Unraveling the Rapid Progression of Non-Target Lesions: Risk Factors and the Therapeutic Potential of PCSK9 Inhibitors in Post-PCI Patients

Unraveling the Rapid Progression of Non-Target Lesions: Risk Factors and the Therapeutic Potential of PCSK9 Inhibitors in Post-PCI Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unraveling the Rapid Progression of Non-Target Lesions: Risk Factors and the Therapeutic Potential of PCSK9 Inhibitors in Post-PCI Patients Jiajie Mei, Xiaodan Fu, Zhenzhu Liu, Lijiao Zhang, Zhaohong Geng, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4625777/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Sep, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract 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 for the rapid progression of NTLs post-PCI. Proprotein convertase subtilisin-kexin 9(PCSK9) inhibitors lower low-density lipoprotein cholesterol(LDL-c) levels significantly, also show the anti-inflammation effect, and may have the potential to reduce the rapid progression of NTLs post-PCI. 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. 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 ,higher 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 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 can predict the rapid progression of NTLs post-PCI. The application of PCSK9 inhibitors has 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. Registration number: ChiCTR2200058529; Date of registration: 2022-04-10 Percutaneous coronary intervention rapid progression of non-target lesions Proprotein convertase subtilisin-kexin 9 inhibitors Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction 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 in a certain period post-PCI(roughly 2 years) 2 – 6 , this suggests the NTLs undergo rapid progression post-PCI 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. 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, our study not only covered the full range of clinical risk factors but also included several specific inflammatory factors which indicate different inflammatory metabolic pathways factors. By screening for characteristic risk factors associated with the rapid progression of NTLs using machine learning, we sought to establish a more accurate clinical prediction model for the rapid progression of NTLs post-PCI. In terms of the intervention of NTLs, early study 16 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 17 , and also show the anti-inflammation effect 18 , 19 . A recent study 20 found that PCSK9 inhibitor evolocumab showed promising results in the regression of NTLs. In our study, we will also observe the relationship between the application of PCSK9 inhibitors and the rapid progression of NTLs. We will also explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI through mediator analysis. Materials and Methods Study Subjects 2068 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 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. 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); collection 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), Inflammatory factors: Interleukin(IL)-10, IL-6, IL-8, IL-1β, soluble interleukin-2 receptor(sIL-2R), and tumor necrosis factor-α(TNF-α). 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 was 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. Results 1. Study flow Initially, a total of 2068 patients were included, whereas 818 patients were excluded (including 412 patients who recived previous PCI or CABG, 167 patients who recived 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 ). 2. 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 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 ± 181.6 338.3 ± 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) 0.07 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 ± 0.7 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 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 2.9 ± 1.56 3.08 ± 1.94 0.074 magnesium, mmol/L 0.91 ± 0.35 0.89 ± 0.3 0.406 sodium, mmol/L 140.61 ± 4.21 140.42 ± 3.31 0.409 potassium, mmol/L 3.93 ± 0.37 3.9 ± 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 1.3 ± 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 ± 3.7 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) x100%; 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. Table 1 should be here 3. 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 of LASSO regression in feature selection, solving multicollinearity, and improving model generalization ability, we used LASSO regression to screen the characteristic risk factors of the progression of NTLs post-PCI. The optimal value of λ was determined by tenfold cross-validation, and the selection criterion was based on the lambda.1se, with lambda.1se = 0.05022, resulting in a model with excellent performance and the lowest number of independent variables, which were hs-CRP, LDL-c, IL-8, and sIL-2R, (see Fig. 2 ). were 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). LASSO, least absolute shrinkage and selection operator; SE, standard error The above 3 models were compared in terms of differentiation, ROC curves were plotted, and all 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. 3 ). DCA, Decision curve analysis 4. 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. We further analyzed by mediation analysis to understand the possible mechanism of PCKS9 inhibitors in reducing the progression of NTLs post-PCI. We took the applications of PCSK9 inhibitors as independent variables, the progression of NTLs as dependent variables, and LDL-c, IL-8, sIL-2R, and hs-CRP as mediator variables. On analysis, it was seen that 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. 