Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia

Immune response and cytokine profiles in post-laminectomy pain syndrome: comparative analysis after treatment with intrathecal opioids, oral opioids, and non-opioid therapies

INTRODUCTION

This study explores the interaction between cytokines, cell-mediated immunity (T cells, B cells, and NK cells), and prolonged morphine administration in chronic neuropathic pain patients without cancer-related issues. Despite evidence of opioid immunomodulation, few studies have compared these interactions.

METHODS

In a cross-sectional and comparative study, 50 patients with chronic low back radicular pain ("Failed Back Surgery Syndrome") were categorized into intrathecal morphine infusion (IT group, n = 18), oral morphine (PO group, n = 17), and non-opioid treatment (NO group, n = 15). Various parameters, including plasma and cerebrospinal fluid (CSF) cytokine concentrations, lymphocyte immunophenotyping, opioid escalation indices, cumulative morphine dose, and treatment duration, were assessed.

RESULTS

CSF IL-8 and IL-1β concentrations exceeded plasma levels in all patients. No differences in T, B, and NK lymphocyte numbers were observed between morphine-treated and non-treated patients. Higher plasma IL-5 and GM-CSF concentrations were noted in IT and PO groups compared to NO. CSF IFNγ concentrations were higher in PO and NO than IT. Positive correlations included CD4 concentrations with opioid escalation indices, and negative correlations involved NK cell concentrations, CSF TNFα concentrations, and opioid escalation indices. Positive correlations were identified between certain cytokines and pain intensity in IT patients, and between NK cells and cumulative morphine dose. Negative correlations were observed between CSF IL-5 concentrations and pain intensity in IT and PO, and between opioid escalation indices and CSF cytokine concentrations in PO and IT.

CONCLUSION

Associations between cytokines, cellular immunity, and prolonged morphine treatment, administered orally and intrathecally were identified.

Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI

BACKGROUND AND OBJECTIVE

Fibromyalgia is a chronic disease that causes pain and affects patients' quality of life. Current treatments focus on pharmacological therapies for pain reduction. However, patients' psychological well-being is also affected, with depression and pain catastrophizing being common. This research addresses the clinicians' need to assess the influence of mental health factors on FM severity compared to pain factors.

METHODS

A co-development study between FM clinicians and data scientists analyzed data from 166 FM-diagnosed patients to assess the influence of mental health factors on FM severity in comparison to pain factors. The study used the Polysymptomatic Distress Scale (PDS) and Fibromyalgia Impact Questionnaire (FIQ) as FM severity indicators and collected 15 variables including regarding demographics, pain intensity perceived, and mental health factors. The team used an author's developed framework to identify the optimal FM severity classifier and explainability by selecting a number of features that lead to obtaining the best classification result. Machine learning classifiers employed in the framework were: decision trees, logistic regression, support vector machines, random forests, AdaBoost, extra trees, and RUSBoost. Explainability analyses were conducted using the following explainable AI techniques: SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Mean Decrease Impurity (MDI).

RESULTS

A balanced random forest with 6 features achieved the best performance with PDS (AUC_ROC, mean = 0.81, std = 0.07). Being FIQ the target variable, due to the imbalance in FM severity levels, a binary and a multiclass classification approaches were considered achieving the optimal performance, respectively, a logistic regression classifier (AUC_ROC, mean = 0.83, std = 0.08) with 6 selected features, and a random forest (AUC_ROC, mean = 0.91, std = 0.04) with 8 selected features. Next, the explainability analysis determined mental health factors were found to be more relevant than pain perceived factors for FM severity.

CONCLUSIONS

This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Methylation factors as biomarkers of fibromyalgia

Background

Fibromyalgia (FM) is a common and intractable chronic musculoskeletal pain syndrome, but its exact underlying mechanisms are unknown. This study sought to identify biomarkers of FM and the underlying molecular mechanisms of the disease.

Methods

FM-related gene expression profiles (GSE67311) and methylation profiles (GSE85506) were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed to identify the methylation factors. Subsequently, an enrichment analysis and gene set enrichment analysis (GSEA) were conducted to examine the methylation factors. In addition, the transcriptional regulators of the methylation factors were predicted, and key methylation factors were identified by a receiver operating characteristic curve analysis and nomogram models. Finally, the relationship between FM and cell death (pyroptosis, necroptosis, and cuproptosis) was assessed by a GSEA and gene set variation analysis.

