PINP levels in patients with newly diagnosed heart failure are associated with myocardial fibrosis and clinical outcomes

Abstract

Objectives

To investigate the association of Procollagen I N-Terminal Propeptide (PINP) with myocardial fibrosis (MF) and its predictive effect on major adverse cardiovascular events (MACEs) in patients with primary diagnosis of heart failure (HF).

Methods

A total of 76 newly diagnosed HF patients with reduced ejection fraction were followed up for one year. Serum PINP levels and extracellular volumes (ECV) quantified by cardiac magnetic resonance(CMR) imaging at baseline were collected. MF was defined when a ECV value was greater than 28.5%. The first MACEs defined as rehospitalization and/or cardiovascular death, were recorded during the visit.

Results

The levels of PINP in patients with MF were higher than those in patients without MF (39.90 VS 49.10 ng/ml, P = 0.030). Multivariate logistic regression analysis showed that PINP was an independent risk factor for MF (OR 1.058, 95% CI 1.018 ~ 1.098, P = 0.004). The linear regression model showed a linear correlation between the PINP levels and ECVs (R = 0.280, P = 0.014). Patients with higher PINP levels had a significant increased risk of incident MACE than those with lower PINP levels (HR 1.95, 95%CI 1.03 ~ 3.69, P = 0.033).

Conclusion

In patients with newly diagnosed HF, serum PINP levels were associated with MF and an increased risk of MACEs during a one-year follow-up.

Heart failure (HF) is a chronic and complex clinical syndrome and imposes a severe burden on patients’ quality of life and economic status due to its recurrent disease course and multi-system involvement [1, 2]. It is generally known that MF contributes a lot to structural remodeling and impair cardiac function, leading to the development and maintenance of HF and creating a vicious cycle [3, 4]. Therefore, early assessment of MF in HF is critically important. Procollagen type I N-terminal propeptide (PINP), as one of the main metabolites of fibrillar collagen metabolism, serves as an indicator for fibrosis in many disease progress, such as PINP can serve as a biomarker for fibrosis in peripartum cardiomyopathy [5]. Additionally, PINP also indicates endothelial damage following myocardial infarction [6]. However, the relationship between PINP and MF in HF remains unclear. We therefore enrolled newly diagnosed HF patients to investigate the association of PINP and MF, and clinical outcomes in a one-year visit.

Materials and methods

Study subjects

Consecutive patients with newly diagnosed HF, who were hospitalized at Huai’an First People’s Hospital between January 2023 and December 2023, were enrolled in this study. The inclusion criteria were as follows: (1) meeting the diagnostic criteria for HF with reduced ejection fraction (HFrEF) as outlined in the Chinese Heart Failure Diagnosis and Treatment Guidelines 2018 [7]; (2) first-time diagnosis of HF; (3) undergone cardiac magnetic resonance (CMR) canning during hospitalization; (4) age greater than 18 years and less than 80 years; (5) Availability of complete clinical data and uninterrupted follow-up. The exclusion criteria were as follows: (1) history of cardiac surgery; (2) diagnosis of malignant tumors; (3) patients with osteoporosis; (4) patients with severe hepatic impairment; (5) presence of concomitant severe organic diseases in other systems. All patients received standardized drug treatments based on the Chinese Heart Failure Diagnosis and Treatment Guidelines 2018 [7]. Written informed consent was obtained from all participants. This study was approved by the Ethics Committee of Huai’an First People’s Hospital (KY-2023-147−01).

Data collection

Demographic and clinical characteristics were systematically collected from medical records. During hospitalization, all participants were instructed to fast after 10:00 PM, with fasting venous blood samples collected in the next morning. Biochemical analyses were then performed: enzymatic methods were employed to quantify total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), creatinine (Cr), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) levels; while immunoassays were used to determine serum concentrations of N-terminal pro-B-type natriuretic peptide (NT-proBNP), hyaluronic acid (HA), laminin (LN), type IV collagen (IV-C), and procollagen type III (PCⅢ). Serum concentrations of PINP were quantitatively measured using liquid chromatography-tandem mass spectrometry analysis.

