prothrombin time and activated partial thromboplastin time. Network pharmacology found 59 candidate targets of DHI in treating COPD, and enrichment analysis found these targets were associated with lymphocyte proliferation and activation, glucocorticoid receptor signaling, TREM1 signaling, IL-12 signaling and production in macrophages, and aryl hydrocarbon receptor signaling. Multiple core targets including AKT1, TNF, and IL1B, etc. Were identified and might play an important role in the action of DHI against COPD.

Conclusion:

Taken together, this study suggested that DHI could ameliorate hemorheological indicators, lung function, arterial blood gas, and as well as coagulation functions of COPD patients and elucidate the underlying mechanism of DHI against COPD.

Keywords: chronic obstructive pulmonary disease, Danhong injection, meta-analysis, network pharmacology

1. Introduction

Chronic obstructive pulmonary disease (COPD), a common respiratory disease characterized by poorly reversible airway obstruction, presents with high global morbidity and mortality.[1] The acute exacerbation of COPD could result in significant adverse consequences for patients, which was associated with increased airway and systemic inflammation and physiological changes. Smoking and air pollution were suggested to be the main risk factors for COPD.[2,3] It was reported that approximately 100 million patients are affected by COPD in China, which causes serious social and economic burden.[4] Currently, medications for COPD included long-acting bronchodilators and a combination of bronchodilators with glucocorticoids.[5] Nevertheless, these medications are always followed by certain adverse effects.[6] Due to the limited acknowledge of the pathogenesis and molecular biology of COPD,[7] the development of novel safe and efficient drugs was time-consuming and laborious.

Traditional Chinese medicine has been developed in China for thousands of years and is effectively used to treat various diseases. It was characterized by multi-components, multi-targets, and multi-pathways in treating diseases. Meanwhile, an excellent safety profile makes it more acceptable. Studies have revealed that various traditional Chinese medicines were effective in ameliorating pulmonary function, inhibiting inflammation, and shortening the acute exacerbation of COPD.[8–10] As an innovative formulation, Chinese herbal injection has been widely employed in the treatment of COPD due to its high bioavailability and rapid action.[11] Danhong injection (DHI) is a commonly used multi-herb Chinese herbal injection with satisfactory efficacy in treating COPD, which was composed of Salvia miltiorrhiza Bunge (Danshen in Chinese) and Carthamus tinctorius L. (Honghua in Chinese). Accumulating clinical studies supported the therapeutic effects of DHI in treating COPD or AECOPD, and a meta-analysis was conducted based on 23 randomized controlled trials with 2059 patients to evaluate the influence of DHI in treating COPD.[12] It pooled the clinical efficacy and influences of DHI on several outcome indicators of COPD. However, due to the limitation of the number and quality of included studies, the conclusions still need more high-quality RCT supplementary validation. As increasing high-quality articles are published, an updated meta-analysis was needed. Meanwhile, the elucidation of the underlying mechanism of the action of DHI against COPD was also urgent.

In this study, an updated meta-analysis was performed to evaluate the effects of DHI on improving clinical efficacy and ameliorating hemorheological indicators, lung function, arterial blood gas index, as well as blood coagulation functions of COPD. Further to this, the network pharmacology approach was adopted to investigate the underlying molecular mechanisms of DHI in treating COPD.

2. Materials and methods

The preferred reporting items for systematic reviews and meta-analyses criteria were used for this meta-analysis.

2.1. Literature search

We performed a computerized literature search of the PubMed, web of science, embase, China national knowledge infrastructure (China national knowledge infrastructure), VIP, and Wanfang databases from their start date to November 8, 2022. We used the following search terms: “chronic obstructive pulmonary disease,” and “Danhong injection,” to obtain relevant articles.

2.2. Inclusion and exclusion criteria

Articles were included when meet the following inclusion criteria: randomized controlled trials; using DHI intervention in the experimental group; providing sufficient data for estimating pooled effects with its 95% confidence interval (CI); published in English or Chinese. The exclusion criteria were as follows: patients comorbid with other diseases, reviews, case reports, conference papers, and studies without sufficient data were excluded. If more than 1 article was published using the same subjects, only the study with the largest sample size was selected.

2.3. Outcome indicator

Fifteen outcome indicators were evaluated in this study, including clinical efficacy, whole blood viscosity (WBV), low-shear WBV, high-shear WBV, fibrinogen, and hematocrit, forced expiratory volume in 1 second (FEV1), FEV1/Forced vital capacity (FVC), partial pressure of carbon dioxide (PCO2), partial pressure of oxygen (PaO2), arterial oxygen saturation (SaO2), pH, thrombin time (TT), prothrombin time (PT), activated partial thromboplastin time (APTT).

2.4. Data extraction and quality assessment

The data were extracted independently by 2 researchers and the dispute was discussed and decided by them. The following data were collected: first author name, publication date, intervention details, sample size, and outcomes indicators. The methodological quality of studies was evaluated according to the scoring standards of the Oxford scoring system (JADAD score). The quality score ranges from 0 to 5 points, with a score of ≤ 2 indicating a low-quality report and a score of ≥ 3 indicating a high-quality report.

