Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders

Investigation of the structural changes in the hippocampus and prefrontal cortex using FTIR spectroscopy in sleep deprived mice

Sleep is a basic, physiological requirement for living things to survive and is a process that covers one third of our lives. Melatonin is a hormone that plays an important role in the regulation of sleep. Sleep deprivation affect brain structures and functions. Sleep deprivation causes a decrease in brain activity, with particularly negative effects on the hippocampus and prefrontal cortex. Despite the essential role of protein and lipids vibrations, polysaccharides, fatty acid side chains functional groups, and ratios between amides in brain structures and functions, the brain chemical profile exposed to gentle handling sleep deprivation model versus Melatonin exposure remains unexplored. Therefore, the present study, aims to investigate a molecular profile of these regions using FTIR spectroscopy measurement's analysis based on lipidomic approach with chemometrics and multivariate analysis to evaluate changes in lipid composition in the hippocampus, prefrontal regions of the brain. In this study, C57BL/6J mice were randomly assigned to either the control or sleep deprivation group, resulting in four experimental groups: Control (C) (n = 6), Control + Melatonin (C + M) (n = 6), Sleep Deprivation (S) (n = 6), and Sleep Deprivation + Melatonin (S + M) (n = 6). Interventions were administered each morning via intraperitoneal injections of melatonin (10 mg/kg) or vehicle solution (%1 ethanol + saline), while the S and S + M groups underwent 6 h of daily sleep deprivation from using the Gentle Handling method. All mice were individually housed in cages with ad libitum access to food and water within a 12-hour light-dark cycle. Results presented that the brain regions affected by insomnia. The structure of phospholipids, changed. Yet, not only changes in lipids but also in amides were noticed in hippocampus and prefrontal cortex tissues. Additionally, FTIR results showed that melatonin affected the lipids as well as the amides fraction in cortex and hippocampus collected from both control and sleep deprivation groups.

Relationship between amide ratio assessed by Fourier-transform infrared spectroscopy: A biomarker candidate for polycythemia vera disease

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

Fibromyalgia: A Review of the Pathophysiological Mechanisms and Multidisciplinary Treatment Strategies

Fibromyalgia is a syndrome characterized by chronic widespread musculoskeletal pain, which may or may not be associated with muscle or joint stiffness, accompanied by other symptoms such as fatigue, sleep disturbances, anxiety, and depression. It is a highly prevalent condition globally, being considered the third most common musculoskeletal disorder, following lower back pain and osteoarthritis. It is more prevalent in women than in men, and although it can occur at any age, it is more common between the ages of thirty and thirty-five. Although the pathophysiology and etiopathogenesis remain largely unknown, three underlying processes in fibromyalgia have been investigated. These include central sensitization, associated with an increase in the release of both excitatory and inhibitory neurotransmitters; peripheral sensitization, involving alterations in peripheral nociceptor signaling; and inflammatory and immune mechanisms that develop concurrently with the aforementioned processes. Furthermore, it has been determined that genetic, endocrine, psychological, and sleep disorders may influence the development of this pathology. The accurate diagnosis of fibromyalgia remains challenging as it lacks specific diagnostic biomarkers, which are still under investigation. Nonetheless, diagnostic approaches to the condition have evolved based on the use of scales and questionnaires for pain identification. The complexity associated with this pathology makes it difficult to establish a single effective treatment. Therefore, treatment is multidisciplinary, involving both pharmacological and non-pharmacological interventions aimed at alleviating symptoms. The non-pharmacological treatments outlined in this review are primarily related to physiotherapy interventions. The effectiveness of physical exercise, both on land and in water, as well as the application of electrotherapy combined with transcranial therapy and manual therapy has been highlighted. All of these interventions aim to improve the quality of life of patients highly affected by fibromyalgia.

