Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis

Abstract

Objective

Inflammatory biomarkers are novel tools to assess the prognosis of different cardiovascular diseases. We evaluated the impact of the monocyte-to-lymphocyte ratio (MLR) on clinical outcomes in patients with coronary heart disease (CHD).

Methods

We systematically screened English-language articles in PubMed, Scopus, and Web of Science to 31 August 2022. Relevant articles reporting the MLR and its association with clinical outcomes (major adverse cardiovascular events (MACE), coronary artery disease (CAD) severity, mortality, cardiac rupture, subclinical CAD, acute coronary syndrome (ACS) prediction, thin-cap fibroatheroma, no-reflow phenomenon, MLR-related differences in percutaneous coronary intervention, heart failure hospitalization, and depression) in patients with CHD were collected for further analysis.

Results

Nineteen articles were selected. The mean MLR was 0.34. A higher MLR was significantly associated with an increased risk of MACE among patients with CHD. The MLR was an independent predictor of MACE in patients with ACS. No significant association was found for CAD severity. A complementary analysis was not performed because of few studies focusing on the other predefined endpoints.

Conclusions

The MLR is a simple and widely available tool to predict MACE in patients with CHD. This biomarker can be utilized in emergency settings to prioritize high-risk patients and optimize therapeutic interventions.

Keywords

Monocyte-to-lymphocyte ratio coronary disease coronary artery disease acute coronary syndrome systematic review meta-analysis

Introduction

Coronary artery disease (CAD) is one of the major causes of morbidity and mortality worldwide.¹ This disorder is the leading cause of death in middle- and high-income nations.² CAD is reportedly the foremost cause of disability-adjusted life years and mortality around the globe, with a higher burden among individuals residing in low- and middle-income countries. Previous studies showed that 129 million disability-adjusted life years and 7 million deaths occurred each year in these nations.^3–6 Although the term CAD might be used interchangeably with the term coronary heart disease (CHD), the latter is the result of CAD.⁷ The most severe and common CAD subcategory is acute coronary syndrome (ACS), defined as the presence of unstable angina, ST segment elevation myocardial infarction (STEMI), and non-ST segment elevation myocardial infarction (NSTEMI).^7,8

Atherosclerosis of the coronary arteries is the underlying mechanism of CAD, and the interplay between atherosclerosis and inflammation has been investigated in the literature.⁹ The inflammatory response plays a significant role in both the initiation and progression of atherosclerotic coronary plaques.^10,11 The main circulating inflammatory cells are neutrophils, monocytes, lymphocytes, and platelets. Previous studies have shown that increased numbers of neutrophils and monocytes and decreased numbers of lymphocytes promote atherosclerosis.^12–14 Additionally, monocytes play more significant roles in atherosclerotic disease than neutrophils.^15,16 Monocytes transmigrate to cardiac tissue, differentiate into macrophages, and activate inflammatory cytokine secretion. Monocytes are thus pivotal cells in atherosclerosis, and assessment of these inflammatory cells might be helpful for prognostic evaluation. Several inflammatory markers have been introduced for different cardiovascular diseases.^17–23 One of these recently introduced markers is the monocyte-to-lymphocyte ratio (MLR), defined as the monocyte count divided by the lymphocyte count. This ratio has been investigated in several cardiovascular diseases.^24–27 However, the role of the MLR in patients with CHD remains controversial.

In this systematic review and meta-analysis, we evaluated all relevant records regarding the impact of the MLR on clinical outcomes among patients with CHD.

Materials and methods

Protocol registration

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used in the current study.²⁸ Moreover, we registered this systematic review and meta-analysis in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022357282).

Inclusion and exclusion criteria

The literature was screened for any relevant studies published in English (cross-sectional studies, case-control studies, cohort studies, and randomized clinical trials) that assessed the potential effect of the MLR on clinical outcomes of patients with CHD. The following study designs were excluded: meeting abstracts, editorials, case reports, case series, animal studies, and any records without complete desired data.

Literature review strategy

The medical databases of PubMed (title, abstract), Scopus, and Web of Science (title, abstract, and keywords) were screened up to 31 August 2022 using the following keyword search strategy: (“monocyte* to lymphocyte* ratio” OR “monocyte *-lymphocyte*” OR “monocyte *-lymphocyte* ratio” OR “monocyte * to lymphocyte*” OR “monocyte *-to-lymphocyte* ratio” OR “monocyte *-to lymphocyte* ratio” OR “monocyte * to-lymphocyte* ratio” OR “monocyte */lymphocyte* ratio” OR “monocyte */lymphocyte*”) AND (“cardiovascular disease*” OR “myocardial infarction*” OR “cardiac infarction” OR “coronary artery disease*” OR “acute coronary syndrome*” OR “unstable angina” OR “ST segment elevation myocardial infarction*” OR “ST-segment elevation myocardial infarction*” OR “non ST segment elevation myocardial infarction*” OR “non ST-segment elevation myocardial infarction*” OR “stable angina” OR “stable cardiac disease*” OR “stable heart disease*”).

