Tang Haiping, Xia Yan, Gao Chenjie, Cai Yufan, Shao Yongqi, Chen Wenji, Yuan Yonggui, Liu Chunyu, Zhang Zhijun, Xu Zhi
Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Department of Molecular Biophysics and Biochemistry, Yale University, CT, New Haven, United States.
BMC Psychiatry. 2025 May 20;25(1):505. doi: 10.1186/s12888-025-06895-0.
Antidepressant efficacy is influenced by a multitude of factors, yet predicting treatment outcomes remains challenging. This difficulty is partly due to the commonly employed dichotomous classifications of treatment response that rely on a single primary endpoint.
The study enrolled 972 patients diagnosed with depression, including both first-episode and recurrent cases. All patients received treatment with a single class of antidepressant medication over an eight-week period. Treatment response trajectories were identified through cluster analysis using normalized score change ratios from the 17-item Hamilton Rating Scale for Depression (HAMD-17) at baseline and weeks 2, 4, 6, and 8. The impact of psychosocial factors-including childhood trauma experience, social support, and family environment-on these response patterns was evaluated using ANOVA and Tukey's HSD tests. Additionally, targeted exome sequencing was conducted to perform rare-variant burden and enrichment analyses to investigate genetic influences on antidepressant response.
Three patterns of antidepressant treatment response were identified: gradual response (C1 cluster), early response (C2 cluster), and fluctuating response (C3 cluster). Notably, patients in the C3 cluster exhibited higher levels of suicidal ideation, alexithymia, and anhedonia after the treatment period, along with the highest baseline levels of family control (a subscale of the family environment). Our rare-variant analysis revealed genes associated with response efficiency between C1 and C2 clusters to be significantly enriched in the neurotrophin signaling pathway (odds ratio = 23.94; p-adjusted = 6.96e-05). In addition, genes linked to response volatility between C1 and C3 clusters were enriched in the regulation of inflammatory mediators of transient receptor potential (TRP) channels (odds ratio = 31.5; p-adjusted = 1.83e-07).
Our findings suggest that patients exhibiting a fluctuating response to antidepressant treatment may endure more severe clinical symptoms throughout the treatment course. The involvement of the neurotrophin signaling pathway and TRP channels in these response patterns highlights their potential as novel targets for therapeutic intervention in depression. This underscores the importance of personalized treatment strategies that consider the underlying genetic and psychological factors influencing antidepressant efficacy.
抗抑郁药的疗效受多种因素影响，但预测治疗结果仍然具有挑战性。这种困难部分归因于常用的基于单一主要终点的二分法治疗反应分类。
该研究纳入了972例诊断为抑郁症的患者，包括首发和复发病例。所有患者在八周内接受单一类别的抗抑郁药物治疗。通过聚类分析确定治疗反应轨迹，使用基线及第2、4、6和8周时17项汉密尔顿抑郁量表（HAMD-17）的标准化评分变化率。使用方差分析和Tukey's HSD检验评估心理社会因素（包括童年创伤经历、社会支持和家庭环境）对这些反应模式的影响。此外，进行靶向外显子组测序以进行罕见变异负担和富集分析，以研究基因对抗抑郁反应的影响。
确定了三种抗抑郁治疗反应模式：逐渐反应（C1簇）、早期反应（C2簇）和波动反应（C3簇）。值得注意的是，C3簇中的患者在治疗期后表现出自杀观念、述情障碍和快感缺失的水平较高，同时家庭控制（家庭环境的一个子量表）的基线水平最高。我们的罕见变异分析显示，与C1和C2簇之间的反应效率相关的基因在神经营养因子信号通路中显著富集（优势比=23.94；校正p值=6.96e-05）。此外，与C1和C3簇之间的反应波动性相关的基因在瞬时受体电位（TRP）通道的炎症介质调节中富集（优势比=31.5；校正p值=1.83e-07）。
我们的研究结果表明，对抗抑郁治疗表现出波动反应的患者在整个治疗过程中可能承受更严重的临床症状。神经营养因子信号通路和TRP通道参与这些反应模式，凸显了它们作为抑郁症治疗干预新靶点的潜力。这强调了考虑影响抗抑郁疗效的潜在遗传和心理因素个性化治疗策略的重要性。