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

The Disease Loophole: Index Terms and Their Role in Disease Misclassification

The definitions of disease proffered by philosophers and medical actors typically require that a state of ill health be linked to some known bodily dysfunction before it is classified as a disease. I argue that such definitions of disease are not fully implementable in current medical discourse and practice. Adhering to the definitions would require that medical actors keep close track of the current state of knowledge on the causes and mechanisms of particular illnesses. Yet, unaddressed problems in medical terminology can make this difficult to do. I show that unrecognized misuse of "heterogeneous," "biomarker," and other important health terms-which I call index terms-can misrepresent the current empirical evidence on illness pathophysiology, such that unvalidated illness constructs become mistaken for diseases. Thus, implementing common definitions of disease would require closing this "loophole" in medical discourse. I offer a simple rule that, if followed, could help do just that.

Portable Mid-Infrared Spectroscopy Combined with Chemometrics to Diagnose Fibromyalgia and Other Rheumatologic Syndromes Using Rapid Volumetric Absorptive Microsampling

The diagnostic criteria for fibromyalgia (FM) have relied heavily on subjective reports of experienced symptoms coupled with examination-based evidence of diffuse tenderness due to the lack of reliable biomarkers. Rheumatic disorders that are common causes of chronic pain such as rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain are frequently found to be comorbid with FM. As a result, this can make the diagnosis of FM more challenging. We aim to develop a reliable classification algorithm using unique spectral profiles of portable FT-MIR that can be used as a real-time point-of-care device for the screening of FM. A novel volumetric absorptive microsampling (VAMS) technique ensured sample volume accuracies and minimized the variation introduced due to hematocrit-based bias. Blood samples from 337 subjects with different disorders (179 FM, 158 non-FM) collected with VAMS were analyzed. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. The OPLS-DA algorithm enabled the classification of the spectra into their corresponding classes with 84% accuracy, 83% sensitivity, and 85% specificity. The OPLS-DA regression plot indicated that spectral regions associated with amide bands and amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases.

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Serum Raman spectroscopy can be used to screen patients with early rheumatoid arthritis

In this study, Raman spectroscopy was used to analyze the serum of patients with early rheumatoid arthritis (RA), and to explore the screening value of Raman spectroscopy in patients with early RA. A total of 216 patients were included in the study. Fasting venous blood was collected for routine biochemical detection, and the remaining samples were tested by serum Raman spectroscopy. Support vector machine was used for model building and training. The area under the curve (AUC) values of the model were as follows: (1) healthy group versus early RA group: 0.860, (2) healthy group versus non-early RA group: 0.903, and (3) early RA group versus non-early RA group: 0.918. This study shows that serum Raman spectroscopy has a good ability to screen RA and can be staged according to the course of the disease, which can provide new ideas and technical support for the diagnosis or screening of early RA.

Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI-MS) and multivariate classification

This study performs a chemical investigation of blood plasma samples from patients with and without fibromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual analogue pain scale (VAS); fibromyalgia impact questionnaire (FIQ); Hamilton anxiety rating scale (HAM); Tampa Scale for Kinesiophobia (TAMPA); quality of life Questionnaire-physical and mental health (QL); and Pain Catastrophizing Scale (CAT). Plasma samples were analyzed by paper spray ionization mass spectrometry (PSI-MS). Spectral data were organized into datasets and related to each of the symptoms measured. The datasets were submitted to multivariate classification using supervised models such as principal component analysis with linear discriminant analysis (PCA-LDA), successive projections algorithm with linear discriminant analysis (SPA-LDA), genetic algorithm with linear discriminant analysis (GA-LDA) and their versions with quadratic discriminant analysis (PCA/SPA/GA-QDA) and support vector machines (PCA/SPA/GA-SVM). These algorithm combinations were performed aiming the best class separation. Good discrimination between the controls and fibromyalgia samples were observed using PCA-LDA, where the spectral data associated with the CAT symptom achieved 100% classification sensitivity, and associated with the VAS symptom achieved 100% classification specificity, with both symptoms at the moderate level of intensity. The spectral variable at 579 m/z was found to be substantially significant for classification according to the PCA loadings. According to the human metabolites database, this variable can be associated with a LysoPC compound, which comprises a class of metabolites already evidenced in other studies for fibromyalgia diagnosis. This study proposed an investigation of spectral data combined with clinical data to compare the classification ability of different datasets. The good classification results obtained confirm this technique is as a good analytical tool for the detection of fibromyalgia, and provides theoretical support for other studies about fibromyalgia diagnosis.

