Predictive Value of Monocyte to High-Density Lipoprotein Cholesterol Ratio for Target Lesion Revascularization in Patients With Drug-Eluting Stent Implantation

Introduction

Coronary heart disease (CHD) constitutes a substantial factor contributing to the global mortality burden.¹ The implementation of drug-eluting stents (DESs) has enhanced the effectiveness of percutaneous coronary intervention (PCI) in the management of severe coronary heart disease.² However, in-stent restenosis (ISR) after the placement of DES continues to pose a considerable challenge, with incidence rates reported to vary between 3% and 20%.^3–5 An increase in clinical risks, encompassing recurrent angina pectoris and acute myocardial infarction, ultimately necessitates revascularization therapies, including plain old balloon angioplasty (POPA), drug-coated balloons (DCB) angioplasty, repeated DES applications, and coronary artery bypass grafting (CABG). Early risk stratification after DES implantation is considered an important means to improve prognosis. In addition to traditional risk factors, the identification of novel predictors for TLR may be of considerable importance in reducing the incidence of this condition.

Previous studies have increasingly emphasized the monocyte to high density lipoprotein cholesterol ratio (MHR) as an emerging and promising prognostic marker for cardiovascular events.^6–9 A study suggested that MHR was a predictive factor for ISR following DES implantation in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS).¹⁰ The predictive ability of the MHR for TLR in patients with DES remains ambiguous. The objective of the current research was to assess the efficacy of the MHR in predicting TLR subsequent to the implantation of DES.

Methods

Study Population

This retrospective, observational, case-control study presents an analysis of 622 consecutive patients who underwent successful primary DES implantation, followed by post-implantation coronary angiography at Tianjin Chest Hospital from December 1, 2014 to December 1, 2022. The research excluded participants who had a previous history of CABG, heart failure, hematological abnormalities, active infectious diseases, autoimmune disorders or chronic connective tissue diseases, severe hepatic pathology, thyroid dysfunction, malignancy, renal insufficiency (estimated glomerular filtration rate below 30 mL/min/1.73 m²). Ultimately, a total of 474 patients were enrolled in the study. Among them, 193 patients underwent TLR, with 25 undergoing POBA, 45 receiving DCB treatment, 96 receiving DES implantation, and 27 undergoing CABG. Figure 1 presents the flowchart depicting the study’s design and progression.

Figure 1 Study flow chart. Abbreviations: TLR, Target Lesion Revascularization; CABG, Coronary Artery Bypass Grafting.

Intervention Procedure

The interventional procedures were executed in full compliance with the interventional therapy guidelines that were followed at our medical institution.¹¹ Patients received antiplatelet therapy that consisted of an initial oral loading dose of 300 mg of aspirin, succeeded by a daily maintenance dose of 100 mg. Furthermore, patients were assigned to one of two antiplatelet regimens: either a loading dose of 300 mg of clopidogrel, followed by a maintenance dose of 75 mg per day, or a loading dose of 180 mg of ticagrelor, maintained at 90 mg administered twice daily. Throughout the course of the interventional procedures, anticoagulation therapy was provided using unfractionated heparin, with an initial bolus of 70–100 IU/kg and subsequent doses as required to maintain an activated clotting time exceeding 250 seconds. Coronary angiography was conducted using the Judkins technique on the Philips Allura Xper FD10 fixed X-ray system, after which digital imaging was obtained for further quantitative analysis. The operator was afforded discretion in selecting the type and size of DES, and making decisions regarding the utilization of glycoprotein IIb/IIIa receptor antagonists. Patients adhered to secondary prevention strategies in accordance with the most recent guidelines after PCI.