4 ). Values adjacent to the arrows depict β-coefficients (95% CIs) and P values from regression models. The total effect of the association of PCSK9 inhibitors with outcomes (progression of NTLs post-PCI) on regression analysis is a prerequisite for mediation analysis. Percent mediated = mediated effect/total effect×100. Beta coefficients reflect the change in the dependent variable (progression of NTLs post-PCI) when the independent variable takes the value 1 (when applying PCSK9 inhibitors). 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 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 artificial intelligence method (machine learning) to analyze the correlation between risk factors and lesions through LASSO regression and was used to screen the key characteristics and select risk factors related to lesions. The major influencing factors for the 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. And the application of PCSK9 inhibitors can 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 mechanism of progression of NTLs is the progression of the original atherosclerotic plaque, and the exact mechanism of rapid plaque progression is not yet fully understood. It is currently believed that the pathologic basis for the rapid progression of NTLs is the vulnerable plaque with thin fibrous cap, the presence of a high number of necrotic cores within the plaque, the distribution of neovascularization at its margins accompanied by wall hypoxia, and neovascularization that 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 by macrophage aggregation (innate immune activation) and T cell aggregation (adaptive immune activation), and increases the systemic inflammation and then increases the level of inflammation in NTLs 21 , 22 , 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 23 , 24 , and current guidelines 25 , 26 . Our finding provides new evidence for the control of lower LDL-c levels. Hs-CRP is another important predictor, study 27 found admission CRP and post-PCI (48 hours) CRP elevation were independent predictors of rapid progression of NTLs in patients with nonST-elevation acute coronary syndrome and underwent PCI. Imai 28 also found CRP is 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 inflammations include neopterin, matrix-degrading metalloproteinase-9, soluble intercellular adhesion molecule-1, Lipoprotein-Associated Phospholipase A2, serumamyloidprotein1, and lipopolysaccharide-binding protein 29 ,30 21 , These inflammatory factors mainly represent endothelial and monocyte/macrophage activation, and vessel injury-triggered acute phase. In our study, IL-8 and sIL-2R can predict the rapid progression of NTLs post-PCI, which expands the range of inflammatory factors associated with rapid progression further. The major cellular sources of IL-8 are usually monocytes and macrophages 31 . 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, and hypoxia promotes IL-8 expression 32 . Zhujun 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 33 . 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. 34 Simonini et al concluded that in human coronary atherosclerosis, IL-8 is an important mediator of angiogenesis, and may contribute to plaque formation through its angiogenic properties 35 . Aihua et al observed an inhibition of capillary tube formation and neovascularization following treatment with anti-IL-8, anti-CXCR1, and anti-CXCR2 antibodies 36 . Generally speaking, IL-8 can respond to 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. Several previous studies have shown higher levels of sIL2-R in patients with coronary artery disease compared to healthy controls 37 – 40 . 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 41 . Activated T lymphocytes play an important role in atherosclerosis promoting chemokine secretion, inflammation, and eventually, the formation of atherosclerotic plaques 42 . 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 37 . 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, and 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 oxidation low lipoprotein (ox-LDL) production and phagocytosis by macrophages and increase 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 the characteristic risk factors contained in the model are consistent with and reflect the internal pathophysiological occurrence and correlation 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 has 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. Studies 43 have 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 44 , 45 , PCSK9 inhibitors reduced IL-2 and thus may influence IL-2-IL-2R signaling complexes to ameliorate atherosclerosis 41 . The analysis showed that hs-CRP was not a mediator of PCSK9 inhibitors, and there was no correlation between PCSK9 inhibitors and hs-CRP in the present study, in agreement with previous reports. 46 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 47 . PCSK9 inhibitors also neutralize PCSK9 in plaques and reduce plaque inflammation, which 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 progression of NTLs is also affected by plaque characteristics, our database did not contain the plaque characteristics observed by IVUS or OCT, so 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 can predict the rapid progression of NTLs post-PCI. Application of PCSK9 inhibitors has 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 Declarations Acknowledgments 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. 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 that they have no competing interests. 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The Evolving Future of PCSK9 Inhibitors. J Am Coll Cardiol. 2018;72:314–29. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Sep, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 16 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviews received at journal 14 Jul, 2024 Reviewers agreed at journal 06 Jul, 2024 Reviewers agreed at journal 04 Jul, 2024 Reviewers invited by journal 04 Jul, 2024 Editor invited by journal 04 Jul, 2024 Editor assigned by journal 01 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 23 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4625777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327874511,"identity":"d18b7da4-2b54-414d-91d2-04abb428bf4b","order_by":0,"name":"Jiajie Mei","email":"","orcid":"","institution":"The Second Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Mei","suffix":""},{"id":327874512,"identity":"81b8dc03-feb9-4405-8caf-ee507d453b6d","order_by":1,"name":"Xiaodan Fu","email":"","orcid":"","institution":"The Second Hospital of Dalian Medical 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15:15:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4625777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4625777/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-04186-2","type":"published","date":"2024-09-18T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61181213,"identity":"d903a2a1-dd86-4bba-b02c-bf345179358c","added_by":"auto","created_at":"2024-07-26 16:46:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75645,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow diagram describing the screening, enrolling, and groupings of patients.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4625777/v1/5ddc6ef8ef02ac25ad97ab9b.jpg"},{"id":61181212,"identity":"7c8630d4-9fa7-4271-8801-3056ee20c794","added_by":"auto","created_at":"2024-07-26 16:46:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94390,"visible":true,"origin":"","legend":"\u003cp\u003ePredictor selection using the LASSO regression analysis with tenfold cross-validation.\u003c/p\u003e\n\u003cp\u003ea. Tuning parameter (lambda) selection of deviance in the LASSO regression based on the minimum criteria (left dotted line) and the 1-SE criteria (right dotted line).\u003c/p\u003e\n\u003cp\u003eb. A coefficient profile plot was created against the log (lambda) sequence. In the present study, predictor’s selection was according to the 1-SE criteria (right dotted line), where 4 nonzero coefficients were selected.\u003c/p\u003e\n\u003cp\u003eLASSO, least absolute shrinkage and selection operator; SE, standard error\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4625777/v1/6cd638f9590d5b6dbbcace97.jpg"},{"id":61181215,"identity":"3d7a8272-65d8-49eb-a4a7-5845e6b4c54d","added_by":"auto","created_at":"2024-07-26 16:46:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67556,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC curves and DCA curves between Model\u003csub\u003e \u003c/sub\u003e1, Model\u003csub\u003e \u003c/sub\u003e2, and Model\u003csub\u003e \u003c/sub\u003e3.\u003c/p\u003e\n\u003cp\u003ea. Model 3 (AUC = 0.632, P \u0026lt; 0.001) was significantly more effective in predicting the progression of NTLs post-PCI than Model 1(AUC = 0.596, P \u0026lt; 0.001), and Model 2(AUC = 0.606, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eb. 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.\u003c/p\u003e\n\u003cp\u003eDCA, Decision curve analysis\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4625777/v1/8436354d74d96bf3a5e668da.jpg"},{"id":61181216,"identity":"a6748214-65dd-4118-bbfa-d4b45dd2be78","added_by":"auto","created_at":"2024-07-26 16:46:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127621,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of PCSK9 inhibitors on the progression of NTLs post-PCI.\u003c/p\u003e\n\u003cp\u003eValues adjacent to the arrows depict β-coefficients (95% CIs) and P values from regression models. The total effect of the association of PCSK9 inhibitors with outcomes (progression of NTLs post-PCI) on regression analysis is a prerequisite for mediation analysis.\u003c/p\u003e\n\u003cp\u003ea. Investigates the assumptions that PCSK9 inhibitors are associated with decreased mediators (LDL-cfollow-up, IL-8,sIL-2R,hs-CRP) and that mediators are associated with increased outcomes (progression of NTLs post-PCI). Outcomes in green fulfill the assumptions for mediation analysis(95% confidence interval without 0), outcomes in light green basically fulfill the assumptions for mediation analysis(90% confidence interval without 0), outcomes in grey do not fulfill the assumptions for mediation analysis(no statistically significant effect).\u003c/p\u003e\n\u003cp\u003eb. 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.\u003c/p\u003e\n\u003cp\u003ePercent mediated = mediated effect/total effect×100.\u003c/p\u003e\n\u003cp\u003eBeta coefficients reflect the change in the dependent variable (progression of NTLs post-PCI) when the independent variable takes the value 1 (when applying PCSK9 inhibitors).\u003c/p\u003e\n\u003cp\u003ePCSK9, 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.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4625777/v1/e81d2f5965445bee63f0a677.jpg"},{"id":65104115,"identity":"d89f63b1-f380-4985-a026-ce05dfaf1fa0","added_by":"auto","created_at":"2024-09-23 16:11:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1097160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4625777/v1/9a79176c-a94c-495b-92f6-bce62c526238.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling the Rapid Progression of Non-Target Lesions: Risk Factors and the Therapeutic Potential of PCSK9 Inhibitors in Post-PCI Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTarget 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 \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. And the incidence of NTL related cardiac events was highest in a certain period post-PCI(roughly 2 years) \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, this suggests the NTLs undergo rapid progression post-PCI\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Most studies define progression of NLTs within a few months to 2 years as rapid progression of NLTs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003ePrevious studies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003ehad 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. 