Biostatistics and Health Informatics Department. Institute of Psychiatry, Psychology and Neuroscience, Kings College London. 16 De Crespigny Park, London, SE5 8AF, UK.
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.
Sci Rep. 2018 Apr 3;8(1):5530. doi: 10.1038/s41598-018-23584-z.
Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.
个体对抗抑郁药的治疗反应存在显著差异。具体的预测指标只能解释这些差异的一小部分。为了有意义地预测哪些患者对哪种抗抑郁药有反应，可能需要将多个生物标志物和临床变量结合起来。我们使用统计学习方法，对 280 名随机分配到接受抗抑郁药依地普仑或去甲替林治疗 12 周的个体的常见遗传变异和临床信息的训练样本进行分析，为每种抗抑郁药物的缓解预测建立模型。我们在未用于模型推导的 150 名验证参与者的样本中测试了每个预测的可重复性。基于 11 个遗传和 6 个临床变量的弹性网络逻辑模型，对验证数据集中依地普仑的缓解预测的曲线下面积为 0.77（95%CI；0.66-0.88；p=0.004），解释了约 30%的缓解患者比例。一个基于 20 个遗传变量的模型，对验证数据集中去甲替林的缓解预测的曲线下面积为 0.77（95%CI；0.65-0.90；p<0.001），解释了约 36%的缓解患者比例。这些预测模型是特定于抗抑郁药的。经过验证的药物特异性预测表明，相对较少的遗传和临床变量可以帮助在依地普仑和去甲替林之间选择治疗方法。
