biomedicines Article 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 and Jesús Castro-Marrero 5, * 1 Department of Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain; saranieves.maurel@uab.cat 2 Department of Psychiatry and Forensic Medicine, School of Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain; lidia.gimenez@uab.cat 3 Institut de Neurosciències, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain 4 Division of Rheumatology, Clinical Unit in ME/CFS and Long COVID, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain; jose.alegre@vallhebron.cat 5 Division of Rheumatology, Research Unit in ME/CFS and Long COVID, Vall d’Hebron Research Institute, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain * Correspondence: jesus.castro@vhir.org 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 neu- ropsychological 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 Citation: Maurel, S.; Giménez-Llort, negative affect; cluster 3: mindfulness and acceptance; and cluster 4: surrender). The second analysis L.; Alegre-Martin, J.; Castro-Marrero, on clinical symptoms revealed three distinct subgroups (cluster 1: fatigue, poorer sleep quality, J. Hierarchical Cluster Analysis Based stiffness and difficulties at work; cluster 2: pain; and cluster 3: anxiety and depression). The third on Clinical and Neuropsychological analysis of neuropsychological variables provided two opposed subgroups (cluster 1: those with Symptoms Reveals Distinct high scores in surrender, injustice, catastrophizing and negative affect, and cluster 2: those with Subgroups in Fibromyalgia: A Population-Based Cohort Study. high scores in acceptance, positive affect and mindfulness). These empirical results support models Biomedicines 2023, 11, 2867. that assume an interaction between neurobiological, psychological and social factors beyond the https://doi.org/10.3390/ classical biomedical model. A detailed assessment of such risk and protective factors is critical to biomedicines11102867 differentiate FM subtypes, allowing for further identification of their specific needs and designing tailored personalized therapeutic interventions. Academic Editor: Ryan S. D’Souza Received: 3 October 2023 Keywords: chronic pain; fibromyalgia; cluster analysis; neuropsychological symptoms; fatigue; Revised: 18 October 2023 mindfulness Accepted: 21 October 2023 Published: 23 October 2023 1. Introduction Copyright: © 2023 by the authors. Fibromyalgia (FM) is a chronic complex condition of unknown etiology, considered Licensee MDPI, Basel, Switzerland. multifactorial and combining genetic and epigenetic factors that condition a persistent This article is an open access article alteration in pain regulation mechanisms [1]. The diagnostic case criteria of the 2010 distributed under the terms and ACR identify it by the presence of widespread and diffuse musculoskeletal pain lasting conditions of the Creative Commons more than 3 months and with painful tenderness to palpation in at least 11 of 18 tender Attribution (CC BY) license (https:// points, defined as trigger points [2]. In FM, pain is the most frequent and disabling creativecommons.org/licenses/by/ symptom; it is considered as a chronic pain syndrome that presents with neurophysiological 4.0/). alterations similar to other chronic pain states but is also accompanied by other functionally Biomedicines 2023, 11, 2867. https://doi.org/10.3390/biomedicines11102867 https://www.mdpi.com/journal/biomedicines Biomedicines 2023, 11, 2867 2 of 15 limiting clinical symptoms. This multiplicity of other symptoms includes fatigue, sleep disturbances, irritable bowel syndrome, paresthesia, concentration and memory problems, muscle stiffness, mood disturbances, high comorbidity of anxiety/depression symptoms and somatoform disorders [3–5]. Fibromyalgia is one of the leading health problems worldwide due to its high preva- lence causing significant clinical and social burden, which may further exacerbate the patients’ level of disability [6], chronic pain [7] and reduction in the quality of life that it entails, as well as the high healthcare expenditure that it generates [8,9]. Furthermore, this debilitating condition can start and profoundly affect the psychology and life of people during their life cycle [10,11]. In many countries, such as the U.S., Canada, Norway, Sweden and Brazil, FM is already recognized as a disease with high rates of disability. The prevalence of FM in Spain according to the EPISER study is high and has been es- timated as approximately 2.4% of the general population over 20 years of age, representing approximately 700,000 people affected [12,13]. It also presents a clear gender difference, being predominantly in women (4.2% in women, compared to 0.2% in men) with a ratio of 1:21. Although FM can manifest itself at all ages, it presents a maximum peak between 40 and 50 years of age (4.9%). Geographically, the highest prevalence is found in Europe, and Spain is one of the European countries with the highest prevalence after Germany, Portugal, Italy and Turkey. It is important to note that heterogeneity and variability have been found in peo- ple with FM, both in clinical symptoms [14] and in differences in psychological process- ing [12,15], altered cardiovascular reactivity and distorted pain perception [15–17]. This heterogeneity and variability in symptomatology, but also the difficulties in reaching a rigorous diagnosis, directly affect prevalence studies, resulting in 2–4% in the general population, 2–6% in primary care consultations and 10–20% in rheumatology consultations. In this regard, different subgroups of FM patients based on pressure–pain thresholds, psychological factors and coping strategies (anxiety/depression; catastrophism) have been defined, and personalized therapeutic approaches proposed. Their main aim is to be able to propose different strategies for coping with pain and to offer the FM patients personalized therapeutic approaches [18]. In the present work, our study aimed to identify subgroups of FM patients using a hierarchical cluster analysis based on clinical and neuropsychological variables. Thus, clini- cal variables such as pain, fatigue, sleep quality, stiffness, anxiety, depression and disability at work with physical and emotional impact were studied. The psychological variables explored were acceptance, mindfulness, positive affect and negative affect, catastrophizing, surrender and perceived injustice in the FM population. 