Cardiac magnetic resonance imaging and extracellular volume measurement

All examinations were performed using a 3.0-T magnetic resonance scanner. Image acquisition was completed during breath-hold to minimize motion artifacts. Left ventricular end-diastolic volume (LVEDV) and end-systolic volume (LVESV) were calculated. The left ventricular ejection fraction (LVEF) was then derived according to the following formula: LVEF = (LVEDV - LVESV)/LVEDV × 100%. Native T1 mapping was obtained to measure the total volume of distribution (TV) of the myocardium. After the intravenous administration of gadolinium-based contrast agent, post-contrast T1 mapping (T1-enhance) was performed. ECV fraction was calculated using the following formula: ECV = (1 - hematocrit) × (ΔR1_myocardium/ΔR1_blood), where ΔR1 is the change in the longitudinal relaxation rate (1/T1) post-contrast relative to pre-contrast. Based on the established ECV values, patients were divided into two groups using a cutoff value of 28.5% [8]: a group with the presence of MF (ECV ≥ 28.5%) and a group without significant MF (ECV < 28.5%).

Follow-up

All patients were prospectively followed for 12 months after discharge. Follow-up assessments were conducted via telephone interviews at 3-month intervals until the end. The primary endpoint was the occurrence of major adverse cardiovascular events (MACEs) during follow-up. MACE was defined as a composite endpoint including rehospitalization due to worsening HF, and/or cardiovascular mortality. The timing of the first MACE event was recorded for analysis.

Statistical analysis

All statistical analyses were performed using R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed continuous variables are presented as mean ± standard deviation (X̄ ± s), while non-normally distributed continuous variables are presented as median with interquartile range [M (Q1, Q3)]. Categorical variables are expressed as frequencies and percentages [n (%)]. Intergroup comparisons were performed using the Independent Samples T-test for normally distributed continuous variables, the Mann-Whitney U test for non-normally distributed continuous variables, and the Chi-square test (or Fisher’s exact test where appropriate) for categorical variables. A linear regression model was fitted to analyze the potential linear relationship between PINP levels and the degree of cardiac fibrosis (as quantified by extracellular volume, ECV). Survival analysis of MACEs was performed using the Kaplan-Meier method and cox proportional hazards regression analysis. A p value < 0.05 was defined as statistical significance.

Results

Comparison of baseline characteristics between groups

A total of 76 patients with newly diagnosed HFrEF were included, of those 39 patients have ECV value greater than ≥ 28.5% and were assigned to MF group, while the other 37 ones were to non-MF group. Significant differences were observed between patients with and without MF in systolic blood pressure (P = 0.037, Table 1).

Table 1.

Comparison of baseline characteristics between patients with and without MF

Variables	Total (n = 76)	MF (n = 39)	Non-MF (n = 37)		P
Age, y	53.20 ± 14.44	51.54 ± 14.37	54.95 ± 14.50		0.307
SBP, mmHg	135.67 ± 24.87	141.69 ± 26.53	129.32 ± 21.57		0.037
DBP, mmHg	90.61 ± 19.68	94.08 ± 20.79	86.95 ± 18.00		0.115
BMI, kg/m2	25.25 ± 7.64	25.77 ± 9.14	24.70 ± 5.74		0.546
HR,/min	97.50 (77.50, 109.25)	98.00 (78.00,110.50)	97.00 (76.00, 106.00)		0.400
Male, n (%)	52(68.42)	28(71.79)	24(64.86)		0.516
Smoking, n (%)	29 (38.16)	17 (43.59)	12 (32.43)		0.317
Drinking, n (%)	16 (21.05)	6 (15.38)	10 (27.03)		0.213
Diabetes, n (%)	23 (30.26)	11 (28.21)	12 (32.43)		0.688
CHD, n (%)	16 (21.05)	7 (17.95)	9 (24.32)		0.496

Data are given as mean ± SD, n (%), or median (interquartile range)

Abbreviations: SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, HR heart rate, CHD coronary heart disease

Comparison of laboratory and imaging parameters

The MF group had significantly higher PINP levels compared to the non-MF group (P = 0.030). Among other parameters, significant differences were observed in T1-enhance TC, TG, LDL-C, LVEF, and LN levels (P = 0.022, 0.003, 0.013, 0.013 and 0.005). No significant intergroup difference was detected in CI, native T1 values, HDL-C, BUN, NT-proBNP, ALT, AST, Cr, HA, IV-C, or PCⅢ levels (Table 2).

