Skip to main content
Download PDF

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

  • Published:

Systemic immune-inflammation index (SII) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in the general population

European Journal of Medical Research volume 28, Article number: 575 (2023) Cite this article

Abstract

Background

An elevated systemic immune-inflammation index (SII) is associated with higher mortality in patients with coronary artery disease and other diseases. However, the potential of SII for predicting mortality in the general population has been underexplored. Therefore, this study aimed to analyze the relationship between the SII and all-cause, cardiovascular disease, and cardiocerebrovascular disease mortality in the general population.

Methods

This study involved 26,855 participants (≥ 18 years) from the National Health and Nutrition Examination Survey 1999–2014 who were grouped according to the SII tertiles. Survival differences between the groups were analyzed using log-rank tests and Kaplan–Meier plots. Furthermore, multivariate Cox regression and restricted cubic spline analyses were used to examine the relationship between the SII and all-cause, cardiovascular, and cardio-cerebrovascular mortality.

Results

Overall, 1947 (7.425%) participants died following an average follow-up of 87.99 ± 54.04 months. Among these, 325 (1.210%) deaths were related to cardiovascular diseases and 392 (1.459%) to cardio-cerebrovascular mortality. Kaplan–Meier analysis revealed statistically significant differences in all-cause, cardiovascular, and cerebrovascular mortality between the SII tertiles (log-rank test: all P < 0.001). Multi-adjusted models showed that participants in the highest tertile of SII had a higher risk of death from all-cause (hazard ratio [HR] = 1.48, 95% confidence interval [CI] 1.48–1.48) and cardiovascular mortality (HR = 1.60, 95% CI 1.60–1.61) compared with those in the lowest tertile. In addition, the restricted cubic spline curve indicated a nonlinear association between SII and all-cause mortality (P < 0.001), with threshold value of SII at 18.284. There was a 15% decrease in the risk of all-cause mortality for each twofold change in SII on the left flank (HR = 0.85, 95% CI 0.69–1.05) and a 42% increase (HR = 1.42, 95% CI 1.23–1.64) on the right flank of the inflection point. In addition, the risk of cardiovascular mortality increased nonlinearly by 39% per twofold change in SII (HR = 1.39, 95% CI 1.07–1.81). There was also a nonlinear increase in the risk of cardio-cerebrovascular mortality per twofold change in SII (HR = 1.29, 95% CI 1.00–1.66).

Conclusions

In the general population, the SII was significantly associated with all-cause, cardiovascular, and cardio-cerebrovascular mortality, regardless of the established risk factors.

Introduction

Nearly one-third of all deaths worldwide are caused by cardiovascular disease (CVD) [1, 2]. In the United States (US), this accounts for 17% of all health expenditures [2, 3]. Therefore, effective mortality prediction tools are essential for preventing and treating CVD as soon as possible [4]. The burden of CVD and disability-adjusted life years is largely attributed to stroke and ischemic heart disease [5], and atherosclerosis is the major cause of these diseases [6]. Atherosclerosis is associated with thrombosis, oxidative stress, and endothelial damage. [7, 8]. Recent research has shown that the immune system has a profound relationship with inflammation during the development of atherosclerosis [8, 9]. The cells of the immune system comprise lymphocytes, neutrophils, monocytes, and macrophages, which play different roles in atherosclerosis development. For example, neutrophils activate macrophages, promote monocyte recruitment, and initiate cytotoxicity at various stages of atherosclerosis. In contrast, lymphocytes regulate inflammation and reduce atherosclerosis [10,11,12]. In addition, platelets adhere to vessel walls, aggregate leukocytes, and initiate atherosclerosis before invading plaques [13,14,15,16].

Recently, various cost-effective and verified inflammatory and immune indicators have been examined. The neutrophil–lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) offer better predictions of CVD outcomes than single cells [16,17,—18]. A new systemic immune-inflammation index (SII), calculated as (neutrophil × platelet)/lymphocyte, has been introduced for CVD, which provides a more comprehensive reflection of immunity and inflammation [19, 20]. SII can be easily obtained by routine whole blood count test (the most commonly used clinical test). This marker was proposed by Hu et al. [21]. In the field of cancer, SII has a better predictive value for prognosis compared to other inflammatory factors [22, 23]. Studies have also indicated that SII may be crucial in CVD prognosis and mortality [24].