Study selection process

In the aforementioned databases, the titles and abstracts were screened by two authors independently. If relevant records were found, the full texts were collected for further assessment. In cases of duplication, only a single record was considered, and any disagreement was resolved by consensus. Figure 1 shows the flow diagram of the current study.

Figure 1.

Flow diagram of current review.

Data collection

The following items were collected from each record: first author’s name and year of publication, study design, total sample size and subgroups (if applicable), frequency of male patients (percentage), age (mean ± standard deviation (SD) or median (interquartile range (IQR)), as reported), follow-up duration (if applicable), MLR (mean ± SD, median (IQR), cut-off, or tertiles, as reported), and clinical outcomes (major adverse cardiovascular events (MACE), severity of CAD, all-cause mortality, cardiac rupture (CR), subclinical CAD, ACS prediction, thin-cap fibroatheroma (TCFA), no-reflow phenomenon, MLR-related differences in percutaneous coronary intervention (PCI), heart failure (HF) hospitalization, and depression, as reported).

Risk of bias assessment

The quality of cross-sectional studies was evaluated using the Appraisal Tool for Cross-Sectional Studies (AXIS).²⁹ The National Institutes of Health Quality Assessment Tool was used for case-control studies.³⁰ The Joanna Briggs Institute critical appraisal checklists for cohort studies and randomized clinical trials were utilized to evaluate the quality of cohort studies and randomized clinical trials, respectively.^31,32

Statistical analysis

The pooled effect size is presented as mean, hazard ratio (HR), and odds ratio (OR) with their specific 95% confidence interval (CI), as appropriate. The method established by Wan et al.³³ was used to convert median (IQR) to mean ± SD. Cochran’s Q statistic, I², and tau squared (τ²) were utilized for heterogeneity evaluation. Random-effects or fixed-effects models were used if I² ≥ 25% or I² < 25%, respectively. Forest plots were created to show the mean MLR, and the HR of MACE and OR of CAD severity were assessed according to the multivariate adjusted models reported in each study, as appropriate. Publication bias was examined using funnel plots as well as Begg’s and Egger’s tests. Data entry was performed with an Excel datasheet, and all analyses were performed using Comprehensive Meta-Analysis (CMA) Version 2.0 software.

Results

After full screening of the aforementioned databases and removal of duplicate and non-relevant records from a total of 203 studies, 19 articles that reported the effects of the MLR on CHD were finally selected (Figure 1).^34–52 With the exception of one article,³⁹ all reported the participants’ ages (mean ± SD or median (IQR)). The mean age of all participants was 62.07 ± 11.19 years, and 64.09% of the population was male. One study was a cohort study, and another was a case-control study.^38,47 In all articles, the MLR was calculated as the monocyte count divided by the lymphocyte count.^34–52 We used a random-effects model because I² ≥ 25%. A summary of all included studies is shown in Table 1. The details of the risk-of-bias assessment based on the study designs are provided in the supplementary appendix (Tables S1, S2, and S3). Three articles did not report the MLR values (either as mean ± SD or median (IQR)).^36,39,47 Figure 2 shows a forest plot of the mean MLR in patients with CHD (0.34; 95% CI, 0.30–0.38). The heterogeneity indices were as follows: Q = 3444.436, I² = 99.56%, τ² = 0.006, and P < 0.001. In terms of publication bias, the P-values of Begg’s and Egger’s tests were 0.172 and 0.015, respectively (the funnel plot is shown in Figure S1).

Table 1.

Summary of included studies reporting MLR and clinical outcomes.

