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. 2023 Oct 23;11(10):2867.
doi: 10.3390/biomedicines11102867.

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

Sara Maurel  1 , Lydia Giménez-Llort  2   3 , Jose Alegre-Martin  4   5 , Jesús Castro-Marrero  5
Affiliations

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

Sara Maurel et al. Biomedicines. .

Abstract

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.

Keywords: chronic pain; cluster analysis; fatigue; fibromyalgia; mindfulness; neuropsychological symptoms.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses or interpretation of data, in the writing of the manuscript or in the decision to publish the results.

Figures

Figure 1
Figure 1
Dendrogram using a mean linkage between pain (FIQ_5); fatigue (FIQ_6); sleep quality (FIQ_7); stiffness (FIQ_8); anxiety (Total HADS-A); depression (Total HADS-D); difficulty at work (FIQ_4); surrender; injustice; catastrophizing (CATAST); negative affect (PANAS_negative); acceptance (CPAQ_TOTAL); positive affect (PANAS_positive); and mindfulness.
Figure 2
Figure 2
Dendrogram using a mean linkage between pain, sleep quality, fatigue, stiffness, anti-anxiety, depression and difficulty in work. Combination of rescaled distance clusters. The following cluster classification is then obtained from cluster analysis 2: cluster 1: FM patients with increased fatigue, sleep quality, stiffness and work difficulties scores; cluster 2: FM patients with pain; cluster 3: FM patients with raised anxiety/depression scores.
Figure 3
Figure 3
Dendrogram using an average linkage distance between surrender, injustice, catastrophizing, negative affect, acceptance, positive affect and mindfulness.
See this image and copyright information in PMC

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