Metabolomics Signature for Non-Responders to Total Joint Replacement Surgery in Primary Osteoarthritis Patients: The Newfoundland Osteoarthritis Study

Unraveling the molecular landscape of osteoarthritis: A comprehensive review focused on the role of non-coding RNAs

Osteoarthritis (OA) poses a significant global health challenge, with its prevalence anticipated to increase in the coming years. This review delves into the emerging molecular biomarkers in OA pathology, focusing on the roles of various molecules such as metabolites, noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Advances in omics technologies have transformed biomarker identification, enabling comprehensive analyses of the complex pathways involved in OA pathogenesis. Notably, ncRNAs, especially miRNAs and lncRNAs, exhibit dysregulated expression patterns in OA, presenting promising opportunities for diagnosis and therapy. Additionally, the intricate interplay between epigenetic modifications and OA progression highlights the regulatory role of epigenetics in gene expression dynamics. Genome-wide association studies have pinpointed key genes undergoing epigenetic changes, providing insights into the inflammatory processes and chondrocyte hypertrophy typical of OA. Understanding the molecular landscape of OA, including biomarkers and epigenetic mechanisms, holds significant potential for developing innovative diagnostic tools and therapeutic strategies for OA management.

Causal association of genetically determined plasma metabolites with osteoarthritis: a two-sample Mendelian randomization study

Background

We aimed to elucidate the causal relationship between plasma metabolites and the vulnerability to Osteoarthritis (OA), encompassing both hip OA and knee OA.

Methods

We conducted a two-way two-sample Mendelian randomization (MR) analysis to investigate the association of 1,400 plasma metabolites with OA. The Inverse Variance Weighted (IVW) model served as the primary two-sample MR Analysis method, with supplementary analysis using the Weighted Median (WM) and MR Egger methods. To ensure the robustness of our findings, sensitivity analyses were performed, incorporating Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and Leave-One-Out analyses. To validate the identified metabolites, we utilized the Steiger test and linkage disequilibrium score regression.

Results

A total of 94 plasma metabolites were associated with osteoarthritis, with 60 associated with hip OA and 106 associated with knee OA. IVW analysis revealed that tryptophan levels showed the strongest positive association with hip OA (OR [95% CI]: 1.119 [1.024, 1.223]), while X-24757 levels exhibited the highest positive association with knee osteoarthritis (OR [95% CI]: 1.095 [1.032, 1.162]). Ethylparaben sulfate levels were found to have the greatest positive association with hip OA (OR [95% CI]: 1.118 [1.015, 1.231]). Notably, the plasma metabolite X-2475 showed a strong robust random effect across all three types of osteoarthritis. Metabolic pathway analysis revealed that the pathogenesis of osteoarthritis in the hip was mediated by acetylarginine, specifically in four important metabolic pathways: ethanol degradation (p = 0.044), amino sugar metabolism (p = 0.090), fatty acid biosynthesis (p = 0.095), and aspartate metabolism (p = 0.097816).

Conclusion

There is a significant association between tryptophan levels and the risk of hip OA, as well as X-24757 levels and the risk of knee osteoarthritis. Additionally, X-24757 levels are also linked to the risk of hip OA. Moreover, this study has identified four crucial metabolic pathways in hip osteoarthritis, which are all regulated by acetylarginine. These findings provide valuable insights into potential biomarkers for OA and highlight potential pathways for its prevention and clinical intervention.

