Association Between Systemic Immune-Inflammatory Indices and Severity of Coronary Artery Lesions in Patients With Coronary Artery Disease in Different Glucose Metabolic States

Introduction

Coronary artery disease (CAD) is a chronic cardiac disorder resulting from varying degrees of coronary artery constriction. Globally, CAD is becoming a major public health concern due to rising incidence and fatality rates.^1,2 Recent research indicates that individuals with diabetes mellitus (DM) have a substantially higher risk of developing CAD, compared to traditional CAD risk factors such as hypertension and hyperlipidemia,^3,4 Abnormal glucose metabolism, including diabetes and impaired glucose tolerance, is a major risk factor for coronary heart disease and is closely linked with the onset and progression of coronary artery lesions.^5,6 Clinically, timely identification and intervention for high-risk CAD individuals is essential to reduce CAD incidence and mortality, highlighting the importance of early detection and intervention in improving patient outcomes.

Inflammation contributes a significant role in the development and progression of atherosclerotic coronary artery disease (CAD)^7.Various inflammatory indicators derived from whole blood counts, including neutrophils, lymphocytes, and C-reactive protein, effectively represent the body’s inflammatory status and are instrumental in predicting the onset and prognosis of cardiovascular disease.^8,9 However, relying on a single marker or a limited set of inflammatory markers may be insufficient to evaluate the body’s overall inflammatory state. When two or three interacting cellular ratios are combined, the overall predictive potential for determining the occurrence and prognosis of cardiovascular disease significantly increases.^10,11

In recent years, a novel inflammatory marker known as the systemic immune-inflammatory index (SII), calculated as (neutrophils × platelets) / lymphocytes, has been demonstrated to play a significant role in predicting the occurrence, progression, and severity of coronary heart disease, as well as hyperlipidemia and stroke.^12–14 SII has also emerged as an independent risk factor for adverse cardiovascular events, including all-cause cardiovascular mortality, myocardial infarction incidence, heart failure onset, and rehospitalization due to heart failure.^15,16 Given the heightened inflammatory response linked with metabolic disorders, individuals with glucose metabolism dysregulation are at an increased risk of developing cardiovascular disease^17,18. However, the association between SII and the severity of coronary artery lesions in CAD patients in different glucose metabolic states remains unclear. Therefore, this study aims to investigate the relationship between SII and coronary artery lesion severity in different glucose metabolic states.

Materials and Methods

Study Population

The inclusion criteria for study participants were as follows: (1) patients hospitalized in the Cardiology Department of Tianjin Union Medical Center between January 2018 and April 2022 due to typical symptoms of chest tightness and chest pain who subsequently underwent coronary angiography; (2) age ≥18 years; The exclusion criteria were as follows: (1) patients with incomplete data; (2) patients with severe liver or renal insufficiency, significant valvular disease, cardiomyopathy, rheumatic heart disease, or malignancy. A total of 27 27 individuals (1287 men and 1440 women) were included in the study. The study protocol was approved by the Human Research Committee of Tianjin Union Medical Center (IRB number: 2021-C03). This study was conducted in accordance with the principles of the Declaration of Helsinki. As this was a retrospective study with no additional interventions, all patient data were anonymized to ensure confidentiality. Therefore, informed consent is not required.

Data Collection

Participant data were obtained from the digital medical record system, encompassing key demographic characteristics, clinical background, blood analysis results, and relevant medical imaging records. Demographic characteristics included age, gender, weight, height, blood pressure, smoking and drinking states. Clinical history covered diagnoses of hypertension and diabetes as well as Treatment status. Medication data included information on antihypertensive, antidiabetic, and antilipidemic therapies.

Blood samples were collected in the morning from fasting venous blood by skilled healthcare professionals. HDL-C, LDL-C, Blood glucose, HbA1c, TC, TG, WBC, RBC, PDW, MPV, NEUT, LYMP, MONO, PLT, TG, ALT and AST were analyzed using an automated blood analyzer. Catheter-based invasive coronary angiography (CAG) was performed via the percutaneous radial or femoral artery. The angiography equipment used was sufficiently versatile to allow accurate assessment of all manifestations of coronary artery conditions.