In some studies, the SII has been associated with CVD risk. However, inconsistent results have been obtained, highlighting the importance of early detection and intervention for CVD. Therefore, we aimed to investigate the clinical significance of the SII in predicting all-cause, cardiovascular, and cardio-cerebrovascular mortality in the adult population. Further, we aimed to assess the effectiveness of the SII as an affordable and accessible CVD risk indicator.

Methods

Study population

The population for this study was from the National Health and Nutrition Survey (NHANES) survey. Informed consent was obtained from all participants, and the NCHS Ethics Review Board approved the study protocol. Additional file 1 (Table S1) that shows the baseline characteristics of the included population and the follow-up missing population.  Overall, 82,091 participants from the surveys conducted between 1999 and 2014 were selected for this study. Exclusion criteria were as follows: (a) individuals aged < 18 years (n = 0); (b) individuals whose peripheral lymphocyte, neutrophil, and platelet counts were not available (n = 14,990); and c) individuals who were lost to follow-up (n = 40,246). Consequently, the final analysis was conducted on 26,855 participants. Figure 1 shows a flowchart of the selection of the study population.

Fig. 1
figure 1

Flow chart of the participants

Full size image

Exposure

The NHANES Laboratory/Medical Technologists Procedures Manual (LPM) describes the procedures for collecting and processing blood specimens. The complete blood count (CBC) parameters were determined using the Coulter® method for counting and sizing, as well as an automatic mixing and dilution device for sample processing. Hemoglobinometry was performed using a single-beam photometer, while VCS technology was used for differential analysis of WBCs. The three simultaneous measurements conducted included individual cell volume (V), high-frequency conductivity (C), and laser light scattering (S). The Scattergrams plot the cells based on these three parameters.

Under established procedures, all blood specimens were withdrawn in the morning following a 9-h fast. CBC for all blood samples was obtained using Beckman Coulter MAXM. Three simultaneous measurements, comprising V, C, and S were used to sort the neutrophils and lymphocytes from the white blood cells. The number of thrombocytes was determined by multiplying the Plt histogram by the calibration constant, and expressing it as n × 103 cells/µL. The SII was calculated as (neutrophils × platelets)/lymphocyte [21].

Outcomes

The NCHS data linkage website provides detailed information on mortality status derived from the NHANES-linked National Death Index records [25]. Study outcomes, including all-cause, cardiovascular, and cardio-cerebrovascular mortality, were defined according to the 10th Revision of the International Classification of Diseases (ICD-10) [26]. All -cause death is the primary observation outcome event, while cardiovascular death and cardio-cerebrovascular death are secondary observation outcome events. The follow-up period was recorded from December 31, 2015, until the date of participation.

Covariates

Demographic information, such as age, sex, and ethnicity, was collected through interview questionnaires. Participants who consumed 12 or more drinks in the last 12 months were classified as drinkers, while those who had smoked at least 100 cigarettes in their lifetime were classified as smokers [27]. The questionnaires also asked participants to self-report medical comorbidities such as hypertension, heart failure, diabetes mellitus, coronary heart disease, cancer, and stroke. Other covariates included hemoglobin, platelets, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, albumin, glycohemoglobin, blood urea nitrogen, creatinine, uric acid, cholesterol, and glucose levels.

Statistical analysis

The study participants were classified into three groups based on their SII tertile: low (< 391,809.52), medium (391,809.52–611,840), and high (> 611,840) SII groups. Continuous variables were expressed as means ± standard deviations, while categorical variables were expressed as frequencies with percentages. One-way ANOVA was used to analyze the differences in categorical or continuous variables among the SII tertiles. Kaplan–Meier plots and log-rank tests were used to determine the differential survival rates of all-cause, cardiovascular, and cardio-cerebrovascular mortality according to SII tertiles.

Multivariate Cox regression models were used to examine the relationship between the SII and all-cause, cardiovascular, and cardio-cerebrovascular mortality with hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 was unadjusted, and model 2 was adjusted for age, sex, ethnicity, educational level, marital status, smoking status, and drinking status. Model 3 was further adjusted for diabetes mellitus (DM), hypertension, heart failure (HF), coronary heart disease (CHD), stroke, cancer, body mass index (BMI), and levels of hemoglobin, glycohemoglobin, platelets, blood urea nitrogen, creatinine, uric acid, cholesterol, glucose, LDL-C, HDL-C, triglycerides, and albumin.