2. Methods 2.1. Study Participants We included 251 FM patients from the primary care health center of Aragon region, Spain (Zaragoza, Teruel and Huesca), and selected those patients who met the following inclusion criteria: patients diagnosed with FM according to the 2010 ACR case criteria [2], aged between 18–65 years, speaking and understanding Spanish, giving informed consent. Exclusion criteria included suffering from severe medical illness or severe neuropsychiatric disorder in Axis I, not collaborating or not signing the informed consent. Patients were contacted by telephone to explain the characteristics of the study pro- tocol. Subsequently, the patients were randomly summoned by a psychologist in post- graduate training in order to carry out the clinical interview and complete the protocols in person. In the personal interview, the conditions and completion of questionnaires, study aims, research conditions and the confidentiality of the process and data were explained. After obtaining informed consent, the tests and interviews were administered, lasting approximately one and a half hours per person. The study followed the norms of the Helsinki Convention and its subsequent mod- ifications and the Madrid Declaration of the World Psychiatric Association. Informed Biomedicines 2023, 11, 2867 3 of 15 consent was requested from all patients before being included in the study and voluntary and informed participation was guaranteed. At any time, the patient could decline to participate or answer any questions he/she considered appropriate, as well as revoke the previously signed informed consent. This study is part of a project financially supported by the Health National Institute Carlos III in Madrid, Spain (grant number PI09/90301) and the data collection took place between July 2011 and May 2014. 2.2. Measures Sociodemographic variables such as age, sex, place of residence, marital status, educa- tional level, cohabitation, employment status and comorbidity were recorded. A general survey was also performed on each participant. 2.3. Health Status Variables Physical impact variables of FM: pain, fatigue, sleep quality, stiffness and difficulty at work. The Spanish consensus version of the Fibromyalgia Impact Questionnaire (FIQ) was used. The Spanish FIQ version was used in this study [19]. The FIQ is composed of 10 items classified on a four-point Likert scale (0–3) and quantifies functional capacity, health status, pain intensity, sleep disturbances, muscle stiffness, fatigue, anxiety/depression and personal perception. It is an instrument recommended by the Spanish Society of Rheumatology (SER) to assess both disability and the global and physical impact of FM. The first item, consisting of 9 sub-items, focuses on the patient’s ability to perform physical activities and can be used in isolation to assess the patient’s degree of disability. The next two require the patient to indicate the number of days in the previous week that he/she felt well and how many days in the last week he/she stopped working due to illness. The remaining seven (4 to 10) refer to the ability to work, pain, fatigue, morning tiredness, stiffness, anxiety and depression, all measured (from 0 to 10) using visual analog scales (VASs). Higher scores indicate a greater degree of impact of the disease, a score of 70 or more is considered severe and the Cronbach’s alpha was 0.80 [14,20]. Variables of Emotional Impact or Distress, Anxiety and Depression. The Hospital Anxiety and Depression Scale (HADS), a self-reported measure, was used to detect anxiety and depression in people with medical illnesses. It comprises 14 items that are scored on a 4-point Likert scale. It includes two subscales—anxiety (7 items) and depression (7 items)—which are scored independently; the higher the score, the greater the anxiety or depression. The HADS has been validated in Spanish. It is recommended by the SER as an instrument to assess the emotional state or impact of patients with FM (Rivera et al., 2006). The Spanish version has shown adequate test–retest reliability and a Cronbach’s alpha of 0.69 [21]. 2.4. Neuropsychological Variables Acceptance: It is measured with the Chronic Pain Acceptance Questionnaire (CPAQ). The Spanish validation of the questionnaire was used in this study. It measures pain acceptance as a predictor of well-being in patients with chronic pain. It is an abbreviated version of the CPCI-42 that maintains its psychometric properties. It analyzes 2 subscales: willingness to perform activities of daily living and acceptance of pain. It is composed of a list of 20 self-administered items, assessed on a rating scale from 0 (never true) to 6 (always true). The result of the two subscales is summed directly and the range is between 0 and 120. The higher the score, the greater the acceptance of pain and/or availability to perform the activities. The Spanish version has shown adequate test–retest reliability, internal consistency and construct validity [22]. Mindfulness: It is measured with the Mindful Attention Awareness Scale (MAAS) validated in Spanish. The mindfulness scale is composed of 15 items, which measure the construct of “being aware”, focused on the present and without judging the situations being experienced. The correctness of the items is from 1 (almost always) to 6 (almost Biomedicines 2023, 11, 2867 4 of 15 never), summed directly. There is only one factor, which is the total of the scale. The range is between 15 (minimum mindfulness) and 90 (maximum mindfulness). In an attempt to control for socially desirable responses, participants are asked to respond according to what actually reflects their experience and not what they think it should be. Cognition, emotions, physical, interpersonal and general domains are assessed. The original MAAS had high internal consistency (Cronbach’s alpha is 0.85) [23]. Positive and Negative Affectivity (PANAS scales): Most studies on the structure of affect agree that affect is made up of two dimensions or factors: positive and negative affect. The