Coronary Angiography

All patients received 300 mg of aspirin and 300 mg of clopidogrel prior to undergoing coronary angiography or percutaneous coronary intervention, which were performed by specialized cardiologists. In this study, the transradial approach was used for coronary angiography in all patients. The standard clinical procedure involves using the Judkins technique to visualize both the left and right coronary arteries, with multiple projection views to provide a comprehensive and accurate assessment of the most severe coronary artery lesions.¹⁹

To ensure the objectivity and accuracy of angiography results, the location and severity of coronary lesions were jointly assessed and documented by 2–3 experienced clinicians. Coronary artery disease (CAD) was diagnosed according to the clinical guidelines of the American College of Cardiology (ACC) and the American Heart Association (AHA): CAD was defined as ≥30% stenosis of the left main coronary artery or ≥50% stenosis in any other major coronary artery branch^2. The Gensini score was used to quantify the severity of coronary artery stenosis.^20,21

Definition

The severity of CAD is determined by the number of diseased vessels and the Gensini score (GS), which takes into account the degree of luminal stenosis and the importance of its location in the following way: obstruction of less than 25% is scored as 1 point, 26–50% obstruction is scored as 2 points, 51–75% obstruction is scored as 4 points, 76–90% obstruction is scored as 8 points and 91–99% obstruction is scored as 16 points. Complete obstruction (100%) is given a score of 32. The score is then multiplied by a coefficient that depends on the functional importance of the area provided by the segment.5 for the left main coronary artery, 2.5 for the proximal segment of the left anterior descending or circumflex artery, and 1.5 for the middle segment of the left anterior descending branch.^11,22

The systemic immunoinflammatory index (SII) was calculated by the formula = platelet count × neutrophil count/lymphocyte count (10 ⁹ /L).^23,24.

The WHO guidelines for diabetes mellitus define diabetes mellitus (DM) as FPG > 7.0 mmol/L, 2-hour plasma glucose level ≥ 11.1 mmol/L based on an oral glucose tolerance test, HbA1c ≥ 6.5%, or a history of T2DM. Normoglycemic regulation (NGR) is characterized by an FPG < 6.1 mmol/L and a 2-hour plasma glucose level < 7.8 mmol/L. Pre-diabetes mellitus (Pre-DM) should be examined in those who have high plasma glucose levels but fail to match the diagnostic categories as T2DM.^25,26

Statistical Analysis

Statistical analyses were conducted with continuous variables expressed as mean ± standard deviation (SD) and compared using the t-test or Mann–Whitney U-test as appropriate. For comparisons involving more than two groups, one-way analysis of variance (ANOVA) or the Kruskal–Wallis test was applied. For continuous variables not following a normal distribution, the data were presented as median (interquartile range, IQR), while categorical variables were expressed as percentages (%), with comparisons made using the chi-square test. Bonferroni corrections were applied to account for multiple comparisons. Correlation analyses utilized Pearson or Spearman correlation coefficients as appropriate. The systemic immune-inflammation index (SII) was divided into quartiles for group-based analysis. A multivariable logistic regression model was employed to examine the association between SII levels and the number and severity of coronary artery lesions in patients with coronary artery disease (CAD) after adjusting for covariates. Subgroup analyses were performed to evaluate the relationship between SII levels (as a continuous variable) and the severity of coronary lesions across various subgroups of CAD patients, and to calculate the P for the interaction. Multivariable logistic analysis was used to estimate odds ratios (ORs) in subgroup analyses.

Additionally, LASSO regression analyses were performed using the ‘glmnet’ package in R to identify factors associated with the severity of coronary artery disease. The discriminatory ability of the model to identify disease risk was assessed using the receiver operator characteristic curve (ROC). A restricted cubic spline (RCS) curve based on the multivariable logistic regression model was used to visualize the association between SII levels and CAD severity, with three knots chosen for the analysis. P<0.05 was considered statistically significant, All data analyses were performed using SPSS software version 26.0, GraphPad Prism 8, and R Studio version 4.2.3.