Rapid Biomarker-Based Diagnosis of Fibromyalgia Syndrome and Related Rheumatologic Disorders by Portable FT-IR Spectroscopic Techniques

Fibromyalgia syndrome (FM), one of the most common illnesses that cause chronic widespread pain, continues to present significant diagnostic challenges. The objective of this study was to develop a rapid vibrational biomarker-based method for diagnosing fibromyalgia syndrome and related rheumatologic disorders (systemic lupus erythematosus (SLE), osteoarthritis (OA) and rheumatoid arthritis (RA)) through portable FT-IR techniques. Bloodspot samples were collected from patients diagnosed with FM (n = 122) and related rheumatologic disorders (n = 70), including SLE (n = 17), RA (n = 43), and OA (n = 10), and stored in conventional protein saver bloodspot cards. The blood samples were prepared by four different methods (blood aliquots, protein-precipitated extraction, and non-washed and water-washed semi-permeable membrane filtration extractions), and spectral data were collected with a portable FT-IR spectrometer. Pattern recognition analysis, OPLS-DA, was able to identify the signature profile and classify the spectra into corresponding classes (Rcv > 0.93) with excellent sensitivity and specificity. Peptide backbones and aromatic amino acids were predominant for the differentiation and might serve as candidate biomarkers for syndromes such as FM. This research evaluated the feasibility of portable FT-IR combined with chemometrics as an accurate and high-throughput tool for distinct spectral signatures of biomarkers related to the human syndrome (FM), which could allow for real-time and in-clinic diagnostics of FM.

Diagnostic biomarker panels of osteoarthritis: UPLC-QToF/MS-based serum metabolic profiling

Osteoarthritis (OA) is the most common joint disease in the world, characterized by pain and loss of joint function, which has led to a serious reduction in the quality of patients' lives. In this work, ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-QToF/MS) in conjunction with multivariate pattern recognition methods and an univariate statistical analysis scheme were applied to explore the serum metabolic signatures within OA group (n = 31), HC (healthy controls) group (n = 57) and non-OA group (n = 19) for early diagnosis and differential diagnosis of OA. Based on logistic regression analysis and receiver operating characteristic (ROC) curve analysis, seven metabolites, including phosphatidylcholine (18:0/22:6), p-cresol sulfate and so on, were identified as critical metabolites for the diagnosis of OA and HC and yielded an area under the curve (AUC) of 0.978. The other panel of unknown m/z 239.091, phosphatidylcholine (18:0/18:0) and phenylalanine were found to distinguish OA from non-OA and achieved an AUC of 0.888. These potential biomarkers are mainly involved in lipid metabolism, glucose metabolism and amino acid metabolism. It is expected to reveal new insight into OA pathogenesis from changed metabolic pathways.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Novel Approaches in Molecular Imaging and Neuroimaging of Fibromyalgia

Fibromyalgia (FM) represents a condition that is still controversial in its entity, pathophysiology, diagnosis and management. The aim of this review is to focus on imaging aspects of FM, especially on novel approaches in molecular imaging, with a special focus on neuroimaging. Novel functional and molecular imaging findings may represent, eventually, future biomarkers both in research settings and in terms of clinical practice. Several imaging techniques have already been tested in clinical trials in the FM field, including functional MRI, positron emission tomography (PET) imaging with ¹⁸F-FDG in FM, PET imaging of the dopaminergic system, PET imaging of the GABAergic system, PET imaging with neuroinflammation and neuroimmune parameters, PET imaging of the opioid system and H₂¹⁵O-PET activation studies. Therefore, the potential role in the FM field of fMRI and different PET tracers has been discussed in different settings, serving as a comprehensive guide of novel imaging options both in research and in the clinical field.

