Plasma Connective Tissue Growth Factor as a Biomarker of Pulmonary Arterial Hypertension Associated With Congenital Heart Disease in Adults

Abstract

Background: Connective tissue growth factor (CTGF) has diagnostic value for pulmonary arterial hypertension (PAH) associated with congenital heart disease (CHD) in children; however, its value in adult patients remains unclear. This study evaluated CTGF as a biomarker in adult PAH-CHD patients.

Methods and Results: Based on mean pulmonary artery pressure (mPAP), 56 CHD patients were divided into 3 groups: without PAH (W; mPAP <25 mmHg; n=28); mild PAH (M; mPAP 25–35 mmHg; n=18); and moderate and severe PAH (H; mPAP ≥35 mmHg; n=10). The control group consisted of 28 healthy adults. Plasma CTGF and B-type natriuretic peptide (BNP) concentrations were determined. Plasma CTGF concentrations were higher in the H and M groups than in the W and control groups, and were higher in the H than M group. Plasma CTGF concentrations were positively correlated with pulmonary artery systolic pressure (PASP), mPAP, and pulmonary vascular resistance, and negatively correlated with mixed venous oxygen saturation. CTGF, BNP, red blood cell distribution width, and World Health Organization Class III/IV were risk factors for PAH in CHD patients, and CTGF was an independent risk factor for PAH-CHD. The efficacy of CTGF in the diagnosis of PAH was not inferior to that of BNP.

Conclusions: CTGF is a biomarker of PAH associated with CHD. It can be used for early diagnosis and severity assessment in adult patients with CHD-PAH.

Congenital heart disease (CHD) is the most common cause of congenital malformations and affects 1–1.2% of liveborn infants.¹ With the development of pediatric care and surgical or catheter techniques, more than 90% of children with CHD are expected to survive into adulthood, with the prevalence of CHD shifting from infancy and childhood to include adult patients.^2,3 Pulmonary arterial hypertension (PAH) is a serious complication of CHD, with more than 4% of CHD patients continuing to develop PAH in adulthood. If there is no early surgical intervention, the proportion of CHD patients with PAH is even higher, reaching 33%.⁴ The development of PAH can lead to poorer exercise tolerance and quality of life in patients with CHD, as well as increased morbidity, mortality, and healthcare expenditure.⁵ Early diagnosis of CHD-PAH and timely repair of defects can cure PAH by permanently reversing PAH; however, the delayed correction of the defect may even accelerate the progression of PAH.⁶ Therefore, for patients with CHD, the timely detection of PAH and determination of its severity are particularly important.

At present, right heart catheterization is the gold standard for the diagnosis and evaluation of PAH, but it is too invasive and costly to be used for long-term follow-up and repeated evaluation of PAH patients. Echocardiography is often used to estimate pulmonary artery systolic pressure (PASP) in clinical practice, but its accuracy is poor because it depends on the skill and experience of the operator. B-Type natriuretic peptide (BNP) and N-terminal pro B-type natriuretic peptide (NT-proBNP) are the only 2 biomarkers recommended by current guidelines for evaluating the prognosis of PAH.⁷ As for the treatment endpoint of PAH, the guidelines refer only to BNP: “normal” BNP level is recommended as the treatment target.⁷ Thus, non-invasive, objective, and efficient biomarkers are urgently needed in clinical practice for the early diagnosis and severity assessment of CHD-PAH patients.

Connective tissue growth factor (CTGF) is a cysteine-rich secreted peptide with a molecular mass of 34–38 kDa. CTGF was originally isolated from umbilical vein endothelial cells and subsequently found in a various cell types, including vascular endothelial cells, fibroblasts, and smooth muscle cells. CTGF is involved in pathophysiological processes such as wound repair, angiogenesis, and cell proliferation, migration, and differentiation.^8,9 A previous study found that plasma CTGF concentrations have good diagnostic and predictive value for CHD-PAH in children.¹⁰ In another study of 30 children with CHD-PAH, CTGF was recognized as a promising biomarker for CHD-PAH in children, but there was no significant correlation between CTGF and hemodynamic parameters, so it could not be used to evaluate the severity of PAH.¹¹ Considering that there are significant differences in vascular structure and function, right heart responsiveness, duration of disease, and responsiveness to targeted drug therapy between children and adults with PAH,¹² whether CTGF can be used as a biomarker for CHD-PAH patients remains to be determined.