Results

Basic General Clinical Characteristics

The characteristics of the study participants are summarized in Table 1.A total of 2727 participants were included and divided into four groups based on quartiles of SII values: Q1 (SII ≤ 351.52), Q2 (351.52 < SII ≤ 485.77), Q3 (485.77 < SII ≤ 682.86), and Q4 (SII > 682.86). Significant differences were observed among the quartiles in terms of gender, SBP, hypertension, antihypertensive drugs, WBC, RBC, PDW, MPV, NEUT, LYMP, MONO, PLT, TG, and AST (P < 0.05); Moreover, the number and severity of coronary artery lesions differed significantly across the SII groups (P < 0.05). Participants in Q4 had a higher likelihood of presenting with more numerous and severe coronary artery lesions compared with those in Q1. Additionally, Q4 participants were more likely to be male and to have higher levels of SBP, WBC, RBC, NEUT, MONO, PLT, hypertension and lower levels of PDW, MPV, LYMP, TG, AST.

Table 1 General Characteristics of Research Participants

Correlation of SII With Risk Factors for Coronary Artery Disease

Pearson and Spearman correlation analyses demonstrated that SII was negatively correlated with Gender (rho = −0.054), PDW (rho = −0.085), and MPV (rho = −0.140) (all P < 0.05). However, SII demonstrated positive correlations with Hypertension (rho = 0.097), WBC (rho =0.423), RBC (rho = 0.169), HbA1c (rho =0.054), The number of coronary lesions (rho =0.101), and Gensini scores (rho =0.110), (all P < 0.05) as in Table 2 and Figure 1.

Table 2 The Correlation Between SII and CAD Risk Factors

Figure 1 The correlation between SII and CAD risk factors. P < 0.05 was considered statistically significant. Abbreviations: CAD, coronary artery disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index (Weight (kg)/Height (m)²); WBC, white blood cell; RBC, red blood cell; MPV, mean platelet volume; PDW, platelet distribution width; HbA1c, glycosylated hemoglobin; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TP, total protein; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; SII, systemic immune-inflammatory index.

Factors Associated With Coronary Lesion Severity and Their Risk Prediction Models Based on LASSO Regression Screening

LASSO regression was employed to identify factors associated with the severity of coronary artery lesions, including laboratory markers and demographic variables (gender, age, and BMI). To determine the optimal penalty parameter λ, we applied 10-fold cross-validation with 100 iterations to ensure model accuracy. A critical and suitable number of variables were selected at λ = 0.000217, as shown in Figure 2A; By examining the regression coefficients at this optimal λ, we explicitly identified the variables associated with coronary lesion severity, which include: gender, age, BMI hypertension, diabetes, hyperlipidaemia, smoking, drinking, SBP, DBP, WBC, NEUT. LYMP, MONO, RBC, PDW, TP, ALB, ALT, AST, TC, TG, HDL-C, LDL-C, Glucose, HbA1c and SII as shown in Figure 2B; Subsequently, we performed logistic regression analysis to further refine the model by selecting variables with statistically significant associations which include: gender, hypertension, PDW, ALT, AST, HDL-C, LDL-C, HbA1c and SII; ROC curve analysis was performed on these variables, and the area under the curve (AUC) of the model’s working characteristics of the subjects was calculated, with a value of 80.45%, indicating that the model had good predictive accuracy, as shown in Figure 2C.

Figure 2 Coronary artery lesion severity correlates and their risk prediction models based on LASSO regression screening, (A) Cross-validation plots; (B) LASSO regression coefficient plots; (C) ROC curves of the predictive power of variables in the estimation model for the severity of coronary artery lesions. Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, Weight (kg) /Height (m) 2; WBC, white blood cell; RBC, red blood cell; PDW, Platelet volume distribution width; NEUT, neutrophils; LYMP, lymphocytes; MONO, monocytes; HbA1c, glycosylated hemoglobin; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TP, total protein; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; SII, systemic immune-inflammatory index; LASSO, least absolute shrinkage; and selection operator; ROC, receiver operator characteristic curve.

