
==== Front
BMC Cardiovasc Disord
BMC Cardiovasc Disord
BMC Cardiovascular Disorders
1471-2261
BioMed Central London

4186
10.1186/s12872-024-04186-2
Research
Unraveling the rapid progression of non-target lesions: risk factors and the therapeutic potential of PCSK9 inhibitors in post-PCI patients
Mei Jiajie 1
Fu Xiaodan 1
Liu Zhenzhu 1
Zhang Lijiao 1
Geng Zhaohong 1
Xie Wenli 1
Yu Ming 1
Wang Yuxing 1
Zhao Jinglin 1
Zhang Xiaodong 1
Yin Lili 17709870029@163.com

2
Qu Peng qupeng963@aliyun.com

1
1 https://ror.org/04c8eg608 grid.411971.b 0000 0000 9558 1426 Department of Cardiology, The Second Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, 116027 China
2 https://ror.org/04c8eg608 grid.411971.b 0000 0000 9558 1426 International Medical Department, The Second Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, 116027 China
18 9 2024
18 9 2024
2024
24 49923 6 2024
11 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the risk factors and establish an accurate clinical prediction model for the rapid progression of NTLs post-PCI. PCSK9 inhibitors lower LDL-c levels significantly, also show the anti-inflammation effect, and may have the potential to reduce the rapid progression of NTLs post-PCI. We tried to test this hypothesis and explore the potential mechanisms.

Methods

This retrospective study included 1250 patients who underwent the first PCI and underwent repeat coronary angiography for recurrence of chest pain within 24 months. General characteristics, laboratory tests and inflammatory factors(IL-10, IL-6, IL-8, IL-1β, sIL-2R, and TNF-α) were collected. Machine learning (LASSO regression) was mainly employed to select the important characteristic risk factors for the rapid progression of NTLs post-PCI and build prediction models. Finally, mediator analysis was employed to explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI.

Results

There were more diabetes, less beta-blockers and PCSK9 inhibitors application, higher HbA1c, LDL-c, ApoB, TG, TC, uric acid, hs-CRP, TNF-α, IL-6, IL-8, and sIL-2R in NTL progressed group. LDL-c, hs-CRP, IL-8, and sIL-2R were characteristic risk factors for the rapid progression of NTLs post-PCI, combining LDL-c, hs-CRP, IL-8, and sIL-2R builds the optimal model for predicting the rapid progression of NTLs post-PCI (AUC = 0.632). LDL-c had a clear and incomplete mediating effect (95% CI, mediating effect: 51.56%) in the reduction of the progression of NTLs by PCSK9 inhibitors, and there was a possible mediating effect of IL-8 (90% CI), and sIL-2R (90% CI).

Conclusions

LDL-c, hs-CRP, IL-8, and sIL-2R may be the key characteristic risk factors for the rapid progression of NTLs post-PCI, and combining these parameters might predict the rapid progression of NTLs post-PCI. The application of PCSK9 inhibitors had a negative correlation with the rapid progression of NTLs. In addition to the significant LDL-c-lowering, PCSK9 inhibitors may reduce the rapid progression of NTLs by reducing local inflammation of plaque.

Trial registration

ChiCTR2200058529; Date of registration: 2022–04-10.

Keywords

Percutaneous coronary intervention
Rapid progression of non-target lesions
Proprotein convertase subtilisin-kexin 9 inhibitors
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Target lesions, also known as culprit lesions, have received great attention because they can lead to cardiac events and are still at high risk after percutaneous coronary intervention(PCI), and have been the focus of previous studies. However, non-target lesion(NTL) related and target lesion related cardiac events accounted for an equal proportion of recurrent cardiac events post-PCI [1]. And the incidence of NTL related cardiac events was highest within about 2 years post-PCI [2–6], this suggests the NTLs undergo rapid progression [7, 8]. Most studies define progression of NLTs within a few months to 2 years as rapid progression of NLTs [9]. Therefore, fully studying the risk factors of rapid progression of NTLs post-PCI and timely prevention and treatment will greatly reduce the incidence of cardiac post-PCI, which is crucial and necessary for long-term maintenance of cardiovascular health and reduction of mortality in patients with coronary heart disease.