A restricted cubic spline (RCS) regression model was used to investigate the nonlinear relationship between the SII (per twofold change) and all-cause, cardiovascular, and cardiocerebrovascular mortality. The threshold point was determined for nonlinear relationships using a two-piece Cox regression model. Further investigation of the effect of the SII on death risk among men and women was conducted using sex-specific models. Empower(R) (X&Y Solutions, Inc., MA, USA) and Stata (version 14.0) were used for statistical analysis. Statistical significance was set at P-value < 0.05.

Results

Characteristics of participants at baseline

Table 1 compares the baseline characteristics of the 26,855 participants included in this study. The participants had an average age of 45.674 ± 17.238 years, and included 48.268% male participants. The average follow-up period was 7.33 ± 4.50 years, during which 1995 (7.43%) deaths occurred, including 325 (1.210%) and 392 (1.459%) caused by cardiovascular and cardio-cerebrovascular mortality, respectively. Most baseline covariates were statistically significant, except for uric acid, blood urea nitrogen, and HDL-C levels (P < 0.05). Kaplan–Meier curves for all-cause mortality showed worse outcomes with increase in the SII (log-rank P < 0.001; Fig. 2A). The fully adjusted Cox regression model (Table 2) showed HRs (95% CI) of 1.018 and 1.479 for participants in the medium and highest tertiles, respectively, compared with those in the lowest tertile. Based on the RCS model (Fig. 3A), the association between the SII and all-cause mortality was nonlinear and U-shaped (P < 0.001).

Table 1 Baseline characteristics of the study population stratified by tertiles of SII value
Full size table
Fig. 2
figure 2

Kaplan–Meier curves for all-cause (A), cardiovascular mortality (B) and cardio-cerebrovascular mortality (C) according to SII tertiles. SII: systemic immune-inflammation index; (Platelet count × Neutrophils)/lymphocytes

Full size image
Table 2 Multivariate Cox regression models between SII with all-cause, cardiovascular and cardio-cerebrovascular mortality
Full size table
Fig. 3
figure 3

Restricted cubic spline curves of relations between SII with all-cause (A), cardiovascular mortality (B) and cardio-cerebrovascular mortality (C). Analysis was adjusted for age, gender, ethnicity, education, marital status, smoker, drinker, DM, hypertension, HF, CHD, stroke, cancer, BMI, hemoglobin, glycohemoglobin, platelets, blood urea nitrogen,uric acid, creatinine, cholesterol glucose, LDL-C, HDL-C, triglyceride, albumin. The solid and dashed lines symbolize the hazard ratios and corresponding 95% confidence intervals, respectively

Full size image

The results of the two-piecewise Cox regression analysis are shown in Table 3. To fit the models, the SII was log2-transformed because of its skewed distribution. The P-values for the logarithmic likelihood ratio test were < 0.001 for the one-line Cox regression model and the two-piecewise regression model. A threshold value of 18.284 was determined for the SII. On the left and right flanks of the inflection point, a twofold change in SII resulted in a 15% decrease (HR = 0.85, 95% CI 0.69–1.05) and a 42% increase (HR = 1.42, 95% CI 1.23–1.64), respectively (see Additional file 1).

Table 3 Threshold effect analysis of SII on mortality using the two-piecewise regression model
Full size table

The association between SII and cardiovascular mortality

The Kaplan–Meier plot (Fig. 2B) showed that increased SII values were associated with reduced survival in CVD (log-rank P = 0.00077). When adjusted for all covariates (Table 2), the HR and CIs of cardiovascular mortality for those in the medium and highest tertiles were 1.33 (1.32–1.33) and 1.60 (1.60–1.61), respectively.

The RCS curve indicated a nonlinear association between SII and cardiovascular mortality (P for non-linearity = 0.071; Fig. 3B).

The association between SII and cardio-cerebrovascular mortality

In the Kaplan–Meier plot for cardiocerebrovascular disease (Fig. 2C), the higher SII values were associated with reduced survival (log-rank P = 0.00018). After adjusting for all covariates (Table 2), the HR and 95% CIs of cardio-cerebrovascular mortality were 1.21 (1.21–1.22) and 1.40 (1.39–1.40) for individuals in the medium and highest tertiles, respectively.

