Could Systemic Inflammation in Healthy Individuals With Obesity Indicate Subclinical Atherosclerosis?

Systemic immune-inflammation index mediates the association between metabolic dysfunction-associated fatty liver disease and sub-clinical carotid atherosclerosis: a mediation analysis

Background

Limited research has been conducted to quantitatively assess the impact of systemic inflammation in metabolic dysfunction-associated fatty liver disease (MAFLD) and sub-clinical carotid atherosclerosis (SCAS). The systemic immune-inflammation index (SII), which integrates inflammatory cells, has emerged as a reliable measure of local immune response and systemic inflammation Therefore, this study aims to assess the mediating role of SII in the association between MAFLD and SCAS in type 2 diabetes mellitus (T2DM).

Method

This study prospectively recruited 830 participants with T2DM from two centers. Unenhanced abdominal CT scans were conducted to evaluate MAFLD, while B-mode carotid ultrasonography was performed to assess SCAS. Weighted binomial logistic regression analysis and restricted cubic splines (RCS) analyses were employed to analyze the association between the SII and the risk of MAFLD and SCAS. Mediation analysis was further carried out to explore the potential mediating effect of the SII on the association between MAFLD and SCAS.

Results

The prevalence of both MAFLD and SCAS significantly increased as the SII quartiles increased (P<0.05). MAFLD emerged as an independent factor for SCAS risk across three adjusted models, exhibiting odds ratios of 2.15 (95%CI: 1.31-3.53, P < 0.001). Additionally, increased SII quartiles and Ln (SII) displayed positive associations with the risk of MAFLD and SCAS (P < 0.05). Furthermore, a significant dose-response relationship was observed (P for trend <0.001). The RCS analyses revealed a linear correlation of Ln (SII) with SCAS and MAFLD risk (P for nonlinearity<0.05). Importantly, SII and ln (SII) acted as the mediators in the association between MAFLD and SCAS following adjustments for shared risk factors, demonstrating a proportion-mediated effect of 7.8% and 10.9%.

Conclusion

SII was independently correlated with MAFLD and SCAS risk, while also acting as a mediator in the relationship between MAFLD and SCAS.

Correlation between blood inflammatory indices and carotid intima-media thickness in the middle-aged and elderly adults

OBJECTIVES

This study aimed to investigate the correlations between carotid intima-media thickness (IMT) and systemic immune inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte (NLR) ratio.

MATERIALS AND METHODS

This was a cross-sectional study enrolling a total of 582 middle-aged and elderly patients. The correlations between SII, PLR, and NLR with IMT were assessed using logistic regression models, which were subsequently incorporated into the underlying models with traditional risk factors and their predictive values for IMT.

RESULTS

NLR exhibited a significant correlation with IMT in the simple regression analysis (β = 0.01, 95 %CI= 0.00-0.02, p < 0.05). After controlling for potential confounding variables in the multivariate analysis, the association between NLR and both Maximum IMT [β = 0.04, 95 %CI = 0.02-0.07, p = 0.0006] and Mean IMT [β = 0.05, 95 %CI = 0.02-0.07, p = 0.0001] remained statistically significant. Additionally, PLR was found to be a significant independent predictor of Maximum IMT [β = 0.04, 95 % CI =0.00-0.07, p = 0.0242] and Mean IMT [β = 0.04, 95 % CI = 0.01-0.07, p = 0.0061]. Similarly, SII was identified as an independent predictor of Maximum IMT [β = 1.87, 95 % CI =1.24, p = 0.0003]. The study found a significant positive correlation between Maximum IMT and the levels NLR, PLR, and SII. Specifically, in the Maximum IMT group, higher quartiles of NLR, PLR, and SII were associated with increased odds ratios (OR) for elevated IMT levels, with statistically significant results for NLR (Q4vsQ1: OR 3.87, 95 % CI 1.81-8.29), PLR (Q4vsQ1: OR 2.84, 95 % CI 1.36-5.95), and SII (Q4vsQ1: OR 2.64, 95 % CI 1.30-5.37). Finally, the inclusion of NLR, PLR, and NLR+PLR+SII in the initial model with traditional risk factors resulted in a marginal improvement in the predictive ability for Maximum IMT, as evidenced by the net reclassification index (p < 0.05).

CONCLUSIONS

This study discovered a positive correlation between SII, PLR, NLR, and IMT, which are likely to emerge as new predictors for IMT thickening. These findings lay a theoretical reference for future predictive research and pathophysiological research on carotid intima-media thickening.