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, our study not only covered the full range of clinical risk factors but also included several specific inflammatory factors which indicate different inflammatory metabolic pathways factors. By screening for characteristic risk factors associated with the rapid progression of NTLs using machine learning, we sought to establish a more accurate clinical prediction model for the rapid progression of NTLs post-PCI.\u003c/p\u003e \u003cp\u003eIn terms of the intervention of NTLs, early study\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e 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\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and also show the anti-inflammation effect\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A recent study\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e found that PCSK9 inhibitor evolocumab showed promising results in the regression of NTLs. In our study, we will also observe the relationship between the application of PCSK9 inhibitors and the rapid progression of NTLs. We will also explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI through mediator analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003e2068 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 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuantitative coronary angiography(QCA) analysis\u003c/b\u003e: QCA analyzes the coronary angiography images to clarify the progression of lesions. Rapid progression of NTLs was defined as follows: (i)\u0026thinsp;\u0026ge;\u0026thinsp;10% diameter reduction of at least one preexisting stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50%, (ii)\u0026thinsp;\u0026ge;\u0026thinsp;30% diameter reduction of a preexisting stenosis\u0026thinsp;\u0026lt;\u0026thinsp;50%, (iii) progression of a lesion to total occlusion within 2 years\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClinical 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); collection 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), Inflammatory factors: Interleukin(IL)-10, IL-6, IL-8, IL-1β, soluble interleukin-2 receptor(sIL-2R), and tumor necrosis factor-α(TNF-α).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll 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\u0026thinsp;\u0026plusmn;\u0026thinsp;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 was 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.\u003c/p\u003e \u003cp\u003eFurther, 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%\u0026times;[Beta-coefficientmodel - Beta-coefficientmodel\u0026thinsp;+\u0026thinsp;intermediatefactor]/[Beta-coefficient- model]. A two-sided p-value of \u0026lt;\u0026thinsp;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.\u003c/p\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e1. Study flow\u003c/h2\u003e\n \u003cp\u003eInitially, a total of 2068 patients were included, whereas 818 patients were excluded (including 412 patients who recived previous PCI or CABG, 167 patients who recived 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\u0026apos;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. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2. The difference in general characteristics and laboratory tests of patients.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn 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\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and there was no statistically significant difference in other medication use.\u003c/p\u003e\n \u003cp\u003eIn terms of laboratory tests, HbA1c was higher in the NTL progressed group (6.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 vs 6.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37,p\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 vs 2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81, ApoB: 0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 vs 0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, TG: 1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 vs 1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1, TC: 3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13 vs 3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97, Rate of LDL-c decline : 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;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 no-progressed group (3.02(0.87\u0026ndash;5.51) vs 3.13(0.93\u0026ndash;9.71), P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with the NTL no-progressed group, the NTL progressed group had higher TNF-\u0026alpha;, IL-6, IL-8, and sIL-2R(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The baseline characteristics, Medication application post-PCI, and Laboratory tests are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics, General information of repeat CAG, medication application, and Laboratory data of patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1250\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNTL No-progressed(N\u0026thinsp;=\u0026thinsp;849)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNTL progressed(N\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge,y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.27\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567(66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285(71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoker,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227(32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136(33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einterval time,d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322.72\u0026thinsp;\u0026plusmn;\u0026thinsp;181.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.3\u0026thinsp;\u0026plusmn;\u0026thinsp;182.