Associations between SII, SIRI, and cardiovascular disease in obese individuals: a nationwide cross-sectional analysis

Abstract

Background: Systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are comprehensive markers of inflammatory status. However, the correlation between SII and SIRI and the prevalence of cardiovascular disease (CVD) in populations with obesity remains unknown.

Methods: This is a cross-sectional study with data obtained from the National Health and Nutrition Examination Survey from 1999 to 2018. SII and SIRI were calculated using the following equations: SII = (platelet count × neutrophil count)/lymphocyte count. SIRI = (neutrophil count × monocyte count)/lymphocyte count. Spearman's rank correlation coefficient was used to assess the relationship between SII and SIRI and baseline variables. Logistic regression models and generalized additive model (GAM) with a spline smoothing function were used to evaluate the association between SIRI and CVD prevalence. Nomogram and receiver operating characteristic curve (ROC) analysis were used to assess the value of the risk prediction model.

Results: A total of 17,261 participants with obesity and SII and SIRI publicly available data were used for this study. Multivariate logistic regression analysis revealed that SIRI, rather than SII, was an independent risk factor for CVD prevalence. For every standard deviation increase in SIRI, there was a 13%, 15%, and 28% increase in the odds ratios of CVD prevalence (OR = 1.13, 95% CI: 1.04-1.22, P = 0.01), coronary heart disease (OR = 1.15, 95% CI: 1.05-1.26, P = 0.002), and congestive heart failure (OR = 1.28, 95% CI: 1.16-1.41, P < 0.001). ROC results demonstrated that SIRI had a certain accuracy in predicting CVD prevalence (AUC = 0.604), especially when combined with other variables used in the nomogram (AUC = 0.828). The smooth curve fitting regression analysis demonstrated a significant linear association between the risk of SIRI and the odds ratio of CVD prevalence (P for nonlinear = 0.275).

Conclusions: SIRI is a relatively stable indicator of inflammation and is independently associated with the prevalence of CVD. It may serve as a novel inflammatory indicator to estimate CVD prevalence in populations with obesity.

Keywords: cardiovascular diseases; inflammation; obesity; prevalence; systemic immune-inflammation index; systemic inflammation response index.

Figure 1

Subgroups were analyzed for the association between an increase in SIRI per one standard deviation and the odds ratios of CVD prevalence, stratified according to different clinical characteristics. The model was adjusted for age, gender, ethnicity, education levels, poverty income ratio (PIR), body mass index (BMI), estimated glomerular filtration rate (eGFR), alanine aminotransferase (ALT), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), diabetes mellitus (DM), hypertension, antihypertensives and glucose-lowering drugs, and smoking and drinking behavior.

Figure 2

Nomogram and receiver operating characteristics (ROC) curves to evaluate the risk of CVD in participants with obesity. (A) Nomogram to predict CVD prevalence. In the DM, hypertension, and hyperlipidemia groups, 0 means “no,” and 1 means “yes”. In the smoking group, 0 means “never”, 1 means “former”, and 2 means “current”. In the gender group, 0 means “female”, and 2 means “male”. (B) ROC curve of SIRI for the prevalence of CVD model. (C) ROC curves for SIRI combined with clinical variables in the nomogram for the CVD prevalence model. SIRI, systemic inflammation response index; ROC, receiver operating characteristic.

Figure 3

Smooth spline analysis of the relationship between SIRI and the risk of CVD prevalence. Smooth spline analysis was performed through generalized addictive model 3. In the plot, the risk of CVD prevalence increased proportionally with the increment of SIRI, and P for nonlinear = 0.275, confirming that the correlation between SIRI and CVD prevalence was linear in the whole range of SIRI.