Meng Ting-Ting, Wang Wen-Rui, Zheng Yan-Qing, Liu Guan-Dong
Department of Thyroid and Breast Surgery, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China.
Front Nutr. 2025 Mar 26;12:1535879. doi: 10.3389/fnut.2025.1535879. eCollection 2025.
To explore the factors influencing hyperuricemia in breast cancer patients based on the National Health and Nutrition Examination Survey (NHANES) database.
The univariate and multivariate generalized linear regression were used to screen the influencing factors of hyperuricemia. Logistic and XGBoost algorithms were used to rank the importance of influencing factors. Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA) curves were used to assess the predictive performance and clinical benefit. Trend analysis, Restricted cubic spline (RCS) analysis, and generalized additive model were used to explore the relationship between key factor and hyperuricemia.
A total of 359 patients with breast cancer were included, of whom 99 patients had hyperuricemia. Among all variables collected, BMI, total calcium, creatinine, hypertension, and gout were found as independent factors of hyperuricemia (all < 0.05). Among them, Both the 2 algorithms indicated that importance of creatinine on hyperuricemia ranked first. Further, BMI and creatinine levels had higher area under the curve than other variables (BMI: 0.626 [95%CI: 0.574-0.685]; creatinine: 0.722 [95%CI: 0.674-0.777]), but prediction performance difference between them was insignificant (P for Delong test = 0.051). DCA next indicated that creatinine achieved better clinical net benefit than BMI. Further, a detailed positive association between creatinine and hyperuricemia was determined (P for trend<0.001), with a linear relationship (P for non-linear = 0.428).
Creatinine was identified as the most important factor of hyperuricemia in breast cancer patients, as it had independent association with hyperuricemia and favorable prediction performance.
基于美国国家健康与营养检查调查（NHANES）数据库，探讨影响乳腺癌患者高尿酸血症的因素。
采用单因素和多因素广义线性回归筛选高尿酸血症的影响因素。使用逻辑回归和XGBoost算法对影响因素的重要性进行排序。采用受试者工作特征（ROC）曲线和决策曲线分析（DCA）曲线评估预测性能和临床获益。采用趋势分析、受限立方样条（RCS）分析和广义相加模型探讨关键因素与高尿酸血症之间的关系。
共纳入359例乳腺癌患者，其中99例患有高尿酸血症。在收集的所有变量中，体重指数、总钙、肌酐、高血压和痛风被发现是高尿酸血症的独立因素（均P<0.05）。其中，两种算法均表明肌酐对高尿酸血症的重要性排名第一。此外，体重指数和肌酐水平的曲线下面积高于其他变量（体重指数：0.626[95%CI：0.574-0.685]；肌酐：0.722[95%CI：0.674-0.777]），但它们之间的预测性能差异不显著（德龙检验P=0.051）。DCA分析表明，肌酐的临床净获益优于体重指数。此外，确定了肌酐与高尿酸血症之间存在详细的正相关（趋势P<0.001），呈线性关系（非线性P=0.428）。
肌酐被确定为乳腺癌患者高尿酸血症的最重要因素，因为它与高尿酸血症独立相关且具有良好的预测性能。
