Abstract

Background: The aim of the present study was to investigate the association between the monocyte-to-high-density lipoprotein–cholesterol ratio (MHR) and the outcomes of patients with coronary artery disease (CAD) who were treated with percutaneous coronary intervention (PCI).

Methods: A total of 5679 CAD patients from CORFCHD-PCI, a retrospective cohort study (identifier: ChiCTR-ORC-16010153), who underwent PCI were included in the study and divided into three tertiles according to their MHR values. The primary outcome was long-term mortality after PCI. The main secondary endpoints were stroke, readmission, and major adverse cardiovascular events (MACEs), defined as the combination of cardiac death, recurrent myocardial infarction, and target vessel reconstruction. The average follow-up time was 35.9 ± 22.6 months.

Results: Patients were divided into three groups according to MHR tertiles: the first tertile (MHR < 0.4; n=1290), second tertile (MHR ≥ 0.4–0.61; n=1878) and third tertile (MHR > 0.61; n=1870). The all-cause mortality (ACM) incidence was significantly lower in the first and second tertiles than in the third tertile (adjusted HR = 0.658, [95% CI: 0.408–0.903], P=0.009 and HR = 0.712, [95% CI: 0.538–0.941], P=0.017, respectively). Cardiac mortality (CM) occurred in 235 patients: 60 (3.1%) in the first tertile group, 74 (3.9%) in the second tertile group and 101 (5.4%) in the third tertile group. There was a significant difference in the CM incidence between the first tertile group and the third tertile group (HR = 0.581, [95% CI: 0.406–0.832], P=0.003), and there was also a difference in the CM incidence between the second tertile group and the third tertile group (HR = 0.690, [95% CI: 0.506–0.940], P=0.019).

Conclusion: The present study indicated that an increased MHR was independently associated with long-term mortality in CAD patients who have undergone PCI.

Introduction

Coronary artery disease (CAD) is considered a complex disease, and the morbidity, mortality, disability, and recurrence rates are high [1,2]. CAD became one of the most common causes of death in the Chinese population in 2015 [3,4]. Atherosclerosis is the pathological basis of CAD. Arterial wall endothelial dysfunction and chronic inflammation continue to promote the development of atherosclerotic lesions [5]. Systemic inflammation, such as high levels of pro-inflammatory cytokines and adhesion molecules, actively promotes the transfer of monocytes to atherosclerotic lesions. Monocytes are important participants in the immune system [6]. The activation of inflammatory factors plays an important role in the initiation of atherosclerotic inflammation, which is involved in plaque progression, destabilization, rupture and thrombosis throughout the development of CAD. Hypercholesterolemia is one of the major risk factors for atherosclerosis. A large number of studies have shown that high-density lipoprotein (HDL) has protective effects against low-density lipoprotein (LDL) oxidation, transports cholesterol from the surrounding tissue to the liver for recycling, and inhibits the expression of endothelial adhesion molecules and the recruitment of monocytes in the arterial wall. HDL inhibits the inflammatory response by acting directly on monocytes. It has antioxidant properties and regulates vascular inflammation, vasomotor function, and thrombosis [7–9].

The monocyte-to-HDL-cholesterol ratio (MHR) has been proposed to have a role in systemic inflammation and to be a possible predictor of atherosclerosis development and progression. An increased MHR level was associated with adverse outcomes and was an indication of high rates of major cardiovascular adverse events (MACEs), including stent thrombosis and mortality after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients [10–13]. In addition, previous studies also suggested that the MHR is a useful predictor of outcomes in stable CAD patients or in acute coronary syndrome [14,15]. However, a few studies have evaluated the association of the MHR with long-term outcomes in Chinese patients with CAD who have undergone PCI. The aim of the present study was to investigate the association between the MHR and long-term outcomes in Chinese patients with CAD after PCI.

Methods

Study design and population

From January 2008 to December 2016, a total of 5679 CAD patients who underwent PCI were enrolled, and all of them were from the CORFCHD-PCI study, which has been described in a previous article [16]. Briefly, CORFCHD-PCI is a large, single-center retrospective cohort study based on case records and a follow-up registry. The details of the design are registered at http://www.chictr.org.cn (identifier: ChiCTR-ORC-16010153). The PCIs were performed by experienced interventionalists. Initially, a total of 6050 patients with CAD who had undergone PCI were evaluated. A total of 371 patients were excluded due to no monocyte count or HDL-c data available; acute infections, including respiratory tract infections, urinary tract infections, oral infections, central nervous system infections, intestinal infections; blood system diseases; and malignant tumors. Finally, 5679 patients were enrolled in the present study. The flow chart for the inclusion and exclusion of the participants is shown in Figure 1.

