Metabolic signatures of osteoarthritis in urine using liquid chromatography-high resolution tandem mass spectrometry

Novel insights into the role of metabolic disorder in osteoarthritis

Osteoarthritis (OA) is a prevalent condition that affects individuals worldwide and is one of the leading causes of disability. Nevertheless, the underlying pathological mechanisms of OA remain inadequately understood. Current treatments for OA include non-drug therapies, pharmacological interventions, and surgical procedures. These treatments are mainly focused on alleviating clinical manifestations and improving patients' quality of life, but are not effective in limiting the progression of OA. The detailed understanding of the pathogenesis of OA is extremely significant for the development of OA treatment. Metabolic syndrome has become a great challenge for medicine and public health, In recent years, several studies have demonstrated that the metabolic syndrome and its individual components play a crucial role in OA. Consequently, this review summarizes the mechanisms and research progress on how metabolic syndrome and its components affect OA. The aim is to gain a deeper understanding of the pathogenesis of OA and explore effective treatment strategies.

The interplay between dysregulated metabolites and signaling pathway alterations involved in osteoarthritis: a systematic review

Background

Osteoarthritis (OA) is a common degenerative joint disease that poses a significant global healthcare challenge due to its complexity and limited treatment options. Advances in metabolomics have provided insights into OA by identifying dysregulated metabolites and their connection to altered signaling pathways. However, a comprehensive understanding of these biomarkers in OA is still required.

Objectives

This systematic review aims to identify metabolomics biomarkers associated with dysregulated signaling pathways in OA, using data from various biological samples, including in vitro models, animal studies, and human research.

Design

A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Data sources and methods

Data were gathered from literature published between August 2017 and May 2024, using databases such as "PubMed," "Scopus," "Web of Science," and "Google Scholar." Studies were selected based on keywords like "metabolomics," "osteoarthritis," "amino acids," "molecular markers," "biomarkers," "diagnostic markers," "inflammatory cytokines," "molecular signaling," and "signal transduction." The review focused on identifying key metabolites and their roles in OA-related pathways. Limitations include the potential exclusion of studies due to keyword selection and strict inclusion criteria.

Results

The meta-analysis identified dysregulated metabolites and associated pathways, highlighting a distinct set of related metabolites consistently altered across the studies analyzed. The dysregulated metabolites, including amino acids, lipids, and carbohydrates, were found to play critical roles in inflammation, oxidative stress, and energy metabolism in OA. Metabolites such as alanine, lysine, and proline were frequently linked to pathways involved in inflammation, cartilage degradation, and apoptosis. Key pathways, including nuclear factor kappa B, mitogen-activated protein kinase, Wnt/β-catenin, and mammalian target of rapamycin, were associated with changes in metabolite levels, particularly in proinflammatory lipids and energy-related compounds.

Conclusion

This review reveals a complex interplay between dysregulated metabolites and signaling pathways in OA, offering potential biomarkers and therapeutic targets. Further research is needed to explore the molecular mechanisms driving these changes and their implications for OA treatment.

Lipidomic Signature of Plasma and Synovial Fluid in Patients with Osteoarthritis: Putative Biomarkers Determined by UHPLC-QTOF-ESI+MS

BACKGROUND

Osteoarthritis (OA) is a prevalent joint condition causing pain and disability, especially in the elderly. Currently, OA diagnosis relies on clinical data and imaging, but recent interest in metabolomics suggests that early biochemical changes in biofluids, particularly synovial fluid (SF), could enable an earlier diagnosis and understanding of the disease.

METHODS

In this regard, we conducted a lipidomics study in 33 plasma and SF samples from OA patients and 20 OA-free controls to assess the diagnostic value of various lipid metabolites, using UHPLC-QTOF-ESI+MS.

