Cattaneo Annamaria, Ferrari Clarissa, Uher Rudolf, Bocchio-Chiavetto Luisella, Riva Marco Andrea, Pariante Carmine M
Stress, Psychiatry and Immunology Laboratory, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom (Drs Cattaneo and Pariante); Biological Psychiatry Unit, IRCCS Fatebenefratelli Institute, Brescia, Italy (Dr Cattaneo); Statistical Service, IRCCS Fatebenefratelli Institute, Brescia, Italy (Dr Ferrari); Genetic Unit, IRCCS Fatebenefratelli Institute Brescia, Italy and Faculty of Psychology, eCampus University, Novedrate, Como, Italy (Dr Bocchio-Chiavetto); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Dr Uher); The Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom (Dr Uher); Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy (Dr Riva).
Int J Neuropsychopharmacol. 2016 Sep 30;19(10). doi: 10.1093/ijnp/pyw045. Print 2016 Oct.
Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms.
Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins.
We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration.
We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs.
在多个临床样本中，炎症水平升高与对抗抑郁药的反应较差有关，但这些发现受到不同实验室生物标志物检测方法可重复性低、难以在个体基础上预测反应概率以及分子机制不明确的限制。
在此，我们测量了巨噬细胞迁移抑制因子和白细胞介素-1β的绝对mRNA值（对分子数量的可靠定量），该样本来自一项比较艾司西酞普兰与去甲替林的随机对照试验（GENDEP），以及一个独立的、自然主义的重复样本。然后，我们使用线性判别分析来计算mRNA值的临界值，该临界值能在使用抗抑郁药12周后最佳地区分反应者和无反应者。由于巨噬细胞迁移抑制因子和白细胞介素-1β可能参与不同途径，我们通过检索相互作用基因/蛋白质的搜索工具构建了一个蛋白质-蛋白质相互作用网络。
我们确定了绝对mRNA测量的临界值，该临界值能在个体基础上准确预测反应概率，两个样本中无反应者的阳性预测值和特异性均为100%（阴性预测值=82%至85%，敏感性=52%至61%）。通过网络分析，我们确定了这两种细胞因子的不同靶点簇，巨噬细胞迁移抑制因子主要与神经发生、神经可塑性和细胞增殖相关的途径相互作用，而白细胞介素-1β主要与炎性小体复合物、氧化应激和神经退行性变相关的途径相互作用。
我们认为这些数据为抗抑郁治疗的个性化提供了一种临床适用的方法：绝对mRNA值高于建议临界值的患者可以更早地采用更积极的抗抑郁策略，包括添加其他抗抑郁药或抗炎药。
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