基于基因及心理因素的曲马多药效模型的研究

Study on a predictive model for tramadol response from genetic and psychological factors

目的:综合患者抑郁、焦虑水平及CYP2D6,P-gp,OPRM1,COMT位点基因多态性共六因素建立曲马多有效性的预测模型。方法:共入组250例,分为A组(200例)和B组(50例),均为在南京某医院进行上肢骨折内固定手术的患者,术前一晚进行心理评估,采用连接酶检测反应(LDR)对CYP2D6,P-gp,OPRM1,COMT进行基因检测。A组实验数据采用二元logistic回归得出预测模型;用非参数检验方法将B组患者的实验数据用于预测模型的检验。结果:根据A组数据得出预测模型:Logit(1)=2.304-4.841×(焦虑Ⅰ)-23.709×(焦虑II)+2.823×(P-gp 3435 CT)+5.737×(P-gp 3435 TT)-1.586×(CYP2D6×10 CT)-4.542×(CYP2D6×10 TT)。模型的阳性预测率为90%。将模型应用于B组患者,得出模型的阳性预测率为86%。回归方程的拟合优度Nagelkerke R2为0.819,Hosmer和Lemeshow检验结果为0.981。非参数检验结果显示B组患者的曲马多实际药效与模型预测药效结果没有显著差异(P=0.083)。结论:本项研究结果可能是有效预测曲马多药效的潜在模型。

OBJECTIVE To develop an algorithm by polymorphisms of CYP2D6, P-gp, OPRM1, COMT and psychological variables to predict tramadol response in Chinese patients. METHODS A total of 250 Han Chinese patients recovering from fracture in upper limb were enrolled. Psychological tests were conducted the night before surgery, and CYP2D6×10, PgpG2677T, P-gp C3435T, OPRM1 A118G and COMT Va1158Met were detected by ligase detection reaction （LDR） method. The algorittun was developed with binary logistic regression in cohort 1 （200 patients） and assessed with Wilcoxon signed-rank test in cohort 2 （50 patients）, liF^ULI＇S According to cohort 1, the predictive equation was calculated with the following logistic regression parameters： Logit（1） = 2. 304 - 4. 841 × （anxiety I ） - 23. 709 × （anxiety Ⅱ ） ＋ 2. 823 × （P-gp 3435 CT） ＋ 5. 737 × （p-gp 3435 TT） - 1. 586 x （CYP2D6 × 10 CT） -4. 542 × （CYP2D6 × 10 TT）. The equation＇s positive predictive value （PPV） was 900/00. When applied to cohort 2, the PPV was 86%. The Nagelkerke R2 of the model was 0. 819, and the Hosmer and Lemeshow test value was 0. 981. Wilcoxon signed-rank test displayed that there was no significant difference between the actual response and predicted one （P = 0. 083）. CONCLUSION The algorithm developed by us might predict tramadol response in Chinese patients.