ABSTRACT
Subclinical atherosclerosis is a key predictor of cardiovascular events. While inflammation plays a crucial role in atherosclerosis, the involvement of Human Neutrophil Peptides 1–3 (HNP1‐3) in its progression remains unclear. The study investigates the association of HNP1‐3 and PCSK9 with coronary atherosclerotic burden and explores the potential mediatory role of PCSK9 in HNP1‐3's effect on atherogenesis. Patients who underwent coronary computed tomographic angiography (CCTA) and had subclinical atherosclerosis (luminal stenosis < 50%) or normal coronary arteries were included in this cross‐sectional study. HNP1‐3 and PCSK9 levels were measured using ELISA, and coronary plaque burden was quantified using the modified Gensini score. Patients with subclinical atherosclerosis had significantly higher levels of HNP1‐3 (p < 0.001), PCSK9 (p < 0.001), and lipoprotein(a) [Lp(a)] (p < 0.001) compared to controls. HNP1‐3 was an independent predictor of subclinical atherosclerosis (p < 0.001), and its levels positively correlated with the modified Gensini score (p < 0.001). In multinomial logistic regression, higher levels of HNP1‐3, PCSK9, and Lp(a) were independently associated with higher modified Gensini score tertiles. Mediation analysis revealed that PCSK9 mediated 48.7% of the effect of HNP1‐3 on the modified Gensini score. After adjusting for hsCRP and cardiovascular risk factors, the direct effect of HNP1‐3 became statistically insignificant, while the indirect effect via PCSK9 remained significant, suggesting that PCSK9 fully mediates the pro‐atherogenic effects of HNP1‐3. In conclusion, HNP1‐3 is a novel independent predictor of subclinical atherosclerosis and coronary plaque burden, with its effects being mediated through PCSK9. These findings suggest that targeting PCSK9 could mitigate the inflammatory actions of HNP1‐3, offering potential therapeutic insights for atherosclerosis prevention.
Keywords: atherosclerotic plaque burden, HNP1‐3, PCSK9, subclinical atherosclerosis
Study Highlights.
- What is the current knowledge on the topic?
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○Atherosclerotic plaque burden, rather than the degree of coronary artery occlusion, is a key determinant of cardiovascular events. Atherosclerosis is increasingly recognized as a systemic inflammatory disease. Human neutrophil peptides 1–3 (HNP1–3) are among the most abundant proteins in neutrophil granules and play a crucial role in inflammation and may contribute to the pathogenesis of atherosclerosis. Proprotein convertase subtilisin/kexin type 9 (PCSK9) has recently been shown to exert direct pro‐atherosclerotic effects independent of its role in lipid metabolism.
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- What question did this study address?
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○This study investigated the roles of PCSK9 and HNP1–3 in subclinical atherosclerosis and explored the potential interaction between these molecules in the development of subclinical atherosclerosis.
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- What does this study add to our knowledge?
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○Our findings demonstrate that both PCSK9 and HNP1–3 are positively associated with coronary atherosclerotic plaque burden in individuals with subclinical atherosclerosis. Moreover, PCSK9 appears to mediate the pro‐atherogenic effects of HNP1–3, suggesting a novel mechanistic link between these two inflammatory mediators.
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- How might this change clinical pharmacology or translational science?
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○In addition to PCSK9, HNP1–3 may serve as a novel biomarker for early atherosclerosis. Furthermore, PCSK9 inhibition could potentially mitigate HNP1–3–related vascular inflammation, offering additional therapeutic benefits of PCSK9 inhibitors in the prevention of atherosclerosis.
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1. Introduction
Atherosclerosis is the leading cause of death worldwide, responsible for approximately 19.1 million deaths annually, according to the American Heart Association [1]. It is a chronic condition that begins as early as childhood or adolescence, with clinical manifestations typically emerging in middle age or later in life. Subclinical atherosclerosis refers to the presence of atheromatous plaques in one or more vascular territories without any clinical symptoms or signs [2]. Early detection of subclinical atherosclerosis is crucial for preventing the severe consequences of the disease and has become a significant focus in recent research and clinical practice. Despite the absence of obstructive lesions, the atherosclerotic plaque burden, which refers to the total accumulation of atherosclerotic plaques and their impact on vascular function, is an important predictor of cardiovascular event (CVE) risk [3]. Furthermore, atherosclerotic plaque burden is recognized as a better determinant of CVE than occlusive coronary artery lesions [3]. Advances in non‐invasive imaging techniques like computed tomography angiography (CTA) have greatly improved our ability to determine atherosclerotic plaque burden.
The role of inflammation in atherosclerosis has gained significant attention in recent years, and atherosclerosis is increasingly recognized as a systemic inflammatory disease. Consequently, the molecular mechanisms linking inflammation to atherosclerosis have become a major focus of research.
Macrophages are the most abundant immune cells in atherosclerotic plaques, and their role in plaque‐associated inflammation is well established. In contrast, neutrophils, despite being the first line of defense in innate immunity, have been relatively understudied in atherosclerosis, largely due to their limited presence in advanced plaque histology. However, recent studies highlight their critical involvement in early atherogenesis, preceding macrophage dominance [4]. Human Neutrophil Peptides (HNPs) are among the most abundant proteins in neutrophil granules and play a crucial role in inflammation [5]. Their positive charge enables them to disrupt cell membranes, allowing them to kill a broad range of pathogens, including bacteria, fungi, and viruses [6]. In addition to their antimicrobial effects, HNPs attract immune cells and stimulate the production of pro‐inflammatory cytokines such as IL‐8, IFN‐γ, and IL‐2, and contribute to blood clot formation by altering fibrin structure [6, 7]. Their relationship with atherosclerosis and cardiovascular diseases has been investigated in a limited number of studies. While some studies found a relationship between cardiovascular diseases and increased HNP1‐3 levels, some studies demonstrated that increased HNP1‐3 levels are related to reduced LDL levels and attenuated atherosclerosis [8, 9]. Nevertheless, most studies suggest that HNPs are related to cardiovascular events, but there are limited studies on their relationship with atherosclerotic plaque burden.
Proprotein convertase subtilisin/kexin type 9 (PCSK9), which is a member of the proprotein convertase family, is another molecule that plays a significant role in the development of atherosclerosis. It directs low‐density lipoprotein (LDL) receptors to lysosomes for degradation in the liver, leading to hyperlipidemia [10]. Besides its proatherosclerotic effects through hypercholesterolemia, recent studies point to its direct atherogenic effects on the vascular wall. Studies have shown that atherosclerotic necrotic core volume correlates with PCSK9 plasma levels, independent of lipid levels [11]. PCSK9 contributes to atherosclerosis by modulating multiple mechanisms, with one of its most prominent effects being the enhancement of inflammation in the arterial wall [12]. More importantly, inflammation leads to increased PCSK9 expression in vascular wall cells, which in turn triggers various cellular effects, including apoptosis, phenotypic changes, migration, and proliferation.
