Brain-derived neurotrophic factor levels and bipolar disorder in patients in their first depressive episode: 3-year prospective longitudinal study. (July 2014)

• Record Type: Journal Article
• Authors: Li, Zezhi; Zhang, Chen; Fan, Jinbo; Yuan, Chengmei; Huang, Jia; Chen, Jun; Yi, Zhenghui; Wang, Zuowei; Hong, Wu; Wang, Yong; Lu, Weihong; Guan, Yangtai; Wu, Zhiguo; Su, Yousong; Cao, Lan; Hu, Yingyan; Hao, Yong; Liu, Mingyuan; Yu, Shunying; Cui, Donghong; Xu, Lin; Song, Yanyan; Fang, Yiru
• Is Part Of: British journal of psychiatry. Volume 205:Number 1(2014)
• Page Start: 29
• Page End: 35
• Publication Date: 2014-07
• Subjects: Psychiatry -- Periodicals; Psychology, Pathological -- Periodicals; 616.89005
• DOI: 10.1192/bjp.bp.113.134064
• Languages: English
• ISSNs: 0007-1250

Abstract

Abstract : Background: Early identification of patients with bipolar disorder during their first depressive episode is beneficial to the outcome of the disorder and treatment, but traditionally this has been a great challenge to clinicians. Recently, brain-derived neurotrophic factor (BDNF) has been suggested to be involved in the pathophysiology of bipolar disorder and major depressive disorder (MDD), but it is not clear whether BDNF levels can be used to predict bipolar disorder among patients in their first major depressive episode. Aims: To explore whether BDNF levels can differentiate between MDD and bipolar disorder in the first depressive episode. Method: A total of 203 patients with a first major depressive episode as well as 167 healthy controls were recruited. After 3 years of bi-annual follow-up, 164 patients with a major depressive episode completed the study, and of these, 21 were identified as having bipolar disorder and 143 patients were diagnosed as having MDD. BDNF gene expression and plasma levels at baseline were compared among the bipolar disorder, MDD and healthy control groups. Logistic regression and decision tree methods were applied to determine the best model for predicting bipolar disorder at the first depressive episode. Results: At baseline, patients in the bipolar disorder and MDD groups showed lower BDNF mRNA levels ( P <0.001 and P = 0.02 respectively) and plasma levels ( P = 0.002 and P = 0.01 respectively) compared with healthy controls. Similarly, BDNF levels in the bipolar disorder group were lower than those in the MDD group. These results showed that the best model for predicting bipolar disorder during a first depressive episode was a combination of BDNF mRNA levels with plasma BDNF levels (receiver operating characteristics (ROC) = 0.80, logistic regression; ROC = 0.84, decision tree). Conclusions: Our findings suggest that BDNF levels may serve as a potential differential diagnostic biomarker for bipolar disorder in a patient's first depressive episode.