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003emedication application post-PCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTiglitazarol, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178(21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103(25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;-blocker, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e413(48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166(41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEI/ARB, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373(43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192(47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estatins, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e824(97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381(95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCSK9i, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003elaboratory parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;HbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApoB, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApoA1, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erLDL-c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-LCR, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPV, fl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDW, fl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW-SD, fl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW-CV, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.92\u0026thinsp;\u0026plusmn;\u0026thinsp;18.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.93\u0026thinsp;\u0026plusmn;\u0026thinsp;18.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emagnesium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esodium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.61\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epotassium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ealbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprealbumin, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258.2\u0026thinsp;\u0026plusmn;\u0026thinsp;59.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257.94\u0026thinsp;\u0026plusmn;\u0026thinsp;61.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etotal bilirubin, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.83\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR, ml/min/1.73 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.75\u0026thinsp;\u0026plusmn;\u0026thinsp;15.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.73\u0026thinsp;\u0026plusmn;\u0026thinsp;18.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCystatin c, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003euric acid, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349.73\u0026thinsp;\u0026plusmn;\u0026thinsp;96.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365.82\u0026thinsp;\u0026plusmn;\u0026thinsp;109.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecreatinine, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.45\u0026thinsp;\u0026plusmn;\u0026thinsp;101.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.03\u0026thinsp;\u0026plusmn;\u0026thinsp;109.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSH, \u0026micro;IU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efT4, pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efT3, pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanti-TPO, IU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.29(28.00-39.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.88(28.00-41.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanti-TG, IU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.00(15.00-19.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.00(15.00-21.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehs-CRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02(0.87\u0026ndash;5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13(0.93\u0026ndash;9.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehomocysteine, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD-Dimer, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50(0.35\u0026ndash;0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49(0.35\u0026ndash;0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNT-ProBNP, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.60(63.15\u0026ndash;742.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.90(71.00-1032.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003einflammatory factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNF-\u0026alpha;, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.10(9.48\u0026ndash;41.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.91(11.20\u0026ndash;47.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-10, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00(5.00\u0026ndash;5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00(5.00\u0026ndash;5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-8, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.75(28.03\u0026ndash;146.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.55(36.53\u0026ndash;230.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-6, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79(2.39\u0026ndash;7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.38(2.79\u0026ndash;9.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esIL-2R, U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395.00(324.00-496.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e447.00(367.50\u0026ndash;601.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-1\u0026beta;, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00(5.00-5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00(5.00-6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mean (25th-75th quantiles) ,or n (%)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eACEI, angiotensin converting enzyme inhibitors; ARB, Angiotensin II Receptor Blocker; PCSK9i, Proprotein convertase subtilisin-kexin 9 inhibitors; HbA1c, Hemoglobin A1c; \u0026Delta;HbA1c\u0026thinsp;=\u0026thinsp;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) x100%; 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-\u0026alpha;, tumor necrosis factor-\u0026alpha;; IL, Interleukin.