Table 2.

Comparison of biomarker profiles between patients with and without MF

Variables	Total (n = 76)	fibrosis (n = 39)	non-fbrosis (n = 37)	P
CI (L/min/m2)	2.10 ± 0.89	2.19 ± 0.95	1.98 ± 0.82	0.577
Native-T1 (ms)	1322.01 ± 95.47	1322.05 ± 56.20	1321.97 ± 125.19	0.997
T1-enhance (ms)	469.07 ± 145.26	507.81 ± 119.93	429.26 ± 159.37	0.022
TC (mmol/L)	4.24 ± 1.47	4.71 ± 1.44	3.77 ± 1.35	0.003
TG (mmol/L)	1.65 ± 0.82	1.88 ± 0.98	1.42 ± 0.56	0.013
HDL-C(mmol/L)	1.00 ± 0.26	0.99 ± 0.26	1.01 ± 0.26	0.745
BUN (mmol/L)	7.47 ± 2.86	7.68 ± 3.60	7.26 ± 1.82	0.522
HA (ng/mL)	76.60 ± 45.56	77.20 ± 53.65	75.98 ± 35.86	0.908
LVEF (%)	0.20 (0.15, 0.27)	0.25 (0.18, 0.30)	0.19 (0.14, 0.21)	0.013
LDL-C (mmol/L)	2.53 (1.92, 3.37)	3.21 (2.16, 3.71)	2.40 (1.87, 3.00)	0.015
NT-proBNP (ng/ml)	2130.50 (935.25, 4825.00)	2054.00 (690.50, 4800.00)	2800.00 (1069.00, 4667.00)	0.427
ALT(U/L)	28.60 (17.20, 48.50)	29.25 (17.02, 42.22)	27.60 (18.40, 54.80)	0.494
AST(U/L)	26.60 (18.93, 34.88)	23.60 (19.35, 31.65)	28.30 (17.20, 36.40)	0.457
Cr (µmol-L)	82.35 (65.03, 96.85)	86.10 (63.45, 105.95)	79.10 (71.70, 91.20)	0.284
IV-C (ng/mL)	40.28 (26.93, 63.61)	37.33 (24.85, 62.25)	43.36 (30.88, 64.25)	0.178
LN (ng/mL)	40.23 (28.96, 86.51)	32.18 (25.70, 73.28)	56.10 (36.04, 120.60)	0.005
PCⅢ(ng/mL)	29.52 (19.00, 41.18)	25.50 (18.21, 34.30)	32.88 (25.10, 44.30)	0.067
PINP (ng/mL)	43.25 (35.48, 56.80)	39.90 (28.35, 53.00)	49.10 (37.10, 62.50)	0.030

Data are given as mean ± SD, n (%), or median (interquartile range)

Abbreviations: CI cardiac index, TC total cholesterol, TG triglycerides, HDL-C high-density lipoprotein cholesterol, BUN blood urea nitrogen, HA hyaluronic acid, LVEF left ventricular ejection fraction, LDL-C low-density lipoprotein cholesterol, NT-proBNP N-terminal pro-B-type natriuretic peptide, ALT Alanine aminotransferase, AST aspartate aminotransferase, Cr creatinine, IV-C type IV collagen, LN laminin, PCⅢ procollagen type III, PINP procollagen type I N-terminal propeptide

Regression analyses of PINP levels and MF

Binary logistic regression analysis revealed a significant positive association between PINP levels and the presence of MF (odds ratio [OR] 1.034, 95% confidence interval [CI] 1.005–1.064, P = 0.022). After adjustment for potential confounders identified in Tables 1 and 2 (including systolic blood pressure, heart rate, hypertension history, LVEF, TC, TG, LDL-C, LN, and T1-enhance values), PINP still significantly associated with the presence of MF (OR 1.058, 95% CI 1.018–1.098, P = 0.004, Table 3). Linear regression analysis demonstrated a significant positive correlation between PINP levels and ECV (R = 0.280, P = 0.014).

