An omics investigation into chronic widespread musculoskeletal pain reveals epiandrosterone sulfate as a potential biomarker

The influence of sex hormones on musculoskeletal pain and osteoarthritis

The association of female sex with certain rheumatic symptoms and diseases is now indisputable. Some of the most striking examples of this association occur in individuals with musculoskeletal pain and osteoarthritis, in whom sex-dependent changes in incidence and prevalence of disease are seen throughout the lifecourse. Joint and muscle pain are some of the most common symptoms of menopause, and there is increasingly compelling evidence that changes in or loss of sex hormones (be it natural, autoimmune, pharmacological, or surgical) influence musculoskeletal pain propensity and perhaps disease. However, the effects of modulation or replacement of sex hormones in this context are far less established, particularly whether these approaches could represent a preventative or therapeutic opportunity once symptoms have developed. In this Review, we present evidence for the association of changes in sex hormones with musculoskeletal pain and painful osteoarthritis, discussing data from diverse natural, therapeutic, and experimental settings in humans and relevant animal models relating to hormone loss or replacement and the consequent effects on health, pain, and disease. We also postulate mechanisms by which sex hormones could mediate these effects. Further research is needed; however, increased scientific understanding of this complex area could lead to real benefits in musculoskeletal and women's health.

Causality of genetically determined metabolites on anxiety disorders: a two-sample Mendelian randomization study

BACKGROUND

Although anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.

METHODS

A two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10^-4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.

RESULTS

In MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (OR_{fixed-effect IVW} = 1.04; 95% CI 1.021-1.06; P_{fixed-effect IVW} = 4.3 × 10^-5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a "leave-one-out" analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = - 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran's Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the "primary bile acid biosynthesis" pathway (P = 0.008) and the "valine, leucine, and isoleucine biosynthesis" pathway (P = 0.03), were identified through metabolic pathway analysis.

CONCLUSION

This study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes

Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.

Comparing metabolic profiles between female endurance athletes and non-athletes reveals differences in androgen and corticosteroid levels

Endurance training is associated with physiological changes in elite athletes, but little is known about female-specific effects of endurance training. Despite the significant rise in female sports participation, findings from studies performed on male athletes are largely extrapolated to females without taking into consideration sex-specific differences in metabolism. Subsequently, this study aimed to investigate the steroid hormone profiles of elite female endurance athletes in comparison with their non-athletic counterparts. Untargeted metabolomics-based mass spectroscopy combined with ultra-high-performance liquid chromatography was performed on serum samples from 51 elite female endurance athletes and 197 non-athletic females. The results showed that, compared to non-athletic females, certain androgen, pregnenolone, and progestin steroid hormones were reduced in elite female endurance athletes, while corticosteroids were elevated. The most significantly altered steroid hormones were 5alpha-androstan-3alpha,17alpha-diol monosulfate (FDR = 1.90 × 10^-05), androstenediol (3alpha, 17alpha) monosulfate (FDR = 2.93 × 10^-04), and cortisol (FDR = 2.93 × 10^-04). Conclusively, the present study suggests that elite female endurance athletes have a unique steroid hormone profile with implications on their general health and performance.

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.

Metabolomics of Synovial Fluid and Infrapatellar Fat Pad in Patients with Osteoarthritis or Rheumatoid Arthritis

Osteoarthritis (OA) and autoimmune-driven rheumatoid arthritis (RA) are inflammatory joint diseases with complex and insufficiently understood pathogeneses. Our objective was to characterize the metabolic fingerprints of synovial fluid (SF) and its adjacent infrapatellar fat pad (IFP) obtained during the same surgical operation from OA and RA knees. Non-targeted metabolite profiling was performed for 5 non-inflammatory trauma controls, 10 primary OA (pOA) patients, and 10 seropositive RA patients with high-resolution mass spectrometry-based techniques, and metabolites were matched with known metabolite identities. Groupwise differences in metabolic features were analyzed with the univariate Welch’s t-test and the multivariate linear discriminant analysis (LDA) and principal component analysis (PCA). Significant discrimination of metabolite profiles was discovered by LDA for both SF and IFP and by PCA for SF based on diagnosis. In addition to a few drug-derived substances, there were 16 and 13 identified metabolites with significant differences between the diagnoses in SF and IFP, respectively. The pathways downregulated in RA included androgen, bile acid, amino acid, and histamine metabolism, and those upregulated included biotin metabolism in pOA and purine metabolism in RA and pOA. The RA-induced downregulation of androgen and bile acid metabolism was observed for both SF and IFP. The levels of 11 lipid metabolites, mostly glycerophospholipids and fatty acid amides, were also altered by these inflammatory conditions. The identified metabolic pathways could be utilized in the future to deepen our understanding of the pathogeneses of OA and RA and to develop not only biomarkers for their early diagnosis but also therapeutic targets.