Both experimental and clinical studies have shown that inflammation and sepsis lead to elevated plasma PCSK9 levels as well as increased expression of PCSK9 in vascular cells [13]. This upregulation is primarily mediated by NF‐κB activation, which enhances both PCSK9 mRNA and protein expression [14]. Notably, HNP1–3 induces NF‐κB signaling, promoting pro‐inflammatory gene expression [15]. Thus, HNP1–3 may act upstream in this pathway, indirectly contributing to the increase in PCSK9 expression and plasma PCSK9 levels.
We hypothesize that in early atherosclerosis, NF‐κB activation by HNP1‐3 leads to upregulation of PCSK9, which then promotes inflammation, apoptosis, proliferation, phenotypic modulation, and foam cell formation in vascular cells. Therefore, in addition to their individual pro‐atherogenic effects, HNP1–3 and PCSK9 may be mechanistically linked, with PCSK9 potentially mediating the vascular effects of HNP1–3.
In this study, we investigated the relationship between HNP1–3 and PCSK9 with atherosclerotic plaque burden, a key determinant of cardiovascular risk, and explored the possibility of a mediating interaction between these two molecules in the progression of subclinical atherosclerosis.
2. Methods
The study was conducted in accordance with the Declaration of Helsinki and received approval from the Local Ethics Committee of Koc University with approval number 2021.113.IRB1.036. Written informed consent was obtained from each patient included in the study.
2.1. Study Population
The study group consists of consecutive patients who underwent coronary computed tomographic angiography (CCTA) at Koç University Hospital between March 2021 and March 2022, had no known cardiovascular disease or clinical symptoms of cardiovascular disease, and had coronary artery plaques with lumen stenosis of < 50%. These patients mostly have one or more cardiovascular risk factors but didn't have any cardiovascular symptoms. Patients who are consistent with inclusion criteria and did not fulfill any exclusion criteria during this period were included in the study. The control group comprised patients without any plaques who also met the inclusion criteria and did not fulfill any exclusion criteria. In total, 179 patients were included in the study, consisting of 105 patients with subclinical atherosclerosis and 74 controls.
Patients were excluded if they had chest pain, coronary artery plaque causing stenosis ≥ 50% on coronary CT angiography, evidence of myocardial ischemia on stress imaging, a history of coronary revascularization, peripheral arterial disease with stenosis ≥ 50%, or a history of cerebrovascular events. Additionally, patients with severe arrhythmia, valvular heart disease, cardiomyopathy, or pulmonary heart disease were not included. Since acute and chronic inflammation can influence PCSK9 and HNP1 levels, patients with conditions associated with inflammation, including chronic liver disease, chronic kidney disease stages G3b, G4, or G5 (eGFR < 45 mL/min), and active cancer or a history of cancer were excluded. Furthermore, patients with acute infections or inflammation—identified by high‐sensitivity C‐reactive protein (hs‐CRP) > 10 mg/L, leukocytosis (white blood cell count > 11 × 109 cells/L), fever > 38°C, or antibiotic use—were also excluded. Additional exclusion criteria included patients under 18 years old and women on hormone replacement therapy.
2.2. Coronary Computed Tomography Angiography
CCTA (Siemens SOMATOM Definition Flash) was utilized to assess coronary artery anatomy and the presence of atherosclerotic plaques. The evaluation included the analysis of 18 coronary artery segments, following the guidelines provided by the American Heart Association (AHA).
Each segment was carefully examined for the presence, location, and severity of coronary artery stenosis.
Prior to image acquisition, beta‐blockers were administered to patients when necessary to achieve an optimal heart rate (typically < 65 beats per minute) to minimize motion artifacts and improve image quality. In cases where additional vasodilation was deemed beneficial by the attending physician, sublingual nitroglycerin was administered to enhance the visualization of the coronary artery lumen.
Image acquisition was performed using a 64‐slice multidetector CT scanner with retrospective ECG gating to synchronize image acquisition with the cardiac cycle. The contrast medium (iodinated contrast agent) was administered intravenously at a flow rate of 5 mL/s, followed by a saline flush to optimize coronary opacification. The images were reconstructed with a slice thickness of 0.75 mm, and the datasets were transferred to a dedicated workstation for further analysis. Coronary plaque burden was assessed using the modified Gensini score [16]. All images were independently reviewed, and the modified Gensini score was analyzed by one experienced radiologist and one experienced cardiologist. Any discrepancies were resolved by consensus. The modified Gensini score was calculated by multiplying the severity index by the location‐specific factor, and the sum of these products provided the total plaque burden. The severity index was graded as follows: 1 for 0%–25% stenosis, 2 for 26%–50%, 4 for 51%–75%, 8 for 76%–90%, 16 for 91%–99%, and 32 for 100% stenosis. The location‐specific factors were assigned as follows: 5 for the left main coronary artery, 2.5 for the proximal left anterior descending artery and the proximal left circumflex artery, 1.5 for the midsegment of the left anterior descending artery, 0.5 for the second diagonal branch and the posterolateral branch, and 1 for other branches.
2.3. Blood Sample Collection and Analysis
Blood samples were collected from patients who met the inclusion criteria after obtaining informed written consent. A 10 mL blood sample was drawn into a dry bio tube and centrifuged at 1000 g for 10 min. The supernatant was then divided into three separate Eppendorf tubes and stored in a −80°C freezer until analysis.
PCSK9 and HNP1 levels were quantified using ELISA kits from Elabscience. Lp(a) levels were measured using the Roche Cobas Tina‐quant Lipoprotein (a) Gen 2 kit, applying the immunoturbidimetric method at a certified local laboratory.
2.4. Data Recording
Patients' cardiovascular risk factors, including hypertension, diabetes, lipid profiles, body mass index (BMI), smoking status, alcohol consumption, estimated glomerular filtration rate (eGFR) calculated using the MDRD formula, high‐sensitivity C‐reactive protein (hs‐CRP) levels, and medication use, were recorded. Hypertension was identified as blood pressure measurements exceeding 140/90 mmHg on at least two occasions, a previous diagnosis of hypertension, or the use of antihypertensive medication. Diabetes was defined as a fasting blood glucose level greater than 126 mg/dL, an HbA1c level above 6.5%, a previous diagnosis of diabetes, or the use of antidiabetic treatment. Hyperlipidaemia was defined as being on statin treatment or having a total cholesterol level > 200 mg/dL or low‐density lipoprotein (LDL) level > 130 mg/dL.
2.5. Statistical Analysis
Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 26.0 for Windows (SPSS Inc., Chicago, IL, USA), GraphPad Prism version 9.4.1 (GraphPad Software, San Diego, CA, USA), and SmartPLS 4. The Kolmogorov–Smirnov test was used to assess the normality of data distribution. Propensity score matching was used to minimize differences between the subclinical atherosclerosis and control groups, and all analyses were performed with 1000 bootstrap resamples to enhance the robustness and stability of the results. Continuous variables were expressed as mean ± standard deviation (SD) or as median with interquartile range (25th–75th percentiles, Q1–Q3), while categorical variables were presented as percentages.
