Evaluation of systemic immune-inflammation index for predicting severity of lower extremity arterial disease

Abstract

Objective

Predictive value of systemic immune-inflammation index (SII) has been shown in clinical outcomes and complexity of coronary artery disease, acute coronary syndrome, and heart failure. We sight to evaluate value of SII in patients with lower extremity arterial disease (LEAD).

Methods

A total of 271 patients diagnosed with LEAD were included to our study. Blood samples of the patients were collected and analyzed for biochemical variables and complete blood count parameters. SII value of each patient was calculated. The complexity of atherosclerotic disease was classified according to Trans-Atlantic Inter-Society Consensus (TASC II) classification.

Results

Patients with TASC C-D were older than patients in TASC A-B group (63.06 ± 9.24 years and 60.85 ± 8.75 years, respectively). Other co-morbidities were comparable in both groups. Hemoglobin level and lymphocyte count were significantly lower, neutrophil, platelet counts, and SII values were significantly higher in patients with TASC C-D disease compared to that of patients with TASC A-B disease. SII showed significant correlation with the severity of LEAD (r = 0.363, p < .001). SII value of 664.24 predicted TASC C-D disease with a sensitivity and specificity of 60.8% and 73.3%, respectively. Results of multivariate logistic regression analysis showed that SII had higher odds ratio compared to platelet, neutrophil, and lymphocyte counts.

Conclusion

Higher SII may indicate probability of more complex LEAD. This relationship seems plausible in terms of similar pathophysiology of coronary artery disease and peripheral artery disease.

Keywords

Systemic immune-inflammation index peripheral artery disease

Introduction

Peripheral artery disease (PAD) with a rate of 16% in seventh decade and older patients is a prevalent atherosclerotic cardiovascular disease and its prevalence increases with age.¹ Atherosclerosis is the leading cause of coronary, peripheral, and cerebral artery disease.² Beyond the solely lipid accumulation in vascular wall, chronic inflammatory reactions play pivotal role of molecular mechanism of atherosclerosis.³ Inflammatory biomarkers markers such as C-reactive protein (CRP), white blood cell (WBC), interleukins, and others have been discovered as risk factors for atherosclerotic coronary heart diseases in many studies addressing contribution of the inflammatory process on atherosclerosis.⁴ Similar to studies on atherosclerotic coronary heart diseases, studies investigating relationship between inflammation and PAD have shown that circulating inflammatory markers such as serum CRP, interleukin-6 (IL-6), and intercellular adhesion molecule-1 are predictors of PAD.^5,6 In addition to that findings complete blood count provides valuable data which can estimate severity and clinical outcomes of coronary artery disease (CAD) and PAD.^7–10

Recently, as a novel indicator, systemic immune-inflammation index (SII) which is derived from complete blood count by multiplying platelet (PLT) count to neutrophil (NEU) count/lymphocyte (LYM) count ratio, has been associated with clinical outcomes and complexity of CAD, acute coronary syndrome, and heart failure.^11–15 Zhang et al. have shown that SII is an independent risk factor for the presence of lower extremity arterial disease (LEAD).¹⁶ Up until now, relationship between SII and severity of LEAD remains unclear. As a cost-effective, reproducible, and easy to use tool, we investigated the relationship between LEAD and SII in patients who underwent lower extremity peripheral angiography.

Materials and methods

Our study was designed as a single-center, retrospective study.

Study population

Between January 2019 and December 2022 all eligible consecutive patients who represented to our tertiary center with the complaint about intermittent claudication and whose lower extremity peripheral artery disease was documented by angiography were included to our study. Exclusion criteria were under 18 years of age, end stage renal disease, active infection, active malignancy, gut disease, thyroid disease, rheumatological disease, anti-inflammatory drug use, and hematological disorders. Local ethical committee approved the study and it was conducted in accordance with the Declaration of Helsinki. All the patients gave informed consent for participation of the study.