Metabolomic biomarkers of low BMD: a systematic review

Due to the metabolic nature of osteoporosis, this study was conducted to identify metabolomic studies investigating the metabolic profile of low bone mineral density (BMD) and osteoporosis. A comprehensive systematic literature search was conducted through PubMed, Web of Science, Scopus, and Embase databases up to April 08, 2020, to identify observational studies with cross-sectional or case-control designs investigating the metabolic profile of low BMD in adults using biofluid specimen via metabolomic platform. The quality assessment panel specified for the "omics"-based diagnostic research (QUADOMICS) tool was used to estimate the methodologic quality of the included studies. Ten untargeted and one targeted approach metabolomic studies investigating biomarkers in different biofluids through mass spectrometry or nuclear magnetic resonance platforms were included in the systematic review. Some metabolite panels, rather than individual metabolites, showed promising results in differentiating low BMD from normal. Candidate metabolites were of different categories including amino acids, followed by lipids and carbohydrates. Besides, certain pathways were suggested by some of the studies to be involved. This systematic review suggested that metabolic profiling could improve the diagnosis of low BMD. Despite valuable findings attained from each of these studies, there was great heterogeneity regarding the ethnicity and age of participants, samples, and the metabolomic platform. Further longitudinal studies are needed to validate the results and confirm the predictive role of metabolic profile on low BMD and fracture. It is also mandatory to address and minimize the heterogeneity in future studies by using reliable quantitative methods. Summary: Due to the metabolic nature of osteoporosis, researchers have considered metabolomic studies recently. This systematic review showed that metabolic profiling including different categories of metabolites could improve the diagnosis of low BMD. However, great heterogeneity was observed and it is mandatory to address and minimize the heterogeneity in future studies.

An association between chronic widespread pain and the gut microbiome

OBJECTIVES

Chronic widespread musculoskeletal pain (CWP) is a characteristic symptom of fibromyalgia, which has been shown to be associated with an altered gut microbiome. Microbiome studies to date have not examined the milder CWP phenotype specifically nor have they explored the role of raised BMI. The aim of this study was to investigate whether the microbiome is abnormal in CWP.

METHODS

CWP was assessed using a standardized screening questionnaire in female volunteers from the TwinsUK cohort including 113 CWP cases and 1623 controls. The stool microbiome was characterized using 16S rRNA amplicon sequencing and amplicon sequence variants, and associations with CWP examined using linear mixed-effects models adjusting for BMI, age, diet, family relatedness and technical factors.

RESULTS

Alpha diversity was significantly lower in CWP cases than controls (Mann-Whitney test, P-values 2.3e-04 and 1.2e-02, for Shannon and Simpson indices respectively). The species Coprococcus comes was significantly depleted in CWP cases (Padj = 3.04e-03). A genome-wide association study (GWAS) performed for C. comes in TwinsUK followed by meta-analysis with three Dutch cohorts (total n = 3521) resulted in nine suggestive regions, with the most convincing on chromosome 4 near the TRAM1L1 gene (rs76957229, P = 7.4e-8). A Mendelian randomization study based on the results of the GWAS did not support a causal role for C. comes on the development of CWP.

CONCLUSIONS

We have demonstrated reduced diversity in the microbiome in CWP, indicating an involvement of the gut microbiota in CWP; prospectively the microbiome may offer therapeutic opportunities for this condition.

Sources of Individual Differences in Pain

Pain is an immense clinical and societal challenge, and the key to understanding and treating it is variability. Robust interindividual differences are consistently observed in pain sensitivity, susceptibility to developing painful disorders, and response to analgesic manipulations. This review examines the causes of this variability, including both organismic and environmental sources. Chronic pain development is a textbook example of a gene-environment interaction, requiring both chance initiating events (e.g., trauma, infection) and more immutable risk factors. The focus is on genetic factors, since twin studies have determined that a plurality of the variance likely derives from inherited genetic variants, but sex, age, ethnicity, personality variables, and environmental factors are also considered.

Sphingomyelin is involved in multisite musculoskeletal pain: evidence from metabolomic analysis in 2 independent cohorts

ABSTRACT

Metabolic dysfunction has been suggested to be involved in musculoskeletal pain; however, few studies have identified metabolic markers associated with multisite musculoskeletal pain (MSMP). This study sought to identify metabolic marker(s) for MSMP by metabolomic analysis. The Tasmanian Older Adult Cohort Study (TASOAC) provided the discovery cohort with the Newfoundland Osteoarthritis Study (NFOAS) providing the replication cohort. Multisite musculoskeletal pain was assessed by a self-reported pain questionnaire and defined as painful sites ≥4 in both the TASOAC and the NFOAS. Furthermore, MSMP was also defined as painful sites ≥7, whereas non-MSMP was defined as either painful sites <7 or ≤1 in the NFOAS. Serum samples of the TASOAC received metabolic profiling using The Metabolomics Innovation Centre Prime Metabolomics Profiling Assay. The data on the identified metabolites were retrieved from NFOAS metabolomic database for the purpose of replication. A total of 409 participants were included in the TASOAC, 38% of them had MSMP. Among the 143 metabolites assessed, 129 passed quality control and were included in the analysis. Sphingomyelin (SM) C18:1 was significantly associated with MSMP (odds ratio [OR] per log µM increase = 3.96, 95% confidence interval, 1.95-8.22; P = 0.0002). The significance remained in multivariable analysis (OR per log µM increase = 2.70, 95% confidence interval, 1.25-5.95). A total of 610 participants were included in the NFOAS, and the association with SM C18:1 was successfully replicated with 3 MSMP definitions (OR ranging from 1.89 to 2.82; all P < 0.03). Our findings suggest that sphingomyelin metabolism is involved in the pathogenesis of MSMP, and the circulating level of SM C18:1 could serve as a potential marker in the management of MSMP.

