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Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: A systematic review and
Zhen Ye1, Tingyi Hu2, Jin Wang1
1Hengyang Medical School, University of South China, Hengyang, China.
Insights
The systemic immune-inflammation index (SII) is linked to a higher risk of developing cardiovascular disease (CVD). Elevated SII may serve as a predictive biomarker for CVD, though evidence quality is currently low.
Area of Science:
- Cardiovascular Medicine
- Inflammation Biomarkers
- Epidemiology
Background:
- Inconsistent findings exist regarding the systemic immune-inflammation index (SII) as a predictor for cardiovascular disease (CVD).
- A comprehensive meta-analysis and systematic review were conducted to clarify the association between SII and CVD risk.
Approach:
- Systematic literature search across major databases (PubMed, Embase, Web of Science, Cochrane, CINAHL).
- Pooled risk estimates and weighted mean differences using fixed- or random-effects models.
- Assessed risk of bias with Newcastle-Ottawa Scale and evidence certainty with GRADE.
Key Points:
- Higher SII levels significantly correlate with an increased risk of overall CVD (HR = 1.39).
- This association was observed across multiple CVD subtypes, including ischemic stroke, hemorrhagic stroke, myocardial infarction, and peripheral arterial disease.
- SII levels were significantly higher in individuals with CVD compared to the general population.
Conclusions:
- The systemic immune-inflammation index (SII) shows potential as a biomarker for CVD development.
- Elevated SII is associated with increased CVD risk, but current evidence quality is low to very low.
- Further high-quality studies are needed to establish optimal cutoff values and identify populations that may benefit from SII monitoring.
Background:
Several studies have investigated the value of the systemic immune-inflammation index (SII) for predicting cardiovascular disease (CVD), but the results were inconsistent. Therefore, a meta-analysis and systematic review were conducted to assess the correlation between SII and risk of CVD.
Materials And Methods:
Two investigators systematically searched PubMed, Embase, Web of Science, Cochrane library, and CINAHL databases to identify all studies that examined the association between SII levels and CVD. The risk estimates of CVD for people with high SII compared to those with low SII levels and the weighted mean difference (WMD) between the CVD and control groups were pooled using fixed- or random-effects models based on the heterogeneity test. We used the Newcastle-Ottawa Scale to assess the risk of bias in eligible studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was applied to rate the certainty of evidence.
Results:
A total of 13 studies with 152,996 participants were included for analysis. The overall pooled results showed that higher SII was significantly associated with an increased risk of CVD (HR = 1.39, 95%CI: 1.20-1.61, P < 0.001). This increased risk could be observed in almost all CVD subtypes, including ischemic stroke (HR = 1.31, 95%CI: 1.06-1.63, P = 0.013), hemorrhagic stroke (HR = 1.22, 95%CI: 1.10-1.37, P < 0.001), myocardial infarction (HR = 1.11, 95%CI: 1.01-1.23, P = 0.027), and peripheral arterial disease (HR = 1.51, 95%CI: 1.18-1.93, P = 0.001). There were no significant but still similar trends in venous thrombosis (HR = 4.65, 95%CI: 0.66-32.71, P = 0.122), cerebral small vessel disease (HR = 1.09, 95%CI: 0.95-1.25, P = 0.233), and acute coronary syndrome (HR = 1.08, 95%CI: 0.96-1.22, P = 0.200). Furthermore, the pooled results showed that SII levels at the onset of CVD were significantly higher than that in the general population (WMD = 355.2, 95%CI: 234.8-475.6, P < 0.001), which was consistent across different CVD subtypes. The GRADE assessment suggested that the quality of current evidence from observational studies was low or very low.
Conclusion:
This study indicated that SII may be a potential biomarker for CVD development and elevated SII is associated with an increased risk of CVD. However, the quality of evidence is generally low. Additional well-designed studies are necessary to determine the optimal cutoff value and to characterize the benefited population.
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