Methods

Study Population

In this study, 56 CHD patients admitted to the Affiliated Hospital of Nantong University for right heart catheterization between September 2018 and January 2021 were included as the experimental group. Of these patients, 45 had an atrial septal defect (ASD; all defects in the anterior tricuspids), 10 had patent ductus arteriosus (PDA), and 1 had a ventricular septal defect (VSD). The inclusion criteria for the experimental group were age >18 years and previous diagnosis of CHD without repair. Patients with another (i.e., not CHD) cause of PAH; those who had undergone surgery under general anesthesia; those with an intolerance or contraindication to surgery; those who had severe hepatic and renal insufficiency, idiopathic pulmonary fibrosis, infectious disease, asthma, acute heart failure, or malignant tumor; and those with PAH treated with targeted drug therapy were excluded from the experimental group. Based on mean pulmonary artery pressure (mPAP) measured during right heart catheterization, patients were divided into 3 groups: those without PAH (W; mPAP <25 mmHg; n=28), those with mild PAH (M; 25 mmHg≤mPAP<35 mmHg; n=18), and those with moderate and severe PAH (H; mPAP ≥35 mmHg; n=10).

The control group consisted of 28 healthy adults recruited from the Affiliated Hospital of Nantong University. The inclusion criteria for the control group were age >18 years and PASP measured by echocardiography within the normal range. Patients with a diagnosis of CHD, severe hepatic and renal insufficiency, idiopathic pulmonary fibrosis, infectious disease, asthma, acute heart failure, malignant tumor, and other conditions that may cause pulmonary hypertension or PAH were excluded from the control group.

The study was approved by the Ethics Committee of the Affiliated Hospital of Nantong University and informed consent was signed by the patients or their families. All procedures were performed in accordance with the Declaration of Helsinki and the ethical standards of the Affiliated Hospital of Nantong University Ethics Committee on human experimentation.

Clinical Data Collection

General data were collected for patients in the experimental group, including age, sex, height, weight (used to calculate body mass index [BMI]), blood pressure at admission, history of present disease, complications (whether patients had diabetes, hypertension, or coronary heart disease), medical history (current use of hypoglycemic drugs, calcium channel blockers, diuretics, and statins), history of smoking (current smoker or smoking for <6 months of ≥5 cigarettes/day), and alcohol consumption (current drinker or drinking for <6 months of ≥50 g/week alcohol). Highly qualified clinicians assessed patients’ cardiac function based on World Health Organization (WHO) functional class. After admission, patients underwent routine blood tests and examinations of liver function, renal function, blood glucose, blood electrolytes, blood coagulation, blood fat, blood gas analysis, echocardiography, abdominal B-scan ultrasound, chest X-ray or chest computed tomography, and other auxiliary examinations. Hemodynamic parameters measured during catheterization (PASP, mPAP, pulmonary vascular resistance [PVR]) were also recorded.

For subjects in the control group, data recorded included age, sex, height, weight, blood pressure, routine blood test results, blood biochemical test results, and echocardiography.

Plasma CTGF Detection by ELISA

After patients had been admitted to the hospital, an appropriate amount of venous blood was collected using an K2-EDTA sampling tubes in the morning after an overnight fast; blood samples were similarly collected from healthy adults in the control group after an overnight fast. After the collection of blood samples, the K2-EDTA tubes were tilted 30–60° and left to stand at room temperature for 10–20 min before being centrifuged at 1,000 g for 10 min at 4℃. The supernatant was transferred to an EP tube and stored at −80℃ before measurement. Plasma CTGF concentrations were determined by ELISA using a human CTGF ELISA kit (ab261851; Abcam, USA); and BNP concentrations were determined using a chemiluminescent microparticle immunoassay with a BNP CARTRIDGE kit (Abbott Laboratories, USA).

