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Data-driven cognitive phenotypes in subjects with bipolar disorder and their clinical markers of severity

Published online by Cambridge University Press:  14 October 2020

Francisco Diego Rabelo-da-Ponte ,
Flavia Moreira Lima ,
Anabel Martinez-Aran ,
Flávio Kapczinski ,
Eduard Vieta ,
Adriane Ribeiro Rosa ,
Maurício Kunz  and
Leticia Sanguinetti Czepielewski Open the ORCID record for Leticia Sanguinetti Czepielewski [Opens in a new window]
Francisco Diego Rabelo-da-Ponte
Affiliation:
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
Flavia Moreira Lima
Affiliation:
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
Anabel Martinez-Aran
Affiliation:
Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
Flávio Kapczinski
Affiliation:
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
Eduard Vieta
Affiliation:
Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
Adriane Ribeiro Rosa
Affiliation:
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 Departamento de Farmacologia, Programa de Pós-Graduação em Farmacologia e Terapêutica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Maurício Kunz
Affiliation:
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
Leticia Sanguinetti Czepielewski*
Affiliation:
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 Programa de Pós-Graduação em Psicologia, Departamento de Psicologia do Desenvolvimento e da Personalidade, Instituto de Psicologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
*
Author for correspondence: Leticia Sanguinetti Czepielewski, E-mail: leticia.czepielewski@ufrgs.br
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Abstract

Background

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.

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.

Results

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.

Conclusion

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.

Keywords

Bipolar disordercluster analysiscognitiondecision treemachine learning
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 9 , July 2022 , pp. 1728 - 1735
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Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press.

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