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Different personality profiles in patients with cluster headache: a data-driven approach

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Abstract

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.

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Data Availability

The data that support the findings of this study are openly available in an open repository at https://doi.org/10.5281/zenodo.6583557.

References

  1. D’Amico D, Raggi A, Grazzi L, Lambru G (2020) Disability, quality of life, and socioeconomic burden of cluster headache: a critical review of current evidence and future perspectives. Headache J Head Face Pain 60(4):809–18

    Article  Google Scholar 

  2. Fischera M, Marziniak M, Gralow I, Evers S (2008) The incidence and prevalence of cluster headache: a meta-analysis of population-based studies. Cephalalgia 28(6):614–618

    Article  CAS  PubMed  Google Scholar 

  3. Giani L, ProiettiCecchini A, Leone M (2020) Cluster headache and TACs: state of the art. Neurol Sci 41(S2):367–375

    Article  PubMed  Google Scholar 

  4. (2018) Headache Classification Committee of the International Headache Society (IHS) The international classification of headache disorders, 3rd edition. Cephalalgia.;38(1):1–211.

  5. Kim B-S, Chung P-W, Kim B-K, Lee MJ, Park JW, Chu MK, et al 2020 The impact of remission and coexisting migraine on anxiety and depression in cluster headache. J Headache Pain 21(1).

  6. Evers S (2005) Cognitive processing in cluster headache. Curr Pain Headache Rep 9(2):109–112

    Article  PubMed  Google Scholar 

  7. Jorge RE, Leston JE, Arndt S, Robinson RG (1999) Cluster headaches: association with anxiety disorders and memory deficits. Neurology 53(3):543

    Article  CAS  PubMed  Google Scholar 

  8. Robbins MS, Bronheim R, Lipton RB, Grosberg BM, Vollbracht S, Sheftell FD et al (2012) Depression and anxiety in episodic and chronic cluster headache: a pilot study. Headache J Head Face Pain 52(4):600–11

    Article  Google Scholar 

  9. Muñoz I, Hernández MS, Santos S, Jurado C, Ruiz L, Toribio E et al (2016) Personality traits in patients with cluster headache: a comparison with migraine patients. J Headache Pain 17(1).

  10. Piacentini SHMJ, Draghi L, Cecchini AP, Leone M (2017) Personality disorders in cluster headache: a study using the Millon Clinical Multiaxial Inventory-III. Neurol Sci 38(S1):181–184

    Article  PubMed  Google Scholar 

  11. Millon T (1997) Millon Clinical Multiaxial Inventory-III [Manual Second Edition]. Pearson Assessments, Bloomington, MN

    Google Scholar 

  12. Millon T, Davis RO (1996) Disorders of personality: DSM-IV and beyond: John Wiley & Sons.

  13. Rushton JP, Irwing P (2009) A general factor of personality in the Millon Clinical Multiaxial Inventory-III, the dimensional assessment of personality pathology, and the personality assessment inventory. J Res Pers 43:1091–1095

    Article  Google Scholar 

  14. Consonni M, Dalla Bella E, Bersano E, Telesca A, Lauria G (2021) Cognitive reserve is associated with altered clinical expression in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degeneration 22(3–4):237–247

    Article  Google Scholar 

  15. Zennaro A, Ferracuti S, Lang M, Roccaro G, Roma P, Sanavio E et al (2013) Diagnostic validity statistics in MCMI-III applied to an Italian sample. BPA-Appl Psychol Bull (Bollettino di Psicologia Applicata) 60(267).

  16. Field A (2013) Discovering statistics using IBM SPSS statistics. 4th Edition ed. London: SAGE Publications Ltd.

  17. Professional I (n.d) Statistical Package for the Social Science. SPSS. 25.0 ed. Inc. Chicago, IL, USA.

  18. Davidsen AH, Hoyt WT, Poulsen S, Waaddegaard M, Lau M (2017) Eating disorder severity and functional impairment: moderating effects of illness duration in a clinical sample. Eat Weight Disord - Stud Anorexia Bulimia Obes 22(3):499–507

    Article  Google Scholar 

  19. Diniz JB, Rosario-Campos MC, Shavitt RG, Curi M, Hounie AG, Brotto SA et al (2004) Impact of age at onset and duration of illness on the expression of comorbidities in obsessive-compulsive disorder. J Clin Psychiatry 65:22–27