Previous studies [5, 10–15] had explored the influencing factors of NTLs, the results of these studies can be briefly summarized as that: diabetes mellitus, dyslipidemia, and inflammation play essential roles in the rapid progression of NTLs. However, there were some limitations in these studies, it is well known that the risk factors for atherosclerosis are extensive, on the other hand, PCI causes a variety of biological indicators to change over a period of time, so the factors influencing the rapid progression of lesions post-PCI may be more complex. But few parameters had been included in these studies, in terms of inflammation, most studies had observed the non-specific inflammatory C-reactive protein, lacking the observation of the specific inflammatory factors. Therefore, a study covering the full range of clinical factors and specific inflammatory factors which represent different inflammatory pathways is urgently needed. Tumor necrosis factor-α(TNF-α) is involved in hypoxia response and lipid disorder [16]; Interleukin(IL)-6 is involved in the acute phase reaction [17]; IL-8 is involved in innate immune activation, neovascularisation, and fibrous cap instability [18–21]; IL-1β is involved in endothelial cell expression of adhesion molecules, lipid metabolism disorder, foam cell formation, and smooth muscle cell proliferation [22]; soluble interleukin-2 receptor(sIL-2R) represents the activation of adaptive immunity [23]; IL-10 has pleiotropic effects, and mainly shows anti-inflammatory effects [24]. The above pathophysiological processes play important roles in the development of atherosclerosis, but whether involved in the progression of NTLs post-PCI remains unclear and is worth to study further. By screening for characteristic risk factors associated with the rapid progression of NTLs using machine learning, we sought to identify the characteristic risk factors and establish an accurate clinical prediction model for the rapid progression of NTLs post-PCI.

In terms of the intervention of NTLs, early study [25] observed the effect of statins on slower the progression of NTLs, they found statins did not affect the progression of percentage of stenosis severity of coronary artery lesions but induced phenotypic plaque transformation. Proprotein convertase subtilisin-kexin 9(PCSK9) inhibitors lower low-density lipoprotein cholesterol(LDL-c) levels significantly [26], and also show the anti-inflammation effect [27, 28]. A recent study [29] found that PCSK9 inhibitor evolocumab showed promising results in the regression of NTLs. In our study, we will observe the relationship between the application of PCSK9 inhibitors and the rapid progression of NTLs and explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI through mediator analysis.

Materials and methods

Study subjects

 Two thousand sixty-eight patients who underwent the first PCI for coronary artery disease with implantation of the New-Generation Drug-Eluting Stents at the Second Affiliated Hospital of Dalian Medical University between January 2018 and June 2023, and underwent repeat coronary angiography(CAG) for recurrence of chest pain within 24 months were consecutively selected. Exclusion criteria: 1. Previous PCI or coronary artery bypass grafting(CABG); 2. Underwent PCI or CABG before repeat CAG; 3. Tumors or severe autoimmune diseases; 4. Incomplete clinical data, 5. Failure to comply with the doctor’s prescription to regulate coronary heart disease medication post-PCI. The research protocol was approved by the ethics committee of the Second Hospital of Dalian Medical University, with a waiver of informed consent.

Quantitative coronary angiography(QCA) analysis

QCA analyzes the coronary angiography images to clarify the progression of lesions. The coronary angiograms were assessed by MEDCON TCS QCA software by two independent cardiologists who were unaware of all other clinical data of the patients. Initially, the edge of the contrasted blood vessel was drawn using an automatic edge-detection algorithm. Following the determination of a start point and an end point in the image of the enhanced coronary artery, a vessel pathline was created. Subsequently, the vessel contour was delineated in accordance with the pathline. The pathline and vessel contour, which were determined automatically in accordance with the contrast density, occasionally require editing by analysts. Measured variables included the minimal luminal diameter and the diameter stenosis [30]. Rapid progression of NTLs was defined as follows: (i) ≥ 10% diameter reduction of at least one preexisting stenosis ≥ 50%, (ii) ≥ 30% diameter reduction of a preexisting stenosis < 50%, (iii) progression of a lesion to total occlusion within 2 years [9].

Clinical data collection

Retrospectively reviewed database of patients, including characteristics such as gender, age, history of diabetes, hypertension, and smoking; use of medication post-PCI including the type of ADP receptor antagonist (Clopidogrel/Ticagrelor), β-blockers, statins, PCSK9 inhibitors, angiotensin-converting enzyme inhibitors / Angiotensin II Receptor Blocker (ACEI/ARB). Collected the laboratory tests at repeat CAG as follows: Hemoglobin A1c(HbA1c), lipid profiles, hematologic parameters, Electrolytes, Liver biochemistry parameters, Renal function parameters, thyroid function parameters, Homocysteine, D-Dimer, N-terminal pro-brain natriuretic peptide(NT-ProBNP), hypersensitive C-reactive protein(hs-CRP), IL-10, IL-6, IL-8, IL-1β, sIL-2R, and TNF-α; collected the levels of LDL-c and HbA1c at first PCI and calculated their changes.