Toward Composite Pain Biomarkers of Neuropathic Pain-Focus on Peripheral Neuropathic Pain

Chronic pain affects ~10-20% of the U.S. population with an estimated annual cost of $600 billion, the most significant economic cost of any disease to-date. Neuropathic pain is a type of chronic pain that is particularly difficult to manage and leads to significant disability and poor quality of life. Pain biomarkers offer the possibility to develop objective pain-related indicators that may help diagnose, treat, and improve the understanding of neuropathic pain pathophysiology. We review neuropathic pain mechanisms related to opiates, inflammation, and endocannabinoids with the objective of identifying composite biomarkers of neuropathic pain. In the literature, pain biomarkers typically are divided into physiological non-imaging pain biomarkers and brain imaging pain biomarkers. We review both types of biomarker types with the goal of identifying composite pain biomarkers that may improve recognition and treatment of neuropathic pain.

Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain

Lipid mediators have been suggested to have a role in pain sensitivity and response; however, longitudinal data on lipid metabolites and persistent multisite musculoskeletal pain (MSMP) are lacking. This study was to identify lipid metabolic markers for persistent MSMP. Lipidomic profiling of 807 lipid species was performed on serum samples of 536 participants from a cohort study. MSMP was measured by a questionnaire and defined as painful sites ≥4. Persistent MSMP was defined as having MSMP at every visit. Logistic regression was used with adjustment for potential confounders. The Benjamini–Hochberg method was used to control for multiple testing. A total of 530 samples with 807 lipid metabolites passed quality control. Mean age at baseline was 61.54 ± 6.57 years and 50% were females. In total, 112 (21%) of the participants had persistent MSMP. Persistent MSMP was significantly associated with lower levels of monohexosylceramide (HexCer)(d18:1/22:0 and d18:1/24:0), acylcarnitine (AC)(26:0) and lysophosphatidylcholine (LPC)(18:1 [sn1], 18:2 [sn1], 18:2 [sn2], and 15-MHDA[sn1] [104_sn1]) after controlling for multiple testing. After adjustment for age, sex, body mass index, comorbidities, and physical activity, HexCer(d18:1/22:0 and d18:1/24:0) and LPC(15-MHDA [sn1] [104_sn1]) were significantly associated with persistent MSMP [Odds Ratio (OR) ranging from 0.25–0.36]. Two lipid classes—HexCer and LPC—were negatively associated with persistent MSMP after adjustment for covariates (OR = 0.22 and 0.27, respectively). This study identified three novel lipid signatures of persistent MSMP, suggesting that lipid metabolism is involved in the pathogenesis of persistent pain.

Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.

Diagnostic Challenges and Management of Fibromyalgia

The World Health Organization regards chronic pain to be a public health concern. In clinical medicine, fibromyalgia (FM) is the most prevalent chronic widespread pain disease. In terms of impairment, consumption of health and social resources, and impact on primary and speciality care systems, it has reached worrisome proportions. This disease is frequently managed by primary care providers. Because of its intricacy, fibromyalgia diagnosis and treatment can be difficult. Fibromyalgia is a controversial condition. It might appear ill-defined in comparison to other pain conditions, with no clear knowledge of pathophysiology and hence no particular targeted therapy. This invariably sparks debates and challenges. There is no obvious cut-off point that distinguishes FM from non-FM. The diagnosis of fibromyalgia has been complicated by several factors, including patients' health-seeking behaviour, symptom identification, and physician labelling of the disease. Fibromyalgia is currently considered a centralized pain condition, according to research that has improved our understanding of its etiopathology. A multidisciplinary strategy combining pharmacological and non-pharmacological therapies based on a biopsychosocial paradigm can result in effective therapy. Cultural and psychosocial variables appear to be a recent development in fibromyalgia, and they appear to have a larger influence on physician diagnosis than severe symptom levels in FM patients. Although physicians rely on FM criteria as the only way to classify FM patients in research and clinical settings, some crucial elements of the diagnostic challenge of fibromyalgia remain unsolved - invalidation, psychosocial variables, and diverse illness manifestation are some examples. Beyond the existing constructional scores, physicians' judgment gained in real communicative contexts with patients, appears to be the only dependable route for a more accurate diagnosis for fibromyalgia. We have performed an exhaustive review of the literature using the keywords "Fibromyalgia", "challenges" and "diagnosis" in PubMed and Google Scholar indexes up to September 2021. This article aims to examine the causes, diagnosis, and current treatment protocols of FM, as well as discuss some continuing debates and diagnostic challenges which physicians face in accurately diagnosing fibromyalgia.

