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Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis

BACKGROUND: Dissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis...

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Autores principales: Fekih-Romdhane, Feten, Hajje, Romy, Haddad, Chadia, Hallit, Souheil, Azar, Jocelyne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10428523/
https://www.ncbi.nlm.nih.gov/pubmed/37582728
http://dx.doi.org/10.1186/s12888-023-05101-3
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author Fekih-Romdhane, Feten
Hajje, Romy
Haddad, Chadia
Hallit, Souheil
Azar, Jocelyne
author_facet Fekih-Romdhane, Feten
Hajje, Romy
Haddad, Chadia
Hallit, Souheil
Azar, Jocelyne
author_sort Fekih-Romdhane, Feten
collection PubMed
description BACKGROUND: Dissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis to identify subgroups of Lebanese patients diagnosed with either schizophrenia or schizoaffective disorder based on NS clusters, and (2) to relate the statistically-derived subgroups to clinically relevant external validators (including measures if state and trait depression, stigma, insight, loneliness, social support). METHOD: A total of 202 adult long-stay, chronic, and clinically remitted patients (166 diagnosed with schizophrenia and 36 with schizoaffective disorder) were enrolled. A cluster analysis approach was adopted to classify patients based on the five NS domains social withdrawal, emotional withdrawal, alogia, avolition and anhedonia. RESULTS: A three-cluster solution was obtained based on unique NS profiles, and divided patients into (1) low NS (LNS; 42.6%) which characterized by the lowest mean scores in all NS domains, (2) moderate NS (MNS; 25.7%), and (3) high NS (HNS; 31.7%). Post-hoc comparisons showed that depression (state and trait), loneliness and social support could accurately distinguish the schizophrenia subgroups. Additionally, individuals in the HNS cluster had longer duration of illness, longer duration of hospitalization, and were given higher dosages of antipsychotic medication compared to those in the other clusters, but these differences did not achieve the statistical significance. CONCLUSION: Findings provide additional support to the categorical model of schizophrenia by confirming the existence of three alternate subtypes based on NS. The determination of distinct NS subgroups within the broad heterogeneous population of people diagnosed with schizophrenia may imply that each subgroup possibly has unique underlying mechanisms and necessitates different treatment approaches.
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spelling pubmed-104285232023-08-17 Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis Fekih-Romdhane, Feten Hajje, Romy Haddad, Chadia Hallit, Souheil Azar, Jocelyne BMC Psychiatry Research BACKGROUND: Dissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis to identify subgroups of Lebanese patients diagnosed with either schizophrenia or schizoaffective disorder based on NS clusters, and (2) to relate the statistically-derived subgroups to clinically relevant external validators (including measures if state and trait depression, stigma, insight, loneliness, social support). METHOD: A total of 202 adult long-stay, chronic, and clinically remitted patients (166 diagnosed with schizophrenia and 36 with schizoaffective disorder) were enrolled. A cluster analysis approach was adopted to classify patients based on the five NS domains social withdrawal, emotional withdrawal, alogia, avolition and anhedonia. RESULTS: A three-cluster solution was obtained based on unique NS profiles, and divided patients into (1) low NS (LNS; 42.6%) which characterized by the lowest mean scores in all NS domains, (2) moderate NS (MNS; 25.7%), and (3) high NS (HNS; 31.7%). Post-hoc comparisons showed that depression (state and trait), loneliness and social support could accurately distinguish the schizophrenia subgroups. Additionally, individuals in the HNS cluster had longer duration of illness, longer duration of hospitalization, and were given higher dosages of antipsychotic medication compared to those in the other clusters, but these differences did not achieve the statistical significance. CONCLUSION: Findings provide additional support to the categorical model of schizophrenia by confirming the existence of three alternate subtypes based on NS. The determination of distinct NS subgroups within the broad heterogeneous population of people diagnosed with schizophrenia may imply that each subgroup possibly has unique underlying mechanisms and necessitates different treatment approaches. BioMed Central 2023-08-15 /pmc/articles/PMC10428523/ /pubmed/37582728 http://dx.doi.org/10.1186/s12888-023-05101-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fekih-Romdhane, Feten
Hajje, Romy
Haddad, Chadia
Hallit, Souheil
Azar, Jocelyne
Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
title Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
title_full Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
title_fullStr Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
title_full_unstemmed Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
title_short Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
title_sort exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10428523/
https://www.ncbi.nlm.nih.gov/pubmed/37582728
http://dx.doi.org/10.1186/s12888-023-05101-3
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