河北医科大学学报 ›› 2025, Vol. 46 ›› Issue (6): 682-688.doi: 10.3969/j.issn.1007-3205.2025.06.010

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基于肿瘤标志物和肾功能指标构建男性痛风疾病活动度的预测模型

  

  1. 1.陕西省西安市第五医院检验科，陕西 西安 710082；2.陕西省西安市第九医院检验科，陕西 西安 710082

  • 出版日期:2025-06-25 发布日期:2025-07-04
  • 作者简介:安沛欣（1984-），女，辽宁锦州人，陕西省西安市第五医院主管检验师，医学学士，从事免疫疾病诊治研究。

  • 基金资助:
    陕西省重点研发计划项目（2021SF-255）

Construction of a prediction model of gout disease activity in men based on tumor markers and renal function indicators

  1. 1.Department of Clinical Laboratory, Xi′an Fifth Hospital, Shanxi Province, Xi′an 710082, China; 
    2.Department of Clinical Laboratory, Xi′an Ninth Hospital, Shanxi Province, Xi′an 710082, China

  • Online:2025-06-25 Published:2025-07-04

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摘要： 目的基于肿瘤标志物和肾功能指标构建男性痛风疾病活动度的风险预测模型。
方法回顾性分析西安市第五医院在2021年1月—2023年12月间收治的男性痛风患者118例临床资料，依据8∶2定律随机分为训练集（94例）和验证集（24例），根据患者疾病活动度将其分为急性期（41例）和缓解期（53例）2组，其中训练集急性期组（41例）、缓解期组（53例），验证集急性期组（10例）、缓解期组（14例）。患者均行肿瘤标志物［糖类抗原125（carbohydrate antigen 125，CA125）、CA72-4、前列腺特异性抗原（prostate specific antigen，PSA）、胃泌素释放肽前体（Pro-gastrin-releasing peptide，proGRP）］检查和肾功能指标［血肌酐（serum creatinine，Scr）、胱抑素C（Cystatin C，Cys-C）、血尿素氮（blood urea nitrogen，BUN）、血β2微球蛋白（β2-microglobulin，β2-MG）、血尿酸（uric acid，UA）］检查。比较2组肿瘤标志物、肾功能指标水平及其他临床资料，分析影响患者疾病活动度的独立因素，并以此构建列线图预测模型，绘制受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under curve，AUC）分析预测模型对患者疾病活动度的预测效能。
结果急性期组CA125［（14.25±2.85）kU/L vs.（12.67±2.53）kU/L］、CA72-4［（3.41±0.85）kU/L vs.（2.11±0.42）kU/L］、proGRP［（71.32±17.83）ng/L vs.（42.65±10.66）ng/L］、UA［（566.43±113.28）μmol/L vs. （372.71±74.54）μmol/L］、Cys-C［（1.21±0.25）mg/L vs. （0.77±0.26）mg/L］、β2-MG［（3.41±1.05）mg/L vs. （1.86±0.62）mg/L］、红细胞分布宽度（red cell distribution width，RDW）［（13.84±1.54）% vs. （12.67±1.41）%］、血小板与淋巴细胞比值（platelet lymphocyte ratio，PLR）［（165.24±33.05）vs. （148.43±29.69）］水平均高于缓解期组，结果比较差异有统计学意义（P＜0.05）。多因素逐步Logistic回归分析显示，CA72-4水平（OR=2.989，95%CI：1.164~7.673）、proGRP水平（OR=3.678，95%CI：1.571~8.610）、Cys-C水平（OR=3.162，95%CI：1.773~5.637）、β2-MG水平（OR=5.236，95%CI：2.621~10.458）、UA水平（OR=4.543，95%CI：2.778~7.430）是患者疾病活动度的独立危险因素（P＜0.05）。基于上述影响因素构建的列线图，C-index指数为0.844（95%CI：0.759~0.929），预测患者疾病活动度的校正曲线趋近于理想曲线（P＞0.05）。训练集ROC显示，模型预测敏感度87.80%、特异度84.90%，AUC为0.896（P＜0.05）。验证集ROC显示，敏感度85.40%、特异度86.80%，AUC为0.861（P＜0.05）。
结论基于CA72-4、proGRP、UA、Cys-C、β2-MG水平构建的列线图预测模型，对识别高风险男性痛风疾病活动度患者具有较好预测价值。


