Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study

Transcriptomics and metabolomics: Challenges of studying obesity in osteoarthritis

Objective

Obesity is a leading risk factor for both the incidence and progression of osteoarthritis (OA). Omic technologies, including transcriptomics and metabolomics are capable of identifying RNA and metabolite profiles in tissues and biofluids of OA patients. The objective of this review is to highlight studies using transcriptomics and metabolomics that contribute to our understanding of OA pathology in relation to obesity.

Design

We conducted a targeted search of PUBMED for articles, and GEO for datasets, published up to February 13, 2024, screening for those using high-throughput transcriptomic and metabolomic techniques to study human or pre-clinical animal model tissues or biofluids related to obesity-associated OA. We describe relevant studies and discuss challenges studying obesity as a disease-related factor in OA.

Results

Of the 107 publications identified by our search criteria, only 15 specifically used transcriptomics or metabolomics to study joint tissues or biofluids in obesity-related OA. Specific transcriptomic and metabolomic signatures associated with obesity-related OA have been defined in select local joint tissues, biofluids and other biological material. However, considerable challenges exist in understanding contributions of obesity-associated modifications of transcriptomes and metabolomes related to OA, including sociodemographic, anthropometric, dietary and molecular redundancy-related factors.

Conclusions

A number of additional transcriptomic and metabolomic studies are needed to comprehensively understand how obesity affects OA incidence, progression and outcomes. Integration of transcriptome and metabolome signatures from multiple tissues and biofluids, using network-based approaches will likely help to better define putative therapeutic targets that could enable precision medicine approaches to obese OA patients.

Changes in the urinary metabolome accompanied alterations in body mass and composition in women with overweight - impact of high versus low protein breakfast

INTRODUCTION

Understanding why subjects with overweight and with obesity vary in their response to dietary interventions is of major interest for developing personalized strategies for body mass regulation.

OBJECTIVES

The aim of this study was to investigate the relationship between changes in the urine metabolome and body mass during a breakfast meal intervention. Furthermore, we aimed to elucidate if the baseline urine metabolome could predict the response to the two types of breakfast meals (high versus low protein) during the intervention.

METHODS

A total of 75 young, women with overweight were randomly allocated to one of two intervention groups: (1) High-protein (HP) or (2) low-protein (LP) breakfast as part of their habitual diet during a 12-week intervention. Beside the breakfast meal, participants were instructed to eat their habitual diet and maintain their habitual physical activity level. Nuclear magnetic resonance-based metabolomics was conducted on urine samples collected at baseline (wk 0), mid-intervention (wk 6), and at endpoint (wk 12). At baseline and endpoint, body mass was measured and DXA was used to measure lean body mass and fat mass.

RESULTS

The baseline urine metabolite profile showed a slightly higher correlation (R2 = 0.56) to body mass in comparison with lean body mass (R2 = 0.51) and fat mass (R2 = 0.53). Baseline 24-h urinary excretion of trigonelline (p = 0.04), N, N-dimethylglycine (p = 0.02), and trimethylamine (p = 0.03) were significantly higher in individuals who responded with a reduction in body mass to the HP breakfast.

CONCLUSIONS

Differences in the urine metabolome were seen for women that obtained a body weight loss in the response to the HP breakfast intervention and women who did not obtain a body weight loss, indicating that the urine metabolome contains information about the metabolic phenotype that influences the responsiveness to dietary interventions.

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.

Metabolomic Profiling and Characterization of a Novel 3D Culture System for Studying Chondrocyte Mechanotransduction

Articular chondrocytes synthesize and maintain the avascular and aneural articular cartilage. In vivo these cells are surrounded by a 3D pericellular matrix (PCM) containing predominantly collagen VI. The PCM protects chondrocytes and facilitates mechanotransduction, and PCM stiffness is critical in transmitting biomechanical signals to chondrocytes. Various culture systems with different hydrogels have been used to encapsulate chondrocytes for 3D culture, but many lack either the PCM or the in vivo stiffness of the cartilage matrix. Here, we demonstrate that primary chondrocytes cultured in alginate will form a pericellular matrix and display a phenotype similar to in vivo conditions. We found that primary human and bovine chondrocytes, when cultured in alginate beads with addition of sodium L-ascorbate for 7 days, had a pronounced PCM, retained their phenotype, and synthesized both collagens VI and II. This novel culture system enables alginate-encapsulated chondrocytes to develop a robust PCM thereby creating a model system to study mechanotransduction. We also observed distinct compression-induced changes in metabolomic profiles between the monolayer-agarose and alginate-released agarose-embedded chondrocytes indicating physiological changes in cell metabolism. Our data suggest that 3D preculture of chondrocytes in alginate before encapsulation in physiologically-stiff agarose leads to a pronounced development of pericellular matrix that is sustained in the presence of ascorbate. This novel model can be useful in studying the mechanism by which chondrocytes respond to cyclical compression and other types of loading simulating in vivo physiological conditions.

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.

Metabolic profiling of synovial fluid in human temporomandibular joint osteoarthritis

Introduction

Temporomandibular joint (TMJ) osteoarthritis (OA) is a common TMJ degenerative disease with an unclear mechanism. Synovial fluid (SF), an important component of TMJ, contains various proteins and metabolites that may directly contribute to OA. The present study aimed to investigate the influence of SF in TMJOA at the metabolite level.

Methods

Untargeted and widely targeted metabolic profiling were employed to identify metabolic changes in SF of 90 patients with different TMJOA grades according to TMJ magnetic resonance imaging.

Results

A total 1498 metabolites were detected. Most of the metabolites were amino acids and associated metabolites, benzene and substituted derivatives, and lipids. Among patients with mild, moderate and severe TMJOA, 164 gradually increasing and 176 gradually decreasing metabolites were identified, indicating that biosynthesis of cofactors, choline metabolism, mineral absorption and selenocompound metabolism are closely related to TMJOA grade. Combined metabolomics and clinical examination revealed 37 upregulated metabolites and 16 downregulated metabolites in patients with pain, of which 19 and 26 metabolites were positively and negatively correlated, respectively, with maximum interincisal opening. A model was constructed to diagnose TMJOA grade and nine biomarkers were identified. The identified metabolites are key to exploring the mechanism of TMJOA.