RESULTS

In plasma samples, 25 metabolites had area-under-the-curve (AUC) values higher than 0.9, suggesting a very good diagnostic potential for phosphatidic acid PA (16:0/16:0), PA (34:0), phosphatidylethanolamine PE (34:2), glucosylceramide, phosphatidylcholine PC (32:1), and other metabolites while in SF 20, metabolites had AUC values higher than 0.8, the vast majority belonging to lipid metabolism as well.

CONCLUSIONS

Although the results align with the previous literature, larger cohort studies are necessary to confirm the diagnostic value of the lipid metabolites.

Osteoarthritis in the UK Armed Forces: a review of its impact, treatment and future research

Within the UK Armed Forces, musculoskeletal injuries account for over half of all medical downgrades and discharges. Data from other Armed Forces show that osteoarthritis (OA), more common in military personnel, is likely to contribute to this, both in its primary form and following injury (post-traumatic OA, PTOA), which typically presents in the third or fourth decade. OA is not a progressive 'wear and tear' disease, as previously thought, but a heterogenous condition with multiple aetiologies and modulators, including joint damage, abnormal morphology, altered biomechanics, genetics, low-grade inflammation and dysregulated metabolism. Currently, clinical diagnosis, based on symptomatic or radiological criteria, is followed by supportive measures, including education, exercise, analgesia, potentially surgical intervention, with a particular focus on exercise rehabilitation within the UK military. Developments in OA have led to a new paradigm of organ failure, with an emphasis on early diagnosis and risk stratification, prevention strategies (primary, secondary and tertiary) and improved aetiological classification using genotypes and phenotypes to guide management, with the introduction of biological markers (biomarkers) potentially having a role in all these areas. In the UK Armed Forces, there are multiple research studies focused on OA risk factors, epidemiology, biomarkers and effectiveness of different interventions. This review aims to highlight OA, especially PTOA, as an important diagnosis to consider in serving personnel, outline current and future management options, and detail current research trends within the Defence Medical Services.

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.

Metabolomics in Osteoarthritis Knee: A Systematic Review of Literature

Introduction

Osteoarthritis (OA) is a common degenerative disorder of the synovial joints and is usually an age-related disease that occurs due to continuous wear and tear of the cartilage in the joints. Presently, there is no proven medical management to halt the progression of the disease in the early stages. The purpose of our systematic review is to analyze the possible metabolites and metabolic pathways that are specifically involved in OA pathogenesis and early treatment of the disease.

Materials and Methods

The articles were collected from PubMed, Cochrane, Google Scholar, Embase, and Scopus databases. "Knee", "Osteoarthritis", "Proteomics", "Lipidomics", "Metabolomics", "Metabolic Methods", and metabolic* were employed for finding the articles. Only original articles with human or animal OA models with healthy controls were included.

Results

From the initial screening, a total of 458 articles were identified from the 5 research databases. From these, 297 articles were selected in the end for screening, of which 53 papers were selected for full-text screening. Finally, 50 articles were taken for the review based on body fluid: 6 urine studies, 15 plasma studies, 16 synovial fluid studies, 11 serum studies, 4 joint tissue studies, and 1 fecal study. Many metabolites were found to be elevated in OA. Some of these metabolites can be used to stage the OA Three pathways that were found to be commonly involved are the TCA cycle, the glycolytic pathway, and the lipid metabolism.

Conclusion

All these studies showed a vast array of metabolites and metabolic pathways associated with OA. Metabolites like lysophospholipids, phospholipids, arginine, BCCA, and histidine were identified as potential biomarkers of OA but a definite association was not identified, Three pathways (glycolytic pathway, TCA cycle, and lipid metabolic pathways) have been found as highly significant in OA pathogenesis. These metabolic pathways could provide novel therapeutic targets for the prevention and progression of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43465-024-01169-5.

