Jianbin Li,1,2,* Jiamin Zhang,1,2,* Hang Lu,1,2,* Suiran Li,1,2 Lei Zhang,3 Ning Tan,4 Shunlai Shang,5 Renhe Li,1,2 Yuxiu Ka,1,2 Siwei Wang,1,2 Wei Liu1,2 

1Department of Rheumatism and Immunity, The First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China; 3Department of Rheumatism and Immunity, Tianjin Academy of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 4Department of Rheumatism and Immunity, Shenzhen Nanshan People’s Hospital, Shenzhen, People’s Republic of China; 5Department of Nephrology, China-Japan Friendship Hospital, Beijing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Wei Liu, Email fengshiliuwei@163.com

Objective: The coexistence of refractory gout and metabolic syndrome may worsen patient prognosis, but the association and the nonlinear relationships with clinical parameters have not been fully elucidated. This study aimed to explore this association and reveal its potential nonlinear patterns in a Chinese cohort.
Methods: This retrospective study included clinical data from 4111 gout patients from a single center in Tianjin, China between 2014 and 20234. Based on international standards, patients were divided into the refractory gout group (n=1972) and the non-refractory gout group (n=2139). Metabolic syndrome was defined according to the NCEP-ATP III criteria. Propensity score matching (1:1) was used to control for confounding factors, and multivariable logistic regression and restricted cubic spline models were employed to assess the associations and nonlinear relationships.
Results: After propensity score matching, the prevalence of metabolic syndrome was significantly higher in the refractory gout group compared to the non-refractory gout group (52.5% vs 15.8%, P< 0.001). Multivariable analysis showed that metabolic syndrome was the strongest independent factor associated with refractory gout (adjusted OR=9.689, 95% CI: 5.727– 16.392, P< 0.001). Restricted cubic spline analysis revealed nonlinear relationships between clinical parameters and disease risk: age showed a rising and then falling trend with metabolic syndrome risk peaking around 58 years; ESR exhibited a bell-shaped relationship with both conditions; CRP showed a positive nonlinear relationship with refractory gout; SBP had a U-shaped relationship with refractory gout. Blood glucose levels were significantly associated with refractory gout, particularly in patients with tophi and longer disease duration.
Conclusion: Metabolic syndrome prevalence is significantly higher in refractory gout, and metabolic burden shows complex nonlinear relationships with clinical parameters. Managing metabolic risk requires special attention in younger patients, those with a long disease duration, and those with high uric acid levels, which are critical for improving patient outcomes.

Keywords: refractory gout, metabolic syndrome, propensity score matching analysis, nonlinear relationships, risk stratification