Patients were considered as hypertensive if they use antihypertensive medications or their blood pressure is greater than 140 and/or 90 mmHg. Use anti-diabetic drugs or fasting glucose levels of greater than 125 mg/dL were considered as diabetes mellitus. Hyperlipidemia was defined as fasting total cholesterol >200 mg/dl or low density lipoprotein cholesterol >130 mg/dl or lipid-lowering chronic use of drugs. CAD was defined as presence of more than 50% stenosis at least in one of the coronary arteries, history of percutaneous coronary and/or coronary artery bypass operation intervention. After 12 h of fasting in the sitting position, the blood samples of the patients were collected in tubes containing trisodium citrate (0.109 µM). Collected samples analyzed for biochemical variables and complete blood count parameters. SII value of each patient was calculated.

Angiographic analyses

Conventional invasive peripheral lower extremity cineangiography was performed and interpreted by two experienced cardiologists. The complexity of atherosclerotic disease was classified according to Trans-Atlantic Inter-Society Consensus (TASC II) classification.¹⁷ We divided the patients into two groups according to their TASC II classification (TASC A-B and TASC C-D).

Statistical analyses

Normality of data was evaluated with the Kolmogorov-Smirnov test. Data with normal or non-normal distribution are expressed as mean±SD or median interquartile range (Q1–Q3), respectively. Categorical data are expressed as number and percentages. Comparison of patients who had TASC A-B disease with patients who had TASC C-D disease was conducted by independent samples-t test or Mann–Whitney U test. Categorical comparisons were made by the Chi-square test. The correlation SII with peripheral arterial disease severity was examined by Spearman correlation analysis. Receiver operator characteristic (ROC) curve analysis was conducted to determine cut-off value of the SII for predicting TASC C-D disease. Univariate logistic regression was performed in order to find the independent predictors of the presence of TASC C-D disease. Variables that had significant value in predicting TASC C-D disease were put into multivariate logistic regression analysis. Quade ANCOVA was performed where age was used as a covariate for comparison of the two groups. P value of less than 0.05 was considered as significant.

Results

271 patients were enrolled to the study population. Patients with TASC-C and D were older than patients in TASC-A and B group. Other co-morbidities were comparable in both groups. Biochemical and complete blood count variables except hemoglobin, platelet, LYM, and NEU count were also similar in two groups. Hemoglobin level and LYM count were significantly lower; NEU, PLT counts and SII values were significantly higher in patients with TASC C-D disease compared to that of patients with TASC A-B disease. SII was found to be higher in patients with TASC C-D disease where age was used as a covariate. Baseline clinical characteristic and laboratory values are shown in Table 1.

Table 1.

Clinical characteristics of the two groups.

	TASC class A-B (n = 150)	TASC class C-D (n = 121)
Age (years)	60.85 ± 8.75	63.06 ± 9.24	0.041
Gender (n,%)			0.871
Male	136 (90.7)	109 (90.1)
Female	14 (9.3)	12 (9.9)
Hypertension (n,%)	65 (43.3)	50 (41.3)	0.437
Diabetes mellitus (n,%)	51 (34)	43 (35.5)	0.792
Cerebrovascular disease (n,%)	6 (4)	6 (5)	0.704
Coronary artery disease (n,%)	47 (31.3)	45 (37.2)	0.311
Atrial fibrillation (n,%)	8 (5.3)	5 (4.1)	0.644
Glucose (mg/dL)	124 (97.85–180.25)	123 (98–163.5)	0.631
Urea (mg/dL)	33.5 (26.95–43.62)	35.7 (27.7–44.5)	0.324
Creatinine (mg/dL)	0.88 (0.78–1.05)	0.9 (0.78–1.1)	0.201
GFR (ml/min/1.73 m2)	91 (74–97)	88 (65–99)	0.369
Total cholesterol (mg/dL)	2034.39 ± 49.34	202.79 ± 58.09	0.520
HDL-C (mg/dL)	38.25 (33–46.92)	38.4 (33.5–45.55)	0.850
TG (mg/dL)	166 (118.5–238.5)	158 (120–207)	0.393
LDL-C (mg/dL)	127.27 ± 40.20	128.40 ± 51.83	0.847
WBC (109/L)	8.98 (7.76–10.73)	9.33 (8.03–11.41)	0.208
Hgb (g/dL)	13.9 (12.65–15.2)	13.4 (11.8–14.65)	0.009
Platelet count (109/L)	241.5 (194.75–303.25)	259 (223–320.5)	0.011
Neutrophil count (109/L)	5.49 (4.43–6.67)	5.91 (4.96–7.44)	0.003
Lymphocyte count (109/L)	2.5 (2.04–3.1)	2.05 (1.6–2.8)	<0.001
Monocyte count (109/L)	0.69 (0.53–0.79)	0.67 (0.54–0.83)	0.744
SII	584.5 (364.687.91)	725.78 (544.39–1102.19)	<0.001

GFR: Glomerular filtration rate, HDL-C: High density lipoprotein cholesterol, WBC: White blood cell, Hgb: Hemogrlobin, SII: Systemic immune inflammatory index.