The Interplay between Chronic Pain, Opioids, and the Immune System

Chronic pain represents one of the most serious worldwide medical problems, in terms of both social and economic costs, often causing severe and intractable physical and psychological suffering. The lack of biological markers for pain, which could assist in forming clearer diagnoses and prognoses, makes chronic pain therapy particularly arduous and sometimes harmful. Opioids are used worldwide to treat chronic pain conditions, but there is still an ambiguous and inadequate understanding about their therapeutic use, mostly because of their dual effect in acutely reducing pain and inducing, at the same time, tolerance, dependence, and a risk for opioid use disorder. In addition, clinical studies suggest that opioid treatment can be associated with a high risk of immune suppression and the development of inflammatory events, worsening the chronic pain status itself. While opioid peptides and receptors are expressed in both central and peripheral nervous cells, immune cells, and tissues, the role of opioids and their receptors, when and why they are activated endogenously and what their exact role is in chronic pain pathways is still poorly understood. Thus, in this review we aim to highlight the interplay between pain and immune system, focusing on opioids and their receptors.

Fibromyalgia: Pathogenesis, Mechanisms, Diagnosis and Treatment Options Update

Fibromyalgia is a syndrome characterized by chronic and widespread musculoskeletal pain, often accompanied by other symptoms, such as fatigue, intestinal disorders and alterations in sleep and mood. It is estimated that two to eight percent of the world population is affected by fibromyalgia. From a medical point of view, this pathology still presents inexplicable aspects. It is known that fibromyalgia is caused by a central sensitization phenomenon characterized by the dysfunction of neuro-circuits, which involves the perception, transmission and processing of afferent nociceptive stimuli, with the prevalent manifestation of pain at the level of the locomotor system. In recent years, the pathogenesis of fibromyalgia has also been linked to other factors, such as inflammatory, immune, endocrine, genetic and psychosocial factors. A rheumatologist typically makes a diagnosis of fibromyalgia when the patient describes a history of pain spreading in all quadrants of the body for at least three months and when pain is caused by digital pressure in at least 11 out of 18 allogenic points, called tender points. Fibromyalgia does not involve organic damage, and several diagnostic approaches have been developed in recent years, including the analysis of genetic, epigenetic and serological biomarkers. Symptoms often begin after physical or emotional trauma, but in many cases, there appears to be no obvious trigger. Women are more prone to developing the disease than men. Unfortunately, the conventional medical therapies that target this pathology produce limited benefits. They remain largely pharmacological in nature and tend to treat the symptomatic aspects of various disorders reported by the patient. The statistics, however, highlight the fact that 90% of people with fibromyalgia also turn to complementary medicine to manage their symptoms.

Opportunities and challenges for nonaddictive interventions in chronic pain

The worlds of chronic pain and addiction continue to intersect too often in many ways. Chronic pain significantly impairs and disrupts the quality of life of millions of people worldwide. Opioids remain the most prescribed pharmacotherapy offered to patients to alleviate chronic pain. The extensive and often unnecessary prescription of opioids has created a surge in the prevalence of opioid use disorders and opioid overdose-related deaths. In this brief review, we aim to provide a bench-to-bedside overview of promising biomarkers, therapeutic targets, and challenges related to treating patients with chronic pain. We hope this review will inspire new opportunities and insights into the development of novel, nonaddictive treatments for chronic pain that will be available to patients in the near future.

