https://orcid.org/0000-0003-2561-3260
Abstract
Objective
Our research was designed to investigate the relationship between systemic immune inflammation (SII) index and all-cause, cardiovascular disease (CVD), and cancer-related mortality in patients with CVD.
Methods
We used the National Health and Nutrition Examination Survey data from 1999 to 2018 to conduct this study. The association between SII index and all-cause, CVD, and cancer-related mortality in patients with CVD was examined using restricted cubic splines (RCS), Cox proportional hazard models, and subgroup analysis, respectively. CVD was defined as a composite of five outcomes of CVD, including coronary heart disease (CHD), congestive heart failure (CHF), angina pectoris, myocardial infarction, and stroke. Additionally, the link between SII index and all-cause, CVD, and cancer-related mortality in patients with a composite of five outcomes of CVD was also explored.
Results
In total, 5329 participants were included. The RCS also showed a U-curve correlation between SII index and the all-cause, CVD, and cancer-related mortality in patients with CVD. As compared with the individuals with lowest quartile of SII index, hazard ratios with 95% confidence intervals for all-cause, CVD, and cancer-related mortality across the quartiles were (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672)), (1.116 (0.815, 1.527), 1.017 (0.740, 1.398), and 1.220 (0.891, 1.670)), and (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672)), respectively, in the full-adjusted model. The SII index also had a U-shaped relationship with all-cause, CVD, and cancer-related mortality in patients with CHD, angina, and myocardial infarction. Additionally, the U-shaped relationship between SII index and all-cause, and cancer-related mortality also exists in CHF, and stroke. However, there was a positive linear correlation between SII index and CVD mortality in patients with CHF, and stroke.
Conclusion
In the United States general population, the correlation between SII index and all-cause, CVD, and cancer-related mortality showed a U-shaped curve in patients with CVD.
Introduction
Worldwide, cardiovascular diseases (CVD) are responsible for nearly one-third of all deaths and account for the majority of disease burdens.Citation1,Citation2 There are many types of CVD, including heart vascular diseases, heart structure and function diseases, heart conduction system diseases.Citation3 Among them, congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris, myocardial infarction (MI), and stroke are all CVD. Systemic inflammation caused by metabolic or immunological conditions is strongly linked to cardiovascular disease.Citation4 In addition, after CVD, cancer is the second leading cause of death in the world.Citation5 Similar to CVD, chronic inflammation is often associated with cancer initiation and progression.Citation6
Systemic immune inflammation (SII) index is a measure of systemic inflammation based on neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio and has been shown to be promising.Citation7,Citation8 There is a correlation between changes in gene expression in peripheral blood cells and various forms of systemic inflammation and immunosuppression, such as CVD.Citation9 Wingrove JA has found that peripheral-blood cell gene expression reflects coronary artery disease (CAD) severity and presence.Citation10 In addition, an analysis of gene expression and demographic characteristics could be useful for identifying obstructive CAD among nondiabetics without known CAD.Citation11 Deng MC and his team revealed that the expression of genes in peripheral blood mononuclear cells can be used to detect moderate/severe rejection in cardiac allograft recipients.Citation12 Additionally, Baechler EC also found global gene expression profiling of peripheral blood mononuclear cells was used to identify distinct gene expression patterns that differentiate most systemic lupus erythematosus patients from healthy controls.Citation13 A recent study shows major cardiovascular events after coronary intervention were better predicted by SII index than traditional risk factors in CAD patients.Citation14 In addition, the SII index has recently been shown to have independent prognostic value in several cancer types.Citation15 Silva-Vaz P also found that SII index is exclusively used for assessing prognosis and therapeutic outcomes in a variety of cancers.Citation16
At present, the relationship between SII index and mortality (all-cause, cardiovascular disease (CVD), and cancer) is unclear in United States (US) general population. Therefore, SII index in population-based cohorts throughout the US general population was analyzed using the National Health and Nutrition Examination Survey (NHANES) 1999–2018 to explore the association of SII index with all-cause mortality, CVD, and cancer-related mortality.