Statistical Analysis

Statistical analyses were conducted using SPSS Statistics 23. If approximate or consistent with the normal distribution, the measurement data are expressed as the mean±SD. A homogeneity test of variance was performed to compare means between groups, and 1-way analysis of variance (ANOVA) was used for comparisons of multiple groups. The measurement data that were not normally are presented as the median with interquartile range (IQR), and were compared between groups using the Kruskal-Wallis non-parametric test. Count data are presented as numbers or percentages, and were compared using the Chi-squared test or Fisher’s exact test. Spearman’s rank correlation analysis was used to analyze correlations between variables.

Variables in univariate logistic regression analysis were chosen taking the following into consideration: previous studies and findings (e.g., red blood cell distribution width [RDW], BNP, defect typing, etc.); our interests (e.g., CTGF); easy access to information (e.g., height, weight, sex, history of tobacco and alcohol use); and routine test results (e.g., routine and biochemistry blood tests). Variables with P<0.10 in the univariate logistic regression analysis were included in the multivariate logistic regression analysis to screen for independent risk factors for PAH among CHD patients.

MedCalc statistical software (version 19.6.3) was used to plot receiver operating characteristic (ROC) curves, and the Hanley and McNeil methods were used to compare differences between 2 areas under the curve (AUC). The cut-off value of the ROC curve was the maximum value of the Youden index. CTGF data among the 4 groups were compared using 1-way ANOVA, followed by multiple comparisons with the Bonferroni method. GraphPad Prism 5 was used to draw images. P values two-tailed 0.05 were considered significant.

Results

Comparisons of General Clinical Data Among Groups

General clinical data were compared among the control group and 3 CHD subgroups. There were significant (P<0.05) differences between the groups in RDW, sodium ion, chloride ion, oxygen partial pressure, WHO functional class, BNP, PASP, mPAP, PVR, and mixed venous oxygen saturation (SV̇O₂). However, there were no significant (P>0.05) differences among the groups in sex composition, age, height, weight, BMI, defect type, complications, medication history, history of tobacco and alcohol use, hemoglobin, creatinine, uric acid, blood glucose, blood lipid, and the pulmonary-systemic flow ratio (Table 1).