Categorical variables were analyzed using the Chi‐square test. For the analysis of two groups, Fisher's Exact Chi‐square test was used, while for comparisons involving more than two groups, the Likelihood Ratio Chi‐square test was applied. Student's t‐test and the Mann–Whitney U test were used for normally and non‐normally distributed continuous variables, respectively. Binary logistic regression analyses, both univariate and multivariate, were performed to assess the independent association of HNP1‐3 and PCSK9 with subclinical atherosclerosis.
For the subgroup analysis of patients with subclinical atherosclerosis, the modified Gensini score was divided into three tertiles based on its percentiles. Patients with a modified Gensini score below the 33rd percentile formed the 1st tertile, those between the 33rd and 66th percentiles comprised the 2nd tertile, and those above the 66th percentile were classified into the 3rd tertile group. Univariate and multivariate multinomial regression analyses were conducted to identify independent predictors of each modified Gensini Score tertile.
Correlation analyses between HNP1‐3 and PCSK9 were conducted with Pearson correlation analyses. Mediation analyses were performed using partial least squares structural equation modeling (PLS‐SEM). Mediation refers to a situation in which the effect of an independent variable (in our case, HNP1–3) on a dependent variable (plaque burden) is transmitted through a third variable, called the mediator (PCSK9). Mediation analysis helps determine whether this indirect path is statistically significant and whether the mediator explains part or all of the observed relationship. PLS‐SEM estimates both direct and indirect effects and allows for testing the significance of the mediation pathway using bootstrapped confidence intervals [17]. The model was run with 10,000 resamples, both without adjustments and with adjustments for cardiovascular risk factors. Statistical significance was defined as a two‐sided p‐value < 0.05.
3. Results
3.1. Subclinical Atherosclerosis Vs. Controls
The basic characteristics of the study population are presented in Table S1. Due to differences in cardiovascular risk factors between the control and subclinical atherosclerosis groups, a propensity score matching analysis was performed. Following this analysis, 44 individuals were excluded: 11 from the subclinical atherosclerosis group and 33 from the control group. Although major cardiovascular risk factors were matched, differences in age and the proportion of females persisted between the groups (Table 1). Following propensity matching, median (Q1–Q3) Lp(a) levels were higher in the subclinical atherosclerosis group [16.5 (8.0–41.8) mg/dL] than in controls [12.0 (6.0–17.0) mg/dL] (p = 0.013). Mean PCSK9 concentrations were also elevated in the subclinical group (251.7 ± 79.1 ng/mL) compared to controls (193.7 ± 109.01 ng/mL) (p = 0.003). Similarly, mean HNP1‐3 levels were higher in the subclinical group (18.63 ± 11.12 ng/mL) than in controls (9.62 ± 6.18 ng/mL) (p = 0.012).
TABLE 1.
Basic characteristics of study group following propensity score match.
| Normal coronary artery (n = 41) | Subclinical atherosclerosis (n = 94) | p value | |
|---|---|---|---|
| Demographic variables | |||
| Age (mean ± SD) | 57.5 ± 12.2 | 63.7 ± 10.3 | 0.034 |
| Women, n (%) | 24 (58.53%) | 38 (40.4%) | 0.002 |
| BMI, kg/m2 (mean ± SD) | 30.95 ± 6.22 | 29.38 ± 4.82 | 0.389 |
| Smokers, n (%) | 12 (31.6%) | 38 (48.1%) | 0.112 |
| Diabetes, n (%) | 10 (24.4%) | 31 (33.7%) | 0.316 |
| Hypertension, n (%) | 27 (65.9%) | 62 (68.1%) | 0.842 |
| Hyperlipidemia | 14 (34.1%) | 43 (47.3%) | 0.186 |
| Medications | |||
| Beta‐blockers, n (%) | 16 (39%) | 38 (41.8%) | 0.849 |
| RAS blockers, n (%) | 20 (48.8%) | 44 (48.4%) | 1 |
| Statins, n (%) | 20 (48.7%) | 51 (50.7%) | 0.145 |
| Biochemical analysis | |||
| eGFR, ml/min/1.73 m2 (mean ± SD) | 87.1 ± 18.2 | 86.1 ± 22.9 | 0.900 |
| Total cholesterol, mg/dL (mean ± SD) | 210.9 ± 32.7 | 202.3 ± 52.2 | 0.602 |
| HDL, mg/dL (median, Q1‐Q3) a | 54.0 (46.0–66.0) | 51.0 (43.0–61.0) | 0.179 |
| LDL, mg/dL(mean ± SD) | 140.2 ± 32.0 | 132.0 ± 46.9 | 0.583 |
| Non‐HDL Cholesterol (mean ± SD) | 167.2 ± 31.4 | 159.1 ± 50.2 | 0.582 |
| Triglycerides, mg/dL (median, Q1–Q3) a | 137.0 (98.8–160.8) | 136.0 (101.0–188.0) | 0.604 |
| Neutrophil count | 4.25 ± 1.61 | 4.52 ± 1.63 | 0.655 |
| hsCRP, mg/L (mean ± SD) | 3.52 ± 3.18 | 4.31 ± 2.71 | 0.212 |
| Lp(a), mg/dL (median, Q1–Q3) a | 12.0 (6.0–17.0) | 16.5 (8.0–41.8) | 0.013 |
| PCSK9 (ng/mL) | 193.7 ± 109.01 | 251.7 ± 79.1 | 0.003 |
| HNP1‐3 (ng/mL) | 9.62 ± 6.18 | 18.63 ± 11.12 | 0.012 |
Note: Statistical significant values (p < 0.05) are indicated in bold.
Abbreviations: BMI, body mass index; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HDL, high density lipoprotein; HNP1‐3, human neutrophil peptide 1–3; hsCRP, high sensitivity CRP; Q1, 25th percentile; Q2, 75th percentile; LDL, low density lipoprotein; Lp(a), lipoprotein (a); PCSK9, Proprotein convertase subtilisin/kexin type 9; SBP, systolic blood pressure.
Nonparametric test was used for statistical analysis.
In a multivariate analysis following propensity score matching, which included age, sex, smoking status, diabetes, hypertension, hyperlipidemia, neutrophil count, hsCRP, PCSK9, Lp(a), and HNP1‐3, only age (p = 0.027), Lp(a) (p = 0.042), and HNP1‐3 (p < 0.001) were identified as independent predictors of subclinical atherosclerosis (Table 2).
TABLE 2.
