PREDICTION OF ANTIDEPRESSANT SIDE EFFECTS IN THE GENETIC LINK TO ANXIETY AND DEPRESSION STUDY

IF 6.1 2区医学 Q1 CLINICAL NEUROLOGY

European Neuropsychopharmacology Pub Date : 2024-10-01 DOI:10.1016/j.euroneuro.2024.08.065

Danyang Li , Yuhao Lin , Helena Davies , Evangelos Vassos , Raquel Iniesta , Gerome Breen

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引用次数: 0

Abstract

Antidepressants are the most common treatment for moderate or severe depression. Side effects are crucial indicators for antidepressants, but their expression varies widely among individuals.

In this study, we leveraged genetic and phenotypic data from self-reported questionnaires in the Genetic Link to Anxiety and Depression (GLAD) study to predict side effects and discontinuation (due to side effect) across three antidepressant classes (SSRI, SNRI, tricyclic antidepressants (TCA)) at the first and the last (most recent) year of prescription. About 260 predictors spanning genetic, clinical, comorbidity, demographic, and antidepressant information were included. XGBoost, random forest, cubist, elastic net, and support vector machine (with RBF and polynomial kernel) were trained, and their performance was compared.

The final dataset comprised 5358 individuals, with 4354 in the first and 3414 in the last year of prescription. The average prevalence of side effects and discontinuation was 74.1% and 28.7%, respectively. In the initial year, the best AUROC for predicting SSRI discontinuation and side effects were 0.65 and 0.60. In the last year of SSRI prescription, the highest AUROC reached 0.73 for discontinuation and 0.87 for side effects. Models for predicting discontinuation and side effects of SNRI and TCA showed comparable performance. The history of side effects and discontinuation of antidepressant use were the most influential predictors of the outcomes in the last year. When examining 30 common antidepressant side effect symptoms, most of them were differentially prevalent between antidepressant classes.

Our findings demonstrate the feasibility of predicting antidepressant side effects using a self-reported questionnaire, particularly for the last prescription. These results contribute valuable insights for the development of clinical decisions aimed at optimising treatment selection with enhanced tolerability.

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焦虑和抑郁遗传关联研究中抗抑郁药副作用的预测

抗抑郁药是治疗中度或重度抑郁症最常用的药物。在这项研究中，我们利用焦虑和抑郁的遗传联系（GLAD）研究中自我报告问卷中的遗传和表型数据，预测了三种抗抑郁药（SSRI、SNRI、三环类抗抑郁药（TCA））在处方第一年和最后一年（最近一年）的副作用和停药（由于副作用）情况。该研究纳入了约 260 个预测因子，涵盖遗传、临床、合并症、人口统计学和抗抑郁药信息。对 XGBoost、随机森林、立方体、弹性网和支持向量机（RBF 和多项式核）进行了训练，并比较了它们的性能。副作用和停药的平均发生率分别为 74.1%和 28.7%。在第一年，预测 SSRI 停药和副作用的最佳 AUROC 分别为 0.65 和 0.60。在处方 SSRI 的最后一年，预测停药和副作用的最高 AUROC 分别为 0.73 和 0.87。预测 SNRI 和 TCA 的停药和副作用的模型表现相当。有副作用史和停用抗抑郁药是对去年治疗结果影响最大的预测因素。我们的研究结果表明，使用自我报告问卷预测抗抑郁药副作用是可行的，尤其是对最近一次处方的副作用。这些结果为临床决策的制定提供了有价值的见解，旨在优化治疗选择，提高耐受性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Neuropsychopharmacology 医学-精神病学

CiteScore

10.30

自引率

5.40%

发文量

730

审稿时长

41 days

期刊介绍： European Neuropsychopharmacology is the official publication of the European College of Neuropsychopharmacology (ECNP). In accordance with the mission of the College, the journal focuses on clinical and basic science contributions that advance our understanding of brain function and human behaviour and enable translation into improved treatments and enhanced public health impact in psychiatry. Recent years have been characterized by exciting advances in basic knowledge and available experimental techniques in neuroscience and genomics. However, clinical translation of these findings has not been as rapid. The journal aims to narrow this gap by promoting findings that are expected to have a major impact on both our understanding of the biological bases of mental disorders and the development and improvement of treatments, ideally paving the way for prevention and recovery.