Cluster analysis of clinical data identifies fibromyalgia subgroups

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

A comparison of pain, fatigue, and function between post-COVID-19 condition, fibromyalgia, and chronic fatigue syndrome: a survey study

ABSTRACT

A growing number of individuals report prolonged symptoms following acute Coronavirus-19 (COVID-19) infection, known as post-COVID-19 condition (post-COVID-19). While studies have emerged investigating the symptom sequelae of post-COVID-19, there has been limited investigation into the characterization of pain, fatigue, and function in these individuals, despite initial reports of a clinical phenotype similar to fibromyalgia syndrome (FMS) and chronic fatigue syndrome (CFS)/myalgic encephalomyelitis (ME). This study aimed to characterize multiple symptom domains in individuals reporting post-COVID-19 and compare its clinical phenotype with those with FMS and CFS. A total of 707 individuals with a single or comorbid diagnosis of post-COVID-19, FMS, and/or CFS completed multiple surveys assessing self-reported pain, fatigue, physical and cognitive function, catastrophizing, kinesiophobia, anxiety, depression, dyspnea, and sleep quality. In all 3 diagnoses, elevated pain, fatigue, anxiety, depression, catastrophizing, and kinesiophobia were reported. Physical and cognitive function were similarly impacted among individuals with post-COVID-19, FMS, and CFS; however, individuals with post-COVID-19 reported lower pain and fatigue than FMS and CFS. The comorbid diagnosis of post-COVID-19 with FMS and/or CFS further exacerbated pain, fatigue, and psychological domains when compared with post-COVID-19 alone. In summary, individuals with post-COVID-19 report a symptom phenotype similar to FMS and CFS, negatively impacting cognitive and physical function, but with less severe pain and fatigue overall. These findings may help direct future investigations of the benefit of a biopsychosocial approach to the clinical management of post-COVID-19.

Working memory dysfunction in fibromyalgia is associated with genotypes of the catechol- O-methyltransferase gene: an event-related potential study

Recent findings have associated different COMT genotypes with working memory capacity in patients with fibromyalgia. Although it is thought that the COMT gene may influence neural correlates (P2 and P3 ERP components) underlying working memory impairment in this chronic-pain syndrome, it has not yet been explored. Therefore, the aim of the present research was to investigate the potential effect of the COMT gene in fibromyalgia patients on ERP working memory indices (P2 and P3 components). For this purpose, 102 participants (51 patients and 51 healthy control participants) took part in the experiment. Event-related potentials and behavioral responses were recorded while participants performed a spatial n-back task. Participants had to decide if the stimulus coincided or not in the same location as the one presented one (1-back condition) or two (2-back condition) trials before. Genotypes of the COMT gene were determined through a saliva sample from all participants. Present results significantly showed lower working memory performance (p < 0.05) in patients with fibromyalgia as compared to control participants (higher rate of errors and slower reaction times). At neural level, we found that patients exhibited enhanced frontocentral and parieto-occipital P2 amplitudes compared to control participants (p < 0.05). Interestingly, we also observed that only fibromyalgia patients carrying the Val/Val genotype of the COMT gene showed higher frontocentral P2 amplitudes than control participants (p < 0.05). Current results (behavioral outcomes and P2 amplitudes) confirmed the presence of an alteration in working memory functioning in fibromyalgia. The enhancement of frontocentral P2 could be reflecting that these patients would manifest an inefficient way of activating executive attention processes, in carriers of the Val/Val genotype of COMT. To our knowledge, the present findings are the first linking neural indices of working memory dysfunctions and COMT genotypes in fibromyalgia. Applying a subgroup of patient's strategy based on this genetic marker could be useful to establish more tailored therapeutical approaches.

Coping and Beliefs as Predictors of Functioning and Psychological Adjustment in Fibromyalgia Subgroups

Objectives

Research has pointed to two profiles of persons with fibromyalgia according to differences in functionality, thus distinguishing between functional and dysfunctional patients. The role of psychological factors underlying such clusters is unclear. This study aims to explore the contribution of pain beliefs and coping on fibromyalgia clustering.

Methods

A cluster analysis was performed to classify 238 women with fibromyalgia using the Fibromyalgia Impact Questionnaire and the Beck Depression Inventory as clustering variables. Cluster differences in physical functioning, depression, pain beliefs, coping, and age were then calculated (Student's t-test). Finally, a binary logistic regression was conducted to study the unique contribution of age, beliefs, and coping on cluster classification.

