Pain Biomarkers in Fibromyalgia Syndrome: Current Understanding and Future Directions

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain

PURPOSE OF REVIEW

This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.

RECENT FINDINGS

In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Repetitive Transcranial Magnetic Stimulation in Fibromyalgia: Exploring the Necessity of Neuronavigation for Targeting New Brain Regions

Fibromyalgia and osteoarthritis are among the most prevalent rheumatic conditions worldwide. Nonpharmacological interventions have gained scientific endorsements as the preferred initial treatments before resorting to pharmacological modalities. Repetitive transcranial magnetic stimulation (rTMS) is among the most widely researched neuromodulation techniques, though it has not yet been officially recommended for fibromyalgia. This review aims to summarize the current evidence supporting rTMS for treating various fibromyalgia symptoms. Recent findings: High-frequency rTMS directed at the primary motor cortex (M1) has the strongest support in the literature for reducing pain intensity, with new research examining its long-term effectiveness. Nonetheless, some individuals may not respond to M1-targeted rTMS, and symptoms beyond pain can be prominent. Ongoing research aims to improve the efficacy of rTMS by exploring new brain targets, using innovative stimulation parameters, incorporating neuronavigation, and better identifying patients likely to benefit from this treatment. Summary: Noninvasive brain stimulation with rTMS over M1 is a well-tolerated treatment that can improve chronic pain and overall quality of life in fibromyalgia patients. However, the data are highly heterogeneous, with a limited level of evidence, posing a significant challenge to the inclusion of rTMS in official treatment guidelines. Research is ongoing to enhance its effectiveness, with future perspectives exploring its impact by targeting additional areas of the brain such as the medial prefrontal cortex, anterior cingulate cortex, and inferior parietal lobe, as well as selecting the right patients who could benefit from this treatment.

Pharmacotherapeutic advances in fibromyalgia: what's new on the horizon?

INTRODUCTION

This review delves into Fibromyalgia Syndrome (FMS), a chronic pain condition demanding thorough understanding for precise diagnosis and treatment. Yet, a definitive pharmacological solution for FMS remains elusive.

AREAS COVERED

In this article, we systematically analyze various pharmacotherapeutic prospects for FMS treatment, organized into sections based on the stage of drug development and approval. We begin with an overview of FDA-approved drugs, discussing their efficacy in FMS treatment. Next, we delve into other medications currently used for FMS but still undergoing further study, including opioids and muscle relaxants. Further, we evaluate the evidence behind medications that are currently under study, such as cannabinoids and naltrexone. Lastly, we explore new drugs that are in phase II trials. Our research involved a thorough search on PUBMED, Google Scholar, and clinicaltrials.gov. We also discuss the action mechanisms of these drugs and their potential use in specific patient groups.

EXPERT OPINION

A focus on symptom-driven, combination therapy is crucial in managing FMS. There is also a need for ongoing research into drugs that target neuroinflammation, immunomodulation, and the endocannabinoid system. Bridging the gap between benchside research and clinical application is challenging, but it holds potential for more targeted and effective treatment strategies.

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.

Causal association between gut microbiota and fibromyalgia: a Mendelian randomization study

Background

Fibromyalgia (FM) is a syndrome characterized by chronic and widespread musculoskeletal pain. A number of studies have implied a potential association between gut microbiota and FM. However, the casual association between gut microbiota and FM remains unknown.

Method

Mendelian randomization (MR) study was conducted using the summary statistics of genetic variants from the genome-wide association study (GWAS). Inverse variance weighted (IVW), combined with MR-Egger and weighted median were used to investigate the causal association between 119 gut microbiota genera and FM. Sensitivity analyses were performed on the MR results, including heterogeneity test, leave-one-out test and pleiotropy test.

Results

A total of 1,295 single nucleotide polymorphism (SNPs) were selected as instrumental variables (IVs), with no significant heterogeneity and pleiotropy according to the sensitivity analyses. Five gut microbiota genera were found to have significant casual association with FM. Coprococcus2 (OR = 2.317, p-value = 0.005, 95% CI: 1.289-4.167), Eggerthella (OR = 1.897, p-value = 0.001, 95% CI: 1.313-2.741) and Lactobacillus (OR = 1.576, p-value =0.020, 95% CI: 1.073-2.315) can increase the risk of FM. FamillyXIIIUCG001 (OR = 0.528, p-value = 0.038, 95% CI: 0.289-0.964) and Olsenella (OR = 0.747, p-value = 0.050, 95% CI: 0.557-1.000) can decrease the risk of FM.

Conclusion

This MR study found that gut microbiota is casually associated with FM. New insights into the mechanisms of FM mediated by gut microbiota are provided.

Serum VEGF and CGRP Biomarkers: Relationships with Pain Intensity, Electric Pain, Pressure Pain Threshold, and Clinical Symptoms in Fibromyalgia-An Observational Study

Fibromyalgia (FM) is a multifactorial syndrome, mainly characterized by chronic widespread pain, whose physiopathology is yet to be determined. Reliable biomarkers for FM and how they are associated with the symptomatology have not yet been identified. We aimed to examine the relationships among serum vascular endothelial growth factor (VEGF) and calcitonin gene-related peptide (CGRP) levels with clinical manifestations and pain-related variables in women with FM. We conducted an observational case study with forty-seven women diagnosed with FM. Serum VEGF and CGRP levels were spectrophotometrically analyzed. We used questionnaires to measure the impact of FM and the degree of central sensitization, fatigue, and anxiety. We also assessed pain intensity, electric pain threshold and magnitude, and pressure pain threshold (PPT) in tender points. The linear regression analysis adjusting for age, menopause status, and body mass index showed that serum VEGF levels were significantly associated with the PPTs of non-dominant trapezius (β = 153.418; p = 0.033), non-dominant second metacarpal (β = 174.676; p = 0.008) and dominant tibialis anterior (β = 115.080; p = 0.049) in women with FM. We found no association between serum CGRP levels and the variables measured (p ≥ 0.152). Our results suggest that VEGF may be related to pain processing in patients with FM.