Discussion

In the present study, a metabolic profile was constructed and assessed using a much larger number of human SF samples from patients with TMJOA, and a model was established to contribute to the diagnosis of TMJOA grade. The findings expand our knowledge of metabolites in human SF of TMJOA patients, and provide an important basis for further research on the pathogenesis and treatment of TMJOA.

The metabolome of individuals with knee osteoarthritis is influenced by 18-months of an exercise and weight loss intervention and sex: the IDEA trial

Objective

The Intensive Diet and Exercise for Arthritis (IDEA) trial was conducted to evaluate the effects of diet and exercise on osteoarthritis (OA), the most prevalent form of arthritis. Various risk factors, such as obesity and sex, contribute to the debilitating nature of OA. While diet and exercise are known to improve OA symptoms, cellular and molecular mechanisms underlying these interventions, as well as effects of participant sex, remain elusive.

Methods

Serum was obtained at three timepoints from IDEA participants assigned to groups of diet, exercise, or combined diet and exercise (n=10 per group). All serum metabolites were extracted and analyzed via liquid chromatography-mass spectrometry combined with metabolomic profiling. Extracted serum was pooled and fragmentation patterns were analyzed to identify metabolites that statistically differentially regulated between groups.

Results

Changes in metabolism across male and female IDEA participants after 18-months of diet, exercise, and combined diet and excise intervention mapped to lipid, amino acid, carbohydrate, vitamin, and matrix metabolism. The diverse metabolic landscape detected across IDEA participants shows that intervention type impacts the serum metabolome of individuals with OA in distinct patterns. Moreover, differences in the serum metabolome corresponded with participant sex.

Conclusions

These findings suggest that intensive weight loss among male and female subjects offers potential metabolic benefits for individuals with knee OA. This provides a deeper understanding of dysregulation occurring during OA development that may pave the way for improved interventions, treatments, and quality of life of those impacted by this disease.

Comparative study of 1H-NMR metabolomic profile of canine synovial fluid in patients affected by four progressive stages of spontaneous osteoarthritis

The study aimed to assess the metabolomic profile of the synovial fluid (SF) of dogs affected by spontaneous osteoarthritis (OA) and compare any differences based on disease progression. Sixty client-owned dogs affected by spontaneous OA underwent clinical, radiographic, and cytologic evaluations to confirm the diagnosis. The affected joints were divided into four study groups based on the Kallgreen-Lawrence classification: OA1 (mild), OA2 (moderate), OA3 (severe), and OA4 (extremely severe/deforming). The osteoarthritic joint's SF was subjected to cytologic examination and 1H-NMR analysis. The metabolomic profiles of the study groups' SF samples were statistically compared using one-way ANOVA. Sixty osteoarthritic joints (45 stifles, 10 shoulders and 5 elbows) were included in the study. Fourteen, 28, and 18 joints were included in the OA1, OA2, and OA3 groups, respectively (0 joints in the OA4 group). Metabolomic analysis identified 48 metabolites, five of which were significantly different between study groups: Mannose and betaine were elevated in the OA1 group compared with the OA2 group, and the 2-hydroxyisobutyrate concentration decreased with OA progression; in contrast, isoleucine was less concentrated in mild vs. moderate OA, and lactate increased in severe OA. This study identified different 1H-NMR metabolomic profiles of canine SF in patients with progressive degrees of spontaneous OA, suggesting 1H-NMR metabolomic analysis as a potential alternative method for monitoring OA progression. In addition, the results suggest the therapeutic potentials of the metabolomic pathways that involve mannose, betaine, 2-hydroxyisobutyrate, isoleucine, and lactate.

Serum Metabolomic Alteration in Rats with Osteoarthritis Treated with Palm Tocotrienol-Rich Fraction Alone or in Combination with Glucosamine Sulphate

Osteoarthritis (OA) is a degenerative joint condition with limited disease-modifying treatments currently. Palm tocotrienol-rich fraction (TRF) has been previously shown to be effective against OA, but its mechanism of action remains elusive. This study aims to compare serum metabolomic alteration in Sprague-Dawley rats with monosodium iodoacetate (MIA)-induced OA which were treated with palm TRF, glucosamine sulphate, or a combination of both. This study was performed on thirty adult male rats, which were divided into normal control (n = 6) and OA groups (n = 24). The OA group received intra-articular injections of MIA and daily oral treatments of refined olive oil (vehicle, n = 6), palm TRF (100 mg/kg, n = 6), glucosamine sulphate (250 mg/kg, n = 6), or a combination of TRF and glucosamine (n = 6) for four weeks. Serum was collected at the study's conclusion for metabolomic analysis. The findings revealed that MIA-induced OA influences amino acid metabolism, leading to changes in metabolites associated with the biosynthesis of phenylalanine, tyrosine and tryptophan as well as alterations in the metabolism of phenylalanine, tryptophan, arginine and proline. Supplementation with glucosamine sulphate, TRF, or both effectively reversed these metabolic changes induced by OA. The amelioration of metabolic effects induced by OA is linked to the therapeutic effects of TRF and glucosamine. However, it remains unclear whether these effects are direct or indirect in nature.

DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative

Background

No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting.

Methods

A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24-48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24-48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric.

Results

The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585-0.743) at baseline, 0.739 (95% CI: 0.703-0.775) at 12 months, and 0.775 (95% CI: 0.686-0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees.

Conclusions

This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.

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.