Results

Two clusters were revealed. Cluster 1 had a poor adaptation to fibromyalgia regarding physical functioning and depression. They generally embraced less adaptive beliefs (i.e., disability, harm, emotion, and requests) and coping strategies (i.e., guarding, resting, and asking for assistance). Cluster 2 showed a better adaptation to fibromyalgia and adopted more favorable beliefs (i.e., control) and coping strategies (i.e., exercise and task persistence). Cluster differences in age were significant but small. The backward binary logistic regression suggested a final model with six predictors (guarding, task persistence, harm, emotion, solicitude, and age) that explained 31% of the variance of group membership. Discussion. These results suggest that only a subset of psychological variables uniquely and independently contribute to functional/dysfunctional group membership. The results support the need to address psychological components in the management of fibromyalgia and point to a subset of preferred target beliefs and coping strategies.

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Hierarchical clustering by patient-reported pain distribution alone identifies distinct chronic pain subgroups differing by pain intensity, quality, and clinical outcomes

Background

In clinical practice, the bodily distribution of chronic pain is often used in conjunction with other signs and symptoms to support a diagnosis or treatment plan. For example, the diagnosis of fibromyalgia involves tallying the areas of pain that a patient reports using a drawn body map. It remains unclear whether patterns of pain distribution independently inform aspects of the pain experience and influence patient outcomes. The objective of the current study was to evaluate the clinical relevance of patterns of pain distribution using an algorithmic approach agnostic to diagnosis or patient-reported facets of the pain experience.

Methods and findings

A large cohort of patients (N = 21,658) completed pain body maps and a multi-dimensional pain assessment. Using hierarchical clustering of patients by body map selection alone, nine distinct subgroups emerged with different patterns of body region selection. Clinician review of cluster body maps recapitulated some clinically-relevant patterns of pain distribution, such as low back pain with radiation below the knee and widespread pain, as well as some unique patterns. Demographic and medical characteristics, pain intensity, pain impact, and neuropathic pain quality all varied significantly across cluster subgroups. Multivariate modeling demonstrated that cluster membership independently predicted pain intensity and neuropathic pain quality. In a subset of patients who completed 3-month follow-up questionnaires (N = 7,138), cluster membership independently predicted the likelihood of improvement in pain, physical function, and a positive overall impression of change related to multidisciplinary pain care.

Conclusions

This study reports a novel method of grouping patients by pain distribution using an algorithmic approach. Pain distribution subgroup was significantly associated with differences in pain intensity, impact, and clinically relevant outcomes. In the future, algorithmic clustering by pain distribution may be an important facet in chronic pain biosignatures developed for the personalization of pain management.

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

BACKGROUND

The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.

OBJECTIVE

This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.

METHODS

A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).

RESULTS

A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).

CONCLUSIONS

Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Fibromyalgia as a Heterogeneous Condition: Subgroups of Patients Based on Physical Symptoms and Cognitive-Affective Variables Related to Pain

Fibromyalgia (FM) is a chronic syndrome characterized by heterogeneous clinical manifestations, and knowing this variability can help to develop tailored treatments. To understand better the heterogeneity of FM the present cross-sectional study analyzed the role of several physical symptoms (pain, fatigue and poor sleep quality) and cognitive-affective variables related to pain (pain catastrophizing, pain vigilance, self-efficacy in pain management, and pain acceptance) in the configuration of clinical profiles. A sample of 161 women with FM fulfilled an interview and several self-report measures to explore physical symptoms, cognitive-affective variables, disability and psychopathology. To establish FM groups a hierarchical cluster analysis was performed. The findings revealed three clusters that differed in the grouping variables, Wilks' λ = .17, F(14, 304) = 31.50, p < .001, ηp2 = .59. Group 1 (n = 72) was characterized by high physical and psychological affectation, Group 2 (n = 19) by low physical affectation and high pain self-efficacy, and Group 3 (n = 70) by moderate physical affectation and low pain catastrophizing. The external validation of the clusters was confirmed, Wilks' λ = .72, F(4, 314) = 14.09, p < .001, ηp2 = .15, showing Group 1 the highest levels of FM impact and psychopathological distress. Considering the distinctive clinical characteristics of each subgroup therapeutic strategies addressed to the specific needs of each group were suggested. Assessing FM profiles may be key for a better understanding and approach of this syndrome.

Assessing our approach to diagnosing Fibromyalgia

Introduction: Fibromyalgia represents the most prevalent of the group of conditions that are known as central sensitivity syndromes. Approximately 2-5% of the adult population in the United States is affected by Fibromyalgia. This pain amplification syndrome has an enormous economic impact as measured by work absenteeism, decreased work productivity, disability and injury compensation, and over-utilization of healthcare resources. Multiple studies have shown that early diagnosis of this condition can improve patient outlook, and redirect valuable health care resources toward more appropriate targeted therapy. Efforts have been made toward improving diagnostic accuracy through updated criteria. Areas Covered: Reviewed here are 1) reasons for the need for more accurate diagnosis of Fibromyalgia, (2) a review of the evolution of Fibromyalgia to current times, and (3) the proliferation of currently available diagnostic criteria and problems related to each of them. From initial literature review until October 2020, PubMed, Embase, and Scopus were searched for applicable literature. Expert Opinion: A discussion of ongoing efforts to obtain a biomarker to enhance diagnostic accuracy concludes this review. A need to include rheumatologists as part of the care team of patients with Fibromyalgia is emphasized.