Statistical analysis

All data were analyzed by statistical software SPSS(version 26.0) and R(version 4.2.2). Categorical data are presented as numbers(percentages) and were compared using the chi-square. Normally distributed continuous variables are expressed as the mean ± SD and were compared by t-test, skewed distributed continuous variables are expressed as the mean (25th-75th quantiles) and were compared by Wilcoxon rank sum test. Multiple logistic regression, hierarchical multiple logistic regression, least absolute shrinkage and selection operator(LASSO) regression technique analysis were employed to predictor selection and model building. The Hosmer–Lemeshow test was used to check the calibration degree of the models, and the receiver operating characteristic(ROC) curve and decision curve analysis(DCA) curve were used to compare the discrimination of models.

Further, we performed a mediation analysis to understand the intermediate effect between the application of PCSK9 inhibitors and the rapid progression of NTLs post-PCI. The proportion explained by the intermediate factors as follows: 100% × [Beta-coefficientmodel—Beta-coefficientmodel + intermediatefactor]/[Beta-coefficient- model]. A two-sided p-value of < 0.05 was considered statistically significant. In mediated analyses, 95% confidence interval without 0 was considered statistically significant, and 90% confidence interval without 0 was considered potentially statistically significant.

Result

Study flow

Initially, a total of 2068 patients were included, whereas 818 patients were excluded (including 412 patients who received previous PCI or CABG, 167 patients who received PCI or CABG before repeat CAG,104 patients with tumors or severe autoimmune diseases,37 patients with incomplete clinical data, 98 patients failed to comply with the doctor’s prescription to regulate coronary heart disease medication post-PCI). 1250 cases were finally included in the analysis, and NTL progressed in 401 patients (See Fig. 1).Fig. 1 Study flow diagram describing the screening, enrolling, and groupings of patients

The difference in general characteristics and laboratory tests of patients

In terms of general characteristics, there were no differences between the NTLs progressed and no-progressed groups in age, gender, history of hypertension, and smoking, while for diabetes, the progressed group had a higher prevalence (47.1% vs 37.2%, p < 0.05). The application of beta-blockers and PCSK9 inhibitors was lower in the NTL progressed group compared with the NTL no-progressed group (beta-blockers:41.4% vs 48.6%, PCSK9 inhibitors:3.2% vs 7.9%,p < 0.05), and there was no statistically significant difference in other medication use.

In terms of laboratory tests, HbA1c was higher in the NTL progressed group (6.84 ± 1.62 vs 6.54 ± 1.37,p < 0.05), but there was no statistically significant difference in change of HbA1c. The levels of LDL-c, ApoB, TG, and TC were higher and the decrease in LDL-c was lower in the NTL progressed group (LDL-c: 1.83 ± 0.7 vs 2.03 ± 0.81, ApoB: 0.75 ± 0.24 vs 0.68 ± 0.21, TG: 1.73 ± 1.27 vs 1.51 ± 1, TC: 3.91 ± 1.13 vs 3.63 ± 0.97, Rate of LDL-c decline: 0.17 ± 0.42 vs 0.23 ± 0.36, p < 0.05). With regards to other laboratory tests, compared to the NTL no-progressed group, higher uric acid levels were observed in the NTL progressed group, in terms of hematologic parameters, electrolytes, thyroid function, liver biochemistry parameters, renal function, homocysteine, D-Dimer, and NT-ProBNP, there were no statistically significant differences between the two groups. Finally, in terms of inflammatory factors, the NTL progressed group had higher hs-CRP levels than the no-progressed group (3.02(0.87–5.51) vs 3.13(0.93–9.71), P < 0.05). Compared with the NTL no-progressed group, the NTL progressed group had higher TNF-α, IL-6, IL-8, and sIL-2R (p < 0.05). The baseline characteristics, medication application post-PCI, and laboratory tests are shown in Table 1. Table 1 Characteristics, General information of repeat CAG, medication application, and Laboratory data of patients