We found significant positive correlation between SII and severity of LEAD (r = 0.363, p < .001). ROC curve analyses revealed that SII had the highest area under the curve (AUC) compared to PLT, NEU, and LYM counts. SII value of 664.24 predicted TASC C-D disease with a sensitivity and specificity of 60.8% and 73.3%, respectively. Table 2 and Figure 1 shows the ROC curve analyses of PLT, NEU, LYM, and SII for prediction of TASC C-D disease.

Table 2.

Results of the ROC curve analyses of platelet, lymphocyte, neutrophil counts, and SII.

	AUC	p	95% CI	Cut-off value	Sensitivity	Specificity
Platelet	0.589	0.011	0.522–0.657	212	81.8	36.7
Neutrophil	0.605	0.034	0.537–0.672	6.22	48.8	68
Lymphocyte	0.362	0.001	0.295–0.430	2.77	27.3	64.7
SII	0.711	<0.001	0.649–0.773	664.24	60.8	73.3

Figure 1.

ROC curve analysis of SII, NEU, PLT, and LYM for TASC C-D disease.

Univariate logistic regression analysis was performed to determine predictors of complexity of lower extremity artery disease (LEAD) (TASC C-D disease). Age, hemoglobin, PLT, NEU, LYM counts, and SII were the predictors of more complex LEAD (Table 3). We conducted four models of multivariate logistic regression analysis in order to find the independent predictors for the presence of more complex LEAD. According to our results SII had higher odds ratio compared to PLT, NEU, and LYM counts (Table 4).

Table 3.

Univariate logistic regression analysis for predictors of TASC C-D disease.

	p	OR	95% CI
Age	0.046	1.048	1.000–1.057
Gender	0.871	1.069	0.475–2.407
Hypertension	0.739	0.921	0.567–1.496
Diabetes mellitus	0.792	1.070	0.647–1.769
WBC	0.596	1.000	1.000–1.001
Hemoglobin	0.002	0.823	0.726–0.933
Platelet	0.008	1.004	1.001–1.008
Neutrophil	0.001	1.002	1.001–1.003
Lymphocyte	0.001	0.621	0.463–0.832
Monocyte	0.585	1.349	0.461–3.945
SII	<0.001	1.264	1.264
Glucose	0.393	0.999	0.996–1.002
Creatinine	0.172	1.668	0.801–3.376
Total cholesterol	0.815	0.999	0.995–1.004
HDL-C	0.217	0.989	0.971–1.008
Triglyceride	0.283	0.999	0.996–1.001
LDL-C	0.852	1.001	0.995–1.006

WBC: White blood cell, HDL-C: High density lipoprotein cholesterol, LDL-C: Low density lipoprotein cholesterol.

Table 4.

Multivariate logistic regression for prediction of TASC C-D disease. MODEL A.

	p	OR	95% CI
MODEL A
Age	0.110	1.024	0.995–1.053
Hemoglobin	0.021	0.857	0.752–0.977
Platelet	0.015	1.004	1.001–1.008
MODEL B
Age	0.107	1.025	0.995–1.055
Hemoglobin	0.016	0.850	0.745–0.970
Neutrophil	0.002	1.002	1.001–1.003
MODEL C
Age	0.249	1.018	0.987–1.050
Hemoglobin	0.033	0.867	0.761–0.988
Lymphocyte	0.016	0.691	0.512–0.934
MODEL D
Age	0.249	1.018	0.987–1.050
Hemoglobin	0.327	0.931	0.806–1.074
SII	<0.001	1.267	1.001–1.003

SII: Systemic immune inflammatory index.