PANAS scales have proven to be a valid and reliable measure to assess the presence and degree of positive and negative affect in clinical and normal populations of adolescents, adults and older adults, validated in Spanish. The PANAS is characterized by an internal consistency with alphas of 0.86 to 0.90 for positive affect and 0.84 to 0.87 for negative affect. The correlation between the two is invariably low, ranging from −0.12 to −0.23, which reinforces the idea that they are independent domains of affective well-being. Currently, this scale has an abbreviated version of 10 items that have proven to be cross-culturally reliable. PANAS consists of two mood scales with 10 items each for the assessment of positive and negative affectivity. The score for each range of the scale goes from 0 to 50. A positive affect is obtained by adding the odd-numbered items and negative affect by adding the even-numbered items. The scores for both affects are obtained by adding the numbers assigned to the 10 items that make up each of the two scales. The mean positive PANAS for a sample of young students was 32 and the mean negative PANAS was 23. People with scores above 38 for positive and below 16 for negative are characterized by an extremely positive affect balance. People with scores below 25 in positive and above 30 in negative give an extremely negative affect balance [24]. Catastrophizing: It is measured with the Spain-validated Pain Catastrophizing Scale (PCS). It consists of 13 items divided into three subscales that analyze rumination or meditation doubt (4 items), magnification or exaggeration (3 items) and hopelessness or helplessness (6 items), and its design focuses on feelings and thoughts related to pain, valuing them on a scale from 0 (not at all) to 4 (all the time). The 3 subscales are summed and the range is between 0 and 52. A higher score would correspond to a higher frequency and intensity of negative thinking and feelings regarding pain. The maximum total score is 52, such that a higher score would correspond to a higher frequency and intensity of negative thinking and feelings regarding pain [25]. Surrender: The Spanish version of the Pain Self Perception Scale was used. It is composed of 24 items that measure the self-processing of thoughts and feelings that can be experienced during an episode of severe pain. It is a self-administered questionnaire, where responses are measured using a 5-point scale between 0 (minimum intensity) and 4 (maximum intensity). There is only one factor, which is the global factor of the scale, and there are no subfactors. It is scored between 0 and 96 [26]. Psychological inflexibility: It is measured by the self-reported Psychological Inflexibil- ity of Pain Scale, which contains 12 items, with two main subscales: avoidance (8 items) and cognitive fusion (4 items); both measure the inability to maintain our values in the presence of unpleasant thoughts, emotions and physical symptoms. The statements are scored on a Likert-type scale ranging from 1 (never true) to 7 (always true). Total 12-item scores can range from 12 to 84. Higher scores would indicate greater psychological inflexibility in the face of pain. Its psychometric properties are considered adequate [27]. Perceived injustice: This construct is measured by a questionnaire that reliably mea- sures how a traumatic situation affects people’s lives validated in Spanish. It contains 12 items, with 2 subscales of severity/irreparability (6 items) and guilt/injustice (6 items) on a 5-point scale, from not at all (0) to all the time (4). The total scale scores range from 0 to 48. The psychometric properties of the injustice experiences questionnaire (IEQ) are considered adequate for use in the study [28,29]. Biomedicines 2023, 11, 2867 5 of 15 2.5. Statistical Analysis SPSS version 21 was used to perform cluster analysis, which is a multivariate technique that seeks to group variables to achieve maximum homogeneity in each group and the greatest difference between groups. We will rely on cumulative hierarchical algorithms, i.e., the method that forms groups by making larger and larger clusters. In our study, three cluster analyses were performed using the hierarchical clustering method (interval measure—Euclidean-by variables), as discussed below. First of all, we can say that the distance considered is the Euclidean distance (interval measure given by the following procedure, i.e., between each pair of elements, the difference in squared coordinates, the sum of all of them and finally their square root is considered). In addition, in order to compare the distances, the distances are standardized into Z-scores. The linkage technique is the inter-group average. Three cluster analyses were performed and the objective consisted first of all in group- ing all the individuals into non-predefined subgroups and classifying or organizing them according to the 14 variables studied in order to obtain similar profiles with respect to psychological and clinical variables. From the result obtained, two other cluster analyses were performed, one with psychological variables (catastrophizing, surrender, mindfulness, acceptance, injustice, positive affect and negative affect) and another cluster analysis with respect to clinical variables (pain, fatigue, sleep quality, stiffness, anxiety, depression and disability at work). 