Reference	Design	Sample size		Male (%)	Age (years)	Follow-up duration	MLR	Outcomes
Li et al. 2022³⁴	Cross-sectional	Total	1701	1305 (76.72)	Mean ± SD: 60 ± 10	Median (IQR): 30 (30–36) months	Mean ± SD: 0.2 ± 0.07Median (IQR): 0.2 (0.16–0.26) MLR cut-off: 0.24	MACE
		Negative MACE	1594	1227 (76.98)	Mean ± SD: 59 ± 10		Mean ± SD: 0.2 ± 0.08Median (IQR): 0.2 (0.15–0.26)
		Positive MACE	107	78 (72.90)	Mean ± SD: 64 ± 12		Mean ± SD: 0.25 ± 0.09Median (IQR): 0.25 (0.19–0.32)
Dai et al. 2022³⁵	Cross-sectional	Total	180	109 (60.56)	Mean ± SD: 70.52 ± 10.75	NA	Mean ± SD: 0.64 ± 0.6MLR cut-off: 0.61	Cardiac rupture
		Negative cardiac rupture	90	55 (61.11)	Mean ± SD: 71.03 ± 11.42		Mean ± SD: 0.41 ± 0.24Median (IQR): 0.41 (0.25–0.58)
		Positive cardiac rupture	90	54 (60.00)	Mean ± SD: 70.01 ± 10.09		Mean ± SD: 0.88 ± 0.76Median (IQR): 0.75 (0.44–1.45)
Song et al. 2021³⁶	Cross-sectional	Total	3461	2383 (68.85)	Mean ± SD: 63.24 ± 10.6	Mean ± SD: 37.59 ± 22.24 months	MLR cut-off: 0.34	MACE
Si et al. 2021³⁷	Cross-sectional	Total	335	204 (60.90)	Mean ± SD: 63.33 ± 6.7Median (IQR): 63 (59–68)	NA	Mean ± SD: 0.25 ± 0.11Median (IQR): 0.25 (0.18–0.33) MLR cut-off: 0.142	Subclinical CAD
Shumilah et al. 2021³⁸	Case-control	Total	100	60 (60.00)	Mean ± SD: 55.5 ± 15	NA	Mean ± SD: 0.61 ± 0.29MLR cut-off: 0.375	ACS presence
Gao et al. 2021³⁹	Cross-sectional	Total	1558	1166 (74.84)	NR	Median: 1142 days	MLR cut-off: 0.33 and 0.43	MACE
Dai et al. 2022⁴⁰	Cross-sectional	Total	304	215 (70.72)	Mean ± SD: 65.09 ± 12.46	NA	Mean ± SD: 0.47 ± 0.39MLR cut-off: 0.64	Cardiac rupture
		Negative cardiac rupture	228	167 (73.25)	Mean ± SD: 63.03 ± 12.65		Mean ± SD: 0.36 ± 0.21Median (IQR): 0.34 (0.23–0.52)
		Positive cardiac rupture	76	48 (63.16)	Mean ± SD: 71.3 ± 9.58		Mean ± SD: 0.81 ± 0.58Median (IQR): 0.76 (0.45–1.22)
Candemir et al. 2021⁴¹	Cross-sectional	Total	669	395 (59.04)	Mean ± SD: 62.21 ± 10.86	NA	Mean ± SD: 0.28 ± 0.13	Severity of CAD
Zhang et al. 2020⁴²	Cross-sectional	Total	72	56 (77.78)	Mean ± SD: 55.65 ± 12.08	NA	Mean ± SD: 0.21 ± 0.1MLR cut-off: 0.25	TCFA
Tanriverdi et al. 2020⁴³	Cross-sectional	Total	421	215 (51.07)	Mean ± SD: 56.3 ± 10.2	NA	Mean ± SD: 0.22 ± 0.06	Severity of CAD
Chen et al. 2020⁴⁴	Cross-sectional	Total	1009	608 (60.26)	Mean ± SD: 56.22 ± 9.45	90 days	Mean ± SD: 0.64 ± 0.37	All-cause mortalityNo-reflow phenomenon
Horváth et al. 2019⁴⁵	Cross-sectional	Total	21	18 (85.71)	Mean ± SD: 62.9 ± 10.6	NA	Mean ± SD: 0.33 ± 0.18	MLR-related difference in PCI
Chen et al. 2019⁴⁶	Cross-sectional	Total	963	758 (78.71)	Mean ± SD: 60.77 ± 11.34	Median (IQR): 22 (12–35) months	Mean ± SD: 0.2 ± 0.14Median (IQR): 0.28 (0.21–0.40) MLR tertiles: T1: <0.23T2: 0.23–0.35T3: >0.35MLR cut-off: 0.43 and 0.31	MACE
Fan et al. 2018⁴⁷	Retrospective cohort	Total	678	435 (64.16)	Mean ± SD: 62.81 ± 15.07	Median (range): 26 (1–30) months	MLR cut-off: 0.36	MACE
Ji et al. 2017⁴⁸	Cross-sectional	Total	381	211 (55.38)	Mean ± SD: 62.79 ± 9.52	NA	Mean ± SD: 0.23 ± 0.09Median (IQR): 0.23 (0.17–0.30) MLR cut-off: 0.18 and 0.25	CAD detectionSeverity of CAD
Gijsberts et al. 2017⁴⁹	Cross-sectional	Total	1754	478 (27.25)	Mean ± SD: 63.97 ± 10.76	484 days	Mean ± SD: 0.33 ± 0.14Median (IQR): 0.32 (0.24–0.43)	HF hospitalization
Fan et al. 2017⁵⁰	Cross-sectional	Total	133	96 (72.18)	Mean ± SD: 62 ± 12	NA	Mean ± SD: 0.28 ± 0.38MLR cut-off: 0.25	TCFA
Serfőző et al. 2016⁵¹	Cross-sectional	Total	23	18 (78.26)	Mean ± SD: 62.9 ± 10.6	NA	Mean ± SD: 0.32 ± 0.14	Depression
Gijsberts et al. 2016⁵²	Cross-sectional	Total	1015	742 (73.10)	Mean ± SD: 65.11 ± 11.32	805 days	Mean ± SD: 0.33 ± 0.14	MACEAll-cause death
		Survived	950	693 (72.95)	Mean ± SD: 64.6 ± 11.2		Mean ± SD: 0.33 ± 0.13Median (IQR): 0.32 (0.25–0.43)
		Death	65	49 (75.38)	Mean ± SD: 72.7 ± 10.4		Mean ± SD: 0.47 ± 0.25Median (IQR): 0.44 (0.32–0.65)

ACS: acute coronary syndrome, CAD: coronary artery disease, HF: heart failure, IQR: interquartile range, MACE: major adverse cardiovascular events, MLR: monocyte-to-lymphocyte ratio, NA: not applicable, NR: not reported, PCI: percutaneous coronary intervention, SD: standard deviation, T: tertile, TCFA: thin-cap fibrous atheroma

Figure 2.

