Clementz Brett A, Trotti Rebekah L, Pearlson Godfrey D, Keshavan Matcheri S, Gershon Elliot S, Keedy Sarah K, Ivleva Elena I, McDowell Jennifer E, Tamminga Carol A
Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, Georgia.
Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2020 Aug;5(8):808-818. doi: 10.1016/j.bpsc.2020.03.011. Epub 2020 Apr 28.
Psychiatry aspires to the molecular understanding of its disorders and, with that knowledge, to precision medicine. Research supporting such goals in the dimension of psychosis has been compromised, in part, by using phenomenology alone to estimate disease entities. To this end, we are proponents of a deep phenotyping approach in psychosis, using computational strategies to discover the most informative phenotypic fingerprint as a promising strategy to uncover mechanisms in psychosis.
Doing this, the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has used biomarkers to identify distinct subtypes of psychosis with replicable biomarker characteristics. While we have presented these entities as relevant, their potential utility in clinical practice has not yet been demonstrated.
Here we carried out an analysis of clinical features that characterize biotypes. We found that biotypes have unique and defining clinical characteristics that could be used as initial screens in the clinical and research settings. Differences in these clinical features appear to be consistent with biotype biomarker profiles, indicating a link between biological features and clinical presentation. Clinical features associated with biotypes differ from those associated with DSM diagnoses, indicating that biotypes and DSM syndromes are not redundant and are likely to yield different treatment predictions. We highlight 3 predictions based on biotype that are derived from individual biomarker features and cannot be obtained from DSM psychosis syndromes.
In the future, biotypes may prove to be useful for targeting distinct molecular, circuit, cognitive, and psychosocial therapies for improved functional outcomes.
精神病学致力于从分子层面理解其疾病,并凭借这些知识实现精准医疗。在精神病领域,部分支持此类目标的研究因仅使用现象学来评估疾病实体而受到影响。为此,我们主张在精神病研究中采用深度表型分析方法,运用计算策略来发现最具信息价值的表型指纹,以此作为揭示精神病发病机制的一种有前景的策略。
在此过程中,双相情感障碍 - 精神分裂症中间型网络(B - SNIP)利用生物标志物来识别具有可重复生物标志物特征的精神病不同亚型。虽然我们已表明这些实体具有相关性,但其在临床实践中的潜在效用尚未得到证实。
在此,我们对表征生物型的临床特征进行了分析。我们发现生物型具有独特且具有决定性的临床特征,可在临床和研究环境中用作初步筛查。这些临床特征的差异似乎与生物型生物标志物谱一致,表明生物学特征与临床表现之间存在联系。与生物型相关的临床特征不同于与《精神疾病诊断与统计手册》(DSM)诊断相关的特征,这表明生物型和 DSM 综合征并非冗余,且可能产生不同的治疗预测。我们强调了基于生物型得出的 3 个预测,这些预测源自个体生物标志物特征,无法从 DSM 精神病综合征中获得。
未来,生物型可能被证明有助于针对不同的分子、神经回路、认知和心理社会疗法,以改善功能结局。