N = 1250	NTL No-progressed(N = 849)	NTL progressed(N = 401)	P value	
Characteristic	
 Age,y	64.27 ± 10.18	63.85 ± 10.21	0.495	
 Male, n(%)	567(66.8)	285(71.1)	0.129	
 Hypertension, n(%)	141(16.6)	71(17.7)	0.629	
 Diabetes,n(%)	316(37.2)	189(47.1)	0.001*	
 Smoker,n(%)	227(32.6)	136(33.9)	0.651	
 interval time,d	322.72 ± 181.60	338.3 ± 182.61	0.158	
Medication application post-PCI	
 Tiglitazarol, n(%)	178(21.0)	103(25.7)	0.062	
 β-blocker, n(%)	413(48.6)	166(41.4)	0.016*	
 ACEI/ARB, n(%)	373(43.9)	192(47.9)	0.191	
 statins, n(%)	824(97.1)	381(95.0)	0.070	
 PCSK9i, n(%)	67(7.9)	13(3.2)	0.002*	
Laboratory parameters	
 HbA1c, %	6.54 ± 1.37	6.84 ± 1.62	0.001*	
 ΔHbA1c, %	-0.11 ± 0.92	-0.09 ± 0.97	0.796	
 ApoB, mmol/L	0.68 ± 0.21	0.75 ± 0.24	 < 0.001*	
 ApoA1, mmol/L	1.29 ± 0.22	1.28 ± 0.24	0.570	
 LDL-C, mmol/L	1.83 ± 0.70	2.03 ± 0.81	 < 0.001*	
 HDL-C, mmol/L	1.07 ± 0.25	1.06 ± 0.28	0.292	
 TG, mmol/L	1.51 ± 1.00	1.73 ± 1.27	0.001*	
 TC, mmol/L	3.63 ± 0.97	3.91 ± 1.13	 < 0.001*	
 rLDL-c, %	0.23 ± 0.36	0.17 ± 0.42	0.016*	
 P-LCR, %	26.16 ± 7.58	26.47 ± 7.64	0.507	
 MPV, fl	10.01 ± 1.09	10.05 ± 1.09	0.543	
 PDW, fl	15.67 ± 1.49	15.71 ± 1.54	0.593	
 PCT, %	0.21 ± 0.06	0.21 ± 0.06	0.894	
 RDW-SD, fl	43.18 ± 3.83	43.09 ± 2.99	0.675	
 RDW-CV, %	13.01 ± 1.14	12.93 ± 0.77	0.188	
 hemoglobin, g/L	135.92 ± 18.32	137.93 ± 18.42	0.076	
 NLR	2.90 ± 1.56	3.08 ± 1.94	0.074	
 magnesium, mmol/L	0.91 ± 0.35	0.89 ± 0.30	0.406	
 sodium, mmol/L	140.61 ± 4.21	140.42 ± 3.31	0.409	
 potassium, mmol/L	3.93 ± 0.37	3.90 ± 0.36	0.199	
 albumin, g/L	39.57 ± 4.61	39.56 ± 4.69	0.998	
 prealbumin, mg/L	258.2 ± 59.24	257.94 ± 61.45	0.944	
 total bilirubin, μmol/L	13.83 ± 6.15	13.92 ± 6.39	0.811	
 eGFR, ml/min/1.73 m2	83.75 ± 15.41	81.73 ± 18.17	0.054	
 Cystatin c, mg/L	1.19 ± 0.82	1.30 ± 1.08	0.052	
 uric acid, μmol/L	349.73 ± 96.81	365.82 ± 109.76	0.009*	
 creatinine, mmol/L	83.45 ± 101.68	92.03 ± 109.55	0.176	
 TSH, μIU/mL	2.67 ± 8.16	2.43 ± 6.39	0.602	
 fT4, pmol/L	15.04 ± 3.70	15.15 ± 3.78	0.635	
 fT3, pmol/L	4.65 ± 1.56	4.64 ± 1.46	0.882	
 anti-TPO, IU/mL	28.29(28.00–39.33)	28.88(28.00–41.42)	0.409	
 anti-TG, IU/mL	15.00(15.00–19.49)	15.00(15.00–21.33)	0.395	
 hs-CRP, mg/L	3.02(0.87–5.51)	3.13(0.93–9.71)	0.028*	
 homocysteine, μmol/L	12.31 ± 7.18	13.07 ± 6.95	0.167	
 D-Dimer, mg/L	0.50(0.35–0.66)	0.49(0.35–0.66)	0.986	
 NT-ProBNP, pg/mL	165.60(63.15–742.70)	155.90(71.00–1032.08)	0.402	
Inflammatory factors			
 TNF-α, pg/mL	14.10(9.48–41.10)	18.91(11.20–47.90)	0.028*	
 IL-10, pg/mL	5.00(5.00–5.00)	5.00(5.00–5.00)	0.323	
 IL-8, pg/mL	61.75(28.03–146.80)	92.55(36.53–230.00)	0.021*	
 IL-6, pg/mL	3.79(2.39–7.04)	5.38(2.79–9.78)	0.012*	
 sIL-2R, U/mL	395.00(324.00–496.00)	447.00(367.50–601.00)	0.001*	
 IL-1β, pg/mL	5.00(5.00–5.71)	5.00(5.00–6.99)	0.226	
Values are mean ± SD, mean (25th-75th quantiles),or n (%)