Materials and Methods
Study Population
The current cross-sectional research was based on the NHANES, a survey of nutrition and health in the United States that is representative nationwide.Citation17 In the total sample of 106,203 participants, there were 11,312 without SII index data. Furthermore, we excluded participants who did not have CVD data (n =88,259) and mortality data (n =1303). Finally, 5329 participants were included in the final analysis. The NHANES website (https://www.cdc.gov/nchs/nhanes/) has comprehensive information on the survey’s design, methodology, and statistics. The National Center for Health Statistics Research Ethics Review Board approved all protocols, and informed permission was acquired from all participants included in the investigation.Citation18
Calculation of the SII Index
The blood samples were collected from fasting participants in the study. The automated hematology analyzing devices (Coulter® DxH 800 analyzer) were used to measure blood count (neutrophil, lymphocyte, and platelet counts). In this study, we calculated SII index for each participant as follows: SII index (×109/L) = neutrophil count (×109/L)/lymphocyte count (×109/L) × platelet count (×109/L).Citation19 Furthermore, SII index was categorized into quartiles: Q1 (4.056–349.500), Q2 (349.501–508.800), Q3 (508.801–736.154), and Q4 (376.155–11,700.000).
All-Cause, CVD, and Cancer-Related Mortality
Overall mortality was the primary outcome and defined as death due to any cause during follow-up, including diseases of heart (I00-I09, I11, I13, I20-I51), malignant neoplasms (C00-C97), chronic lower respiratory diseases (J40-J47), accidents (unintentional injuries) (V01-X59, Y85-Y86), cerebrovascular diseases (I60-I69), Alzheimer’s disease (G30), Diabetes mellitus (E10-E14), influenza and pneumonia (J09-J18), nephritis, nephrotic syndrome and nephrosis (N00-N07, N17-N19, N25-N27), all other causes (residual). Follow-up commenced at the baseline examination date. CVD mortality was considered as the secondary outcome and included death due to diseases of heart (I00-I09, I11, I13, I20-I51). The comprehensive information on this program and its procedures were published on the NHANES website (https://www.cdc.gov/nchs/nhanes/).
Ascertainment of CVD
CVD was defined as a composite of self-reported doctor diagnoses of CHF, CHD, angina pectoris, MI, and stroke. NHANES surveys asked about the five diseases used to define CVD.Citation20
Covariates
The following covariates were considered in the study: age, sex, race/ethnicity, family poverty income ratio (PIR), education level, marital status, the complication of hypertension, and diabetes mellitus (DM), smoker, drinker, body mass index (BMI), waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean energy intake, hemoglobin (Hb), fast glucose (FBG), glycosylated hemoglobin (HbA1c), alanine transaminase (Alt), aspartate aminotransferase (Ast), albumin, total cholesterol (TC), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), uric acid (UA), blood urea nitrogen (BUN), serum creatinine (Scr), and estimated glomerular filtration rate (eGFR). Individuals who have smoked less than 100 cigarettes in their lifetime/smoked less than 100 cigarettes in their lifetime, do not smoke at all at present/smoked more than 100 cigarettes in their lifetime, and smoke some days or every day were defined as never smoke, former smokers, and now smokers, respectively. There are three categories of drinkers: current heavy alcohol consumption were defined as ≥3 drinks per day for females, ≥4 drinks per day for males, or binge drinking [≥4 drinks on same occasion for females, ≥5 drinks on same occasion for males] on 5 or more days per month; current moderate alcohol consumption were defined as ≥2 drinks per day for females, ≥3 drinks per day for males, or binge drinking ≥2 days per month. Those who did not meet the above criteria were classified as current mild alcohol user.Citation21 Hypertension was defined as an average systolic blood pressure more than 140 mmHg/diastolic blood pressure greater than 90 mmHg or self-reported use of antihypertensive medication. DM will be assessed by measures of blood glycohemoglobin, fasting plasma glucose, 2-hour glucose (Oral Glucose Tolerance Test), serum insulin in participants aged 12 years and over. Hb, FBG, HbA1c, Alt, Ast, albumin, TC, TG, HDL-C, UA, BUN, Scr, and eGFR were all determined in the laboratory. More information regarding the variables used is available at https://www.cdc.gov/nchs/nhanes/index.htm.