Figure 3C shows that the SII was nonlinearly associated with cardio-cerebrovascular mortality (P for non-linearity = 0.136).

Subgroup analysis

Table 4 shows that the HR and 95% CIs of cardiovascular mortality for women in the medium and highest tertiles were 3.906 (3.89–3.93) and 3.575 (3.56–3.595), respectively. In the same tertiles, the cardiovascular mortality HR and 95% CIs were 0.688 (0.68–0.69) and 1.24 (1.24–1.25), respectively.

Table 4 Stratified association between SII with all-cause, cardiovascular and cardio-cerebrovascular mortality by sex
Full size table

Additionally, Fig. 4A shows a nonlinear relationship between the SII and all-cause mortality in women (P for nonlinearity = 0.011) and men (P for nonlinearity = 0.012). Figure 4B shows a nonlinear relationship between the SII and cardiovascular mortality for women (P for nonlinearity = 0.150) and men (P for nonlinearity = 0.372). Finally, Fig. 4C indicates that a higher SII was nonlinearly associated with an increased risk of cardio-cerebrovascular mortality in both women (P for nonlinearity = 0.123) and men (P = 0.555).

Fig. 4
figure 4

Restricted cubic spline curves of relations between SII and mortality in different sex groups. A all-cause mortality; B cardiovascular mortality; C cardio-cerebrovascular mortality. Analysis was adjusted for age, gender, ethnicity, education, marital status, smoker, drinker, DM, hypertension, HF, CHD, stroke, cancer, BMI, hemoglobin, glycohemoglobin, platelets, blood urea nitrogen, creatinine,uric acid, cholesterol glucose, LDL-C, HDL-C, triglyceride, albumin

Full size image

Discussion

This study examined whether the SII could predict long-term outcomes in the general population. Increase in the SII appeared to be significantly related to cardiovascular and cardiocerebrovascular mortality, while it was nonlinearly related to all-cause mortality. The SII was associated with the lowest risk of death at a threshold of 18.28. Similar association patterns were observed in women and men. Although men and women showed comparable associations with all-cause mortality, women exhibited a stronger association with cardiovascular and cardiocerebrovascular mortality. Previous studies have confirmed that sex differences in CVD are attributable to the comprehensive expression of genetic and hormonal differences between men and women [28].

Recently, several studies have reported that the SII can predict CVD's severity, complications, and mortality [29]. Specifically, researchers found that the SII level was an independent prognostic indicator of poor prognosis at 3 months, which was related to the severity of stroke [30]. Furthermore, SII was significantly correlated with cerebral venous thrombosis. Xu et al. [31] reported that SII increased the total stroke risk, while Zhang et al. [32] found a significant correlation between SII and cerebral venous thrombosis. In multiple regression analysis, the degree of SII was a strong independent predictor.

Compared to other biological indicators such as PLR, NLR, or C-reactive protein, SII possesses unique advantages, making it a better predictor of coronary heart disease [33]. The SII is more stable than the blood cell count alone, which can be influenced by dehydration and fluid overload [33, 34]. This study’s results are consistent with previous research demonstrating a positive relationship between SII and cardiovascular mortality. Each twofold increase in the SII resulted in 20.4%, 21.8%, and 16.3% increases in all-cause, cardiovascular, and cardiocerebrovascular mortality, respectively.

The SII was also found to be significantly associated with all-cause mortality in several specific diseases, such as pancreatic [35], oral cavity squamous cell carcinoma [36], lung cancer [37], gastrointestinal cancer [38], urinary system cancer [39]. This study found a significant association between a higher SII and an increased risk of all-cause mortality over an average follow-up period of 88 months.

In part of the curve (SII > 18.28), all-cause mortality increased with increasing SII, and there was a positive correlation between the SII and cardiovascular mortality. This may be because of the significant association between inflammatory and immune status, as reflected by SII and CVD risk. Endothelial activation and platelet coordination are closely related, and recent research has shown a close relationship between cardiovascular mortality, platelet number, and platelet aggregation ability. Combining platelets with fibrin gives rise to a coronary thrombus, which is a crucial player in ACS pathophysiology [40]. In addition to causing blood clots, platelets also deliver mediators contributing to local inflammation [41]. Similarly, neutrophils secrete inflammatory mediators that can cause vessel wall degradation and endothelial dysfunction [42].