关键词: 痛风, 男性, 疾病活动度, 肿瘤标志物, 肾功能, 风险模型

Abstract: Objective To construct a risk prediction model for gout disease activity in male patients based on tumor markers and renal function indicators. 
Methods Clinical data of 118 male gout patients admitted to Xi′an Fifth Hospital from January 2021 to December 2023 were retrospectively analyzed. They were randomly divided into training set (n=94) and verification set (n=24) according to an 8∶2 ratio, and divided into two groups according to disease activity: acute stage (n=41) and remission stage (n=53). Among them, the training set had acute stage group (n=41) and remission stage group (n=53), and the verification set had acute stage group (n=10) and remission stage group (n=14). Tumor markers ［carbohydrate antigen 125 (CA125), carbohydrate antigen72-4 (CA72-4), prostate specific antigen (PSA), pro-gastrin-releasing peptide (proGRP)］ and renal function indexes［serum creatinine (Scr), cystatin C (Cys-C), blood urea nitrogen (BUN), blood β2 microglobulin (β2-MG), blood uric acid (UA)］ were detected in all patients. The levels of tumor markers, renal function indexes and other clinical data of the two groups were compared, and the independent factors affecting the disease activity of the patients were analyzed. The area under the receiver operating characteristic (ROC)curve (AUC) was used to analyze the predictive efficiency of the prediction model for the disease activity of patients. 
Results 〖JP2〗The levels of CA125 ［(14.25±2.85)kU/L vs. (12.67±2.53)kU/L］〖JP〗, CA72-4［(3.41±0.85)kU/L vs. (2.11±0.42)kU/L］, proGRP［(71.32±17.83)ng/L vs. (42.65±10.66)ng/L］, UA［(566.43±113.28)μmol/L vs. (372.71±74.54)μmol/L］, Cys-C ［(1.21±0.25) mg/L vs. (0.77±0.26）mg/L］, β2-MG［(3.41±1.05)mg/L vs. (1.86±0.62)mg/L］, erythrocyte distribution width (RDW) ［(13.84±1.54)% vs. (12.67±1.41)%］ and platelet-lymphocyte ratio (PLR) ［(165.24±33.05) vs. (148.43±29.69)］ in acute stage were higher than those in remission stage, and the differences were significant (P＜0.05). Multivariate stepwise Logistic regression analysis showed that CA72-4 level (OR=2.989, 95%CI: 1.164-7.673), proGRP level (OR=3.678, 95%CI: 1.571-8.610), Cys-C level (OR=3.162, 95%CI: 1.773-5.637), β2-MG level (OR=5.236, 95%CI: 2.621-10.458) and UA level (OR=4.543, 95%CI: 2.778-7.430) were independent risk factors for disease activity (P＜0.05). The C-index was 0.844 (95%CI: 0.759-0.929) in the nomogram constructed based on the above influencing factors, and the correction curve for predicting disease activity was close to the ideal curve (P＞0.05). ROC of the training set showed that the prediction sensitivity of the model was 87.80%, the specificity was 84.90%, and the AUC was 0.896 (P＜0.05). ROC of the validation set showed that the sensitivity was 85.40%, the specificity was 86.80%, and the AUC was 0.861 (P＜0.05). 
Conclusion The nomogram prediction model based on CA72-4, proGRP, UA, Cys-C and β2-MG levels has a good predictive value for identifying high-risk male gout patients with disease activity. 


Key words: gout, male, disease activity, tumor markers, renal function, risk model

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