Statistical Analysis
Mean (standard deviation) and quantity (percentage, %) are used to represent continuous and categorical variables, respectively. For continuous variables, Student’s t-test or one-way ANOVA were used. In addition, to compare the constituent ratios between each group, the chi-square test was performed. The SII index was divided into four groups: Q1 (4.056–349.500), Q2 (349.501–508.800), Q3 (508.801–736.154), and Q4 (376.155–11,700.000), with Q1 serving as the reference group. Cox regression analysis was used to examine the relationship between SII index and all-cause, cardiovascular, and cancer-related mortality in patients with CVD. First, model 1 was adjusted for age and sex. Second, model 2 was further adjusted for race/ethnicity, education level, marital status, family PIR, the complication of hypertension, and DM, smoker, and drinker. Finally, model 3 was further adjusted for BMI, waist circumference, mean energy intake, SBP, DBP, Hb, FBG, HbA1c, Alt, Ast, albumin, TC, TG, HDL-C, UA, BUN, Scr, and eGFR, as our final model. Then, using the above methods and models, we also explored the relationship between SII index and all-cause, CVD, and cancer-related mortality in patients with a composite of 5 CVD outcomes (CHF, CHD, angina pectoris, MI, and stroke). All statistical analyses were performed using the “survey”, “openxlsx”, “dplyr”, “reshape2”, and “do” packages of R version 3.6.4 (R Foundation for Statistical Computing, Vienna, Austria), Stata version 13.0 (Stata Corporation, College Station, TX, USA), and SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). Two-side P-value <0.05 was regarded as statistically significant.
Results
Baseline Characteristics
shows the baseline characteristics of included participants. The all-cause, CVD, and cancer-related mortality were 44.1%, 18.1, and 7.4%, respectively. The average age of included 5329 participants in our study was 64.70 ± 0.29 years, with 2997 (56.2%) were male. The education level, the complication of DM, smoke status, drink status, the prevalence of CHD, Angina, MI, and Stroke, BMI, waist circumference, SBP, FBG, HbA1c, UA, TC, TG, and HDL-C had no significant difference among Q1, Q2, and Q3, and Q4 group. Individuals in Q2 group were the youngest, with 13.8% of males. Q2 group occupied the lowest proportion of hypertension, DM, CHF, MI, and cancer-related mortality, and the highest proportion of stroke. And, Q2 group had the highest level of HbA1c, mean energy intake, albumin, Hb, and eGFR, and the lowest level of waist circumference, SBP, DBP, mmHg, UA, Scr, and HDL-C. In addition, participants in Q3 group occupied a lowest proportion of angina pectoris, and had the highest level of BMI, waist circumference, SBP, and TG. Compared with the Q1, Q2, and Q3 group, individuals in Q4 occupied a highest proportion of CVD, CHF, MI, all-cause mortality, CVD mortality, and cancer-related mortality, and had the highest level of FBG, CRP, BUN, UA, Scr, TC, and HDL-C.
Table 1 Characteristics of the Study Population Based on SII Index Quartiles
Associations of SII Index with All-Cause, CVD, and Cancer-Related Mortality in CVD
According to , the restricted cubic spline (RCS) curve illustrates the U-shaped association between SII index and all-cause, CVD, and cancer-related mortality in participants with CVD (P for nonlinearity <0.05). After adjusting for underlying confounding variables, compared to Q1 group, the hazard ratios (HRs) with 95% confidence intervals (CIs) for all-cause, CVD, and cancer-related mortality across rising quartiles were (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672)), (1.116 (0.815, 1.527), 1.017 (0.740, 1.398), and 1.220 (0.891, 1.670)), and (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672)) for SII index (). According to the SII index quartiles, the differences in survival rate of all-cause (), CVD (), and cancer-related () mortality were shown in Kaplan–Meier survival curves. Individuals in Q4 group had the highest risk of all-cause, CVD, and cancer-related mortality (Log-rank P <0.001, Log-rank P <0.001, and Log-rank P <0.001, respectively). Subgroup analysis for the associations of SII index with all-cause, cardiovascular, and cancer-related mortality was conducted based on age, sex, hypertension, DM, and BMI (). Additionally, RCS curves for subgroup analysis were shown in Supplementary Figures 1–3.