Table 1. Patient Characteristics

	Control (n=28)	W group (n=28)	M group (n=18)	H group (n=10)	P value
Characteristics
Female sex	21 (75.0)	20 (71.4)	14 (77.8)	7 (70.0)	0.954
Age (years)	49.7±14.3	41.6±15.0	46.0±13.3	49.1±19.5	0.222
Height (cm)	163.6±8.8	164.2±7.6	161.4±6.0	161.1±7.8	0.549
Weight (kg)	62.9±8.7	61.9±14.4	62.5±8.3	60.2±14.2	0.934
BMI (kg/m2)	23.5±2.9	22.7±3.6	24.0±2.9	23.0±3.8	0.626
SBP (mmHg)	129.6±7.6	122.7±13.5	124.1±8.3	126.1±14.1	0.111
DBP (mmHg)	74.6±8.4	72.6±8.7	71.6±9.2	67.0±14.7	0.197
ASD/PDA/VSD (n)	NA	25/2/1	14/4/0	6/4/0	0.081
WHO Class III/IV	NA	1 (3.6)	4 (22.2)	6 (60.0)	0.001
Diabetes	NA	1 (3.6)	4 (22.2)	2 (20.0)	0.106
Hypertension	NA	7 (25)	7 (38.9)	2 (20.0)	0.484
Coronary heart disease	NA	2 (7.1)	3 (16.7)	1 (10.0)	0.636
CCB	NA	4 (14.3)	3 (16.7)	3 (30.0)	0.576
Diuretic	NA	2 (7.1)	3 (16.7)	3 (30.0)	0.167
Statin	NA	5 (17.9)	4 (22.2)	2 (20.0)	0.907
Smoking	NA	3 (10.7)	1 (5.6)	1 (10.0)	1.000
Drinking	NA	3 (10.7)	1 (5.6)	1 (10.0)	1.000
Laboratory data
Leukocytes (×109/L)	5.7±1.5	5.5±1.3	5.6±1.5	5.9±1.2	0.889
Neutrophil granulocytes (×109/L)	3.2±0.9	3.2±1.1	3.3±1.3	3.5±0.9	0.776
Lymphocytes (×109/L)	1.9±0.6	1.8±0.5	1.8±0.6	1.8±0.7	0.789
Hemoglobin (g/L)	133.0±12.5	132.5±16.0	126.3±16.1	128.0±27.9	0.509
RDW (%)	12.5 [1.3]	12.7 [1.0]	13.3 [0.6]	14.5 [5.3]	0.001
GPT (U/L)	17.8 [17.5]	20.0 [25.5]	18.0 [14.5]	27.5 [24.8]	0.669
GOT (U/L)	NA	20.5 [11.3]	21.5 [4.8]	28.0 [10.0]	0.327
Direct bilirubin (μmol/L)	NA	2.3 [2.4]	3.2 [1.9]	3.0 [3.0]	0.387
Urea (mmol/L)	5.5±1.3	5.2±1.1	5.8±1.5	5.9±2.1	0.396
Creatinine (μmol/L)	58.0 [12.0]	56.0 [15.0]	49.5 [15.0]	55.0 [18.0]	0.122
Uric acid (μmol/L)	293.5 [93.2]	296.0 [111.5]	263.5 [104.0]	357.0 [207.5]	0.283
FBG (mmol/L)	5.2 [0.9]	4.8 [1.0]	5.1 [0.8]	4.8 [1.3]	0.098
Sodium ion (mmol/L)	NA	139.9±2.2	140.4±2.5	137.9±2.4	0.026
Chloride ion (mmol/L)	NA	104.8±2.0	105.3±2.8	101.6±4.4	0.004
APTT (s)	NA	28.9±3.3	27.7±1.8	29.4±2.2	0.218
Prothrombin time (s)	NA	10.9±0.7	10.8±0.6	11.4±0.9	0.146
D-dimer (μg/mL)	NA	0.2 [0.2]	0.3 [0.3]	0.2 [0.3]	0.627
Fibrinogen (g/L)	NA	2.2±0.6	2.5±0.9	2.5±0.5	0.302
Total cholesterol (mmol/L)	NA	4.1±0.9	4.3±0.8	4.2±0.9	0.885
Triglyceride (mmol/L)	NA	1.0 [0.8]	1.2 [0.9]	1.1 [0.5]	0.666
HDL-C (mmol/L)	NA	1.2 [0.3]	1.1 [0.3]	1.2 [0.2]	0.744
LDL-C (mmol/L)	NA	2.6±0.8	2.4±0.7	2.7±0.6	0.582
Partial pressure of oxygen (mmHg)	NA	89.8±9.5	90.9±12.2	80.4±11.4	0.039
BNP (pg/mL)	NA	32.3 [49.5]	69.7 [112.4]	123.4 [140.5]	0.001
Right heart catheterization
PASP (mmHg)	NA	31.5 [10.0]	42.0 [6.5]	66.5 [19.5]	0.001
mPAP (mmHg)	NA	21.0 [6.8]	29.0 [5.3]	43.5 [12.8]	0.001
PVR (Wood units)	NA	2.1 [1.1]	3.3 [2.3]	8.8 [5.6]	0.001
SV̇O2 (%)	NA	85.8±4.0	85.6±5.5	75.7±9.4	0.001
Qp/Qs	NA	2.2 [2.3]	2.0 [1.8]	1.5 [1.1]	0.124

Unless indicated otherwise, data are given as the mean±SD, median [interquartile range], or n (%). APTT, activated partial thromboplastin time; ASD, atrial septal defect; BMI, body mass index; BNP, B-type natriuretic peptide; CCB, calcium channel blocker; DBP, diastolic blood pressure; FBG, fasting blood glucose; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase; H, congenital heart disease (CHD) patients with moderate or severe pulmonary arterial hypertension (PAH); HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; M, CHD patients with mild PAH; mPAP, mean pulmonary arterial pressure; PASP, pulmonary artery systolic pressure; PDA, patent ductus arteriosus; PVR, pulmonary vascular resistance; Qp/Qs, pulmonary-systemic flow ratio; RDW, red blood cell distribution width; SBP, systolic blood pressure; SV̇O₂, mixed venous oxygen saturation; VSD, ventricular septal defect; W, CHD patients without PAH; WHO, World Health Organization.