Results

A total of 455 methylation factors were identified. The enrichment analysis and GSEA results showed that methylation factors were clearly involved in the biological functions and signaling pathways related to neural, immune inflammation, and pain responses. The transcriptional regulator specificity protein 1 (SP1) may have a broad regulatory role. Finally, seven key methylation factors were identified, of which amino beta (A4) precursor protein binding family B member 2 (APBB2), A-kinase anchor protein 12 (AKAP12), and cluster of differentiation 38 (CD38) had strong clinical diagnostic power. In addition, AKAP12 and CD38 were significantly and negatively associated with sepsis, necrotizing sepsis, and cupular sepsis.

Conclusions

Our study suggests that FM is associated with deoxyribonucleic acid methylation. The methylation factors APBB2, AKAP12, and CD38 may be potential biomarkers and should be further examined to provide a new biological framework of the possible disease mechanisms underlying FM.

Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis

Changes in cytokine profiles and cytokine networks are known to be a hallmark of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). However, cytokine profiles research studies are usually based on the analysis of a small number of cytokines and give conflicting results. In this work, we analyzed cytokine profiles of 41 analytes in patients with SLE and MS compared with healthy donors using multiplex immunoassay. The SLE group included treated patients, while the MS patients were drug-free. Levels of 11 cytokines, IL-1b, IL-1RA, IL-6, IL-9, IL-10, IL-15, MCP-1/CCL2, Fractalkine/CX3CL1, MIP-1a/CCL3, MIP-1b/CCL4, and TNFa, were increased, but sCD40L, PDGF-AA, and MDC/CCL22 levels were decreased in SLE patients. Thus, changes in the cytokine profile in SLE have been associated with the dysregulation of interleukins, TNF superfamily members, and chemokines. In the case of MS, levels of 10 cytokines, sCD40L, CCL2, CCL3, CCL22, PDGF-AA, PDGF-AB/BB, EGF, IL-8, TGF-a, and VEGF, decreased significantly compared to the control group. Therefore, cytokine network dysregulation in MS is characterized by abnormal levels of growth factors and chemokines. Cross-disorder analysis of cytokine levels in MS and SLE showed significant differences between 22 cytokines. Protein interaction network analysis showed that all significantly altered cytokines in both SLE and MS are functionally interconnected. Thus, MS and SLE may be associated with impaired functional relationships in the cytokine network. A cytokine correlation networks analysis revealed changes in correlation clusters in SLE and MS. These data expand the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases.

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

Technologies utilizing cutting-edge methodologies, including artificial intelligence (AI), machine learning (ML) and deep learning (DL), present powerful opportunities to help evaluate, predict, and improve patient outcomes by drawing insights from real-world data (RWD) generated during medical care. They played a role during and following the Coronavirus Disease 2019 (COVID-19) pandemic by helping protect healthcare providers, prioritize care for vulnerable populations, predict disease trends, and find optimal therapies. Potential applications across therapeutic areas include diagnosis, disease management and patient journey mapping. Use of fit-for-purpose datasets for ML models is seeing growth and may potentially help additional enterprises develop AI strategies. However, biopharmaceutical companies often face specific challenges, including multi-setting data, system interoperability, data governance, and patient privacy requirements. There remains a need for evolving regulatory frameworks, operating models, and data governance to enable further developments and additional research. We explore recent literature and examine the hurdles faced by researchers in the biopharmaceutical industry to fully realize the promise of AI/ML/DL for patient-centric purposes.