Discussion

Our retrospective study was carried out with the aim of discovering the impact of SII on LEAD. SII is statistically significantly higher in patients of TASC-C and D than whom in TASC-A and B class in our study. Moreover SII was an independent predictor for the presence of more severe LEAD.

Atherosclerosis is a complex process involving endothelial cells, lipid accumulation, inflammatory cytokines, smooth muscle cells, and leukocytes.¹⁸ As LEAD is one of the manifestations of the atherosclerotic cardiovascular disease, conventional risk factors of atherosclerosis, such as hyperlipidemia, high blood pressure, age, gender, and smoking, increase likelihood of development of arterial stenosis or occlusion in lower extremities.¹⁹ Besides, chronic kidney disease, metabolic syndrome, hyperviscosity, hypercoagulable states, and inflammatory process are some of non-traditional risk factors of atherosclerotic cardiovascular disease.^2,20 In recent years, the contribution of inflammatory processes in the development of atherosclerosis have been emerged with compelling evidence. It has been confirmed the fact that inflammatory mechanisms and atherosclerosis have a strong link on the basis of development and evolvement of atherosclerotic plaque.^21,22 Inflammation is found to be closely related to endothelial dysfunction which is one of the crucial step for the pathogenesis of atherosclerosis.²³ Several lines of evidence have suggested that a network of cytokines regulate immune inflammatory response in the arterial wall during atherosclerotic plaque formation and progression.²⁴

The associations between LEAD and inflammation have been shown in various studies. Smoking and diabetes mellitus, both of which are related to increased levels of oxidative stress and inflammation, are strongly linked to the development of LEAD.²⁵ In the study performed by Cauley et al. men who had higher levels of inflammatory markers including interleukin-6, tumor necrosis factor-α and CRP had higher prevalence of LEAD compared to the men who had lower levels of inflammatory markers.²⁶ In the InCHIANTI study, McDermott et al. has showed the association between circulating fibrinogen and CRP levels and PAD.²⁷ In parallel, The Framingham Offspring Study has emphasized that pro-inflammatory cytokines such as IL-6 have relationship with PAD.²⁸

Beyond the conventional biomarkers of inflammatory state, inflammatory cell ratios developed in recent years are associated with severity and prognosis of several diseases. It has been suggested that, compared to single parameter, combinations of two or more parameters could more precisely reflect inflammatory activity.²⁹ Aykan et al. have shown that neurtrophil to lymphocyte ratio predicted severity and complexity of LEAD.²⁹ Besides from their diagnostic values, NEU/LYM and PLT/LYM ratios have been suggested to help in identifying LEAD patients who have higher readmission or mortality rate within 1 year.³⁰

SII, one of those novel indicators, has the potential to predict prognosis and/or severity in several different clinical scenarios such as malignancies, pancreatitis, subarachnoid hemorrhage, and acute ischemic stroke.^31–34 SII, reflecting inflammatory and immune status, has been focus of cardiology researches in recent years. The prognostic role of SII in patients with CAD, acute myocardial infarction, or heart failure has been well-established.^13–15 For the best of our knowledge only one study have investigated the value of SII in LEAD. In that study, performed by Zhang et al., SII was found to be an independent predictor for the presence of LEAD.¹⁶ In that study, diagnosis of LEAD was made by ankle brachial index measurement and angiographic examinations of the patients were not done. In the present study all patients had lower extremity angiographic examination and we could therefore assess the relation between SII and severity of LEAD. Compared to other blood count parameters, SII better discriminated the presence of severe disease. SII had highest odds ratio compared to neutrophil, lymphocyte, and platelet counts. SII could be used to estimate the extent of LEAD, reflecting the inflammatory nature of atherosclerosis. This index would be a valuable tool to determine the severity of disease in newly diagnosed or medically followed patients. Routine use of this parameter could allow early detection of possible worsening of the diagnosed disease and prevent morbidity by providing appropriate treatment to these patients at an early stage.