Independent predictors of subclinical atherosclerosis.
| Multivariate analysis | |||
|---|---|---|---|
| Variables | OR | 95% CI | p value |
| Age | 1.086 | 1.009–1.168 | 0.027 |
| Male Sex | 5.467 | 0.822–36.37 | 0.079 |
| Smoking | 2.089 | 0.827–5.28 | 0.119 |
| Diabetes | 3.704 | 0.902–14.71 | 0.069 |
| Hypertension | 1.066 | 0.612–6.981 | 0.853 |
| Hyperlipidemia | 1.494 | 0.582–3.833 | 0.086 |
| Neutrophil count | 1.332 | 0.955–1.859 | 0.091 |
| hsCRP | 0.920 | 0.807–1.049 | 0.211 |
| PCSK9 | 1.003 | 0.0998–1.008 | 0.268 |
| Lp(a) | 1.028 | 1.001–1.055 | 0.042 |
| HNP1‐3 | 1.116 | 1.050–1.186 | < 0.001 |
Note: Statistical significant values (p < 0.05) are indicated in bold.
Abbreviations: HNP1‐3, human neutrophil peptide 1–3, Lp(a), lipoprotein (a); PCSK9, Proprotein convertase subtilisin/kexin type 9.
3.2. Subclinical Atherosclerosis Subgroup
In a subgroup analysis of patients with subclinical atherosclerosis, the modified Gensini score was evaluated. Modified Gensini Score was positively correlated with HNP1‐3 (r = 0.454, p < 0.001) (Figure 1A), hsCRP (r = 0.377, p < 0.001) (Figure 1B), PCSK9 (r = 0.581, p < 0.001) (Figure 1C), and Lp(a) (r = 0.317, p < 0.001) (Figure 1D). When the relationship of HNP1‐3 and other parameters was analyzed, HNP1‐3 was positively correlated with hsCRP (r = 0.672, p < 0.001) (Figure 1E) and PCSK9 (Figure 1F) (r = 0.468, p < 0.001). The correlation plot is presented in Table S1.
FIGURE 1.
Correlation Graphs of Modified Gensini Score and HNP1‐3. (A) Correlation between modified Gensini score and HNP1‐3 (r = 0.454, p < 0.001). (B) Correlation between modified Gensini score and hsCRP (r = 0.377, p < 0.001). (C) Correlation between modified Gensini score and PCSK9 (r = 0.581, p < 0.001). (D) Correlation between modified Gensini Score and Lp (a) (r = 0.317, p < 0.001). (E) Correlation between HNP1‐3 and hsCRP (r = 0.672, p < 0.001). (F) Correlation between HNP1‐3 and PCSK9.
The distribution of key characteristics across the modified Gensini score tertiles is presented in Table 3. Lp(a) levels progressively increased across tertiles, with the highest levels observed in Tertile 3 (Tertile 3: 40 [15–56] mg/dL vs. Tertile 2: 14 [6–38] mg/dL vs. Tertile 1: 11 [5–20] mg/dL, p < 0.001). Similarly, PCSK9 levels exhibited a significant stepwise increase across tertiles (Tertile 3: 325.86 ± 93.54 ng/mL vs. Tertile 2: 222.57 ± 189.05 ng/mL vs. Tertile 1: 201.88 ± 79.28 ng/mL, p < 0.001). HNP1‐3 levels also demonstrated a significant upward trend, with the highest levels observed in Tertile 3 (Tertile 3: 25.44 ± 10.77 ng/mL vs. Tertile 2: 19.16 ± 8.18 ng/mL vs. Tertile 1: 15.37 ± 9.26 ng/mL, p < 0.001). The comparisons of HNP1‐3, PCSK9, and Lp(a) across modified Gensini score tertiles are shown in Figure S2A–C, respectively.
TABLE 3.
Characteristics of patients according to modified gensini score tertiles.
| Modified Gensini Score Tertile | Tertile 1 (n = 35) Gensini score < 12 | Tertile 2 (n = 35) Gensini Score 12–32 | Tertile 3 (n = 35) Gensini Score > 32 | p value |
|---|---|---|---|---|
| Demographic variables | ||||
| Age (mean ± SD) | 61.94 ± 12.43 | 65.71 ± 11.84 | 63.37 ± 10.46 | 0.393 |
| Women, n (%) | 17 (48.6) | 20 (57.1) | 7 (20) | 0.004 |
| BMI, m2/kg (mean ± SD) | 29.12 ± 4.55 | 28.03 ± 4.59 | 28.51 ± 4.62 | 0.609 |
| Smokers, n (%) | 14 (46.7) | 12 (42.9) | 16 (50) | 0.858 |
| Diabetes, n (%) | 13 (38.2) | 9 (26.5) | 12 (34.3) | 0.576 |
| Hypertension, n (%) | 25 (73.5) | 20 (60.6) | 26 (74.3%) | 0.392 |
| Hyperlipidemia, n (%) | 13 (38.2) | 12 (36.4) | 15 (44.1) | 0.794 |
| Medications | ||||
| Beta‐blockers, n (%) | 10 (28.5) | 13 (39.4) | 22 (64.7) | 0.008 |
| RAS blockers, n (%) | 14 (40) | 18 (54.5) | 18 (52.9) | 0.416 |
| Statins, n (%) | 14 (40) | 21 (63.6) | 22 (64.7) | 0.065 |
| Biochemical analysis | ||||
| eGFR, mL/min/1.73 m2 (mean ± SD) | 93.94 ± 20.81 | 80.79 ± 20.61 | 85.44 ± 22.11 | 0.040 |
| Total cholesterol, mg/dL (mean ± SD) | 208.84 ± 44.28 | 210.83 ± 57.42 | 202.86 ± 38.18 | 0.448 |
| HDL, mg/dL (median, Q1‐Q3) a | 56 (41–66) | 52 (43–68) | 49 (41–58) | 0.258 |
| LDL, mg/dL (mean ± SD) | 130.63 ± 35.52 | 127.83 ± 50.73 | 132.26 ± 35.15 | 0.510 |
| Non‐HDL Cholesterol (mean ± SD) | 149.73 ± 41.27 | 150.65 ± 52.52 | 153.21 ± 37.62 | 0.654 |
| Triglycerides, mg/dL (median, Q1–Q3) a | 140 (96–218.5) | 144.5 (107.5–182) | 126 (101–179) | 0.467 |
| hsCRP mg/L (mean ± SD) | 4 ± 3.15 | 3.29 ± 2.62 | 5.34 ± 3.72 | 0.111 |
| Neutrophil count (mean ± SD) | 4.86 ± 1.92 | 4.15 ± 1.32 | 4.66 ± 1.55 | 0.227 |
| Lp(a), mg/dL (median, Q1‐Q3) a | 11 (5–20) | 14 (6–38) | 40 (15–56) | < 0.001 |
| PCSK9 ng/mL (mean ± SD) | 201.88 ± 79.28 | 222.57 ± 189.05 | 325.86 ± 93.54 | < 0.001 |
| HNP1‐3 ng/mL (mean ± SD) | 15.37 ± 9.26 | 19.16 ± 8.18 | 25.44 ± 10.77 | < 0.001 |
Note: Statistical significant values (p < 0.05) are indicated in bold.