Forest plot for mean MLR based on studies that reported this marker.

MLR and MACE

Six studies reported MACE as the clinical endpoint among patients with CHD.^{34,36,39,46,47,52} MACE was defined as the following in each study: Li et al.³⁴: composite of all-cause death, non-fatal ischemic stroke, and non-fatal myocardial infarction (MI); Song et al.³⁶: combination of cardiovascular death, recurrent MI, stroke, and target vessel reconstruction; Gao et al.³⁹: all-cause mortality, cardiac mortality (death due to HF, MI, fatal arrhythmia, or other heart-related conditions), and readmission for severe HF; Chen et al.⁴⁶: in-hospital MACE (cardiac arrest, cardiac death, cardiogenic shock or syncope, CR, malignant arrhythmia, and acute congestive HF) and long-term MACE (cardiac arrest, malignant arrhythmia, MI, unstable angina, cardiogenic shock or syncope, cardiac death, readmission for coronary revascularization, or HF decompensation); Fan et al.⁴⁷: composite of all-cause death, stroke, non-fatal MI, cardiac death, and target vessel or target lesion revascularization; and Gijsberts et al.⁵²: cardiovascular death, stroke, non-fatal MI, and unplanned coronary revascularization. Among all 9376 patients, MACE occurred in 1150 (12.26%). The mean MLR in three articles (n = 3679; male patients, 76.24%) that reported this endpoint was 0.24 (95% CI, 0.17–0.32) (data not shown).^34,46,52

Five articles reported the HR of the MACE incidence among patients with CHD (including both CAD and ACS).^{34,36,39,46,47} Further analysis revealed that a high MLR was associated with a 1.87-times increased risk of MACE (95% CI, 1.30–2.68; P = 0.001) (Figure 3, heterogeneity indices: Q = 17.338, I² = 76.93%, τ² = 0.123, P = 0.002, Begg’s test P-value = 0.231, Egger’s test P-value = 0.033) (funnel plot: Figure S2).

Figure 3.

Forest plot for hazard ratio of MACE in coronary heart disease (coronary artery disease and acute coronary syndrome) based on MLR.

For a more uniform description of MACE, we selected studies that reported at least death (either cardiovascular or all-cause mortality), MI, and stroke. This complementary analysis revealed a similar outcome in that a high MLR was associated with a significantly increased risk of MACE among patients with CHD (HR = 1.62, 95% CI = 1.12–2.34, P = 0.010) (Figure 4). The heterogeneity indices were low (Q = 7.613, I² = 73.73%, τ² = 0.077, P = 0.022), and neither the funnel plot (Figure S3) nor Begg’s (P = 0.500) or Egger’s test (P = 0.106) was in favor of publication bias.

Figure 4.

Forest plot for hazard ratio of MACE (uniform definition) in coronary heart disease (coronary artery disease and acute coronary syndrome) based on MLR.

As shown in Figure 5, a higher MLR was significantly associated with an increased occurrence of MACE among patients with ACS (HR = 2.14, 95% CI = 1.59–2.87, P < 0.001). Heterogeneity was low among the included studies (Q = 4.920, I² = 39.02%, τ² = 0.035, P = 0.178). The funnel plot showed no publication bias among the studies (Figure S4, Begg’s test P-value = 0.500, Egger’s test P-value = 0.335).

Figure 5.

Forest plot for hazard ratio of MACE in acute coronary syndrome based on MLR.

MLR and severity of CAD

Three studies involving 1471 patients assessed the effect of the MLR on CAD severity.^41,43,48 Figure 6 shows the forest plot of the MLR odds on CAD severity. Although the MLR was associated with the severity of CAD, this relationship was not statistically significant (OR = 1.25, 95% CI = 0.95–1.65, P = 0.115). The heterogeneity data were as follows: Q = 27.577, I² = 92.74%, τ² = 0.046, P < 0.001. The funnel plot, as shown in Figure S5, as well as Begg’s test (P = 0.500) and Egger’s test (P = 0.101), were in favor of no publication bias.

Figure 6.

Forest plot for MLR odds ratio of coronary artery disease severity.

