Coronary heart disease (CHD) is the leading cause of mortality and morbidity worldwide [1]. Acute coronary syndrome (ACS) is one of the most severe manifestations of CHD [2]. ACS is characterized by acute myocardial ischemia because of the formation of intracoronary thrombi due to the rupture or erosion of unstable atherosclerotic plaques, and encompasses unstable angina (UA), ST-segment elevation myocardial infarction (STEMI), and non-ST-segment elevation myocardial infarction (NSTEMI) [3, 4]. Despite advances in revascularization techniques such as percutaneous coronary intervention (PCI), the prognosis of patients with ACS remains poor and unsatisfactory [5, 6]. Furthermore, the incidence rates of ACS in China have consistently increased over the past few decades [7]. Therefore, identifying risk factors associated with an adverse prognosis in patients with ACS is of paramount importance to identify patients at higher risk and to delineate personalized therapeutic strategies.

Inflammation is an important feature in all stages of atherosclerosis, including acute thrombotic complications and clinical events [8]. Inflammation is pivotal in the development and instability of the coronary plaques and contributes significantly to plaque rupture. Coronary plaques contain activated macrophages, which promote plaque rupture, arterial wall thrombosis, and vessel constriction [9]. Interleukin (IL)-17 is a pro-inflammatory cytokine that exerts a significant influence on atherosclerosis and ACS [10]. More than half of the patients with atherosclerotic cardiovascular disease are associated with systemic inflammation. The incidences of major adverse cardiovascular events (MACEs), heart failure (HF), and mortality increase significantly when the C-reactive protein (CRP) levels are $\ge$2 mg/L [11]. IL-6 is a pro-inflammatory cytokine primarily secreted by macrophages and T cells. Plasma IL-6 levels show significant prognostic value in patients with ACS [12]. Moreover, plasma IL-6 levels are positively correlated with CRP levels in patients with ACS [13]. In addition to inflammatory factors such as RCP, higher leukocyte counts (e.g., neutrophils, monocytes) in newly diagnosed ACS patients have been shown to significantly correlate with increased mortality [14]. However, the most optimal inflammatory predictor indicator that is both simple and practical remains uncertain. There is an urgent need to characterize novel clinical biomarkers for the routine and accurate estimation of the chronic inflammatory status of patients. Recent studies have shown that, compared to simple blood cell counts, ratios based on blood cells (such as the monocyte-to-lymphocyte ratio (MLR)) demonstrate significantly more reliable performance in predicting MACEs [15]. In a cohort of ACS patients undergoing PCI, multivariate Cox regression analysis demonstrated that MACEs were significantly and independently associated with five hematological inflammatory indices, namely, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), MLR, systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) [16]. In a recent meta-analysis, higher SII levels demonstrated significant independent prognostic value for MACEs and all-cause mortality in patients with ACS [17]. As a novel inflammatory index derived from lymphocyte parameters, the systemic immune-inflammatory response index (SIIRI) has been found to exhibit independent prognostic value for MACEs in newly diagnosed coronary artery disease (CAD) patients [18]. The SIIRI is also an independent predictor of severe CAD [19]. The SIIRI emerged as an independent prognostic predictor of MACEs in newly diagnosed CAD patients after comparative analysis of established inflammatory indices (NLR, PLR, MLR, SII, and SIRI) using multivariable adjusted models [20]. However, compared to MLR and SIIRI, only SIIRI was a predictor of severe CAD [21]. Currently, the clinical values of novel inflammatory biomarkers such as NLR, PLR, MLR, SII, SIRI, and SIIRI are inconclusive for predicting adverse clinical outcomes in newly diagnosed ACS patients. Furthermore, the definitions of MACEs vary widely across observational studies, with only 8.6% of the studies matching the traditional three-point MACE definitions and none of the studies matching the four-point or five-point MACE definitions [22].

In this study, we compared the prognostic values of six different inflammatory markers to identify indicators that can precisely predict MACEs in patients with newly diagnosed ACS. The aim was to identify the best inflammatory indicator that can be used for clinical monitoring and strategizing personalized treatment plans for patients with ACS to improve their prognosis and quality of life.

In this study, we assessed the prognostic value of six novel identified lymphocyte-derived inflammatory indices and multiple traditional clinical characteristics in predicting MACEs in newly diagnosed ACS patients. Among these inflammatory indices based on blood cell analysis, the MLR $\ge$0.304 demonstrated independent associations with MACEs in newly diagnosed ACS patients through multivariate analysis. Notably, the MLR exhibited superior predictive performance compared to traditional clinical characteristics. This indicates its potential clinical value as a prognostic indicator for cardiovascular poor outcomes.