Multiple Logistic Regression Analysis of SII Levels and the Number of Coronary Lesions in Patients With Coronary Artery Disease

The results of the multiple logistic regression analysis indicated that SII levels in the highest quartile (Q4) were significantly associated with a higher risk of the number of coronary lesions in patients with coronary artery disease (P < 0.05), as presented in Table 3. Even after adjusting for these variables, including age, gender, diabetes, antilipidemic drugs, smoking, drinking, RBC, PDW, ALT, AST, TG, HDL-C, and HbA1c levels. SII levels remained significantly associated with coronary lesion risk, Q4 was 1.175 times higher in the number of coronary lesions compared to those in Q1 (95% CI: 1.044–1.324, P < 0.05), as shown in Table 3.

Table 3 Multiple Logistic Regression Analysis of SII Levels and the Number of Coronary Lesions

Multiple Logistic Regression Analysis of SII Levels and Coronary Stenosis Severity in Patients With Coronary Artery Disease

Gensini scores were adopted to evaluate the severity of coronary artery stenosis: Mild (Gensini scores ≤ 24), Moderate (24 < Gensini scores ≤ 45), and Severity (Gensini scores > 45). The study results indicated SII was significantly associated with the severity of coronary artery stenosis (P < 0.05), as shown in Table 4. In addition, after adjusting for confounders such as hypertension, antihypertensive drugs, diabetes, antidiabetes drugs, hyperlipidemia, antilipidemic drugs, smoking, drinking, BMI, SBP, TC, TG, HDL-C, LDL-C, Glucose and HbA1c in the multivariate regression analysis, SII levels remained significantly associated with the level of CAD severity, the highest SII quartile (Q4) exhibited a 1.430 times higher in coronary stenosis severity compared to those in Q1 (95% CI 1.209–1.691, P < 0.05), as shown in Table 4.

Table 4 Multiple Logistic Regression Analysis of SII Levels and the Severity of Coronary Lesions

Association Between SII Levels and the Number and Severity of Coronary Lesions in Patients With Coronary Artery Disease

To further investigate the relationship between SII and the number and severity of coronary artery lesions in patients with coronary artery disease. Participants were grouped according to SII quartiles to assess the number and severity of coronary artery lesions. The study results indicated that the number of coronary artery lesions vessels and severity were significantly higher in Q4 compared with Q1 and Q2 (P < 0.05) as shown in Figures 3A and B. Additionally, participants were grouped according to the number of coronary vascular lesions and stenosis severity grouping to assess SII levels. SII levels were significantly higher in those with three coronary vessel lesions than in those with single- and two-vessel lesions; and SII levels were significantly higher in those with severe coronary stenosis than in those with mild coronary stenosis (P < 0.05) as shown in Figures 3C and D.

Figure 3 Association Between SII Levels and the Number and Severity of Coronary artery diseased in Patients With Coronary Artery Disease; (A) Number of diseased coronary artery vessels across SII quartiles. (B) Severity of coronary artery stenosis across SII quartiles. (C) SII levels by the number of coronary vessel lesions. (D) SII levels by the severity of coronary artery stenosis. Abbreviations: Quartile 1, the first SII quartile; Quartile 4, the fourth SII quartile; SII, systemic immune-inflammatory index. Note: Statistical differences were assessed using ANOVA; Bonferroni corrections were applied to account for multiple comparisons; The whiskers in the bar chart represent the mean ± SD of the data. P < 0.05 was considered statistically significant, *: P<0.05; **: P<0.005; ***: P<0.0005,****: P<0.0001.