Table 2 Cox Regression Analysis of SII Index for All-Cause, CVD, and Cancer-Related Mortality in CVD Patients
Table 3 Subgroups Analysis for the Associations of SII Index with All-Cause Mortality in CVD Patients
Table 4 Subgroups Analysis for the Associations of SII Index with CVD Mortality in CVD Patients
Table 5 Subgroups Analysis for the Associations of SII Index with Cancer-Related Mortality in CVD Patients
Figure 1 The RCS curve of the association between SII index and all-cause (A), CVD (B and C) cancer-related mortality in CVD patients.
Figure 2 Kaplan-Meier survival curve for all-cause (A), CVD (B and C) cancer-related mortality in CVD patients.
Associations of SII Index with All-Cause, CVD, and Cancer-Related Mortality in CHD
There were the U-shaped relationships between SII index and all-cause, CVD, and cancer-related mortality in participants with CHD (, P for nonlinearity <0.05). After adjusting for underlying confounding variables, compared to Q1 group, the HRs with 95% CIs for all-cause, CVD, and cancer-related mortality across rising quartiles were (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672), 1.116 (0.815, 1.527), 1.017 (0.740, 1.398), and 1.220 (0.891, 1.670), and 0.808 (0.499, 1.308), 0.931 (0.586, 1.478), and 1.067 (0.668, 1.703)) for SII index (Supplementary Table 1). In accordance with the SII index quartiles, the differences in survival rate of all-cause (), cardiovascular (), and cancer-related () mortality were shown in Kaplan–Meier survival curves. Individuals in Q4 group had the highest risk of all-cause, CVD, and cancer-related mortality (Log-rank P <0.001, Log-rank P <0.001, and Log-rank P =0.180, respectively). On the basis of age, gender, hypertension, DM, and BMI, subgroup analyses were performed to determine the relationships between the SII index and all-cause, CVD, and cancer-related mortality (Supplementary Tables 2–4). Moreover, RCS curves for subgroup analysis were shown in Supplementary Figures 4–6.
Figure 3 The RCS curve of the association between SII index and all-cause (A), CVD (B and C) cancer-related mortality in CHD patients.
Figure 4 Kaplan-Meier survival curve for all-cause (A), CVD (B and C) cancer-related mortality in CHD patients.
Associations of SII Index with All-Cause, CVD, and Cancer-Related Mortality in CHF
The RCS plot also shows the U-shaped association between SII index and all-cause, and cancer-related mortality in participants with CHF (, and , P for nonlinearity <0.05). However, there was a positive and linear relationship between SII index and CVD mortality (). After adjusting for underlying confounding variables, compared to Q1 group, the HRs with 95% CIs for all-cause, CVD, and cancer-related mortality across rising quartiles were (1.040 (0.832, 1.299), 1.009 (0.813, 1.252), and 1.212 (0.987, 1.488)), (1.165 (0.825, 1.645), 1.058 (0.754, 1.484), and 1.292 (0.935, 1.785)), and (0.528 (0.293, 0.952), 0.632 (0.365, 1.093), and 0.942 (0.576, 1.541)) for SII index (Supplementary Table 5). Kaplan–Meier survival curves illustrated differences in survival rate of all-cause (), CVD (), and cancer-related () mortality. Patients in Q4 group also had the highest risk of all-cause, CVD, and cancer-related mortality (Log-rank P <0.001, Log-rank P <0.001, and Log-rank P =0.009, respectively). Based on age, sex, hypertension, DM, and BMI, subgroup analysis for the correlations of SII index with all-cause, CVD, and cancer-related mortality was performed (Supplementary Tables 6–8). Additionally, Supplementary Figures 7–9 included RCS curves for subgroup analysis.