3. Results 3.1. Participants’ Demographic and Clinical Characteristics Table 1 shows the demographic and clinical features of the study population. In relation to sex, 96.1% were women and 3.9% were men. Participants had a mean age (±SD) of 52.4 ± 8.0 years (95% CI: 51.3–53.4, range: 3–70 years) and a mean length of time from onset of symptoms until inclusion of 18.3 ± 11.1 years. In all, 82.9% of the FM patients lived in Zaragoza and 11.2% in Teruel in Spain. Most (84%) had finished primary and secondary education. As for their living arrangements, most were married or living with a partner (73.7%) and almost half lived in their own home with their partner and children (47.4%). With regard to employment, 25.1% were working, 21.1% were permanently disabled and 12.3% were on sick leave. Table 1. Demographic and clinical characteristics of the study population (n = 251). Variable n (%) or Mean ± SD Age (years) 52.4 ± 8 Gender (F/M) 241 (96.1)/10 (3.9) Illness duration at diagnosis (years) 10.2 ± 9.3 Illness duration at inclusion (years) 18.3 ± 11.1 Current smoking 70 (27.9) Ex-smoker (≥1 year) 50 (20) Non-smoker 131 (52.2) Marital status Married/living with partner 185 (73.7) Separated/divorced 32 (12.8) Single 23 (9.2) Widower 11 (4.3) Place of residence Zaragoza 221 (88.1) Huesca 20 (7.9) Teruel 10 (3.9) Living arrangements Living with partner/spouse and children 119 (47.4) Biomedicines 2023, 11, 2867 6 of 15 Table 1. Cont. Variable n (%) or Mean ± SD Living with partner/spouse 80 (31.8) Living alone 28 (11.2) Living with other family 10 (3.9) Others 14 (5.6) Education Finished primary school 116 (46.2) Finished secondary school 95 (37.8) University graduate 32 (12.8) No qualifications 8 (3.2) Employment Employed 63 (25.1) Disabled 53 (21.1) Unemployed 38 (15.1) Retired 34 (13.5) Homemaker 32 (12.7) Sick leave 31 (12.3) Comorbid health conditions Chronic neck pain 238 (94.8) Low back pain 230 (91.6) Dry eyes 223 (88.8) Anxiety/Depression 175 (69.7) Cephalea/Migraine 193 (76.9) Intestinal bowel syndrome 187 (74.5) Menopause 169 (67.3) Osteoarthritis 153 (60.9) Rheumatoid arthritis 61 (24.3) All values, except for age at inclusion and illness duration at diagnosis and at the time of study inclusion (as mean ± standard deviation (SD)), are displayed as numbers (percentages) of individuals. 3.2. Cluster Analysis of Clinical and Neuropsychological Parameters Global cluster 1 analysis included pain, fatigue, sleep quality, stiffness, difficulty at work, anxiety/depression, surrender, injustice, mindfulness, catastrophizing, acceptance, positive affect and negative affect. These variables have been analyzed from a sample collected from 251 patients; 96.8% are valid cases and 3.2% are missing values. In our study, the matrix of distances between the different clinical and psychological variables mentioned above was performed and below we present the average (inter-group) linkage of the psychological constructs and clinical variables mentioned above (Table 2). The dendrogram shows the formation of the clusters, as well as the distances between them. It can be seen, for example, that the variables (observations) closest to each other were pain (FIQ_5); fatigue (FIQ_6); sleep quality (FIQ_7); stiffness (FIQ_8); anxiety (Total HADS-A); depression (Total HADS-D); and difficulty at work (FIQ_4), which form the first group (distance closest to 0) and were the closest to each other. These are joined with injustice, catastrophizing (CATAST), negative affect (PANAS_negative) and positive affect (PANAS_positive). On the other hand, it was joined by proximity acceptance (CPAQ) and mindfulness and, finally, a fourth cluster with the variable surrender (Figure 1). The summary of the subgroups obtained from the global analysis was as follows: cluster 1: FM patients with pain, fatigue, sleep quality, stiffness, anxiety, depression and difficulty at work; cluster 2: FM patients with injustice, catastrophizing, negative affect and positive affect; cluster 3: FM patients with mindfulness and acceptance; cluster 4: FM patients with surrender. Biomedicines 2023, 11, 2867 7 of 15 Table 2. Cluster history. 1. Pain; 2. fatigue; 3. sleep quality; 4. rigidity; 5. difficulty of work; 6. anxiety; Biomedicines 2023, 11, x FOR PEER REVIEW 7 of 15 7. depression; 8. surrender; 9. injustice; 10. mindfulness; 11. catastrophization; 12. acceptance; 13. positive affect; 14. negative affect. 10 1 Stage 9 Cluster * 1108,588.036 Cluster * 2 Coefficients 6 9 Cluster ** 1 Cluster ** 2 Next12Stage 11 10 12 151,021.000 0 0 12 1 2 3 886.000 0 0 3 12 1 10 513,915.114 10 11 13 2 1 5 972.250 0 0 4 13 1 3 8 2 526,653.865 4 12 1058.000 1 0 0 04 * Cluster 4 combined.1 ** Cluster stage2 appearing for the first time. 1560.958 2 3 5 5 1 7 4534.450 4 0 6 The 6 dendrogram 1 shows the 6 formation of the clusters, 7649.042 5 as well as the 0 distances between 10 them.7 It can be seen, 11 14 for example, that31,097.000 0 the variables (observations) 0 closest to each8 other were 8pain (FIQ_5); 9 fatigue (FIQ_6); 11 36,317.500 sleep quality (FIQ_7); 0 stiffness (FIQ_8); 7 anxiety9(Total 9 9 13 77,097.667 8 0 10 HADS-A); 10 depression 1 (Total HADS-D); 9 and difficulty at 108,588.036 6 work (FIQ_4),9 which form 12 the first group 11 (distance 10 closest to120) and were the closest to 0each other. These 151,021.000 0 are joined 12 with injustice, 12 catastrophizing 1 (CATAST), 10 negative affect (PANAS_negative) 513,915.114 10 11 and positive13affect (PANAS_positive). 13 1 On the other 8 hand,526,653.865 it was joined by proximity 12 acceptance 0 (CPAQ) 0 and mindfulness and,** finally, * Cluster combined. a fourth Cluster stage cluster appearing with for the firstthe time.variable surrender (Figure 1). Dendrogramusing Figure 1. Dendrogram usingaamean meanlinkage linkagebetween betweenpain pain(FIQ_5); (FIQ_5);fatigue fatigue (FIQ_6); (FIQ_6); sleep sleep qual- quality (FIQ_7); stiffness (FIQ_8); anxiety (Total HADS-A); depression (Total HADS-D); difficulty ity (FIQ_7); stiffness (FIQ_8); anxiety (Total HADS-A); depression (Total HADS-D); difficulty at at work (FIQ_4); surrender; work (FIQ_4); injustice; surrender; catastrophizing injustice; (CATAST); catastrophizing negative (CATAST); affectaffect negative (PANAS_negative); (PANAS_negative);ac- ceptance (CPAQ_TOTAL); positive affect (PANAS_positive); and mindfulness. acceptance (CPAQ_TOTAL); positive affect (PANAS_positive); and mindfulness. The summary Cluster of thepain, 2 analysis: subgroups obtainedfatigue, sleep quality, from the global analysis stiffness, was as follows: anxiety, depression and cluster 1: FM patients with pain, fatigue, sleep quality, stiffness, difficulty at work. Of the sample of 251 patients, 100% were valid cases. Below, anxiety, depression and we present difficulty the distance at work; matrixcluster 2: FM patients of the variables analyzed with injustice, (Table 3). catastrophizing, negative affect and positive affect; The cluster clustershowed analysis 3: FM patients with mindfulness the following: in the firstand acceptance; stage, cluster fatigue was 4: FM clustered patients with work with surrender. difficulty. In the second, anxiety was clustered with depression. In the third stage,Cluster 2 analysis: sleep quality was pain, sleepwith clustered quality, fatigue, rigidity. In thestiffness, anxiety, fourth stage, depression fatigue, and dif- work difficulty, ficulty at work. sleep quality andOfstiffness the samplewereofclustered. 