Relationship Between SII Levels and Severity of Coronary Artery Lesions in Patients With Coronary Artery Disease in Different Glucose Metabolic States

As shown in Table 5, without adjustment for confounders, the risk of developing coronary artery lesions was higher in Q4 than in Q1 regardless of glucose metabolism status (P<0.05). After adjusting for confounders, A significant relationship was observed between SII and the severity of coronary artery lesions in the Pre-DM and DM states (Pre-DM: OR 1.610, 95% CI: 1.055–2.457; DM: OR 1.749, 95% CI: 1.275–2.398; P < 0.05), however, there was no statistical significance in the NGR state (P > 0.05).

Table 5 Relationship Between SII Levels and Severity of Coronary Artery Lesions in Patients With Coronary Artery Disease in Different Glucose Metabolic States

Further subgroup analyses were conducted to evaluate whether the relationship between SII and coronary artery lesion severity was influenced by other covariates, as shown in Figure 4. The results indicated that there was no significant interaction between SII and age, gender, hypertension, antihypertensive drugs, hyperlipidaemia, antilipidemic drugs, smoking, drinking (all P for interaction > 0.05). Subgroup analyses determined the robustness of the association between SII and coronary artery lesion severity. The correlation between SII and severity of coronary artery lesions was further determined.

Figure 4 Subgroup analysis for the association between SII and Severity of coronary stenosis.

Linear Correlation Between SII and Severity of Coronary Artery Lesions

The relationship between SII and the severity of coronary artery lesions was assessed using the RCS model analysis (Figure 5), and there was a significant linear correlation between SII and the severity of coronary artery lesions. The results showed a positive correlation between SII and the severity of coronary lesions after adjustment for potential confounders in the model. In particular, a one-unit increase in SII was associated with a 41.6% in the severity of coronary lesions.

Figure 5 RCS model showing the association between the SII and severity of coronary artery lesions. Abbreviation: SII, systemic immune-inflammatory index. Notes: Restricted cubic spline (RCS) analysis was applied to analyze the data; Blue lines and shaded areas represent the odds ratios and 95% confidence intervals; P < 0.05 was considered statistically significant.

Discussion

In this retrospective cohort study of patients hospitalised with coronary artery disease in northern China, the present study first appraised the relationship between SII and coronary artery lesion severity, and the results indicated that there was a significant correlation between SII and the severity of coronary artery disease in patients with coronary artery disease and that there was a positive correlation. In addition, the severity of coronary lesions was significantly increased in patients in the highest quartile of SII (Q4) compared with the lowest quartile of SII (Q1). Next, the relationship between SII and coronary artery lesion severity was further evaluated in different glucose metabolic states in this study, Unadjusted for confounders, the relationship between SII and coronary artery lesion severity was statistically significant regardless of glucose metabolism states. After adjusting for confounders, a significant correlation between SII and coronary lesion severity remained in the Pre-DM and DM states; however, there was no statistical significance in the NGR state. The results of subgroup analyses showed that this association was consistent across populations, further validating the robustness of the findings; the study also found a linear relationship between SII and coronary lesion severity, increasing coronary lesion severity by 41.6% for each unit increase in SII.

Previous studies have revealed that inflammatory and immune responses are continuously active during the development and progression of coronary atherosclerosis.^27,28 Neutrophil aggregation and release of various pro-inflammatory factors at the area of inflammation exacerbate vascular endothelial cell damage and promote the progression of atherosclerotic lesions, ultimately leading to atherosclerosis and cardiovascular disease.²⁹ In addition, platelet activation can promote inflammatory responses and participate in the formation and progression of atherosclerotic plaques.^30,31 And lymphocytes have also been shown to be involved in atherosclerotic plaque formation.^32,33 Abnormal glucose metabolism leads to enhanced levels of oxidative stress, which further triggers vascular endothelial damage. High inflammation and high oxidative stress states are key factors contributing to the progression of coronary artery disease,^34,35 Studies have also indicated that abnormal glucose metabolism not only accelerates the development of coronary atherosclerosis, but also leads to long-term activation and accumulation of inflammatory factors, which makes coronary artery lesions more severe.³⁶

