基于临床血液生物标志物构建帕金森病辅助鉴别模型  

Construction of an auxiliary identification model for Parkinson's disease based on clinical blood bio⁃markers

作　　者：韩家奇 安宇 孙梦鸽[5] HAN Jiaqi;AN Yu;SUN Mengge(Department of Laboratory Medicine,Shanghai East Hospital,Tongji University School of Medicine,Shanghai 200123,China;School of Life Sciences Fudan University,Shanghai 200433,China;Human Phenome Institute,Fudan University,Shanghai 200433,China;Institute of Medical Genetics and Genomics,Fudan University,Shanghai 200032,China;Department of Laboratory Medicine,Shanghai Municipal Hospital of Traditional Chinese Medicine,Shanghai 200071,China)

机构地区：[1]同济大学附属上海市东方医院南院检验科,上海200123 [2]复旦大学生命科学学院,上海200433 [3]复旦大学人类表型组研究院,上海200433 [4]复旦大学医学遗传研究院,上海200032 [5]上海中医药大学附属市中医医院检验科,上海200071

出　　处：《老年医学研究》2025年第1期1-7,14,共8页Geriatrics Research

摘　　要：目的探索临床实验室中与帕金森病(PD)相关的血液生物标志物,寻找潜在的PD血液生物标志物并构建PD辅助鉴别模型。方法回顾性收集2019年1月-2023年10月于上海市东方医院神经内科和功能神经科就诊的PD患者216例作为PD组,随机选取同期性别、年龄匹配的健康体检者216例作为对照组。记录PD组和对照组的人口学特征和血液生物标志物检查结果。基于两组数据利用LASSO回归筛选PD特征生物标志物,将两组数据按照7∶3随机拆分为训练集和验证集,在训练集中利用特征生物标志物构建多因素逻辑回归模型并绘制列线图。分别对训练集和验证集作受试者工作特征(ROC)曲线分析、校正曲线和决策曲线分析(DCA)用于模型的验证和评估。结果经变量筛选和模型构建得到多因素逻辑回归模型,包含性别、天冬氨酸氨基转移酶/谷丙转氨酶比值(AST/ALT)、总胆固醇(TC)、超氧化物歧化酶(SOD)、尿酸(UA)5个变量。性别(OR=0.294,95%CI:0.134~0.643,P=0.002)、AST/ALT(OR=3.112,95%CI:1.411~6.864,P=0.005)、SOD(OR=0.933,95%CI:0.916~0.951,P<0.001)、TC(OR=0.564,95%CI:0.383~0.832,P=0.004)、UA(OR=0.988,95%CI:0.983~0.993,P<0.001)为PD发生的影响因素。ROC曲线分析显示训练集的ROC曲线下面积(AUC)为0.896(95%CI:0.861~0.930);验证集的AUC=0.853(95%CI:0.787~0.918),模型分类性能良好。训练集和验证集的拟合优度检验结果分别为P=0.673和P=0.138,模型线性拟合程度良好。训练集和验证集阈值概率分别在0.05~0.97和0.10~1.00的范围内存在净收益,模型收益表现良好。结论AST/ALT、TC、SOD、UA可能是PD潜在的生物标志物;基于血液标志物的PD辅助鉴别模型具有一定的临床参考价值。Objective To explore blood biomarkers related to Parkinson's disease(PD)in clinical laboratories,to find potential PD blood biomarkers and to construct a PD auxiliary prediction model.Methods A total of 216 PD pa-tients treated in the department of Neurology and Functional Neurology of Shanghai East Hospital from January 2019 to Oc-tober 2023 were retrospectively collected as the PD cohort,and 216 healthy subjects matched by gender and age during the same period were randomly selected as the control cohort.Demographic characteristics and blood biomarker results of the PD and control cohorts were recorded.Based on the two data sets,LASSO regression was used to screen PD characteristic biomarkers.The two data sets were randomly split into a training set and a validation set according to 7∶3.In the training set,the characteristic biomarkers were used to construct a multi-factor logistic regression model and draw a nomogram.Re-ceiver Operating Characteristic(ROC)curve analysis,calibration curve,and Decision Curve Analysis(DCA)were per-formed on the training and validation sets for model verification and evaluation.Results After variable screening and model construction,a multi-factor logistic regression model was obtained,including five variables:gender,aspartate ami-notransferase/alanine aminotransferase ratio(AST/ALT),total cholesterol(TC),superoxide dismutase(SOD),and uric acid(UA).Gender(OR=0.294,95%CI:0.134-0.643,P=0.002),AST/ALT(OR=3.112,95%CI:1.411-6.864,P=0.005),SOD(OR=0.933,95%CI:0.916-0.951,P<0.001),TC(OR=0.564,95%CI:0.383-0.832,P=0.004),UA(OR=0.988,95%CI:0.983-0.993,P<0.001)are the influencing factors of PD occurrence.ROC curve analysis showed that the area under the ROC curve(AUC)of the training set was 0.896(95%CI:0.861-0.930),the AUC of the validation set was 0.853(95%CI:0.787-0.918),and the model classification performance was good.The goodness-of-fit test results of the training and validation sets were P=0.673 and P=0.138 respectively,indicating a reasonable degree of model fit.There is a net gain

关 键 词：帕金森病 鉴别模型 氧化应激 生物标志物 

分 类 号：R446.1[医药卫生—诊断学]