Flow chart of participant inclusion

Figure 1
Flow chart of participant inclusion
Figure 1
Flow chart of participant inclusion

Blood testing

For the routine blood tests, 2-ml venous blood samples were collected in standardized dipotassium EDTA tubes. The routine blood samples were measured using an automated blood counter within 2 h of collection to minimize variations due to sample aging. We measured the serum concentrations of uric acid, total cholesterol, triglycerides, blood urea nitrogen (BUN), creatinine (Cr), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and fasting glucose using chemical analysis equipment (Dimension AR/AVL Clinical Chemistry System, Newark, NJ) in the Clinical Laboratory Department of the First Affiliated Hospital of Xinjiang Medical University, as described previously [17].

Calculation of the MHR

We calculated the MHR by dividing the absolute value of the monocyte count by that of the HDL-C. The normal range of monocytes is (0.12–0.8) × 109/l. The normal range of HDL-C is 0.78–2 mmol/l.

Clinical and demographic characteristics collection

We collected demographic data, laboratory data, including routine blood parameters and biochemical indicators, cardiovascular risk factor data, including smoking status, alcohol consumption, previously diagnosed diabetes, history of hypertension, familial history of CAD and history of medication and surgical disease, ECG data, data from echocardiography, coronary angiography and PCI procedures, and the short-term and long-term outcomes of these patients. The medical history included the use of antiplatelet therapy, CCBs, ACEIs or ARBs, β-blockers, and statins.

According to the diagnostic criteria for hypertension, a patient was considered to have hypertension if he was undergoing active treatment with antihypertensive drugs or had a blood pressure ≥140/90 mmHg on at least two separate occasions [18]. A diagnosis of diabetes mellitus was considered positive in patients with a definite history of diabetes who were undergoing treatment with glucose-lowering agents, in those with a fasting glucose level of at least 7.1 mmol/l, and in those with a 2-h postload glucose level of at least 11.1 mmol/l [19]. The diagnosis of hyperlipidemia comes from the ‘guidelines for the prevention and treatment of dyslipidemia in Chinese adults (2016)’ [20]. The smoking status classifications were current smokers, former smokers, and never-smokers. Persons reporting regular tobacco use in the previous 6 months were considered current smokers.

Ethical approval of the study protocol

The study protocol was consistent with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University.

Endpoints

The primary endpoints were long-term all-cause mortality (ACM) and cardiac mortality (CM). Deaths were considered to be a result of a cardiac condition unless a definite noncardiac cause of death was identified. Secondary endpoints included stroke, bleeding events, readmission, and major adverse cardiac events (MACEs). MACEs were defined as the combination of cardiac death, recurrent myocardial infarction and target vessel reconstruction.

Follow-up

All patients were scheduled for elective clinical follow-up at 1 month, 3 months, 6 months, 1 year, 3 years, and 5 years. We clinically monitored the patients for cardiovascular events and their medication status. The patients were followed up for at least 2 years, and the longest follow-up time was 10 years. The investigators followed the patients by either office visits or telephone contact as necessary.

Statistical analysis

Data were analyzed by using SPSS 22.0 software. The continuous variables are presented as the mean ± standard deviation (SD), and the categorical variables are presented as the number of patients and percentages. The MHR tertiles categorized into three groups by trisection (<0.04, ≥0.4 U/l to 0.61 and >0.61) and compared with one-way ANOVA (for continuous variables). Kruskal–Wallis tests (for nonparametric variables) and chi-square tests or Fisher’s exact tests were performed for the categorical variables. The chi-square test was employed for the comparison of categorical variables. Kaplan–Meier analysis was applied to the cumulative incidence rates of the long-term outcomes, and the log-rank test was used to compare groups. Multivariable analysis was performed to assess the predictive value of the MHR for outcomes during and up to a 10-year follow-up. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. A P<0.05 (two-sided) was considered significant.