A large cohort study indicated that neutrophil counts were strongly associated with the prevalence of CVD, and high neutrophil counts were associated with a higher prevalence of CVD.³⁷ Comparatively, lymphocytes play a different role in regulating the inflammatory response of the body. Clinical studies have indicated that low lymphocyte counts are associated with a poorer prognosis in patients with CVD.³⁸ As SII is a combination of three inflammatory markers, namely neutrophils, lymphocytes and platelets, it can more fully reflect the balance between inflammation and immunity. SII components represent key pathophysiological mechanisms in coronary artery disease. In patients with coronary artery disease, the increasing number of neutrophils and platelets and decreasing number of lymphocytes indicate that the body is in a state of high inflammation and low immunosuppression, which contributes to the progression of atherosclerosis. This finding further supports that SII levels can, to a certain extent, reflect the stability of coronary plaques, thereby contributing to the assessment of lesion progression and prognostic evaluation.¹⁵

SII, which consists of neutrophils, lymphocytes and platelets, was first proposed by HU B et al in order to predict the prognosis of patients suffering from hepatocellular carcinoma after radical resection.³⁹ SII has been extensively investigated in a multitude of medical fields in diseases such as oncology, acute and critical illnesses and cerebrovascular diseases.^40–42 In recent years SII has also been used in the diagnosis and prognosis of various cardiovascular diseases.^43,44 Studies have indicated that higher SII is an independent risk factor for the development of CVD.⁴⁵ A recent study demonstrated that SII is a useful target for risk stratification and prognostic assessment of patients with acute decompensated heart failure, providing important prognostic information.¹⁶ Furthermore, another study demonstrated that SII was an independent predictor of lesion severity in patients with acute coronary syndrome (ACS).⁴⁶ In the present study, we identified a significant association between SII and the severity of coronary artery lesions. Meanwhile, we observed that SII levels were significantly elevated in patients with abnormal glucose metabolism (including diabetes mellitus and impaired glucose tolerance) and positively correlated with the severity of coronary artery lesions. This finding indicates that SII may not only serve as an independent predictor of coronary artery disease risk in patients with glucose metabolism abnormalities, but may also provide a convenient indicator for assessing the inflammatory status of these patients, and further subgroup analyses also reinforced the robustness of our findings.

The innovation of this study is that it is the first time to combine the analysis of inflammatory indicators of glucose metabolism status and severity of coronary artery disease, proposing the potential clinical value of SII as a joint assessment tool, and providing a new perspective for the development of individualised diagnostic and therapeutic strategies. At the same time, the group analysis based on different glucose metabolism status reveals more precisely the interactive effects of glucose metabolism on inflammation level and coronary artery disease.

Despite this study provides useful valuable in exploring the relationship between SII and the severity of coronary artery lesions, some limitations remain. Firstly, the present study was a single-centre retrospective study with a limited sample size, which may affect the generalisability of the results; secondly, the present study was a cross-sectional study, which only provided an association between SII and the severity of coronary artery lesions, and not revealed a causal relationship between the two. Future longitudinal studies could further explore the predictive value of SII on disease progression and clinical prognosis in patients with coronary artery disease. Additionally, CRP was not included as an analytical marker in this study. Although CRP is an important marker of inflammation and may have a correlation with the SII index, it was not included in the data, and thus we were unable to analyze it further. Future research should consider including CRP to comprehensively assess its relationship with the SII index. Finally, although some of the confounders were adjusted for in the analyses of this study, there were other possible influences (lifestyle factors, etc.) That were not included in the analyses. Future studies should consider controlling for additional confounders to enhance the explanatory power of the findings.

Conclusion

SII has a significant relationship with the severity of coronary artery disease; even in different states of glucose metabolism, SII remains correlated with the severity of coronary artery disease. Therefore, SII has potential application as a combined evaluation tool in clinical practice, providing a new perspective of individualised diagnostic and treatment strategies for coronary artery disease patients with abnormal glucose metabolism.