In addition, neutrophils interact with platelets, proteolyze coagulation factors, and release prothrombin molecules, activating the inflammatory response [43]. In addition to regulating inflammation, lymphocytes have anti-atherothrombotic properties [44].

Our study provides novel insights by demonstrating a nonlinear association between the SII and all-cause mortality, although the mechanism underlying this association is unclear. A reasonable explanation could be that participants with a lower SII had lower platelet counts. Suppose the platelet count decreases to a certain extent, bleeding symptoms may occur, which could be mild, such as in the skin and mucous membrane areas, or more serious and life-threatening, such as bleeding in the gastrointestinal or intracranial cavities [45, 46]. This may be one of the reasons for the U-shaped relationship between all-cause mortality and the SII.

Furthermore, we observed that the percentages of women and cancer patients in the group with a higher SII were higher, indicating that sex and cancer may explain the increased mortality risk associated with an increase in SII. Further research is needed to confirm the relationship between the SII increase and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality.

Strengths and limitations of the study

Our study, which had a large research population and a long follow-up period, allowed us to better understand the relationship between the SII and all-cause mortality rate of the general population and cardiovascular and cerebrovascular deaths. Furthermore, we used the RCS model to analyze the nonlinear relationship between the SII and all-cause mortality. However, the study has some limitations, such as using self-reported data for complications and living habits, which may have memory bias. Additionally, we only collected the baseline value of the SII, which may not reflect changes in the SII during follow-up. Although we adjusted for many confounding factors, other variables may have influenced the results.

Conclusions

In summary, this study showed that the SII was independently related to all-cause, cardiovascular, and cardio-cerebrovascular mortality in the general population. These findings suggest that the SII could be useful for identifying individuals at risk of poor clinical outcomes. Considering that the SII is easy and inexpensive to acquire, it could be a convenient tool for clinicians to stratify patient risks and plan preventive and treatment strategies.

Availability of data and materials

The data of this study are publicly available on the NHANES website.

Abbreviations

SII:

Systemic immune-inflammation index

NLR:

Neutrophil-lymphocyte ratios

PLR:

Platelet-lymphocyte ratios

LPM:

Laboratory/Medical Technologists Procedures Manual

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

ACS:

Acute coronary syndrome

NHANES:

National Health and Nutrition Examination Survey

NCHS:

National Center for Health Statistics

ICD:

International Classification of Diseases

DM:

Diabetes mellitus

HF:

Heart failure

CHD:

Coronary heart disease

BMI:

Body mass index

HR:

Hazard ratio

CI:

Confidence interval

RCS:

Restricted cubic spline

CAD:

Coronary artery disease

NIHSS:

National Institutes of Health Stroke Score

References

  1. Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J. 2016;37(42):3232–45. https://doi.org/10.1093/eurheartj/ehw334.

    Article  PubMed  Google Scholar 

  2. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, et al. Heart disease and stroke Statistics-2018 update: a report from the American Heart Association. Circulation. 2018;137(12):e67–492. https://doi.org/10.1161/CIR.0000000000000558.

    Article  PubMed  Google Scholar 

  3. Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123(8):933–44. https://doi.org/10.1161/CIR.0b013e31820a55f5.

    Article  PubMed  Google Scholar 

  4. Liu C, Du L, Wang S, Kong L, Zhang S, Li S, Zhang W, Du G. Differences in the prevention and control of cardiovascular and cerebrovascular diseases. Pharmacol Res. 2021;170: 105737. https://doi.org/10.1016/j.phrs.2021.105737.

    Article  PubMed  Google Scholar 

  5. Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392:1736–88. https://doi.org/10.1016/S0140-6736(18)32203-7.

    Article  Google Scholar 

  6. Herrington W, Lacey B, Sherliker P, Armitage J, Lewington S. Epidemiology of atherosclerosis and the potential to reduce the global burden of atherothrombotic disease. Circ Res. 2016;118(4):535–46. https://doi.org/10.1161/CIRCRESAHA.115.307611.

    Article  PubMed  CAS  Google Scholar 

  7. Wolf D, Ley K. Immunity and inflammation in atherosclerosis. Herz. 2019;44(2):107–20. https://doi.org/10.1007/s00059-019-4790-y.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Merei B. Atherosclerotic plaque adhesion strength and its role in plaque rupture, Doctoral dissertation. University of South Carolina. 2017.