Results

Baseline data

The study included 5679 patients who were divided into three groups according to MHR: the first tertile (MHR < 0.04; n=1923), second tertile (MHR ≥ 0.04–0.61; n=1880), and third tertile (MHR ≥ 0.61; n=1876). The baseline data are shown in Table 1. All patients were administered dual antiplatelet therapy with aspirin and one P2Y12 receptor antagonist after PCI. Several variables were significantly different among these three tertiles, including age, sex, smoking status, blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), triglycerides (TG), total cholesterol (TC), HDL, LDL, and Apo-a1 (all P<0.05). There was no significant difference in hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), diabetes mellitus, hyperlipidemia, blood glucose, Apo-B, heart failure, or stroke.

Table 1
Baseline characteristics of patients
VariablesMHRP
Tertile 1 (n=1923)Tertile 2 (n=1880)Tertile 3 (n=1876)
Age (years) 60.41 ± 10.34 59.64 ± 10.78 58.36 ± 11.14 <0.001 
Gender (male) 1277 (66.4%) 1422 (75.6%) 1523 (81.2%) <0.001 
Smoking 637 (35%) 756 (40.2%) 852 (45.4%) <0.001 
Hypertension 818 (42.5%) 793 (42.2%) 814 (43.4%) 0.743 
SBP 127.65 ± 18.98 127.13 ± 18.93 126.42 ± 18.35 0.126 
DBP 76.28 ± 11.30 76.05 ± 11.25 76.57 ± 11.41 0.381 
Diabetes mellitus 455 (23.7%) 481 (25.6%) 452 (24.1%) 0.352 
Hyperlipidemia 163 (8.5%) 162 (8.7%) 174 (9.3%) 0.647 
BUN 5.43 ± 1.58 5.51 ± 1.66 5.62 ± 1.76 0.003 
Cr 73.25 ± 19.36 76.29 ± 20.58 78.43 ± 20.02 <0.001 
UA 315.18 ± 86.55 325.95 ± 89.60 328.61 ± 94.12 <0.001 
Blood glucose 6.59 ± 3.21 6.57 ±3.17 6.70 ± 3.90 0.453 
TG 1.73 ± 1.19 1.90 ± 1.21 2.07 ± 1.37 <0.001 
TC 4.09 ± 1.13 3.98 ± 1.10 3.80 ± 107 <0.001 
HDL 1.25 ± 0.71 0.98 ± 0.21 0.82 ± 0.21 <0.001 
LDL 2.55 ± 0.95 2.47 ± 0.89 2.36 ± 0.89 <0.001 
Apo-A1 1.26 ± 0.35 1.16 ± 0.28 1.08 ± 0.29 <0.001 
Apo-B 0.86 ± 0.39 0.85 ± 0.39 0.84 ± 0.41 0.22 
Lp(a) 219.65 ± 177.99 215.62 ± 168.88 225.67 ± 183.16 0.215 
CCB (n,%) 226 (11.8) 210 (11.2) 207(11.1) 0.734 
β-Blockers (n,%) 799 (41.8) 727 (38.8) 775 (41.5) 0.125 
ARB (n,%) 448 (23.5) 433(23.2) 410(21.9) 0.488 
Statin (n,%) 1072 (56.3) 992 (53.2) 984(52.9) 0.065 
VariablesMHRP
Tertile 1 (n=1923)Tertile 2 (n=1880)Tertile 3 (n=1876)
Age (years) 60.41 ± 10.34 59.64 ± 10.78 58.36 ± 11.14 <0.001 
Gender (male) 1277 (66.4%) 1422 (75.6%) 1523 (81.2%) <0.001 
Smoking 637 (35%) 756 (40.2%) 852 (45.4%) <0.001 
Hypertension 818 (42.5%) 793 (42.2%) 814 (43.4%) 0.743 
SBP 127.65 ± 18.98 127.13 ± 18.93 126.42 ± 18.35 0.126 
DBP 76.28 ± 11.30 76.05 ± 11.25 76.57 ± 11.41 0.381 
Diabetes mellitus 455 (23.7%) 481 (25.6%) 452 (24.1%) 0.352 
Hyperlipidemia 163 (8.5%) 162 (8.7%) 174 (9.3%) 0.647 
BUN 5.43 ± 1.58 5.51 ± 1.66 5.62 ± 1.76 0.003 
Cr 73.25 ± 19.36 76.29 ± 20.58 78.43 ± 20.02 <0.001 
UA 315.18 ± 86.55 325.95 ± 89.60 328.61 ± 94.12 <0.001 
Blood glucose 6.59 ± 3.21 6.57 ±3.17 6.70 ± 3.90 0.453 
TG 1.73 ± 1.19 1.90 ± 1.21 2.07 ± 1.37 <0.001 
TC 4.09 ± 1.13 3.98 ± 1.10 3.80 ± 107 <0.001 
HDL 1.25 ± 0.71 0.98 ± 0.21 0.82 ± 0.21 <0.001 
LDL 2.55 ± 0.95 2.47 ± 0.89 2.36 ± 0.89 <0.001 
Apo-A1 1.26 ± 0.35 1.16 ± 0.28 1.08 ± 0.29 <0.001 
Apo-B 0.86 ± 0.39 0.85 ± 0.39 0.84 ± 0.41 0.22 
Lp(a) 219.65 ± 177.99 215.62 ± 168.88 225.67 ± 183.16 0.215 
CCB (n,%) 226 (11.8) 210 (11.2) 207(11.1) 0.734 
β-Blockers (n,%) 799 (41.8) 727 (38.8) 775 (41.5) 0.125 
ARB (n,%) 448 (23.5) 433(23.2) 410(21.9) 0.488 
Statin (n,%) 1072 (56.3) 992 (53.2) 984(52.9) 0.065 