Specialized, pro-resolving mediators as potential therapeutic agents for alleviating fibromyalgia symptomatology

OBJECTIVE

To present a hypothesis on a novel strategy in the treatment of fibromyalgia (FM).

DESIGN

A narrative review.

SETTING

FM as a disease remains a challenging concept for numerous reasons, including undefined etiopathogenesis, unclear triggers and unsuccessful treatment modalities. We hypothesize that the inflammatome, the entire set of molecules involved in inflammation, acting as a common pathophysiological instrument of gut dysbiosis, sarcopenia, and neuroinflammation, is one of the major mechanisms underlying FM pathogenesis. In this setup, dysbiosis is proposed as the primary trigger of the inflammatome, sarcopenia as the peripheral nociceptive source, and neuroinflammation as the central mechanism of pain sensitization, transmission and symptomatology of FM. Whereas neuroinflammation is highly-considered as a critical deleterious element in FM pathogenesis, the presumed pathogenic roles of sarcopenia and systemic inflammation remain controversial. Nevertheless, sarcopenia-associated processes and dysbiosis have been recently detected in FM individuals. The prevalence of pro-inflammatory factors in the cerebrospinal fluid and blood has been repeatedly observed in FM individuals, supporting an idea on the role of inflammatome in FM pathogenesis. As such, failed inflammation resolution might be one of the underlying pathogenic mechanisms. In accordance, the application of specialized, inflammation pro-resolving mediators (SPMs) seems most suitable for this goal.

CONCLUSIONS

The capability of various SPMs to prevent and attenuate pain has been repeatedly demonstrated in laboratory animal experiments. Since SPMs suppress inflammation in a manner that does not compromise host defense, they could be attractive and safe candidates for the alleviation of FM symptomatology, probably in combination with anti-dysbiotic medicine.

Metabolomic analysis coupled with extreme phenotype sampling identified that lysophosphatidylcholines are associated with multisite musculoskeletal pain

ABSTRACT

Musculoskeletal pain often occurs simultaneously at multiple anatomical sites. The aim of the study was to identify metabolic biomarkers for multisite musculoskeletal pain (MSMP) by metabolomics with an extreme phenotype sampling strategy. The study participants (n = 610) were derived from the Newfoundland Osteoarthritis Study. Musculoskeletal pain was assessed using a self-reported pain questionnaire where painful sites were circled on a manikin by participants and the total number of painful sites were calculated. Targeted metabolomic profiling on fasting plasma samples was performed using the Biocrates AbsoluteIDQ p180 kit. Plasma cytokine concentrations including tumor necrosis factor-α, interleukin-6, interleukin-1β, and macrophage migration inhibitory factor were assessed by enzyme-linked immunosorbent assay. Data on blood cholesterol profiles were retrieved from participants' medical records. Demographic, anthropological, and clinical information was self-reported. The number of reported painful sites ranged between 0 and 21. Two hundred and five participants were included in the analysis comprising 83 who had ≥7 painful sites and 122 who had ≤1 painful site. Women and younger people were more likely to have MSMP (P ≤ 0.02). Multisite musculoskeletal pain was associated with a higher risk of having incontinence, worse functional status and longer period of pain, and higher levels of low-density lipoprotein and non-high-density lipoprotein cholesterol (all P ≤ 0.03). Among the 186 metabolites measured, 2 lysophosphatidylcholines, 1 with 26 carbons with no double bond and 1 with 28 carbons with 1 double bond, were significantly and positively associated with MSMP after adjusting for multiple testing with the Bonferroni method (P ≤ 0.0001) and could be considered as novel metabolic markers for MSMP.

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.

Multi-OMICS analyses of frailty and chronic widespread musculoskeletal pain suggest involvement of shared neurological pathways

Chronic widespread musculoskeletal pain (CWP) and frailty are prevalent conditions in older people. We have shown previously that interindividual variation in frailty and CWP is genetically determined. We also reported an association of frailty and CWP caused by shared genetic and common environmental factors. The aim of this study was to use omic approaches to identify molecular genetic factors underlying the heritability of frailty and its genetic correlation with CWP. Frailty was quantified through the Rockwood Frailty Index (FI) as a proportion of deficits from 33 binary health deficit questions in 3626 female twins. Common widespread pain was assessed using a screening questionnaire. OMICS analysis included 305 metabolites and whole-genome (>2.5 × 10 SNPs) and epigenome (∼1 × 10 MeDIP-seq regions) assessments performed on fasting blood samples. Using family-based statistical analyses, including path analysis, we examined how FI scores were related to molecular genetic factors and to CWP, taking into account known risk factors such as fat mass and smoking. Frailty Index was significantly correlated with 51 metabolites after correction for multiple testing, with 20 metabolites having P-values between 2.1 × 10 and 4.0 × 10. Three metabolites (uridine, C-glycosyl tryptophan, and N-acetyl glycine) were statistically independent and thought to exert a direct effect on FI. Epiandrosterone sulphate, previously shown to be highly inversely associated with CWP, was found to exert an indirect influence on FI. Bioinformatics analysis of genome-wide association study and EWAS showed that FI and its covariation with CWP was through genomic regions involved in neurological pathways. Neurological pathway involvement accounts for the associated conditions of aging CWP and FI.