Plasma CTGF Concentrations in the Control Group and 3 CHD Subgroups

Plasma CTGF concentrations were significantly higher in the H than M, W, and control groups (16.8±4.1 vs. 12.6±2.7, 8.5±3.2, and 7.2±2.5 ng/mL, respectively; all P<0.05). The plasma CTGF concentrations in the M group were significantly (P<0.05) higher than in the W and control groups. There was no significant difference in plasma CTGF concentrations between the W and control groups (Figure 1).

Figure 1.

Mean (±SD) plasma connective tissue growth factor (CTGF) concentrations in the control group and in adult congenital heart disease (CHD) patients without (W group) pulmonary arterial hypertension (PAH) or with mild (M) or moderate and severe (H) PAH. **P<0.01, ***P<0.001 (ANOVA).

Correlations Between Plasma CTGF Concentrations and Hemodynamic Parameters in CHD Patients

There was a significant positive correlation between plasma CTGF concentrations and PASP, mPAP, PVR, BNP, RDW, and WHO Class III/IV, with correlation coefficients (r) of 0.581, 0.680, 0.621, 0.377, 0.366, and 0.393, respectively (P<0.05). Plasma CTGF concentrations were significantly negatively correlated with SV̇O₂ (r=−0.287, P<0.05; Table 2; Figure 2).

Table 2. Correlation Between Plasma CTGF Concentrations and Different Variables

Variables	Correlation coefficient (r)	P value
Female sex	−0.046	0.736
Age (years)	0.073	0.592
BMI (kg/m2)	0.021	0.877
ASD	−0.065	0.632
SBP (mmHg)	−0.055	0.688
DBP (mmHg)	−0.112	0.412
Hemoglobin (g/L)	−0.101	0.461
RDW (%)	0.366	0.006**
Creatinine (μmol/L)	−0.066	0.631
Uric acid (μmol/L)	0.117	0.389
FBG (mmol/L)	−0.096	0.481
Sodium ion (mmol/L)	−0.068	0.619
Chloride ion (mmol/L)	−0.201	0.137
HDL-C (mmol/L)	0.030	0.823
Partial pressure of oxygen (mmHg)	−0.253	0.059
WHO Class III/IV	0.393	0.003**
BNP (pg/mL)	0.377	0.004**
PASP (mmHg)	0.581	0.001***
mPAP (mmHg)	0.680	0.001***
PVR (Wood units)	0.621	0.001***
SV̇O2 (%)	−0.287	0.032*
Qp/Qs	−0.191	0.159

CTGF, connective tissue growth factor. Other abbreviations as in Table 1. *P<0.05, **P<0.01, ***P<0.001.

Figure 2.

Correlation analysis between plasma connective tissue growth factor (CTGF) concentrations and hemodynamic parameters. Plasma CTGF concentrations were positively correlated with (A) pulmonary artery systolic pressure (PASP), (B) mean pulmonary arterial pressure (mPAP), and (C) pulmonary vascular resistance (PVR), and (D) negatively correlated with mixed venous oxygen saturation (SV̇O₂).

Univariate and Multivariate Logistic Regression of Risk Factors for PAH in CHD Patients

Univariate logistic regression analysis showed that WHO Class III/IV, RDW, BNP, and CTGF were risk factors for PAH in CHD patients. Each 1-ng/mL increase in plasma CTGF was associated with a 59.1% increased risk of PAH in CHD patients (odds ratio 1.591; 95% confidence interval [CI] 1.256–2.016; P<0.05). When factors with P<0.10 were included in multivariate logistic regression analysis, plasma CTGF remained an independent risk factor for PAH in CHD patients after multivariate adjustment for diabetes, RDW, and BNP (Table 3).

Table 3. Univariate and Multivariate Logistic Regression Analysis of Factors Predicting PAH in CHD Patients