Mechanisms and Preventative Strategies for Persistent Pain following Knee and Hip Joint Replacement Surgery: A Narrative Review

Chronic postsurgical pain (CPSP) following total knee arthroplasty (TKA) and total hip arthroplasty (THA) is a prevalent complication of joint replacement surgery which has the potential to decrease patient satisfaction, increase financial burden, and lead to long-term disability. The identification of risk factors for CPSP following TKA and THA is challenging but essential for targeted preventative therapy. Recent meta-analyses and individual studies highlight associations between elevated state anxiety, depression scores, preoperative pain, diabetes, sleep disturbances, and various other factors with an increased risk of CPSP, with differences observed in prevalence between TKA and THA. While the etiology of CPSP is not fully understood, several factors such as chronic inflammation and preoperative central sensitization have been identified. Other potential mechanisms include genetic factors (e.g., catechol-O-methyltransferase (COMT) and potassium inwardly rectifying channel subfamily J member 6 (KCNJ6) genes), lipid markers, and psychological risk factors (anxiety and depression). With regards to therapeutics and prevention, multimodal pharmacological analgesia, emphasizing nonopioid analgesics like acetaminophen and non-steroidal anti-inflammatory drugs (NSAIDs), has gained prominence over epidural analgesia. Nerve blocks and local infiltrative anesthesia have shown mixed results in preventing CPSP. Ketamine, an N-methyl-D-aspartate (NMDA)-receptor antagonist, exhibits antihyperalgesic properties, but its efficacy in reducing CPSP is inconclusive. Lidocaine, an amide-type local anesthetic, shows tentative positive effects on CPSP. Selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs) have mixed results, while gabapentinoids, like gabapentin and pregabalin, present hopeful data but require further research, especially in the context of TKA and THA, to justify their use for CPSP prevention.

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies

OBJECTIVE

Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades.

DESIGN

We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease.

RESULTS

Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues.

CONCLUSIONS

Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

ABSTRACT

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS's initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

Emerging molecular biomarkers in osteoarthritis pathology

Osteoarthritis (OA) is the most common form of arthritis resulting in joint discomfort and disability, culminating in decline in life quality. Attention has been drawn in recent years to disease-associated molecular biomarkers found in readily accessible biofluids due to low invasiveness of acquisition and their potential to detect early pathological molecular changes not observed with traditional imaging methodology. These biochemical markers of OA have been found in synovial fluid, blood, and urine. They include emerging molecular classes, such as metabolites and noncoding RNAs, as well as classical biomarkers, like inflammatory mediators and by-products of degradative processes involving articular cartilage. Although blood-based biomarkers tend to be most studied, the use of synovial fluid, a more isolated biofluid in the synovial joint, and urine as an excreted fluid containing OA biomarkers can offer valuable information on local and overall disease activity, respectively. Furthermore, larger clinical studies are required to determine relationships between biomarkers in different biofluids, and their impacts on patient measures of OA. This narrative review provides a concise overview of recent studies of OA using these four classes of biomarkers as potential biomarker for measuring disease incidence, staging, prognosis, and therapeutic intervention efficacy.

Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study

BACKGROUND

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and is the leading cause of pain and disability in the aged population. However, the underlying biological mechanism has not been fully understood. This study aims to reveal the causal effect of circulation metabolites on OA susceptibility.

METHODS

A two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of GDMs on OA. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas 8 different OA phenotypes, including any-site OA (All OA), knee and/or hip OA (knee/hip OA), knee OA, hip OA, spine OA, finger and/or thumb OA (hand OA), finger OA, thumb OA, were set the outcomes. Inverse-variance weighted (IVW) was used for calculating causal estimates. Methods including weight mode, weight median, MR-egger, and MR-PRESSO were used for the sensitive analysis. Furthermore, metabolic pathway analysis was performed via the web-based Metaconflict 4.0. All statistical analyses were performed in R software.

RESULTS

In this MR analysis, a total of 235 causative associations between metabolites and different OA phenotypes were observed. After false discovery rate (FDR) correction and sensitive analysis, 9 robust causative associations between 7 metabolites (e.g., arginine, kynurenine, and isovalerylcarnitine) and 5 OA phenotypes were finally identified. Additionally, eleven significant metabolic pathways in 4 OA phenotypes were identified by metabolic pathway analysis.

