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Hierarchical Cluster Analysis Based on Clinical and Neuropsychological Symptoms Reveals Distinct Subgroups in Fibromyalgia: A Population-Based Cohort Study

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Title: Hierarchical Cluster Analysis Based on Clinical and Neuropsychological Symptoms Reveals Distinct Subgroups in Fibromyalgia: A Population-Based Cohort Study
Authors: Maurel Ibáñez, Sara Nieves, Giménez-Llort, Lydia, Alegre, Jose, Castro-Marrero, Jesus
Contributors: Institut Català de la Salut, Maurel S Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. Giménez-Llort L Departament de Psiquiatria i Medicina Forense, Escola de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. Institut de Neurociències, Universitat Autònoma de Barcelona, Bellaterra, Spain. Alegre-Martin J Grup de Recerca de Reumatologia, Unitat d’Encefalomielitis Miàlgica/Síndrome de Fatiga Crònica (EM/SFC), Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca de Reumatologia, Unitat d’Encefalomielitis Miàlgica/Síndrome de Fatiga Crònica (EM/SFC), Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. Castro-Marrero J Grup de Recerca de Reumatologia, Unitat d’Encefalomielitis Miàlgica/Síndrome de Fatiga Crònica (EM/SFC), Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain, Vall d'Hebron Barcelona Hospital Campus
Source: Scientia
Publisher Information: MDPI
Publication Year: 2023
Subject Terms: Dolor crònic, Fatiga, Fibromiàlgia - Tractament, DISEASES::Musculoskeletal Diseases::Muscular Diseases::Fibromyalgia, Other subheadings::Other subheadings::/therapy, DISEASES::Pathological Conditions, Signs and Symptoms::Signs and Symptoms::Neurologic Manifestations::Pain::Chronic Pain, Signs and Symptoms::Signs and Symptoms::Fatigue, ENFERMEDADES::enfermedades musculoesqueléticas::enfermedades musculares::fibromialgia, Otros calificadores::Otros calificadores::/terapia, ENFERMEDADES::afecciones patológicas, signos y síntomas::signos y síntomas::manifestaciones neurológicas::dolor::dolor crónico, signos y síntomas::signos y síntomas::fatiga
Description: Cluster analysis; Fibromyalgia; Neuropsychological symptoms ; Análisis de clústers; Fibromialgia; Síntomas neuropsicológicos ; Anàlisi de clústers; Fibromiàlgia; Símptomes neuropsicològics ; Fibromyalgia (FM) is a condition characterized by musculoskeletal pain and multiple comorbidities. Our study aimed to identify four clusters of FM patients according to their core clinical symptoms and neuropsychological comorbidities to identify possible therapeutic targets in the condition. We performed a population-based cohort study on 251 adult FM patients referred to primary care according to the 2010 ACR case criteria. Patients were aggregated in clusters by a K-medians hierarchical cluster analysis based on physical and emotional symptoms and neuropsychological variables. Four different clusters were identified in the FM population. Global cluster analysis reported a four-cluster profile (cluster 1: pain, fatigue, poorer sleep quality, stiffness, anxiety/depression and disability at work; cluster 2: injustice, catastrophizing, positive affect and negative affect; cluster 3: mindfulness and acceptance; and cluster 4: surrender). The second analysis on clinical symptoms revealed three distinct subgroups (cluster 1: fatigue, poorer sleep quality, stiffness and difficulties at work; cluster 2: pain; and cluster 3: anxiety and depression). The third analysis of neuropsychological variables provided two opposed subgroups (cluster 1: those with high scores in surrender, injustice, catastrophizing and negative affect, and cluster 2: those with high scores in acceptance, positive affect and mindfulness). These empirical results support models that assume an interaction between neurobiological, psychological and social factors beyond the classical biomedical model. A detailed assessment of such risk and protective factors is critical to differentiate FM subtypes, allowing for further identification of their specific needs and designing tailored personalized therapeutic interventions. ; This work was partially supported by the ...
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: Biomedicines;11(10); https://doi.org/10.3390/biomedicines11102867; https://hdl.handle.net/11351/10577
DOI: 10.3390/biomedicines11102867
Availability: https://hdl.handle.net/11351/10577
https://doi.org/10.3390/biomedicines11102867
Rights: Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
Accession Number: edsbas.5A3E16D6
Database: BASE
Description
Abstract:Cluster analysis; Fibromyalgia; Neuropsychological symptoms ; Análisis de clústers; Fibromialgia; Síntomas neuropsicológicos ; Anàlisi de clústers; Fibromiàlgia; Símptomes neuropsicològics ; Fibromyalgia (FM) is a condition characterized by musculoskeletal pain and multiple comorbidities. Our study aimed to identify four clusters of FM patients according to their core clinical symptoms and neuropsychological comorbidities to identify possible therapeutic targets in the condition. We performed a population-based cohort study on 251 adult FM patients referred to primary care according to the 2010 ACR case criteria. Patients were aggregated in clusters by a K-medians hierarchical cluster analysis based on physical and emotional symptoms and neuropsychological variables. Four different clusters were identified in the FM population. Global cluster analysis reported a four-cluster profile (cluster 1: pain, fatigue, poorer sleep quality, stiffness, anxiety/depression and disability at work; cluster 2: injustice, catastrophizing, positive affect and negative affect; cluster 3: mindfulness and acceptance; and cluster 4: surrender). The second analysis on clinical symptoms revealed three distinct subgroups (cluster 1: fatigue, poorer sleep quality, stiffness and difficulties at work; cluster 2: pain; and cluster 3: anxiety and depression). The third analysis of neuropsychological variables provided two opposed subgroups (cluster 1: those with high scores in surrender, injustice, catastrophizing and negative affect, and cluster 2: those with high scores in acceptance, positive affect and mindfulness). These empirical results support models that assume an interaction between neurobiological, psychological and social factors beyond the classical biomedical model. A detailed assessment of such risk and protective factors is critical to differentiate FM subtypes, allowing for further identification of their specific needs and designing tailored personalized therapeutic interventions. ; This work was partially supported by the ...
DOI:10.3390/biomedicines11102867