Metabolomic Pathways to Osteoporosis in Middle-Aged Women: A Genome-Metabolome-Wide Mendelian Randomization Study

The metabolic state of the body can be a major determinant of bone health. We used a Mendelian randomization approach to identify metabolites causally associated with bone mass to better understand the biological mechanisms of osteoporosis. We tested bone phenotypes (femoral neck, total hip, and lumbar spine bone mineral density [BMD]) for association with 280 fasting blood metabolites in 6055 women from TwinsUK cohort with genomewide genotyping scans. Causal associations between metabolites and bone phenotypes were further assessed in a bidirectional Mendelian randomization study using genetic markers/scores as instrumental variables. Significant associations were replicated in 624 participants from the Hong Kong Osteoporosis Study (HKOS). Fifteen metabolites showed direct associations with bone phenotypes after adjusting for covariates and multiple testing. Using genetic instruments, four of these metabolites were found to be causally associated with hip or spine BMD. These included androsterone sulfate, epiandrosterone sulfate, 5alpha-androstan-3beta17beta-diol disulfate (encoded by CYP3A5), and 4-androsten-3beta17beta-diol disulfate (encoded by SULT2A1). In the HKOS population, all four metabolites showed significant associations with hip and spine BMD in the expected directions. No causal reverse association between BMD and any of the metabolites were found. In the first metabolome-genomewide Mendelian randomization study of human bone mineral density, we identified four novel biomarkers causally associated with BMD. Our findings reveal novel biological pathways involved in the pathogenesis of osteoporosis. © 2017 American Society for Bone and Mineral Research.

Genome-wide methylation analysis of a large population sample shows neurological pathways involvement in chronic widespread musculoskeletal pain

Chronic widespread musculoskeletal pain (CWP), has a considerable heritable component, which remains to be explained. Epigenetic factors may contribute to and account for some of the heritability estimate. We analysed epigenome-wide methylation using MeDIPseq in whole blood DNA from 1708 monozygotic and dizygotic Caucasian twins having CWP prevalence of 19.9%. Longitudinally stable methylation bins (lsBINs), were established by testing repeated measurements conducted ≥3 years apart, n = 292. DNA methylation variation at lsBINs was tested for association with CWP in a discovery set of 50 monozygotic twin pairs discordant for CWP, and in an independent dataset (n = 1608 twins), and the results from the 2 samples were combined using Fisher method. Functional interpretation of the most associated signals was based on functional genomic annotations, gene ontology, and pathway analyses. Of 723,029 signals identified as lsBINs, 26,399 lsBINs demonstrated the same direction of association in both discovery and replication datasets at nominal significance (P ≤ 0.05). In the combined analysis across 1708 individuals, whereas no lsBINs showed genome-wide significance (P < 10-8), 24 signals reached p≤9E-5, and these included association signals mapping in or near to IL17A, ADIPOR2, and TNFRSF13B. Bioinformatics analyses of the associated methylation bins showed enrichment for neurological pathways in CWP. We estimate that the variance explained by epigenetic factors in CWP is 6%. This, the largest study to date of DNA methylation in CWP, points towards epigenetic modification of neurological pathways in CWP and provides proof of principle of this method in teasing apart the complex risk factors for CWP.

Musculoskeletal pain and menopause

Musculoskeletal pain, arthralgia and arthritis are all more common in women, and their frequency increases with age and in some appears to be associated with the onset of menopause. The clinical assessment, investigation and management of women presenting with musculoskeletal pain, arthralgia or arthritis at the time of menopause are reviewed. Common causes of arthralgia and arthritis in this population are discussed. The epidemiological and trials evidence for the effects of hormone replacement therapy on musculoskeletal pain and arthritis (primarily from RCTs of HRT for other menopausal symptoms) are discussed. Lastly, the possible underlying aetiological roles of sex hormones including estrogen, and their deficiency, in predisposing to musculoskeletal pain and arthritis are overviewed. Although the association appears strong, a causal link between estrogen deficiency and musculoskeletal pain or different types of arthritis is lacking; there have been few studies specifically within this group of symptomatic patients, and there is much still to understand about musculoskeletal pain and arthritis at the time of the menopause, and about how we might prevent or treat this.