Figure 5 The RCS curve of the association between SII index and all-cause (A), CVD (B and C) cancer-related mortality in CHF patients.
Figure 6 Kaplan-Meier survival curve for all-cause (A), CVD (B and C) cancer-related mortality in CHF patients.
Associations of SII Index with All-Cause, CVD, and Cancer-Related Mortality in Angina Pectoris
As shown in , the RCS curve also indicates a U-shaped relationship between SII index and all-cause mortality, CVD, and cancer-related mortality (P for nonlinearity <0.05). After adjusting for underlying confounding variables, compared to Q1 group, the HRs with 95% CIs for all-cause, cardiovascular, and cancer-related mortality across rising quartiles were (0.972 (0.759, 1.244), 0.991 (0.777, 1.263), and 1.250 (0.987, 1.583)), (0.933 (0.641, 1.357), 0.877 (0.604, 1.273), and 1.204 (0.841, 1.722)), and (1.420 (0.752, 2.681), 1.287 (0.676, 2.449), and 1.761 (0.949, 3.269)) for SII index (Supplementary Table 9). The variations in survival rate of all-cause (), cardiovascular (), and cancer-related () mortality were shown in Kaplan–Meier survival curves. Participants in Q4 group had the highest risk of all-cause, cardiovascular, and cancer-related mortality (Log-rank P <0.001, Log-rank P =0.013, and Log-rank P =0.079, respectively). Based on age, sex, hypertension, DM, and BMI, subgroup analysis was performed to determine the link between the SII index and all-cause, CVD, and cancer-related mortality (Supplementary Tables 10–12). In addition, RCS curves for subgroup analysis were also included in Supplementary Figures 10–12.
Figure 7 The RCS curve of the association between SII index and all-cause (A), CVD (B and C) cancer-related mortality in angina pectoris patients.
Figure 8 Kaplan-Meier survival curve for all-cause (A), CVD (B and C) cancer-related mortality in angina pectoris patients.
Associations of SII Index with All-Cause, CVD, and Cancer-Related Mortality in Myocardial Infarction
The correlation between the U-curve is also between SII index and all-cause, CVD, and cancer-related mortality in individuals with myocardial infarction (, P for nonlinearity <0.05). After adjusting for underlying confounding variables, compared to Q1 group, the HRs with 95% CIs for all-cause, CVD, and cancer-related mortality across rising quartiles were (1.096 (0.904, 1.328), 0.980 (0.803, 1.196), and 1.249 (1.035, 1.507), (1.090 (0.818, 1.453), 0.891 (0.657, 1.208), and 1.176 (0.886, 1.563)), and 0.815 (0.523, 1.270), 0.739 (0.469, 1.166), and 1.041 (0.685, 1.584)) for SII index (Supplementary Table 13). The differences in survival rate of all-cause (), cardiovascular (), and cancer-related () mortality were shown in Kaplan–Meier survival curves. Q4 group had the highest risk of all-cause, cardiovascular, and cancer-related mortality (Log-rank P <0.001, Log-rank P <0.001, and Log-rank P =0.056, respectively). Subgroup analysis for SII index and all-cause, cardiovascular, and cancer-related mortality was done by age, sex, hypertension, DM, and BMI (Supplementary Tables 14–16). Moreover, Supplementary Figures 13–15 show RCS curves for subgroup analysis.
Figure 9 The RCS curve of the association between SII index and all-cause (A), CVD (B and C) cancer-related mortality in MI patients.
Figure 10 Kaplan-Meier survival curve for all-cause (A), CVD (B and C) cancer-related mortality in MI patients.