	Univariate analysis		Multivariate analysis
	OR (95% CI)	P value	OR (95% CI)	P value
Female sex	1.200 (0.367–3.924)	0.763
Age	1.025 (0.989–1.062)	0.182
Height	0.943 (0.873–1.019)	0.140
Weight	0.998 (0.957–1.041)	0.944
BMI	1.082 (0.922–1.269)	0.334
ASD	0.300 (0.070–1.281)	0.104
Diabetes	7.364 (0.824–65.833)	0.074	2.772 (0.021–361.168)	0.682
Hypertension	1.421 (0.443–4.563)	0.555
Coronary heart disease	2.167 (0.363–12.920)	0.396
Smoking	0.641 (0.099–4.166)	0.641
Drinking	0.641 (0.099–4.166)	0.641
SBP	1.015 (0.971–1.061)	0.510
DBP	0.974 (0.924–1.028)	0.338
RDW	1.583 (1.031–2.429)	0.036	1.296 (0.800–2.101)	0.292
Hemoglobin	0.983 (0.954–1.013)	0.259
GPT	0.991 (0.959–1.024)	0.603
Direct bilirubin	1.126 (0.865–1.466)	0.377
Urea	1.410 (0.927–2.146)	0.109
Creatinine	0.985 (0.954–1.017)	0.355
Uric acid	1.002 (0.997–1.008)	0.367
FBG	1.474 (0.752–2.891)	0.259
Sodium ion	0.940 (0.755–1.171)	0.580
Chloride ion	0.915 (0.762–1.097)	0.337
D-dimer	0.691 (0.212–2.253)	0.540
Total cholesterol	1.164 (0.632–2.145)	0.625
Triglyceride	1.049 (0.452–2.437)	0.911
HDL-C	2.717 (0.271–27.240)	0.395
LDL-C	0.856 (0.408–1.793)	0.680
Partial pressure of oxygen	0.979 (0.933–1.027)	0.383
WHO Class III/IV	15.0 (1.764–127.54)	0.013	3.198 (0.165–62.063)	0.442
BNP	1.022 (1.007–1.037)	0.003	1.015 (0.995–1.035)	0.136
CTGF	1.591 (1.256–2.016)	0.001	1.526 (1.176–1.981)	0.001

CI, confidence interval; OR, odds ratio. Other abbreviations as in Tables 1,2.

ROC Curves for Plasma CTGF and BNP Concentrations, Alone and in Combination, for the Diagnosis of PAH in CHD Patients

A BNP cut-off value of 60.60 pg/mL had 64% sensitivity and 79% specificity for the diagnosis of PAH, with a positive predictive value (PPV) of 75% and a negative predictive value (NPV) of 69%. A CTGF cut-off value of 9.94 ng/mL had 89% sensitivity, 68% specificity, a PPV of 74%, and an NPV of 86% for the diagnosis of PAH. The efficacy of CTGF in the diagnosis of PAH was not inferior to that of BNP (AUC difference 0.093 [P=0.199]; CTGF AUC 0.869; BNP AUC 0.776). The AUC of CTGF combined with BNP was better than that of BNP alone (AUC difference 0.124 [P=0.02]; BNP AUC 0.776 [95% CI 0.645–0.877]; CTGF+BNP AUC 0.900 [95% CI 0.790–0.964]), with higher sensitivity, PPV, and NPV (Table 4; Figure 3).

Table 4. Receiver Operating Characteristic Curve Analysis for Plasma CTGF and BNP Concentrations, Alone and in Combination, for the Diagnosis of PAH

	AUC	SE	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	P value
CTGF	0.869 (0.752–0.944)	0.0459	89 (72–98)	68 (48–84)	74 (62–83)	86 (68–95)	0.0001
BNP	0.776 (0.645–0.877)	0.0608	64 (44–81)	79 (59–92)	75 (58–87)	69 (56–79)	0.0001
CTGF+BNP	0.900 (0.790–0.964)	0.0408	96 (82–99.9)	71 (51–87)	77 (65–86)	95 (74–99)	0.0001

Unless indicated otherwise, the values in parentheses are 95% confidence intervals. AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value. Other abbreviations as in Tables 1,2.

Figure 3.

Receiver operating characteristic curves for plasma connective tissue growth factor (CTGF) and B-type natriuretic peptide (BNP) concentrations, alone and in combination, for the diagnosis of pulmonary arterial hypertension.