Profiles of Women With Fibromyalgia and Social Comparison Processes

Background

Due to uncertainty regarding chronic pain in Fibromyalgia (FM) patients, there has been a growing interest in social comparison and its influence on emotional responses.

Aims

to analyze profiles in FM patients according to pain perception, social comparison strategies and anxiety and depression.

Methods

The sample consisted of 131 FM outpatients (Mean age: 50.15, SD = 11.1). Two scales were used: the Social Comparison Illness Scale and the Hospital Anxiety and Depression Scale.

Results

Two profiles were found by cluster analysis (K-means method): one (66%) with a higher level of pain perception, anxiety and depression and greater use of upward contrast and downward identification social comparison; and another (34%) with lower levels of pain perception, anxiety and depression and greater use of upward identification and downward contrast.

Conclusion

These profiles underline the interest in social comparison strategies and their role in FM.

AliClu - Temporal sequence alignment for clustering longitudinal clinical data

Background

Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient’s current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierarchical clustering. To find the best number of clusters and assess their stability, a resampling technique is applied.

Results

We propose the AliClu, a novel tool for clustering temporal clinical data based on the TNW algorithm coupled with clustering validity assessments through bootstrapping. The AliClu was applied for the analysis of the rheumatoid arthritis EMRs obtained from the Portuguese database of rheumatologic patient visits (Reuma.pt). In particular, the AliClu was used for the analysis of therapy switches, which were coded as letters corresponding to biologic drugs and included their durations before each change occurred. The obtained optimized clusters allow one to stratify the patients based on their temporal therapy profiles and to support the identification of common features for those groups.

Conclusions

The AliClu is a promising computational strategy to analyse longitudinal patient data by providing validated clusters and by unravelling the patterns that exist in clinical outcomes. Patient stratification is performed in an automatic or semi-automatic way, allowing one to tune the alignment, clustering, and validation parameters. The AliClu is freely available at https://github.com/sysbiomed/AliClu.

Identification of different clinical faces of obstructive sleep apnea in children

OBJECTIVE

This study aimed to identify the heterogeneity of obstructive sleep apnea syndrome clinical presentation in children.

PARTICIPANTS

Children who were 3-14 years old and with obstructive sleep apnea syndrome after polysomnography monitoring (apnea and hypopnea index>5 or obstructive apnea index>1) in the sleep center of Beijing Children's Hospital were included.

METHODS

A sleep disorder questionnaire including different combinations of symptoms and co-morbidities of obstructive sleep apnea syndrome in children was used. A cluster analysis was used to classify the data.

RESULTS

The apnea hypopnea index alone is not adequate to predict clinical phenotypes. Based on symptoms and co-morbidities of obstructive sleep apnea syndrome, three distinct clusters were identified. They were "nocturnal snoring and daytime sleepiness group" (cluster 1), "hyperactivity group" (cluster 2), and "minimally symptomatic group" (cluster 3). A prediction model was built according to eight variables which showed statistical significance by pairwise comparison among clusters. Overall accuracy of the prediction model could reach 86%. Both the sensitivity and specificity of cluster 2 and 3 prediction were around 90%.

CONCLUSION

Children with obstructive sleep apnea syndrome have different patterns of clinical presentation and the identification of the different clinical profiles of obstructive sleep apnea syndrome can provide clues for more personalised diagnoses and therapies.

Comorbidity clusters in generalized osteoarthritis among female patients: A cross-sectional study

OBJECTIVES

To investigate the prevalence of comorbidities among female patients with generalized osteoarthritis (GOA) in comparison to an age- and sex matched control group. To identify clusters of comorbidities in both groups.

METHODS

An observational, cross-sectional study was conducted. Consecutive female patients with hand and knee osteoarthritis according to the American College of Rheumatology (ACR) classification criteria were invited to participate in the study. A control group of participants without musculoskeletal symptoms, history or evidence of osteoarthritis or inflammatory rheumatic disease were also included. Cardiovascular, obstructive pulmonary, gastrointestinal, endocrine, neurological, malignant diseases and depression were recorded in both groups. In both study groups comorbidity cluster and factor analysis was performed.