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Artificial Intelligence in healthcare is growing quickly in diagnostics and treatment management. Despite the quantity and variety of studies its role in clinical care is not clear. To identify the evidence gaps and characteristics of the Artificial Intelligence and Machine Learning techniques in predicting/diagnosing the pain? Pubmed/Embase were searched from the inception to October 2021 for articles without any language restrictions specifically addressing the following: the accuracy of AI in pain considering Brain Imaging, Patient-reported measures, and Electrophysiology, the ability of AI to differentiate stratify severity/types of pain, the ability of AI to predict pain and lastly the most accurate AI technique for given inputs. All the included studies were on humans. Eight hundred forty abstracts were reviewed, and 23 articles were finally included. Identified records were independently screened and relevant data was extracted. We performed conceptual synthesis by grouping the studies using available concepts of AL/ML techniques in diagnosing pain. Then we summarized the number of features/physiological measurements. Structured tabulation synthesis was used to show patterns predictions along with a narrative commentary. A total of 23 articles, published between 2015 and 2020 from 12 countries were included. Most studies were experimental in design. The most common design was cross sectional. Chronic or acute pains were predicted more often. Compared to control, the pain prediction was in the range of 57%–96% by AI techniques. Support Vector Machine and deep learning showed higher accuracy for classifying pain. From this study, it can be inferred that AI/ML can be used to differentiate healthy controls from patients. It can also facilitate categorizing them into new and different clinical subgroups. Lastly, it can predict future pain. The limitations are with respect to studies done after the search period. AL/ ML has a supportive role in pain diagnostics.

Subcategories of Fibromyalgia: A New Concept

Fibromyalgia has previously been categorized as primary, secondary, and juvenile fibromyalgia. However, these definitions do not adequately explain the etiopathology of disease, nor do they help direct new specific therapies. Herein, we review the previously known categorizations of fibromyalgia. Based on common patient characteristics and previously studied pathophysiologies, we propose new subcategorizations of fibromyalgia that we have self-narrated, including hormonal fibromyalgia, neuroendocrine fibromyalgia, psychologic fibromyalgia, inflammatory fibromyalgia, and lastly, neuropathic fibromyalgia. Future research needs to be done to verify, add to, and fully describe these self-narrated categories of fibromyalgia that we have proposed.

Potential role of blood biomarkers in patients with fibromyalgia: a systematic review with meta-analysis

ABSTRACT

Fibromyalgia (FM) is a complex chronic pain condition. Its symptoms are nonspecific, and to date, no objective test exists to confirm FM diagnosis. Potential objective measures include the circulating levels of blood biomarkers. This systematic review and meta-analysis aim to review studies assessing blood biomarkers' levels in patients with FM compared with healthy controls. We systematically searched the PubMed, MEDLINE, EMBASE, and PsycINFO databases. Fifty-four studies reporting the levels of biomarkers in blood in patients with FM were included. Data were extracted, and the methodological quality was assessed independently by 2 authors. The methodological quality of 9 studies (17%) was low. The results of most studies were not directly comparable given differences in methods and investigated target immune mediators. Thus, data from 40 studies only were meta-analyzed using a random-effects model. The meta-analysis showed that patients with FM had significantly lower levels of interleukin-1 β and higher levels of IL-6, IL-8, tumor necrosis factor-alpha, interferon gamma, C-reactive protein, and brain-derived neurotrophic factor compared with healthy controls. Nevertheless, this systematic literature review and meta-analysis could not support the notion that these blood biomarkers are specific biomarkers of FM. Our literature review, however, revealed that these same individual biomarkers may have the potential role of identifying underlying pathologies or other conditions that often coexist with FM. Future research is needed to evaluate the potential clinical value for these biomarkers while controlling for the various confounding variables.

Elevated highly sensitive C-reactive protein in fibromyalgia associates with symptom severity

Objectives

Fibromyalgia (FM), a common pain syndrome, is thought to be a non-inflammatory, nociplastic condition, but evidence implicating neuroinflammation has been increasing. Systemic inflammation may be associated with more severe symptoms in some FM patients. We studied healthy controls and FM patients with and without systemic inflammation detectable using high-sensitivity CRP (hsCRP) measurement.

Methods

We measured hsCRP levels and gathered clinical and questionnaire data [including the Fibromyalgia Impact Questionnaire (FIQ)] from 40 female FM patients and 30 age-matched healthy women. An hsCRP level >3 mg/l was considered elevated.