MLR and mortality

Three studies investigated the association of the MLR with death in patients with CHD.^36,44,52 Song et al.³⁶ enrolled 3461 patients with CAD and categorized them into two groups based on the reported MLR cut-off point (MLR < 0.34, n = 2338 and MLR ≥ 0.34, n = 1123). CAD was defined as ≥1 coronary artery stenosis (≥50%) confirmed through coronary angiography with ≥1 implemented stent. After follow-up for 37.59 ± 22.24 months, the rates of both all-cause and cardiac death were significantly higher in patients with an MLR of ≥0.34 than in those with an MLR of <0.34 (6.8% vs. 2.3%, P < 0.001 and 4.3% vs. 1.4%, P < 0.001, respectively). The multivariate adjusted hazard models indicated that a high MLR was associated with a 1.766-times higher (95% CI = 1.336–3.650, P = 0.001) and 2.379-times higher (95% CI = 1.611–3.511, P < 0.001) rate of all-cause and cardiovascular mortality, respectively.³⁶ In another study, 1009 patients with STEMI were followed for 90 days, and 83 (8.23%) deaths were observed. Further analysis revealed that a high MLR significantly increased the 90-day mortality risk in both the univariate and multivariate models (HR = 1.423, 95% CI = 1.183–1.702, P < 0.001 and HR = 1.313, 95% CI = 1.150–1.586, P = 0.012, respectively).⁴⁴ Gijsberts et al.⁵² followed 1015 patients with suspected CAD for 805 days and observed 65 (6.40%) death events. The MLR was significantly associated with all-cause and cardiovascular mortality (HR = 1.35, 95% CI = 1.14–1.59, P < 0.001 and HR = 1.42, 95% CI = 1.11–1.81, P = 0.005, respectively).⁵²

MLR and CR

Dai et al.³⁵ investigated the predictive role of the MLR on CR among patients with acute MI. CR was defined as signs and symptoms of pericardial tamponade, electromechanical separation, large pericardial effusion, and pericardial puncture with production of non-coagulable fluids. After propensity score matching, 180 patients were selected and divided into two groups of equal numbers. The MLR differed significantly between patients with and without CR (0.75 (0.44–1.45) vs. 0.41 (0.25–0.58), respectively; P < 0.001). The authors defined 0.61 as the optimal cut-off point for CR prediction. The complementary analysis indicated that the MLR was an independent risk factor for CR (univariate OR = 6.64, 95% CI = 2.69–16.43, P < 0.001 and multivariate OR = 3.57, 95% CI = 1.28–9.97, P = 0.015).³⁵

Another study involved 76 patients who had MI with concurrent CR and 228 patients who had MI without CR.⁴⁰ The MLR was higher in patients with than without CR (0.76 (0.45–1.22) vs. 0.34 (0.23–0.52), P < 0.001). An MLR of 0.64 was defined as the optimal cut-off point for prediction of CR. A high MLR was found to increase the likelihood of CR in patients with MI (univariate OR = 9.81, 95% CI = 4.11–23.35 and multivariate OR = 5.99, 95% CI = 2.09–17.16, P = 0.001).⁴⁰

MLR and subclinical CAD

Si et al.³⁷ investigated the ability of the MLR to predict subclinical CAD in 335 patients with CAD and 362 controls. Subclinical CAD was defined as a coronary artery calcium score of ≥100 Agatston units and/or coronary stenosis of ≥50%. The total MLR (median (IQR)) in the study population was 0.23 (0.17–0.31), and patients with CAD had significantly higher MLR values than controls (0.25 (0.18–0.33) vs. 0.21 (0.16–0.29), P < 0.001). Further analysis revealed that an MLR of 0.142 was the optimal cut-off point for predicting subclinical CAD. Moreover, the multivariable adjusted regression model indicated that the MLR was an independent predictor of subclinical CAD (OR = 1.693, 95% CI = 1.032–2.775, P = 0.037).³⁷

MLR and ACS prediction

A case-control study of 100 patients with ACS and 100 controls with no significant differences in age or sex was performed to investigate the ability of the MLR to predict the incidence of ACS. The mean MLR was significantly different between the groups (ACS group: 0.61 ± 0.29 vs. control group: 0.23 ± 0.10, P < 0.001). The optimal MLR cut-off was determined to be 0.375. However, the complementary analysis failed to prove any independency of the MLR to predict ACS (OR = 4.12, P = 0.597).³⁸

MLR and TCFA

Two studies assessed the impact of the MLR on TCFA in patients with CHD.^42,50 Zhang et al.⁴² enrolled 72 patients with ACS and divided them into lower and higher MLR groups according to the median value (0.21). The median (IQR) of the MLR was 0.26 (0.23–0.36) and 0.15 (0.11–0.18) in the high and low groups, respectively (P < 0.001). Fibrous cap thickness was defined as the thickness of the fibrous layer covering a lipid plaque, with a criterion of <65 µm, and a lipid core involving more than two quadrants. It was measured by optical coherence tomography. Lower thickness was found among patients with higher MLR values (112.37 ± 60.24 vs. 153.49 ± 73.29 µm, P = 0.013). The authors defined 0.25 as the optimal cut-off for the presence of TCFA, and further analyses were in favor of MLR independency for predicting TCFA in patients with ACS (multivariate OR = 3.316, 95% CI = 1.448–7.593, P = 0.005).⁴²

Another study of 133 patients with stable angina (age, 62 ± 12 years; male patients, 72.18%) indicated that the MLR was higher among those with TCFA (0.36 ± 0.12 vs. 0.22 ± 0.5, P < 0.001).⁵⁰ The authors defined TCFA as a necrotic core of >10% in at least three consecutive frames with no evidence of overlying fibrous tissue in the presence of a ≥40% plaque burden. The optimal cut-off value was proposed as 0.25, and they suggested that the MLR could independently identify TCFA among patients with stable angina (OR = 2.61, 95% CI = 1.13–6.04, P = 0.025).⁵⁰