Previous studies have shown a U-shaped relationship between hemoglobin concentrations and cardiovascular disease in the general population.³⁵ The Atherosclerosis Risk in Communities (ARIC) study demonstrated that anemia was an independent predictor of the development of adverse cardiovascular outcomes.³⁶ In addition to causing ventricular remodeling, myocardial ischemia, and cardiac dysfunction, anemia may be a simple marker of an underlying inflammatory process.³⁶ A study by Choi et al. showed that low hemoglobin levels were associated with the development of carotid plaque after adjustment for metabolic abnormalities in a relatively healthy adult population.³⁷ Similarly, Dijk et al. showed that in patients with manifest arterial disease, increasing hemoglobin levels are associated with reduced severity of atherosclerosis.³⁸ In our study, patients with TASC C-D disease had lower levels of hemoglobin concentrations and remained as an independent predictor of more severe LEAD in multivariate regression analysis.

Limitations

Our study was a single-center study and the sample size was small. It had a retrospective study design. We did not assess the effect of treatment modalities that decrease SII on disease severity. Follow-up of the study population was not done and prognostic value of SII was not evaluated.

Conclusions

There is lack of data about SII and complexity of PAD in the literature. Our study has revealed that SII has a power of predicting more complex LEAD. Higher SII may indicate probability of more complex LEAD. This relationship seems plausible in terms of similar pathophysiology of CAD and PAD.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Cennet Yildiz

Ali Aycan Kavala

Saygin Turkyilmaz

References

Eraso

Fukaya

Mohler

, et al. Peripheral arterial disease, prevalence and cumulative risk factor profile analysis. Eur J Prev Cardiol 2014; 21(6): 704–711.

Bauersachs

Zeymer

Brière

, et al. Burden of coronary artery disease and peripheral artery disease: a literature review. Cardiovasc Ther 2019; 2019: 8295054.

Kattoor

Pothineni

NVK

Palagiri

, et al. Oxidative stress in atherosclerosis. Curr Atherosclerosis Rep 2017; 19(11): 42.

Madjid

Willerson

. Inflammatory markers in coronary heart disease. Br Med Bull 2011; 100: 23–38.

Ridker

Cushman

Stampfer

, et al. Plasma concentration of C-reactive protein and risk of developing peripheral vascular disease. Circulation 1998; 97(5): 425–428.

Tzoulaki

Murray

Lee

, et al. C-reactive protein, interleukin-6, and soluble adhesion molecules as predictors of progressive peripheral atherosclerosis in the general population: Edinburgh Artery Study. Circulation 2005; 112(7): 976–983.

Arbel

Finkelstein

Halkin

, et al. Neutrophil/lymphocyte ratio is related to the severity of coronary artery disease and clinical outcome in patients undergoing angiography. Atherosclerosis 2012; 225(2): 456–460.

Bhat

Afari

Garcia

. Neutrophil lymphocyte ratio in peripheral vascular disease: a review. Expert Rev Cardiovasc Ther 2016; 14(7): 871–875.

Verdoia

Barbieri

Di Giovine

, et al. Neutrophil to lymphocyte ratio and the extent of coronary artery disease: results from a large cohort study. Angiology 2016; 67(1): 75–82.

10.

Haumer

Amighi

Exner

, et al. Association of neutrophils and future cardiovascular events in patients with peripheral artery disease. J Vasc Surg 2005; 41(4): 610–617.

11.

Candemir

Kiziltunç

Nurkoç

, et al. Relationship between systemic immune-inflammation index (SII) and the severity of stable coronary artery disease. Angiology 2021; 72(6): 575–581.

12.

Zhang

Xiao

, et al. Systemic immune-inflammation index as a promising predictor of mortality in patients with acute coronary syndrome: a real-world study. J Int Med Res 2021; 49(5): 3000605211016274.

13.

Yang

Hsu

, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest 2020; 50(5): e13230.

14.

Huang

Zhang

Wang

, et al. Systemic immune-inflammatory index predicts clinical outcomes for elderly patients with acute myocardial infarction receiving percutaneous coronary intervention. Med Sci Mon Int 2019; 25: 9690–9701.

15.

Tang

Zeng

Feng

, et al. Association of systemic immune-inflammation index with short-term mortality of congestive heart failure: a retrospective cohort study. Front Cardiovasc Med 2021; 8: 753133.

16.

Zhang

Chen

. Higher systemic immune-inflammation index is associated with higher likelihood of peripheral arterial disease. Ann Vasc Surg 2022; 84: 322–326.