CONCLUSION

The finding of our study suggested that identified metabolites and metabolic pathways can be considered useful circulating metabolic biomarkers for OA screening and prevention in clinical practice, and can also serve as candidate molecules for future mechanism exploration and drug target selection.

Causal associations between serum amino acid levels and osteoarthritis: A Mendelian randomization study

OBJECTIVE

The association between serum amino acid (AA) levels and osteoarthritis (OA) risk remains unclear.

METHOD

We performed a two-sample Mendelian randomization (MR) analysis to analyze the causal effects of. serum AA levels on the OA risk by using summary-level genome-wide association study (GWAS) data. Inverse variance weighted (IVW) and Wald ratio were used as the main analysis.We also applied MR-Egger, Weighted median and Robust Adjusted Profile Score (MR.RAPS) methods. Heterogeneity and horizontally pleiotropic outliers were checked. The causal effects of AAs on early-onset all OA were explored. We also performed multivariable MR (MVMR) and conducted the bidirectional MR.

RESULTS

The results suggested that genetically predicted alanine (Ala), tyrosine (Tyr) and isoleucine (Ile) levels were significantly associated with OA risk (e.g., association between Ala and hip/knee OA risk: OR = 0.82, 95% CI = 0.75 ∼ 0.90, p = 1.54E-05). The study yielded little evidence of associations between genetically predicted AA levels with early-onset all OA risk. When adjusting the BMI in the MVMR model, suggestive causal effects of Ala and Tyr were also identified, while the effects of Ile substantially attenuated with OA risk. No significant associations between OA and AA levels were observed after testing for bidirectionality.

CONCLUSIONS

Some AAs, such as Ala, Tyr and Ile likely affects the OA risk especially at hip or knee joints. The findings highlight the important role that serum AAs might play in the development of OA and provided new treatment approaches to OA.

Mechanical Loading Promotes the Migration of Endogenous Stem Cells and Chondrogenic Differentiation in a Mouse Model of Osteoarthritis

Osteoarthritis (OA) is a major health problem, characterized by progressive cartilage degeneration. Previous works have shown that mechanical loading can alleviate OA symptoms by suppressing catabolic activities. This study evaluated whether mechanical loading can enhance anabolic activities by facilitating the recruitment of stem cells for chondrogenesis. We evaluated cartilage degradation in a mouse model of OA through histology with H&E and safranin O staining. We also evaluated the migration and chondrogenic ability of stem cells using in vitro assays, including immunohistochemistry, immunofluorescence, and Western blot analysis. The result showed that the OA mice that received mechanical loading exhibited resilience to cartilage damage. Compared to the OA group, mechanical loading promoted the expression of Piezo1 and the migration of stem cells was promoted via the SDF-1/CXCR4 axis. Also, the chondrogenic differentiation was enhanced by the upregulation of SOX9, a transcription factor important for chondrogenesis. Collectively, the results revealed that mechanical loading facilitated cartilage repair by promoting the migration and chondrogenic differentiation of endogenous stem cells. This study provided new insights into the loading-driven engagement of endogenous stem cells and the enhancement of anabolic responses for the treatment of OA.

Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.

Glutathione, polyamine, and lysophosphatidylcholine synthesis pathways are associated with circulating pro-inflammatory cytokines

INTRODUCTION

Pro-inflammatory cytokines are responsible for initiating an effective defense against exogenous pathogens, and their regulation has a vital role in maintaining physiological homeostasis. The involvement of pro-inflammatory cytokines in pathological conditions have been explored in great detail, however, studies investigating metabolic pathways associated with these cytokines under normal homeostatic conditions are scarce.

OBJECTIVES

The aim of the current study was to identify metabolites and metabolic pathways associated with circulating pro-inflammatory cytokines under homeostatic conditions using a metabolomics approach.