Abbreviations: ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; BUN, blood urea nitrogen; Cr, creatinine; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; Lp(a), lipoprotein a; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; UA, uric acid.

Clinical outcome

To predict mortality, the MHR (AUC = 0.600, 95% CI: 0.529–0.670, P=0.005) was stronger than monocytes (AUC = 0.532, 95% CI: 0.465–0.600, P=0.363) or HDL-C (AUC = 0.513, 95% CI: 0.442–0.583, P=0.722) (Supplementary Tables S1 and S2). There were 293 (5.16%) ACMs during the follow-up. The incidence of ACM in the first tertile was 80 (4.2%), it was 91 (4.8%) in the second tertile, and it was 122 (6.5%) in the third tertile. The ACM incidence was significantly lower in the first tertile than in the second tertile or in the third tertile (both P<0.05). The ACM incidence was significantly lower in the first and second tertiles than in the third tertile (adjusted HR = 0.658 [0.408–0.903], P=0.009 and HR = 0.712 [0.538–0.941], P=0.017, respectively). As shown in Figure 2, Tables 2 and 3, the incidence of CM in the first tertile was 60 (3.1%), which was lower than that in the second tertile [74 (3.9%)] and third tertile [101 (5.4%)]. Univariate Cox regression analysis also showed significant differences in CM between patients in the third tertile and those in the first tertile (HR = 0.581, 95% CI: 0.406–0.832, P=0.003) and second tertile (HR = 0.690, 95% CI: 0.506–0.9401, P=0.019). As shown in Figure 3, Tables 2 and 4, we also found that the incidence of MACEs was higher in the third tertile group than in the first tertile group (P=0.002) in a univariate analysis. However, the difference was not significant after multivariable Cox regression analysis, as shown in Figure 4, Tables 2 and 5. Since the MHR was a continuous variable, we also performed a restricted cubic spline regression to show the relation between the MHR and the hazard risk. The results are shown in Supplementary Figure S1.

Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of ACM

Figure 2
Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of ACM
Figure 2
Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of ACM

Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of CM

Figure 3
Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of CM
Figure 3
Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of CM

Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of a MACE

Figure 4
Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of a MACE
Figure 4
Cumulative Kaplan–Meier estimates of the time to the first adjudicated occurrence of a MACE
Table 2
Clinical outcomes among the three groups
VariablesMHRP
Tertile 1 (n=1923)Tertile 2 (n=1880)Tertile 3 (n=1876)
ACM 80 (4.2%) 91 (4.8%) 122 (6.5%) 0.004 
CM 60 (3.1%) 74 (3.9%) 101 (5.4%) 0.002 
MACE 258 (13.4%) 276 (14.7%) 327 (17.4%) 0.002 
HF 49 (2.5%) 59 (3.1%) 60 (3.2%) 0.424 
Stroke 24 (1.2%) 21 (1.1%) 30 (1.6%) 0.408 
Bleeding 52 (2.7%) 62 (3.3%) 52 (2.8%) 0.495 
Readmission 263(13.7%) 247(13.1%) 265(14.1%) 0.677 
Secondary MI 56(2.9%) 66(3.5%) 59(3.1%) 0.571 
Secondary PCI 80 (4.2%) 83 (4.4%) 89 (4.7%) 0.681 
Secondary CABG 20 (1.0%) 15 (0.8%) 15 (0.8%) 0.654 
TVR 99 (5.1%) 96 (5.1) 103 (5.5) 0.845 
VariablesMHRP
Tertile 1 (n=1923)Tertile 2 (n=1880)Tertile 3 (n=1876)
ACM 80 (4.2%) 91 (4.8%) 122 (6.5%) 0.004 
CM 60 (3.1%) 74 (3.9%) 101 (5.4%) 0.002 
MACE 258 (13.4%) 276 (14.7%) 327 (17.4%) 0.002 
HF 49 (2.5%) 59 (3.1%) 60 (3.2%) 0.424 
Stroke 24 (1.2%) 21 (1.1%) 30 (1.6%) 0.408 
Bleeding 52 (2.7%) 62 (3.3%) 52 (2.8%) 0.495 
Readmission 263(13.7%) 247(13.1%) 265(14.1%) 0.677 
Secondary MI 56(2.9%) 66(3.5%) 59(3.1%) 0.571 
Secondary PCI 80 (4.2%) 83 (4.4%) 89 (4.7%) 0.681 
Secondary CABG 20 (1.0%) 15 (0.8%) 15 (0.8%) 0.654 
TVR 99 (5.1%) 96 (5.1) 103 (5.5) 0.845 

Abbreviations: ACM, all-cause mortality; CABG, coronary artery bypass grafting; CM, cardiac mortality; MACE, major adverse cardiovascular events; hF, heart failure; MI, myocardial infarction; PCI, percutaneous coronary intervention.