Current pathophysiological theories of coronary artery disease encompass inflammatory cascades, lipid plaque formation, platelet activation, and vascular injury responses [24, 25]. Previous studies have demonstrated that elevated heart rate serves as a critical determinant of adverse clinical outcomes in ACS patients undergoing PCI [26]. Our investigation revealed that both elevated heart rate and systolic blood pressure emerged as significant clinical correlates of MACEs in ACS patients. However, multivariate analysis demonstrated that neither parameter maintained independent predictive value for MACEs occurrence following comprehensive adjustment for established cardiovascular risk factors. The underlying reason might be related to the fact that sustained tachycardia and hypertension can cause damage to arterial endothelial cells. Specifically, at curved or bifurcated arteries, blood flow patterns characterized by oscillatory shear stress promote endothelial transformation into a pro-inflammatory phenotype [27]. This transformation increases cellular inflammation, oxidative stress response, metabolic abnormalities, and endothelial permeability, thereby promoting the occurrence and progression of atherosclerosis [28]. Histopathological analysis of atherosclerotic coronary arteries has revealed that unstable plaques are histologically characterized by infiltration of macrophages, lymphocytes, and mast cells [29]. Notably, activated platelets not only recruit leukocytes but also regulate monocyte migration and subsequent differentiation into macrophages. Atherosclerosis, recognized as a chronic inflammatory disease, progresses through a pathological continuum spanning from endothelial injury, inflammatory cell recruitment, and lipid deposition to eventual plaque rupture. Throughout this disease progression, multiple leukocyte subtypes-including monocytes, neutrophils, and lymphocytes-are actively involved in mediating these pathophysiological transitions [30, 31]. This mechanistic pathway may explain the observed associations between the inflammatory index based on blood cell analysis and adverse outcomes in our cohort study.

Like previous reports [3, 32], older age, hypertension, and diabetes are also risk factors for poor prognosis of ACS. Our study revealed that among ACS patients with advanced age or comorbid diabetes/hypertension, the high-MLR cohort demonstrated significantly elevated MACE incidence compared to their counterparts without these comorbidities. Particularly in three clinically relevant subgroups—geriatric patients (age $>$60 years), diabetic individuals, and hypertensive cases—MLR measurement exhibited robust capacity for risk stratification, enabling effective prognostic differentiation within these vulnerable populations. MLR, composed of monocytes and lymphocytes, has its earliest traceable record indicating its potential application in the risk stratification of tuberculosis [33]. Subsequently, MLR was studied for conditions such as bipolar disorder [34], chronic kidney disease [35], diabetes [36], and malignant tumors [37, 38, 39], where it can be used to predict poor outcomes. In recent years, the MLR has emerged as a valuable biomarker, finding applications in both the diagnosis and prognosis of cardiovascular diseases [7, 40]. A recent retrospective study showed a significant association between a higher MLR and an increased risk of cardiovascular and all-cause mortality [41]. The meta-analysis showed that MLR was a simple and widely available tool to predict MACEs in patients with CHD [42]. We observed a similar significant association between elevated MLR and mace in patients with newly diagnosed ACS. The difference is that the mean cut-off value in the 19 studies included in the meta-analysis was 0.34, while the cut-off value of MLR in our study was 0.304. In a study that used MLR to predict the mortality of ACS patients, the cut-off value could reach 0.414 [43], which was higher than the cutoff value of MLR in our study. This might be related to the differences in the definitions of MACEs among different studies, as the MACEs in our study included not only death but also four other conditions, such as UA, acute myocardial infarction, etc. Additionally, the smaller sample size might also be the reason for the incomplete consistency of data among different studies. Therefore, currently, the level of evidence regarding the prognostic prediction of the MLR in CHD is generally low, and the optimal threshold remains to be determined.

The immunopathological cascade critically orchestrates atherosclerotic lesion formation and progression, wherein monocytes and lymphocytes emerge as principal mediators of inflammatory pathogenesis. The MLR, calculated from monocytes and lymphocytes, is effective in identifying the presence of vulnerable plaques in ACS patients [44]. Song et al. [15] included MLR in the inflammatory prognostic score and reported that a higher score was closely associated with poorer long-term prognosis in patients with ACS undergoing PCI. However, Shumilah et al. [45] reported that NLR was the strongest predictor of ACS (p 0.001) and MLR was not a significant predictor of ACS (p $>$ 0.05). Gao et al. [20] analyzed six novel lymphocyte-based inflammatory indices in predicting MACEs in patients with newly diagnosed CAD, and discovered that only SIIRI showed significantly high predictive performance. Bani et al. [21] identified SIRI as a predictor of severe CAD. However, due to potential collinearity among the six lymphocyte-derived inflammatory indices in our study, only MLR was selected. These differences may be because Gao et al. [20] defined three events as MACEs, whereas we defined five events as MACEs in this study. Based on the definition of MACEs [22], this study was relatively complete. Our findings suggested that the superior predictive performance of MLR compared to the other five inflammatory indices was context-specific and depended on both the study population characteristics and the clinical definitions of MACEs used in the study. This highlights the need for further validation studies to comprehensively evaluate the prognostic value of MLR across diverse patient populations and varying clinical endpoint criteria. Future studies should focus on elucidating the biological mechanisms underlying the predictive utility of MLR while systematically examining the potential confounding factors that might influence its clinical applicability.

