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抗抑郁剂疗效的预测因素分析
引用本文:徐贵云,郭扬波,欧阳惠怡,党亚梅,张敏玲,林康广,XU Gui-yun,GUO Yang-bo,OUYANG Hui-yi,DANG Ya-mei,ZHANG Min-ling,LIN Kang-guang. 抗抑郁剂疗效的预测因素分析[J]. 临床精神医学杂志, 2014, 0(5): 296-300
作者姓名:徐贵云  郭扬波  欧阳惠怡  党亚梅  张敏玲  林康广  XU Gui-yun  GUO Yang-bo  OUYANG Hui-yi  DANG Ya-mei  ZHANG Min-ling  LIN Kang-guang
作者单位:1. 广州市脑科医院、广州医科大学附属脑科医院情感障碍科
2. 广州市脑科医院、广州医科大学附属脑科医院临床心理科
3. 广州市脑科医院、广州医科大学附属脑科医院精神神经研究所
4. Departmnet of Affective Disorder,Brain Hospital Affiliated to Guangzhou Medical University,Guangzhou 510370,China
摘    要:目的:探索影响抑郁症患者抗抑郁剂疗效的预测因素。方法:241例抑郁症患者给予抗抑郁药治疗6周,治疗前后进行汉密顿抑郁量表17项(HAMD)评分,采用减分率评定疗效。分析人口学因素、基线HAMD、明尼苏达多项人格测定(MMPI-2)、认知功能评分及脑源性神经营养因子(BDNF)、5-羟色胺转运体(5-HTTLPR)和糖皮质激素受体(GR)3种基因多态对疗效的预测。结果:基线HAMD评分(β=0.771,P0.001)、MMPI-2中偏执(Pa)分(β=-0.322,P=0.032,R2=0.451)、连线测验B评分(TMT-B)(β=-0.045,P=0.013)、汉诺塔总分(β=-0.067,P=0.026)、数字广度(倒序)分(β=-0.974,P=0.025)及GR BclI基因G-等位基因携带者(P=0.05)与抗抑郁剂HAMD减分率有关。整合模型回归分析显示,结合基线HAMD评分(β=0.894,P0.001)、MMPI-2-Pa分(β=-0.155,P=0.036)和TMT-B分(β=-0.038,P=0.034)3个预测因子可解释57.1%的变异。结论:基线HAMD评分、MMPI-2-Pa分和TMT-B分可预测抗抑郁剂的疗效。

关 键 词:抑郁症  抗抑郁剂  疗效的预测因素  临床药物实验

Analysis of predictor factors of antidepressant efficacy
XU Gui-yun,GUO Yang-bo,OUYANG Hui-yi,DANG Ya-mei,ZHANG Min-ling,LIN Kang-guang,XU Gui-yun,GUO Yang-bo,OUYANG Hui-yi,DANG Ya-mei,ZHANG Min-ling,LIN Kang-guang. Analysis of predictor factors of antidepressant efficacy[J]. Journal of Clinical Psychological Medicine, 2014, 0(5): 296-300
Authors:XU Gui-yun  GUO Yang-bo  OUYANG Hui-yi  DANG Ya-mei  ZHANG Min-ling  LIN Kang-guang  XU Gui-yun  GUO Yang-bo  OUYANG Hui-yi  DANG Ya-mei  ZHANG Min-ling  LIN Kang-guang
Affiliation:XU Gui-yun, GUO Yang-bo, OUYANG Hui-yi, DANG Ya-mei, ZHANG Min-ling, LIN Kang-guang( Departmnet of Affective Disorder, Brain Hospital Affiliated to Guangzhou Medical University, Guangzhou 510370, China)
Abstract:Objective: To explore the predictors factors of antidepressant efficacy in patients with major depressive disorder (MDD). Method: Two hundred and forty-one MDD patients were treated with anti depreessants for 6 weeks, and were evaluated with 17-item Hamilton rating scale for depression(HAMD) at baseline and after treatment,the rates of decrease in HAMD scores from baseline to 6 weeks after treatment was used to measure the antidepressant treatment efficacy. Predict analysis for the efficacy of antidepressant were analyzed by demographic variables, baseline HAMD scores, Minnesota muhiphasic personality inventory-( MMPI- 2) scores, neumcognitive performances and seven polymorphisms of brain derived neurophic factor( BDNF), 5- hydroxy tryptamine transporter-linked polymorphic region (5-HTTLPR) and glucocorticoid receptor(GR) genes. Results: HAMD scores at baseline (β = 0. 771, P 〈 0. 001 ), MMPI-2 subscale paranoia (β = - 0. 322, P = 0.032,R2 =0.451 ) ,performance on trail making test-part B(TMT-B) (β = -0.045 ,P =0.013) ,tower of Ha- noi(TOH) (β = - 0.067, P = 0. 026), digit backward (β = - 0. 974, P = 0.025 ) and G-allele of glucocorticoid receptor gene BclI polymorphism carriers (P = 0.05 ) were associated with the efficacy of antidepressant in treatment of patients with major depressive disorder. The integrate regression model showed that three predictors,combination of HAMD scores at baseline (β = 0. 894, P 〈 0.001 ), MMPI-2 subscale paranoia (β = -0. 155 ,P = 0. 036) and TMT-B test (β = - 0. 038, P = 0. 034), explained 57. 1% of the variance. Conclusion:HAMD scores at baseline, MMPI-2 subscale paranoia and TMT-B test, may serve as predictors of antidepressant treatment outcome.
Keywords:major depressive disorder  antidepressants  predictors factor of efficacy  clinical pharmacology
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