2.5. Statistical analysis

Statistical analysis was conducted by the Stata SE15.0 software (Stata, China). The risk rate was applied to evaluate the rate of improvement of DHI in the treatment of COPD, and the standard mean difference (SMD) and risk ratio were adopted to assess the pooled effects on other continuous outcome indicators. The heterogeneity across studies was tested using the I2 statistic, and a random-effect mode was used when I2 > 50%, otherwise, a fix-effect model was employed. The funnel plot was generated to assess the publication bias. Meta-regression analysis was performed to investigate the source of heterogeneity.

2.6. Network pharmacology

The compounds and targets of DHI were retrieved from the (traditional Chinese medicine systems pharmacology database [TCMSP], https://old.tcmsp-e.com/tcmsp.php) database, and those compounds with drug-likeness of ≥ 0.1 were considered as active compounds and used for subsequent analysis. The targets symbol was unified using the UniProt database (https://www.uniprot.org/) and non-human targets were removed. COPD-related genes were identified from the GeneCards (https://www.genecards.org/) and DisGeNET (https://www.disgenet.org/) databases, and the candidate targets of DHI against COPD were obtained by intersecting the COPD-related genes and DHI targets. The protein-protein interaction data of the candidate targets were obtained from the STRING database (https://string-db.org/) and were visualized using the Cytoscape 3.9.1 software. Enrichment analyses were conducted and visualized using the TCGAbiolinks R package.[13] Core bioactive compounds and targets were identified using the cytoHubba plugin in the Cytoscape software.[14]

3. Results

3.1. Search results and basic information of included articles

A total of 203 documents were obtained by initial database searching. Of these, 116 duplications were found and removed, and the rest 87 articles were eliminated by reading the abstracts and titles. Finally, 35 eligible studies were retained for full-text evaluation, which excluded 1 article without available data for meta-analysis. The rest 34 studies were finally included in the meta-analysis.[15–48] Figure 1 showed the literature searching and screening process. The basic information of the 34 studies were shown in Table 1. All studies were conducted in China and published between 2007 and 2022. The sample size ranged from 46 to 306, and the majority of patients were AECOPD. Most studies adopted a similar intervention in the experimental group (DHI, 40 mL/d for 14 days). In terms of outcomes measurement, the clinical efficacy, hemorheological alterations, pulmonary function, arterial blood gas, and coagulation function were generally tested. In addition, 4 studies reported adverse effects and more than half of the studies (29/34) had a high-quality methodology.

Figure 1.

Figure 1.

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Literature search and screening process.

Table 1.

The basic information of included articles.