METHODS

The study participants (n = 133) were derived from the Newfoundland Osteoarthritis Study (NFOAS) and the Complex Diseases in the Newfoundland population: Environment and Genetics (CODING) study. Plasma concentrations of cytokines including tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and macrophage migration inhibitory factor (MIF) were assessed by enzyme-linked immunosorbent assay. Targeted metabolomic profiling on fasting plasma samples was performed using Biocrates MxP® Quant 500 kit which measures a total of 630 metabolites. Associations between natural log-transformed metabolite concentrations and metabolite sums/ratios and cytokine levels were assessed using linear regression with adjustment for age, sex, body mass index (BMI), and osteoarthritis status.

RESULTS

Seven metabolites and 11 metabolite sums/ratios were found to be significantly associated with TNF-α, IL-1β, and MIF (all p ≤ 5.13 × 10^- 5) after controlling multiple testing with Bonferroni method, indicating the association between glutathione (GSH), polyamine, and lysophosphatidylcholine (lysoPC) synthesis pathways and these pro-inflammatory cytokines.

CONCLUSION

GSH, polyamine, and lysoPC synthesis pathways were positively associated with circulating TNF-α, IL-1β, and MIF levels under homeostatic conditions.

Restricting Branched-Chain Amino Acids within a High-Fat Diet Prevents Obesity

Obesity is a global pandemic, but there is yet no effective measure to control it. Recent metabolomics studies have identified a signature of altered amino acid profiles to be associated with obesity, but it is unclear whether these findings have actionable clinical potential. The aims of this study were to reveal the metabolic alterations of obesity and to explore potential strategies to mitigate obesity. We performed targeted metabolomic profiling of the plasma/serum samples collected from six independent cohorts and conducted an individual data meta-analysis of metabolomics for body mass index (BMI) and obesity. Based on the findings, we hypothesized that restriction of branched-chain amino acids (BCAAs), phenylalanine, or tryptophan may prevent obesity and tested our hypothesis in a dietary restriction trial with eight groups of 4-week-old male C57BL/6J mice (n = 5/group) on eight different types of diets, respectively, for 16 weeks. A total of 3397 individuals were included in the meta-analysis. The mean BMI was 30.7 ± 6.1 kg/m2, and 49% of participants were obese. Fifty-eight metabolites were associated with BMI and obesity (all p ≤ 2.58 × 10-4), linked to alterations of the BCAA, phenylalanine, tryptophan, and phospholipid metabolic pathways. The restriction of BCAAs within a high-fat diet (HFD) maintained the mice's weight, fat and lean volume, subcutaneous and visceral adipose tissue weight, and serum glucose and insulin at levels similar to those in the standard chow group, and prevented obesity, adipocyte hypertrophy, adipose inflammation, and insulin resistance induced by HFD. Our data suggest that four metabolic pathways, BCAA, phenylalanine, tryptophan, and phospholipid metabolic pathways, are altered in obesity and restriction of BCAAs within a HFD can prevent the development of obesity and insulin resistance in mice, providing a promising strategy to potentially mitigate diet-induced obesity.

A review of applications of metabolomics in osteoarthritis

Osteoarthritis (OA) represents the most prevalent and disabling arthritis worldwide due to its heterogeneous and progressive articular degradation. However, effective and timely diagnosis and fundamental treatment for this disorder are lacking. Metabolomics, a growing field in life science research in recent years, has the potential to detect many metabolites and thus explains the underlying pathophysiological processes. Hence, new specific metabolic markers and related metabolic pathways can be identified for OA. In this review, we aimed to provide an overview of studies related to the metabolomics of OA in animal models and humans to describe the metabolic changes and related pathways for OA. The present metabolomics studies reveal that the pathogenesis of OA may be significantly related to perturbations of amino acid metabolism. These altered amino acids (e.g., branched-chain amino acids, arginine, and alanine), as well as phospholipids, were identified as potential biomarkers to distinguish patients with OA from healthy individuals.

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.