Murine cartilage microbial DNA deposition occurs rapidly following the introduction of a gut microbiome and changes with obesity, aging, and knee osteoarthritis

Cartilage microbial DNA patterns have been recently characterized in osteoarthritis (OA). The objectives of this study were to evaluate the gut origins of cartilage microbial DNA, to characterize cartilage microbial changes with age, obesity, and OA in mice, and correlate these to gut microbiome changes. We used 16S rRNA sequencing performed longitudinally on articular knee cartilage from germ-free (GF) mice following oral microbiome inoculation and cartilage and cecal samples from young and old wild-type mice with/without high-fat diet-induced obesity (HFD) and with/without OA induced by destabilization of the medial meniscus (DMM) to evaluate gut and cartilage microbiota. Microbial diversity was assessed, groups compared, and functional metagenomic profiles reconstructed. Findings were confirmed in an independent cohort by clade-specific qPCR. We found that cartilage microbial patterns developed at 48 h and later timepoints following oral microbiome inoculation of GF mice. Alpha diversity was increased in SPF mouse cartilage samples with age (P = 0.013), HFD (P = 5.6E-4), and OA (P = 0.029) but decreased in cecal samples with age (P = 0.014) and HFD (P = 1.5E-9). Numerous clades were altered with aging, HFD, and OA, including increases in Verrucomicrobia in both cartilage and cecal samples. Functional analysis suggested changes in dihydroorotase, glutamate-5-semialdehyde dehydrogenase, glutamate-5-kinase, and phosphoribosylamine-glycine ligase, in both cecum and cartilage, with aging, HFD, and OA. In conclusion, cartilage microbial DNA patterns develop rapidly after the introduction of a gut microbiome and change in concert with the gut microbiome during aging, HFD, and OA in mice. DMM-induced OA causes shifts in both cartilage and cecal microbiome patterns independent of other factors.

Metabolomic Profiles and Pathways in Osteoarthritic Human Cartilage: A Comparative Analysis with Healthy Cartilage

Osteoarthritis (OA) is a chronic joint disease with heterogenous metabolic pathology. To gain insight into OA-related metabolism, metabolite extracts from healthy (n = 11) and end-stage osteoarthritic cartilage (n = 35) were analyzed using liquid chromatography-mass spectrometry metabolomic profiling. Specific metabolites and metabolic pathways, including lipid and amino acid pathways, were differentially regulated in osteoarthritis-derived and healthy cartilage. The detected alterations in amino acids and lipids highlighted key differences in bioenergetic resources, matrix homeostasis, and mitochondrial alterations in OA-derived cartilage compared to healthy cartilage. Moreover, the metabolomic profiles of osteoarthritic cartilage separated into four distinct endotypes, highlighting the heterogenous nature of OA metabolism and the diverse landscape within the joint in patients. The results of this study demonstrate that human cartilage has distinct metabolomic profiles in healthy and end-stage OA patients. By taking a comprehensive approach to assess metabolic differences between healthy and osteoarthritic cartilage and within osteoarthritic cartilage alone, several metabolic pathways with distinct regulation patterns were detected. Additional investigation may lead to the identification of metabolites that may serve as valuable indicators of disease status or potential therapeutic targets.

Metabolomic Profiles and Pathways in Osteoarthritic Human Cartilage: A Comparative Analysis with Healthy Cartilage

Objective

Osteoarthritis (OA) is a chronic joint disease with heterogenous metabolic pathology. To gain insight into OA-related metabolism, healthy and end-stage osteoarthritic cartilage were compared metabolically to uncover disease-associated profiles, classify OA-specific metabolic endotypes, and identify targets for intervention for the diverse populations of individuals affected by OA.

Design

Femoral head cartilage (n=35) from osteoarthritis patients were collected post-total joint arthroplasty. Healthy cartilage (n=11) was obtained from a tissue bank. Metabolites from all cartilage samples were extracted and analyzed using liquid chromatography-mass spectrometry metabolomic profiling. Additionally, cartilage extracts were pooled and underwent fragmentation analysis for biochemical identification of metabolites.