Genome-Wide Association Study on Immunoglobulin G Glycosylation Patterns

Immunoglobulin G (IgG), a glycoprotein secreted by plasma B-cells, plays a major role in the human adaptive immune response and are associated with a wide range of diseases. Glycosylation of the Fc binding region of IgGs, responsible for the antibody’s effector function, is essential for prompting a proper immune response. This study focuses on the general genetic impact on IgG glycosylation as well as corresponding subclass specificities. To identify genetic loci involved in IgG glycosylation, we performed a genome-wide association study (GWAS) on liquid chromatography electrospray mass spectrometry (LC–ESI-MS)—measured IgG glycopeptides of 1,823 individuals in the Cooperative Health Research in the Augsburg Region (KORA F4) study cohort. In addition, we performed GWAS on subclass-specific ratios of IgG glycans to gain power in identifying genetic factors underlying single enzymatic steps in the glycosylation pathways. We replicated our findings in 1,836 individuals from the Leiden Longevity Study (LLS). We were able to show subclass-specific genetic influences on single IgG glycan structures. The replicated results indicate that, in addition to genes encoding for glycosyltransferases (i.e., ST6GAL1, B4GALT1, FUT8, and MGAT3), other genetic loci have strong influences on the IgG glycosylation patterns. A novel locus on chromosome 1, harboring RUNX3, which encodes for a transcription factor of the runt domain-containing family, is associated with decreased galactosylation. Interestingly, members of the RUNX family are cross-regulated, and RUNX3 is involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis. Besides the involvement of glycosyltransferases in IgG glycosylation, we suggest that, due to the impact of variants within RUNX3, potentially mechanisms involved in B-cell activation and T-cell differentiation during the immune response as well as cell migration and invasion involve IgG glycosylation.

Metabolomic markers of fatigue: Association between circulating metabolome and fatigue in women with chronic widespread pain

BACKGROUND

Fatigue is a sensation of unbearable tiredness that frequently accompanies chronic widespread musculoskeletal pain (CWP) and inflammatory joint disease. Its mechanisms are poorly understood and there is a lack of effective biomarkers for diagnosis and onset prediction. We studied the circulating metabolome in a population sample characterised for CWP to identify biomarkers showing specificity for fatigue.

MATERIAL AND METHODS

Untargeted metabolomic profiling was conducted on fasting plasma and serum samples of 1106 females with and without CWP from the TwinsUK cohort. Linear mixed-effects models accounting for covariates were used to determine relationships between fatigue and metabolites. Receiver operating curve (ROC)-analysis was used to determine predictive value of metabolites for fatigue.

RESULTS

While no association between fatigue and metabolites was identified in twins without CWP (n=711), in participants with CWP (n=395), levels of eicosapentaenoate (EPA) ω-3 fatty acid were significantly reduced in those with fatigue (β=-0.452±0.116; p=1.2×10-4). A significant association between fatigue and two other metabolites also emerged when BMI was excluded from the model: 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF), and C-glycosyltryptophan (p=1.5×10-4 and p=3.1×10-4, respectively). ROC analysis has identified a combination of 15 circulating metabolites with good predictive potential for fatigue in CWP (AUC=75%; 95% CI 69-80%).

CONCLUSION

The results of this agnostic metabolomics screening show that fatigue is metabolically distinct from CWP, and is associated with a decrease in circulating levels of EPA. Our panel of circulating metabolites provides the starting point for a diagnostic test for fatigue in CWP.

Shared genetic influence on frailty and chronic widespread pain: a study from TwinsUK

Introduction

frailty is an increased vulnerability to adverse health outcomes, across multiple physiological systems, with both environmental and genetic drivers. The two most commonly used measures are Rockwood’s frailty index (FI) and Fried’s frailty phenotype (FP).

Material and methods

the present study included 3626 individuals from the TwinsUK Adult Twin Registry. We used the classical twin model to determine whether FI and FP share the same latent aetiological factors. We also investigated the relationship between frailty and chronic widespread musculoskeletal pain (CWP), another holistic age-related condition with significant clinical impact.

Results

FP and FI shared underlying genetic and environmental aetiology. CWP was associated with both frailty measures, and health deficits appeared to mediate the relationship between phenotypic frailty and pain. Latent genetic factors underpinning CWP were shared with frailty. While frailty was increased in the twins reporting pain, co-twin regression analysis indicated that the relationship between CWP and frailty is reduced after accounting for shared genetic and environmental factors.

Conclusions

both measures of frailty tap the same root causes, thus this work helps unify frailty research. We confirmed a strong association between CWP and frailty, and showed a large and significant shared genetic aetiology of both phenomena. Our findings argue against pain being a significant causative factor in the development of frailty, favouring common causation. This study highlights the need to manage CWP in frail individuals and undertake a Comprehensive Geriatric Assessment in individuals presenting with CWP. Finally, the search for genetic factors underpinning CWP and frailty could be aided by integrating measures of pain and frailty.

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms

Background

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different “omics” layers. Existing tools only consider single-nucleotide polymorphism (SNP)–SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables.

Results

We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different “omics” layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels.

