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Classification of patients with bipolar disorder using k-means clustering.

ABSTRACT: INTRODUCTION:Bipolar disorder (BD) is a heterogeneous disorder needing personalized and shared decisions. We aimed to empirically develop a cluster-based classification that allocates patients according to their severity for helping clinicians in these processes. METHODS:Naturalistic, cross-sectional, multicenter study. We included 224 subjects with BD (DSM-IV-TR) under outpatient treatment from 4 sites in Spain. We obtained information on socio-demography, clinical course, psychopathology, cognition, functioning, vital signs, anthropometry and lab analysis. Statistical analysis: k-means clustering, comparisons of between-group variables, and expert criteria. RESULTS AND DISCUSSION:We obtained 12 profilers from 5 life domains that classified patients in five clusters. The profilers were: Number of hospitalizations and of suicide attempts, comorbid personality disorder, body mass index, metabolic syndrome, the number of comorbid physical illnesses, cognitive functioning, being permanently disabled due to BD, global and leisure time functioning, and patients' perception of their functioning and mental health. We obtained preliminary evidence on the construct validity of the classification: (1) all the profilers behaved correctly, significantly increasing in severity as the severity of the clusters increased, and (2) more severe clusters needed more complex pharmacological treatment. CONCLUSIONS:We propose a new, easy-to-use, cluster-based severity classification for BD that may help clinicians in the processes of personalized medicine and shared decision-making.

SUBMITTER: Fuente-Tomas L

PROVIDER: S-EPMC6343877 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Classification of patients with bipolar disorder using k-means clustering.

Fuente-Tomas Lorena de la L Arranz Belen B Safont Gemma G Sierra Pilar P Sanchez-Autet Monica M Garcia-Blanco Ana A Garcia-Portilla Maria P MP

PloS one 20190123 1

<h4>Introduction</h4>Bipolar disorder (BD) is a heterogeneous disorder needing personalized and shared decisions. We aimed to empirically develop a cluster-based classification that allocates patients according to their severity for helping clinicians in these processes.<h4>Methods</h4>Naturalistic, cross-sectional, multicenter study. We included 224 subjects with BD (DSM-IV-TR) under outpatient treatment from 4 sites in Spain. We obtained information on socio-demography, clinical course, psycho ...[more]

PMID: 30673717

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Project description:BackgroundPsychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.MethodsIn this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification.ResultsWhen studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension).ConclusionsThe proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.

Project description:Bipolar Disorder (BD) is a complex neuropsychiatric disorder that is characterized by intermittent episodes of mania and depression and, without treatment, 15% of patients commit suicide1. Hence, among all diseases, BD has been ranked by the WHO as a top disorder of morbidity and lost productivity2. Previous neuropathological studies have revealed a series of alterations in the brains of BD patients or animal models3, such as reduced glial cell number in the patient prefrontal cortex4, up-regulated activities of the PKA/PKC pathways5-7, and changes in dopamine/5-HT/glutamate neurotransmission systems8-11. However, the roles and causation of these changes in BD are too complex to exactly determine the pathology of the disease; none of the current BD animal models can recapitulate both the manic and depressive phenotypes or spontaneous cycling of BD simultaneously12,13. Furthermore, while some patients show remarkable improvement with lithium treatment, for yet unknown reasons, other patients are refractory to lithium treatment. Therefore, developing an accurate and powerful biological model has been a challenge for research into BD. The development of induced pluripotent stem cell (iPSC) technology has provided such a new approach. Here, we developed a human BD iPSC model and investigated the cellular phenotypes of hippocampal dentate gyrus neurons derived from the patient iPSCs. Using patch clamp recording, somatic Ca2+ imaging and RNA-seq techniques, we found that the neurons derived from BD patients exhibited hyperactive action potential (AP) firing, up-regulated expression of PKA/PKC/AP and mitochondria-related genes. Moreover, lithium selectively reversed these alterations in the neurons of patients who responded to lithium treatment. Therefore, hyper-excitability is one endophenotype of BD that is probably achieved through enhancement in the PKA/PKC and Na+ channel signaling systems, and our BD iPSC model can be used to develop new therapies and drugs aimed at clinical treatment of this disease. total RNAseq from neurons generated from BD patient-specific iPS cells

Dataset Information

Classification of patients with bipolar disorder using k-means clustering.

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Classification of patients with bipolar disorder using k-means clustering.

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