Results

Specific metabolites and metabolic pathways, including lipid- and amino acid pathways, were differentially regulated between osteoarthritis-derived and healthy cartilage. The detected alterations of amino acids and lipids highlight key differences in bioenergetic resources, matrix homeostasis, and mitochondrial alterations in osteoarthritis-derived cartilage compared to healthy. Moreover, metabolomic profiles of osteoarthritic cartilage separated into four distinct endotypes highlighting the heterogenous nature of OA metabolism and diverse landscape within the joint between patients.

Conclusions

The results of this study demonstrate that human cartilage has distinct metabolomic profiles between healthy and end-stage osteoarthritis patients. By taking a comprehensive approach to assess metabolic differences between healthy and osteoarthritic cartilage, and within osteoarthritic cartilage alone, several metabolic pathways with distinct regulation patterns were detected. Additional investigation may lead to the identification of metabolites that may serve as valuable indicators of disease status or potential therapeutic targets.

Differential Metabolites in Osteoarthritis: A Systematic Review and Meta-Analysis

(1) Many studies have attempted to utilize metabolomic approaches to explore potential biomarkers for the early detection of osteoarthritis (OA), but consistent and high-level evidence is still lacking. In this study, we performed a systematic review and meta-analysis of differential small molecule metabolites between OA patients and healthy individuals to screen promising candidates from a large number of samples with the aim of informing future prospective studies. (2) Methods: We searched the EMBASE, the Cochrane Library, PubMed, Web of Science, Wan Fang Data, VIP Date, and CNKI up to 11 August 2022, and selected relevant records based on inclusion criteria. The risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. We performed qualitative synthesis by counting the frequencies of changing directions and conducted meta-analyses using the random effects model and the fixed-effects model to calculate the mean difference and 95% confidence interval. (3) Results: A total of 3798 records were identified and 13 studies with 495 participants were included. In the 13 studies, 132 kinds of small molecule differential metabolites were extracted, 58 increased, 57 decreased and 17 had direction conflicts. Among them, 37 metabolites appeared more than twice. The results of meta-analyses among four studies showed that three metabolites increased, and eight metabolites decreased compared to healthy controls (HC). (4) Conclusions: The main differential metabolites between OA and healthy subjects were amino acids (AAs) and their derivatives, including tryptophan, lysine, leucine, proline, phenylalanine, glutamine, dimethylglycine, citrulline, asparagine, acetylcarnitine and creatinine (muscle metabolic products), which could be potential biomarkers for predicting OA.

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.

Utilizing metabolomics to identify potential biomarkers and perturbed metabolic pathways in osteoarthritis: A systematic review

PURPOSE

Osteoarthritis (OA) is a joint disease that is clinically diagnosed using components of history, physical exam, and characteristic radiographic findings, such as joint space narrowing. Currently, there are no laboratory findings that are specific to a diagnosis of OA. The purpose of this systematic review is to evaluate the state of current studies of metabolomic biomarkers that can aid in the diagnosis and treatment of OA.

METHODS

Articles were gathered from PubMed and Web of Science using the search terms "osteoarthritis" and "biomarkers" and "metabolomics". Last search of databases took place December 3rd, 2022. Duplicates were manually screened, along with any other results that were not original journal articles. Only original reports involving populations with diagnosed primary or secondary OA (human participants) or surgically induced OA (animal participants) and a healthy control group for comparison were considered for inclusion. Metabolites and metabolic pathways reported in included articles were then manually extracted and evaluated for importance based on reported a priori p-values and/or area under the receiver-operator curve (AUC).

RESULTS

Of the 161 results that were returned in the database searches, 43 unique articles met the inclusion criteria. Articles were categorized based on body fluid analyzed: 6 studies on urine samples, 13 studies on plasma samples, 11 studies on synovial fluid (SF) samples, 11 studies on serum samples, 1 study on both synovial fluid and serum, and 1 study that involved both plasma and synovial fluid. To synthesize results, individual metabolites, as well as metabolic pathways that involve frequently reported metabolites, are presented for each study. Indications as to whether metabolite levels were increased or decreased are also included if this data was included in the original articles.