RESULTS

The study population included 200 GOA and 200 control participants. The following comorbidities were observed adjusted to Bonferroni correction with a significantly higher prevalence among individuals with GOA: hypertension, uterine leiomyoma, gastroesophageal reflux disease, diverticulosis, upper gastrointestinal tract ulcers, depression, diseases with vertigo (benign paroxysmal positional vertigo and vertebrobasilar insufficiency) and surgery due to otoclerosis. In the GOA group 5 clusters were identified with different comorbidity patterns.

CONCLUSION

We report a high comorbidity rate in GOA. Cluster analysis allowed us to identify different comorbidity subsets for vascular, gastrointestinal and malignant gynaecological disorders. Further research is required to understand the links between GOA and non-musculoskeletal comorbidities.

Clinical phenotypes and survival of pre-capillary pulmonary hypertension in systemic sclerosis

Pre-capillary pulmonary hypertension (PH) in systemic sclerosis (SSc) is a heterogeneous condition with an overall bad prognosis. The objective of this study was to identify and characterize homogeneous phenotypes by a cluster analysis in SSc patients with PH. Patients were identified from two prospective cohorts from the US and France. Clinical, pulmonary function, high-resolution chest tomography, hemodynamic and survival data were extracted. We performed cluster analysis using the k-means method and compared survival between clusters using Cox regression analysis. Cluster analysis of 200 patients identified four homogenous phenotypes. Cluster C1 included patients with mild to moderate risk pulmonary arterial hypertension (PAH) with limited or no interstitial lung disease (ILD) and low DLCO with a 3-year survival of 81.5% (95% CI: 71.4–88.2). C2 had pre-capillary PH due to extensive ILD and worse 3-year survival compared to C1 (adjusted hazard ratio [HR] 3.14; 95% CI 1.66–5.94; p = 0.0004). C3 had severe PAH and a trend towards worse survival (HR 2.53; 95% CI 0.99–6.49; p = 0.052). Cluster C4 and C1 were similar with no difference in survival (HR 0.65; 95% CI 0.19–2.27, p = 0.507) but with a higher DLCO in C4. PH in SSc can be characterized into distinct clusters that differ in prognosis.

Longitudinal stability of fibromyalgia symptom clusters

Background

Using self-report questionnaires of key fibromyalgia symptom domains (pain, fatigue, sleep disturbance, function, stiffness, dyscognition, depression, and anxiety), we previously identified four unique symptom clusters. The purpose of this study was to examine the stability of fibromyalgia symptom clusters between baseline and 2-year follow-up.

Methods

Women with a diagnosis of fibromyalgia completed the Brief Pain Inventory, Profile of Mood States, Medical Outcomes Study Sleep measure, Multidimensional Fatigue Inventory, Multiple Ability Self-Report Questionnaire, Revised Fibromyalgia Impact Questionnaire, and the 36-Item Short Form Survey Instrument at baseline. Follow-up measures were completed approximately 2 years later. The hierarchical agglomerative clustering algorithm previously developed was applied; agreement between baseline and follow-up was assessed with the κ statistic.

Results

Among 433 participants, the mean age was 56 (range 20–85) years. The median Revised Fibromyalgia Impact Questionnaire total score was 57 (range 8–96). More than half of participants (58%) remained in the same cluster at follow-up as at baseline, which represented moderate agreement between baseline and follow-up (κ = 0.44, 95% confidence interval (CI) 0.37–0.50). Only two patients changed from high symptom intensity to low symptom intensity; similarly, only three moved from low to high.

Conclusions

Fibromyalgia patients classified into four unique symptom clusters based on the key domains of pain, fatigue, sleep disturbance, function, stiffness, dyscognition, depression, and anxiety showed moderate stability in cluster assignment after 2 years. Few patients moved between the two extremes of severity, and it was slightly more common to move to a lower symptom level than to worsen.

Trial registration

Not applicable.

Informatics and machine learning to define the phenotype

INTRODUCTION

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

A narrative review on the difficulties associated with fibromyalgia diagnosis

Fibromyalgia presents a clinical enigma as its pathophysiology is not well understood and its symptoms are nonspecific and overlap with many disorders, making its diagnosis a challenge for clinicians and researchers. Efforts have been made to develop a set of diagnostic criteria for this disorder. However, these criteria rely heavily on expert clinician opinion and produce a large heterogeneity within the diagnosed population. With no present specific technique reflecting the underlying pathophysiology of fibromyalgia, a definitive diagnosis of fibromyalgia remains elusive. This review discusses some problems and challenges associated with fibromyalgia diagnosis and presents some novel findings on the pathophysiological nature of fibromyalgia.