251 patients, 100% In the were fifth, valid pain wascases. Below,with combined we present fatigue, work the difficulty, distance sleepofquality matrix and stiffness. the variables analyzed In (Table the sixth, 3). pain, fatigue, work difficulty, sleep quality and stiffness were combined with anxiety/depression (Table 4). TableThe 3. Matrix of distances dendrogram between shows pain, sleeporders the grouping quality,according fatigue, stiffness, anxiety,fatigue to intensity: depression and (FIQ_6) difficulty and workatdifficulty work. (FIQ_4) were the closest. Anxiety and depression were strongly linked Variables Pain and Fatigue so on, as were sleep quality andStiffness Sleep stiffness (FIQ_7 and FIQ_8), Anxiety (FIQ6 + FIQ4) with (FIQ_7 Depression Difficulty at Work Pain 0.0000 and FIQ_8) 206.240 and this group 283.696 with pain (FIQ_5) 232.052 (Figure 329.424 2). 314.157 191.962 Fatigue 206.240 0.0000 189.356 185.192 286.871 259.451 173.276 Sleep 283.696 189.356 0.0000 182.618 352.678 324.184 207.396 Stiffness 232.052 185.192 182.618 0.0000 312.836 279.140 182.064 Anxiety 329.424 286.871 352.678 312.836 0.0000 173.849 233.890 Depression 314.157 259.451 324.184 279.140 173.849 0.0000 227.812 (Total HADS-A); 6 = depression (Total HADS-D); 7 = difficulty at work (FIQ_4). The cluster analysis showed the following: in the first stage, fatigue was clustered with work difficulty. In the second, anxiety was clustered with depression. In the third stage, sleep quality was clustered with rigidity. In the fourth stage, fatigue, work diffi- Biomedicines 2023, 11, 2867 8 of 15 culty, sleep quality and stiffness were clustered. In the fifth, pain was combined with fa- tigue, work difficulty, sleep quality and stiffness. In the sixth, pain, fatigue, work diffi- culty, sleep quality and stiffness were combined with anxiety/depression (Table 4). Table 3. Matrix of distances between pain, sleep quality, fatigue, stiffness, anxiety, depression and difficulty Table at work. 4. History of clusters of the variables pain (1), fatigue (2), sleep quality (3), rigidity (4), anxiety (5), depression (6) and difficulty at work (7). Variables Pain Fatigue Sleep Stiffness Anxiety Depression Difficulty at Work StagePain Cluster *1 0.0000 Cluster * 2 283.696 206.240 Coefficients232.052 Cluster 329.424** 1 Cluster 314.157 ** 2 Next Stage 191.962 1Fatigue 2206.240 0.00007 189.356 173.276 185.192 0 286.871 0 259.451 173.2764 2 Sleep 5283.696 189.3566 0.0000 173.849 182.618 0 352.678 324.184 0 207.3966 Stiffness 232.052 185.192 182.618 0.0000 312.836 279.140 182.064 3Anxiety 3329.424 286.8714 352.678 182.618 312.836 0 0.0000 0 173.849 233.8904 4 Depression 2314.157 259.4513 324.184 191.002 279.140 1 173.849 3 0.0000 227.8125 Difficulty 5 at work 1191.962 173.2762 207.396 228.488 182.064 0 233.890 227.812 4 0.00006 6 1 Note: 1 = 5pain (FIQ_5); 2 = 292.044 fatigue (FIQ_6); 3 = sleep5quality (FIQ_7); 4 = stiffness 2 (FIQ_8); 5 = anxiety 0 (Total HADS-A); 6 = depression (Total HADS-D); 7 = difficulty at work * Cluster that is combined. ** Cluster appearing for the first time. (FIQ_4). 4. History TableThe of clusters dendrogram showsof the the grouping variables pain (1), according orders fatigue (2),tosleep qualityfatigue intensity: (3), rigidity (4), (FIQ_6) anxiety (5), depression (6) and difficulty at work (7). and work difficulty (FIQ_4) were the closest. Anxiety and depression were strongly linked andStage so on, as were sleep quality and stiffness (FIQ_7 and FIQ_8), (FIQ6 + FIQ4) with Cluster * 1 Cluster * 2 Coefficients Cluster ** 1 Cluster ** 2 Next Stage (FIQ_7 and FIQ_8) and this group with pain (FIQ_5) (Figure 2). 1 2 7 173.276 Cluster 1 consisted of patients with fatigue, work 0difficulty, sleep 0 4 quality, stiffness 2 5 6 173.849 0 0 6 and pain; 3 however, 3 a separate 4group with pain was identified 182.618 0 in cluster 0 2 and anxiety4 and depression 4 were linked 2 in cluster 3 3. The interpretation 191.002 of 1 the combination 3 scale indicates 5 that the 5 relationship 1 between the 2 variables anxiety and depression 228.488 0 and4 functional disabil- 6 6 ity (assessed 1 FIQ) was 25 using 5 times less292.044 5 stronger relationship relevant than the 2 0 between *fatigue Cluster that is combined. ** Cluster appearing for the first time. and work difficulty or between anxiety and depression symptoms. Figure 2. Figure Dendrogram using 2. Dendrogram using aa mean mean linkage linkage between between pain, pain, sleep sleep quality, quality, fatigue, fatigue, stiffness, stiffness, anti- anti- anxiety, depression and difficulty difficulty in work. Combination of rescaled distance clusters. The following following cluster cluster classification classification is is then obtained obtained from cluster analysis 2: cluster 1: FM FM patients with increased fatigue, fatigue, sleep sleep quality, stiffness and quality, stiffness and work work difficulties difficulties scores; scores; cluster cluster 2: 2: FM FM patients patients with with pain; pain; cluster cluster 3: 3: FM FM patients patients with with raised raised anxiety/depression anxiety/depressionscores. scores. Cluster 1 consisted of patients with fatigue, work difficulty, sleep quality, stiffness and pain; however, a separate group with pain was identified in cluster 2 and anxiety and depression were linked in cluster 3. The interpretation of the combination scale indicates that the relationship between the variables anxiety and depression and functional disability (assessed using FIQ) was 25 times less relevant than the stronger relationship between fatigue and work difficulty or between anxiety and depression symptoms. Cluster 3 analysis: The variables analyzed were: catastrophizing, surrender, mindful- ness, acceptance, injustice, positive affect and negative affect. Of the sample of 251 patients, Biomedicines 2023, 11, 2867 9 of 15 96.8% were valid cases and 3.2% were missing values. In our study, a matrix of distances between the different psychological variables mentioned was performed (Table 5). Table 5. Matrix of distances between the variables of catastrophizing, surrender, mindfulness, acceptance, injustice, positive affect and negative affect. Positive Negative Variables Catastrophizing Surrender Mindfulness Acceptance Injustice Affect Affect Catastrophizing 0.0000 155.560 169.251 813.405 706.444 733.237 211.856 Surrender 155.560 0.0000 137.812 790.621 763.422 741.268 205.045 Mindfulness 169.251 137.812 0.0000 787.965 704.242 721.446 219.548 Acceptance 813.405 790.621 787.965 0.0000 298.573 192.085 742.781 Injustice 706.444 763.422 704.242 298.573 0.0000 278.596 703.264 Positive affect 733.237 741.268 721.446 192.085 278.596 0.0000 723.220 Negative affect 211.856 205.045 219.548 742.781 703.264 723.220 0.0000 In addition, we present the average (inter-group) linkage of the aforementioned psychological domains (Table 6). Table 6. Clustering history. 