CONCLUSIONS

These studies clearly show that there are a wide range of metabolic pathways perturbed in OA. For this period, there was no consensus on a single metabolite, or panel of metabolites, that would be clinically useful in early diagnosis of OA or distinguishing OA from a healthy control. However, many common metabolic pathways were identified in the studies, including TCA cycle, fatty acid metabolism, amino acid metabolism (notably BCAA metabolism and tryptophan metabolism via kynurenine pathway), nucleotide metabolism, urea cycle, cartilage matrix components, and phospholipid metabolism. Future research is needed to define effective clinical biomarkers of osteoarthritis from metabolomic and other data.

Untargeted Metabolomics Combined with Metabolic Flux Analysis Reveals the Mechanism of Sodium Citrate for High S-Adenosyl-Methionine Production by Pichia pastoris

S-adenosyl-methionine (SAM) is crucial for organisms to maintain some physiological functions. However, the inconsistency between high L-methionine feeding rate and yield during SAM production at an industrial scale and its metabolic mechanism have not been elucidated. Here, the cellular metabolic mechanism of feeding sodium citrate to the Pichia pastoris (P. pastoris) G12’/AOX-acs2 strain to enhance SAM production was investigated using untargeted metabolomics and metabolic flux analysis. The results indicated that the addition of sodium citrate has a facilitative effect on SAM production. In addition, 25 metabolites, such as citrate, cis-aconitate, and L-glutamine, were significantly up-regulated, and 16 metabolites, such as glutathione, were significantly down-regulated. Furthermore, these significantly differential metabolites were mainly distributed in 13 metabolic pathways, such as the tricarboxylic acid (TCA) cycle. In addition, the metabolic fluxes of the glycolysis pathway, pentose phosphate pathway, TCA cycle, and glyoxylate pathway were increased by 20.45–29.32%, respectively, under the condition of feeding sodium citrate compared with the control. Finally, it was speculated that the upregulation of dihydroxyacetone level might increase the activity of alcohol oxidase AOX1 to promote methanol metabolism by combining metabolomics and fluxomics. Meanwhile, acetyl coenzyme A might enhance the activity of citrate synthase through allosteric activation to promote the flux of the TCA cycle and increase the level of intracellular oxidative phosphorylation, thus contributing to SAM production. These new insights into the L-methionine utilization for SAM biosynthesis by systematic biology in P. pastoris provides a novel vision for increasing its industrial production.

Stage related metabolic profile of the synovial fluid in patients with acute flares of knee osteoarthritis

Background and aim

Osteoarthritis (OA) is the most common joint condition and the leading cause of pain and disability in elderly patients. Currently, there is no biomarker available for the early diagnosis of OA, and limited data is available regarding the molecular basis of progression for OA. For this reason, this study aimed to identify the metabolomic profile of early and late OA using high-performance liquid chromatography coupled with untargeted mass spectrometry (LC-MS).

Methods

31 patients with knee OA and joint effusion were enrolled. Based on Kellgren/Laurence scale, 12 patients were classified as early OA (eOA) and 19 as late OA (lOA). The synovial fluid (SF) was collected and characterized by untargeted LC-MS. Only the metabolites identified in more than 25% of each group were kept for further analysis. Principal component analysis (PCA) enabled the unsupervised clustering of the eOA and lOA groups. Further, for classification, the best three principal components (PCs) were used as input for two machine learning algorithms (random forest and naïve Bayes), which were trained to discriminate between the eOA and lOA groups.

