Relationship Between Comprehensive Inflammatory Indicators and Gout Risk: A Cross-sectional Study Based on NHANES 2015–2018
Fan Zeng, Ou Mei, Min Lu, li wang, Gao-yan KuangIntroduction:
Gout development and progression are closely linked to inflammatory processes. Six novel systemic inflammation indices, including the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), inflammation score, systemic inflammation response index (SIRI), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) have shown significant value in assessing systemic inflammation severity and related comorbidities. This study aims to explore the relationship between these systemic inflammation indices and the risk of gout.
Methods:
Data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed in this study to explore the link between systemic inflammation indices and gout risk. Demographic information of all participants was collected, and multivariable logistic regression was applied to evaluate the relationship between the inflammation indices and gout risk. Restricted cubic splines (RCS) were used to investigate the nonlinear association between inflammation scores and gout risk. Stratified and interaction analyses were also conducted to examine the relationships in different populations.
results:
A total of 7,681 participants were included in the analysis. After adjusting for all covariates in the multivariable logistic regression model, the inflammation score demonstrated the most significant association with gout risk. Further RCS analysis revealed a nonlinear relationship (P=0.0134) with a turning point at 1.164. When the inflammation score was below 1.164, an increase in the score was significantly associated with a higher risk of gout. Stratified analyses indicated that age, race, and smoking status interacted with the relationship between inflammation scores and gout risk.
Results:
A total of 7,681 participants were included in the analysis. After adjusting for all covariates in the multivariable logistic regression model, the inflammation score demonstrated the most significant association with gout risk. Further RCS analysis revealed a nonlinear relationship (P=0.0134) with a turning point at 1.641. When the inflammation score was below 1.641, an increase in the score was significantly associated with a higher risk of gout. Stratified analyses indicated that age, race, and smoking status interacted with the relationship between inflammation scores and gout risk.
Discussion:
This study identified a significant association between the inflammation score and gout risk, particularly at low-to-moderate inflammation levels, suggesting that systemic inflammation may play an important role in the early stages of gout development. The inflammation score remained independently associated with gout after full adjustment, indicating its potential utility for early risk identification.
Conclusion:
The findings suggest that the inflammation score is a robust and independent predictor of gout risk, particularly at low-to-moderate levels, highlighting its potential for the early identification of high-risk individuals.