Chronic Pain: What Does It Mean? A Review on the Use of the Term Chronic Pain in Clinical Practice

Chronic pain is nowadays used as an umbrella term referring to a wide range of clinical conditions, such as fibromyalgia, migraine, or long-standing pain states without actual known causes. However, labeling a patient’s clinical condition with the term “chronic pain”, when dealing with pain lasting longer than 3 months, might be misleading. This paper aims at analyzing the possible pitfalls related to the use of the term “chronic pain” in the clinical field. It appears, indeed, that the term “chronic pain” shows a semantic inaccuracy on the basis of emerging scientific evidences on the pathogenesis of different long-standing pain states. The major pitfalls in using this label emerge in clinical settings, especially with patients having a biomedical perspective on pain or from different cultures, or with healthcare providers of other medical specialties or different disciplines. A label solely emphasizing temporal features does not help to discern the multifaceted complexity of long-standing pain states, whose onset, maintenance and exacerbation are influenced by a complex and interdependent set of bio-psycho-social factors. Thus, finding a more meaningful name might be important. We call upon the necessity of bringing awareness and implementing educational activities for healthcare providers, as well as for the public, on the biopsychosocial approach to assess, prevent and care of chronic pain. Further research on the etiopathogenetic processes of chronic pain states is also required, together with examinative diagnostic methods, to individuate the most appropriate label(s) representing the complex long-standing pain states and to avoid adopting the term “chronic pain” inappropriately.

Intraepidermal Nerve Fiber Density as Measured by Skin Punch Biopsy as a Marker for Small Fiber Neuropathy: Application in Patients with Fibromyalgia

Small fiber neuropathy (SFN) is a type of peripheral neuropathy that occurs from damage to the small A-delta and C nerve fibers that results in the clinical condition known as SFN. This pathology may be the result of metabolic, toxic, immune-mediated, and/or genetic factors. Small fiber symptoms can be variable and inconsistent and therefore require an objective biomarker confirmation. Small fiber dysfunction is not typically captured by diagnostic tests for large-fiber neuropathy (nerve conduction and electromyographic study). Therefore, skin biopsies stained with PGP 9.5 are the universally recommended objective test for SFN, with quantitative sensory tests, autonomic function testing, and corneal confocal imaging as secondary or adjunctive choices. Fibromyalgia (FM) is a heterogenous syndrome that has many symptoms that overlap with those found in SFN. A growing body of research has shown approximately 40-60% of patients carrying a diagnosis of FM have evidence of SFN on skin punch biopsy. There is currently no clearly defined phenotype in FM at this time to suggest whom may or may not have SFN, though research suggests it may correlate with severe cases. The skin punch biopsy provides an objective tool for use in quantifying small fiber pathology in FM. Skin punch biopsy may also be repeated for surveillance of the disease as well as measuring response to treatments. Evaluation of SFN in FM allows for better classification of FM and guidance for patient care as well as validation for their symptoms, leading to better use of resources and outcomes.

The Search for Biomarkers in Fibromyalgia

Fibromyalgia is the most common of the central sensitivity syndromes affecting 2–5% of the adult population in the United States. This pain amplification syndrome has enormous societal 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 healthcare resources towards more appropriate targeted therapy. Efforts have been made towards improving diagnostic accuracy through updated criteria. The search for biomarkers for diagnosis and verification of Fibromyalgia is an ongoing process. Inadequacies with current diagnostic criteria for this condition have fueled these efforts for identification of a reproducible marker that can verify this disease in a highly sensitive, specific and reproducible manner. This review focuses on areas of research for biomarkers in fibromyalgia and suggests that future efforts might benefit from approaches that utilize arrays of biomarkers to identify this disorder that presents with a diverse clinical phenotype.

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.

Fibromyalgia: Recent Advances in Diagnosis, Classification, Pharmacotherapy and Alternative Remedies

Fibromyalgia (FM) is a syndrome that does not present a well-defined underlying organic disease. FM is a condition which has been associated with diseases such as infections, diabetes, psychiatric or neurological disorders, rheumatic pathologies, and is a disorder that rather than diagnosis of exclusion requires positive diagnosis. A multidimensional approach is required for the management of FM, including pain management, pharmacological therapies, behavioral therapy, patient education, and exercise. The purpose of this review is to summarize the recent advances in classification criteria and diagnostic criteria for FM as well as to explore pharmacotherapy and the use of alternative therapies including the use of plant bioactive molecules.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical "fingerprint" with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