Metabolite asymmetric dimethylarginine (ADMA) functions as a destabilization enhancer of SOX9 mediated by DDAH1 in osteoarthritis

Osteoarthritis (OA) is a degenerative disease with a series of metabolic changes accompanied by many altered enzymes. Here, we report that the down-regulated dimethylarginine dimethylaminohydrolase-1 (DDAH1) is accompanied by increased asymmetric dimethylarginine (ADMA) in degenerated chondrocytes and in OA samples. Global or chondrocyte-conditional knockout of ADMA hydrolase DDAH1 accelerated OA development in mice. ADMA induces the degeneration and senescence of chondrocytes and reduces the extracellular matrix deposition, thereby accelerating OA progression. ADMA simultaneously binds to SOX9 and its deubiquitinating enzyme USP7, blocking the deubiquitination effects of USP7 on SOX9 and therefore leads to SOX9 degradation. The ADMA level in synovial fluids of patients with OA is increased and has predictive value for OA diagnosis with good sensitivity and specificity. Therefore, activating DDAH1 to reduce ADMA level might be a potential therapeutic strategy for OA treatment.

The cortical bone metabolome of C57BL / 6J mice is sexually dimorphic

Cortical bone quality, which is sexually dimorphic, depends on bone turnover and therefore on the activities of remodeling bone cells. However, sex differences in cortical bone metabolism are not yet defined. Adding to the uncertainty about cortical bone metabolism, the metabolomes of whole bone, isolated cortical bone without marrow, and bone marrow have not been compared. We hypothesized that the metabolome of isolated cortical bone would be distinct from that of bone marrow and would reveal sex differences. Metabolite profiles from liquid chromatography–mass spectrometry (LC‐MS) of whole bone, isolated cortical bone, and bone marrow were generated from humeri from 20‐week‐old female C57Bl/6J mice. The cortical bone metabolomes were then compared for 20‐week‐old female and male C57Bl/6J mice. Femurs from male and female mice were evaluated for flexural material properties and were then categorized into bone strength groups. The metabolome of isolated cortical bone was distinct from both whole bone and bone marrow. We also found sex differences in the isolated cortical bone metabolome. Based on metabolite pathway analysis, females had higher lipid metabolism, and males had higher amino acid metabolism. High‐strength bones, regardless of sex, had greater tryptophan and purine metabolism. For males, high‐strength bones had upregulated nucleotide metabolism, whereas lower‐strength bones had greater pentose phosphate pathway metabolism. Because the higher‐strength groups (females compared with males, high‐strength males compared with lower‐strength males) had higher serum type I collagen cross‐linked C‐telopeptide (CTX1)/procollagen type 1 N propeptide (P1NP), we estimate that the metabolomic signature of bone strength in our study at least partially reflects differences in bone turnover. These data provide novel insight into bone bioenergetics and the sexual dimorphic nature of bone material properties in C57Bl/6 mice. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Association of Increased Serum Lipopolysaccharide, But Not Microbial Dysbiosis, With Obesity-Related Osteoarthritis

OBJECTIVE

To test the hypothesis that an altered gut microbiota (dysbiosis) plays a role in obesity-associated osteoarthritis (OA).

METHODS

Stool and blood samples were collected from 92 participants with a body mass index (BMI) ≥30 kg/m2 , recruited from the Johnston County Osteoarthritis Project. OA patients (n = 50) had hand and knee OA (Kellgren/Lawrence [K/L] grade ≥2 or arthroplasty). Controls (n = 42) had no hand OA and a K/L grade of 0-1 for the knees. Compositional analysis of stool samples was carried out by 16S ribosomal RNA amplicon sequencing. Alpha- and beta-diversity and differences in taxa relative abundances were determined. Blood samples were used for multiplex cytokine analysis and measures of lipopolysaccharide (LPS) and LPS binding protein. Germ-free mice were gavaged with patient- or control-pooled fecal samples and fed a 40% fat, high-sucrose diet for 40 weeks. Knee OA was evaluated histologically.

RESULTS

On average, OA patients were slightly older than the controls, consisted of more women, and had a higher mean BMI, higher mean Western Ontario and McMaster Universities Osteoarthritis Index pain score, and higher mean K/L grade. There were no significant differences in α- or β-diversity or genus level composition between patients and controls. Patients had higher plasma levels of osteopontin (P = 0.01) and serum LPS (P < 0.0001) compared to controls. Mice transplanted with patient or control microbiota exhibited a significant difference in α-diversity (P = 0.02) and β-diversity, but no differences in OA severity were observed.

CONCLUSION

The lack of differences in the gut microbiota, but increased serum LPS levels, suggest the possibility that increased intestinal permeability allowing for greater absorption of LPS, rather than a dysbiotic microbiota, may contribute to the development of OA associated with obesity.

Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis

OBJECTIVE

The objective of this exploratory study was to determine if perturbations in gut microbial composition and the gut metabolome could be linked to individuals with obesity and osteoarthritis (OA).

METHODS

Fecal samples were collected from obese individuals diagnosed with radiographic hand plus knee OA (n = 59), defined as involvement of at least 3 joints across both hands, and a Kellgren-Lawrence (KL) grade 2-4 (or total knee replacement) in at least one knee. Controls (n = 33) were without hand OA and with KL grade 0-1 knees. Fecal metabolomes were analyzed by a UHPLC/Q Exactive HFx mass spectrometer. Microbiome composition was determined in fecal samples by 16 S ribosomal RNA amplicon sequencing (rRNA-seq). Stepwise logistic regression models were built to determine microbiome and/or metabolic characteristics of OA.

RESULTS

Untargeted metabolomics analysis indicated that OA cases had significantly higher levels of di- and tripeptides and significant perturbations in microbial metabolites including propionic acid, indoles, and other tryptophan metabolites. Pathway analysis revealed several significantly perturbed pathways associated with OA including leukotriene metabolism, amino acid metabolism and fatty acid utilization. Logistic regression models selected metabolites associated with the gut microbiota and leaky gut syndrome as significant predictors of OA status, particularly when combined with the rRNA-seq data.

CONCLUSIONS

Adults with obesity and knee plus hand OA have distinct fecal metabolomes characterized by increased products of proteolysis, perturbations in leukotriene metabolism, and changes in microbial metabolites compared with controls. These metabolic perturbations indicate a possible role of dysregulated proteolysis in OA.

Identification and development of a novel 5-gene diagnostic model based on immune infiltration analysis of osteoarthritis

Background

Osteoarthritis (OA), which is due to the progressive loss and degeneration of articular cartilage, is the leading cause of disability worldwide. Therefore, it is of great significance to explore OA biomarkers for the prevention, diagnosis, and treatment of OA.