ROC Curves for Plasma CTGF or BNP Concentrations, for the Diagnosis of Moderate and Severe PAH in CHD Patients

A BNP cut-off value of 79.70 pg/mL had the 80% sensitivity and 61% specificity for the diagnosis of moderate and severe PAH, with a PPV of 53% and an NPV of 85%. A CTGF cut-off value of 16.93 ng/mL had 60% sensitivity, 100% specificity, 100% PPV, and 82% NPV for the diagnosis of moderate and severe PAH. Although there was no significant difference between the AUCs of CTGF and BNP in the diagnosis of moderate and severe PAH in CHD patients (AUC difference 0.122 [P=0.35]; CTGF AUC 0.806; BNP AUC 0.683), CTGF had a higher specificity and PPV (P<0.05; Supplementary Table; Figure 4).

Figure 4.

Receiver operating characteristic curves for plasma connective tissue growth factor (CTGF) and B-type natriuretic peptide (BNP) concentrations, alone and in combination, for the diagnosis of moderate and severe pulmonary arterial hypertension (PAH) in congenital heart disease patients with PAH.

Discussion

The main findings of this study are as follows. First, plasma CTGF concentrations are higher in patients with moderate and severe PAH than in patients with mild PAH, and thus can be used to evaluate the severity of PAH. In addition, plasma CTGF concentrations are positively correlated with PASP, mPAP, and PVR, and thus have potential in evaluating patient prognosis. Second, each 1-ng/mL increase in CTGF concentration increases the risk of PAH in CHD patients by 52.6%, and the plasma CTGF concentration is an independent risk factor for the development of PAH in CHD patients. Finally, the efficacy of CTGF in the diagnosis of PAH in CHD patients is not inferior to that of BNP, and the value of CTGF combined with BNP in the diagnosis of PAH is superior to that of BNP alone. CTGF also has diagnostic value for moderate and severe PAH in CHD patients.

Previous experimental cell and animal studies showed that CTGF is involved in the process of pulmonary vascular remodeling associated with PAH and plays a key role in that process. For example, the expression of CTGF mRNA was upregulated in monocrotaline-induced and high pulmonary blood flow PAH rat models.^13,14 Wang et al indicated that CTGF contributes to the proliferation of pulmonary artery smooth muscle cells,¹⁵ and plasmid-based short hairpin RNA targeting CTGF inhibits pulmonary vascular remodeling in rats exposed to cigarette extract.¹⁶ The effects of CTGF on pulmonary vascular remodeling can be achieved by actions on 3 cell layers in the vessel wall.^17–21 Vascular endothelial cells can secrete CTGF in an autocrine manner. CTGF acts on the nucleus of endothelial cells, promotes mitosis, regulates their growth, and stabilizes the intracellular environment for their growth. CTGF also plays an important role in the proliferation, migration, and deposition of vascular smooth muscle cells. CTGF secreted by vascular smooth muscle cells can increase the formation of extracellular matrix. Oversecretion of CTGF also promotes smooth muscle cells to produce large amounts of collagen I, fibronectin, and other extracellular matrix components by increasing the activity of matrix metalloproteinase-2. CTGF also promotes the growth of fibroblasts.^17–21 The effects of CTGF on pulmonary vascular remodeling play an important role in the formation of PAH.

Plasma CTGF concentrations are used to diagnose or predict idiopathic pulmonary fibrosis,²² acute heart failure,²³ and airway obstruction in adult patients with asthma.²⁴ Some studies have also shown that simvastatin downregulates CTGF expression.²⁵ Therefore, in the present study, diseases that may affect plasma CTGF concentrations were included in the exclusion criteria, and the use of statins in the experimental group was recorded to reduce the interference of confounding factors in the study conclusions.

In the present study, we found that plasma CTGF concentrations in patients with moderate and severe PAH and mild PAH were higher than in patients with CHD alone. CTGF secretion is increased under various conditions, such as mechanical stretch, pressure overload, and oxidative stress,²⁶ which are all present in patients with CHD-PAH. Our data showed a significant negative correlation between plasma CTGF concentrations and SV̇O₂, suggesting that the secretion of CTGF may be affected by hypoxia. There is evidence that hypoxia is an important inducer of PAH.²⁷ These data support the close correlation between the secretion of CTGF and PAH. Hemodynamic parameters are the key indicators for the diagnosis and evaluation of PAH.²⁸ In the present study, plasma CTGF concentrations were positively correlated with the hemodynamic parameters PASP, mPAP, and PVR; this differs from the conclusions of Li et al, which are there was no correlation between CTGF and hemodynamic parameters.²⁹ One of the reasons for this apparent discrepancy could be the difference in CHD-PAH between children and adults, such as the proportion of defect types and the duration of the disease. We also found that the plasma CTGF concentration in the moderate and severe PAH group was higher than in the mild PAH group, suggesting that plasma CTGF concentrations can be used to evaluate the severity of PAH in CHD patients. Because plasma CTGF concentrations are significantly correlated with hemodynamic parameters, it have potential in evaluating the prognosis of PAH. Interestingly, plasma CTGF concentrations were positively correlated with WHO functional class, suggesting that they could reflect right heart function in CHD-PAH patients to some extent.