Data Collection and Definitions

Data were collected from patient information records by physicians who were adequately trained and blinded to the objectives of the study, including demographics characteristics (age, sex, hypertension, smoking, diabetes), echocardiography (left ventricular ejection fraction[LVEF]), data from coronary angiography, and prescribed medications. Prior to the implantation of the stent, blood samples from the peripheral veins were gathered and then put through an analysis. The subsequent laboratory results were recorded: fasting blood glucose (FBG), high-sensitivity C-reactive protein (hs-CRP), creatinine, uric acid, white blood cell (WBC) counts, total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, triglyceride (TG) levels, and high-density lipoprotein (HDL) cholesterol. The MHR is determined utilizing the subsequent equation: . Hypertension is characterized by a systolic reading of 140 mmHg or more and/or a diastolic reading of 90 mmHg or above, as determined by at least three separate measurements conducted on different days, or the patient is undergoing antihypertensive treatment. Diabetes mellitus is identified when a person’s fasting blood glucose reaches 7.0 mmol/L or higher, or by the utilization of hypoglycemic medications for management. TLR is characterized as any repeat PCI or CABG performed on the target lesion due to: 1) ischemic symptoms that are functionally confirmed and/or myocardial ischemia, accompanied by a luminal diameter stenosis of 50% or greater, as assessed quantitatively by angiography; or 2) any revascularization procedure required due to a luminal diameter stenosis of 70% or greater, confirmed quantitatively by angiography.^12,13

Statistical Analysis

We employed the Kolmogorov–Smirnov test to evaluate the normality of the continuous variables. Following this assessment, the data were reported as follows: for variables conforming to a normal distribution, the mean ± standard deviation was utilized; for those not conforming to a normal distribution, the median with interquartile range was presented. Moreover, the categorical variables were presented in terms of their frequencies and percentages (%). For data that followed a normal distribution, we utilized independent sample t-tests to analyze the results, whereas Mann–Whitney U-tests were applied to data that deviated from normal distribution. We evaluated the categorical variables for their statistical significance by employing the chi-square test. A univariate logistic regression analysis was performed to ascertain the factors that were correlated with TLR. Variables that exhibited a P-value of less than 0.1 or were clinically relevant to TLR were subsequently incorporated into the multivariate logistic regression analysis model. MHR was analyzed as a continuous variable. To investigate the relationship between MHR and TLR, we conducted an analysis after excluding the impact of confounding factors. The predictive value of MHR for TLR was conducted through the application of the receiver operating characteristic (ROC) curve, followed by the calculation of the area under the curve (AUC) and determination of the optimal cut-off value. Two models were constructed to assess whether the incorporation of MHR among established risk factors would enhance predictive accuracy. We employed the DeLong test for comparing the areas under the curves (AUCs), and assessed the predictive value of MHR through the calculation of net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The alpha level for all two-tailed tests was established at 0.05. Statistical analyses were performed using SPSS software (version 25.0) and R software (version 4.2.3).

Results

Baseline Characteristics of the Study Participants

The patient cohort had an average age of 60.4 ± 8.6 years, with 59.7% (n = 283) being male. The research participants were divided into two groups: the non-TLR group (281 patients) and the TLR group (193 patients). The baseline demographic and clinical characteristics of the study participants are summarized in Table 1.

Table 1 Baseline Characteristics of the Study Participants

The TLR and non-TLR groups exhibited comparable characteristics in respect to age, hypertension, smoking status, LVEF, interval between the two coronary angiographies, and the use of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and β-blockers. Equivalent concentrations of lymphocyte counts, TC, LDL, TG, and uric acid were observed between the two groups. However, a greater proportion of males and diabetics were present in the TLR group, accompanied by a higher incidence of multivessel disease. The TLR group exhibited narrower minimum stent diameters and longer total stent lengths. Additionally, the TLR group demonstrated significantly higher levels of FBG, creatinine, and hs-CRP. When compared with the non-TLR group, there was a significant increase in the counts of WBCs, neutrophils, and monocytes, coupled with a notable decrease in HDL levels.