First Author Year Age case (e.g.,/CG) Patients Intervention Treatment details Treatment duration Outcome measures Adverse effects Quality
EG CG EG CG
Chen Hongci 2007 63–87;78.45 65–85;77.68 40/36 AECOPD DHI RT 40 mL 14d clinical efficacy, high-shear WBV, low-shear WBV, WBV, hematokrit no 2
Liang Xiao 2009 52.3 ± 2.2 53.1 ± 3.2 52/56 AECOPD DHI RT 30mL 14d hospital day, white blood cell count, PaO2, SaO2, CPIS SCORE Allergic reactions, gastrointestinal reactions 4
Shi Yiying 2009 52–69;58 51–68;57 23/23 AECOPD DHI RT 20–30mL 14d clinical efficacy, WBV, erythrocyte electrophoresis time, fibrinogen, hematokrit, FEV1, FEV1/FVC no 3
Tang Tianzhong 2009 43–82;68 40–78;62 68/54 COPD DHI RT 40 mL 14d clinical efficacy, FEV1, FEV1/FVC, high-shear WBV, low-shear WBV, WBV, hematokrit, WBV, hematokrit, fibrinogen, D-dimer, platelet, Dyspnea index no 4
Liu Zhong 2010 67.7 ± 9.8 66.1 ± 9.2 50/46 AECOPD DHI RT 40 mL 14d clinical efficacy, IL-8, TNF-ɑ no 2
Yang Ruifang 2010 66.5 ± 5.8 67.2 ± 4.6 42/40 AECOPD DHI RT 20 mL 14d clinical efficacy, blood routine examination, chest X-ray no 4
Zuo Xiqing 2010 70.35 ± 5.12 70.85 ± 4.04 30/30 AECOPD DHI RT 30 mL 14d clinical efficacy, PaCO2, PaO2, WBV, hematokrit no 4
Guan Wei 2011 65 ± 4.5 67 ± 3 40/40 AECOPD DHI RT 20 mL 14d low-shear WBV, high-shear WBV, WBV, hematokrit, PT, TT, APTT, fibrinogen, platelet, D-dimer no 2
Wang Hua 2012 61.2 ± 12.5 65.3 ± 12.7 40/40 AECOPD DHI RT 20 mL 14d clinical efficacy, CRP, MMP-9, TIMP-1, MMP-9/TIMP-1 dizziness 3
Yang Jiewu 2012 N.M N.M 110/110 AECOPD DHI RT 20–30mL 14d clinical efficacy, FEV1, FEV1/FVC no 4
Wu Wen 2012 67.8 67.5 30/30 AECOPD DHI RT 30 mL 14d 6-min walk test, dyspnea index, FEV1, FEV1/FVC no 4
Chen Zhuo 2013 66.6 ± 7.4 63.8 ± 7.8 52/48 AECOPD DHI RT 40 mL 14d clinical efficacy, IL-8, TNF-ɑ no 4
Wang Jinhai 2013 76.35 ± 5.70 69.28 ± 5.18 31/31 AECOPD DHI RT 30 mL 14d clinical efficacy, low-shear WBV, high-shear WBV, WBV, fibrinogen, hematokrit no 4
Wang Yanli 2013 N.M N.M 43/42 AECOPD DHI RT 40 mL 14d TCM syndrome score, PaCO2, PaO2, pH, low-shear WBV, high-shear WBV, WBV, hematokrit, SGRQ score no 2
Xia Jingfen 2013 63 ± 4.2 64 ± 4.5 25/25 AECOPD DHI RT 20 mL 15d low-shear WBV, high-shear WBV, WBV, hematokrit, erythrocyte sedimentation rate, fibrinogen, TT, PT, APTT, D-dimer no 3
Zhang Li 2013 N.M N.M 60/60 AECOPD DHI RT 20 mL 14d clinical efficacy no 3
Du Jin 2014 62 ± 9.8 65 ± 8.4 30/30 AECOPD DHI RT 30 mL 14d clinical efficacy,TT, PT, APTT, fibrinogen, D-dimer no 3
Qi Chunhui 2014 68.35 ± 5.14 70.25 ± 5.14 65/63 AECOPD DHI RT 30 mL 14d clinical efficacy, low-shear WBV, high-shear WBV, WBV, hematokrit, fibrinogen, PaCO2, PaO2, SaO2 no 3
Yu Shufen 2014 69.5 ± 2.36 68.3 ± 2.14 153/153 COPD RT ± DHI ± heparin RT ± heparin 30 mL 10d clinical efficacy, FEV1, FEV1/FVC, PaCO2, PaO2 no 4
Wang Jialie 2014 63.3 ± 4.4 61.4 ± 5.3 50/50 COPD RT ± DHI RT 40 mL 14d clinical efficacy, pH, PaCO2, PaO2, WBV, hematokrit, erythrocyte sedimentation rate, fibrinogen, no 4
Zhang Qiong 2014 64.45 ± 9.46 64.41 ± 9.45 64/64 AECOPD RT ± DHI RT 40 mL 14d clinical efficacy, low-shear WBV, high-shear WBV, WBV no
Zhu Renqian 2014 62.5 ± 7.92 61.5 ± 8.84 40/40 AECOPD RT ± ambroxol ± DHI RT ± ambroxol 40 mL 15d clinical efficacy, FEV1, FEV1/FVC no 4
Ge Zongkai 2016 54.9 ± 4.4 55.7 ± 4.6 32/32 COPD RT ± DHI RT 40 mL 14d clinical efficacy, WBV, erythrocyte electrophoresis time, fibrinogen, hematokrit no 4
Zhao Liang 2016 59.6 ± 7.9 58.1 ± 8.2 50/50 AECOPD RT ± DHI RT 30 mL 14d Thrnmbomodulin, vWF, D-dimer, PT, APTT, TT, fibrinogen no 3
Jia Zhongrui 2017 62.7 ± 6.1 63.5 ± 5.8 62/62 AECOPD RT ± DHI RT 20 mL 10d clinical efficacy, D-dimer, APTT, TT, fibrinogen no 4
Shen Qing 2017 65.4 ± 6.4 65.8 ± 7.3 56/56 AECOPD RT ± DHI RT 40 mL 14d clinical efficacy, pH, PaCO2, PaO2, IgG, IgK, IgM, INF-γ, TNF-ɑ, IL-10, T lymphocyte subsets Palpitations, nausea, headaches 4
Yang Aiping 2018 N.M N.M 98/98 AECOPD RT ± DHI ± phentolamine RT ± phentolamine 20 mL/d 7d clinical efficacy, Time of symptom resolution no 2
Yang Yuxia 2018 66.2 ± 8.0 66.9 ± 7.6 118/118 AECOPD RT ± DHI RT 2 × 40 mL 4d clinical efficacy, TCM syndrome score, PaCO2, PaO2, SaO2, sE-selection, vWF, APTT, D-dimer, platelet, thromboelastography no 4
Liu Xinyan 2019 64.0 ± 4.68 62.5 ± 3.72 30/30 AECOPD DHI RT 40 mL 14d lipid peroxidase, glutathione peroxidase, catalase, TNF- α, IL-6, CRP, FEV1, FEV1/FVC no 5
Wang Jiazhen 2019 52.3 ± 7.6 52.1 ± 7.5 60/60 AECOPD RT ± DHI RT 20 mL 14d clinical efficacy, S100A12, procalcitonin, CRP, IL-1β, IL-8, TNF-ɑ, pH, PaCO2, PaO2, SaO2, FEV1, FEV1/FVC, left ventricular ejection fraction, cardiac output, pulmonary artery systolic pressure, right ventricular outflow tract no 5
Chen Xiuhong 2020 58.11 ± 4.02 59.03 ± 3.98 37/37 COPD RT ± DHI ± Montelukast RT ± Montelukast 40 mL 14d clinical efficacy, FEV1, FEV1/FVC, procalcitonin, hematokrit, fibrinogen Vomiting, nausea, dizziness 4
Huang Yajing 2021 68.83 ± 4.41 68.32 ± 4.38 33/32 AECOPD RT ± DHI RT 2 × 40 mL 30d clinical efficacy, PaCO2, PaO2, SaO2, APTT, D-dimer, platelet, sE-selevtion, vWF no 4
Zheng Shenghua 2022 79.5 ± 8.5 79.4 ± 8.3 40/40 AECOPD RT ± DHI RT 40 mL 7d clinical efficacy, PaCO2, PaO2, FEV1, FEV1/FVC, white blood cell count, neutral cell count, CRP, AT, D-dimer, fibrinogen, hemoglobin, red blood cells, platelet, alanine aminotransferase, serum creatinine, blood urea nitrogen no 4
Gao Xiaoyu 2022 71.7 ± 8.2 72.2 ± 6.7 56/56 AECOPD RT ± DHI RT 40 mL 10d clinical efficacy, PaCO2, PaO2, SaO2, FEV1, FEV1/FVC, TCM syndrome score no 5
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AECOPD = acute exacerbation of chronic obstructive pulmonary disease, APTT = activated partial thromboplastin time, CG = control group, COPD = chronic obstructive pulmonary disease, CRP = C-reactive protein, DHD = Danhong injection, EG = experimental group, FCV = forced vital capacity, FEV1 = forced expiratory volume in 1 second, IL-10 = interleukin 10, IL-8 = interleukin 8, MMP-9 = matrix metallopeptidase 9, PaCO2 = partial pressure of carbon dioxide, PaO2 = partial pressure of oxygen, PT = prothrombin time, RT = routine treatment, SaO2 = arterial oxygen saturation, TCM = traditional Chinese medicine, TIMP-1 = TIMP metallopeptidase inhibitor 1, TNF-α = tumor necrosis factor alpha, TT = thrombin time, vWF = von willebrandfactor, WBV = whole blood viscosity.