Pain and Fatigue Variability Patterns Distinguish Subgroups of Fibromyalgia Patients

The current study examined between- and within-subject variability in pain-related symptoms as predictors of pain and fatigue, and identified patient subgroups on the basis of symptom variability characteristics. Two hundred fifty-six fibromyalgia (FM) patients completed daily diaries up to a period of 154 days and reported on symptoms of pain intensity, pain unpleasantness, fatigue, anxiety, and depressed mood. Measures of health status, quality of life, and somatic symptoms were obtained at baseline, and hierarchical linear modeling and cluster analyses were used. Significant intra- and interindividual variability in daily FM symptoms was observed. Higher levels of pain were associated with greater fluctuations in pain unpleasantness, fatigue, and depressed mood. Similar effects were observed for fatigue and individual variability in anxiety also emerged as a robust predictor. Three FM subgroups were revealed: low variability in symptoms (cluster 1), high symptom variability (cluster 2), and a mixed variability group characterized by low fluctuation in pain unpleasantness; moderate pain, fatigue, and depressed mood variability; and high anxiety variability (cluster 3). Cluster 3 exhibited lower social functioning and higher levels of pain, compared with cluster 1. These findings support the dynamic nature of FM pain and suggest the presence of FM subgroups on the basis of variation in mood and pain symptomatology.

PERSPECTIVE

FM patients show significant intra- and interindividual variability in pain, mood, and fatigue. Subgroups in mood and pain-related variability emerged, with phenotypic clusters differing across levels of pain intensity and social functioning. Better understanding of the processes affecting pain variability may facilitate targeted treatments for the control of pain.

Adaptation profiles comprising objective and subjective measures in fibromyalgia: the al-Ándalus project

Objectives

The aim of this study was to identify subgroups in terms of adaptation to FM and to test differences in FM severity between these subgroups.

Methods

The al-Ándalus project made it possible to perform a comprehensive population-based cross-sectional study in 486 FM patients including multiple assessments of modifiable (could be targeted in therapy) resilience and vulnerability factors, measured by objective and subjective assessments, related to psychological and physical function. FM severity was assessed by means of FM impact (total score of the Revised Fibromyalgia Impact Questionnaire) and distress (Polysymptomatic Distress Scale of the modified 2011 preliminary criteria for FM). Exploratory factor analysis, cluster analysis and analysis of variance were conducted.

Results

Factor analysis yielded eight factors: three included objective measures (declarative memory, active lifestyle and objective physical fitness) and five included subjective measures (fatigue, psychological distress, catastrophizing, resilience and subjective physical fitness). Cluster analysis based on these eight factors identified five profiles: Adapted (16%), Fit (18%), Poor performer (20%), Positive (20%) and Maladapted (26%). Most profile comparisons revealed different levels of FM severity varying from Adapted (the most favourable profile) to Maladapted (the most unfavourable profile) with Fit, Poor performer and Positive obtaining intermediate positions.

Conclusions

Heterogeneity of FM was shown by five clinically meaningful profiles of modifiable factors that were associated with FM severity. It is of clinical interest to examine whether these profiles are associated with FM prognosis and the effectiveness of interventions, which would enhance the development of customized interventions based on adaptation profiles in FM.

Somatic symptom presentations in women with fibromyalgia are differentially associated with elevated depression and anxiety

This study examined whether depression and anxiety differentially relate to fatigue, sleep disturbance, pain catastrophizing, fear of movement, and pain severity in women with fibromyalgia. Baseline data from the Fibromyalgia Activity Study with Transcutaneous Electrical Nerve Stimulation were analyzed. Of 191 participants, 50 percent reported high anxiety and/or depression (17% high anxiety, 9% high depression, and 24% both). Fatigue and sleep impairment were associated with high depression (p < 0.05). Pain severity, pain catastrophizing, and fear of movement were associated with high anxiety and high depression (p < 0.05). Possible implications for underlying mechanisms and the need for targeted treatments are discussed.

Unravelling Fibromyalgia-Steps Toward Individualized Management

The heterogeneity of the clinical presentation and the pathophysiologic mechanisms associated with fibromyalgia (FM), and the modest results on average for any therapy, call for a more individualized management strategy. Individualized treatment can be on the basis of subgrouping of patients according to associated conditions (mental health problems, chronic overlapping pain conditions, other somatic diseases) or on disease severity. Categorizing FM as mild, moderate, or severe can be on the basis of clinical assessment (eg, degree of daily functioning) or on questionnaires. Shared decision-making regarding treatment options can be directed according to patient preferences, comorbidities, and availability in various health care settings. The European League Against Rheumatism guidelines recommend a tailored approach directed by FM key symptoms (pain, sleep disorders, fatigue, depression, disability), whereas the German guidelines recommend management tailored to disease severity, with mild disease not requiring any specific treatment, and more severe disease requiring multicomponent therapy (combination of drug treatment with aerobic exercise and psychological treatments). When indicated, treatments should follow a stepwise approach beginning with easily available therapies such as aerobic exercise and amitriptyline. Successful application of a tailored treatment approach that is informed by individual patient characteristics should improve outcome of FM.