MLR and no-reflow phenomenon

Chen et al.⁴⁴ investigated the association of the MLR with the no-reflow phenomenon in patients with STEMI. In total, 1009 patients underwent PCI. A no-reflow event was defined as no effective myocardial tissue perfusion (thrombolysis in MI grade of <3) after coronary recanalization with no evidence of spasm, residual stenosis, or dissection. The mean MLR in the no-reflow group (n = 262) was higher than that in the normal flow group (n = 747) (0.56 ± 0.48 vs. 0.43 ± 0.33, P < 0.001). Logistic regression analysis showed independency of the MLR for predicting the no-reflow phenomenon (univariable OR = 1.432, 95% CI = 1.221–1.632, P < 0.001 and multivariable OR = 1.335, 95% CI = 1.176–1.558, P = 0.018).⁴⁴

MLR differences in PCI

Horváth et al.⁴⁵ investigated the MLR alterations before PCI, immediately after PCI, and 1 day after PCI. Twenty-three patients with stable CHD (age, 62.9 ± 10.6 years) were selected. The mean MLR before the PCI procedure was 0.33 ± 0.18. The MLR was also assessed immediately after completion of PCI (0.33 ± 0.17), and no significant difference was found between these two time points. However, the mean MLR was significantly higher 1 day after PCI (0.42 ± 0.20) than before PCI (P < 0.01). The authors proposed that stenting might be associated with coronary wall injury, leading to an increased monocyte count.⁴⁵

MLR and HF hospitalization

The association of the MLR with HF admission among patients with CAD was also investigated.⁴⁹ Among 1754 patients who underwent coronary angiography for unstable angina, MI, or stable CAD (defined as chest pain or dyspnea upon exertion and >50% stenosis in at least one coronary artery), the MLR was calculated before the procedure and showed a median (IQR) of 0.32 (0.24–0.43). Recruited patients were followed for a mean of 1.3 years, and 46 HF admissions were reported. The authors defined an MLR value in the upper quartile (0.43) as the cut-off and divided the patients into high and low groups accordingly. Multivariate adjusted Cox regression analysis showed that a high MLR was significantly associated with an increased risk of HF hospitalization (HR = 2.1, 95% CI = 1.1–4.1, P = 0.039).⁴⁹

MLR and depression

The association of the MLR with depression in patients with CHD was examined by Serfőző et al.⁵¹ They enrolled 23 patients (age, 62.9 ± 10.6 years) with stable CAD, defined as >50% stenosis of at least two major coronary arteries, and assessed depression using the Beck Depression Inventory. The mean MLR in the study population was 0.32 ± 0.14. The multivariate correlation analysis was in favor of a significant correlation between the MLR and the depression questionnaire responses (r = 0.624, P = 0.004). The authors suggested that the MLR was associated with depressive symptoms among patients with CAD.⁵¹

MLR cut-off points

Twelve articles reported specific MLR cut-off values for their endpoints in patients with CHD.^{34–40,42,46–48,50} In terms of MACE, five studies indicated different cut-off points of 0.24, 0.34, 0.33, 0.43, 0.31, and 0.36.^{34,36,39,46,47} Two different cut-off points (0.61 and 0.64) were reported to predict CR.^35,40 Si et al.³⁷ and Shumilah et al.³⁸ defined cut-off points of 0.142 and 0.375 for prediction of subclinical CAD and the presence of ACS, respectively. Two studies revealed a cut-off point of 0.25 for detection of TCFA.^42,50 The best cut-off for prediction of CAD severity was also 0.25. Moreover, the cut-off value to determine CAD was 0.18.⁴⁸ Detailed information regarding each cut-off value according to each study endpoint is shown in Table 2.

Table 2.

Summary of MLR cut-off characteristics.

Reference	Outcome	MLR cut-off	Sensitivity (%)	Specificity (%)	Area under curve	95% confidence interval	P-value
Li et al. 2022³⁴	MACE	0.24	59.1	NR	NR	NR	NR
Dai et al. 2022³⁵	Cardiac rupture	0.61	63	80	0.74	0.65–0.83	NR
Song et al. 2021³⁶	MACE	0.34	NR	NR	NR	NR	NR
Si et al. 2021³⁷	Subclinical CAD	0.142	90.1	18.2	0.582	0.540–0.624	<0.001
Shumilah et al. 2021³⁸	ACS presence	0.375	79	91	0.896	NR	<0.001
Gao et al. 2021³⁹	MACE 1	0.33	52.5	66.1	0.588	0.508–0.668	NR
Gao et al. 2021³⁹	MACE 2	0.43	44.4	81.3	0.627	0.521–0.734	NR
Dai et al. 2022⁴⁰	Cardiac rupture	0.64	62.2	86.4	0.811	0.734–0.889	<0.001
Zhang et al. 2020⁴²	TCFA	0.25	60	85.1	0.741	0.619–0.864	0.001
Chen et al. 2019⁴⁶	In-hospital MACE	0.43	63.2	80.8	0.728	0.604–0.852	0.001
Chen et al. 2019⁴⁶	Long-term MACE	0.31	57.4	61.5	0.609	0.556–0.661	<0.001
Fan et al. 2018⁴⁷	MACE	0.36	54.74	73.57	0.683	0.647–0.718	0.022
Ji et al. 2017⁴⁸	CAD detection	0.18	69.03	64.81	0.727	0.683–0.771	NR
Ji et al. 2017⁴⁸	Severity of CAD	0.25	60.26	78.49	0.761	0.702–0.820	<0.001
Fan et al. 2017⁵⁰	TCFA	0.25	73.7	61.8	0.759	0.676–0.842	<0.001