3.2. Comparison of clinical efficacy

The clinical efficacy was reported in 27 studies, with 1540 and 1509 cases in the experimental and control groups, respectively. The pooled results were calculated using a fix-effect model due to the insignificant heterogeneity across included studies (I2 = 0.0%, P = 1.000), and demonstrated in almost all of these studies, the pooled risk ratio was 1.11 (95% CI: 1.05–1.17), and the difference was statistically significant (Z = 3.627 and P < .0001, Fig. 2A). It was observed that the funnel plot of the included studies was symmetrical with the midline, suggesting that the research accuracy was high and that there was no publication bias (Fig. 2B).

Figure 2.

Figure 2.

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The meta-analytic results of the clinical efficacy of DHI in treating COPD. COPD = chronic obstructive pulmonary disease, DHI = Danhong injection.

3.3. Comparison of hemorheological indicators

To compare the differences in hemorheological indicators between the experimental and control groups, we evaluated the pooled effects of WBV, low-shear WBV, high-shear WBV, fibrinogen, and hematocrit. As shown in Figure 3A, ten studies containing 819 patients reported the WBV data and were used to conduct a meta-analysis. The overall heterogeneity was significant (I2 = 84.2%, P < .0001), and the random effect model was applied to analyze the whole. The pooled results demonstrated that the WBV in the experimental group was significantly lower than that in the control group (SMD: −0.93, 95%CI: −1.31 to −0.56, Z = −11.307, P < .0001). In addition, the pooled data also revealed a significantly lower fibrinogen (SMD: −1.94, 95%CI: −2.84 to −1.05, Z = −4.257, P < .0001, Fig. 3B), high-shear WBV (SMD: −2.24, 95%CI: −3.05 to −1.53, Z = −5.416, P < .0001, Fig. 3C), shear-WBV (SMD: −1.64, 95%CI: −2.18 to −1.10, Z = −5.968, P < .0001, Fig. 3D), and hematocrit (SMD: −0.86, 95%CI: −1.33 to −0.39, Z = −3.595, P < .0001, Fig. 3E) in the experimental group as compared to that in the control group. All pooled effects were calculated using a random effect model due to the heterogeneity across studies.

