A predictive algorithm to identify genes that discriminate individuals with fibromyalgia syndrome diagnosis from healthy controls

A Comprehensive Review of the Genetic and Epigenetic Contributions to the Development of Fibromyalgia

This narrative review summarizes the current knowledge of the genetic and epigenetic contributions to the development of fibromyalgia (FM). Although there is no single gene that results in the development of FM, this study reveals that certain polymorphisms in genes involved in the catecholaminergic pathway, the serotonergic pathway, pain processing, oxidative stress, and inflammation may influence susceptibility to FM and the severity of its symptoms. Furthermore, epigenetic changes at the DNA level may lead to the development of FM. Likewise, microRNAs may impact the expression of certain proteins that lead to the worsening of FM-associated symptoms.

Relationship Between Vitamin D Receptor Gene BsmI Polymorphism and Fibromyalgia Syndrome

Purpose

Vitamin D receptor (VDR) has been proposed as a possible marker for fibromyalgia syndrome (FMS). The purpose of this study is to characterize the expression pattern of BsmI polymorphism (rs1544410) in the VDR gene in women with FMS and the genotype-phenotype association.

Methods

A total of 105 FMS patients and 105 controls were included in this study. VDR gene BsmI polymorphism was assessed by polymerase chain reaction (PCR) and restriction fragment length polymerase (RFLP) method.

Results

There was no significant difference in the frequency distribution of both genotypes and alleles for VDR gene BsmI polymorphism between FMS patients and controls (p>0.05). The frequencies of BB, Bb, and bb in the VDR gene BsmI polymorphism were 19%, 43%, and 37% in patients, while in controls were 22.9%, 55.2%, and 21.9%. However, we did not find any significant association between the clinical symptoms of this disease and VDR BsmI genotypes among FMS patients (p>0.05).

Conclusions

The relationship between the VDR gene BsmI polymorphism and FMS could not be determined in this study. However, further studies with a larger sample size may be required to show a relation between the VDR gene BsmI polymorphism and FMS.

Interplay between genetics and lifestyle on pain susceptibility in women with fibromyalgia: The al-Ándalus project

Objectives

It is widely acknowledged that the experience of pain is promoted by both genetic susceptibility and environmental factors such as engaging in physical activity (PA), and that pain-related cognitions are also important. Thus, the purpose of the present study was to test the association of 64 polymorphisms (34 candidate genes) and the gene–gene, gene–PA and gene–sedentary behaviour interactions with pain and pain-related cognitions in women with FM.

Methods

Saliva samples from 274 women with FM [mean (s.d.) age 51.7 (7.7) years] were collected for extracting DNA. We measured PA and sedentary behaviour by accelerometers for a week, pain with algometry and questionnaires, and pain-related cognitions with questionnaires. To assess the robustness of the results, a meta-analysis was also performed.

Results

The rs6311 and rs6313 polymorphisms (5-hydroxytryptamine receptor 2A, HTR2A) were individually related to algometer scores. The interaction of rs4818 (catechol-O-methyltransferase, COMT) and rs1799971 (opioid receptor μ gene, OPRM1) was related to pain catastrophizing. Five gene–behaviour interactions were significant: the interactions of sedentary behaviour with rs1383914 (adrenoceptor alpha 1A, ADRA1A), rs6860 (charged multivesicular body protein 1A, CHMP1A), rs4680 (COMT), rs165599 (COMT) and rs12994338 (SCN9A) on bodily pain subscale of the Short Form 36. Furthermore, the meta-analysis showed an association between rs4680 (COMT) and severity of FM symptoms (codominant model, P-value 0.032).

Conclusion

The HTR2A gene (individually), COMT and OPRM1 gene–gene interaction, and the interactions of sedentary behaviour with ADRA1A, CHMP1A, COMT and SCN9A genes were associated with pain-related outcomes. Collectively, findings from the present study indicate a modest contribution of genetics and gene–sedentary behaviour interaction to pain and pain catastrophizing in women with FM. Future research should examine whether reducing sedentary behaviour is particularly beneficial for reducing pain in women with genetic susceptibility to pain.

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches

BACKGROUND

Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

METHOD

We carried out a retrospective analysis of two different microarray datasets, using machine learning techniques to understand the defective pathways involved in this disease. We have modeled two data sets that are publicly accessible. The first was used to establish the list of most discriminatory genes; whereas, the second one was utilized for validation purposes.

RESULTS

The analysis provided a list of high discriminatory genes with predictive cross-validation accuracy higher than 95%, both in learning and in blind validation. The results were confirmed via the holdout sampler. The most discriminatory genes were related to the production of Hemoglobin. The biological processes involved were related to T-cell Receptor Signaling and co-stimulation, Interferon-Gamma Signaling and Antigen Processing and Presentation. Drug repositioning via CMAP methodologies highlighted the importance of Trichostatin A and other HDAC inhibitors.

CONCLUSIONS

The defective pathways suggest viral or bacterial infections as plausible mechanisms involved in MS development. The pathway analysis also confirmed coincidences with Epstein-Barr virus, Influenza A, Toxoplasmosis, Tuberculosis and Staphylococcus Aureus infections. Th17 Cell differentiation, and CD28 co-stimulation seemed to be crucial in the development of this disease. Furthermore, the additional knowledge provided by this analysis helps to identify new therapeutic targets.

Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia

Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality of life. As such, the detection of extreme cases of sleep quality and fatigue level is a prerequisite for any intervention that can improve sleep quality and reduce fatigue level for people with fibromyalgia and enhance their daytime functionality. In this study, we propose a new supervised machine learning method called Learning Using Concave and Convex Kernels (LUCCK). This method employs similarity functions whose convexity or concavity can be configured so as to determine a model for each feature separately, and then uses this information to reweight the importance of each feature proportionally during classification. The data used for this study was collected from patients with fibromyalgia and consisted of blood volume pulse (BVP), 3-axis accelerometer, temperature, and electrodermal activity (EDA), recorded by an Empatica E4 wristband over the courses of several days, as well as a self-reported survey. Experiments on this dataset demonstrate that the proposed machine learning method outperforms conventional machine learning approaches in detecting extreme cases of poor sleep and fatigue in people with fibromyalgia.

Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments

Fibromyalgia (FM) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are diseases of unknown etiology presenting complex and often overlapping symptomatology. Despite promising advances on the study of miRNomes of these diseases, no validated molecular diagnostic biomarker yet exists. Since FM and ME/CFS patient treatments commonly include polypharmacy, it is of concern that biomarker miRNAs are masked by drug interactions. Aiming at discriminating between drug-effects and true disease-associated differential miRNA expression, we evaluated the potential impact of commonly prescribed drugs on disease miRNomes, as reported by the literature. By using the web search tools SM2miR, Pharmaco-miR, and repoDB, we found a list of commonly prescribed drugs that impact FM and ME/CFS miRNomes and therefore could be interfering in the process of biomarker discovery. On another end, disease-associated miRNomes may incline a patient’s response to treatment and toxicity. Here, we explored treatments for diseases in general that could be affected by FM and ME/CFS miRNomes, finding a long list of them, including treatments for lymphoma, a type of cancer affecting ME/CFS patients at a higher rate than healthy population. We conclude that FM and ME/CFS miRNomes could help refine pharmacogenomic/pharmacoepigenomic analysis to elevate future personalized medicine and precision medicine programs in the clinic.