    Article  PubMed  Google Scholar 

  20. Mendes De Leon CF (2005) Social engagement and successful aging. Eur J Ageing 2(1):64–66

    Article  PubMed  PubMed Central  Google Scholar 

  21. Penninkilampi R, Casey A-N, Singh MF, Brodaty H (2018) The association between social engagement, loneliness, and risk of dementia: a systematic review and meta-analysis. J Alzheimers Dis 66:1619–1633

    Article  PubMed  Google Scholar 

  22. Bjelland I, Krokstad S, Mykletun A, Dahl AA, Tell GS, Tambs K (2008) Does a higher educational level protect against anxiety and depression? HUNT study 66(6):1334–1345

    Google Scholar 

  23. Cano-López I, Aliño M, Duque A, Martínez P, Almela M, García-Rubio MJ et al (2021) Educational level as a protective factor against the influence of depressive symptoms on cognition in older adults: implications for functional independence during a 10-year follow-up. Int Psychogeriatr 33(8):813–825

    Article  PubMed  Google Scholar 

  24. Navarro-Bravo B, Latorre JM, Jiménez AML, Cabello R, Fernández-Berrocal P (2019) Ability emotional intelligence in young people and older adults with and without depressive symptoms, considering gender and educational level. PeerJ 7.

  25. Sass H, Jünemann K (2003) Affective disorders, personality and personality disorders. Acta Psychiatr Scand 108(s418):34–40

    Article  Google Scholar 

  26. Lovati C, Bernasconi G, Capogrosso C, Molteni L, Giorgetti F, Dell’Osso B et al (2022) Personality traits and efficacy of anti-CGRP monoclonal antibodies in migraine prevention. Neurol Sci

  27. McMahon RC (2008) Substance abuse problems, psychiatric symptoms, and post-treatment status in MCMI psychopathology subgroups of cocaine dependent males. Am J Drug Alcohol Abuse 34(2):195–202

    Article  PubMed  Google Scholar 

  28. Craig RJ, Olson RE (1995) MCMI-II profiles and typologies for patients seen in marital therapy. Psychol Rep 76(1):163–170

    Article  CAS  PubMed  Google Scholar 

  29. Piekarski AM, Sherwood R, Funari DJ (1993) Personality subgroups in an inpatient Vietnam veteran treatment program. Psychol Rep 72(2):667–674

    Article  CAS  PubMed  Google Scholar 

  30. Gatchel RJ, Peng YB, Peters ML, Fuchs PN, Turk DC (2007) The biopsychosocial approach to chronic pain: scientific advances and future directions. Psychol Bull 133(4):581–624

    Article  PubMed  Google Scholar 

  31. Pulvers K, Hood A (2013) The role of positive traits and pain catastrophizing in pain perception 17(5):330

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Acknowledgements

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Italian Ministry of Health, Research Grant RF-2016-02364909.

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Authors and Affiliations

  1. Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Alessandra Telesca, Alberto Proietti Cecchini, Massimo Leone, Susanna Usai, Licia Grazzi & Monica Consonni

  2. Ph.D. program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy

    Alessandra Telesca

  3. Clinical Neuropsychology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Sylvie Piacentini

Authors
  1. Alessandra Telesca
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  2. Alberto Proietti Cecchini
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  3. Massimo Leone
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  4. Sylvie Piacentini
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  5. Susanna Usai
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  6. Licia Grazzi
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  7. Monica Consonni
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Corresponding author

Correspondence to Licia Grazzi.

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Conflict of interest

Licia Grazzi has received consultancy and advisory fees from Allergan SpA; Electrocore LLC; EliLilly; Novartis AG. Moreover, collaborators for RCTs are sponsored by EliLilly; Novartis; TEVA Pharm Ind. The other authors declare no competing interests.

Ethical approval and Informed consent

The study was performed in accordance with the ethical standards of the Declaration of Helsinki 2013 and was approved by the local ethical committee. All patients gave written informed consent before enrolment.

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Telesca, A., Proietti Cecchini, A., Leone, M. et al. Different personality profiles in patients with cluster headache: a data-driven approach. Neurol Sci 44, 2853–2861 (2023). https://doi.org/10.1007/s10072-023-06713-z

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  • DOI: https://doi.org/10.1007/s10072-023-06713-z

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