Analgesic and Antidepressant Effects of the Clinical Glutamate Modulators Acetyl-L-Carnitine and Ketamine

Pain and depression are leading causes of disability and of profound social and economic burden. Their impact is aggravated by their chronicity and comorbidity and the insufficient efficacy of current treatments. Morphological and functional metabolism studies link chronic pain and depressive disorders to dysfunctional neuroplastic changes in fronto-limbic brain regions that control emotional responses to painful injuries and stressful events. Glutamate modulators are emerging new therapies targeting dysfunctional brain areas implicated in the generation and maintenance of chronic pain and depression. Here, we report the effects of two clinically approved glutamate modulators: acetyl-L-carnitine (ALCAR) and S, R(±)ketamine (KET). ALCAR is a natural neurotrophic compound currently marketed for the treatment of neuropathies. KET is the prototypical non-competitive antagonist at N-methyl-D-aspartate glutamate receptors and a clinically approved anesthetic. Although they differ in pharmacological profiles, ALCAR and KET both modulate aminergic and glutamatergic neurotransmissions and pain and mood. We assessed in rats the effects of ALCAR and KET on cerebral metabolic rates for glucose (rCMRglc) and assessed clinically the effects of ALCAR in chronic pain and of KET in post-operative pain. ALCAR and KET increased rCMRglc at similar degrees in prefrontal, somatosensory, and cingulate cortices, and KET increased rCMRglc at a different, much larger, degree in limbic and dopaminergic areas. While rCMRglc increases in prefrontal cortical areas have been associated with analgesic and antidepressant effects of ALCAR and KET, the marked metabolic increases KET induces in limbic and dopaminergic areas have been related to its psychotomimetic and abuse properties. In patients with chronic neuropathic pain, ALCAR (1,000 mg/day) yielded to a fast (2 weeks) improvement of mood and then of pain and quality of life. In day-surgery patients, KET improved dischargeability and satisfaction. In obese patients undergoing bariatric surgery, a single, low dose of KET (0.5 mg/kg) at induction of anesthesia determined a very fast (hours) amelioration of post-operative depression and pain and an opioid-sparing effect. These findings indicate that ALCAR and KET, two non-selective glutamate modulators, still offer viable therapeutic options in comorbid pain and depression.

Association Between Epidemiological Factors and Nonresponders to Total Joint Replacement Surgery in Primary Osteoarthritis Patients

BACKGROUND

While total joint replacement (TJR) is the most effective treatment for end-stage osteoarthritis (OA), one-third of patients do not experience clinically important improvement in pain or function following the surgery. Thus, it is important to identify factors for nonresponders and develop strategies to improve TJR outcomes.

METHODS

Study participants were patients who underwent TJR (hip/knee) due to OA and completed the WOMAC before and on average 4 years after surgery. Nonresponders (pain nonresponders, function nonresponders, pain and function nonresponders) were determined using the WOMAC change score from baseline to follow-up under two previously reported criteria. Eighty-eight self-reported factors collected by a general health questionnaire were examined for associations with nonresponders.

RESULTS

A total of 601 patients (30.8% hip and 69.2% knee replacement) were included; 18% of them were found to be either pain or function nonresponders. Nine factors were identified in the univariable analyses to be associated with nonresponders, and 5 of them (clinical depression, multisite musculoskeletal pain [MSMP], younger age, golfer's elbow, and driving more than 4 hours on average per working day) remained significant in the multivariable analyses in at least one of six categories. Clinical depression, having MSMP, and younger age were the major factors to be independently associated with nonresponders across five categories. In addition, two factors (age at menopause and age at hysterectomy) were significantly associated with female nonresponders.

CONCLUSION

Our data suggested potential roles of pain perception, widespread pain sensitization, patient expectations, and early menopause in females in TJR outcomes, warranting further investigation.