Associations of SII Index with All-Cause, CVD, and Cancer-Related Mortality in Stroke
The RCS plot demonstrates a U-shaped connection between SII index and all-cause, and cancer-related mortality in CHF patients (, and ; P for nonlinearity <0.05). However, SII index and CVD mortality were positively correlated (). After adjusting for underlying confounding variables, compared to Q1 group, the HRs with 95% CIs for all-cause, cardiovascular, and cancer-related mortality across rising quartiles were (1.022 (0.633, 1.650), 0.924 (0.567, 1.507), and 1.369 (0.847, 2.211), 1.514 (0.671, 3.4140), 1.200 (0.524, 2.748), and 1.925 (0.855, 4.333), and (0.440 (0.131, 1.474), 0.240 (0.063, 0.912), and 0.674 (0.202, 2.250)) for SII index (Supplementary Table 17). Kaplan–Meier survival curves showed the difference in survival rates between all-cause (), cardiovascular (), and cancer-related () mortality. Patients with stroke in Q4 group had the highest risk of all-cause, cardiovascular, and cancer-related mortality (Log-rank P =0.200, Log-rank P =0.330, and Log-rank P =0.520, respectively). A subgroup analysis was conducted to examine the associations between SII index and all-cause, CVD, and cancer-related mortality based on age, sex, hypertension, DM, and BMI (Supplementary Tables 18–20). Finally, Supplementary Figures 16–18 show RCS curves for subgroup analysis.
Figure 11 The RCS curve of the association between SII index and all-cause (A), CVD (B and C) cancer-related mortality in stroke patients.
Figure 12 Kaplan-Meier survival curve for all-cause (A), CVD (B and C) cancer-related mortality in stroke patients.
Discussion
Using the large general population dataset in the US, our study found that there was the U-shaped association between SII index and all-cause, CVD, and cancer-related mortality in participants with CVD. SII index, a novel inflammatory biomarker, might provide a more accurate and thorough evaluation of the immune and inflammatory responses.Citation22 In addition, clinical outcomes may be poor in diseases with high SII levels due to high inflammatory activity.Citation23 High SII levels have been shown to be an effective predictor of survival in cancer patients, chronic heart failure patients, and elderly non-ST-elevation myocardial infarction patients over the long run.Citation24,Citation25 The platelets are essential for prothrombotic potential during arterial thrombosis, as well as in atherogenesis and inflammation. And, platelets are crucial for preserving hemostasis and coagulation as well as preventing bleeding.Citation26,Citation27 By interacting with the endothelium, leukocytes, and non-activated platelets, platelets actively contribute to the inflammatory and atherosclerotic process and advance atherosclerosis.Citation28,Citation29 Neutrophils, the most abundant subtype of white blood cell in the blood, are essential in mediating inflammation. By causing smooth muscle cells to lyse and die, neutrophils have been shown to cause tissue damage and inflammation in advanced stages of atherosclerosis.Citation30 Additionally, research has shown that neutrophils interact with platelets to influence important biological processes connected to atherosclerosis, thrombosis, and ischemic stroke.Citation31 Lymphocytes are essential for controlling the inflammatory response at every stage of the atherosclerotic process. The development of atherosclerosis is linked to low lymphocyte numbers. After lymphocytes undergo apoptosis, the lipid core of an atherosclerotic plaque ruptures and creates a thrombus.Citation32 Furthermore, Yao J and his team found that a low lymphocyte count is positively connected with cardiovascular events and is linked to a poor prognosis in a number of illnesses, including stable coronary artery disease.Citation33 Thus, based on the above-mentioned evidence, it is reasonable to suggest a U-shaped relationship between SII index and all-cause, CVD, and cancer-related mortality in patients with CVD in American population.