Conclusions

The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1838-y) contains supplementary material, which is available to authorized users.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Chronic pain, depression and cardiovascular disease linked through a shared genetic predisposition: Analysis of a family-based cohort and twin study

BACKGROUND

Depression and chronic pain are the two most important causes of disability (Global Burden of Disease Study 2013). They occur together more frequently than expected and both conditions have been shown to be co-morbid with cardiovascular disease. Although shared socio-demographic risk factors (e.g. gender, deprivation) might explain the co-morbidity of these three conditions, we hypothesised that these three long-term, highly prevalent conditions co-occur and may be due to shared familial risk, and/or genetic factors.

METHODS AND FINDINGS

We employed three different study designs in two independent cohorts, namely Generation Scotland and TwinsUK, having standardised, validated questionnaire data on the three traits of interest. First, we estimated the prevalence and co-occurrence of chronic pain, depression and angina among 24,024 participants of a population-based cohort of extended families (Generation Scotland: Scottish Family Health Study), adjusting for age, gender, education, smoking status, and deprivation. Secondly, we compared the odds of co-morbidity in sibling-pairs with the odds in unrelated individuals for the three conditions in the same cohort. Lastly, examination of similar traits in a sample of female twins (TwinsUK, n = 2,902), adjusting for age and BMI, allowed independent replication of the findings and exploration of the influence of additive genetic (A) factors and shared (C) and non-shared (E) environmental factors predisposing to co-occurring chronic widespread pain (CWP) and cardiovascular disease (hypertension, angina, stroke, heart attack, elevated cholesterol, angioplasty or bypass surgery). In the Generation Scotland cohort, individuals with depression were more than twice as likely to have chronic pain as those without depression (adjusted OR 2·64 [95% CI 2·34-2·97]); those with angina were four times more likely to have chronic pain (OR 4·19 [3·64-4·82]); those with depression were twice as likely to have angina (OR 2·20 [1·90-2·54]). Similar odds were obtained when the outcomes and predictors were reversed and similar effects seen among sibling pairs; depression in one sibling predicted chronic pain in the other (OR 1·34 [1·05-1·71]), angina predicted chronic pain in the other (OR 2·19 [1·63-2·95]), and depression, angina (OR 1·98 [1·49-2·65]). Individuals with chronic pain and angina showed almost four-fold greater odds of depression compared with those manifesting neither trait (OR 3·78 [2·99-4·78]); angina showed seven-fold increased odds in the presence of chronic pain and depression (OR 7·76 [6·05-9·95]) and chronic pain nine-fold in the presence of depression and angina (OR 9·43 [6·85-12·98]). In TwinsUK, the relationship between CWP and depression has been published (R = 0.34, p<0.01). Considering the CWP-cardiovascular relationship, the most suitable model to describe the observed data was a combination of A, C and E, with a small but significant genetic predisposition, shared between the two traits (2·2% [95% CI 0·06-0·23]).

CONCLUSION

We found an increased co-occurrence of chronic pain, depression and cardiovascular disease in two independent cohorts (general population-based cohort, twins cohort) suggesting a shared genetic contribution. Adjustment for known environmental influences, particularly those relating to socio-economic status (Generation Scotland: age, gender, deprivation, smoking, education; Twins UK: age,BMI) did not explain the relationship observed between chronic pain, depression and cardiovascular disease. Our findings from two independent cohorts challenge the concept of traditional disease boundaries and warrant further investigation of shared biological mechanisms.

A Metabolome-Wide Study of Dry Eye Disease Reveals Serum Androgens as Biomarkers

PURPOSE

To test the association between serum metabolites and dry eye disease (DED) using a hypothesis-free metabolomics approach.

DESIGN

Cross-sectional association study.

PARTICIPANTS

A total of 2819 subjects from the population-representative TwinsUK cohort in the United Kingdom, with a mean age of 57 years (range, 17-82 years).

METHODS

We tested associations between 222 known serum metabolites and DED. All subjects underwent nontargeted metabolomic analysis of plasma samples using gas and liquid chromatography in combination with mass spectrometry (Metabolon Inc., Durham, NC). Dry eye disease was defined from the validated Short Questionnaire for Dry Eye Syndrome (SQDES) as a previous diagnosis of DED by a clinician or "often" or "constant" symptoms of dryness and irritation. Analyses were performed with linear mixed effect models that included age, BMI, and sex as covariates, corrected for multiple testing.

MAIN OUTCOME MEASURES

Primary outcome was DED as defined by the SQDES, and secondary outcomes were symptom score of DED and a clinical diagnosis of DED.

RESULTS

Prevalence of DED as defined by the SQDES was 15.5% (n = 436). A strong and metabolome-wide significant association with DED was found with decreased levels of the metabolites androsterone sulfate (P = 0.00030) and epiandrosterone sulfate (P = 0.00036). Three other metabolites involved in androgen metabolism, 4-androsten-3beta,17beta-diol disulfate 1 and 2, and dehydroepiandrosterone sulfate, were the next most strongly associated of the 222 metabolites, but did not reach metabolome-wide significance. Dryness and irritation symptoms, as opposed to a clinical diagnosis, were particularly strongly associated with decreased androgen steroid metabolites, with all reaching metabolome-wide significance (androsterone sulfate, P = 0.000000029; epiandrosterone sulfate, P = 0.0000040; 4-androsten-3beta,17beta-diol disulfate 1, P = 0.000016; 4-androsten-3beta,17beta-diol disulfate 2, P = 0.000064; and dehydroepiandrosterone sulfate, P = 0.00011). Of these 5 androgens, epiandrosterone sulfate (P = 0.0076) was most associated with 2-year incidence of clinician-diagnosed DED. In addition, we found decreased glycerophosphocholines to be associated with DED, although not at metabolome-wide significance.