Figure 3.

Figure 3.

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The pooled results of the influence of DHI on hemorheological indicators of COPD patients. (A-D) represented the pooled SMD of WBV, fibrinogen, high-shear WBV, low-shear WBV, and hematocrit, respectively. COPD = chronic obstructive pulmonary disease, DHI = Danhong injection, SMD = standard mean difference, WBV = whole blood viscosity.

3.4. Comparison of lung function indexes

The pulmonary function was assessed by calculating the pooled effects of FEV1 and FEV1/FVC between the experimental and control groups. In terms of FEV1, 6 studies reported relevant data and were included in the meta-analysis. The random-effect model was adopted due to the higher heterogeneity across studies (I2 = 83.7%, P < .0001), and the pooled data demonstrated that DHI intervention significantly elevated the FEV1 as compared to routine treatment. (SMD: 0.85, 95%CI: 0.42–1.27, Z = 3.909, P < .0001, Fig. 4A). In addition, 9 studies reported FEV1/FVC data of 1078 patients with COPD, and significant heterogeneity was observed among studies (I2 = 85.9%, P < .0001). The pooled results revealed that the FEV1/FVC in the experimental group was markedly increased as compared to the control group (SMD: 0.95, 95%CI: 0.59–1.30, Z = 5.198, P < .0001, Fig. 4B).

Figure 4.

Figure 4.

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The pooled results of the influence of DHI on lung function index of COPD patients. (A and B) represented the pooled SMD of FEV1 and FEV1/FVC, respectively. COPD = chronic obstructive pulmonary disease, DHI = Danhong injection, FVC = Forced vital capacity, SMD = standard mean difference.

3.5. Comparison of arterial blood gas indexes

Four arterial blood gas indexes including PCO2, PaO2, SaO2, and pH were included for meta-analysis. As shown in Figure 5A, eleven studies provided available data on PaO2 and presented with high heterogeneity (I2 = 83.8%, P < .0001). The meta-analytic results showed that administration of DHI for patients with COPD significantly increased PaO2 as compared to the routine treatment (SMD: 1.11, 95%CI: 0.80–1.42, Z = 6.998, P < .0001). The pooled effects on PCO2 and pH were calculated under fix-effect models, and the results showed a significantly decreased PCO2 (SMD: −0.74, 95%CI: −0.87 to −0.62, Z = −11.875, P < .0001, Fig. 5B) and increased pH (SMD: 0.26, 95%CI: 0.07–0.45, Z = 2.648, P = .008, Fig. 5C) in the experimental group as compared to the control group. A random effect model was adopted to calculate the pooled effects of SaO2, and the results showed that DHI treatment significantly elevated the SaO2 of COPD patients as compared to the routine treatment (SMD: 0.95, 95%CI: 0.46–1.44, Z = 11.150, P = .008, Fig. 5D).

Figure 5.

Figure 5.

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The pooled results of the influence of DHI on arterial blood gas index of COPD patients. (A-D) represented the pooled SMD of PaO2, PCO2, pH, and SaO2, respectively. COPD = chronic obstructive pulmonary disease, DHI = Danhong injection, PCO2 = partial pressure of carbon dioxide, SaO2 = arterial oxygen saturation, SMD = standard mean difference.

3.6. Comparison of blood coagulation indexes

Three indexes including PT, TT, and APTT were used to evaluate pooled effects of DHI on blood coagulation functions. As shown in Figure 6A, 4 studies with 290 COPD patients reported available data on PT, and high heterogeneity was observed across studies (I2 = 98.9%, P < .0001). The pooled results revealed an insignificantly higher level of PT in the experimental group as compared to the control group (SMD: 2.74, 95%CI: −0.55 to 6.03, Z = 1.630, P = .103). In terms of TT, the pooled results showed a significantly higher TT level in the experimental group as compared to the control group (SMD: 2.79, 95%CI: 0.75–4.83, Z = 2.680, P = .007, Fig. 6B). In addition, insignificant pooled effects on APTT were also observed between the 2 groups (SMD: 0.54, 95%CI: −0.55 to 1.62, Z = 0.964, P = .335, Fig. 6C).

Figure 6.

Figure 6.

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The pooled results of the influence of DHI on blood coagulation indexes of COPD patients. (A-C) represented the pooled SMD of PT, TT, and APTT, respectively. APTT = activated partial thromboplastin time, COPD = chronic obstructive pulmonary disease, DHI = Danhong injection, PT = prothrombin time, SMD = standard mean difference, TT = thrombin time.

3.7. Meta-regression analysis

Heterogeneity was observed across studies when evaluating the pooled influences of Danhong injection on outcomes of COPD. Therefore, meta-regression analysis was performed to explore the source of heterogeneity. As shown in Table 2, we assessed the contribution of the number of cases, dosage, duration, and combination of dosage and duration to the heterogeneity, and it was found that the number of cases was a contributor to the heterogeneity of FEV1 and the heterogeneity when analyzing fibrinogen was attributed to duration. However, these factors did not contribute to heterogeneity in the analyses of other outcomes.