1. Catastrophizing; 2. surrender; 3. perceived injustice; 4. acceptance; 5. mindfulness; 6. positive affect; 7. negative affect. Stage Cluster * 1 Cluster * 2 Coefficients Cluster ** 1 Cluster ** 2 Next Stage 1 2 3 137.812 0 0 2 2 1 2 162.406 0 1 4 3 4 6 192.085 0 0 5 4 1 7 212.150 2 0 6 5 4 5 288.584 3 0 6 6 1 4 744.276 4 5 0 * Cluster that is combined. ** Cluster appearing for the first time. According to the clustering history under the criterion of proximity, they were grouped as follows: in the first stage, surrender and injustice are united in clusters. In the second stage, catastrophizing was united with surrender and injustice. In the third stage, accep- tance and positive affect were clustered together. In the fourth stage, catastrophizing, surrender and injustice were clustered together with negative affect. In the fifth, mind- fulness was joined with acceptance and positive affect, and, in the sixth, mindfulness, acceptance and positive affect were joined with catastrophizing, surrender, injustice and negative affect. The result of the previous process is represented by a graph called a dendrogram in the form of an inverted tree, where, on the vertical axis, the variables (and groupings) are placed and on the horizontal axis a relative measure of intensity is represented, rescaled inversely in distance. The dendrogram shows the formation of the clusters, as well as the distances between them. It can be seen, for example, that the variables (observations) closest to each other are surrender and injustice, which form the first group (distance closest to 0). These two are joined with catastrophizing and then these three with negative affect, forming a relatively cohesive cluster. On the other hand, acceptance and positive affect were joined by proximity, and this cluster is joined in turn by mindfulness, forming the second cluster (Figure 3). The observations that are most similar to each other in the first cluster are surrender, injustice and catastrophizing, and the last to be incorporated is negative affect. In the second cluster, the most similar and close variables are acceptance and positive affect and the most different is mindfulness. The two large clusters above are obviously part of a set of variables, which is the final total cluster (Figure 3). 2023, 11, Biomedicines 2023, 11, 2867 x FOR PEER REVIEW 10 10of of 15 Dendrogram using an Figure 3. Dendrogram an average average linkage linkage distance distancebetween betweensurrender, surrender, injustice, injustice, catastrophiz- catastrophiz- affect, acceptance, positive affect ing, negative affect, affect and mindfulness. 3.3. Combination of 3.3. Combination of Rescaled Rescaled Distance Distance Clusters Clusters The following cluster classification was then The following cluster classification was then obtained obtained from from the the third third cluster cluster analysis: analysis: cluster 1: FM patients with high scores of surrender, unfairness, catastrophizing cluster 1: FM patients with high scores of surrender, unfairness, catastrophizing and neg- and negative affect; and cluster 2: FM patients with high scores of acceptance, positive ative affect; and cluster 2: FM patients with high scores of acceptance, positive affect and affect and mindfulness. mindfulness. 4. Discussion 4. Discussion Our research has revealed that, by using cluster analysis on clinical data and psy- Our research chological variables, haswe revealed that, by can identify using cluster different analysis on FM subgroups. Inclinical the firstdata andanalysis, cluster psycho- logical variables, we can identify different FM subgroups. In which included clinical variables, anxiety and depression components and psychological the first cluster analysis, which included domains, clinical variables, four subgroups anxietycluster were defined: and depression 1 displayed components high scores and in psychological pain, fatigue, domains, four subgroups were defined: cluster 1 displayed high scores poor sleep quality, rigidity, anxiety/depression and difficulties at work; cluster 2 displayed in pain, fatigue, high scores in injustice, catastrophizing, negative affect and positive affect; cluster23 dis- poor sleep quality, rigidity, anxiety/depression and difficulties at work; cluster dis- played high played high scores scores in in injustice, mindfulnesscatastrophizing, and acceptance; negative and, affect finally,and positive cluster affect; cluster 4 displayed high 3 displayed scores high scores in surrender. In thein second mindfulness clusterand acceptance; analysis, whichand, finally, included cluster clinical 4 displayed variables and high scores in surrender. In the second cluster analysis, anxiety/depression components, three subgroups were defined: cluster 1 reported which included clinical variables high and anxiety/depression fatigue scores, sleep quality, components, stiffness and three subgroups difficulties werecluster at work; defined: cluster 1 high 2 displayed reported pain high fatigue scores; scores,3 displayed and cluster sleep quality, highstiffness and difficulties anxiety/depression at work; cluster 2 displayed scores. high In painthescores; secondand cluster cluster 3 displayed analysis, high anxiety/depression the objective of which was to scores.group individuals into In the second cluster analysis, predefined subgroups and classify or organize