CONCLUSIONS

This hypothesis-free metabolomic approach found decreased serum androgens to be highly associated with DED and adds important evidence to the growing body of research that links androgens to ocular surface disease and DED.

Urinary metabolomics of complete Freund's adjuvant-induced hyperalgesia in rats

The aim of this study was to demonstrate the differences of metabolomics changes in a hyperalgesia model and find potent biomarkers of hyperalgesia. Seven rats were placed in metabolic cages. An emulsion containing 500 μg of Complete Freund's adjuvant (CFA) was used to induce hyperalgesia. Urine samples were collected prior to the injection of CFA and on post-injection days 1, 3 and 7. Ultraperformance liquid chromatography, coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS), was used for a quantitative analysis of urinary metabolic changes in the CFA-induced hyperalgesia model. Differences between the metabolic profiles of the rats in the four groups were analyzed using partial least squares discriminant analysis. Thirty-four potential urine metabolite biomarkers were identified, which changed in a trend similar to the pain threshold. These potential biomarkers were involved in 11 metabolic pathways, as follows: alanine, aspartate, and glutamate metabolism; ascorbate and aldarate metabolism; glycerolipid metabolism; glycerophospholipid metabolism; histidine metabolism; phenylalanine metabolism; sphingolipid metabolism; tryptophan metabolism; tyrosine metabolism; valine, leucine and isoleucine biosynthesis; and vitamin B6 metabolism. These results may improve our understanding of hyperalgesia and provide a basis for the clinical diagnosis of hyperalgesia.

Systemic inflammation and painful joint burden in osteoarthritis: a matter of sex?

OBJECTIVE

We investigated the association between serum levels of C-reactive protein (CRP) and the extent of multijoint pain among individuals with hip/knee osteoarthritis (OA) and determined whether the association differs by sex.

DESIGN

Serum CRP and cartilage oligomeric matrix protein (COMP) were determined by enzyme-linked immunosorbent assay (ELISA) in 189 individuals (101 female, 88 male) scheduled for total hip/knee arthroplasty for OA. Patients indicated on a homunculus all painful joints; a summed count was derived. A series of negative binomial regression models was used to investigate the cross-sectional association between painful joint count (outcome) and serum CRP concentrations, adjusting for age, sex, body mass index (BMI), comorbidity count and COMP. An interaction between sex and these biomarkers was tested.

RESULTS

Mean age: 66 among women, 65 among men. Women had higher mean joint count (3.7 vs 2.5, P < 0.01; 4+ joint count reported by 37% women, 25% men). Median CRP concentration was higher in women (15.4 mg/l vs 9.3, P = 0.07). From adjusted analyses, the effects of both ln(CRP) and ln(COMP) were modified by sex (P < 0.05). Increasing ln(CRP) was associated with greater painful joint count among women, but not men.

CONCLUSIONS

There may be a dose-response association between painful joint burden in OA and systemic inflammation, and it appears the association is sex-specific, which may in part explain inconsistent findings in the literature. Our results underline the importance of showing sex-specific associations in OA, especially when studying the influence of inflammation.

Are Epigenetic Factors Implicated in Chronic Widespread Pain?

BACKGROUND

Chronic widespread musculoskeletal pain (CWP) is the cardinal symptom of fibromyalgia and affects about 12% of the general population. Familial aggregation of CWP has been repeatedly demonstrated with estimated heritabilities of around 50%, indicating a genetic susceptibility. The objective of the study was to explore genome-wide disease-differentially methylated positions (DMPs) for chronic widespread pain (CWP) in a sample of unrelated individuals and a subsample of discordant monozygotic (MZ) twins.

METHODOLOGY/PRINCIPLE FINDINGS

A total of N = 281 twin individuals from the TwinsUK registry, including N = 33 MZ twins discordant for self-reported CWP, were part of the discovery sample. The replication sample included 729 men and 756 women from a subsample of the KORA S4 survey-an independent population-based cohort from Southern Germany. Epigenome-wide analysis of DNA methylation was conducted using the Illumina Infinium HumanMethylation 450 DNA BeadChip in both the discovery and replication sample. Of our 40 main loci that were carried forward for replication, three CPGs reached significant p-values in the replication sample, including malate dehydrogenase 2 (MDH2; p-value 0.017), tetranectin (CLEC3B; p-value 0.039), and heat shock protein beta-6 (HSPB6; p-value 0.016). The associations between the collagen type I, alpha 2 chain (COL1A2) and monoamine oxidase B (MAOB) observed in the discovery sample-both of which have been previously reported to be biological candidates for pain-could not be replicated.