Table 2.

The results of meta-regression analysis on evaluated variables

Variables SMD Coefficient Standard error t P
Low-shear WBV case 0.4102257 0.5316285 0.77 .521
dosage −0.2567426 0.5339007 −0.48 .678
duration 2.346904 0.7959273 2.95 .098
WBV case −0.2400809 0.5237196 −0.46 .663
dosage 0.3866834 0.5032922 0.77 .471
duration −0.2893227 0.8594685 −0.34 .748
High-shear WBV case −2.006294 3.102205 −0.65 .584
dosage 1.683941 3.102764 0.54 .642
duration −2.557254 4.016248 −0.64 .589
Fibrinogen case −0.1071032 0.6183996 −0.17 .867
dosage −1.593431 0.7175171 −2.22 .062
duration 3.063377 0.7430993 4.12 .004
Hematocrit case 0.6872588 0.5803166 1.18 .302
dosage 0.3209759 0.5349864 0.6 .581
duration 0.6028882 0.819984 0.74 .503
FEV1 case 0.9778324 0.2135377 4.58 .02
dosage −0.4118172 0.200968 −2.05 .133
FEV1/FVC case 0.8730151 0.3632285 2.4 .074
dosage −1.417356 0.5118696 −2.77 .05
duration −0.406218 0.4605663 −0.88 .428
dosage_duration 0.2632677 0.6195555 0.42 .693
PT insufficient observations
TT insufficient observations
APTT case 2.456392 1.388916 1.77 .152
dosage −1.992919 1.389979 −1.43 .225
PaO2 case 0.4503308 0.428293 1.05 .334
dosage 0.0812959 0.6643012 0.12 .907
duration −0.3257211 0.5781588 −0.56 .594
dosage_duration −0.0669454 0.8336396 −0.08 .939
PCO2 case 0.3096247 0.178157 1.74 .143
dosage 0.0150301 0.2554227 0.06 .955
duration −0.1894276 0.2308398 −0.82 .449
dosage_duration −0.0040817 0.3346344 −0.01 .991
pH insufficient observations
SaO2 insufficient observations
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APTT = activated partial thromboplastin time, FEV1 = forced expiratory volume in 1 second, FVC = FEV1/Forced vital capacity, PaO2 = partial pressure of oxygen, PCO2 = partial pressure of carbon dioxide, PT = prothrombin time, SaO2 = arterial oxygen saturation, SMD = standard mean difference, TT = pH, thrombin time, WBV = whole blood viscosity.

3.8. Active compounds and targets of DHI

To elucidate the underlying mechanism of DHI against COPD, we first retrieved the chemical constituents and targets of DHI. As a result, we found 376 chemical constituents of DHI from the TCMSP database, including 202 from Danshen and 189 from Honghua. Further, 124 compounds were identified as bioactive compounds due to their drug-likeness ≥ 0.1 (Table S1, Supplemental Digital Content, http://links.lww.com/MD/I423). Therefore, we collected the targets of these bioactive compounds from the TCMSP database and removed those that were not human genes. As shown in Figures 7, 85 bioactive compounds of DHI targeted 249 proteins belonging to human (Table S2, Supplemental Digital Content, http://links.lww.com/MD/I424). Table 3 showed the top 10 core bioactive compounds arranged by the degree value, which had the most number of targets and might be crucial for the action of DHI against COPD.

Figure 7.

Figure 7.

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The network contains the connections among active compounds and their targets.

Table 3.

The main topological characteristics of the top ten core bioactive compounds arranged by degree value.

MOL_ID Compounds Betweenness Centrality Closeness Centrality Degree Neighborhood Connectivity
MOL000050 2-Azaniumylacetate 0.32 0.47 105 5.75
MOL000042 (2S)-2-azaniumylpropanoate 0.05 0.40 47 8.55
MOL002686 Glyoxylic acid 0.03 0.38 43 8.14
MOL000346 Succinic Acid 0.05 0.38 38 8.87
MOL001801 Salicylic acid 0.15 0.38 36 7.08
MOL000065 (3S)-3-azaniumyl-4-hydroxy-4-oxobutanoate 0.04 0.38 34 9.03
MOL003969 d,l-Serine 0.04 0.39 33 10.91
MOL000922 (-)-Terpinen-4-ol 0.04 0.38 28 11.96
MOL000052 (2S)-2-azaniumyl-5-hydroxy-5-oxopentanoate 0.03 0.38 26 11.81
MOL007134 Danshensu 0.04 0.34 23 8.96
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3.9. Candidate targets of DHI against COPD