Spectrochemical analysis in blood plasma combined with subsequent chemometrics for fibromyalgia detection

Fibromyalgia is a rheumatologic condition characterized by multiple and chronic body pain, and other typical symptoms such as intense fatigue, anxiety and depression. It is a very complex disease where treatment is often made by non-medicated alternatives in order to alleviate symptoms and improve the patient’s quality of life. Herein, we propose a method to detect patients with fibromyalgia (n = 252, 126 controls and 126 patients with fibromyalgia) through the analysis of their blood plasma using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques, hence, providing a low-cost, fast and accurate diagnostic approach. Different chemometric algorithms were tested to classify the spectral data; genetic algorithm with linear discriminant analysis (GA-LDA) achieved the best diagnostic results with a sensitivity of 89.5% in an external test set. The GA-LDA model identified 24 spectral wavenumbers responsible for class separation; amongst these, the Amide II (1,545 cm⁻¹) and proteins (1,425 cm⁻¹) were identified to be discriminant features. These results reinforce the potential of ATR-FTIR spectroscopy with multivariate analysis as a new tool to screen and detect patients with fibromyalgia in a fast, low-cost, non-destructive and minimally invasive fashion.

The Metabolomics of Chronic Pain Conditions: A Systematic Review

BACKGROUND

Chronic pain is a significant public health problem in the United States, affecting approximately 100 million people. Yet there is a lack of robust biomarkers for clinical use in chronic pain conditions. Downstream effects of environmental, genomic, and proteomic variations in individuals with chronic pain conditions can be identified and quantified using a metabolomic approach.

AIM/DESIGN

The purpose of this systematic review was to examine the literature for reports of potential metabolomic signatures associated with chronic pain conditions.

METHODS

We searched relevant electronic databases for published studies that used various metabolomic approaches to investigate chronic pain conditions among subjects of all ages.

RESULTS

Our search identified a total of 586 articles, 18 of which are included in this review. The reviewed studies used metabolomics to investigate fibromyalgia (n = 5), osteoarthritis (n = 4), migraine (n = 3), musculoskeletal pain (n = 2), and other chronic pain conditions (n = 1/condition). Results show that several known and newly identified metabolites differ in individuals with chronic pain conditions compared to those without these conditions. These include amino acids (e.g., glutamine, serine, and phenylalanine) and intermediate products (e.g., succinate, citrate, acetylcarnitine, and N-acetylornithine) of pathways that metabolize various macromolecules.

CONCLUSION

Though more high-quality research is needed, this review provides insights into potential biomarkers for future metabolomics studies in people with chronic pain conditions.

Metabolomics in Central Sensitivity Syndromes

Central sensitization syndromes are a collection of frequently painful disorders that contribute to decreased quality of life and increased risk of opiate abuse. Although these disorders cause significant morbidity, they frequently lack reliable diagnostic tests. As such, technologies that can identify key moieties in central sensitization disorders may contribute to the identification of novel therapeutic targets and more precise treatment options. The analysis of small molecules in biological samples through metabolomics has improved greatly and may be the technology needed to identify key moieties in difficult to diagnose diseases. In this review, we discuss the current state of metabolomics as it relates to central sensitization disorders. From initial literature review until Feb 2020, PubMed, Embase, and Scopus were searched for applicable studies. We included cohort studies, case series, and interventional studies of both adults and children affected by central sensitivity syndromes. The majority of metabolomic studies addressing a CSS found significantly altered metabolites that allowed for differentiation of CSS patients from healthy controls. Therefore, the published literature overwhelmingly supports the use of metabolomics in CSS. Further research into these altered metabolites and their respective metabolic pathways may provide more reliable and effective therapeutics for these syndromes.

Untargeted Tumor Metabolomics with Liquid Chromatography-Surface-Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface-enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC-SERS approach has metabolite detection capabilities comparable to the state-of-art LC-MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC-SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC-SERS technology for metabolomics-based diagnosis and treatment of cancer.

