文章摘要
基于血清尿酸、维生素D、胱抑素C构建健康体检人群骨质疏松症发生风险的列线图预测模型
Nomogram Prediction Model for the Risk of Osteoporosis in Health Examination Population Based on Serum Uric Acid, Vitamin D and Cystatin C
投稿时间:2025-01-15  修订日期:2025-01-15
DOI:
中文关键词: 骨质疏松症  尿酸  维生素D  胱抑素C  风险列线图预测模型
英文关键词: Osteoporosis  Uric acid  Vitamin D  Cystatin C  Risk nomogram prediction model
基金项目:山东省中医药科技发展计划(2017-279)
作者单位邮编
李蓬* 天门市第一人民医院 431700
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中文摘要:
      目的 构建基于血清尿酸(UA)、维生素D、胱抑素C(CysC)的健康体检人群骨质疏松症发生风险列线图预测模型。方法 选择2022年1月~2023年12月于我院接受骨密度检查的健康体检者1082例,根据骨质疏松症发生情况分为骨质疏松组和非骨质疏松组。比较两组血清UA、维生素D、CysC水平及两组人群特征,应用多因素Logistic回归分析骨质疏松症的影响因素,并建立风险预测模型,应用受试者工作特征(ROC)曲线分析列线图预测模型对健康体检人群骨质疏松症发生风险的预测价值。结果 1082例体检者中骨质疏松者151例,骨质疏松症发生率为13.96%(P<0.05)。相较于非骨质疏松组,骨质疏松组血清UA、维生素D水平显著降低,CysC水平显著升高(P<0.05)。女性、年龄偏大、总胆固醇(TC)升高、空腹血糖(FBG)升高、CysC升高是骨质疏松症发生的危险因素,UA升高、维生素D升高则为保护因素(P<0.05)。基于上述的影响因素构建预测模型,Bootstrap(B=1000)验证结果显示,Bias-corrected预测曲线与Ideal线基本重合,一致性指数(C-index)为0.786,表明该模型具有良好的预测能力。预测模型对健康体检人群骨质疏松症发生风险的曲线下面积(AUC)为0.809(95%CI=0.767~0.861)。该模型的阈值概率范围为0.03~0.89,其净收益率>0,高于无效线。结论 血清CysC升高是骨质疏松症发生的危险因素,而UA升高、维生素D升高则是保护因素,基于上述指标构建的列线图预测模型对健康体检人群发生骨质疏松症具有较高的预测价值。
英文摘要:
      Objective: To construct nomogram prediction model for the risk of osteoporosis in health examination population based on serum uric acid(UA), vitamin D and cystatin C(CysC). Methods: 1082 healthy subjects who underwent bone mineral density examination in our hospital from January 2022 to December 2023 were selected, they were divided into osteoporosis group and non-osteoporosis group according to the occurrence of osteoporosis. The serum UA, vitamin D and CysC levels and the characteristics of the two groups were compared, the influencing factors of osteoporosis were analyzed by multivariate logistic regression, and a risk prediction model was established, the predictive value of the nomogram prediction model for the risk of osteoporosis in healthy physical examination population was analyzed by receiver operating characteristic (ROC) curve. Results: There were 151 osteoporosis patients in 1082 subjects, the incidence of osteoporosis was 13.96% (P<0.05). Compared with non-osteoporosis group, the serum UA and vitamin D levels in osteoporosis group were significantly decreased, and the CysC level was significantly increased (P<0.05). Female, older age, total cholesterol (TC) increased, fasting blood glucose (FBG) increased and CysC increased were risk factors for osteoporosis, UA increased and vitamin D increased were protective factors (P<0.05). A prediction model was constructed based on the above influencing factors, Bootstrap (B=1000) verification results showed that, the Bias-corrected prediction curve basically coincided with the Ideal line, and the consistency index (C-index) was 0.786, indicating that the model had good prediction ability. The area under the curve (AUC) of the prediction model for the risk of osteoporosis in healthy physical examination population was 0.809 (95%CI=0.767~0.861). The threshold probability range of the model was 0.03~0.89, and its net return rate was >0, which was higher than that of the invalid lines. Conclusion: Serum CysC increased are the risk factors of osteoporosis, UA increased and vitamin D increased are protective factors. The nomogram prediction model based on the above indicators has a high predictive value for osteoporosis in healthy physical examination population.
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