  9. Libby P. The changing landscape of atherosclerosis. Nature. 2021;592(7855):524–33. https://doi.org/10.1038/s41586-021-03392-8.

    Article  PubMed  CAS  Google Scholar 

  10. Paquissi FC. The role of inflammation in cardiovascular diseases: the predictive value of neutrophil-lymphocyte ratio as a marker in peripheral arterial disease. Ther Clin Risk Manag. 2016;27(12):851–60. https://doi.org/10.2147/TCRM.S107635.

    Article  Google Scholar 

  11. Abdolmaleki F, Gheibi Hayat SM, Bianconi V, Johnston TP, Sahebkar A. Atherosclerosis and immunity: a perspective. Trends Cardiovasc Med. 2019;29(6):363–71. https://doi.org/10.1016/j.tcm.2018.09.017.

    Article  PubMed  CAS  Google Scholar 

  12. Libby P. Inflammation in atherosclerosis. Arterioscler Thromb Vasc Biol. 2012;32(9):2045–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Gounopoulos P, Merki E, Hansen LF, Choi SH, Tsimikas S. Antibodies to oxidized low density lipoprotein: epidemiological studies and potential clinical applications in cardiovascular disease. Min Cardioangiol. 2007;55:821.

    CAS  Google Scholar 

  14. Mass berg S, Brand K, Grüner S, Page S, Müller E, Müller I, et al. Acritical role of platelet adhesion in the initiation of atherosclerotic lesion formation. J Exp Med. 2002;196:887–96. https://doi.org/10.1084/jem.20012044.

    Article  CAS  Google Scholar 

  15. Daub K, Langer H, Seizer P, Stellos K, May AE, Goyal P, et al. Platelets induce differentiation of human CD34 + progenitor cells into foam cells and endothelial cells. FASEB J. 2006;20:2559–61. https://doi.org/10.1096/fj.06-6265fje.

    Article  PubMed  CAS  Google Scholar 

  16. Afari ME, Bhat T. Neutrophil to lymphocyte ratio (NLR) and cardiovascular diseases: an update. Expert Rev Cardiovasc Ther. 2016;14(5):573–7. https://doi.org/10.1586/14779072.2016.1154788.

    Article  PubMed  CAS  Google Scholar 

  17. Falk E, Nakano M, Bentzon JF, Finn AV, Virmani R. Update on acute coronary syndromes: the pathologists’ view. Eur Heart J. 2013;34(10):719–28.

    Article  PubMed  CAS  Google Scholar 

  18. Croce K, Libby P. Intertwining of thrombosis and inflammation in atherosclerosis. Curr Opin Hematol. 2007;14:55–61. https://doi.org/10.1097/00062752-200701000-00011.

    Article  PubMed  CAS  Google Scholar 

  19. Li M, Li Z, Wang Z, Yue C, Hu W, Lu H. Prognostic value of systemic immune-inflammation index in patients with pancreatic cancer: a meta-analysis. Clin Exp Med. 2022;22(4):637–46. https://doi.org/10.1007/s10238-021-00785-x.

    Article  PubMed  Google Scholar 

  20. Hu T, Wang J, Xiao R, Liao X, Liu M, Sun Z. Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: a systematic review and meta-analysis. Front Cardiovasc Med. 2022;8(9): 933913. https://doi.org/10.3389/fcvm.2022.933913.

    Article  CAS  Google Scholar 

  21. Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20:6212–22. https://doi.org/10.1158/1078-0432.CCR-14-0442.

    Article  PubMed  CAS  Google Scholar 

  22. Huang H, Liu Q, Zhu L, Zhang Y, Lu X, Wu Y, Liu L. Prognostic value of preoperative systemic immune-inflammation index in patients with cervical cancer. Sci Rep. 2019;9(1):3284. https://doi.org/10.1038/s41598-019-39150-0.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Yamamoto T, Kawada K, Obama K. Inflammation-related biomarkers for the prediction of prognosis in colorectal cancer patients. Int J Mol Sci. 2021;22(15):8002. https://doi.org/10.3390/ijms22158002.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Zhou Y-X, Li W-C, Xia S-H, Xiang T, Tang C, Luo J-L, et al. Predictive value of the systemic immune inflammation index for adverse outcomes in patients with acute ischemic stroke. Front Neurol. 2022;13: 836595. https://doi.org/10.3389/fneur.2022.836595.