Abbreviations: BMI, body mass index; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HDL, high density lipoprotein; hsCRP, high sensitivity CRP; LDL, low density lipoprotein; Lp(a), lipoprotein (a); PCSK9, Proprotein convertase subtilisin/kexin type 9; SBP, systolic blood pressure, Q1, 25th percentile; Q3, 75th percentile.
Nonparametric tests were used for statistical analysis.
Multinomial logistic regression analysis was conducted to determine independent predictors of modified Gensini score tertiles, with the lowest tertile (Tertile 1) serving as the reference category. Odds ratios (ORs) represent the increased likelihood of being in Tertile 2 or Tertile 3 compared to Tertile 1 (Table 4). Clinically relevant risk factors, along with eGFR, hsCRP, Lp(a), PCSK9, and HNP1‐3, were included in the model. Higher hsCRP levels were associated with increased odds of being in both Tertile 2 (p = 0.013) and Tertile 3 (p = 0.017). An elevated Lp(a) level was a significant predictor of being in Tertile 3 compared to Tertile 1 (p = 0.007). Increased PCSK9 levels were also independently associated with higher odds of being in Tertile 2 (p = 0.041) and Tertile 3 (p = 0.002). Similarly, HNP1‐3 levels were significant predictors for both Tertile 2 (p = 0.024) and Tertile 3 (p = 0.011).
TABLE 4.
Independent predictors of modified Gensini score tertiles. a
| Variables | OR | 95% CI | p value | |
|---|---|---|---|---|
| Male sex | Tertile 2 | 0.564 | 0.045–7.146 | 0.659 |
| Tertile 3 | 2.339 | 0.110–49.939 | 0.586 | |
| Age | Tertile 2 | 1.015 | 0.893–1.153 | 0.819 |
| Tertile 3 | 0.965 | 0.813–1.146 | 0.687 | |
| Hypertension | Tertile 2 | 4.599 | 0.323–65.465 | 0.26 |
| Tertile 3 | 2.327 | 0.084–64.711 | 0.619 | |
| Diabetes | Tertile 2 | 16.172 | 0.771–339.076 | 0.073 |
| Tertile 3 | 4.604 | 0.191–110.995 | 0.347 | |
| Smoking | Tertile 2 | 2.251 | 0.194–26.149 | 0.517 |
| Tertile 3 | 1.763 | 0.102–30.531 | 0.697 | |
| Non‐HDL cholesterol | Tertile 2 | 0.972 | 0.942–1.003 | 0.12 |
| Tertile 3 | 0.974 | 0.936–1.013 | 0.192 | |
| hsCRP | Tertile 2 | 3.033 | 1.268–7.257 | 0.013 |
| Tertile 3 | 3.124 | 1.225–7.968 | 0.017 | |
| eGFR | Tertile 2 | 0.995 | 0.932–1.062 | 0.872 |
| Tertile 3 | 0.982 | 0.902–1.068 | 0.665 | |
| Lp(a) | Tertile 2 | 1.037 | 0.982–1.096 | 0.187 |
| Tertile 3 | 1.096 | 1.025–1.173 | 0.007 | |
| PCSK9 | Tertile 2 | 1.023 | 1.001–1.046 | 0.041 |
| Tertile 3 | 1.04 | 1.014–1.066 | 0.002 | |
| HNP1‐3 | Tertile 2 | 1.308 | 1.036–1.651 | 0.024 |
| Tertile 3 | 1.379 | 1.076–1.768 | 0.011 |
Note: Statistical significant values (p < 0.05) are indicated in bold.
Abbreviations: eGFR, estimated glomerular filtration rate; HDL, high density lipoprotein; hsCRP, high sensitivity CRP; Lp(a), lipoprotein(a).
Tertile 1 was used as the reference category.
3.3. Mediation Analysis
We conducted a mediation analysis to investigate the role of PCSK9 in the association between HNP1‐3 and the atherosclerotic plaque burden (modified Gensini score) (Figure 2, Table 5). In the unadjusted model, the indirect effect mediated through PCSK9 was β = 0.221 (p < 0.001), indicating that 48.7% of the total effect (β = 0.454, 95% CI: 0.256–0.625, p < 0.001) was mediated by PCSK9. Similarly, when adjusted for neutrophil count, partial mediation was observed, with 48% of the total effect explained by PCSK9 (total effect β = 0.460, p < 0.001; indirect effect β = 0.222, p < 0.001).
FIGURE 2.
Mediation effect of PCSK9 on HNP1‐3 relation to modified Gensini score. (A) Mediation effect without adjustment. (B) Mediation effect under the adjustment of age, sex, hypertension, diabetes, hyperlipidemia, smoking. (C) Mediation effect under the adjustment of hsCRP. (D) Mediation effect under the adjustment of hsCRP and neutrophil count.
TABLE 5.
Mediation effect of PCSK9 on HNP1‐3 relation to modified Gensini score under different adjustments.
| Adjusted factors | Total effect (95% CI) | Direct effect (95% CI) | Indirect effect (95% CI) | Mediated proportion | Mediation |
|---|---|---|---|---|---|
| T statistics | T statistics | T statistics | |||
| p value | p value | p value | p value | ||
| No adjustment |
0.454 (0.256–0.625) 4.774 p < 0.001 |
0.233 (0.039–0.429) 2.330 p = 0.020 |
0.221 (0.116–0.329) 4.043 p < 0.001 |
48.7% | < 0.001 |
| hsCRP |
0.412 (0.195–0.614) 3.862 p < 0.001 |
0.190 (−0.022–0.411) 1.727 p = 0.088 |
0.221 (0.117–0.330) 4.026 p < 0.001 |
100% a | < 0.001 |
| Neutrophil Count |
0.460 (0.260–0.630) 4.854 p < 0.001 |
0.237 (0.038–0.430) 2.368 p = 0.018 |
0.222 (0.118–0.331) 4.069 p < 0.001 |
48% | < 0.001 |
| hsCRP, Neutrophil Count |
0.424 (0.212–0.620) 4.111 p < 0.001 |
0.202 (−0.010–0.417) 1.863 p = 0.065 |
0.222 (0.118–0.332) 4.044 p < 0.001 |
100% a | < 0.001 |
|
Age, Sex Hypertension, Diabetes, Hyperlipidemia, Smoking |
0.426 (0.207–0.605) 4.249 p < 0.001 |
0.203 (−0.010–0.400) 1.959 p = 0.055 |
0.223 (0.118–0.336) 3.983 p < 0.001 |
100% a | < 0.001 |
Note: Statistical significant values (p < 0.05) are indicated in bold.
Abbreviations: eGFR, estimated glomerular filtration rate; hsCRP, high sensitivity CRP; Lp(a), lipoprotein (a); PCSK9, Proprotein convertase subtilisin/kexin type 9.
Full mediation occurs when the direct effect is non‐significant (p > 0.05) while the indirect effect remains significant (p < 0.05). This suggests that the independent variable influences the dependent variable entirely through the mediator, without a direct path remaining.
