Suppr超能文献

抗抑郁药和抗精神病药物反应的遗传决定因素。

Genetic determinants of antidepressant and antipsychotic drug response.

作者信息

Stassen Hans H, Bachmann S, Bridler R, Cattapan K, Hartmann A M, Rujescu D, Seifritz E, Weisbrod M, Scharfetter Chr

机构信息

Institute for Response-Genetics, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, Zurich, CH-8032, Switzerland.

Sanatorium Kilchberg, Alte Landstrasse 70, Kilchberg, CH-8802, Switzerland.

出版信息

Eur Arch Psychiatry Clin Neurosci. 2024 Oct 9. doi: 10.1007/s00406-024-01918-5.

Abstract

Today, more than 90% of inpatients hospitalized with Major Depression or Schizophrenia are treated with psychotropic drugs. Since none of the treatment options is causal, response rates are modest and the course of recovery is very heterogeneous. Genetic studies on the etiology and pathogenesis of major psychiatric disorders over the past decades have been largely unsuccessful. Likewise, genetic studies to predict response to psychopharmacological treatment have also not been particularly successful. In this project we have recruited 902 inpatients with ICD-10 diagnoses of schizophrenic ("F2 patients") or depressive disorders ("F3 patients"). The study assessed today's acute inpatient treatment regimens with up to 8 repeated measurements regarding the time course of recovery and adverse side effects. The genotyping included 100 candidate genes with genotypic patterns computed from 549 Single Nucleotide Polymorphisms (SNPs). To predict response to psychopharmacological treatment, we relied on a multidimensional approach to analyzing genetic diversity in combination with multilayer Neural Nets (NNs). Central to this new method were the "gene vectors" that (1) assessed the multidimensional genotypic patterns observed with genes; and (2) evaluated the correlations between genes. By means of these methods, we searched for combinations of multidimensional genotypic patterns that were characteristic of treatment responders while being rare among non-responders. The chosen method of approach provided a powerful technique to detail the complex structures of SNP data that are not detectable by conventional association methods. Molecular-genetic NNs enabled correct classification of 100% "non-responders", along with 94.7% correctly classified "responders" among the F2 patients, and 82.6% correctly classified "responders" among the F3 patients. The F2 and F3 classifiers were not disjoint but showed an overlap of 29.6% and 35.7% between the diagnostic groups, thus indicating that clinical diagnoses may not constitute etiologic entities. Our results suggested that patients may have an unspecific physical-genetic disposition that enables, facilitates, impedes or prevents recovery from major psychiatric disorders by setting various thresholds for exogenous triggers that initiate improvement ("recovery disposition"). Even though this disposition is not causally linked to recovery, it can nonetheless be clinically used in the sense of a "surrogate". Indeed, clinicians are also interested in reliable tools that can "do the job", despite the fact that etiology and pathogenesis of the treated disorders remain unknown.

摘要

如今,超过90%因重度抑郁症或精神分裂症住院的患者接受精神药物治疗。由于现有的治疗方法均非病因性治疗,缓解率一般,且康复过程差异很大。在过去几十年里,针对主要精神疾病的病因和发病机制的基因研究大多没有成功。同样,预测对精神药物治疗反应的基因研究也不太成功。在这个项目中,我们招募了902名根据国际疾病分类第10版(ICD - 10)诊断为精神分裂症(“F2患者”)或抑郁症(“F3患者”)的住院患者。该研究评估了当今的急性住院治疗方案,针对康复过程和不良副作用进行了多达8次重复测量。基因分型包括100个候选基因,其基因型模式由549个单核苷酸多态性(SNP)计算得出。为了预测对精神药物治疗的反应,我们采用了一种多维方法,将分析基因多样性与多层神经网络(NN)相结合。这种新方法的核心是“基因向量”,它(1)评估通过基因观察到的多维基因型模式;(2)评估基因之间的相关性。通过这些方法,我们寻找多维基因型模式的组合,这些组合在治疗有反应者中具有特征性,而在无反应者中很少见。所选用的方法提供了一种强大的技术,能够详细分析常规关联方法无法检测到的SNP数据的复杂结构。分子遗传学神经网络能够正确分类100%的“无反应者”,在F2患者中94.7%的“有反应者”被正确分类,在F3患者中82.6%的“有反应者”被正确分类。F2和F3分类器并非完全不相交,诊断组之间有29.6%和35.7%的重叠,这表明临床诊断可能并不构成病因实体。我们的结果表明,患者可能有一种非特异性的生理 - 遗传倾向,通过为启动改善的外源性触发因素设置各种阈值(“康复倾向”),这种倾向能够促成、促进、阻碍或防止从主要精神疾病中康复。尽管这种倾向与康复没有因果关系,但在“替代物”的意义上它仍可用于临床。实际上,临床医生也对能够“发挥作用”的可靠工具感兴趣,尽管所治疗疾病的病因和发病机制仍然未知。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验