Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023
Different personality profiles in patients with cluster headache: a data-driven approach.
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Telesca Alessandra, Proietti Cecchini Alberto, Leone Massimo, Piacentini Sylvie, Usai Susanna, Grazzi Licia, Consonni Monica
Аннотация
INTRODUCTION: Cluster headache (CH) is usually comorbid to mood spectrum disorders, but the psychopathological aspects are poorly explored. We aimed at identifying discrete profiles of personality traits and their association with clinical features.
METHODS: Based on the personality scales of the Millon Clinical Multiaxial Inventory-III, principal component analysis (PCA) identified psychological patterns of functioning of 56 CH patients. PCA outcomes were used for hierarchical cluster analysis (HCA) for sub-groups classification.
RESULTS: Eighty-seven percent of patients had personality dysfunctions. PCA found two bipolar patterns: (i) negativistic, sadic-aggressive, borderline, and compulsive traits were distinctive of the psychological dysregulation (PD) dimension, and (ii) narcissistic, histrionic, avoidant, and schizoid traits loaded under the social engagement (SE) component. PD was associated with disease duration and psychopathology. SE was related to educational level and young age. HCA found three groups of patients, and the one with high PD and low SE had the worst psychological profile.
CONCLUSIONS: Personality disorders are common in CH. Our data-driven approach revealed distinct personality patterns which can appear differently among patients. The worst combination arguing against mental health is low SE and high PD. Linking this information with medical history may help clinicians to identify tailored-based therapeutic interventions for CH patients.
METHODS: Based on the personality scales of the Millon Clinical Multiaxial Inventory-III, principal component analysis (PCA) identified psychological patterns of functioning of 56 CH patients. PCA outcomes were used for hierarchical cluster analysis (HCA) for sub-groups classification.
RESULTS: Eighty-seven percent of patients had personality dysfunctions. PCA found two bipolar patterns: (i) negativistic, sadic-aggressive, borderline, and compulsive traits were distinctive of the psychological dysregulation (PD) dimension, and (ii) narcissistic, histrionic, avoidant, and schizoid traits loaded under the social engagement (SE) component. PD was associated with disease duration and psychopathology. SE was related to educational level and young age. HCA found three groups of patients, and the one with high PD and low SE had the worst psychological profile.
CONCLUSIONS: Personality disorders are common in CH. Our data-driven approach revealed distinct personality patterns which can appear differently among patients. The worst combination arguing against mental health is low SE and high PD. Linking this information with medical history may help clinicians to identify tailored-based therapeutic interventions for CH patients.