PERSPECTIVE

This article presents suggestions for an individualized treatment strategy for FM patients on the basis of subgroups and disease severity. Categorizing FM as mild, moderate, or severe can be on the basis of clinical assessment (eg, degree of daily functioning) or questionnaires. Subgroups can be defined according to mental health and somatic comorbidities.

Vulvodynia is not created equally: empirical classification of women with vulvodynia

Background

Vulvodynia classification is based on the sensory dimensions of pain and does not include psychological factors associated with the pain experience and treatment outcomes. Previous work has shown that individuals with chronic pain can be classified into subgroups based on pain sensitivity, psychological distress, mood, and symptom severity.

Objective

The aim of this study was to identify distinct subgroups of women with vulvodynia enrolled in the National Vulvodynia Registry. We hypothesized that women with vulvodynia can be clustered into subgroups based on distress and pain sensitivity.

Design

A cross-sectional study.

Methods

We conducted an exploratory hierarchical agglomerative cluster analysis using Ward’s cluster method and squared Euclidean distances to identify unique subgroups based on baseline psychological distress and pain sensitivity. The variables included the catastrophizing subscale of the Coping Strategies Questionnaire, the Beck Depression Inventory, the State Trait Anxiety Index-Trait scale, McGill Pain Questionnaire-Affective subscale, and vulvar and pelvic muscle pressure pain sensitivity.

Subjects

Eight sites enrolled women who presented with vaginal or vulval pain of at least 3-month duration.

Results

Two distinct subgroups, high pain sensitivity with high distress (n=27) and low pain sensitivity with low distress (n=100), emerged from the cluster analysis. Validation indicated that subgroups differed in terms of clinical pain intensity, sensory aspects of pain, and intercourse pain.

Conclusion

Empirical classification indicates that unique subgroups exist in women with vulvodynia. Providers should be aware of the heterogeneity of this condition with respect to pain-related distress and pain sensitivity.

Exploring symptoms of somatization in chronic widespread pain: latent class analysis and the role of personality

Chronic widespread musculoskeletal pain (CWP) is a condition manifesting varied co-symptomatology and considerable heterogeneity in symptom profiles. This poses an obstacle for disease definition and effective treatment. Latent class analysis (LCA) provides an opportunity to find subtypes of cases in multivariate data. In this study, LCA was used to investigate whether and how individuals with CWP could be classified according to 12 additional somatic symptoms (migraine headaches, insomnia, stiffness, etc.). In a second step, the role of psychological and coping factors for the severity of these co-symptoms was investigated. Data were available for a total of N = 3,057 individuals (mean age = 56.6 years), with 15.4% suffering from CWP. In the latter group, LCA resulted in a three-class solution (n_group1 = 123; n_group2 = 306; n_group3 = 43) with groups differing in a graded fashion (i.e., severity) rather than qualitatively for somatic co-symptom endorsements. A consistent picture emerged, with individuals in the first group reporting the lowest scores and individuals in group 3 reporting the highest. Additionally, more co-symptomatology was associated with higher rates of anxiety sensitivity and depression, as well as more extraversion and emotional instability. No group differences for any of the coping strategies could be identified. The findings suggest that CWP has several detectable subtypes with distinct psychological correlates. The identification of CWP subgroups is important for understanding disease mechanisms and refining prognosis as well as stratifying patients in clinical trials and targeting specific treatment at the subgroups most likely to respond.

Fibromyalgia: A Critical and Comprehensive Review

Fibromyalgia is a disorder that is part of a spectrum of syndromes that lack precise classification. It is often considered as part of the global overview of functional somatic syndromes that are otherwise medically unexplained or part of a somatization disorder. Patients with fibromyalgia share symptoms with other functional somatic problems, including issues of myalgias, arthralgias, fatigue and sleep disturbances. Indeed, there is often diagnostic and classification overlap for the case definitions of a variety of somatization disorders. Fibromyalgia, however, is a critically important syndrome for physicians and scientists to be aware of. Patients should be taken very seriously and provided optimal care. Although inflammatory, infectious, and autoimmune disorders have all been ascribed to be etiological events in the development of fibromyalgia, there is very little data to support such a thesis. Many of these disorders are associated with depression and anxiety and may even be part of what has been sometimes called affected spectrum disorders. There is no evidence that physical trauma, i.e., automobile accidents, is associated with the development or exacerbation of fibromyalgia. Treatment should be placed on education, patient support, physical therapy, nutrition, and exercise, including the use of drugs that are approved for the treatment of fibromyalgia. Treatment should not include opiates and patients should not become poly pharmacies in which the treatment itself can lead to significant morbidities. Patients with fibromyalgia are living and not dying of this disorder and positive outlooks and family support are key elements in the management of patients.