Results

43 metabolites were identified in both eOA and lOA, but after selecting the metabolites present in at least 25% of the patients in each group, the metabolomics analysis yielded a panel of only nine metabolites: four metabolites related to phospholipids (phosphatidylcholine 20:0/18:2 and 18:0/20:2, sphingomyelin, and ceramide), three metabolites belonging to purine metabolites (inosine 5'-phosphate, adenosine thiamine diphosphate, and diadenosine 5',5'-diphosphate), one metabolite was a gonadal steroid hormone (estrone 3-sulfate), and one metabolite represented by heme, with all but ceramide (d18:1/20:0) being enriched in the lOA group. By using as features the best three PCs (PC2, PC8 and PC9), random forest and naïve Bayes machine learning algorithms yielded a classification accuracy of 0.81 and 0.78, respectively.

Conclusion

Our LC-MS analysis of SF from patients with eOA and lOA indicates stage-dependent differences, lOA being associated with a perturbed metabolome of phospholipids, purine metabolites, gonadal steroid hormones (estrone 3-sulfate) and a heme molecule. Specific questions need to be answered regarding the biosynthesis and function of these metabolites in osteoarthritic joints, with the aim of developing new relevant biomarkers and therapeutic strategies.

Synovial tissue metabolomic profiling reveal biomarkers of synovial inflammation in patients with osteoarthritis

Objective

Inflammatory responses are associated with changes in tissue metabolism. Prior studies find altered metabolomic profiles in both the synovial fluid (SF) and serum of osteoarthritis subjects. Our study determined the metabolomic profile of synovial tissue (ST) and SF of individuals with osteoarthritis (OA) and its association with synovial inflammation.

Design

37 OA ST samples were collected during joint replacement, 21 also had SF. ST samples were fixed in formalin for histological analysis, cultured (explants) for cytokine analysis by enzyme-linked immunosorbent assay, or snap-frozen for metabolomic analysis. ST samples were categorized by Krenn synovitis score and picrosirius red. CD68 and vimentin expression was assessed by immunohistochemistry and semi-quantified using Image J. Proton-nuclear magnetic resonance (1H NMR) was used to acquire a spectrum from ST and SF samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. Metaboanalyst 5.0, SPSS v26, and R (v4.1.2) were used for statistical analysis.

Results

42 and 29 metabolites were detected in the ST and SF respectively by 1H NMR. Only 3 metabolites, lactate, dimethylamine, and creatine positively correlated between SF and ST. ST concentrations of several metabolites (lactate, alanine, fumarate, glutamine, glycine, leucine, lysine, methionine, trimethylamine N-oxide, tryptophan and valine) were associated with synovitis score, mostly to the lining score. IL-6, acetoacetate, and tyrosine in SF predicted high Krenn synovitis scores in ST.

Conclusion

Metabolomic profiling of ST identified metabolic changes associated with inflammation. Further studies are needed to determine whether metabolomic profiling of synovial tissue can identify new therapeutic targets in osteoarthritis.

Tai Chi Improves Brain Functional Connectivity and Plasma Lysophosphatidylcholines in Postmenopausal Women With Knee Osteoarthritis: An Exploratory Pilot Study

Objective: A pre/post pilot study was designed to investigate neurobiological mechanisms and plasma metabolites in an 8-week Tai-Chi (TC) group intervention in subjects with knee osteoarthritis.

Methods: Twelve postmenopausal women underwent Tai-Chi group exercise for 8 weeks (60 min/session, three times/week). Outcomes were measured before and after Tai Chi intervention including pain intensity (VAS), Brief Pain Inventory (BPI), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), plasma metabolites (amino acids and lipids), as well as resting-state functional magnetic resonance imaging (rs-fMRI, 10 min, eyes open), diffusion tensor imaging (DTI, 12 min), and structural MRI (4.5 min) in a subgroup. Clinical data was analyzed using paired t-tests; plasma metabolites were analyzed using Wilcoxon signed-rank tests; and rs-fMRI data were analyzed using seed-based correlations of the left and right amygdala in a two-level mixed-effects model (FSL software). Correlations between amygdala-medial prefrontal cortex (mPFC) connectivity and corresponding changes in clinical outcomes were examined. DTI connectivity of each amygdala was modeled using a Bayesian approach and probabilistic tractography. The associations between neurobiological effects and pain/physical function were examined.