After data mining, 922 genes were obtained to be associated with COPD, and 59 candidate targets were identified by overlapping these genes with DHI targets (Fig. 8A). Figure 8B illustrated the protein-protein interaction network of the candidate targets, which contains 57 nodes and 531 edges. The core targets were identified by the cytoHubba plugin and the top 10 targets were illustrated in Table 4. GO and KEGG enrichment analyses demonstrated that these candidate targets were significantly enriched in 315 biological processes, 41 cellular components, 53 molecular functions, and 190 pathways (Table S3, Supplemental Digital Content, http://links.lww.com/MD/I425). Figure 8C showed the top 10 terms with lower false discovery rate values. It suggested that the candidate targets were mainly involved in lymphocyte proliferation and activation. Meanwhile, multiple pathways including glucocorticoid receptor signaling, TREM1 signaling, IL-12 signaling and production in macrophages, and aryl hydrocarbon receptor signaling were also associated with these candidate targets.

Figure 8.

Figure 8.

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The candidate targets of DHI against COPD. (A) The Venn diagram of the COPD-related genes and DHI targets; (B) The PPI network of the candidate targets; (C) The top ten enriched terms of biological process, cellular components, molecular function, and pathways with a lower FDR value. DHI = Danhong injection, COPD = chronic obstructive pulmonary disease, FDR = false discovery rat, PPI = protein-protein interaction.

Table 4.

The main topological characteristics of the top 10 core targets arranged by degree value.

Symbol Gene name Betweenness Centrality Closeness Centrality Degree Neighborhood Connectivity
AKT1 AKT serine/threonine kinase 1 0.13 0.91 48 19.56
TNF tumor necrosis factor 0.08 0.90 47 20.23
IL1B Interleukin-1 beta 0.07 0.88 46 20.48
IL6 Interleukin-6 0.10 0.88 46 20.37
PTGS2 Prostaglandin-endoperoxide synthase 2 0.04 0.80 40 22.18
CASP3 Caspase-3 0.04 0.77 37 22.78
IL10 Interleukin-10 0.02 0.75 35 23.89
SRC SRC proto-oncogene, non-receptor tyrosine kinase 0.02 0.73 33 24.70
CAT Catalase 0.03 0.70 30 23.37
NOS3 Nitric oxide synthase, endothelial 0.02 0.70 30 24.57
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4. Discussion

Since a meta-analysis was conducted in 2019, accumulating studies provided additional evidence to evaluate the clinical efficacy of DHI in treating COPD, as well as the influences of DHI on other outcome indicators of COPD. Therefore, this study pooled the data of recently published data and the last meta-analysis and comprehensively analyzed the impacts of DHI on hemorheological alterations, pulmonary function, arterial blood gas, and coagulation function of patients with COPD. Subsequently, network pharmacology was employed to elucidate the pharmacological mechanism of DHI in the treatment of COPD to make up for its theoretical deficiencies for clinical application.

Consistent with the previous meta-analysis, our data revealed a significantly improved clinical efficacy of DHI in treating COPD as compared to the control group. A basically symmetrical funnel plot indicated no publication bias, and the heterogeneity of the papers was not observed. COPD progression could result in significant adverse consequences such as systemic inflammation and physiological changes. Herein, the 34 studies included in the meta-analysis reported the influence of DHI on a variety of indexes, including hemorheological alterations, pulmonary function, arterial blood gas, and coagulation function. In the present study, we selected and assessed fifteen outcome indicators including clinical efficacy and the above domains. Besides, part of these studies provided available data on inflammation, platelet, and immune cells.

Chronic obstructive pulmonary disease is characterized by the decreased maximal volume of gas and increased residual volume. With the development of the disease, the vascular bed area after lung injury decreases and the pulmonary vascular constriction forms a state of hypoxia and ischemia in the body. This study evaluated the improvement of 2 indicators (FEV1 and FEV1/FVC) after DHI as compared to the control group. The results revealed a favorable improvement by DHI as compared to routine treatment. Therefore, DHI was beneficial for improving lung function. Cryptotanshinone, a lipophilic compound extracted from the root of Danshen, could protect against pulmonary fibrosis by inhibiting Smad and STAT3 signaling pathways.[49] In addition, tanshinones from Danshen also exert pharmacological activities in ameliorating pulmonary diseases.[50] It was reported that hydroxysafflor yellow A, a main constituent of Honghua, showed vasodilatation effects on the pulmonary artery[51] and can attenuate lung injury by inhibiting platelet activation.[52] COPD is characterized by airflow limitation which is progressive in the course of illness, and by the changes in arterial blood gases that can lead to respiratory failure.[53] The pooled data showed DHI administration could make that course slower by decreasing PCO2 and increasing PaO2 and pH, and SaO2. Hydroxysafflor yellow A from the flower of Carthamus tinctorius L. was revealed to ameliorate the alterations in arterial blood gases in mice with acute lung injury.[54]