Gene Expression Factor Analysis to Differentiate Pathways Linked to Fibromyalgia, Chronic Fatigue Syndrome, and Depression in a Diverse Patient Sample

OBJECTIVE

To determine if independent candidate genes can be grouped into meaningful biologic factors, and whether these factors are associated with the diagnosis of chronic fatigue syndrome (CFS) and fibromyalgia syndrome (FMS), while controlling for comorbid depression, sex, and age.

METHODS

We included leukocyte messenger RNA gene expression from a total of 261 individuals, including healthy controls (n = 61), patients with FMS only (n = 15), with CFS only (n = 33), with comorbid CFS and FMS (n = 79), and with medication-resistant (n = 42) or medication-responsive (n = 31) depression. We used exploratory factor analysis (EFA) on 34 candidate genes to determine factor scores and regression analysis to examine whether these factors were associated with specific diagnoses.

RESULTS

EFA resulted in 4 independent factors with minimal overlap of genes between factors, explaining 51% of the variance. We labeled these factors by function as 1) purinergic and cellular modulators, 2) neuronal growth and immune function, 3) nociception and stress mediators, and 4) energy and mitochondrial function. Regression analysis predicting these biologic factors using FMS, CFS, depression severity, age, and sex revealed that greater expression in factors 1 and 3 was positively associated with CFS and negatively associated with depression severity (Quick Inventory for Depression Symptomatology score), but not associated with FMS.

CONCLUSION

Expression of candidate genes can be grouped into meaningful clusters, and CFS and depression are associated with the same 2 clusters, but in opposite directions, when controlling for comorbid FMS. Given high comorbid disease and interrelationships between biomarkers, EFA may help determine patient subgroups in this population based on gene expression.

Identifying the symptom and functional domains in patients with fibromyalgia: results of a cross-sectional Internet-based survey in Italy

Objective

The aims of this cross-sectional study were to investigate the usefulness of using an Internet survey of patients with fibromyalgia in order to obtain information concerning symptoms and functionality and identify clusters of clinical features that can distinguish patient subsets.

Methods

An Internet website has been used to collect data. Fibromyalgia Impact Questionnaire Revised version, self-administered Fibromyalgia Activity Score, and Self-Administered Pain Scale were used as questionnaires. Hierarchical agglomerative clustering was applied to the data obtained in order to identify symptoms and functional-based subgroups.

Results

Three hundred and fifty-three patients completed the study (85.3% women). The highest scored items were those related to sleep quality, fatigue/energy, pain, stiffness, degree of tenderness, balance problems, and environmental sensitivity. A high proportion of patients reported pain in the neck (81.4%), upper back (70.1%), and lower back (83.2%). A three-cluster solution best fitted the data. The variables were significantly different (P<0.0001) among the three clusters: cluster 1 (117 patients) reflected the lowest average scores across all symptoms, cluster 3 (116 patients) the highest scores, and cluster 2 (120 patients) captured moderate symptom levels, with low depression and anxiety.

Conclusion

Three subgroups of fibromyalgia samples in a large cohort of patients have been identified by using an Internet survey. This approach could provide rationale to support the study of individualized clinical evaluation and may be used to identify optimal treatment strategies.

Coadministration of Resveratrol and Rice Oil Mitigates Nociception and Oxidative State in a Mouse Fibromyalgia-Like Model

The mechanism underlying pain symptoms in fibromyalgia (FM) is not fully understood. Oxidative stress has emerged as pathophysiological event occurring during the development of the disease. The present study aimed at investigating the efficacy of resveratrol associated with rice bran oil on fibromyalgia-like mice model. Subcutaneous injection of reserpine (0.25 mg/Kg) during 3 days produced fibromyalgia-like symptoms. Resveratrol and/or rice oil or pregabalin were administered through oral route in therapeutic (single dose) and preventive (four doses) schemes. In both schemes, treatment with resveratrol associated with rice bran oil and pregabalin significantly reduced mechanical allodynia and thermal hyperalgesia in animals. The preventive scheme displayed antidepressant effect which was demonstrated by the forced swimming test as well as reduced reactive species in the cerebrospinal fluid of reserpinized animals. Taken together, our data provide evidences that the intake of resveratrol associated with rice bran oil plays antinociceptive and antidepressant actions probably through reducing reactive species and suggests the involvement of oxidative stress in this model of FM as possible underlying mechanism of pathogenesis of the disease.