Interestingly, when adjusted for hsCRP alone, the direct effect of HNP1–3 on subclinical atherosclerosis became statistically non‐significant (β = 0.190, p = 0.088), while the total effect remained significant (p < 0.001), accompanied by a significant indirect effect (β = 0.221, p < 0.001), suggesting complete (100%) mediation of the HNP1–3 effect through PCSK9.
Similarly, when both hsCRP and neutrophil count were included in the model, the direct effect again became non‐significant (β = 0.202, p = 0.065), and the indirect effect remained significant (β = 0.222, p < 0.001), indicating complete mediation. In the fully adjusted model (age, sex, hypertension, diabetes, hyperlipidemia, and smoking), the total effect of HNP1–3 remained significant (β = 0.426, p < 0.001), but the direct effect was no longer significant (β = 0.203, p = 0.055), and the indirect effect remained robust (β = 0.223, p < 0.001), again indicating full mediation (100%) by PCSK9. The mediation of PCSK9 on the effect of HNP1‐3 on the modified Gensini Score without adjustment, with adjustment for traditional risk factors, and with adjustment for inflammatory markers is demonstrated in Figure 2A–D, respectively.
4. Discussion
In this study, we investigated the associations between HNP1‐3, PCSK9, and subclinical atherosclerosis, as well as their predictive value for coronary atherosclerotic burden assessed by the modified Gensini score. We also analyzed Lp(a) levels, which are a well‐established cardiovascular risk factor. Our findings highlight that patients with subclinical atherosclerosis exhibited significantly higher levels of PCSK9, HNP1‐3, and Lp(a) compared to individuals with normal coronary arteries, with HNP1‐3 emerging as an independent predictor of subclinical atherosclerosis. In patients with subclinical atherosclerosis, HNP1‐3, PCSK9, and Lp(a) were positively correlated with the modified Gensini score. In the regression model, higher levels of HNP1‐3, PCSK9, and Lp(a) were independently associated with higher modified Gensini score tertiles. Mediation analysis revealed that PCSK9 mediated 48.7% of the effect of HNP1‐3 on the modified Gensini score. After adjusting for hsCRP and cardiovascular risk factors, the direct effect of HNP1‐3 became statistically insignificant, while the indirect effect via PCSK9 remained significant, suggesting that PCSK9 acts as a key mediator in the HNP1‐3‐related pathway of coronary atherosclerosis.
Inflammation is a key factor in atherosclerosis, and elevated inflammatory markers, including increased neutrophil counts, have been associated with atherosclerotic cardiovascular disease and subclinical atherosclerosis [18]. Neutrophils play a role in various stages of atherosclerosis; however, their involvement in early atherogenesis, preceding macrophage dominance, has recently attracted increasing attention [4, 15]. HNP1–3, also known as α‐defensins found in the azurophilic granules of neutrophils and beyond their antimicrobial functions, HNP1–3 is involved in modulating immune responses, such as enhancing phagocytosis and influencing cytokine production [19]. In our study, HNP1–3 levels and neutrophil counts were higher in patients with subclinical atherosclerosis than in those without coronary plaque, and HNP1–3 was identified as an independent predictor of subclinical atherosclerosis. The expression of defensins in the atherosclerotic arterial wall has been previously described [20]. However, clinical studies on α‐defensins, particularly HNP1–3, have primarily focused on their role in acute coronary events [21, 22]. Although a limited number of studies have reported higher HNP1–3 levels in patients with stable coronary artery disease compared to controls, to our knowledge, no other study has specifically examined the role of HNP1–3 in subclinical atherosclerosis [22].
In patients with subclinical atherosclerosis, we observed a positive correlation between HNP1–3 levels and the modified Gensini score, with HNP1–3 emerging as an independent predictor of being in the highest tertile of the modified Gensini score. Consistent with our findings, previous studies have demonstrated a relationship between HNP1–3 and the severity of coronary artery disease; however, these studies primarily included patients with clinically manifest coronary artery disease [8, 23].
Regarding PCSK9, levels were higher in patients with subclinical atherosclerosis compared to controls. In subclinical atherosclerosis patients, PCSK9 levels were positively correlated with the modified Gensini score, and higher PCSK9 levels predicted placement in higher modified Gensini score tertiles. Many studies have reported higher PCSK9 levels in CAD patients compared to controls [24]. In the IMPROVE Study, PCSK9 levels were elevated in patients with atherosclerotic changes in the carotid arteries; however, similar to our findings, the relationship between PCSK9 and subclinical atherosclerosis was not sustained after adjusting for other factors [25]. These findings are consistent with previous studies showing a relationship between PCSK9 levels and CAD severity. However, to our knowledge, no previous study has investigated the relationship between HNP1‐3 and PCSK9. Beyond its well‐established hyperlipidemic effects, PCSK9 plays a significant role in atherosclerotic plaque development and progression through its direct effects on vascular wall cells [26].
Our findings suggest that the effect of HNP1‐3 on the modified Gensini score was mediated by PCSK9. When adjusted for traditional risk factors and inflammatory markers such as CRP and neutrophil count, the total effect of HNP1‐3 on the modified Gensini score remained significant; however, its direct effect became non‐significant, while the indirect effect through PCSK9 remained significant, indicating that PCSK9 fully mediates the impact of HNP1‐3 on atherosclerosis severity.
We hypothesize that the mediation effect observed in our analysis is supported by emerging mechanistic evidence. HNP1–3, released from activated neutrophils, is known to activate NF‐κB signaling in vascular endothelial cells, immune cells, and the liver [15]. PCSK9 expression is also regulated by NF‐κB activation and is upregulated in response to inflammatory stimuli in both vascular cells and hepatocytes [27, 28]. Elevated plasma PCSK9 not only modulates lipid metabolism but also exerts direct pro‐atherogenic effects. These include endothelial dysfunction, oxidative stress, smooth muscle cell phenotypic modulation, foam cell formation, and the expression of inflammatory transcription factors, cytokines, and adhesion molecules, which collectively lead to enhanced monocyte recruitment and macrophage accumulation in the vascular wall, further amplifying the inflammatory response [29].
Since our study focuses on subclinical atherosclerosis, we believe that neutrophil‐derived signals such as HNP1–3 may play a dominant role in triggering vascular inflammation during these early stages [4]. As atherosclerosis progresses, however, other inflammatory mediators, including cytokines and immune cell crosstalk, likely become more prominent.
Taken together, these findings support a biologically plausible pathway whereby HNP1–3 initiates inflammation, subsequently increasing PCSK9 levels, which in turn promotes a cascade of cellular events contributing to early atherosclerotic plaque development. However, as our study is observational, it lacks direct in vitro or in vivo mechanistic validation. Future experimental studies are needed to confirm the causal role of the HNP1–3–PCSK9 axis and to elucidate the specific molecular mechanisms involved in vascular inflammation and plaque progression.