Table 3
Multivariable Cox regression analysis of ACM
VariablesBSEWaldPHR95%CI
Gender (male) -0.056 0.153 0.134 0.714 0.945 0.701–1.276 
Age (years) 0.028 0.006 21.655 1.028 1.016–1.040 
Smoking 0.035 0.135 0.066 0.797 1.035 0.795–1.348 
BUN 0.055 0.036 2.281 0.131 1.057 0.984–1.135 
Cr 0.004 0.003 1.898 0.168 1.004 0.998–1.009 
TG -0.022 0.049 0.206 0.65 0.978 0.889–1.076 
TC 0.117 0.088 1.786 0.181 1.124 0.947–1.335 
HDL 0.155 0.112 1.903 0.168 1.168 0.937–1.455 
LDL -0.184 0.105 3.096 0.078 0.832 0.677–1.021 
Apo-AI 0.047 0.198 0.058 0.81 1.049 0.712–1.545 
UA 0.001 0.06 0.806 0.999–1.002 
MHR classify   8.601 0.014   
MHR(1) -0.418 0.161 6.726 0.009 0.658 0.480–0.903 
MHR(2) -0.34 0.142 5.69 0.017 0.712 0.538-0.941 
VariablesBSEWaldPHR95%CI
Gender (male) -0.056 0.153 0.134 0.714 0.945 0.701–1.276 
Age (years) 0.028 0.006 21.655 1.028 1.016–1.040 
Smoking 0.035 0.135 0.066 0.797 1.035 0.795–1.348 
BUN 0.055 0.036 2.281 0.131 1.057 0.984–1.135 
Cr 0.004 0.003 1.898 0.168 1.004 0.998–1.009 
TG -0.022 0.049 0.206 0.65 0.978 0.889–1.076 
TC 0.117 0.088 1.786 0.181 1.124 0.947–1.335 
HDL 0.155 0.112 1.903 0.168 1.168 0.937–1.455 
LDL -0.184 0.105 3.096 0.078 0.832 0.677–1.021 
Apo-AI 0.047 0.198 0.058 0.81 1.049 0.712–1.545 
UA 0.001 0.06 0.806 0.999–1.002 
MHR classify   8.601 0.014   
MHR(1) -0.418 0.161 6.726 0.009 0.658 0.480–0.903 
MHR(2) -0.34 0.142 5.69 0.017 0.712 0.538-0.941 
Table 4
Multivariable Cox regression analysis of CM
VariablesBSEWaldPHR95%CI
Gender (male) −0.064 0.171 0.139 0.71 0.938 0.671–1.312 
Age [years] 0.019 0.007 8.174 0.004 1.019 1.006–1.032 
Smoking 0.131 0.151 0.757 0.384 1.14 0.848–1.533 
BUN 0.08 0.04 3.968 0.046 1.083 1.001–1.172 
Cr 0.005 0.003 2.432 0.119 1.005 0.999–1.010 
TG −0.079 0.058 1.844 0.175 0.924 0.824–1.036 
TC 0.192 0.093 4.28 0.039 1.212 1.010–1.454 
HDL 0.158 0.131 1.447 0.229 1.171 0.905–1.516 
LDL −0.225 0.112 4.008 0.045 0.799 0.641–0.995 
Apo-AI 0.068 0.218 0.098 0.754 1.071 0.698–1.643 
UA 0.001 0.007 0.932 0.999–1.002 
MHR classify   10.171 0.006   
MHR(1) −0.543 0.183 8.786 0.003 0.581 0.406–0.832 
MHR(2) −0.371 0.158 5.539 0.019 0.69 0.506–0.9401 
VariablesBSEWaldPHR95%CI
Gender (male) −0.064 0.171 0.139 0.71 0.938 0.671–1.312 
Age [years] 0.019 0.007 8.174 0.004 1.019 1.006–1.032 
Smoking 0.131 0.151 0.757 0.384 1.14 0.848–1.533 
BUN 0.08 0.04 3.968 0.046 1.083 1.001–1.172 
Cr 0.005 0.003 2.432 0.119 1.005 0.999–1.010 
TG −0.079 0.058 1.844 0.175 0.924 0.824–1.036 
TC 0.192 0.093 4.28 0.039 1.212 1.010–1.454 
HDL 0.158 0.131 1.447 0.229 1.171 0.905–1.516 
LDL −0.225 0.112 4.008 0.045 0.799 0.641–0.995 
Apo-AI 0.068 0.218 0.098 0.754 1.071 0.698–1.643 
UA 0.001 0.007 0.932 0.999–1.002 
MHR classify   10.171 0.006   
MHR(1) −0.543 0.183 8.786 0.003 0.581 0.406–0.832 
MHR(2) −0.371 0.158 5.539 0.019 0.69 0.506–0.9401 
Table 5
Multivariable Cox regression analysis of MACE
VariablesBSEWaldPHR95%CI
Gender (male) 0.098 0.092 1.138 0.286 1.103 0.921–1.320 
Age [years] 0.004 0.003 1.672 0.196 1.004 0.998–1.011 
Smoking 0.199 0.078 6.515 0.011 1.22 1.047–1.421 
BUN 0.049 0.022 4.979 0.026 1.05 1.006–1.096 
Cr 0.002 0.988 0.996–1.004 
TG -0.014 0.03 0.213 0.645 0.986 0.931–1.045 
TC 0.035 0.056 0.389 0.533 1.035 0.928–1.155 
HDL 0.022 0.084 0.066 0.797 1.022 0.867–1.204 
LDL -0.11 0.066 2.807 0.094 0.896 0.787–1.019 
Apo-AI -0.133 0.126 1.125 0.289 0.875 0.684–1.120 
UA 0.001 1.55 0.213 1.001 1.000–1.001 
MHR classify   3.199 0.202   
MHR(1) -0.089 0.094 0.903 0.342 0.915 0.762–1.099 
MHR(2) -0.15 0.084 3.191 0.074 0.861 0.730–1.015 
VariablesBSEWaldPHR95%CI
Gender (male) 0.098 0.092 1.138 0.286 1.103 0.921–1.320 
Age [years] 0.004 0.003 1.672 0.196 1.004 0.998–1.011 
Smoking 0.199 0.078 6.515 0.011 1.22 1.047–1.421 
BUN 0.049 0.022 4.979 0.026 1.05 1.006–1.096 
Cr 0.002 0.988 0.996–1.004 
TG -0.014 0.03 0.213 0.645 0.986 0.931–1.045 
TC 0.035 0.056 0.389 0.533 1.035 0.928–1.155 
HDL 0.022 0.084 0.066 0.797 1.022 0.867–1.204 
LDL -0.11 0.066 2.807 0.094 0.896 0.787–1.019 
Apo-AI -0.133 0.126 1.125 0.289 0.875 0.684–1.120 
UA 0.001 1.55 0.213 1.001 1.000–1.001 
MHR classify   3.199 0.202   
MHR(1) -0.089 0.094 0.903 0.342 0.915 0.762–1.099 
MHR(2) -0.15 0.084 3.191 0.074 0.861 0.730–1.015 

Discussion

To the best of our knowledge, a few studies have evaluated the association of the MHR with long-term outcomes in Chinese CAD patients who have undergone PCI. The present study indicated that an increased MHR was independently associated with long-term mortality in CAD patients who underwent PCI in China. A higher MHR was one of the strongest independent predictors of long-term mortality. The patients with CAD who underwent PCI and were in the highest MHR quartile had the highest mortality risk.