Identification of MOR-Positive B Cell as Possible Innovative Biomarker (Mu Lympho-Marker) for Chronic Pain Diagnosis in Patients with Fibromyalgia and Osteoarthritis Diseases

Fibromyalgia (FM) diagnosis follows the American College of Rheumatology (ACR) criteria, based on clinical evaluation and written questionnaires without any objective diagnostic tool. The lack of specific biomarkers is a tragic aspect for FM and chronic pain diseases in general. Interestingly, the endogenous opioid system is close to the immune one because of the expression of opioid receptors on lymphocytes membrane. Here we analyzed the role of the Mu opioid receptor on B lymphocytes as a specific biomarker for FM and osteoarthritis (OA) patients. We enrolled three groups of females: FM patients, OA patients (chronic pain control group) and healthy subjects (pain-free negative control group). We collected blood samples to apply immunophenotyping analysis. Written tests were administrated for psychological analysis. Data were statistically analyzed. Final results showed that the percentage of Mu-positive B cells were statistically lower in FM and OA patients than in pain-free subjects. A low expression of Mu-positive B cell was not associated with the psychological characteristics investigated. In conclusion, here we propose the percentage of Mu-positive B cells as a biological marker for an objective diagnosis of chronic pain suffering patients, also contributing to the legitimacy of FM as a truly painful disease.

Fibromyalgia

Fibromyalgia is characterized by generalized pain, specific sites of musculoskeletal tenderness, fatigue, sleep disturbance, headaches, and many other visceral and cognitive maladies. The epidemiology is not well-elucidated and the diagnoses and management can be difficult. Surgery may not be the most appropriate management of some of these pain conditions like fibromyalgia. It may even be more difficult to discern some surgical conditions from points of heightened sensitivity in the fibromyalgia patient. Close attention to the current and past medical history in such patients should aid the surgeon in his attempt to rid the patient of painful conditions through surgery.

Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain

In this review, we will explore the use of biomarkers in chronic pain, using the examples of two prototypical facial pain conditions: trigeminal neuralgia and temporomandibular disorder. We will discuss the main categories of biomarkers and identify various genetic/genomic, molecular, neuroradiological, and psychophysical biomarkers in both facial pain conditions, using them to compare and contrast features of neuropathic, nonneuropathic, and mixed pain. By using two distinct model facial pain conditions to explore pain biomarkers, we aim to familiarize readers with different types of biomarkers currently being studied in chronic pain and explore how these biomarkers may be used to develop new precision medicine approaches to pain diagnosis, prognosis, and management.

A laboratory approach for characterizing chronic fatigue: what does metabolomics tell us?

INTRODUCTION

Manifestations of fatigue range from chronic fatigue up to a severe syndrome and myalgic encephalomyelitis. Fatigue grossly affects the functional status and quality of life of affected individuals, prompting the World Health Organization to recognize it as a chronic non-communicable condition.

OBJECTIVES

Here, we explore the potential of urinary metabolite information to complement clinical criteria of fatigue, providing an avenue towards an objective measure of fatigue in patients presenting with the full spectrum of fatigue levels.

METHODS

The experimental group consisted of 578 chronic fatigue female patients. The measurement design was composed of (1) existing clinical fatigue scales, (2) a hepatic detoxification challenge test, and (3) untargeted proton nuclear magnetic resonance (¹H-NMR) procedure to generate metabolomics data. Data analysed via an in-house Matlab script that combines functions from a Statistics and a PLS Toolbox.

RESULTS

Multivariate analysis of the original 459 profiled ¹H-NMR bins for the low (control) and high (patient) fatigue groups indicated complete separation following the detoxification experimental challenge. Important bins identified from the ¹H-NMR spectra provided quantitative metabolite information on the detoxification challenge for the fatigue groups.

CONCLUSIONS

Untargeted ¹H-NMR metabolomics proved its applicability as a global profiling tool to reveal the impact of toxicological interventions in chronic fatigue patients. No clear potential biomarker emerged from this study, but the quantitative profile of the phase II biotransformation products provide a practical visible effect directing to up-regulation of crucial phase II enzyme systems in the high fatigue group in response to a high xenobiotic-load.