ACEI Angiotensin converting enzyme inhibitors, ARB Angiotensin II Receptor Blocker, PCSK9i Proprotein convertase subtilisin-kexin 9 inhibitors, HbA1c Hemoglobin A1c; ΔHbA1c = HbA1c(repeat CAG)- HbA1c(initial PCI), Apo-B, apolipoprotein B Apo-A1, apolipoprotein A1, LDL-c Low-density lipoprotein cholesterol, HDL-c High-density lipoprotein cholesterol, TC Total cholesterol, TG Triglycerides, eGFR Estimated glomerular filtration rate, TC Total cholesterol, rLDL-c Rate of LDL-c decline, rLDL-c = [LDL-c(initial PCI)—LDL-c(repeat CAG)] / LDL-c(initial PCI) × 100%, P-LCR Platelet-large cell rate, MPV Mean platelet volume, PDW Platelet distribution width, PCT Platelet crit, RDW-SD Red blood cell distribution width SD, RDW-CV Red blood cell distribution width CV, NLR Neutrophil–lymphocyte ratio, TSH Thyroid-stimulating hormone, fT4 Free thyroxine, fT3 Free triiodothyronine, Anti-TPO Anti-thyroid peroxidase, Anti-TG Antithyroglobulin, hs-CRP High-sensitivity C-reactive protein, NT-ProBNP N-terminal pro-brain natriuretic peptide, TNF-α Tumor necrosis factor-α, IL Interleukin

*p < 0.05

Regression analysis of the progression of NTLs post-PCI and building of prediction models

We selected the variables (excluding the application of medication and inflammatory factors) that differed at P < 0.2 as independent variables, the progression of NTLs post-PCI as dependent variable, and logistic regression was performed with the stepwise method. Independent variables include gender, history of Diabetes, interval time between PCI and repeat CAG, HbA1c, ApoB, LDL-c, TC, TG, Rate of LDL-c decline, RDW-CV, hemoglobin, NLR, potassium, eGFR, uric acid, cystatin, creatinine, hs-CRP, and homocysteine, and the variables that enter the equation included hemoglobin, hs-CRP, cystatin C, and LDL-c. We recorded the equation as Model 1, and the model was meaningful by the Omnibus test (p < 0.001) and the H–L test (p = 0.378).

Based on model 1, we further performed hierarchical multiple logistic regression, inflammatory factors including TNF-α, IL-10, IL-8, IL-6, sIL-2R, and IL-1β were selected as additional independent variables. Finally, IL-8 entered into the equation. We recorded the equation as Model 2, and the model was meaningful by the Omnibus test (p < 0.001) and the H–L test (p = 0.989).

Given the significant advantages in feature selection, solving multicollinearity, and improving model generalization ability, LASSO regression was performed. The optimal value of λ was determined by tenfold cross-validation, the regression model shows the highest efficiency at 1-SE with the value 0.05022, resulting in a model with excellent performance and the lowest number of independent variables, the selected variables are LDL-c, CRP, IL-8, and sIL-2R. This means these 4 variables are the core characteristic predictors. We recorded this model as Model 3, and the model was meaningful by the Omnibus test (p < 0.001) and the H–L test(p = 0.194).