Methods and materials

The GSE129147, GSE57218, GSE51588, GSE117999, and GSE98918 datasets with normal and OA samples were downloaded from the Gene Expression Omnibus (GEO) database. The GSE117999 and GSE98918 datasets were integrated, and immune infiltration was evaluated. The differentially expressed genes (DEGs) were analyzed using the limma package in R, and weighted gene co-expression network analysis (WGCNA) was used to explore the co-expression genes and co-expression modules. The co-expression module genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and hub genes were identified by the degree, MNC, closeness, and MCC algorithms. The hub genes were used to construct a diagnostic model based on support vector machines.

Results

The Immune Score in the OA samples was significantly higher than in the normal samples, and a total of 2313 DEGs were identified. Through WGCNA, we found that the yellow module was significantly positively correlated with the OA samples and Immune Score and negatively correlated with the normal samples. The 142 DEGs of the yellow module were related to biological processes such as regulation of inflammatory response, positive regulation of inflammatory response, blood vessel morphogenesis, endothelial cell migration, and humoral immune response. The intersections of the genes obtained by the 4 algorithms resulted in 5 final hub genes, and the diagnostic model constructed with these 5 genes showed good performance in the training and validation cohorts.

Conclusions

The 5-gene diagnostic model can be used to diagnose OA and guide clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03183-9.

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.

The microbiome mediates epiphyseal bone loss and metabolomic changes after acute joint trauma in mice

OBJECTIVE

To compare the early responses to joint injury in conventional and germ-free mice.

DESIGN

Post-traumatic osteoarthritis (PTOA) was induced using a non-invasive anterior cruciate ligament rupture model in 20-week old germ-free (GF) and conventional C57BL/6 mice. Injury was induced in the left knees of n = 8 GF and n = 10 conventional mice. To examine the effects of injury, n = 5 GF and n = 9 conventional naïve control mice were used. Mice were euthanized 7 days post-injury, followed by synovial fluid recovery for global metabolomic profiling and analysis of epiphyseal trabecular bone by micro-computed tomography (μCT). Global metabolomic profiling assessed metabolic differences in the joint response to injury between GF and conventional mice. Magnitude of trabecular bone volume loss measured using μCT assessed early OA progression in GF and conventional mice.

RESULTS

μCT found that GF mice had significantly less trabecular bone loss compared to conventional mice, indicating that the GF status was protective against early OA changes in bone structure. Global metabolomic profiling showed that conventional mice had greater variability in their metabolic response to injury, and a more distinct joint metabolome compared to their corresponding controls. Furthermore, differences in the response to injury in GF compared to conventional mice were linked to mouse metabolic pathways that regulate inflammation associated with the innate immune system.

CONCLUSIONS

These results suggest that the gut microbiota promote the development of PTOA during the acute phase following joint trauma possibly through the regulation of the innate immune system.

The role of metabolism in chondrocyte dysfunction and the progression of osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease characterized by low-grade inflammation and high levels of clinical heterogeneity. Aberrant chondrocyte metabolism is a response to changes in the inflammatory microenvironment and may play a key role in cartilage degeneration and OA progression. Under conditions of environmental stress, chondrocytes tend to adapt their metabolism to microenvironmental changes by shifting from one metabolic pathway to another, for example from oxidative phosphorylation to glycolysis. Similar changes occur in other joint cells, including synoviocytes. Switching between these pathways is implicated in metabolic alterations that involve mitochondrial dysfunction, enhanced anaerobic glycolysis, and altered lipid and amino acid metabolism. The shift between oxidative phosphorylation and glycolysis is mainly regulated by the AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) pathways. Chondrocyte metabolic changes are likely to be a feature of different OA phenotypes. Determining the role of chondrocyte metabolism in OA has revealed key features of disease pathogenesis. Future research should place greater emphasis on immunometabolism and altered metabolic pathways as a means to understand the pathophysiology of age-related OA. This knowledge will advance the development of new drugs against therapeutic targets of metabolic significance.

Phenylalanine Is a Novel Marker for Radiographic Knee Osteoarthritis Progression: The MOST Study

OBJECTIVE

To identify plasma markers associated with an increased risk of radiographic knee osteoarthritis(OA) progression using a metabolomics approach.

METHODS

Study participants were from the Multicenter Osteoarthritis Study (MOST) and were categorized into 2 groups based on the presence of baseline radiographic OA. Subjects in group 1 had unilateral knee OA and subjects in group 2 had bilateral knee OA. Progression was defined as a half-grade or greater worsening in joint space width at 30-month follow-up. For group 1, a participant progressed when their OA knee showed radiographic progression and the contralateral knee developed OA; for group 2, a participant progressed when both knees with OA showed radiographic progression. Metabolomic profiling was performed on plasma samples collected at baseline and logistic regression was performed to test the association between each metabolite and knee OA progression after adjustment for age, sex, BMI, and clinic site. Significance was defined as P ≤ 0.0003 in the combined analysis.

RESULTS

There were 234 progressors (57 in group 1 and 177 in group 2) and 322 nonprogressors (206 in group 1 and 116 in group 2) included in the analyses. Among 157 metabolites studied, we found that odds of progression were 1.46 times higher per SD increase of phenylalanine level (95% CI 1.20-1.77, P = 0.0001) in the combined analysis. Sex-specific analysis showed that an association was seen in women (P = 0.0002) but not in men.

CONCLUSION

Our data suggest that phenylalanine might be a novel plasma marker for higher risk of bilateral radiographic knee OA progression in women.