Results

FM patients had significantly higher mean hsCRP levels than controls, explained by overweight and lower leisure-time physical activity. Eight FM patients had elevated hsCRP levels and 29 had normal hsCRP levels. Levels of hsCRP were significantly correlated with FIQ scores. Patients with elevated hsCRP had higher FIQ scores, with worse physical functioning and greater pain and were less likely to be employed than patients with normal hsCRP. These patient groups did not differ by blood count, liver function or lipid profiles, nor by education, psychological measures, sleep disturbance, smoking or comorbidities.

Conclusion

Some FM patients have elevated hsCRP, mostly due to overweight and physical inactivity. They have worse symptoms and their ability to work is impaired. Measurement of hsCRP may help to identify FM patients in greatest need of interventions supporting working ability.

Trial registration

ClinicalTrials.gov (https://clinicaltrials.gov), NCT03300635

Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence

Background/Objective

This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT).

Method

The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT. Discriminant capacity was evaluated using the area under the curve (AUC) and the Relief algorithm. A diagnostic aid system with an automatic classifier was implemented.

Results

No significant difference (p ≥ .660) was found anywhere in the choroid. In the RNFL, a significant difference was found in the inner inferior region (p = .010). In the GCL+, GCL++ layers and complete retina, a significant difference was found in the 4 regions defining the inner ring: temporal, superior, nasal and inferior. Applying an ensemble RUSBoosted tree classifier to the features with greatest discriminant capacity achieved accuracy = .82 and AUC = .82.

Conclusions

This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

IL-6 and hsCRP in Somatic Symptom Disorders and related disorders

Interleukin 6 (IL-6) and high-sensitivity C-reactive protein (hsCRP) are biomarkers of systemic low-grade inflammation (SLI) in depression and anxiety. The question if SLI in those conditions is related to comorbid chronic medical conditions has not been resolved. DSM-5 Somatic symptom disorders and related disorders (SSRD) are conditions with serious distress related to physical symptoms as main criterion. They can occur in patients with medically unexplained symptoms (MUS) and in patients with known comorbid chronic medical conditions. Often, comorbid depression and anxiety are present. SSRDs offer the opportunity to explore the role of SLI in relation to mental distress, including trauma, MUS, chronic medical conditions and comorbid mental disorder.

AIM

We hypothesized that increased IL-6 and hsCRP may be directly linked to SLI in SSRD, and that comorbid chronic medical conditions, childhood trauma, current stress and comorbid depression and anxiety may be risk factors that account for some of the variance of SLI in SSRD.

METHODS

We explored these relationships in a large sample of 241 consecutive outpatients with SSRD.

RESULTS

Mean hsCRP level was 3.66 ​mg/l, and mean IL-6 level was 3.58 ​pg/ml. IL-6 and hsCRP levels were associated with each other: τ ​= ​0.249, p ​< ​.001; a medium size correlation. Comorbid chronic medical conditions, adverse childhood events other than sexual trauma, and current stress levels were not associated with IL-6 or hsCRP levels.

CONCLUSION

IL-6 and hsCRP are elevated in SSRD, indicating SLI in SSRD independently of comorbid chronic medical conditions. In clinical research, elevated IL-6 and hsCRP can be used as biomarkers of SLI and can indicate risk for childhood sexual abuse in SSRD. Elevated hsCRP may be a biomarker indicating risk for comorbid depression or high pain levels in SSRD as well.

Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders

Major depressive disorder (MDD) is associated with increased suicidal risk and reduced productivity at work. Neuroimmunology, the study of the immune system and nervous system, provides further insight into the pathogenesis and outcome of MDD. Cytokines are the main modulators of neuroimmunology, and their levels are somewhat entangled in depressive disorders as they affect depressive symptoms and are affected by antidepressant treatment. The use of cytokine-derived medication as a treatment option for MDD is currently a topic of interest. Although not very promising, cytokines are also considered as possible prognostic or diagnostic markers for depression. The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily clinical practice. This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.

Overtraining Syndrome as a Complex Systems Phenomenon

The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS.

