Gout is a globally prevalent metabolic inflammatory syndrome. Inflammation serves as a key driver in the pathogenesis and progression of gout. To investigate the relationships between inflammatory markers and gout acute (GA) flares, we performed comprehensive analyses utilizing data sourced from the Chinese Rheumatology Data Center (CRDC).

The data for this study were sourced from the CRDC database established at the Second Affiliated Hospital of Nanchang University between 2014 and 2025. After applying strict inclusion and exclusion criteria and performing data cleaning, a total of 688 eligible samples were identified. To further control for potential confounding by baseline characteristics, propensity score matching (PSM) matched 328 pairs at a 1:1 ratio for analysis. Based on peripheral blood cell counts from patients, we calculated five inflammatory biomarkers (NLR, PLR, PAR, SII, HALP). Adjusting for known confounders, we systematically explored the associations between these inflammatory biomarkers and GA using multiple statistical methods, including multivariable logistic regression models, restricted cubic spline (RCS) models, extreme gradient boosting (XGBoost) machine learning models, and receiver operating characteristic (ROC) curves. Additionally, this study used limited follow-up data from gout patients before and after treatment to preliminarily assess the potential clinical value of these inflammatory biomarkers in diagnosis value.

Results

High levels of NLR and PLR significantly increase the risk of GA. The OR of NLR in the T2 group (vs. T1) was 2.02 (95% CI = 1.15, 3.56), and in the T3 group it was 2.43 (95% CI = 1.36, 4.33). The OR of PLR in the T2 group was 2.00 (95% CI = 1.12, 3.60). High levels of HALP significantly reduced the risk of GA, with an OR of 0.56 (95% CI = 0.31, 0.99) in the T2 group and 0.55 (95% CI = 0.30, 0.98) in the T3 group. After log2 transformation, NLR, SII, and HALP remained significantly associated with risk (log2NLR: OR = 1.37, 95% CI = 1.08–1.75; log2SII: OR = 1.32, 95% CI = 1.07–1.61; log2HALP: OR = 0.69, 95% CI = 0.50–0.94). log2NLR and log2SII showed a linear dose-response relationship with GA, while log2HALP exhibited a nonlinear "L"-shaped relationship. Notably, subgroup analyses suggested that body mass index and uric acid levels may influence the evaluation of GA risk using these inflammatory markers. Machine learning models further confirmed that log2NLR, log2SII, and log2HALP are important variable features for identifying GA. ROC curve analysis indicated that these inflammatory biomarkers have some role in assessing GA risk, although combining multiple indicators did not significantly improve predictive performance. Additionally, comparison analyses before and after treatment in GA patients preliminarily validated the potential clinical diagnostic value of these inflammatory biomarkers.

Log2NLR, log2SII elevation, and log2HALP reduction show potential as non-invasive biomarkers for early detection and risk stratification, and dynamic monitoring based on these indices may provide new insights and directions for the diagnosis of GA.