themthe objective of which was toto according group similarindividuals into profiles with predefined subgroups and classify or organize them according respect to the variables of pain, fatigue, sleep quality, stiffness, anxiety, depression andto similar profiles with respect to at difficulty the variables work, of pain, fatigue, fatigue, difficulty sleep sleep at work, quality, stiffness, quality anxiety, depression and stiffness appeared toand be difficulty linked. It at work, fatigue, is important difficultythat to highlight at work, difficultysleepat quality work doesandnot stiffness appear appeared associatedto be or linked. to linked It pain is important (since it to highlight is the last one that to difficulty be incorporatedat workinto does thenot appear group, its associated distance toor it linkedgreater). being to pain (since it is the last one to be incorporated into the group, its distance to it beingIngreater). the third cluster analysis that included psychological constructs, a first subgroup In the third was defined, withcluster analysis high scores that included in surrender, psychological injustice, constructs, catastrophizing andanegative first subgroup affect, and was adefined, second with subgroup with high high scores scores in acceptance, in surrender, positive affect and negative injustice, catastrophizing mindfulness.affect, and aOur secondstudy has empirically subgroup with high demonstrated that the psychological scores in acceptance, positive affect and clinical factors mindfulness. studied Ourcould studybe haspartly responsible empirically for both the demonstrated thatexplanation of the health the psychological outcomes and clinical of factors these studiedFMcouldpatients and their be partly chronification, responsible for bothsupporting models that the explanation of theassume healthanoutcomes interactionof between biological, psychological and social factors [7]. these FM patients and their chronification, supporting models that assume an interaction A systematic between biological,review in the scientific psychological and socialliterature factors identified [7]. different subgroups of FM according to criteria A systematic of patients review without concomitant in the scientific disease different literature identified (FM typesubgroups I), patientsofwithFM according to criteria of patients without concomitant disease (FM type I), patients with Biomedicines 2023, 11, 2867 11 of 15 rheumatic and autoimmune diseases (FM type II), with severe psychiatric disturbance (FM type III) and FM simulator patients (FM type IV) [30]. In this line, D’Souza et al. [31] identified two groups of FM patients, differentiated: one group by the degree of distress (anxiety and depression) and morning tiredness, and another group with psychological variables of catastrophism and perception of control. Similarly, Giesecke et al. [18] classified FM into different subgroups: the first, without symptoms of depression or anxiety, not catastrophic, with an internal locus of control and psychological factors not having a negative influence, which, in principle, leads to a better response to treatment; a second subgroup, with high levels of anxiety and depression, with an external locus of control, high level of pain, more years of disease progression and numerous symptoms, suffering significant negative social, cognitive and behavioral consequences, usually frequent medical visits and requiring multidisciplinary treatment; and a third subgroup, with severe pain, no anxiety or depression, their locus of control is internal, they are not catastrophic, they resolve or cope better with the situation than the previous ones and pharmacological treatment (analgesics, antidepressants and anticonvulsants) is more effective. Our data agree with these studies, as they suggest the diagnostic heterogeneity that characterizes FM patients and that could be due to anxiety and depression symptomatology. These differences could be interpreted as being associated with one type of patients and not with others presenting with levels of pain, fatigue and joint stiffness [15]. Our study confirms the existence of clearly distinct patient profiles, which have also been described in previous studies [31,32]. However, the inclusion of various psychological variables highlights the importance of personalizing and optimizing pharmacological and psychological treatments; these results align with those of other reports [33,34]. Much of the multifactorial research on the biopsychosocial approach to FM shows significantly more psychological problems than healthy controls and also more than patients with chronic pain disorders, such as those with rheumatoid arthritis. Differences have been found in active coping strategies and, in the case of patients with FM, they present worse physical functioning, greater pain, poor coping strategies for their pain and a more limited support network than the healthy controls studied [35]. On the other hand, Fietta et al. [36] indicated that between 20–80% of FM patients present depressive disorders, according to DSM-IV and ICD diagnostic criteria. Other studies indicate that 47% of FM patients have an anxiety disorder and 50% present a depressive condition [14]. Studies have shown that there is a correlation between symptoms of psychopathology with the duration of the disease and pain [37]. Other findings studied the impact of FM on health-related quality of life and found that it was even greater than in other chronic diseases, such as rheumatoid arthritis or osteoarthritis [17]. In order to analyze the causes of the variability in depressive disorders, researchers have studied various patient subgroups. A study conducted by Broderick et al. [38] found that 92% of FM patients belonged to the dysfunctional group, 81% came from the interper- sonal distress group and 39% from the adaptive group had severe depression scores. Later, the study by Verra et al. [39] replicated that study and found that the dysfunctional group had higher levels of depression than the interpersonal distress group and the adaptive group. The adaptative group had significantly lower levels of anxiety/depression, negative mood and catastrophizing compared to the other groups. There is great heterogeneity in