The above 3 models were compared in terms of differentiation, and ROC curves were plotted. All of the 3 models were tested to be statistically significant. ROC curves were used to compare the ability of the three models to predict the rapid progression of NTLs post-PCI with the AUC area Model 3 > Model 2 > Model 1. To comprehensively evaluate the effect of the three models on clinical decision-making, we plotted the DCA curve. All of the 3 models could add net benefits in the range 0.2 to 0.4 when compared with either the treat-all or the treat-none. Model 3 adds the most benefits (see Fig. 2).Fig. 2 Comparison of ROC curves and DCA curves between Model 1, Model 2, and Model 3. a Model 3 (AUC = 0.632, P < 0.001) was significantly more effective in predicting the progression of NTLs post-PCI than Model 1(AUC = 0.596, P < 0.001), and Model 2(AUC = 0.606, P < 0.001). b The decision curve indicates that when the threshold probability is between 20 and 40%, all 3 models could add net benefits when compared with either the treat-all or the treat-none. Model 3 adds the most benefits. DCA, Decision curve analysis

Mediation analysis revealed a potential link between PCSK9 inhibitors and the rapid progression of NTLs post-PCI

There was a statistically significant negative correlation between PCSK9 inhibitors application and the rapid progression of NTLs. the LDL-c had a clear mediating effect (95% CI) in the reduction of the progression of NTLs by PCSK9 inhibitors, whereas the mediating effect was 51.56%, which was an incomplete mediation. there was a possible mediating effect of IL-8 (90% CI), and sIL-2R (90% CI). no mediating effect of hs-CRP was observed. In a word, the application of PCSK9 inhibitors can reduce the progression of NTLs post-PCI, not only by lowering LDL-c levels but also possibly by lowering IL-8 and sIL-2R (See Fig. 3).Fig. 3 Mediation analysis of PCSK9 inhibitors on the progression of NTLs post-PCI. Values adjacent to the arrows depict β-coefficients (95% CIs) and P values from regression models. a Investigates the assumptions that PCSK9 inhibitors are associated with decreased mediators and mediators are associated with increased outcomes (progression of NTLs post-PCI).Mediators in green fulfill the assumptions for mediation analysis, in light green basically fulfill the assumptions, in grey do not fulfill the assumptions. b Mediated effect in mediation analysis of parameters indicating that 51.56% of the association of PCSK9 inhibitors with decreased progression of NTLs post-PCI is mediated by LDL-c. PCSK9, Proprotein convertase subtilisin-kexin 9; LDL-c, low-density lipoprotein cholesterol; IL-8, interleukin-8; sIL-2R, soluble interleukin-2 receptor; hs-CRP, hypersensitive C-reactive protein; NTL, non-target lesion

Discussion

In our study, multi-system parameters were comprehensively collected, and in addition to hs-CRP, the inflammatory factors representing different mechanisms were specifically collected. At the same time, different from logistic regression, which can only reflect the correlation of statistical methods, this study applied the machine learning method (LASSO regression) to screen the key characteristics and select risk factors related to the rapid progression of NTLs. The major influencing factors for the rapid progression of NTLs post-PCI are the levels of LDL-c, followed by inflammatory markers. In addition to the specific inflammatory marker hs-CRP, it is interesting to note that IL-8 and sIL-2R are likewise influences on the rapid progression of NTLs post-PCI. Finally, mediation analysis was used to explore the mechanism by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI. Our study showed that the application of PCSK9 inhibitors might reduce the rapid progression of NTLs post-PCI, not only by lowering LDL-c levels but also possibly by lowering IL-8 and sIL-2R.

The essence of the progression of NTLs post-PCI is the progression of the original atherosclerotic plaque, but the exact mechanism is not yet fully understood. It is currently believed that the pathologic basis for the rapid progression of NTLs is the vulnerable plaque with a thin fibrous cap, rich in necrotic cores, distribution of neovascularization at its margins accompanied with wall hypoxia, and the neovascular is not encapsulated by smooth muscle cells, which allows plasma macromolecules, such as LDL-c and erythrocytes (which are enriched in cholesterol) to pass through easily [7]. Stent implantation results in artificial plaque rupture and release of inflammatory mediators and chemotactic factors within the plaque, accompanied with macrophage aggregation (innate immune activation) and T cell aggregation (adaptive immune activation), and increasing the systemic inflammation and then increasing the level of inflammation in NTLs [31, 32], This promotes the rapid progression of NTLs.

The most important predictor of the progression of NTLs post-PCI is LDL-c, the lower the LDL-c level, the slower the progression of atherosclerosis, this study is in line with the previous studies [33, 34], and current guidelines [35, 36]. Our finding provides new evidence for controlling LDL-c to lower levels.

Hs-CRP is another important predictor. Although it is uncertain whether CRP directly promotes atherosclerosis, it is clear that CRP levels are associated with the progression of atherosclerosis [37]. Study [38] found admission CRP and post-PCI (48 h) CRP elevation were independent predictors of rapid progression of NTLs in patients with nonST-elevation acute coronary syndrome and underwent PCI. Imai [39] also found CRP was a predictor of NTL revascularization and cardiac events following coronary stenting in patients with stable and unstable angina pectoris. Our finding is consistent with the above studies.