    Article  PubMed  PubMed Central  Google Scholar 

  25. National Center for Health Statistics, Centers for Disease Control and Prevention. The linkage of national center for health statistics survey data to the National Death Index—2015 Linked Mortality File (LMF): methodology overview and analytic considerations. Hyattsville: National Center for Health Statistics. Office of Analysis and Epidemiology; 2015.

    Google Scholar 

  26. Li J, Covassin N, Bock JM, Mohamed EA, Pappoppula LP, Shafi C, et al. Excessive daytime sleepiness and cardiovascular mortality in US adults: a NHANES 2005–2008 follow-up study. Nat Sci Sleep. 2021;13:1049–59. https://doi.org/10.2147/NSS.S319675.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liao S, Wu N, Gong D, Tang X, Yin T, Zhang H, et al. Association of aldehydes exposure with obesity in adults. Ecotoxicol Environ Saf. 2020;201: 110785. https://doi.org/10.1016/j.ecoenv.2020.110785.

    Article  PubMed  CAS  Google Scholar 

  28. Shufelt CL, Pacheco C, Tweet MS, Miller VM. Sex-specific physiology and cardiovascular disease. Adv Exp Med Biol. 2018;1065:433–54. https://doi.org/10.1007/978-3-319-77932-4_27.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zhang X, Ding R, Li H, Liu Y, Ou W, Hu J, et al. An association between inflammation and cerebral venous thrombosis: a retrospective study. J Stroke Cerebrovasc Dis. 2021;30: 106084. https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.106084.

    Article  PubMed  Google Scholar 

  30. Weng Y, Zeng T, Huang H, Ren J, Wang J, Yang C, et al. Systemic immune-inflammation index predicts 3-month functional outcome in acute ischemic stroke patients treated with intravenous thrombolysis. Clin Interv Aging. 2021;16:877–86. https://doi.org/10.2147/CIA.S311047.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Xu M, Chen R, Liu L, Liu X, Hou J, Liao J, et al. Systemic immune-inflammation index and incident cardiovascular diseases among middle-aged and elderly Chinese adults: the Dongfeng-Tongji cohort study. Atherosclerosis. 2021;323:20–9. https://doi.org/10.1016/j.atherosclerosis.2021.02.012.

    Article  PubMed  CAS  Google Scholar 

  32. Liu Y, Ye T, Chen L, Jin T, Sheng Y, Wu G, et al. Systemic immune-inflammation index predicts the severity of coronary stenosis in patients with coronary heart disease. Coron Artery Dis. 2021;32:715–20. https://doi.org/10.1097/MCA.0000000000001037.

    Article  PubMed  Google Scholar 

  33. Azab B, Zaher M, Weiserbs KF, Torbey E, Lacossiere K, Gaddam S, et al. Usefulness of neutrophil to lymphocyte ratio in predicting short-and long-term mortality after Non ST-elevation myocardial infarction. Am J Cardiol. 2010;106:470–6. https://doi.org/10.1016/j.amjcard.2010.03.062.

    Article  PubMed  Google Scholar 

  34. Tekesin A, Tunç A. Inflammatory markers are beneficial in the early stages of cerebral venous thrombosis. Arq Neuropsiquiatr. 2019;77:101–5. https://doi.org/10.1590/0004-282x20190001.

    Article  PubMed  Google Scholar 

  35. Shui Y, Li M, Su J, Chen M, Gu X, Guo W. Prognostic and clinicopathological significance of systemic immune-inflammation index in pancreatic cancer: a meta-analysis of 2365 patients. Aging (Albany NY). 2021;13(16):20585–97. https://doi.org/10.18632/aging.203449.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hung SP, Chen PR, Ho TY, Chang KP, Chou WC, Lee CH, Wu YY, Chen PJ, Lin CH, Chou YC, Fan KH, Lin CY, Huang BS, Tung-Chieh Chang J, Wang CC, Tsang NM. Prognostic significance of the preoperative systemic immune-inflammation index in patients with oral cavity squamous cell carcinoma treated with curative surgery and adjuvant therapy. Cancer Med. 2021;10(2):649–58. https://doi.org/10.1002/cam4.3650.