Table 3.

Logistic regression analysis results of PINP values and the presence of MF

Model	β	SE	Waldχ2	P	OR(95%CI)
Model 1	0.034	0.015	5.268	0.022	1.034(1.005 ~ 1.064)
Model 2	0.056	0.019	8.192	0.004	1.058(1.018 ~ 1.098)

Model 1: Univariable analysis of PINP. Model 2: Multivariable model adjusted for PINP, SBP, T1enhance, TC, TG, LDL-C, LVEF, and LN

All p-values indicate statistical significance at < 0.05. The odds ratio (OR) with 95% confidence interval (CI) are provided for each factor

Predictive value of PINP for maces

During the one-year follow-up, 39 MACEs were recorded, consisting of 37 rehospitalizations and 2 cardiovascular deaths. Cox regression analysis showed that PINP levels were significantly associated with a high risk of MACEs (HR 1.02, 95%CI 1.00 ~ 1.04, P = 0.019). Moreover, SBP, DBP, TC, and LDL-C were associated with a low risk of MACEs (HR 0.98, 95% CI 0.97–0.99, P = 0.005, HR 0.80, 95% CI 0.65–0.98, P = 0.032 and HR 0.71, 95% CI 0.53–0.95, P = 0.020, respectively, Fig. 1). Patients were then stratified into high- and low-PINP groups based on the median serum PINP concentration. Cox analysis revealed that patients with PINP levels ≥ 43.25 ng/ml exhibited a lower MACEs-free rate compared with those with PINP levels < 43.25 ng/ml (HR 1.95, 95%CI 1.03 ~ 3.69, P = 0.033). Kaplan-Meier survival curve was presented in Fig. 2.

Fig. 1.

Cox Regression Analysis of Factors Associated with One-Year MACEs

Fig. 2.

Kaplan-Meier curve of PINP related to MACEs within one year

Discussion

In patients with newly diagnosed HFrEF, serum PINP levels at baseline were independently associated with MF, with a linear correlation with ECVs. In addition, higher PINP levels indicated an increased risk of one-set MACEs during a short-term follow-up.

HF is the end-stage clinical syndrome of cardiovascular disease, defined by impaired cardiac contraction and/or relaxation due to structural or functional changes [1]. HFrEF represents the most prevalent phenotype of HF and is associated with a higher rate of rehospitalization compared to HF with preserved ejection fraction [9, 10]. Myocardial remodeling, as a key pathophysiological marker in HF, has a critical characteristic feature is MF [11]. MF leads to increased cardiac stiffness, diastolic dysfunction, and electrophysiological disturbances, thereby collectively exacerbating HF [12, 13]. Currently, ECV fraction measured by cardiac magnetic resonance imaging serves as a non-invasive “gold standard” for assessing MF [14]. ECV has been shown to provide consistent prognostic information across HF phenotypes [15]. Additionally, it can be used to evaluate MF in aortic stenosis and, notably, to stratify the risk of sudden cardiac death in non-ischemic dilated cardiomyopathy (HR: 2.91; 95% CI: 1.63–5.22; P = 0.001) [16, 17].However, its widespread clinical application remains limited due to high costs and stringent equipment requirements. Identifying novel, accessible, and easily measurable biomarkers related to MF holds significant clinical importance for risk stratification, prognosis assessment, and treatment guidance in HF patients [18].