CONCLUSION/SIGNIFICANCE

Our results may serve as a starting point to encourage further investigation in large and independent population-based cohorts of DNA methylation and other epigenetic changes as possible disease mechanisms in CWP. Ultimately, understanding the key mechanisms underlying CWP may lead to new treatments and inform clinical practice.

‘Omics’: an emerging field in pain research and management

Precision medicine is an emerging approach for prevention and treatment of diseases considering individuals’ uniqueness. Omics provide one step forward toward advanced precision medicine and include technologies such as genomics, proteomics and metabolomics generating valuable data through characterization of entire biological systems. With the aid of omics, a major shift has been started to occur in understanding of diseases followed by potential fundamental changes in medical care strategies. This short review aims at providing some examples of current omics that are applied in the field of pain in terms of new biomarkers for diagnosis of different pain types, stratification of patients and new therapeutic targets. Implementation of omics would most likely offer breakthrough in the future of pain management.

The Association Between Low Back Pain and Composition of IgG Glycome

Low back pain (LBP) is a common debilitating condition which aetiology and pathogenesis are poorly understood. We carried out a first so far analysis of associations between LBP and plasma IgG N-glycome in a sample of 4511 twins from TwinsUK database assessed for LBP, lumbar disc degeneration (LDD) as its possible cause, and IgG-glycan levels. Using weighted correlation network analysis, we established a correlation between LBP and glycan modules featured by glycans that either promote or block antibody-dependent cell-mediated cytotoxicity (ADCC). The levels of four glycan traits representing two of those modules were statistically significantly different in monozygotic twins discordant for LBP. Also, the trend to higher prevalence of systemic inflammatory disorders was shown for twins with low level of fucosylated glycans and high level of non-fucosylated glycans. Core fucosylation of IgG is a “safety switch” reducing ADCC, thus our results suggest the involvement of ADCC and associated inflammation in pathogenesis of LBP. No correlation between LDD scores and glycans was found assuming that the inflammation may not be a part of LDD. These data provide a new insight into understanding the complex pathophysiology of LBP and suggest glycan levels as a possible biomarker for inflammation-related subtypes of LBP.

Diagnostic and Prognostic Metabolites Identified for Joint Symptoms in the KORA Population

This study aims at identifying metabolites that significantly associate with self-reported joint symptoms (diagnostic) and metabolites that can predict the change from a symptom-free status to the development of self-reported joint symptoms after a 7 years period (prognostic). More than 300 metabolites were analyzed for 2246 subjects from the longitudinal study of the KORA (Cooperative Health Research in the Region of Augsburg, Germany), specifically the fourth survey S4 and its 7-year follow-up study F4. Two types of self-reported symptoms, chronic joint inflammation and worn out joints, were used for the analyses. Diagnostic analysis identified dysregulated metabolites in cases with symptoms compared with controls. Prognostic analysis identified metabolites that differentiate subjects in S4 who remained symptom-free after 7 years (F4) from those who developed any combination of symptoms. 48 metabolites were identified as nominally significantly (p < 0.05) associated with the self-reported symptoms in the diagnostic analysis, among which steroids show Bonferroni significance. 45 metabolites were identified as nominally significantly associated with developing symptoms after 7 years, among which hippurate showed Bonferroni significance. We show that metabolic profiles of self-reported joint symptoms are in line with metabolites known to associate with various forms of arthritis and suggest that future studies may benefit from that by investigating the possible use of self-reporting/questionnaire along with metabolic markers for the early referral of patients for further diagnostic workup and treatment of arthritis.

The Association between Chronic Widespread Musculoskeletal Pain, Depression and Fatigue Is Genetically Mediated

BACKGROUND

Chronic widespread muscoloskeletal pain (CWP) is prevalent in the general population and associated with high health care costs, so understanding the risk factors for chronic pain is important for both those affected and for society. In the present study we investigated the underlying etiological structure of CWP to understand better the association between the major clinical features of fatigue, depression and dihydroepiandrosterone sulphate (DHEAS) using a multivariate twin design.

METHODOLOGY/PRINCIPLE FINDINGS

Data were available in 463 UK female twin pairs including CWP status and information on depression, chronic fatigue and serum DHEAS levels. High to moderate heritabilities for all phenotypes were obtained (42.58% to 74.24%). The highest phenotypic correlation was observed between fatigue and CWP (r = 0.45), and the highest genetic correlation between CWP and fatigue (rg = 0.78). Structural equation modeling revealed the AE Cholesky model to provide the best model of the observed data. In this model, two additive genetic factors could be detected loading heavily on CWP-A2 explaining 40% of the variance and A3 20%. The factor loading heaviest on DHEAS showed only a small loading on the other phenotypes and none on fatigue at all. Furthermore, one distinct non-shared environmental factor loading specifically on CWP-but not on any of the other phenotypes-could be detected suggesting that the association between CWP and the other phenotypes is due only to genetic factors.

CONCLUSIONS/SIGNIFICANCE

Our results suggest that CWP and its associated features share a genetic predisposition but that they are relatively distinct in their environmental determinants.