CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France.
Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France.
Drugs. 2017 Dec;77(18):1967-1986. doi: 10.1007/s40265-017-0819-9.
Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.
Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.
We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.
Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515).
Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.
抗抑郁药物广泛应用于临床，但在 3 个月后仅有约三分之一的患者有应答，因此寻找客观可测量的标志物来预测积极的治疗反应至关重要。这些标志物在不同领域用不同的技术、样本量、成本和效率进行开发，这使得我们难以知道哪些标志物最有前途。
本研究旨在计算研究水平和标志物水平的可比（即标准化）效应量，从而对每种技术和所有分析标志物的疗效得出结论。
我们在 PubMed 数据库中进行了系统检索，收集了自 2000 年以来发表的所有使用客观可测量标志物预测抗抑郁反应的文章，这些标志物来自五个领域，分别是认知、电生理学、影像学、遗传学和转录组学/蛋白质组学/表观遗传学。对这些文章的摘要和参考文献进行了手动筛选，以完成检索过程。
执行功能、额前扣带回皮质（rACC）的θ活动和多导睡眠图测量可被视为最佳的客观测量标志物，其综合 d 值约为 1，至少有四项阳性研究。对于类别间的比较，显示最高效应量的方法依次为影像学（综合 d 值在 0.703 至 1.353 之间）、电生理学（0.294 至 1.138）、认知（0.929 至 1.022）、蛋白质/核苷酸（0.520 至 1.18）和遗传学（0.021 至 0.515）。
抗抑郁治疗结果的标志物很多，但准确性参差不齐。许多生物标志物和认知具有足够的预测价值（d≥1），可能有助于临床医生预测治疗结果并实现个体化抗抑郁治疗。