Macrophage migration inhibitory factor may play a protective role in osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most prevalent form of arthritis and the major cause of disability and overall diminution of quality of life in the elderly population. Currently there is no cure for OA, partly due to the large gaps in our understanding of its underlying molecular and cellular mechanisms. Macrophage migration inhibitory factor (MIF) is a procytokine that mediates pleiotropic inflammatory effects in inflammatory diseases such as rheumatoid arthritis (RA) and ankylosing spondylitis (AS). However, data on the role of MIF in OA is limited with conflicting results. We undertook this study to investigate the role of MIF in OA by examining MIF genotype, mRNA expression, and protein levels in the Newfoundland Osteoarthritis Study.

METHODS

One hundred nineteen end-stage knee/hip OA patients, 16 RA patients, and 113 healthy controls were included in the study. Two polymorphisms in the MIF gene, rs755622, and -794 CATT_5-8, were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and PCR followed by automated capillary electrophoresis, respectively. MIF mRNA levels in articular cartilage and subchondral bone were measured by quantitative polymerase chain reaction. Plasma concentrations of MIF, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1 beta (IL-1β) were measured by enzyme-linked immunosorbent assay.

RESULTS

rs755622 and -794 CATT_5-8 genotypes were not associated with MIF mRNA or protein levels or OA (all p ≥ 0.19). MIF mRNA level in cartilage was lower in OA patients than in controls (p = 0.028) and RA patients (p = 0.004), while the levels in bone were comparable between OA patients and controls (p = 0.165). MIF protein level in plasma was lower in OA patients than in controls (p = 3.01 × 10^-10), while the levels of TNF-α, IL-6 and IL-1β in plasma were all significantly higher in OA patients than in controls (all p ≤ 0.0007). Multivariable logistic regression showed lower MIF and higher IL-1β protein levels in plasma were independently associated with OA (OR per SD increase = 0.10 and 8.08; 95% CI = 0.04-0.19 and 4.42-16.82, respectively), but TNF-α and IL-6 became non-significant.

CONCLUSIONS

Reduced MIF mRNA and protein expression in OA patients suggested MIF might have a protective role in OA and could serve as a biomarker to differentiate OA from other joint disorders.

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.

Identification of the key gene and pathways associated with osteoarthritis via single-cell RNA sequencing on synovial fibroblasts

Osteoarthritis (OA) is a chronic degenerative joint disease with its onset closely related to the growth of synovial fibroblasts (SFs), yet the genes involved in are few reported. In our study, we aimed to identify the OA-associated key gene and pathways via the single-cell RNA sequencing (scRNA-seq) analysis on SFs.scRNA-seq data of SFs from OA sufferers were accessed from GEO database, then the genes involved in were subjected to principal component analysis (PCA) and T-Stochastic Neighbor Embedding (TSNE) Analysis. GO and KEGG enrichment analyses were performed to find the most enriched functions and pathways associated with marker genes and a PPI network was constructed to identify the key gene associated with OA occurrence.Findings revealed that marker genes in three cell types identified by TSNE were mainly activated in pathways firmly related to fibroblasts growth, such as extracellular matrix, immune and cell adhesion molecule binding-associated functions and pathways. Moreover, fibronectin1 (FN1) was validated as the key gene that was tightly related to the growth of SFs, as well as had the potential to play a key role in OA occurrence.Our study explored the key gene and pathways associated with OA occurrence, which were of great value in further investigation of OA diagnosis as well as pathogenesis.

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.

Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients

INTRODUCTION

Up to one third of total joint replacement patients (TJR) experience poor surgical outcome.

OBJECTIVES

To identify metabolomic signatures for non-responders to TJR in primary osteoarthritis (OA) patients.

METHODS

A newly developed differential correlation network analysis method was applied to our previously published metabolomic dataset to identify metabolomic network signatures for non-responders to TJR.

RESULTS

Differential correlation networks involving 12 metabolites and 23 metabolites were identified for pain non-responders and function non-responders, respectively.

CONCLUSION

The differential networks suggest that inflammation, muscle breakdown, wound healing, and metabolic syndrome may all play roles in TJR response, warranting further investigation.