Modern medical research has found that COPD and hypoxemia can increase the aggregation of red blood cells, damage vascular endothelial cells, activate blood clotting factors, lead to the increase of clotting substances, lead to the increase of whole blood viscosity, the formation of a hypercoagulable state. Herein, it was observed that DHI was beneficial for ameliorating hemorheological alterations and coagulation function in COPD patients. The main manifestations in the DHI-treated groups were decreased blood viscosity, fibrinogen and hematocrit and increased thrombin time. Blood viscosity is a significant factor that plays an important role in pulmonary and cardiovascular diseases.[55] The water-soluble extracts of Danshen have been reported to improve abnormal hemorheological parameters including blood viscosity and viscoelasticity in aging guinea pigs.[56] Fibrinogen is an FDA-qualified prognostic biomarker in COPD, and elevated fibrinogen was associated with a higher risk of mortality.[57] The inhibition of fibrinogen by DHI might be partially attributed to phenolic acids obtained from Danshen.[58] Hematocrit is negatively associated with mortality and morbidity of COPD,[59] and the carthamins yellow from Honghua was responsible for this induction of hematocrit by DHI.[60] The available data do not provide sufficient evidence to determine the effect of DHI on PT and APTT in COPD patients. This phenomenon may be caused by the significant heterogeneity among samples and the conflicting effects of DHI components on coagulation function.[58,61,62]

Given the influence of DHI on various outcome indicators of COPD, it is urgent to elucidate the underlying mechanism. Herein, a total of 59 candidate targets were identified to contribute to the anti-COPD action of DHI, and the protein-protein interaction topological characteristics showed that AKT1, TNF and IL6 might be pivotal in the treatment of COPD by DHI. AKT1 is a serine-threonine protein kinase that is involved in various crucial signal transduction and biological processes. Modern pharmacological research demonstrated that AKT1 was regulated by various compounds from DHI, such as tanshinone.[63] Hydroxysafflor yellow A has been reported to alleviate increased TNF and IL6, leading to the inhibition of inflammation in patients with COPD.[64] In addition, it was observed the action of DHI against COPD was associated with the regulation of multiple pathways, such as glucocorticoid receptor signaling, TREM1 signaling, and production in macrophages. COPD is associated with loss of GCR in senescent CD28null and NKT-like cells[65] and synthetic glucocorticoids are widely prescribed drugs for COPD.[66] A recent study demonstrated that TREM-1 aggravates the development of COPD via activating NLRP3 inflammasome-mediated pyroptosis.[67] Macrophages circulate in the blood and control innate and acquired immunity, as well as homeostasis. It plays a crucial role in the pathogenesis of COPD by different polarization.[68]

5. Conclusion

In the present study, we pooled the data of various outcome indicators in COPD patients treated by DHI. The results demonstrated that DHI administration significantly improved the clinical efficacy and ameliorated alterations of hemorheology and arterial blood gas, and alleviate coagulation and pulmonary functions. Network pharmacology analysis revealed the potential targets and pathways of the action of DHI against COPD. However, further experimental studies are required to validate these findings.

Acknowledgments

We are grateful to all researchers in the enrolled studies.

Author contributions

Conceptualization: Xiaoyu Gao, Jinsong Gao.

Data curation: Xiaoyu Gao, Jinsong Gao.

Formal analysis: Xiaoyu Gao, Jinsong Gao.

Methodology: Xiaoyu Gao, Jinsong Gao.

Software: Xiaoyu Gao, Jinsong Gao.

Visualization: Xiaoyu Gao, Jinsong Gao.

Writing – original draft: Xiaoyu Gao, Jinsong Gao.

Writing – review & editing: Xiaoyu Gao, Jinsong Gao.

Supplementary Material

medi-102-e32846-s001.xlsx (16.9KB, xlsx)
medi-102-e32846-s002.xlsx (47.2KB, xlsx)
medi-102-e32846-s003.xlsx (32.6KB, xlsx)

Abbreviations:

APTT
activated partial thromboplastin time
CI
confidence interval
COPD
chronic obstructive pulmonary disease
DHI
Danhong injection
FDR
false discovery rat
FEV1
forced expiratory volume in 1 second
FVC
Forced vital capacity
PaO2
partial pressure of oxygen
PCO2
partial pressure of carbon dioxide
PPI
protein-protein interaction
PT
prothrombin time
SaO2
arterial oxygen saturation
SMD
standard mean difference
TCMSP
traditional Chinese medicine systems pharmacology database
TT
thrombin time
WBV
whole blood viscosity

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Both the authors have given their consent for publication.

Supplemental Digital Content is available for this article.

The authors have no conflicts of interest to disclose.

How to cite this article: Gao X, Gao J. Investigation of the efficacy and pharmacological mechanism of Danhong injections for treating chronic obstructive pulmonary disease: A PRISMA-compliant meta-analysis and network pharmacology analysis. Medicine 2023;102:5(e32846).

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Supplementary Materials

medi-102-e32846-s001.xlsx (16.9KB, xlsx)
medi-102-e32846-s002.xlsx (47.2KB, xlsx)
medi-102-e32846-s003.xlsx (32.6KB, xlsx)

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