Previous research also shows various diseases have been linked to inflammation, such as CVD, and even cancer.Citation34 Karaaslan T found that COVID-19 mortality can be predicted independently using the SII index, a proinflammatory marker of systemic inflammation.Citation35 Among elderly non-ST-segment elevation myocardial infarction patients, Orhan AL demonstrated an independent link between SII index and in-hospital and long-term mortality.Citation25 He L and his team found that, in a US atherosclerotic cardiovascular disease population (ASCVD), there was a non-linear association between the SII index and all-cause mortality. Above the threshold value of 6.57 of SII index, ASCVD patients were associated with a higher probability of all-cause death.Citation36 The results of this study are consistent with this conclusion. In addition, as a result of Li H, they found that high SII index levels may contribute to an increase in all-cause and CVD mortality in general populations, while physical activity may have a beneficial effect on these relationships.Citation37 Accordingly, we concluded that maintaining appropriate levels of SII in vivo can effectively reduce the incidence of cardiovascular events. Meanwhile, Öcal L suggested that in patients with ST-segment elevation myocardial infarction, the systemic immune-inflammation index predicts in-hospital and long-term outcomes. According to Kaplan–Meier survival methods, Q1, Q2, Q3 and Q4 group had 97.6%, 96.9%, 91.6%, and 81.0% overall survival, respectively.Citation38 Compared to high levels of SII index, low levels of SII index can effectively reduce all-cause, and CVD mortality. However, there have been no studies exploring the effect of further dividing low levels of SII on all-cause, and CVD mortality. Therefore, the effect of lowering SII index on all-cause, and CVD patients’ survival requires further study. For the association between SII index and cancer-related mortality, Nøst TH observed that even out of 17 cancers had positive associations with SII index. Among them, colorectal and lung cancer showed the strongest associations.Citation39 In addition, Chen et al also revealed that patients with colorectal cancer benefit from SII in terms of predicting survival outcome and identifying high-risk patients.Citation40
In addition, in the research, we also explored the link between SII index and all-cause, CVD, and cancer-related mortality in patients with a composite of five outcomes of CVD, including CHD, CHF, angina pectoris, myocardial infarction, and stroke. Yang YL found that in CAD patients after coronary angioplasty, SII index fared better than traditional risk factors in predicting major cardiovascular events.Citation14 In addition, an analysis of patients with segment elevation myocardial infarction undergoing percutaneous coronary intervention found that SII index was a better predictor of in-hospital and long-term outcomes than traditional risk factors.Citation38 Meanwhile, unselected patients with acute coronary syndromes may benefit from SII, which as a prognostic indicator.Citation41 Wang Z found that SII index was a significant risk factor for all-cause mortality in HCM patients.Citation42 Agus HZ also revealed that high SII levels are independently associated with in-hospital mortality and patients with infective endocarditis were predicted well by the SII index.Citation43 The high SII index levels can predict an increased risk of 30-day and 90-day in-hospital mortality, as well as major adverse cardiac events in critically ill CHF patients.Citation44 The 30-day all-cause mortality was higher in patients with acute ischemic stroke who had elevated SII. The SII may be useful in elucidating the role of inflammation, thrombocytosis, and immunity interaction in the development of acute ischemic stroke.Citation45
Limitation
This study also had several limitations. Firstly, the samples we analyzed all came from the NHANES public database, which covered the years 1999–2018. There is a need to recruit participants from other nations in order to corroborate our findings, particularly the inflection point. Secondly, due to the limitation of the NHANES database, we did not have data on the medication duration and dosage of antiplatelet drug, antilipidemic drug, and antihypertensive drug, which may be bias of the factors in the Cox regression models. Finally, as a retrospective study, the bias cannot be avoided to some relevant results, prospective studies are thus needed to validate the findings.
Conclusion
In US general population, there was U-shaped association between the SII index and all-cause mortality, CVD, and cancer-related mortality in patients with CVD. In addition, this correlation was also found in patients with CHD, angina pectoris, and myocardial infarction. The association between SII and all-cause, and cancer-related mortality in patients with CHF, and stroke was also U-shaped. However, with the increase of SII index, CVD mortality also increased in patients with CHF, and stroke. The SII index might be used as a clinical predictor for all-cause, CVD, and cancer-related mortality in patients with CVD. The potential mechanisms of SII index in all-cause, CVD, and cancer-related mortality need further exploration.
Data Sharing Statement
The survey data are publicly available on the Internet for data users and researchers throughout the world https://www.cdc.gov/nchs/nhanes/.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare that they have no competing interests.
Acknowledgments
The authors thank the staff and the participants of the NHANES study for their valuable contributions.
Additional information
Funding
References
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