The inflammatory response has a vital function in the progression of atherosclerosis. Bath et al. [21] reported that monocytes in patients with hypercholesterolemia were more sensitive to the stimulation of chemokines. This may explain the increased involvement of monocytes in the formation of atherosclerosis associated with high cholesterol. Murphy et al. [22] reported that HDL-C has anti-inflammatory effects on human monocytes by inhibiting the activation of CD11b. HDL-C inhibits LDL-C oxidation by reducing monocyte chemotaxis and monocyte chemotaxis protein 1 expression, thereby affecting the inflammatory index. The MHR is a novel inflammation-based marker and may be an independent predictor of future cardiovascular events [23]. Previous studies have shown that a higher MHR is associated with thrombus burden and mortality in STEMI patients who undergo successful primary PCI [10–13]. A higher MHR can predict CIN development after primary PCI in STEMI patients [24], and it is also a powerful predictor of in-stent restenosis in patients with stable or unstable angina pectoris who have undergone successful bare metal stent implantation [25]. Sercelik et al. [26] found that the MHR was significantly higher in the STEMI group than in the control group and in the high TIMI score group than in the low TIMI score group.

In the past two decades, China’s rapid economic growth has led to dramatic changes in people’s lifestyles, transportation patterns and eating habits. The number of deaths due to CAD has doubled, reaching 1 million per year [27]. Although the development of CAD is unclear, its etiology and interaction with lifestyle are undoubtedly complex. Inflammation and lipid accumulation are two basic hallmarks of CAD [28]. A high monocyte count and low HDL-C levels may be relevant to inflammation and oxidative stress [29–33]. It has been reported that the MHR is a new prognostic marker for several CVDs [32,34].

In the present study, we included 5679 CAD patients to investigate the relationship between the MHR and outcomes and found that the MHR is an independent predictor after adjustment for confounders. Therefore, the MHR can be used as a valuable and low-cost predictor of clinical outcomes in CAD patients who have undergone PCI. Furthermore, this predictor is worthy of application in clinical practice. The large sample size is a strength of our study. However, there were also several limitations that should be mentioned. We only collected baseline data for monocytes and HDL-C in the present study. The single retrospective cohort design is another limitation.

Conclusion

In conclusion, the present study suggests that the baseline MHR is a simple, inexpensive and independent predictor of mortality in CAD patients who have undergone PCI. An elevated MHR was associated with all-cause mortality and cardiac mortality in CAD patients.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China [grant numbers U1603381 and 81760043]; the China Postdoctoral Science Foundation [grant number 2019M652593]; and the Henan Postdoctoral Science Foundation [grant number 1902006].

Author Contribution

D.P.Z. and Y.Y.Z. made substantial contributions to the study conception and design and to the drafting and critical revision of the manuscript for important intellectual content. G.B., T.T.W., Y.C., and X.G.H. made substantial contributions to the study conception and design and provided critical revision of the manuscript for important intellectual content. Y.Y., Y.P., and X.M. participated in the collection and analysis of the data and the writing and reviewing of the manuscript. YYZ made substantial contributions to the study conception and design and the drafting and critical revision of the manuscript for important intellectual content, while providing study supervision. All authors read and approved the final manuscript.

Ethics Approval

This study protocol was approved by the ethics committee of the First Affiliated Hospital of Xinjiang Medical University and was in line with the Declaration of Helsinki. Due to the retrospective design of the study, the need to obtain informed consent from eligible patients was waived by the ethics committee.

Acknowledgements

The authors are grateful to the Department of Cardiology at the First Affiliated Hospital of Xinjiang Medical University for their help and expertise in conducting this study.

Abbreviations

     
  • CAD

    coronary artery disease

  •  
  • HDL

    high-density lipoprotein

  •  
  • LDL

    low-density lipoprotein

  •  
  • MACE

    major cardiovascular adverse event

  •  
  • MHR

    monocyte-to-high-density lipoprotein–cholesterol ratio

  •  
  • PCI

    percutaneous coronary intervention

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Author notes

*

These authors have contributed equally to this work.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data