Tanimura et al. [46] demonstrated that a history of cancer in ACS patients was independently associated with worse clinical outcomes, including MACEs, compared to those without a cancer history (odds ratio: 4.00, p 0.001). In our study, there was no statistically significant association between cancer history and MACEs in ACS patients. This discrepancy may be attributed to a relatively short follow-up duration in our study. Previous studies have reported that radiation therapy-induced coronary artery disease may manifest 5 to 20 years post-exposure [47]. In the future, we will conduct long-term follow-up to determine whether there is a correlation between the cancer history and MACEs in patients with ACS. Both HDL and LDL are associated with prognosis in the ACS patients [48, 49], which aligns with our observational findings.

Our findings are also in agreement with the results of previously published meta-analyses, which reported significant associations between AF and adverse outcomes in patients with ACS [50, 51]. Current evidence indicates that AF pathogenesis originates from inflammation-mediated myocardial necrosis and fibrotic remodeling [52]. Mechanistically, these structural alterations induce electrophysiological instability through inflammation-induced membrane potential destabilization, which directly facilitates ectopic impulse generation that disrupts the normal rhythm of the heart [53, 54]. Because of the intrinsic pathophysiological interplay between AF and systemic inflammation, AF did not show independent prognostic significance in the multivariable analysis (HR 0.645, 95% CI 0.277–1.503, p = 0.309). This suggested that the predictive value of AF may be mediated through inflammatory pathways rather than AF functioning as an autonomous risk determinant. However, Saleh et al. [51] suggested that AF served as an independent prognostic indicator for predicting adverse outcomes in ACS patients. This discrepancy may be caused by our study only including newly diagnosed ACS patients, of which only 1.61% were diagnosed with AF. In contrast, 25% of ACS patients included in the study by Saleh et al. [51] were previously diagnosed with AF. Therefore, this difference in the inclusion criteria is likely the main contributing factor for the differences in the outcome prediction between the two studies.

This study has several limitations. First, as an observational study, the results merely indicate a correlation rather than a causal relationship. Second, being a single-center retrospective analysis with a relatively small sample size, it may have introduced selection bias and restricted the generalizability of the results. Third, we could not evaluate body mass index as a potential prognostic factor because data for height and weight were not available for a significant proportion of participants. Finally, seasonal variations in blood cell counts could affect the broader applicability of our conclusions. Therefore, larger-cohort multicenter prospective studies are necessary in the future to externally validate our findings and minimize bias through comprehensive clinical data collection.

Elevated MLR was independently associated with MACEs in patients with newly diagnosed ACS. MLR demonstrated superior predictive performance compared to the other five inflammatory indicators. These findings suggested that MLR is a promising low-cost clinical tool for non-invasive inflammatory monitoring, precise risk stratification, and personalized therapeutic strategies in the management of ACS. The optimal cutoff value of MLR requires further validation through large-scale cohort multicenter studies to establish standardized criteria for the clinical application of these biomarkers.

CHD, coronary heart disease; CAD, coronary artery disease; ACS, acute coronary syndrome; UA, unstable angina; STEMI, ST-segment elevation myocardial infarction; NSTEMI, non-ST-segment elevation myocardial infarction; PCI, percutaneous coronary intervention; MACEs, major adverse cardiovascular events; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; SIIRI, systemic immune-inflammatory response index; HR, hazard ratio; CI, confidence interval; HF, heart failure; AF, atrial fibrillation; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ROC, receiver operating characteristic; AUC, area under the curve.

The data that support the findings of this study are available on request from the corresponding author.

ML and GPL initially conceived the research conception. ML, HLL, KYC, and MY then designed the research study. HL and XPC organized the database. ML and HLL performed the statistical analysis. ML, YZ, TL, and GPL contributed to the interpretation of the statistical data. ML wrote the first draft of the manuscript, and HL wrote sections of the manuscript. KYC, MY, and XPC contributed to drafting or critically revising sections of the manuscript. YZ, TL, and GPL provided critical revisions for important intellectual content. All authors contributed to editorial changes, read and approved the final manuscript, and agreed to be accountable for all aspects of the work.

The study was conducted in accordance with the Declaration of Helsinki. The research protocol was approved by the Ethics Committee of Binzhou People’s Hospital (Ethic Approval Number [YYKYLL20250202]). The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because this study was retrospective, and no informed consent was signed.

The authors would like to thank all the participants for their cooperation when conducting the study.

This work was supported by the National Natural Science Foundation of China (Grant number [82470527]), Tianjin Key Medical Discipline (Specialty) Construction Project (Grant number [TJYXZDXK-029A]), the Shandong Province Medical and Health Science and Technology Development Program (Grant number [202203010589]) and the Project of Binzhou People’s Hospital (Grant number [XJ2022006307]).

The authors declare no conflict of interest. Tong Liu is serving as a Guest Editor and an Editorial Board member of this journal. We declare that Tong Liu had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to Allison B. Reiss.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/RCM39917.