In addition to CRP, multiple inflammatory factors have been found to be associated with the rapid progression of NTLs. These inflammatory factors include neopterin, matrix-degrading metalloproteinase-9, soluble intercellular adhesion molecule-1, Lipoprotein-Associated Phospholipase A2, serumamyloidprotein1, and lipopolysaccharide-binding protein [31, 40, 41], these inflammatory factors mainly represent endothelial and monocyte/macrophage activation, and vessel injury-triggered acute phase reaction. In our study, IL-8 and sIL-2R could be the predictors, which expands the range of inflammatory factors associated with the rapid progression of NTLs further.

The major cellular sources of IL-8 are usually monocytes and macrophages. IL-8 acts by binding to its two receptors, CXC chemokine receptor(CXCR) 1 and CXCR2, which are mainly responsible for recruiting monocytes and neutrophils and promoting their activation to play a role in promoting the inflammatory response [42], and hypoxia promotes IL-8 expression [43]. Zhu jun et al. found that IL-8 triggered the release of neutrophil extracellular traps (NETs) from neutrophils via the IL-8/CXCR2 signaling pathway, and activated NETs further induced macrophages to produce IL-8 via the TLR9/NF-κB pathway, thereby exacerbating the development of atherosclerosis [44]. Moreau et al. demonstrated that IL-8 inhibits the accumulation of Tissue Inhibitor of Metalloproteinase (TIMP)-1 in vitro, and concluded that IL-8 may play a potential atherogenic role by inhibiting local TIMP-1 expression, thereby leading to an imbalance between matrix-degrading metalloproteinases and TIMPs at focal sites in the atherosclerotic plaque [19]. Geetika et al. observed that IL-8 plays a crucial role in neovascularization [45]. A recent study found the antagonist of the IL-8 receptor suppressed the development of atherosclerosis through a mouse model [46]. Generally speaking, IL-8 can represent the pathological changes in lesions such as vessel wall hypoxia, innate immune activation, neovascularisation, and fibrous cap instability. These are important mechanisms of rapid progression of NTLs post-PCI.

The truncated form of the IL-2 receptor, termed sIL-2R, is secreted from activated T cells. It is a marker of lymphocyte activation and represents adaptive immunity [23]. Activated T lymphocytes play an important role in atherosclerosis promoting chemokine secretion, inflammation, and eventually, the formation of atherosclerotic plaques [47]. Murine models have shown that IL-2 increases regulatory T cell numbers in atherosclerotic plaques and reduces the plaque burden, when the IL-2 receptor is blocked, the plaque reduction is negated [48]. Therefore, sIL-2R represents adaptive immunity activation, this may promote the rapid progression of NTLs post-PCI by promoting chemokine secretion and inflammation.

In our study, the optimal model for predicting the rapid progression of NTLs post-PCI concludes LDL-c, hs-CRP, IL-8, and sIL-2R. LDL-c represents the circulating LDL-c levels post-PCI; hs-CRP represents systemic inflammation; IL-8 is associated with neovascularisation, fibrous cap instability, vessel wall hypoxia, and innate immunity; sIL-2R represents adaptive immunity. Neovascularization and circulating LDL-c levels together determine the level of LDL-c that leaks into the plaque microenvironment. On the other hand, elevated levels of systemic inflammation and activation of innate and adaptive immunity promote oxidized low-density lipoprotein (ox-LDL) production and phagocytosis by macrophages. These lead to an increase in foam cell formation, which manifests as plaque expansion and rapid progression of NTLs. In conclusion, the prediction model not only shows the optimization of prediction ability statistically but also reflect the internal pathophysiological occurrence of plaque progression. Therefore, it is worth verifying the generalization ability of this prediction model through a larger sample size in future studies.

