Molecular Psychiatry Laboratory, Hospital de Clinicas de Porto Alegre, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain.
Psychol Med. 2022 Jul;52(9):1728-1735. doi: 10.1017/S0033291720003499. Epub 2020 Oct 14.
Subjects with bipolar disorder (BD) show heterogeneous cognitive profile and that not necessarily the disease will lead to unfavorable clinical outcomes. We aimed to identify clinical markers of severity among cognitive clusters in individuals with BD through data-driven methods.
We recruited 167 outpatients with BD and 100 unaffected volunteers from Brazil and Spain that underwent a neuropsychological assessment. Cognitive functions assessed were inhibitory control, processing speed, cognitive flexibility, verbal fluency, working memory, short- and long-term verbal memory. We performed hierarchical cluster analysis and discriminant function analysis to determine and confirm cognitive clusters, respectively. Then, we used classification and regression tree (CART) algorithm to determine clinical and sociodemographic variables of the previously defined cognitive clusters.
We identified three neuropsychological subgroups in individuals with BD: intact (35.3%), selectively impaired (34.7%), and severely impaired individuals (29.9%). The most important predictors of cognitive subgroups were years of education, the number of hospitalizations, and age, respectively. The model with CART algorithm showed sensitivity 45.8%, specificity 78.4%, balanced accuracy 62.1%, and the area under the ROC curve was 0.61. Of 10 attributes included in the model, only three variables were able to separate cognitive clusters in BD individuals: years of education, number of hospitalizations, and age.
These results corroborate with recent findings of neuropsychological heterogeneity in BD, and suggest an overlapping between premorbid and morbid aspects that influence distinct cognitive courses of the disease.
双相障碍（BD）患者表现出异质的认知特征，而且疾病不一定会导致不良的临床结局。我们旨在通过数据驱动的方法，在 BD 患者的认知聚类中确定严重程度的临床标志物。
我们招募了来自巴西和西班牙的 167 名 BD 门诊患者和 100 名未受影响的志愿者，他们接受了神经心理评估。评估的认知功能包括抑制控制、加工速度、认知灵活性、言语流畅性、工作记忆、短期和长期言语记忆。我们分别进行了层次聚类分析和判别函数分析，以确定和确认认知聚类。然后，我们使用分类和回归树（CART）算法来确定先前定义的认知聚类的临床和社会人口统计学变量。
我们在 BD 患者中确定了三个神经心理学亚组：完整（35.3%）、选择性受损（34.7%）和严重受损（29.9%）。认知亚组最重要的预测因子分别是受教育年限、住院次数和年龄。CART 算法模型的灵敏度为 45.8%，特异性为 78.4%，平衡准确性为 62.1%，ROC 曲线下面积为 0.61。在纳入模型的 10 个属性中，只有三个变量能够区分 BD 个体的认知聚类：受教育年限、住院次数和年龄。
这些结果与 BD 中神经心理学异质性的最新发现相吻合，并表明了发病前和发病后因素的重叠，这些因素影响了疾病的不同认知过程。