Systems biology approach to understanding post-traumatic stress disorder

This review paper presents known biological facts about PTSD and the mathematical/systems biology tools used to understand the underpinning molecular principles.

OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis

Introduction

The aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains.

Methods

Female patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical Outcomes Sleep Scale, the Multidimensional Fatigue Inventory, the Multiple Ability Self-Report Questionnaire, the Fibromyalgia Impact Questionnaire–Revised (FIQ-R) and the Short Form-36 between 1 June 2011 and 31 October 2011. Hierarchical agglomerative clustering was used to identify subgroups of patients with similar symptom profiles. To validate the results from this sample, hierarchical agglomerative clustering was repeated in an external sample of female patients with fibromyalgia with similar inclusion criteria.

Results

A total of 581 females with a mean age of 55.1 (range, 20.1 to 90.2) years were included. A four-cluster solution best fit the data, and each clustering variable differed significantly (P <0.0001) among the four clusters. The four clusters divided the sample into severity levels: Cluster 1 reflects the lowest average levels across all symptoms, and cluster 4 reflects the highest average levels. Clusters 2 and 3 capture moderate symptoms levels. Clusters 2 and 3 differed mainly in profiles of anxiety and depression, with Cluster 2 having lower levels of depression and anxiety than Cluster 3, despite higher levels of pain. The results of the cluster analysis of the external sample (n = 478) looked very similar to those found in the original cluster analysis, except for a slight difference in sleep problems. This was despite having patients in the validation sample who were significantly younger (P <0.0001) and had more severe symptoms (higher FIQ-R total scores (P = 0.0004)).

Conclusions

In our study, we incorporated core OMERACT symptom domains, which allowed for clustering based on a comprehensive symptom profile. Although our exploratory cluster solution needs confirmation in a longitudinal study, this approach could provide a rationale to support the study of individualized clinical evaluation and intervention.

The different clinical faces of obstructive sleep apnoea: a cluster analysis

Although commonly observed in clinical practice, the heterogeneity of obstructive sleep apnoea (OSA) clinical presentation has not been formally characterised. This study was the first to apply cluster analysis to identify subtypes of patients with OSA who experience distinct combinations of symptoms and comorbidities. An analysis of baseline data from the Icelandic Sleep Apnoea Cohort (822 patients with newly diagnosed moderate-to-severe OSA) was performed. Three distinct clusters were identified. They were classified as the "disturbed sleep group" (cluster 1), "minimally symptomatic group" (cluster 2) and "excessive daytime sleepiness group" (cluster 3), consisting of 32.7%, 24.7% and 42.6% of the entire cohort, respectively. The probabilities of having comorbid hypertension and cardiovascular disease were highest in cluster 2 but lowest in cluster 3. The clusters did not differ significantly in terms of sex, body mass index or apnoea-hypopnoea index. Patients with OSA have different patterns of clinical presentation, which need to be communicated to both the lay public and the professional community with the goal of facilitating care-seeking and early identification of OSA. Identifying distinct clinical profiles of OSA creates a foundation for offering more personalised therapies in the future.

Profiles in fibromyalgia: algometry, auditory evoked potentials and clinical characterization of different subtypes

The heterogeneity found in fibromyalgia (FM) patients has led to the investigation of disease subgroups, mainly based on clinical features. The aim of this study was to test the hypothesis that clinical FM subgroups are associated with different underlying pathophysiological mechanisms. Sixty-three FM patients were classified in type I or type II, according to the Fibromyalgia Impact Questionnaire (FIQ), and in mild/moderate versus severe FM, according to the severity of three cardinal symptoms considered in the American College of Rheumatology (ACR) 2010 criteria (unrefreshed sleep, cognitive problems and fatigue). To validate the subgroups obtained by these two classifications, we calculated the area under the receiver operating characteristic curves for various clinical variables and for two potential biomarkers of FM: Response to experimental pressure pain (algometry) and the amplitude/intensity slopes of the auditory evoked potentials (AEPs) obtained to stimuli of increasing intensity. The variables that best discriminated type I versus type II were those related to depression, while the indices of clinical or experimental pain (threshold or tolerance) did not significantly differ between them. The variables that best discriminated the mild/moderate versus severe subgroups were those related to the algometry. The AEPs did not allow discrimination among the generated subsets. The FIQ-based classification allows the identification of subgroups that differ in psychological distress, while the index based on the ACR 2010 criteria seems to be useful to characterize the severity of FM mainly based on hyperalgesia. The incorporation of potential biomarkers to generate or validate classification criteria is crucial to advance in the knowledge of FM and in the understanding of pathophysiological pathways.