Inflammatory and Noninflammatory Synovial Fluids Exhibit New and Distinct Tribological Endotypes

Inferior synovial lubrication is a hallmark of osteoarthritis (OA), and synovial fluid (SF) lubrication and composition are variable among OA patients. Hyaluronic acid (HA) viscosupplementation is a widely used therapy for improving SF viscoelasticity and lubrication, but it is unclear how the effectiveness of HA viscosupplements varies with arthritic endotype. The objective of this study was to investigate the effects of the HA viscosupplement, Hymovis®, on the lubricating properties of diseased SF from patients with noninflammatory OA and inflammatory arthritis (IA). The composition (cytokine, HA, and lubricin concentrations) of the SF was measured as well as the mechanical properties (rheology, tribology) of the SF alone and in a 1:1 mixture with the HA viscosupplement. Using rotational rheometry, no difference in SF viscosity was detected between disease types, and the addition of HA significantly increased all fluids' viscosities. In noninflammatory OA SF, friction coefficients followed a typical Stribeck pattern, and their magnitude was decreased by the addition of HA. While some of the IA SF also showed typical Stribeck behavior, a subset showed more erratic behavior with highly variable and larger friction coefficients. Interestingly, this aberrant behavior was not eliminated by the addition of HA, and it was associated with low concentrations of lubricin. Aberrant SF exhibited significantly lower effective viscosities compared to noninflammatory OA and IA SF with typical tribological behavior. Collectively, these results suggest that different endotypes of arthritis exist with respect to lubrication, which may impact the effectiveness of HA viscosupplements in reducing friction.

Metabolomic Profiling in the Characterization of Degenerative Bone and Joint Diseases

Osteoarthritis and inflammatory arthropathies are a cause of significant morbidity globally. New research elucidating the metabolic derangements associated with a variety of bone and joint disorders implicates various local and systemic metabolites, which further elucidate the underlying molecular mechanisms associated with these destructive disease processes. In osteoarthritis, atty acid metabolism has been implicated in disease development, both locally and systemically. Several series of rheumatoid arthritis patients have demonstrated overlapping trends related to histidine and glyceric acid, while other series showed similar results of increased cholesterol and glutamic acid. Studies comparing osteoarthritis and rheumatoid arthritis reported elevated gluconic acid and glycolytic- and tricarboxylic acid-related substrates in patients with osteoarthritis, while lysosphingolipids and cardiolipins were elevated only in patients with rheumatoid arthritis. Other bone and joint disorders, including osteonecrosis, intervertebral disc degeneration, and osteoporosis, also showed significant alterations in metabolic processes. The identification of the molecular mechanisms of osteoarthritis and inflammatory arthropathies via metabolomics-based workflows may allow for the development of new therapeutic targets to improve the quality of life in these patient populations.

Molecular taxonomy of osteoarthritis for patient stratification, disease management and drug development: biochemical markers associated with emerging clinical phenotypes and molecular endotypes

PURPOSE OF REVIEW

This review focuses on the molecular taxonomy of osteoarthritis from the perspective of molecular biomarkers. We discuss how wet biochemical markers may be used to understand disease pathogenesis and progression and define molecular endotypes of osteoarthritis and how these correspond to clinical phenotypes.

RECENT FINDINGS

Emerging evidence suggests that osteoarthritis is a heterogeneous and multifaceted disease with multiple causes, molecular endotypes and corresponding clinical phenotypes. Biomarkers may be employed as tools for patient stratification in clinical trials, enhanced disease management in the primary care centres of the future and for directing more rational and targeted osteoarthritis drug development. Proximal molecular biomarkers (e.g synovial fluid) are more likely to distinguish between molecular endotypes because there is less interference from systemic sources of biomarker noise, including comorbidities.

SUMMARY

In this review, we have focused on the molecular biomarkers of four distinct osteoarthritis subtypes including inflammatory, subchondral bone remodelling, metabolic syndrome and senescent age-related endotypes, which have corresponding phenotypes. Progress in the field of osteoarthritis endotype and phenotype research requires a better understanding of molecular biomarkers that may be used in conjunction with imaging, pain and functional assessments for the design of more effective, stratified and individualized osteoarthritis treatments.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Phenotypes of osteoarthritis: current state and future implications

In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic development and unravelling the pathogenesis of this complex condition. Several phenotypes and endotypes have been proposed albeit none has been sufficiently validated for clinical or research use as yet. This review discusses the latest advances in OA phenotyping including how new modern statistical strategies based on machine learning and big data can help advance this field of research.

Characterization of synovial fluid metabolomic phenotypes of cartilage morphological changes associated with osteoarthritis

OBJECTIVE

Osteoarthritis (OA) is a multifactorial disease with etiological heterogeneity. The objective of this study was to classify OA subgroups by generating metabolomic phenotypes from human synovial fluid.

DESIGN

Post mortem synovial fluids (n = 75) were analyzed by high performance-liquid chromatography mass spectrometry (LC-MS) to measure changes in the global metabolome. Comparisons of healthy (grade 0), early OA (grades I-II), and late OA (grades III-IV) donor populations were considered to reveal phenotypes throughout disease progression.

RESULTS

Global metabolomic profiles in synovial fluid were distinct between healthy, early OA, and late OA donors. Pathways differentially activated among these groups included structural deterioration, glycerophospholipid metabolism, inflammation, central energy metabolism, oxidative stress, and vitamin metabolism. Within disease states (early and late OA), subgroups of donors revealed distinct phenotypes. Synovial fluid metabolomic phenotypes exhibited increased inflammation (early and late OA), oxidative stress (late OA), or structural deterioration (early and late OA) in the synovial fluid.

CONCLUSION

These results revealed distinct metabolic phenotypes in human synovial fluid, provide insight into pathogenesis, represent novel biomarkers, and can move toward developing personalized interventions for subgroups of OA patients.

Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach

PURPOSE OF THE REVIEW

Osteoarthritis (OA) is a multifactorial and progressive disease affecting whole synovial joint. The extract pathogenic mechanisms and diagnostic biomarkers of OA remain unclear. In this article, we review the studies related to metabolomics of OA, discuss the biomarkers as a tool for early OA diagnosis. Furthermore, we examine the major studies on the application of metabolomics methodology in the complex context of OA and create a bridge from findings in basic science to their clinical utility.

RECENT FINDINGS

Recently, the tissue metabolomics signature permits a view into transitional phases between the healthy and OA joint. Both nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry-based metabolomics approaches have been used to interrogate the metabolic alterations that may indicate the complex progression of OA. Specifically, studies on alterations pertaining to lipids, glucose, and amino acid metabolism have aided in the understanding of the complex pathogenesis of OA. The discovery of identified metabolites could be important for diagnosis and staging of OA, as well as for the assessment of efficacy of new drugs.