Comparison of the MHR

The statistical analysis revealed a notable elevation in MHR within the TLR group when compared to the non-TLR group with respective values of 12.34 and 8.97, as illustrated in Figure 2A. Based on the tertile distribution of MHR, patients were stratified into three groups, with a statistically significant upward trend observed in the proportion of patients undergoing TLR from the lowest tertile (T1) to the highest tertile (T3), as depicted in Figure 2B.

Figure 2 Comparison of the monocyte count to high-density lipoprotein cholesterol ratio (MHR) between the two groups (A); as well as the proportion of patients undergoing target lesion revascularization (TLR) by MHR tertiles (B).

Logistic Regression Analyses

Multivariate logistic regression analyses revealed associations between TLR and several factors, including the MHR, sex, diabetes mellitus, the target vessel, and the number of stenosed coronary arteries. Following adjustment for potential confounding variables, MHR was identified as an independent and significant predictor of TLR, exhibiting an odds ratio (OR) of 1.162 (95% confidence interval [CI]: 1.102–1.225), as presented in Table 2.

Table 2 Logistic Regression Analyses of Predictors for Target Lesion Revascularization (TLR)

Receiver Operating Characteristic (ROC) Curve Analysis

Figure 3A illustrated the AUC for TLR, which was 0.712 (95% CI: 0.664–0.759). The optimal cut-off value was determined to be 10.98, with a resulting sensitivity of 58.0% and a specificity of 75.8%.

Figure 3 Receiver operating characteristic (ROC) curve analysis of the monocyte count to high-density lipoprotein cholesterol ratio (MHR) for predicting target lesion revascularization (TLR) (A), and area under curve (AUC) comparison between the models (B).

The incorporation of the MHR into a predictive model, which already included established risk factors such as male gender, diabetes, target coronary vessel, and the number of stenosed coronary arteries, resulted in an enhanced AUC from 0.689 to 0.749 (P < 0.001), as illustrated in Figure 3B. Additionally, improvements were noted in the categorical NRI of 0.183 (P < 0.001) and the IDI of 0.074 (P < 0.001), as detailed in Table 3.

Table 3 Assessment of the Predictive Efficacy of the Models Concerning Target Lesion Revascularization (TLR)

Discussion

The present study examined the correlation between MHR and TLR in patients undergoing DES implantation. An increase in MHR was correlated with a higher probability of TLR following DES implantation. Upon adjustment for potential confounding variables, MHR was identified as an independent predictor of TLR. Incorporating MHR into the mix of conventional risk factors could significantly improve the early assessment of risk for TLR.

Previous research has shown that in patients with ST-segment elevation myocardial infarction (STEMI), the MHR can predict the risk of stent thrombosis with a hazard ratio (HR) of 1.08 (95% CI: 1.02–1.17).¹⁴ Analysis of data from multiple studies has demonstrated that the preprocedural MHR serves as an independent predictor of BMS-ISR. Specifically, the ORs and 95% CIs reported in these studies are as follows: OR = 1.45 (95% CI: 1.06–1.88),¹⁵ OR = 1.29 (95% CI: 1.15–1.49),¹⁶ and OR = 3.64 (95% CI: 2.45–4.84).¹⁷ Nan et al performed a study involving 214 patients who were diagnosed with NSTE-ACS. Their findings indicated that the MHR independently predicted DES-ISR with an OR of 1.02 (95% CI: 1.01–1.03; P = 0.041).¹⁰ Previous studies examining the relationship between MHR and ISR have piqued our interest in investigating the link between MHR and TLR. Our study differs from that of Nan et al in several respects. First, the study population in our research comprises individuals with unstable angina rather than those with NSTE-ACS; Second, the clinical outcomes investigated in our study differ. We included 31 patients in the non-TLR group who were diagnosed with ISR but did not undergo revascularization through repeat PCI or CABG, instead for medical therapy. Essentially, the target vessel revascularization rate reflects the incidence of severe in-stent restenosis, indicating more severe clinical outcomes. Nan et al confirmed the correlation between MHR and ISR, while we have demonstrated the connection between MHR and severe ISR.

