摘要目的:基于实验室检验指标构建湿性体质临床预测模型。方法:采用回顾性队列研究，收集2022年10月1日至2023年10月31日在广东省中医院健康体检中心体检的1 355名表观健康人的基本资料和血常规、血糖、肝功能、脂类代谢以及肾功能检验结果。根据中医湿性体质诊断标准分为湿性体质组683名（痰湿质394名，湿热质289名）和非湿性体质组672名，其中湿性体质组男性547名，女性136名，年龄38.0（32.0，45.0）岁；非湿性体质组男性355名，女性317名，年龄33.0（27.0，41.0）岁。将1 355名表观健康人按7∶3比例通过计算机生成的随机数划分为948名训练集和407名验证集，采用Logistic回归分析筛选湿性体质的危险因素，基于风险因素构建预测模型并将模型可视化，绘制受试者工作特征曲线（ROC）、校准曲线、临床决策曲线（DCA）评价模型的预测价值、一致性和临床效能。结果:在1 355名受检者中，湿性体质与非湿性体质在性别、年龄、体重指数（body mass index，BMI）、血糖、肾功能部分指标、血常规部分指标、肝功能、血脂四项指标中均存在统计学差异（均 P<0.05）。性别（ OR=0.434，95% CI 0.253~0.738）、肌酐（ OR=0.981，95% CI 0.967~0.996）、BMI（ OR=1.366，95% CI 1.290~1.450）、低密度脂蛋白（ OR=1.388，95% CI 1.014~1.897）均为湿性体质的独立危险因素（ P<0.05）。根据筛选出的危险因素绘制了预测列线图。训练集和验证集ROC曲线下面积（AUC）分别为0.810（95% CI 0.783~0.837）、0.804（95% CI 0.762~0.846）。 结论:性别、BMI、肌酐、低密度脂蛋白是湿性体质发生的危险因素，构建的临床预测模型可预测湿性体质发生的风险。

abstractsObjective:To develop a clinical predictive model for dampness constitution based on laboratory testing indicators.Methods:A retrospective cohort study was conducted on 1 355 healthy individuals who underwent physical examinations at the Health Examination Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from October 1 st, 2022 to October 31 st, 2023. Basic information and blood routine, blood glucose, liver function, lipid metabolism, and kidney function test results of 1 355 apparently healthy individuals were collected. According to the diagnostic criteria for dampness constitution in traditional Chinese medicine, they were divided into a dampness constitution group (683 cases, including 394 with phlegm-dampness constitution and 289 with damp-heat constitution) and a non-dampness constitution group (672 cases). Among them, there were 547 males and 136 females in the dampness constitution group, with an age of 38.0 (32.0, 45.0) years; and there were 355 males and 317 females in the non-dampness constitution group, with an age of 33.0 (27.0, 41.0) years. A total of 1 355 apparently healthy individuals were randomly divided into a training set ( n=948) and a validation set ( n=407) using computer-generated random numbers in a 7∶3 ratio. Logistic regression analysis was employed to identify risk factors associated with dampness constitution. Utilizing these identified risk factors, a predictive model was constructed and subsequently visualized. The model′s predictive accuracy, consistency, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results:Among 1 355 subjects, there were statistically significant differences ( P<0.05) in gender, age, body mass index (BMI), blood glucose, some indicators of renal function, some indicators of blood routine, liver function, and four indicators of lipid metabolism between the dampness constitution group and the non-dampness constitution group. Gender ( OR=0.434,95 %CI 0.253-0.738), Cr ( OR=0.981,95 %CI 0.967-0.996), BMI ( OR=1.366,95 %CI 1.290-1.450), and LDL-C ( OR=1.388,95 %CI 1.014-1.897) were independent risk factors for dampness constitution ( P<0.05). A nomogram was subsequently developed based on these identified risk factors. The areas under the ROC curves (AUC) of the training set and validation set were 0.810 (95 %CI 0.783-0.837) and 0.804 (95 %CI 0.762-0.846), respectively. Conclusion:Gender,BMI,Cr and LDL-C were risk factors for the development of dampness constitution, and the clinical predictive model has clinical application value in predicting the risk of dampness constitution.