Alteration of Metabolic Pathways in Osteoarthritis

Sir Archibald Edward Garrod, who pioneered the field of inborn errors of metabolism and first elucidated the biochemical basis of alkaptonuria over 100 years ago, suggested that inborn errors of metabolism were "merely extreme examples of variations of chemical behavior which are probably everywhere present in minor degrees, just as no two individuals of a species are absolutely identical in bodily structure neither are their chemical processes carried out on exactly the same lines", and that this "chemical individuality [confers] predisposition to and immunities from various mishaps which are spoken of as diseases". Indeed, with advances in analytical biochemistry, especially the development of metabolomics in the post-genomic era, emerging data have been demonstrating that the levels of many metabolites do show substantial interindividual variation, and some of which are likely to be associated with common diseases, such as osteoarthritis (OA). Much work has been reported in the literature on the metabolomics of OA in recent years. In this narrative review, we provided an overview of the identified alteration of metabolic pathways in OA and discussed the role of those identified metabolites and related pathways in OA diagnosis, prognosis, and treatment.

The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology

Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis.

Metabonomics delineates allergic reactions induced by Shuang-huang-lian injection in rats using ultra performance liquid chromatography-mass spectrometry

Shuang-huang-lian Injection (SHLI) is the first successfully developed drug from traditional Chinese medicine (TCM) powder for injection, since its use for the treatment of acute respiratory tract infection, pneumonia, influenza, etc. At the same time, its allergic reactions have also emerged, which limits clinical applications. However, few scholars pay attention to the mechanism of allergic reactions. In this present study, metabonomics technology was used to explore the changes in endogenous metabolites in urine of the rat model of SHLI induced allergic reaction; we and analyzed the metabolites, metabolic pathway, and the mechanism which were closely related to the allergic reactions. The levels of serum histamine and tryptase were examined and changes in histomorphology were also observed. Based on the UPLC-Q-TOF/MS metabonomics, we carried out the pattern recognition analysis, selected potential biomarkers associated with allergic reactions, and explored the pathological mechanism for SHLI induced allergic reaction, which laid the foundation for the safety research of SHLI. Our results showed that SHLI increased the levels of serum histamine and tryptase in rats with allergic reaction; we determined 15 biomarkers in rat allergic reaction model induced by SHLI and found multiple metabolic pathways involved, such as metabolism of linolenic acid, phenylalanine, amino acid, 2-oxo acid, and purine and other metabolic pathways.

Soluble biochemical markers of osteoarthritis: Are we close to using them in clinical practice?

Osteoarthritis (OA) is the most common form of arthritis and a major cause of pain and disability. Recent work suggests that the global burden of OA is increasing, and costs associated with treatment are expected to increase dramatically as the aging human population expands. OA is currently diagnosed using radiography, but this technique is an indirect and insensitive measure of alterations in articular cartilage and fails to measure dynamic inflammatory processes in the joint. Radiographic changes detected overtime are small and occur in only a subset (progressors) of patients with OA. Therefore, we diagnose patients with OA on the basis of a diagnostic classification that is outdated. We also use the same tools and approaches for assessing the efficacy of new pharmacological and nonpharmacological interventions. In this review, we discuss the utility of soluble biochemical markers as biomarkers of OA and discuss whether we are close to using them in clinical practice. Combining patient information, functional imaging and carefully selected panels of biomarkers can help in achieving enhanced patient stratification and lead to better designed clinical trials. Biomarkers can be used for molecular endotyping and for developing more effective and more personalized treatments that will enhance clinical care for patients with OA.

Developing a toolkit for the assessment and monitoring of musculoskeletal ageing

The complexities and heterogeneity of the ageing process have slowed the development of consensus on appropriate biomarkers of healthy ageing. The Medical Research Council–Arthritis Research UK Centre for Integrated research into Musculoskeletal Ageing (CIMA) is a collaboration between researchers and clinicians at the Universities of Liverpool, Sheffield and Newcastle. One of CIMA’s objectives is to ‘Identify and share optimal techniques and approaches to monitor age-related changes in all musculoskeletal tissues, and to provide an integrated assessment of musculoskeletal function’—in other words to develop a toolkit for assessing musculoskeletal ageing. This toolkit is envisaged as an instrument that can be used to characterise and quantify musculoskeletal function during ‘normal’ ageing, lend itself to use in large-scale, internationally important cohorts, and provide a set of biomarker outcome measures for epidemiological and intervention studies designed to enhance healthy musculoskeletal ageing. Such potential biomarkers include: biochemical measurements in biofluids or tissue samples, in vivo measurements of body composition, imaging of structural and physical properties, and functional tests. This review assesses candidate biomarkers of musculoskeletal ageing under these four headings, details their biological bases, strengths and limitations, and makes practical recommendations for their use. In addition, we identify gaps in the evidence base and priorities for further research on biomarkers of musculoskeletal ageing.

Osteoarthritis induced by destabilization of the medial meniscus is reduced in germ-free mice

OBJECTIVE

To determine the contribution of the gut microbiota to the development of injury-induced osteoarthritis (OA).

DESIGN

OA was induced using the destabilized medial meniscus (DMM) model in 20 germ-free (GF) C57BL/6J male mice housed in a gnotobiotic facility and 23 strain-matched specific pathogen free (SPF) mice in 2 age groups -13.5 weeks avg age at DMM (17 SPF and 15 GF) and 43 weeks avg age at DMM (6 SPF and 5 GF). OA severity was measured using scores for articular cartilage structure (ACS), loss of safranin O (SafO) staining, osteophyte size, and synovial hyperplasia. Microbiome analysis by 16S rRNA amplicon sequencing was performed on stool samples and LPS and LPS binding protein (LBP) were measured in plasma.