Lp(a) is a lipoprotein which contains apoprotein a and its relation to atherosclerosis cardiovascular diseases is well established [30]. We found Lp(a) as an independent predictor of subclinical atherosclerosis and being in the third tertile of modified Gensini score. The relationship between Lp(a) level and subclinical atherosclerosis and coronary artery disease severity has been demonstrated by many previous studies. Our findings on Lp(a) are consistent with these studies. Higazi et al. suggested that defensins promote Lp(a) binding to vascular endothelial cells and vascular smooth muscle cells, leading to atherosclerotic plaque progression [31]. The relationship of high Lp(a) level and high HNP1‐3 level with higher atherosclerotic burden is consistent with this finding.
The correlation plot analysis further supported the potential contribution of HNP1‐3, PCSK9, and Lp(a) to coronary plaque burden by demonstrating strong and significant positive correlations between the modified Gensini score and each of these markers. Additionally, HNP1‐3 showed significant correlations with both PCSK9 and hsCRP, suggesting a shared inflammatory pathway. Interestingly, neutrophil count exhibited weak correlations with most variables, none of which reached statistical significance.
Our study has some limitations. It is a single‐center cross‐sectional study with a limited number of patients. The atherosclerosis burden was assessed using the modified Gensini Score rather than automated programs that provide a more precise calculation of plaque burden. Additionally, ELISA kits and measurement methods for PCSK9 and HNP1‐3 are not yet standardized, and the ELISA kits used in this study are intended for experimental purposes. Moreover, due to the observational design of the study, no direct mechanistic experiments were performed to validate the hypothesized HNP1‐3–PCSK9 pathway. Despite performing propensity score matching and bootstrapping analyses, a residual imbalance in age and sex distribution between the control group and the subclinical atherosclerosis cohort persisted. Since both age and gender are well‐established determinants of cardiovascular risk, this limitation may have influenced our findings. Future studies with larger, well‐matched cohorts are needed to determine the independent contributions of age and gender and to strengthen the generalizability of our results. Furthermore, translational studies integrating in vitro, in vivo, and clinical approaches may provide additional mechanistic evidence for the interaction between HNP1‐3 and PCSK9 and help clarify their role in atherosclerosis progression.
Despite these limitations, this is the first study to demonstrate the relationship between HNP1‐3 and subclinical atherosclerosis, as well as the mediating effect of PCSK9.
5. Conclusion
Our findings suggest that HNP1‐3 may serve as a marker for both the presence and severity of subclinical atherosclerosis. Additionally, HNP1‐3 could be considered a potential target for atherosclerosis prevention and therapy. Furthermore, since its pro‐atherogenic effects appear to be mediated by PCSK9, targeting PCSK9 may help mitigate atherosclerosis by inhibiting the inflammatory actions of HNP1‐3. If confirmed by larger clinical and translational studies, these findings would provide further insight into the anti‐inflammatory mechanisms of PCSK9 inhibition in atherosclerosis.
Author Contributions
Ece Yurtseven wrote the manuscript; Ece Yurtseven, Dilek Ural, and Kemal Baysal designed the research; Said İncir, Ata Alpay Canbaz, Kayhan Çetin Atasoy, Gamze Aslan, Erol Gursoy, and Yasemin Demirci performed the research; and Mert Veznikli and Arzu Baygul analyzed the data and contributed new analytical tools.
Conflicts of Interest
Ece Yurtseven has participated in a clinical trial with Novartis during the past 3 years. Dilek Ural has received honoraria from Amgen, Astra Zeneca, Boehringer Ingelheim, Novartis, and Novo Nordisk; has participated in clinical trials with Vifor International and Novartis during the past 3 years. All other authors declared no competing interests for this work.
Supporting information
Figure S1: Comparison of HNP1‐3, PCSK9, and Lp(a) across modified Gensini score tertiles.
Table S1: Basic characteristics of study group.
Table S2: Correlation plot.
Acknowledgments
The authors gratefully acknowledge the use of the services and facilities of the Koç University Research Center for Translational Medicine (KUTTAM). Artificial intelligence was used only to assist with grammar correction.
Yurtseven E., Baysal K., İncir S., et al., “Association Between HNP1‐3 and Atherosclerotic Plaque Burden in Subclinical Atherosclerosis and the Mediating Effect of PCSK9 ,” Clinical and Translational Science 18, no. 10 (2025): e70369, 10.1111/cts.70369.
Funding: This work was supported by Koç University School of Medicine through funding for the purchase of HNP1‐3 and PCSK9 ELISA kits.
References
- 1. Heart Association A , Heart Disease & Stroke Statistical Update Fact Sheet Global Burden of Disease (Heart Association, 2022), 10.1161/CIR.0000000000001052. [DOI] [Google Scholar]
- 2. Kawai K., Finn A. V., Virmani R., et al., “Subclinical Atherosclerosis: Part 1: What Is It? Can It be Defined at the Histological Level?,” Arteriosclerosis, Thrombosis, and Vascular Biology 44 (2024): 12–23, 10.1161/ATVBAHA.123.319932/ASSET/05954965-D0FA-452C-914E-C8751ECC779B/ASSETS/GRAPHIC/ATVBAHA.123.319932.FIG07.JPG. [DOI] [PubMed] [Google Scholar]
- 3. Dzaye O., Razavi A. C., Blaha M. J., and Mortensen M. B., “Evaluation of Coronary Stenosis Versus Plaque Burden for Atherosclerotic Cardiovascular Disease Risk Assessment and Management,” Current Opinion in Cardiology 36 (2021): 769–775, 10.1097/HCO.0000000000000911. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Simon D. I. and Zidar D., “Neutrophils in Atherosclerosis: Alarmin Evidence of a Hit and Run?,” Circulation Research 110 (2012): 1036–1038, 10.1161/CIRCRESAHA.112.268367. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Choi K. Y., Chow L. N. Y., and Mookherjee N., “Cationic Host Defence Peptides: Multifaceted Role in Immune Modulation and Inflammation,” Journal of Innate Immunity 4 (2012): 361–370, 10.1159/000336630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Schuerholz T., Brandenburg K., and Marx G., “Antimicrobial Peptides and Their Potential Application in Inflammation and Sepsis,” Critical Care 16 (2012): 1–8, 10.1186/CC11220/FIGURES/5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Abu‐Fanne R., Stepanova V., Litvinov R. I., et al., “Neutrophil α‐Defensins Promote Thrombosis In Vivo by Altering Fibrin Formation, Structure, and Stability,” Blood 133 (2019): 481–493, 10.1182/BLOOD-2018-07-861237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Abu Fanne R., Arbel Y., Chorin E., et al., “Association Between Tissue Human Neutrophil Peptide 1‐3 Levels and Cardiovascular Phenotype: A Prospective, Longitudinal Cohort Study,” Journal of International Medical Research 50 (2022): 3000605221127099, 10.1177/03000605221127099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Paulin N., Döring Y., Kooijman S., et al., “Human Neutrophil Peptide 1 Limits Hypercholesterolemia‐Induced Atherosclerosis by Increasing Hepatic LDL Clearance,” eBioMedicine 16 (2017): 204–211, 10.1016/J.EBIOM.2017.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Seidah N. G., Abifadel M., Prost S., Boileau C., and Prat A., “The Proprotein Convertases in Hypercholesterolemia and Cardiovascular Diseases: Emphasis on Proprotein Convertase Subtilisin/Kexin 9,” Pharmacological Reviews 69 (2016): 33–52, 10.1124/pr.116.012989. [DOI] [PubMed] [Google Scholar]
- 11. Cheng J. M., Oemrawsingh R. M., Garcia‐Garcia H. M., et al., “PCSK9 in Relation to Coronary Plaque Inflammation: Results of the ATHEROREMO‐IVUS Study,” Atherosclerosis 248 (2016): 117–122, 10.1016/j.atherosclerosis.2016.03.010. [DOI] [PubMed] [Google Scholar]
- 12. Momtazi‐Borojeni A. A., Sabouri‐Rad S., Gotto A. M., et al., “PCSK9 and Inflammation: A Review of Experimental and Clinical Evidence,” European Heart Journal 5 (2019): 237–245, 10.1093/EHJCVP/PVZ022. [DOI] [PubMed] [Google Scholar]
- 13. Yuan Y., Wu W., Sun S., Zhang Y., and Chen Z., “PCSK9: A Potential Therapeutic Target for Sepsis,” Journal of Immunology Research 2020 (2020): 2687692, 10.1155/2020/2687692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Ma B., Wang X., Zhang R., et al., “Cigarette Smoke Extract Stimulates PCSK9 Production in HepG2 Cells via ROS/NF‐κB Signaling,” Molecular Medicine Reports 23 (2021): 331, 10.3892/mmr.2021.11970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Zhang X., Kang Z., Yin D., and Gao J., “Role of Neutrophils in Different Stages of Atherosclerosis,” Innate Immunity 29 (2023): 97–109, 10.1177/17534259231189195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Rampidis G. P., Benetos G., Benz D. C., Giannopoulos A. A., and Buechel R. R., “A Guide for Gensini Score Calculation,” Atherosclerosis 287 (2019): 181–183, 10.1016/j.atherosclerosis.2019.05.012. [DOI] [PubMed] [Google Scholar]
- 17. Tönnies T., Schlesinger S., Lang A., and Kuss O., “Mediation Analysis in Medical Research,” Deutsches Ärzteblatt International 120 (2023): 681–687, 10.3238/ARZTEBL.M2023.0175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Luo J., Thomassen J. Q., Nordestgaard B. G., Tybjærg‐Hansen A., and Frikke‐Schmidt R., “Neutrophil Counts and Cardiovascular Disease,” European Heart Journal 44 (2023): 4953–4964, 10.1093/EURHEARTJ/EHAD649. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Fu J., Zong X., Jin M., Min J., Wang F., and Wang Y., “Mechanisms and Regulation of Defensins in Host Defense,” Signal Transduction and Targeted Therapy 8 (2023): 1–30, 10.1038/s41392-023-01553-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Barnathan E. S., Raghunath P. N., Tomaszewski J. E., Ganz T., Cines D. B., and Al‐Roof Higazit A., “Immunohistochemical Localization of Defensin in Human Coronary Vessels,” American Journal of Pathology 150 (1997): 1009–1020. [PMC free article] [PubMed] [Google Scholar]
- 21. Katkat F., Varol S., Isiksaçan N., et al., “Human Neutrophil Peptides 1‐3 Level in Patients With Acute Myocardial Infarction and Its Relation With Coronary Artery Disease Severity,” Türk Kardiyoloji Derneği Arşivi 49 (2021): 120–126, 10.5543/TKDA.2021.99537. [DOI] [PubMed] [Google Scholar]
- 22. Zhao H., Yan H., Yamashita S., et al., “Acute ST‐Segment Elevation Myocardial Infarction Is Associated With Decreased Human Antimicrobial Peptide LL‐37 and Increased Human Neutrophil Peptide‐1 to 3 in Plasma,” Journal of Atherosclerosis and Thrombosis 19 (2012): 357–368, 10.5551/JAT.10108. [DOI] [PubMed] [Google Scholar]
- 23. Ungan I., Caglar F. N. T., Biyik İ., Ciftci S., Sahin A., and Akturk I. F., “The Correlation Between Plasma Human Neutrophil Peptide 1‐3 Levels and Severity of Coronary Artery Disease,” Archives of Medical Science 1 (2016): 133–138, 10.5114/AMSAD.2016.64164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Peng J., Xing C. Y., Zhao K., et al., “Associations of Pro‐Protein Convertase Subtilisin‐Like Kexin Type 9, Soluble Low‐Density Lipoprotein Receptor and Coronary Artery Disease: A Case‐Control Study,” International Journal of Cardiology 350 (2022): 9–15, 10.1016/J.IJCARD.2022.01.014. [DOI] [PubMed] [Google Scholar]
- 25. Coggi D., Frigerio B., Bonomi A., et al., “Relationship Between Circulating PCSK9 and Markers of Subclinical Atherosclerosis—The Improve Study,” Biomedicine 9 (2021): 841, 10.3390/BIOMEDICINES9070841/S1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Yurtseven E., Ural D., Baysal K., and Tokgözoğlu L., “An Update on the Role of PCSK9 in Atherosclerosis,” Journal of Atherosclerosis and Thrombosis 27 (2020): 909–918, 10.5551/JAT.55400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Shin D., Kim S., Lee H., et al., “PCSK9 Stimulates Syk, PKCδ, and NF‐κB, Leading to Atherosclerosis Progression Independently of LDL Receptor,” Nature Communications 15 (2024): 1, 10.1038/s41467-024-46336-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Tang Z. H., Peng J., Ren Z., et al., “New Role of PCSK9 in Atherosclerotic Inflammation Promotion Involving the TLR4/NF‐κB Pathway,” Atherosclerosis 262 (2017): 113–122, 10.1016/j.atherosclerosis.2017.04.023. [DOI] [PubMed] [Google Scholar]
- 29. Seidah N. G. and Garçon D., “Expanding Biology of PCSK9: Roles in Atherosclerosis and Beyond,” Current Atherosclerosis Reports 24 (2022): 821–830, 10.1007/S11883-022-01057-Z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Nordestgaard B. G., Chapman M. J., Ray K., et al., “Lipoprotein(a) as a Cardiovascular Risk Factor: Current Status,” European Heart Journal 31 (2010): 2844–2853, 10.1093/EURHEARTJ/EHQ386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Higazi A. A. R., Lavi E., Bdeir K., et al., “Defensin Stimulates the Binding of Lipoprotein (a) to Human Vascular Endothelial and Smooth Muscle Cells,” Blood 89 (1997): 4290–4298, 10.1182/BLOOD.V89.12.4290. [DOI] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Figure S1: Comparison of HNP1‐3, PCSK9, and Lp(a) across modified Gensini score tertiles.
Table S1: Basic characteristics of study group.
Table S2: Correlation plot.