Mediation analysis showed the application of PCSK9 inhibitors had a negative correlation with the rapid progression of NTLs post-PCI. This suggested that PCSK9 inhibitors may reduce the rapid progression of NTLs post-PCI. This effect was mainly but incompletely mediated by lowering LDL-c levels, and was possibly mediated by lowering IL-8 and sIL-2R. Study [21] had shown potential PCSK9 involvement pathways in Trimethylamine N-Oxide and cardiovascular disease risk may be mediated by IL-8. In studies of depression and alcoholic liver [49, 50], PCSK9 inhibitors reduced IL-2 and thus may influence IL-2-IL-2R signaling complexes to ameliorate atherosclerosis [51]. The analysis showed no correlation between PCSK9 inhibitors and hs-CRP, so hs-CRP wasn’t a mediator, which was in agreement with previous reports [52]. Hs-CRP represents systemic inflammation, while IL-8 and sIL-2R mainly represent to local inflammation of plaque. This suggests that PCSK9 inhibitors were more likely to reduce the rapid progression of NTLs by reducing local inflammation of plaque rather than systemic inflammation. Both plaque macrophages and smooth muscle cells secrete PCSK9 and play a role in promoting inflammation of plaque [53]. PCSK9 inhibitors also neutralize PCSK9 in plaques and reduce plaque inflammation, this may partially explain our findings. This finding provides the direction for future research on the mechanism of PCSK9 inhibitors to reduce the rapid progression of NTLs post-PCI.

In conclusion, in addition to the significant LDL-c-lowering, PCSK9 inhibitors may reduce the rapid progression of NTLs by reducing local inflammation of plaque. PCSK9 inhibitors may be ideal drugs to reduce the repaid progression of NTLs post-PCI.

Study limitations

This is a single-center retrospective study, multicenter prospective studies are needed to support the results of our study.

The evaluation tool of the progression of NTLs was CAG, lacking the information of plaque characteristics, so the evaluation was not comprehensive enough, there may be some bias.

Inflammatory factors, hs-CRP, and some biochemical parameters fluctuate greatly at different times, and this study chose a single postoperative time point, which failed to adequately reflect the changes in these parameters.

Conclusion

LDL-c, hs-CRP, IL-8, and sIL-2R may be the key characteristic risk factors for the rapid progression of NTLs post-PCI, and combining these parameters predict the rapid progression of NTLs post-PCI. Application of PCSK9 inhibitors has a negative correlation with rapid progression of NTLs post-PCI. In addition to the significant LDL-c-lowering, PCSK9 inhibitors may reduce the rapid progression of NTLs post-PCI by reducing local inflammation of plaque.

Abbreviations

PCI Percutaneous coronary intervention

NTL Non-target lesion

hs-CRP Hypersensitive C-reactive protein

NLR Neutrophil–lymphocyte ratio

sIL-2R Soluble interleukin-2 receptor

PCSK9 Proprotein convertase subtilisin-kexin 9

QCA Quantitative coronary angiography

ACEI Angiotensin-converting enzyme inhibitors

ARB Angiotensin II Receptor Blocker

LASSO Least absolute shrinkage and selection operator

ROC Receiver operating characteristic

DCA Decision curve analysis

CXCR CXC chemokine receptor

NETs Neutrophil extracellular traps

TIMP Tissue Inhibitor of Metalloproteinase

ox-LDL Oxidation low lipoprotein

HbA1c Hemoglobin A1c

Apo-B Apolipoprotein B

Apo-A1 Apolipoprotein A1

TC Total cholesterol

HDL-c High-density lipoprotein cholesterol

LDL-c Low-density lipoprotein cholesterol

TG Triglycerides

P-LCR Platelet-large cell rate

MPV Mean platelet volume

PDW Platelet distribution width

PCT Platelet crit

RDW-SD Red blood cell distribution width SD

RDW-CV Red blood cell distribution width CV

eGFR Estimated glomerular filtration rate

fT4 Free thyroxine

fT3 Free triiodothyronine

TSH Thyroid-stimulating hormone

anti-TPO Antithyroglobulin

anti-TG Antithyroglobulin

NT-ProBNP N-terminal pro-brain natriuretic peptide

Acknowledgements

The investigators are grateful to the dedicated participants and all research staff of the study.

Authors’ contributions

MJJ, YLL, and QP initiated and designed the study; LZZ, ZJL, and XWL collected the clinical data, GZH, ZLJ, YM, ZXD and WYX read and analyzed the CAG image, MJJ and FXD performed the statistical analysis, and draft the manuscript; QP and YLL critically revised the manuscript. All authors read and approved the final manuscript.

Funding

No external funding.

Availability of data and materials

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

Declarations

Ethics approval and consent to participate

This study was approved by the institutional ethics of The Second Hospital of Dalian Medical University, with a waiver of informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jiajie Mei and Xiaodan Fu contributed equally to this work and share the first authorship.
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