ACS: acute coronary syndrome, CAD: coronary artery disease, MACE: major adverse cardiovascular events, MLR: monocyte-to-lymphocyte ratio, NR: not reported, TCFA: thin-cap fibrous atheroma.

MLR tertiles

One article reported specific MLR tertiles. Chen et al.⁴⁶ selected 963 patients with NSTEMI to assess MACE according to MLR tertiles. The median (IQR) MLR was 0.28 (0.21–0.40), and the authors categorized the patients into low, intermediate, and high groups based on the MLR tertiles (T1: <0.23 (n = 321), T2: 0.23–0.35 (n = 322), and T3: >0.35 (n = 320). They found that the MLR differed significantly between the groups (P < 0.001). Twenty-seven patients developed MACE during admission (acute HF: n = 10, cardiogenic shock: n = 10, malignant arrhythmia: n = 3, cardiac arrest: n = 2, and CR: n = 2). The rate of in-hospital MACE was remarkably higher among patients in the highest tertile than in the other tertiles (4.7% vs. 2.2% vs. 1.6%, P = 0.016). After a median follow-up of 22 months, 176 long-term MACE occurred with a similar trend (high group: 27.2%, intermediate group: 16.2%, and low group: 13.3%; P< 0.001).⁴⁶

Discussion

The main aim of the current study was to assess the potential effects of the MLR on patients with CHD. The mean MLR in all studies that reported this ratio was 0.34 (95% CI = 0.30–0.38). We also found that the MLR affected the prognosis in that a higher MLR was associated with a 1.87-times higher risk of MACE (95% CI = 1.30–2.68, P = 0.001) in patients with CHD, and this association remained significant even after selection of studies with quite similar definitions of MACE. This impact was more robust for ACS (HR = 2.14, 95% CI = 1.59–2.87, P < 0.001). This widely available tool is easily accessible in healthcare settings and can be used to evaluate the prognosis of patients with CHD.

Atherosclerotic plaque formation is a multifactorial process involving intimal inflammation, fibrosis, calcification, and angiogenesis. Inflammation is a major player in this regard, influencing the entire process from the development of atherosclerosis to its final life-threatening outcome (i.e., plaque instability and rupture).⁵³ The major inflammatory cytokines, including C-reactive protein, tumor necrosis factor-α, and interleukin-1, are reportedly elevated in patients with atherosclerotic heart disease.^54,55 Two main subgroups of interplaying inflammatory cells in patients with CHD are monocytes and lymphocytes. The former has been reported to be the main component of the innate immune system.⁵⁶ Circulating monocytes and resident vascular macrophages are the first leukocytes to be recruited to an atheromatous plaque.⁵⁷ In response to various inflammatory cytokines, these cells transmigrate from the circulation to the involved tissue and subsequently differentiate into macrophages, foam cells, and dendritic cells and activate the secretion of matrix metalloproteinases, reactive oxidative substances, and several proinflammatory cytokines. These factors are responsible for atheromatous plaque formation as well as plaque degradation and rupture.^15,16 Furthermore, monocytes are capable of producing higher cytokine levels than neutrophils, and both monocytes and monocyte-derived macrophages contribute more to atherosclerotic plaque initiation and progression.^16,58 Interestingly, MI itself can induce the bone marrow to release hematopoietic stem cells and increase the monocyte frequency and availability.⁵⁹

Lymphocytes are major cells responsible for inflammatory response regulation. These cells also play important roles in atherosclerosis development.⁶⁰ Under conditions of stress-induced increased cortisol and catecholamine secretion, as in patients with CHD, lymphocyte apoptosis increases, resulting in lymphocytopenia.^61,62 Low lymphocyte counts have been related to poorer outcomes in patients with CAD and ACS.^63,64 Moreover, lymphopenia is an index of impaired coronary microcirculation and is considered a pathogenic mechanism for CAD.^65,66 Thus, the composite of high monocytes and low lymphocytes into a single inflammatory index (the MLR) could provide more prognostic information than each parameter alone.⁴⁶