The primary underlying factor contributing to TLR is the occurrence of excessive neointimal hyperplasia within the stented region.^18,19 Additionally, patients with DES exhibit an earlier and more prevalent occurrence of neoatherosclerosis compared to those with BMS. Inflammation is a crucial factor in the process of endothelial hyperplasia. Upon activation, monocytes are capable of releasing pro-inflammatory cytokines. These cytokines facilitate the proliferation and activation of vascular smooth muscle cells within the vascular wall.^20,21 A study conducted by Fukuda et al revealed that circulating monocytes contribute to the development of neointimal hyperplasia in the context of stent implantation.²² Neoatherosclerosis is defined by the presence of lipid-rich foamy macrophages that accumulate in the neointimal layer. In this context, the critical processes involve the activity of monocytes and their differentiation into macrophages. Our study revealed a statistically significant increase in monocyte counts in the TLR group compared to the control group (0.46 vs 0.37, P < 0.001), indicating a positive correlation between the two variables.

HDL exhibits several properties that may confer anti-atherogenic potential. The inherent antioxidant and anti-inflammatory characteristics of HDL collectively enhance its anti-atherogenic effect, thereby mitigating atherogenic processes.²³ HDL exhibits a multifaceted array of protective effects, encompassing the inhibition of endothelial cell apoptosis, facilitation of re-endothelialization, augmentation of endothelial cell-derived prostacyclin synthesis, reduction of platelet aggregability, and regulation of endothelial function via enhanced endothelial nitric oxide production.^23–25 Moreover, HDL plays a pivotal role in maintaining cholesterol homeostasis by enabling the elimination of excess cholesterol from peripheral tissues via the reverse cholesterol transport pathway.²⁶ Nagano et al observed that a reduced ability of HDL to accommodate additional cholesterol could potentially result in future stent failure, mediated through the induction of atherogenic alterations within the neointima of stents.²⁷ Our study revealed a statistically significant decrease in HDL levels in the TLR group compared to the control group (1.02 vs 1.12, P < 0.001), indicating an inverse correlation between the two variables.

Moreover, existing literature has documented an interaction between HDL and monocytes, indicating that HDL possesses the ability to reduce the manifestation of adhesion molecules on endothelial cells. This reduction effectively hinders the migration of monocytes to the arterial wall.²⁸ HDL demonstrates a significant interaction with monocytes, which inhibits their activation and disrupts their differentiation process into macrophages. This interaction ultimately contributes to a decrease in the subsequent inflammatory response.²⁹ MHR, which serves as a biomarker reflecting the combined influence of monocytes and HDL, has been shown to exhibit an independent correlation with TLR. In our study, the MHR exhibited a predictive AUC of 0.712 for TLR, which demonstrates moderate predictive capability. The incorporation of the MHR into a predictive model resulted in an enhanced AUC from 0.689 to 0.749. Additionally, improvements were noted in the categorical NRI of 0.183 and the IDI of 0.074. Incorporating MHR into the risk stratification following stent implantation will facilitate the identification of high-risk individuals and guide clinicians in providing intensified pharmacotherapy, thereby reducing the rate of target vessel revascularization.

However, some limitations of our study must be acknowledged. Due to the inherent constraints of this retrospective, single-center, observational study design, a definitive causal relationship between MHR and TLR cannot be established. More prospective cohort studies are needed to validate the predictive value of MHR. To discern the causes of in-stent restenosis, additional research utilizing intracoronary imaging techniques is necessary, rather than relying solely on angiography. More research needs to be conducted in order to identify the underlying causes of in-stent restenosis to seek further explanations for relationship between MHR and TLR.

Conclusion

In conclusion, the MHR, serving as a biomarker, demonstrates an independent and positive association with the risk of TLR in patients undergoing DES implantation. Incorporating MHR into predictive models has the potential to enhance their predictive accuracy. Further validation of the predictive significance of MHR requires the conduct of multicenter, prospective studies.