The identification of risk factors or predictors of PAH in CHD patients is beneficial for the early diagnosis and specific treatment of the disease. In the present study, univariate logistic regression analysis revealed that WHO Class III/IV and increased RDW, BNP, and CTGF concentrations were associated with an increased risk of PAH in CHD patients. WHO functional class is considered a predictor of survival in PAH patients.^30–32 In the present study, WHO Class III/IV CHD patients had a 15-fold increased risk of PAH than patients with WHO Class I/II, but after multivariate adjustment this increased risk was no longer significant. RDW has been proven to be a prognostic indicator in patients with pulmonary hypertension,^33–35 and was recently found to be a related factor in PAH patients with systemic sclerosis.³⁶ We also found that RDW was a risk factor for PAH in CHD patients, but it was not significant in multivariate analysis. Furthermore, similar to the conclusions of Long et al,³⁷ we found that the plasma BNP concentration is a risk factor for PAH in CHD patients. Insulin resistance and type II diabetes are closely associated with PAH.^38,39 We observed that the risk of PAH was 7.364-fold higher in CHD patients with than without diabetes, but the difference was not statistically significant (P=0.074). When variables with P<0.10 in univariate analyses were included in the multivariate logistic regression analysis, plasma CTGF concentration was the only independent risk factor for PAH in CHD patients after multivariate adjustment for diabetes, WHO functional class, RDW, and BNP. For every 1-ng/mL increase in CTGF, CHD patients had a 52.6% increased risk of PAH.

ROC curve analysis showed that the plasma CTGF concentration can be used for the diagnosis of PAH. Using a cut-off value of 9.94 ng/mL CTGF, the sensitivity and specificity for PAH were as high as 89% and 68%, respectively. Increasing evidence shows that the combination of multiple biomarkers is beneficial for obtaining more accurate information about the diseases.⁴⁰ Therefore, we further investigated the value of CTGF combined with BNP in the diagnosis of PAH. The results showed that the AUC of combined CTGF plus BNP was superior to that of BNP alone (AUC difference 0.124; P<0.05), with higher sensitivity, PPV, and NPV, improving diagnostic efficacy. We also studied the efficacy of the plasma CTGF concentration in the diagnosis of moderate and severe PAH. When the cut-off value was set to 16.93 ng/mL CTGF, the sensitivity and specificity of plasma CTGF concentration in the diagnosis of moderate and severe PAH were 60% and 100%, respectively, suggesting that the plasma CTGF concentration also has a certain value in the diagnosis of moderate and severe PAH.

The present study has some limitations. First, this was a single-center study, requiring a larger sample size. Second, this study excluded some confounding factors that may have affected plasma CTGF concentrations, but other potential confounding factors may still have existed. Third, future studies should include adults and children with CHD to further clarify the potential of CTGF as a biomarker for PAH in CHD patients.

In summary, this study is the first to investigate the association between plasma CTGF concentrations and the presence and severity of PAH associated with CHD in adults. The plasma CTGF concentration can be used as a biomarker for the diagnosis and severity assessment of PAH in CHD patients and can serve as an independent risk factor for PAH in CHD patients.

Acknowledgments

None.

Sources of Funding

This study was supported by a grant from the Nantong Science and Technology Bureau (No. JCZ19107).

Disclosures

The authors have no conflicts of interest to declare.

IRB Information

The study was approved by the Ethics Committee of the Affiliated Hospital of Nantong University and informed consent was signed by the patients or their families.

Data Availability

The deidentified participant data will be shared upon reasonable request. Requests for data sharing should be made directly to the corresponding author.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-22-0172

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