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e should be here\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3. Regression analysis of the progression of NTLs post-PCI and building of prediction models.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe selected the variables (excluding the application of medication and inflammatory factors) that differed at p\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the H-L test( p\u0026thinsp;=\u0026thinsp;0.378).\u003c/p\u003e\n \u003cp\u003eBased on model 1, we further performed hierarchical multiple logistic regression, inflammatory factors including TNF-\u0026alpha;, IL-10, IL-8, IL-6, sIL-2R, and IL-1\u0026beta; 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\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the H-L test( p\u0026thinsp;=\u0026thinsp;0.989).\u003c/p\u003e\n \u003cp\u003eGiven the significant advantages of LASSO regression in feature selection, solving multicollinearity, and improving model generalization ability, we used LASSO regression to screen the characteristic risk factors of the progression of NTLs post-PCI. The optimal value of \u0026lambda; was determined by tenfold cross-validation, and the selection criterion was based on the lambda.1se, with lambda.1se\u0026thinsp;=\u0026thinsp;0.05022, resulting in a model with excellent performance and the lowest number of independent variables, which were hs-CRP, LDL-c, IL-8, and sIL-2R, (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). were recorded this model as Model 3, and the model was meaningful by the Omnibus test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the H-L test( p\u0026thinsp;=\u0026thinsp;0.194).\u003c/p\u003e\n \u003cp\u003eLASSO, least absolute shrinkage and selection operator; SE, standard error\u003c/p\u003e\n \u003cp\u003eThe above 3 models were compared in terms of differentiation, ROC curves were plotted, and all 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\u0026thinsp;\u0026gt;\u0026thinsp;Model 2\u0026thinsp;\u0026gt;\u0026thinsp;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. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eDCA, Decision curve analysis\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4. Mediation analysis revealed a potential link between PCSK9 inhibitors and the rapid progression of NTLs post-PCI.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThere was a statistically significant negative correlation between PCSK9 inhibitors application and the rapid progression of NTLs. We further analyzed by mediation analysis to understand the possible mechanism of PCKS9 inhibitors in reducing the progression of NTLs post-PCI. We took the applications of PCSK9 inhibitors as independent variables, the progression of NTLs as dependent variables, and LDL-c, IL-8, sIL-2R, and hs-CRP as mediator variables.\u003c/p\u003e\n \u003cp\u003eOn analysis, it was seen that 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. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eValues adjacent to the arrows depict \u0026beta;-coefficients (95% CIs) and P values from regression models. The total effect of the association of PCSK9 inhibitors with outcomes (progression of NTLs post-PCI) on regression analysis is a prerequisite for mediation analysis.\u003c/p\u003e\n \u003cp\u003ePercent mediated\u0026thinsp;=\u0026thinsp;mediated effect/total effect\u0026times;100.\u003c/p\u003e\n \u003cp\u003eBeta coefficients reflect the change in the dependent variable (progression of NTLs post-PCI) when the independent variable takes the value 1 (when applying PCSK9 inhibitors).\u003c/p\u003e\n \u003cp\u003ePCSK9, 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.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMulti-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 artificial intelligence method (machine learning) to analyze the correlation between risk factors and lesions through LASSO regression and was used to screen the key characteristics and select risk factors related to lesions. The major influencing factors for the 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. And the application of PCSK9 inhibitors can 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.\u003c/p\u003e \u003cp\u003eThe mechanism of progression of NTLs is the progression of the original atherosclerotic plaque, and the exact mechanism of rapid plaque progression is not yet fully understood. It is currently believed that the pathologic basis for the rapid progression of NTLs is the vulnerable plaque with thin fibrous cap, the presence of a high number of necrotic cores within the plaque, the distribution of neovascularization at its margins accompanied by wall hypoxia, and neovascularization that 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\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Stent implantation results in artificial plaque rupture and release of inflammatory mediators and chemotactic factors within the plaque, accompanied by macrophage aggregation (innate immune activation) and T cell aggregation (adaptive immune activation), and increases the systemic inflammation and then increases the level of inflammation in NTLs\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, This promotes the rapid progression of NTLs.\u003c/p\u003e \u003cp\u003eThe 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\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and current guidelines\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our finding provides new evidence for the control of lower LDL-c levels.\u003c/p\u003e \u003cp\u003eHs-CRP is another important predictor, study\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e found admission CRP and post-PCI (48 hours) CRP elevation were independent predictors of rapid progression of NTLs in patients with nonST-elevation acute coronary syndrome and underwent PCI. Imai\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e also found CRP is 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.