RESULTS

Compared to the SPF DMM mice, the maximum (MAX) ACS score per joint was 28% lower (p = 0.036) in GF DMM mice while the SafO sum score of all sections evaluated per joint was decreased by 31% (p = 0.009). The differences between SPF and GF mice in these scores were greater when only the younger mice were included in the analysis. The younger GF DMM mice also had significant reductions in osteophyte size (36%, P = 0.0119) and LBP (27%, P = 0.007) but not synovial scores or LPS. Differences in relative abundance of a number of Operational Taxonomic Units (OTUs) were noted between SPF mice with high vs low maximum ACS scores.

CONCLUSIONS

These results suggest factors related to the gut microbiota promote the development of OA after joint injury.

What did we learn from 'omics' studies in osteoarthritis

PURPOSE OF REVIEW

'Omics' technologies developed for the massive analysis of the major biologically relevant molecules (genes, proteins, metabolites) have been applied to the study of osteoarthritis (OA) for more than a decade.

RECENT FINDINGS

'Omics' studies have undoubtedly contributed to increase the knowledge on pathogenic processes related with OA and have provided hundreds to thousands of molecules that might have a putative biomarker utility for this disease.

SUMMARY

This review describes the most recent 'omics' studies in OA research, their conclusions, and discuss those remaining challenges. Still many validation studies must be performed in large and well-characterized cohorts for the translation of the findings from 'omics' strategies to clinical applications. The development of tools for the intelligent integration of 'omics' data with clinical and imaging information is also mandatory to take full profit of the work that has been already performed.

Human microbiota, blood group antigens, and disease

Far from being just "bugs in our guts," the microbiota interacts with the body in previously unimagined ways. Research into the genome and the microbiome has revealed that the human body and the microbiota have a long-established but only recently recognized symbiotic relationship; homeostatic balance between them regulates body function. That balance is fragile, easily disturbed, and plays a fundamental role in human health-our very survival depends on the healthy functioning of these microorganisms. Increasing rates of cardiovascular, autoimmune, and inflammatory diseases, as well as epidemics in obesity and diabetes in recent decades are believed to be explained, in part, by unintended effects on the microbiota from vaccinations, poor diets, environmental chemicals, indiscriminate antibiotic use, and "germophobia." Discovery and exploration of the brain-gut-microbiota axis have provided new insights into functional diseases of the gut, autoimmune and stress-related disorders, and the role of probiotics in treating certain affective disorders; it may even explain some aspects of autism. Research into dietary effects on the human gut microbiota led to its classification into three proposed enterotypes, but also revealed the surprising role of blood group antigens in shaping those populations. Blood group antigens have previously been associated with disease risks; their subsequent association with the microbiota may reveal mechanisms that lead to development of nutritional interventions and improved treatment modalities. Further exploration of associations between specific enteric microbes and specific metabolites will foster new dietary interventions, treatment modalities, and genetic therapies, and inevitably, their application in personalized healthcare strategies. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Translational, Genomic, and Systems Medicine > Translational Medicine Physiology > Mammalian Physiology in Health and Disease.

Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers

Osteoarthritis affects over 250 million individuals worldwide. Currently, there are no options for early diagnosis of osteoarthritis, demonstrating the need for biomarker discovery. To find biomarkers of osteoarthritis in human synovial fluid, we used high performance liquid-chromatography mass spectrometry for global metabolomic profiling. Metabolites were extracted from human osteoarthritic (n = 5), rheumatoid arthritic (n = 3), and healthy (n = 5) synovial fluid, and a total of 1233 metabolites were detected. Principal components analysis clearly distinguished the metabolomic profiles of diseased from healthy synovial fluid. Synovial fluid from rheumatoid arthritis patients contained expected metabolites consistent with the inflammatory nature of the disease. Similarly, unsupervised clustering analysis found that each disease state was associated with distinct metabolomic profiles and clusters of co-regulated metabolites. For osteoarthritis, co-regulated metabolites that were upregulated compared to healthy synovial fluid mapped to known disease processes including chondroitin sulfate degradation, arginine and proline metabolism, and nitric oxide metabolism. We utilized receiver operating characteristic analysis to determine the diagnostic value of each metabolite and identified 35 metabolites as potential biomarkers of osteoarthritis, with an area under the receiver operating characteristic curve >0.9. These metabolites included phosphatidylcholine, lysophosphatidylcholine, ceramides, myristate derivatives, and carnitine derivatives. This pilot study provides strong justification for a larger cohort-based study of human osteoarthritic synovial fluid using global metabolomics. The significance of these data is the demonstration that metabolomic profiling of synovial fluid can identify relevant biomarkers of joint disease.

Osteoarthritis biomarkers: year in review

OBJECTIVE

To summarise important findings from biomarker studies relevant to osteoarthritis (OA), published between April 2016 and March 2017; to consider these findings in the context of new discoveries and technologies, and clinical and scientific need in OA.

DESIGN

Studies were selected by PubMed search, conducted between 01/04/2016 and 01/03/2017. MeSH terms [biomarker] AND [OA] were used; the search was restricted to Human, English language and Full Text Available publications, which yielded 50 eligible publications. Any biomarker was considered, including non-proteins and other clinical measurements.

RESULTS

Three main areas are overviewed: 1) Studies examining highly validated biomarkers, in the FNIH OA Biomarkers Consortium and elsewhere, particularly their ongoing application and validation. Control reference intervals, work on predictive validity and other longitudinal studies examining prognostic value of biomarkers in large cohorts are reviewed. 2) Novel studies relating to biomarkers of inflammation are discussed, including complement, the performance of markers of so-called 'cold inflammation' and results from clinical trials including biomarkers. 3) Discovery studies, including whole blood RNA, proteomics and metabolomics are reviewed, with an emphasis on new technologies.

CONCLUSIONS

Discovery, characterisation and qualification of various biomarkers is ongoing; several novel protein and non-protein candidate biomarkers have been reported this year. Biomarkers provide us with an opportunity to better diagnose and stratify the disease, via established panels or new discovery approaches. Improving quality of sampling and testing, and measuring large numbers of markers simultaneously in large cohorts would seem likely to identify new clinically applicable biomarkers, which are still much needed in this disease.