relation to the presence of anxiety and depression disorders in the population affected with FM and although their prevalence is high, it cannot be established that these disorders are causal in nature or intrinsic to FM. However, there is scientific evidence that people with a higher level of anxiety and depression have a worse prognosis and their disease progresses negatively; it is also necessary to highlight a greater negative impact on their daily lives and a poorer quality of life. The subgroups of the study by Hasset et al. [40] was based on the affect balance style and used the PANAS questionnaire; therefore, four groups were obtained: (a) healthy with Biomedicines 2023, 11, 2867 12 of 15 high positive affect/low negative affect; (b) low with low positive affect/low negative affect; (c) reactive with high positive affect/high negative affect; and (d) depressive with low positive affect/high negative affect. Boersma and Linton’s study [41] pointed out that because psychological factors are significantly associated with disability and pain in the FM patients, new therapeutic alterna- tives that include different psychological variables should be sought. Scientific research on FM and chronic pain has shown that cognitive-behavioral treatments have obtained good results and, when combined with pharmacological treatments and programmed physical exercise, greater effectiveness is achieved. This type of multidisciplinary intervention is usually applied in accredited hospital units and with people affected by FM with a degree of severity or severity much higher than that of patients with FM who are usually treated in primary care, the level of care where the present study was carried out. Given that there is scientific consensus on the high explanatory value of the psychosocial model in FM, and that the empirical data also support it, all this leads us to think of the importance of the application of cognitive and behavioral interventions. It is crucial to highlight the significance of introducing these multidimensional treat- ments for FM in primary care and making them accessible to all individuals with FM. This approach aims to enhance their overall health status, improve their quality of life and reduce the social and health burden and other costs derived from disability and sick leave in individuals with fibromyalgia. Study Limitations To the best of our knowledge, only a few previous reports used cluster analysis to ex- plore clinical and neuropsychological symptoms in the Spanish FM population. Moreover, we took into account the comorbid health conditions and clinical and neuropsychological domains in our analysis, allowing for a wide extension of our outcomes in routine clinical practice. However, several limitations need to be addressed. The main limitation is the cross-sectional study design, correlational in nature, where the type of sampling was performed in primary care; this limits the generalization of the results and may affect the risk of over-adjustment of the clusters found. Therefore, experimental and longitudinal study designs are more appropriate to better understand the differentiating profile of FM patients and their subsequent classification into various subgroups. However, the high effect sizes indicate that these results reflect the intergroup differences found. On the other hand, the potential to combine machine/deep learning to further enhance the performance and extend the application scenarios of the proposed methods is foreseen as a valuable tool for future studies [42,43]. 5. Conclusions and Future Directions The psychological (catastrophizing, surrender, acceptance, mindfulness, perceived injustice, positive affect and negative affect) and clinical (pain, fatigue, sleep quality, stiff- ness, anxiety, depression and difficulty or disability at work) factors studied are partially responsible for both the explanation for the health outcomes of these FM patients and their chronification. These empirical results support models that assume an interaction between biological, psychological and social factors. In the assessment of the FM patient, it is very important to use clinical variables, such as pain, fatigue, disability or difficulty working, sleep quality and stiffness, as well as other psychological variables related to anxiety, depression and other psychological constructs, in order to define the presence of subgroups and to consider the therapeutic approach in each of them. Biomedicines 2023, 11, 2867 13 of 15 Author Contributions: Conceptualization, S.M., L.G.-L. and J.A.-M.; methodology, S.M.; software, S.M. and L.G.-L.; validation, S.M., J.A.-M. and J.C.-M.; formal analysis, S.M. and J.A.-M.; investi- gation, S.M. and J.A.-M.; resources, S.M.; data curation, S.M. and J.A.-M.; writing—original draft preparation, S.M. and J.C.-M.; writing—review and editing, S.M., L.G.-L. and J.C.-M.; visualiza- tion, S.M. and J.A.-M.; supervision, J.A.-M. and J.C.-M.; project administration, J.A.-M. and J.C.-M.; funding acquisition, S.M. and J.A.-M. All authors have read and agreed to the published version of the manuscript. Funding: This work was partially supported by the National Institute of Health “Carlos III” in Madrid, Spain through the grant (reference number: PI09/90301). Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and its later amendments. The study protocol has been approved by the Institutional Review Board Ethics Committee at the Zaragoza University Hospital (Zaragoza, Spain) (reference number: PI09/041). Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. Data Availability Statement: All data generated or analyzed during this study are included in this published article. Acknowledgments: We are extremely grateful to the participants who took part in the study. We thank the Association of Fibromyalgia, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Multiple Chemical Sensitivity from Teruel region, Spain (AFIFASEN, https://www.afifasenteruel.es/; accessed on 21 October 2013) for recruiting the study participants. We also thank Jose Luis Gonzalez for his support in the statistical analysis and interpretation of the data. Conflicts of Interest: The authors declare no conflict of interest. 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