17.

Libby

. Inflammation in atherosclerosis. Arterioscler Thromb Vasc Biol 2012; 32(9): 2045–2051.

18.

Vinereanu

. Risk factors for atherosclerotic disease: present and future. Herz 2006; 3(S3): 5–24.

19.

Norgren

Hiatt

Dormandy

, et al. Inter-society Consensus for the management of peripheral arterial disease (TASC II). J Vasc Surg 2007; 45(Suppl S): S5–S67.

20.

Olin

Sealove

. Peripheral artery disease: current insight into the disease and its diagnosis and management. Mayo Clin Proc 2010; 85(7): 678–692.

21.

Wong

Meredith

Lin

, et al. The biological role of inflammation in atherosclerosis. Can J Cardiol 2012; 28(6): 631–641.

22.

Corti

Hutter

Badimon

, et al. Evolving concepts in the triad of atherosclerosis, inflammation and thrombosis. J Thromb Thrombolysis 2004; 17(1): 35–44.

23.

Signorelli

Marino

Scuto

. Inflammation and peripheral arterial disease. Multidisciplinary Scientific Journal 2019; 2: 142–151.

24.

Galkina

Ley

. Immune and inflammatory mechanisms of atherosclerosis. Annu Rev Immunol 2009; 27: 165–197.

25.

Brevetti

Giugliano

Brevetti

, et al. Inflammation in peripheral artery disease. Circulation 2010; 122(18): 1862–1875.

26.

Cauley

Kassem

Lane

, et al. Prevalent peripheral arterial disease and inflammatory burden. BMC Geriatr 2016; 16(1): 213.

27.

McDermott

Guralnik

Corsi

, et al. Patterns of inflammation associated with peripheral arterial disease: the InCHIANTI study. Am Heart J. 2005; 150(2): 276–281.

28.

Murabito

Keyes

Guo

, et al. Cross-sectional relations of multiple inflammatory biomarkers to peripheral arterial disease: the Framingham Offspring Study. Atherosclerosis 2009; 203(2): 509–514.

29.

Aykan

AÇ

Hatem

Kalaycıoğlu

, et al. Neutrophil-to-lymphocyte ratio may be a marker of peripheral artery disease complexity. Anatol J Cardiol 2016; 16(7): 497–503.

30.

Qian

Guo

, et al. Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio predict severity and prognosis of lower limb arteriosclerosis obliterans. Ann Vasc Surg 2020; 64: 221–227.

31.

Yang

Chang

Meng

, et al. Prognostic value of Systemic immune-inflammation index in cancer: a meta-analysis. J Cancer 2018; 9(18): 3295–3302.

32.

Liu

Guan

Cui

, et al. Systemic immune-inflammation index (SII) can Be an early indicator for predicting the severity of acute pancreatitis: a retrospective study. Int J Gen Med 2021; 14: 9483–9489.

33.

Yun

Lee

, et al. Systemic inflammation response index and systemic immune-inflammation index for predicting the prognosis of patients with aneurysmal subarachnoid hemorrhage. J Stroke Cerebrovasc Dis 2021; 30(8): 105861.

34.

Zhou

Xia

, et al. Predictive value of the systemic immune inflammation index for adverse outcomes in patients with acute ischemic stroke. Front Neurol 2022; 13: 836595.

35.

Gagnon

Zhang

Brand

, et al. Hematocrit and the risk of cardiovascular disease—the Framingham Study: a 34-year follow-up. Am Heart J 1994; 127: 674–682.

36.

Sarnak

Tighiouart

Manjunath

, et al. Anemia as a risk factor for cardiovascular disease in the Atherosclerosis Risk in Communities (ARIC) study. J Am Coll Cardiol 2002; 40(1): 27–33.

37.

Choi

Won

Kang

, et al. Association of serum hemoglobin level with the risk of carotid plaque beyond metabolic abnormalities among asymptomatic adults without major adverse clinical events: a cross-sectional cohort study. BMC Cardiovasc Disord 2021; 21(1): 35.

38.

Dijk

Wangge

Graaf

, et al. Hemoglobin and atherosclerosis in patients with manifest arterial disease. The SMART-study. Atherosclerosis 2006; 188(2): 444–449.