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

• 论著 • 上一篇    下一篇

原发性痛风患者发生骨质疏松症的危险因素分析及预测模型构建

王鹏1,2,3, 余玲2, 胥力川1, 庞敏1, 李果1, 郑洪锟1, 张全波3, 青玉凤3()   

  1. 1.西充县人民医院 内科,四川 南充 637200
    2.南充市第二人民医院 老年疾病科,四川 南充 637000
    3.川北医学院附属医院 高尿酸血症与痛风研究中心,四川 南充 637000
  • 收稿日期:2026-04-03 出版日期:2026-04-20 发布日期:2026-04-24
  • 通讯作者: 青玉凤,Email: qingyufengqq@163.com
  • 基金资助:
    南充市应用技术研究与开发专项项目——原发性痛风患者继发骨质疏松的危险因素分析以及预测模型构建(24YFZJZC0034);四川省科技计划项目——ATG4在痛风炎症的分子机制研究(2024YFFK0223);国家自然科学基金资助项目——非编码miRNA-lncRNA相互调控痛风自噬与炎症的分子机制研究(81974250)

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

摘要:

目的 分析男性原发性痛风患者合并骨质疏松症的临床特点及独立危险因素,构建适用于该人群的骨质疏松风险预测工具,为早期临床筛查提供参考。方法 采用回顾性队列设计,共纳入西充县人民医院和川北医学院附属医院就诊的348例男性原发性痛风患者。采集所有对象的临床资料、实验室指标与骨密度结果。将单因素分析中P<0.05的变量纳入多因素logistic回归模型,建立预测公式。采用受试者工作特征曲线评估模型区分能力,Bootstrap重抽样法进行内部验证,Hosmer-Lemeshow检验及校准曲线评估校准度。结果 348例男性患者中,骨质疏松症31例(8.9%)。单因素分析显示年龄、病程、痛风石、糖尿病、超敏C反应蛋白及血肌酐与骨质疏松显著相关(P<0.05)。多因素logistic回归确立年龄、痛风石和血肌酐为独立危险因素。预测模型:Logit(P)=-6.425+0.055×年龄(岁)+1.038×痛风石(有=1,无=0)+0.005×血肌酐(μmol/L)。模型初始AUC为0.796,Bootstrap校正后AUC为0.781,Hosmer-Lemeshow检验P=0.422,校准曲线显示拟合良好。 结论 高龄、合并痛风石及血肌酐升高是男性原发性痛风患者发生骨质疏松症的独立危险因素。构建的风险预测模型具有良好的区分能力与校准度,可作为筛查中老年男性痛风患者骨质疏松高危人群的有效工具。

关键词: 痛风, 骨质疏松症, 危险因素, 预测模型

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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