\u003c/p\u003e \u003cp\u003eIn addition to CRP, multiple inflammatory factors have been found to be associated with the rapid progression of NTLs. These inflammations include neopterin, matrix-degrading metalloproteinase-9, soluble intercellular adhesion molecule-1, Lipoprotein-Associated Phospholipase A2, serumamyloidprotein1, and lipopolysaccharide-binding protein\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,30 21\u003c/sup\u003e, These inflammatory factors mainly represent endothelial and monocyte/macrophage activation, and vessel injury-triggered acute phase. In our study, IL-8 and sIL-2R can predict the rapid progression of NTLs post-PCI, which expands the range of inflammatory factors associated with rapid progression further.\u003c/p\u003e \u003cp\u003eThe major cellular sources of IL-8 are usually monocytes and macrophages\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. 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, and hypoxia promotes IL-8 expression\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Zhujun 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\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. 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.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Simonini et al concluded that in human coronary atherosclerosis, IL-8 is an important mediator of angiogenesis, and may contribute to plaque formation through its angiogenic properties\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Aihua et al observed an inhibition of capillary tube formation and neovascularization following treatment with anti-IL-8, anti-CXCR1, and anti-CXCR2 antibodies\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Generally speaking, IL-8 can respond to 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.\u003c/p\u003e \u003cp\u003eSeveral previous studies have shown higher levels of sIL2-R in patients with coronary artery disease compared to healthy controls\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. 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\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Activated T lymphocytes play an important role in atherosclerosis promoting chemokine secretion, inflammation, and eventually, the formation of atherosclerotic plaques\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. 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\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Therefore, sIL-2R represents adaptive immunity activation, this may promote the rapid progression of NTLs post-PCI by promoting chemokine secretion and inflammation.\u003c/p\u003e \u003cp\u003eIn 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, and 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 oxidation low lipoprotein (ox-LDL) production and phagocytosis by macrophages and increase 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 the characteristic risk factors contained in the model are consistent with and reflect the internal pathophysiological occurrence and correlation of plaque progression. Therefore, it is worth verifying the generalization ability of this prediction model through a larger sample size in future studies\u003c/p\u003e \u003cp\u003eMediation analysis showed the application of PCSK9 inhibitors has 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. Studies\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e have 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\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, PCSK9 inhibitors reduced IL-2 and thus may influence IL-2-IL-2R signaling complexes to ameliorate atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The analysis showed that hs-CRP was not a mediator of PCSK9 inhibitors, and there was no correlation between PCSK9 inhibitors and hs-CRP in the present study, in agreement with previous reports.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e 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\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. PCSK9 inhibitors also neutralize PCSK9 in plaques and reduce plaque inflammation, which 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.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e This is a single-center retrospective study, multicenter prospective studies are needed to support the results of our study.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe progression of NTLs is also affected by plaque characteristics, our database did not contain the plaque characteristics observed by IVUS or OCT, so there may be some bias.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInflammatory 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.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLDL-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 can predict the rapid progression of NTLs post-PCI. Application of PCSK9 inhibitors has 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCI, percutaneous coronary intervention\u003c/p\u003e\n\u003cp\u003eNTL, non-target lesion\u003c/p\u003e\n\u003cp\u003ehs-CRP, hypersensitive C-reactive protein\u003c/p\u003e\n\u003cp\u003eNLR, neutrophil-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003esIL-2R, soluble interleukin-2 receptor\u003c/p\u003e\n\u003cp\u003ePCSK9, Proprotein convertase subtilisin-kexin 9\u003c/p\u003e\n\u003cp\u003eQCA, Quantitative coronary angiography\u003c/p\u003e\n\u003cp\u003eACEI, angiotensin-converting enzyme inhibitors\u003c/p\u003e\n\u003cp\u003eARB, Angiotensin II Receptor Blocker\u003c/p\u003e\n\u003cp\u003eLASSO, least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eDCA, decision curve analysis\u003c/p\u003e\n\u003cp\u003eCXCR, CXC chemokine receptor\u003c/p\u003e\n\u003cp\u003eNETs, neutrophil extracellular traps\u003c/p\u003e\n\u003cp\u003eTIMP, Tissue Inhibitor of Metalloproteinase\u003c/p\u003e\n\u003cp\u003eox-LDL, oxidation low