Within the cardiovascular system, collagen is a major constituent of the extracellular matrix, primarily comprising fibril-forming types I and III [19]. MF is characterized by significantly enhanced collagen synthesis and deposition, accompanied by relative or absolute deficiency in collagen degradation, including PINP, procollagen type I carboxy-terminal propeptide (PICP), and procollagen type III amino-terminal propeptide (PIIINP) [20–22]. Previous studies have repeatedly demonstrated associations between PICP, PIIINP and MF, that PICP and PIIINP can serve as biomarkers for MF, particularly in the context of HF [23, 24]. For example, low PICP (< 108.1 ng/mL) is associated with a lower risk of heart failure-related outcomes(P = 0.018) [25]. Compared to PINP, PICP and PIIINP exhibit greater variability in their assay measurements [23]. In a study of fibrosis among patients with liver cirrhosis, serum concentrations of PINP were found to be significantly elevated compared to those without the condition (87.9 µg/L VS 52.6 µg/L) [26]. It is also a valuable biomarker for the diagnosis and monitoring of fibrotic post-tuberculosis tracheobronchial stenosis [27]. PINP can promote cardiac remodeling in patients with metabolic syndrome and increase the risk of atrial fibrillation (r = 0.623) [28]. However, a study in patients with coronary artery disease found no significant correlation between PINP and the MF index T1 relaxation time, nevertheless, also reported a certain correlation between PINP and impaired left ventricular diastolic function [29]. This implies that PINP’s role may transition from a structural marker to a functional indicator, reflecting myocardial dysfunction secondary to fibrosis. Consistent with this interpretation, our study demonstrates a significant association between PINP and newly diagnosed HF, along with its predictive value for MACEs risk.

This study focused on treatment-naïve HFrEF patients receiving guideline-directed medical therapy, investigating the relationship between PINP and the MF index ECV, as well as PINP’s predictive value for MACE. This design aimed to minimize confounding from suboptimal treatment. However, aspects of long-term management, such as medication adherence and dose titration during follow-up, were not investigated. Future work will involve extended follow-up duration with detailed medication usage tracking, expanded sample sizes, and multi-center collaboration to validate our findings. These efforts will provide crucial evidence supporting the potential of PINP as a simple, cost-effective blood biomarker for prognostic risk stratification in HF.

Conclusion

In summary, this study demonstrated a significant association between serum PINP levels and MF in initial diagnosis HF patients. Higher PINP levels were observed in the MF group, and PINP was identified as an independent predictor of fibrosis, showing a linear correlation. Furthermore, our one-year follow-up data indicate that PINP holds predictive value for short-term MACEs. Subsequent multi-center studies with expanded samples and follow-up will be essential to definitively confirm these results.

Abbreviations

PINP

Procollagen I N-Terminal Propeptide

MF

Myocardial fibrosis

MACEs

Major adverse cardiovascular events

HF

Heart failure

ECV

Extracellular volumes

CMR

Cardiac magnetic resonance

HFrEF

HF with reduced ejection fraction

TC

Total cholesterol

TG

Triglycerides

HDL-C

High-density lipoprotein cholesterol

LDL-C

Low-density lipoprotein cholesterol

BUN

Blood urea nitrogen

Cr

Creatinine

AST

Aspartate aminotransferase

ALT

Alanine aminotransferase

NT-proBNP

N-terminal pro-B-type natriuretic peptide

HA

Hyaluronic acid

LN

Laminin

IV-C

Type IV collagen

PCⅢ

Procollagen type III

LVEDV

Left ventricular end-diastolic volume

LVESV

Left ventricular end-systolic volume

LVEF

Left ventricular ejection fraction

TV

Total volume

T1-enhance

Post-contrast T1 mapping

PICP

Procollagen type I carboxy-terminal propeptide

PIIINP

Procollagen type III amino-terminal propeptide

Authors’ contributions

Mengjie Liu-writing-original draft; Hailang Liu-language editing and polishing; Qiuyao DU -formal analysis; Shiyu Lei -data curation; Tingting Hu-Project Supervision, and Final Manuscript Approval; Jin Geng - investigation and funding support; Qing Zhang -conceptualization.

Funding

Financial support was received from Jiangsu Provincial Health Commission Scientific Research Project; Grant ID H2023086, Chinese Cardiovascular Association-Natural lipid-lowering drugs fund (2023-CCA-NLD-055), and Huai’an Science Project (HAB202307).

Data availability

The data that support the findings of this study are available on request from the corresponding author, Tingting Hu, Jin Geng, Qing Zhang, upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Huai’an First People’s Hospital (KY-2023-147-01). Written informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki.

Consent for publication

All subjects provided their informed consent when they enrolled in the study.

Competing interests

The authors declare no competing interests.