Clinical Focus ›› 2026, Vol. 41 ›› Issue (4): 323-327.doi: 10.3969/j.issn.1004-583X.2026.04.006

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Risk factors for osteoporosis in patients with primary gout and development of a prediction model

Wang Peng1,2,3, Yu Ling2, Xu Lichuan1, Pang Min1, Li Guo1, Zheng Hongkun1, Zhang Quanbo3, Qing Yufeng3()   

  1. 1. Department of Internal Medicine,Xichong County People's Hospital,Nanchong 637200,China
    2. Department of Geriatrics,Nanchong Second People's Hospital,Nanchong 637000,China
    3. Research Center for Hyperuricemia and Gout,Affiliated Hospital of North Sichuan Medical College, Nanchong 637000,China
  • Received:2026-04-03 Online:2026-04-20 Published:2026-04-24
  • Contact: Qing Yufeng,Email: qingyufengqq@163.com

Abstract:

Objective To analyze the clinical characteristics and independent risk factors of osteoporosis in male patients with primary gout, and to develop an osteoporosis risk prediction tool for this population to support early clinical screening. Methods A retrospective cohort design was used, involving 348 male patients with primary gout at Xichong County People's Hospital and Affiliated Hospital of North Sichuan Medical College. Clinical data, laboratory indices, and bone mineral density results were collected for all participants. Variables with P<0.05 in the univariate analysis were entered into a multivariate logistic regression model to establish a prediction formula. Receiver operating characteristic curve analysis was used to evaluate the discriminative ability of the model. Bootstrap resampling was performed for internal validation, and the Hosmer-Lemeshow test and calibration curve were used to assess calibration. Results Among the 348 male patients, 31 cases (8.9%) had osteoporosis. Univariate analysis showed that age, disease duration, tophi, diabetes, high-sensitivity C-reactive protein, and serum creatinine were significantly associated with osteoporosis (P<0.05). Multivariate logistic regression identified age, tophi, and serum creatinine as independent risk factors. The prediction model was as follows: Logit(P)=-6.425+0.055×age (years)+1.038×tophi (yes=1, no=0)+0.005×serum creatinine (μmol/L). The initial area under the curve (AUC) of the model was 0.796, and the Bootstrap-corrected AUC was 0.781. The Hosmer-Lemeshow test yielded P=0.422, and the calibration curve indicated good model fit. Conclusion Advanced age, presence of tophi, and elevated serum creatinine are independent risk factors for osteoporosis in male patients with primary gout. The constructed risk prediction model demonstrates good discriminative ability and calibration, and may serve as an effective tool for screening high-risk osteoporosis in middle-aged and elderly male patients with gout.

Key words: gout, osteoporosis, risk factors, prediction model

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