Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.
Department of Psychiatry, University of Antioquía, Medellín, Colombia.
Lancet Psychiatry. 2020 May;7(5):411-419. doi: 10.1016/S2215-0366(20)30098-5.
Severe mental illness diagnoses have overlapping symptomatology and shared genetic risk, motivating cross-diagnostic investigations of disease-relevant quantitative measures. We analysed relationships between neurocognitive performance, symptom domains, and diagnoses in a large sample of people with severe mental illness not ascertained for a specific diagnosis (cases), and people without mental illness (controls) from a single, homogeneous population.
In this case-control study, cases with severe mental illness were ascertained through electronic medical records at Clínica San Juan de Dios de Manizales (Manizales, Caldas, Colombia) and the Hospital Universitario San Vicente Fundación (Medellín, Antioquía, Colombia). Participants were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases had structured interview-based diagnoses of schizophrenia, bipolar 1, bipolar 2, or major depressive disorder. Linear mixed models, using CNB tests as repeated measures, modelled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses in cases included symptom factor scores obtained from exploratory factor analysis of symptom data as main effects.
Between Oct 1, 2017, and Nov 1, 2019, 2406 participants (1689 cases [schizophrenia n=160; bipolar 1 disorder n=519; bipolar 2 disorder n=204; and major depressive disorder n=806] and 717 controls; mean age 39 years (SD 14); and 1533 female) were assessed. Participants with bipolar 1 disorder and schizophrenia had similar impairments in accuracy and speed across cognitive domains. Participants with bipolar 2 disorder and major depressive disorder performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, whereas high depression scores were associated with increased social cognition accuracy.
Cross-diagnostic investigations showed that neurocognitive function in severe mental illness is characterised by two distinct profiles (bipolar 1 disorder and schizophrenia, and bipolar 2 disorder and major depressive disorder), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating severe mental illness causes and trajectories.
US National Institute of Mental Health.
严重精神疾病的诊断具有重叠的症状和共同的遗传风险，这促使人们对相关的疾病定量指标进行跨诊断研究。我们分析了大量未因特定诊断而确定的严重精神疾病患者（病例）和无精神疾病者（对照）的神经认知表现、症状域和诊断之间的关系，这些患者来自单一、同质的人群。
在这项病例对照研究中，通过哥伦比亚马利雅莱斯的圣胡安德迪奥斯诊所（Clínica San Juan de Dios de Manizales）和安蒂奥基亚的麦德林圣维森特基金会医院（Hospital Universitario San Vicente Fundación）的电子病历确定严重精神疾病患者。使用宾夕法尼亚电脑化神经认知电池（CNB）对参与者进行速度和准确性评估。病例通过基于结构访谈的诊断确定为精神分裂症、1 型双相情感障碍、2 型双相情感障碍或重性抑郁障碍。使用 CNB 测试作为重复测量的线性混合模型，将神经认知建模为诊断、性别和所有交互作用的函数。在病例中进行的随访分析包括从症状数据的探索性因素分析中获得的症状因子评分作为主要效应。
2017 年 10 月 1 日至 2019 年 11 月 1 日，评估了 2406 名参与者（1689 名病例[精神分裂症 n=160；1 型双相情感障碍 n=519；2 型双相情感障碍 n=204；和重性抑郁障碍 n=806]和 717 名对照；平均年龄 39 岁[14 岁标准差]；1533 名女性）。1 型双相情感障碍和精神分裂症患者在认知领域的准确性和速度方面存在类似的损害。2 型双相情感障碍和重性抑郁障碍患者的表现与对照组相似，仅在执行功能和社会认知方面存在细微缺陷。三因素模型（精神病、躁狂和抑郁）最好地代表了症状数据。在控制诊断、前期智商和疾病严重程度后，高终生精神病得分与认知领域的准确性和速度降低有关，而高抑郁得分与社会认知准确性增加有关。
跨诊断研究表明，严重精神疾病患者的神经认知功能表现出两种不同的特征（1 型双相情感障碍和精神分裂症，以及 2 型双相情感障碍和重性抑郁障碍），并与特定的症状域相关。这些结果表明，这种设计对于阐明严重精神疾病的病因和病程具有实用性。
美国国立精神卫生研究所。