Prognostic value of Geriatric Nutritional Risk Index and systemic immune-inflammatory index in elderly patients with acute coronary syndromes

The objective of this study was to evaluate the predictive value of the Geriatric Nutritional Risk Index (GNRI) combined with the Systemic Immunoinflammatory Index (SII) for the risk of major adverse cardiovascular events (MACE) following percutaneous coronary intervention in elderly patients with acute coronary syndrome (ACS). We retrospectively reviewed the medical records of 1202 elderly patients with acute coronary syndromes divided into MACE and non-MACE groups according to whether they had a MACE. The sensitivity analysis utilized advanced machine learning algorithms to preliminarily identify the critical role of GNRI versus SII in predicting MACE risk. We conducted a detailed analysis using a restricted cubic spline approach to investigate the nonlinear relationship between GNRI, SII, and MACE risk further. We constructed a clinical prediction model based on three key factors: GNRI, SII, and Age. To validate the accuracy and usefulness of this model, we compared it to the widely used GRACE score using subject work and recall curves. Additionally, we compared the predictive value of models and GRACE scores in assessing the risk of MACE using the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI). This study included 827 patients. The GNRI scores were lower in the MACE group than in the non-MACE group, while the SII scores were higher in the MACE group (P < 0.001). The multifactorial analysis revealed a low GNRI (OR = 2.863, 95% CI: 2.026-4.047, P = 0.001), High SII (OR = 3.102, 95% CI: 2.213-4.348, P = 0.001). The area under the curve (AUC) for the predictive model was 0.778 (95% CI: 0.744-0.813, P = 0.001), while the AUC for the GRACE score was 0.744 (95% CI: 0.708-0.779, P = 0.001). NRI was calculated to be 0.5569, with NRI + at 0.1860 and NRI- at 0.3708. The IDI was found to be 0.0571, with a P-value of less than 0.001. These results suggest that the newly developed prediction model is more suitable for use with the population in this study than the GRACE score. The model constructed using GNRI and SII demonstrated good standardization and clinical impact, as evidenced by the standard, DCA, and clinical impact curves. The study shows that combining GNRI and SII can be a simple, cost-effective, and valuable way to predict the risk of MACE within one year in elderly acute coronary syndromes.

Lipid-Associated GWAS Loci Predict Antiatherogenic Effects of Rosuvastatin in Patients with Coronary Artery Disease

We have shown that lipid-associated loci discovered by genome-wide association studies (GWAS) have pleiotropic effects on lipid metabolism, carotid intima-media thickness (CIMT), and CAD risk. Here, we investigated the impact of lipid-associated GWAS loci on the efficacy of rosuvastatin therapy in terms of changes in plasma lipid levels and CIMT. The study comprised 116 CAD patients with hypercholesterolemia. CIMT, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) were measured at baseline and after 6 and 12 months of follow-up, respectively. Genotyping of fifteen lipid-associated GWAS loci was performed by the MassArray-4 System. Linear regression analysis adjusted for sex, age, body mass index, and rosuvastatin dose was used to estimate the phenotypic effects of polymorphisms, and p-values were calculated through adaptive permutation tests by the PLINK software, v1.9. Over one-year rosuvastatin therapy, a decrease in CIMT was linked to rs1689800, rs4846914, rs12328675, rs55730499, rs9987289, rs11220463, rs16942887, and rs881844 polymorphisms (Pperm < 0.05). TC change was associated with rs55730499, rs11220463, and rs6065906; LDL-C change was linked to the rs55730499, rs1689800, and rs16942887 polymorphisms; and TG change was linked to polymorphisms rs838880 and rs1883025 (Pperm < 0.05). In conclusion, polymorphisms rs1689800, rs55730499, rs11220463, and rs16942887 were found to be predictive markers for multiple antiatherogenic effects of rosuvastatin in CAD patients.

The systemic immune-inflammation index in predicting sepsis mortality

BACKGROUND

The systemic immune-inflammation index (SII) is a novel parameter and its role in the prognosis of sepsis has never been explored previously.

METHODS

We retrospectively assessed 267 patients with blood-culture confirmed sepsis. Clinical and laboratory data recorded at intensive care unit (ICU) admission were analyzed. Outcomes of interest included in-hospital mortality and length-of-stay (LOS) in the ICU. Sequential Organ Failure Assessment (SOFA) scores, SII, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) were calculated. Multivariable regression analysis was used to identify independent predictors of LOS and mortality. Area under receiver operator characteristic (AUROC) curves were used to determine optimum cutoffs, and the incremental effect of SII on the SOFA score was assessed using model discrimination and calibration properties.

RESULTS

There were 76 (28.5%) non-survivors. SII, NLR, and PLR were independent predictors of sepsis mortality, with adjusted odds ratios of 1.51 (1.24-1.84), 1.67 (1.30-2.13) and 1.24 (1.11-1.39). SII and SOFA score were independent predictors of LOS. SII had an AUROC of 0.848, and the optimum cutoff was 564 with a sensitivity and specificity of 85.5% and 71.2%. The addition of SII to the model had a significant incremental effect on the predictive ability of SOFA score (Net Reclassification Index = 0.084, P = 0.025; Integrated Discrimination Index = 0.056, P = 0.001).

CONCLUSION

The SII is an inexpensive parameter that can be used in addition to clinical sepsis scores to improve the accuracy of patient assessment.