    Article  PubMed  CAS  Google Scholar 

  37. Zhang Y, Chen B, Wang L, Wang R, Yang X. Systemic immune-inflammation index is a 19 promising noninvasive marker to predict survival of lung cancer: a meta-analysis. Medicine (Baltimore). 2019;98(3): e13788. https://doi.org/10.1097/MD.0000000000013788.

    Article  PubMed  Google Scholar 

  38. Zhang Y, Lin S, Yang X, Wang R, Luo L. Prognostic value of pretreatment systemic immune-inflammation index in patients with gastrointestinal cancers. J Cell Physiol. 2019;234(5):5555–63. https://doi.org/10.1002/jcp.27373.

    Article  PubMed  CAS  Google Scholar 

  39. Li X, Gu L, Chen Y, Chong Y, Wang X, Guo P, He D. Systemic immune-inflammation index is a promising non-invasive biomarker for predicting the survival of urinary system cancers: a systematic review and meta-analysis. Ann Med. 2021;53(1):1827–38. https://doi.org/10.1080/07853890.2021.1991591.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Rawish E, Nording H, Münte T, Langer HF. Platelets as mediators of neuroinflammation and thrombosis. Front Immunol. 2020;6(11): 548631. https://doi.org/10.3389/fimmu.2020.548631.

    Article  CAS  Google Scholar 

  41. Khodadi E. Platelet function in cardiovascular disease: activation of molecules and activation by molecules. Cardiovasc Toxicol. 2020;20(1):1–10. https://doi.org/10.1007/s12012-019-09555-4.

    Article  PubMed  CAS  Google Scholar 

  42. Bonaventura A, Vecchié A, Abbate A, Montecucco F. Neutrophil extracellular traps and cardiovascular diseases: an update. Cells. 2020;9(1):231. https://doi.org/10.3390/cells9010231.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Shirakawa K, Sano M. Neutrophils and neutrophil extracellular traps in cardiovascular disease: an overview and potential therapeutic approaches. Biomedicines. 2022;10(8):1850. https://doi.org/10.3390/biomedicines10081850.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Chistiakov DA, Orekhov AN, Bobryshev YV. Immune-inflammatory responses in atherosclerosis: role of an adaptive immunity mainly driven by T and B cells. Immunobiology. 2016;221(9):1014–33. https://doi.org/10.1016/j.imbio.2016.05.010.

    Article  PubMed  CAS  Google Scholar 

  45. Moulis G, Palmaro A, Montastruc JL, Godeau B, Lapeyre-Mestre M, Sailler L. Epidemiology of incident immune thrombocytopenia: a nationwide population-based study in France. Blood. 2014;124(22):3308–15. https://doi.org/10.1182/blood-2014-05-578336.

    Article  PubMed  CAS  Google Scholar 

  46. Neunert C, Noroozi N, Norman G, Buchanan GR, Goy J, Nazi I, et al. Severe bleeding events in adults and children with primary immune thrombocytopenia: a systematic review. J Thromb Haemost. 2015;13(3):457–64. https://doi.org/10.1111/jth.12813.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank the staff and the participants of the NHANES study for

their valuable contributions.

Funding

This study received no funding.

Author information

Authors and Affiliations

  1. Department of Pain Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an , 710061, China

    Huan Wang, Huiyong Nie & Gang Bu

  2. Department of Clinical Laboratory, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, China

    Xiaoning Tong

  3. The Department of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277, Yanta West Road, Xi’an, 710061, Shaanxi Province, China

    Xiaofang Bai

Authors
  1. Huan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huiyong Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Bu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoning Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaofang Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WH contributed to data collection, analysis and writing of the manuscript. BXF contributed to study design and writing of the manuscript. WH and BXF and NHY contributed to revise the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaofang Bai.

Ethics declarations

Ethics approval and consent to participate

The ethics review board of the National Center for Health Statistics approved all NHANES protocols and written informed consents were obtained from all participants.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Baseline characteristics of the included population and the follow-up missing population.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Nie, H., Bu, G. et al. Systemic immune-inflammation index (SII) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in the general population. Eur J Med Res 28, 575 (2023). https://doi.org/10.1186/s40001-023-01529-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40001-023-01529-1

Keywords

European Journal of Medical Research

ISSN: 2047-783X

Contact us