lipoprotein\u003c/p\u003e\n\u003cp\u003eHbA1c, Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eApo-B, apolipoprotein B\u003c/p\u003e\n\u003cp\u003eApo-A1, apolipoprotein A1\u003c/p\u003e\n\u003cp\u003eTC, total cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-c, high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-c, low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eTG, triglycerides\u003c/p\u003e\n\u003cp\u003eP-LCR, Platelet-large cell rate\u003c/p\u003e\n\u003cp\u003eMPV, Mean platelet volume\u003c/p\u003e\n\u003cp\u003ePDW, platelet distribution width\u003c/p\u003e\n\u003cp\u003ePCT, platelet crit\u003c/p\u003e\n\u003cp\u003eRDW-SD, red blood cell distribution width SD\u003c/p\u003e\n\u003cp\u003eRDW-CV, red blood cell distribution width CV \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eeGFR, estimated glomerular filtration rate \u0026nbsp;\u003c/p\u003e\n\u003cp\u003efT4, free thyroxine \u0026nbsp;\u003c/p\u003e\n\u003cp\u003efT3, free triiodothyronine \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTSH, thyroid-stimulating hormone\u003c/p\u003e\n\u003cp\u003eanti-TPO, antithyroglobulin\u003c/p\u003e\n\u003cp\u003eanti-TG, antithyroglobulin\u003c/p\u003e\n\u003cp\u003eNT-ProBNP, N-terminal pro-brain natriuretic peptide \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigators are grateful to the dedicated participants and all research staff of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJJ, 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics of The Second Hospital of Dalian Medical University, with a waiver of informed consent\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStone GW, Maehara A, Lansky AJ, De Bruyne B, Cristea E, Mintz GS, Mehran R, McPherson J, Farhat N, Marso SP, Parise H, Templin B, White R, Zhang Z, Serruys PW. 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PCSK9 inhibition as a novel therapeutic target for alcoholic liver disease. Sci Rep. 2019;9:17167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHendawy N, Salaheldin TH, Abuelezz SA. PCSK9 Inhibition Reduces Depressive like Behavior in CUMS-Exposed Rats: Highlights on HMGB1/RAGE/TLR4 Pathway, NLRP3 Inflammasome Complex and IDO-1. J Neuroimmune Pharmacol. 2023;18:195\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y-X, Li S, Liu H-H, Li J-J. Impact of PCSK9 monoclonal antibodies on circulating hs-CRP levels: a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2018;8:e022348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenson RS, Hegele RA, Fazio S, Cannon CP. The Evolving Future of PCSK9 Inhibitors. J Am Coll Cardiol. 2018;72:314\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Percutaneous coronary intervention, rapid progression of non-target lesions, Proprotein convertase subtilisin-kexin 9 inhibitors","lastPublishedDoi":"10.21203/rs.3.rs-4625777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4625777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRapid 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 for the rapid progression of NTLs post-PCI. Proprotein convertase subtilisin-kexin 9(PCSK9) inhibitors lower low-density lipoprotein cholesterol(LDL-c) levels significantly, also show the anti-inflammation effect, and may have the potential to reduce the rapid progression of NTLs post-PCI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 1250 patients who underwent the first PCI and underwent repeat coronary angiography for recurrence of chest pain within 24 months. 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were more diabetes, less beta-blockers and PCSK9 inhibitors application, higher HbA1c, LDL-c, ApoB, TG, TC, uric acid ,higher 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 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\u0026thinsp;=\u0026thinsp;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).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLDL-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 can predict the rapid progression of NTLs post-PCI. The application of PCSK9 inhibitors has 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.\u003c/p\u003e\u003ch2\u003eRegistration number:\u003c/h2\u003e \u003cp\u003eChiCTR2200058529; Date of registration: 2022-04-10\u003c/p\u003e","manuscriptTitle":"Unraveling the Rapid Progression of Non-Target Lesions: Risk Factors and the Therapeutic Potential of PCSK9 Inhibitors in Post-PCI Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-26 16:46:17","doi":"10.21203/rs.3.rs-4625777/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-16T19:38:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T15:41:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-14T11:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126770869639203472586456038286668653994","date":"2024-07-06T20:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223905852818509816921120941050119652144","date":"2024-07-05T03:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-04T20:01:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-04T13:08:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-01T12:31:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T12:30:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-06-23T15:13:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cd34f14-de53-4679-a0b0-df7397264f3c","owner":[],"postedDate":"July 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:06:09+00:00","versionOfRecord":{"articleIdentity":"rs-4625777","link":"https://doi.org/10.1186/s12872-024-04186-2","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2024-09-18 15:57:02","publishedOnDateReadable":"September 18th, 2024"},"versionCreatedAt":"2024-07-26 16:46:17","video":"","vorDoi":"10.1186/s12872-024-04186-2","vorDoiUrl":"https://doi.org/10.1186/s12872-024-04186-2","workflowStages":[]},"version":"v1","identity":"rs-4625777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4625777","identity":"rs-4625777","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}