Mitochondria and immunity in chronic fatigue syndrome

It is widely accepted that the pathophysiology and treatment of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) could be considerably improved. The heterogeneity of ME/CFS and the confusion over its classification have undoubtedly contributed to this, although this would seem a consequence of the complexity of the array of ME/CFS presentations and high levels of diverse comorbidities. This article reviews the biological underpinnings of ME/CFS presentations, including the interacting roles of the gut microbiome/permeability, endogenous opioidergic system, immune cell mitochondria, autonomic nervous system, microRNA-155, viral infection/re-awakening and leptin as well as melatonin and the circadian rhythm. This details not only relevant pathophysiological processes and treatment options, but also highlights future research directions. Due to the complexity of interacting systems in ME/CFS pathophysiology, clarification as to its biological underpinnings is likely to considerably contribute to the understanding and treatment of other complex and poorly managed conditions, including fibromyalgia, depression, migraine, and dementia. The gut and immune cell mitochondria are proposed to be two important hubs that interact with the circadian rhythm in driving ME/CFS pathophysiology.

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

The Influence of Pro-inflammatory Cytokines and Genetic Variants in the Development of Fibromyalgia: A Traditional Review

Fibromyalgia is a complex syndrome characterized by widespread chronic pain, without any obvious etiology, and it is often accompanied by a constellation of symptoms such as fatigue, sleep disturbances and cognitive dysfunction, to name a few. The syndrome may be associated with a variety of autoimmune and psychiatric conditions. Fibromyalgia can occur with other musculoskeletal pathologies and its symptoms can overlap with other chronic painful conditions such as chronic myofascial pain syndromes seen in cervical and lumbar spinal osteoarthritis and degenerative disc disease. Gene polymorphisms have been related to a decreased pain threshold and an increased susceptibility to disorders associated with chronic pain. Some of those genetic variants might trigger the onset of fibromyalgia. Researchers are looking into the possible factors that might contribute to its pathophysiology. It is important to study the connections between pro-inflammatory cytokines and genetic variants in pain-related genes and their roles in predisposition and development of fibromyalgia. The objective of this review article is to provide a brief overview of the pro-inflammatory cytokines commonly associated with fibromyalgia, as well as to look into the genes that have shown some level of involvement in the development of fibromyalgia and its symptomatology.

Activation of Transposable Elements in Immune Cells of Fibromyalgia Patients

Advancements in nucleic acid sequencing technology combined with an unprecedented availability of metadata have revealed that 45% of the human genome constituted by transposable elements (TEs) is not only transcriptionally active but also physiologically necessary. Dysregulation of TEs, including human retroviral endogenous sequences (HERVs) has been shown to associate with several neurologic and autoimmune diseases, including Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). However, no study has yet addressed whether abnormal expression of these sequences correlates with fibromyalgia (FM), a disease frequently comorbid with ME/CFS. The work presented here shows, for the first time, that, in fact, HERVs of the H, K and W types are overexpressed in immune cells of FM patients with or without comorbid ME/CFS. Patients with increased HERV expression (N = 14) presented increased levels of interferon (INF-β and INF-γ) but unchanged levels of TNF-α. The findings reported in this study could explain the flu-like symptoms FM patients present with in clinical practice, in the absence of concomitant infections. Future work aimed at identifying specific genomic loci differentially affected in FM and/or ME/CFS is warranted.

T Cell Subpopulations in the Physiopathology of Fibromyalgia: Evidence and Perspectives

Fibromyalgia is one of the most important “rheumatic” disorders, after osteoarthritis. The etiology of the disease is still not clear. At the moment, the most defined pathological mechanism is the alteration of central pain pathways, and emotional conditions can trigger or worsen symptoms. Increasing evidence supports the role of mast cells in maintaining pain conditions such as musculoskeletal pain and central sensitization. Importantly, mast cells can mediate microglia activation through the production of proinflammatory cytokines such as IL-1β, IL-6, and TNFα. In addition, levels of chemokines and proinflammatory cytokines are enhanced in serum and could contribute to inflammation at systemic level. Despite the well-characterized relationship between the nervous system and inflammation, the mechanism that links the different pathological features of fibromyalgia, including stress-related manifestations, central sensitization, and dysregulation of the innate and adaptive immune responses is largely unknown. This review aims to provide an overview of the current understanding of the role of adaptive immune cells, in particular T cells, in the physiopathology of fibromyalgia. It also aims at linking the latest advances emerging from basic science to envisage new perspectives to explain the role of T cells in interconnecting the psychological, neurological, and inflammatory symptoms of fibromyalgia.