The MLR is frequently used to assess the prognosis of non-cardiovascular diseases including tuberculosis, lung and laryngeal squamous cell cancer, and rheumatoid arthritis.^67–70 This inflammatory marker has been less frequently investigated in cardiac disorders. Zhai et al.⁷¹ investigated the prognostic utility of the MLR in 5512 patients admitted to cardiac intensive care units. Their final analysis showed that higher MLR values were associated with increased in-hospital mortality (MLR Q4 vs. MLR Q1: OR = 1.87, 95% CI = 1.38–2.56, P < 0.001). They also found that patients within the last MLR quartile had remarkably longer intensive care unit and hospital stays (median (IQR)) than patients within the first quartile (Q4 vs. Q1: 2.8 (1.7–5.4) vs. 2.1 (1.2–3.7) days, P < 0.001 and 8.3 (4.8–11.1) vs. 5.3 (3.1–9.3) days, P < 0.001, respectively).⁷¹

Although it was not possible to perform a meta-analysis on CR because of the few available records, the enrolled articles suggested a detrimental effect of the MLR on this life-threatening outcome.^35,40 Monocyte activation and accumulation results in initiation of considerable inflammatory responses, leading to infiltration of inflammatory cells to the infarcted area. This process can disrupt and dissolve the cardiac cell membrane and fibrin cytoskeleton.⁷² Additionally, the combination of tumor necrosis factor-α and cell surface receptors aggravates the inflammatory cascade in myocardial cells, causing cardiac collagen fiber degradation and fragmentation and elevating the risk of CR.⁷³

The enrolled articles indicated that the MLR increases the risk of plaque rupture.^42,50 A potential mechanism is the presence of higher monocyte-derived macrophages in the plaque cap.⁷⁴ Lymphocytopenia may also be involved as suggested by the fact that lower lymphocyte counts have been associated with growth of atherosclerotic plaques, development of a lipid core, and finally plaque rupture.⁷⁵

Data regarding an association between the MLR and psychiatric issues in patients with CAD are very limited.⁵¹ However, the association of inflammatory cytokines released by activated monocytes in the context of CHD increases the likelihood of depressive symptoms by interfering with neurotransmitter functions.^76,77

Hematologic indices are widely available in most centers and routinely ordered for patients with CHD. Thus, calculation of the MLR can be rapidly performed at the bedside and used to simply evaluate the prognosis of CHD before use of further expensive and time-consuming risk assessment tools, which might be unavailable in some nations with restricted healthcare resources.

This systematic review and meta-analysis is the first to investigate the potential prognostic impact of the MLR on patients with CHD. We utilized a comprehensive search strategy for collection of all relevant articles with no time restriction. However, the current study is not free from limitations. We only screened records published in English; no non-English studies were evaluated. Because of the presence of few studies on some predefined variables (including CR, subclinical CAD, ACS prediction, TCFA, no-reflow phenomenon, MLR-related differences in PCI, HF hospitalization, and depression), performing a meta-analysis on these variables was not feasible. A meta-analysis of all-cause mortality was also not performed because of inconsistent CIs in the published articles. Additionally, we were unable to define the optimal MLR cut-off point for each endpoint because the studies were heterogeneous and had different sample sizes. Although we assessed the HR of the MLR on MACE, this variable was defined differently among the studies, and dichotomous MLR values were quite variable based on the proposed MLR cut-off values. However, we selected the studies with very similar MACE definitions and performed a further downstream analysis to cover this limitation. Finally, because the main focus of the current review was the MLR, we did not screen studies that investigated the inverse ratio (lymphocyte-to-monocyte ratio).

In conclusion, this study indicates that the MLR is a practical inflammatory marker that can be used in the emergency setting to appropriately categorize high-risk patients with CHD, especially in developing countries with limited healthcare resources. However, complementary studies are imperative to define the optimal MLR cut-off value with which to assess the prognosis of different clinical outcomes.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605231204469 - Supplemental material for Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis

Supplemental material, sj-pdf-1-imr-10.1177_03000605231204469 for Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis by Mehrbod Vakhshoori, Sepehr Nemati, Sadeq Sabouhi, Mehrnaz Shakarami, Behzad Yavari, Sayed Ali Emami, Niloofar Bondariyan and Davood Shafie in Journal of International Medical Research

Footnotes

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available because of confidentiality issues but are available from the corresponding author on reasonable request.

Authors’ contributions

Study concept and design: M.V., N.B., S.S., D.S., S.N., M.S., B.Y., SA.E.

Acquisition of data: N.B, M.V., S.S., SA.E.

Analysis and interpretation of data: M.V., N.B., D.S.

Drafting of the manuscript: N.B., M.V., S.S, SA.E., S.N., B.Y., M.S., D.S.

Critical revision of the manuscript for valuable intellectual content: M.V., N.B., S.S., SA.E., S.N., B.Y., M.S., D.S.

Statistical analysis: M.V.

Administrative, technical, and material support: M.V., D.S., S.S., S.N.

Supervision: D.S., M.V.

Consent for publication

Not applicable.

Declaration of conflicting interest

The authors declare that there is no conflict of interest.

Ethical approval and consent to participate

The requirement for ethics approval was waived because of the nature of this study (review).

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

ORCID iD

Mehrbod Vakhshoori

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