Results: Significant pre/post changes were observed with reduced knee pain (VAS with most pain: p = 0.018; WOMAC-pain: p = 0.021; BPI with worst level: p = 0.018) and stiffness (WOMAC-stiffness, p = 0.020), that likely contributed to improved physical function (WOMAC-physical function: p = 0.018) with TC. Moderate to large effect sizes pre/post increase in rs-fMRI connectivity were observed between bilateral mPFC and the amygdala seed regions (i.e., left: d = 0.988, p = 0.355; right: d = 0.600, p = 0.282). Increased DTI connectivity was observed between bilateral mPFC and left amygdala (d = 0.720, p = 0.156). There were moderate-high correlations (r = 0.28–0.60) between TC-associated pre-post changes in amygdala-mPFC functional connectivity and pain/physical function improvement. Significantly higher levels of lysophosphatidylcholines were observed after TC but lower levels of some essential amino acids. Amino acid levels (alanine, lysine, and methionine) were lower after 8 weeks of TC and many of the lipid metabolites were higher after TC. Further, plasma non-HDL cholesterol levels were lower after TC.

Conclusion: This pilot study showed moderate to large effect sizes, suggesting an important role that cortico-amygdala interactions related to TC have on pain and physical function in subjects with knee osteoarthritis pain. Metabolite analyses revealed a metabolic shift of higher lyso-lipids and lower amino acids that might suggest greater fatty acid catabolism, protein turnover and changes in lipid redistribution in response to TC exercise. The results also support therapeutic strategies aimed at strengthening functional and structural connectivity between the mPFC and the amygdala. Controlled clinical trials are warranted to confirm these observed preliminary effects.

Insights into the molecular landscape of osteoarthritis in human tissues

PURPOSE OF REVIEW

To provide an overview of recent developments in the field of osteoarthritis research with a focus on insights gleaned from the application of different -omic technologies.

RECENT FINDINGS

We searched for osteoarthritis-relevant studies focusing on transcriptomics, epigenomics, proteomics and metabolomics, published since November of 2019. Study designs showed a trend towards characterizing the genomic profile of osteoarthritis-relevant tissues with high resolution, for example either by using single-cell technologies or by considering several -omic levels and disease stages.

SUMMARY

Multitissue interactions (cartilage-subchondral bone; cartilage-synovium) are prevalent in the pathophysiology of osteoarthritis, which is characterized by substantial matrix remodelling in an inflammatory milieu. Subtyping approaches using -omic technologies have contributed to the identification of at least two osteoarthritis endotypes. Studies using data integration approaches have provided molecular maps that are tissue-specific for osteoarthritis and pave the way for expanding these data integration approaches towards a more comprehensive view of disease aetiopathogenesis.

Potential roles of gut microbiota and microbial metabolites in chronic inflammatory pain and the mechanisms of therapy drugs

Observational findings achieved that gut microbes mediate human metabolic health and disease risk. The types of intestinal microorganisms depend on the intake of food and drugs and are also related to their metabolic level and genetic factors. Recent studies have shown that chronic inflammatory pain is closely related to intestinal microbial homeostasis. Compared with the normal intestinal flora, the composition of intestinal flora in patients with chronic inflammatory pain had significant changes in Actinomycetes, Firmicutes, Bacteroidetes, etc. At the same time, short-chain fatty acids and amino acids, the metabolites of intestinal microorganisms, can regulate neural signal molecules and signaling pathways, thus affecting the development trend of chronic inflammatory pain. Glucocorticoids and non-steroidal anti-inflammatory drugs in the treatment of chronic inflammatory pain, the main mechanism is to affect the secretion of inflammatory factors and the abundance of intestinal bacteria. This article reviews the relationship between intestinal microorganisms and their metabolites on chronic inflammatory pain and the possible mechanism.