Effects of dietary weight loss with and without exercise on interstitial matrix turnover and tissue inflammation biomarkers in adults with knee osteoarthritis: the Intensive Diet and Exercise for Arthritis trial (IDEA)

OBJECTIVE

To examine the effects of dietary weight loss, with and without exercise, on selected soluble biomarkers in overweight and obese older adults with symptomatic knee osteoarthritis (OA).

DESIGN

Blood samples were analyzed from 429 participants in the Intensive Diet and Exercise for Arthritis (IDEA) trial randomized to either an 18 month exercise control group (E), weight loss diet (D), or D + E. C1M, C2M, C3M and CRPM biomarkers and interleukin-6 (IL-6) were quantitated using ELISAs. Radiographic progression was defined as a decrease in joint space width of ≥0.7 mm. Statistical modeling of group means and associations used mixed models adjusted for visit, baseline body mass index (BMI), gender, and baseline values of the outcome.

RESULTS

Compared to the E control group, C1M was significantly lower in the D and D + E groups at both 6 and 18 months while C3M was significantly lower in D and D + E at 6 months and in D + E at 18 months. C2M did not change in any group. Using data from all groups, change in C1M (P < 0.0001), C3M (P < 0.0001), as well as CRPM (P = 0.0004) from baseline to 18 months was positively associated with change in weight. No marker was associated with change in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain or radiographic progression. C3M (P = 0.008) and CRPM (P = 0.028) were positively associated with change in WOMAC function. Change in IL-6 was positively associated with change in C1M, C3M, and CRPM.

CONCLUSION

Overweight and obese adults with knee OA who lost weight from diet and diet plus exercise reduced serum markers of interstitial matrix turnover and inflammation but not type II collagen degradation.

Metabolomics as a promising tool for early osteoarthritis diagnosis

Osteoarthritis (OA) is the main cause of disability worldwide, due to progressive articular cartilage loss and degeneration. According to recent research, OA is more than just a degenerative disease due to some metabolic components associated to its pathogenesis. However, no biomarker has been identified to detect this disease at early stages or to track its development. Metabolomics is an emerging field and has the potential to detect many metabolites in a single spectrum using high resolution nuclear magnetic resonance (NMR) techniques or mass spectrometry (MS). NMR is a reproducible and reliable non-destructive analytical method. On the other hand, MS has a lower detection limit and is more destructive, but it is more sensitive. NMR and MS are useful for biological fluids, such as urine, blood plasma, serum, or synovial fluid, and have been used for metabolic profiling in dogs, mice, sheep, and humans. Thus, many metabolites have been listed as possibly associated to OA pathogenesis. The goal of this review is to provide an overview of the studies in animal models and humans, regarding the use of metabolomics as a tool for early osteoarthritis diagnosis. The concept of osteoarthritis as a metabolic disease and the importance of detecting a biomarker for its early diagnosis are highlighted. Then, some studies in plasma and synovial tissues are shown, and finally the application of metabolomics in the evaluation of synovial fluid is described.

Mixing omics: combining genetics and metabolomics to study rheumatic diseases

Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

Osteoarthritis Year in Review 2016: biomarkers (biochemical markers)

PURPOSE

The aim of this "Year in Review" article is to summarize and discuss the implications of biochemical marker related articles published between the Osteoarthritis Research Society International (OARSI) 2015 Congress in Seattle and the OARSI 2016 Congress in Amsterdam.

METHODS

The PubMed/MEDLINE bibliographic database was searched using the combined keywords: 'biomarker' and 'osteoarthritis'. The PubMed/MEDLINE literature search was conducted using the Advanced Search Builder function (http://www.ncbi.nlm.nih.gov/pubmed/advanced).

RESULTS

Over two hundred new biomarker-related papers were published during the literature search period. Some papers identified new biomarkers whereas others explored the biological properties and clinical utility of existing markers. There were specific references to several adipocytokines including leptin and adiponectin. ADAM Metallopeptidase with Thrombospondin Type 1 motif 4 (ADAMTS-4) and aggrecan ARGS neo-epitope fragment (ARGS) in synovial fluid (SF) and plasma chemokine (CeC motif) ligand 3 (CCL3) were reported as potential new knee biomarkers. New and refined proteomic technologies and novel assays including a fluoro-microbead guiding chip (FMGC) for measuring C-telopeptide of type II collagen (CTX-II) in serum and urine and a novel magnetic nanoparticle-based technology (termed magnetic capture) for collecting and concentrating CTX-II, were described this past year.

CONCLUSION

There has been steady progress in osteoarthritis (OA) biomarker research in 2016. Several novel biomarkers were identified and new technologies have been developed for measuring existing biomarkers. However, there has been no "quantum leap" this past year and identification of novel early OA biomarkers remains challenging. During the past year, OARSI published a set of recommendations for the use of soluble biomarkers in clinical trials, which is a major step forward in the clinical use of OA biomarkers and bodes well for future OA biomarker development.

Blood type biochemistry and human disease

Associations between blood type and disease have been studied since the early 1900s when researchers determined that antibodies and antigens are inherited. In the 1950s, the chemical identification of the carbohydrate structure of surface antigens led to the understanding of biosynthetic pathways. The blood type is defined by oligosaccharide structures, which are specific to the antigens, thus, blood group antigens are secondary gene products, while the primary gene products are various glycosyltransferase enzymes that attach the sugar molecules to the oligosaccharide chain. Blood group antigens are found on red blood cells, platelets, leukocytes, plasma proteins, certain tissues, and various cell surface enzymes, and also exist in soluble form in body secretions such as breast milk, seminal fluid, saliva, sweat, gastric secretions, urine, and amniotic fluid. Recent advances in technology, biochemistry, and genetics have clarified the functional classifications of human blood group antigens, the structure of the A, B, H, and Lewis determinants and the enzymes that produce them, and the association of blood group antigens with disease risks. Further research to identify differences in the biochemical composition of blood group antigens, and the relationship to risks for disease, can be important for the identification of targets for the development of nutritional intervention strategies, or the identification of druggable targets. WIREs Syst Biol Med 2016, 8:517-535. doi: 10.1002/wsbm.1355 For further resources related to this article, please visit the WIREs website.