Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.
Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.
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DOI：
10.1038/s41598-018-23584-z
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发表时间：
2018-04-03
影响因子：
4.6
通讯作者：
Uher R
中科院分区：
文献类型：
--
作者：
Iniesta R;Hodgson K;Stahl D;Malki K;Maier W;Rietschel M;Mors O;Hauser J;Henigsberg N;Dernovsek MZ;Souery D;Dobson R;Aitchison KJ;Farmer A;McGuffin P;Lewis CM;Uher R
抑郁症患者对抗抑郁药治疗的反应存在很大差异。具体的预测因素只能解释这些差异的一小部分。为了有意义地预测谁会对哪种抗抑郁药有反应，可能需要联合收割机结合多种生物标志物和临床变量。使用统计学习的常见遗传变异和临床信息的训练样本280人随机分配到12周的抗抑郁药艾司西酞普兰或去甲替林治疗，我们推导出模型来预测缓解与每种抗抑郁药。我们在一个由150名未用于模型推导的参与者组成的验证集中测试了每个预测的可重复性。基于11个遗传变量和6个临床变量的弹性网络逻辑模型预测艾司西酞普兰在验证数据集中的缓解，曲线下面积为0.77（95%CI; 0.66-0.88; p = 0.004），解释了达到缓解的患者中约30%的方差。在验证数据集中，来自20个遗传变量的模型预测去甲替林缓解，曲线下面积为0.77（95%CI; 0.65-0.90; p < 0.001），解释了约36%达到缓解的变异。预测模型是抗抑郁药物特异性的。经验证的药物特异性预测表明，相对少量的遗传和临床变量可以帮助在艾司西酞普兰和去甲替林之间选择治疗。
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.
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影响因子：
3.7
作者：
Comuzzie AG;Cole SA;Laston SL;Voruganti VS;Haack K;Gibbs RA;Butte NF
通讯作者：
Butte NF
影响因子：
4.8
作者：
Franchini, L;Serretti, A;Smeraldi, E
通讯作者：
Smeraldi, E
影响因子：
2
作者：
Liu, Xinmin;Cheng, Rong;Clark, Lorraine N.
通讯作者：
Clark, Lorraine N.
影响因子：
30.8
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Nalls, Mike A.;Pankratz, Nathan;Lill, Christina M.;Do, Chuong B.;Hernandez, Dena G.;Saad, Mohamad;DeStefano, Anita L.;Kara, Eleanna;Bras, Jose;Sharma, Manu;Schulte, Claudia;Keller, Margaux F.;Arepalli, Sampath;Letson, Christopher;Edsall, Connor;Stefansson, Hreinn;Liu, Xinmin;Pliner, Hannah;Lee, Joseph H.;Cheng, Rong;Ikram, M. Arfan;Ioannidis, John P. A.;Hadjigeorgiou, Georgios M.;Bis, Joshua C.;Martinez, Maria;Perlmutter, Joel S.;Goate, Alison;Marder, Karen;Fiske, Brian;Sutherland, Margaret;Xiromerisiou, Georgia;Myers, Richard H.;Clark, Lorraine N.;Stefansson, Kari;Hardy, John A.;Heutink, Peter;Chen, Honglei;Wood, Nicholas W.;Houlden, Henry;Payami, Haydeh;Brice, Alexis;Scott, William K.;Gasser, Thomas;Bertram, Lars;Eriksson, Nicholas;Foroud, Tatiana;Singleton, Andrew B.
通讯作者：
Singleton, Andrew B.
影响因子：
1.8
作者：
Kim, Ji-Hyun
通讯作者：
Kim, Ji-Hyun