Relationship Between Blood Plasma and Synovial Fluid Metabolite Concentrations in Patients with Osteoarthritis

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

Background

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

Methods

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

Results

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

Conclusion

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

Omega-3 Supplementation and Its Effects on Osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease characterized by the destruction of the articular cartilage, resulting in a pro-inflammatory response. The progression of OA is multifactorial and is influenced by the underlying cause of inflammation, which includes but is not limited to trauma, metabolism, biology, comorbidities, and biomechanics. Although articular cartilage is the main tissue affected in osteoarthritis, the chronic inflammatory environment negatively influences the surrounding synovium, ligaments, and subchondral bone, further limiting their functional abilities and enhancing symptoms of OA. Treatment for osteoarthritis remains inconsistent due to the inability to determine the underlying mechanism of disease onset, severity of symptoms, and complicating comorbidities. In recent years, diet and nutritional supplements have gained interest regarding slowing the disease process, prevention, and treatment of OA. This is due to their anti-inflammatory properties, which result in a positive influence on pain, joint mobility, and cartilage formation. More specifically, omega-3 polyunsaturated fatty acids (PUFA) have demonstrated an influential role in the progression of OA, resulting in the reduction of cartilage destruction, inhibition of pro-inflammatory cytokine cascades, and production of oxylipins that promote anti-inflammatory pathways. The present review is focused on the assessment of evidence explaining the inflammatory processes of osteoarthritis and the influence of omega-3 supplementation to modulate the progression of osteoarthritis.

Nuclear magnetic resonance-based metabolomic study of rat serum after anterior cruciate ligament injury

Anterior cruciate ligament (ACL) injury, a common sports injury, is associated with a high risk of subsequent osteoarthritis (OA), which can cause serious pain and disability. Understanding the detailed mechanism underlying the predisposition of knee with ACL injury to secondary OA at an early stage is key to preventing future degradation and progression to a clinically significant disease. A total of 56 male Sprague Dawley rats (age, 8 weeks; weight, 180-220 g) were randomly divided into three experimental groups: control, ACL transection (ACLT; where surgical procedure was performed with ACLT), and sham (where surgical procedure was performed without ACLT). The ACLT and sham groups were further divided into three subgroups based on when the rats were sacrificed: 4, 8, and 12 weeks after the surgical procedure. The control group and the aforementioned subgroups contained 8 rats each. We used nuclear magnetic resonance (NMR)-based metabolomic analysis to analyze rat serum samples for the metabolic characteristics and the underlying mechanisms. In total, 28 metabolites were identified in the NMR spectra of the rat sera. At 4 and 8 weeks postoperatively, the sham group demonstrated metabolic profiles different from those of the ACLT group. However, this difference was not observed 12 weeks postoperatively. In total, five metabolites (acetate, succinate, sn-glycero-3-phosphocholine, glucose, and phenylalanine) and five metabolic pathways (phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; pyruvate metabolism; starch and sucrose metabolism; and histidine metabolism) demonstrated significant differences between the ACLT and sham groups. ACL injury was noted to considerably affect biochemical homeostasis and metabolism; however, these metabolic changes persisted briefly. Moreover, glucose was a characteristic metabolite, and several energy-related metabolic pathways were significantly disturbed. Therefore, an ACL injury may lead to considerable impairments in energy metabolism. Abnormal glucose levels facilitate chondrocyte function impairment and thereby lead to OA progression. Furthermore, lactate may aid in identifying metabolic changes specific to knee trauma not related to an ACL injury. Overall, the metabolic changes in rat serum after an ACL injury were closely related to disturbances in energy metabolism and amino acid metabolism. The current results may aid in understanding the pathogenesis of posttraumatic osteoarthritis.

Relevance of Biomarkers in Serum vs. Synovial Fluid in Patients with Knee Osteoarthritis

The association between structural changes and pain sensation in osteoarthritis (OA) remains unclear. Joint deterioration in OA leads to the release of protein fragments that can either systemically (serum) or locally (synovial fluid; SF) be targeted as biomarkers and describe structural changes and potentially pain. Biomarkers of collagen type I (C1M), type II (C2M), type III (C3M), type X (C10C), and aggrecan (ARGS) degradation were measured in the serum and SF of knee OA patients. Spearman's rank correlation was used to assess the correlation of the biomarkers' levels between serum and SF. Linear regression adjusted for confounders was used to evaluate the associations between the biomarkers' levels and clinical outcomes. The serum C1M levels were negatively associated with subchondral bone density. The serum C2M levels were negatively associated with KL grade and positively associated with minimum joint space width (minJSW). The C10C levels in SF were negatively associated with minJSW and positively associated with KL grade and osteophyte area. Lastly, the serum C2M and C3M levels were negatively associated with pain outcomes. Most of the biomarkers seemed to mainly be associated with structural outcomes. The overall biomarkers of extracellular matrix (ECM) remodeling in serum and SF may provide different information and reflect different pathogenic processes.

Correlations between metabolites in the synovial fluid and serum: A mouse injury study

Osteoarthritis occurs frequently after joint injury. Currently, osteoarthritis is diagnosed by radiographic changes that are typically found after the disease has progressed to multiple tissues. The primary objective was to compare potential metabolomic biomarkers of joint injury between synovial fluid and serum in a mouse model of posttraumatic osteoarthritis. The secondary objective was to gain insight into the pathophysiology of osteoarthritis by examining metabolomic profiles after joint injury. Twelve-week-old adult female C57BL/6 mice (n = 12) were randomly assigned to control, Day 1, or Day 8 postinjury groups. Randomly selected stifle joints were subjected to a single rapid compression. At Days 1 and 8 postinjury, serum was extracted before mice were euthanized for synovial fluid collection. Metabolomic profiling detected ~2500 metabolites across serum and synovial fluid. Of these, 179 were positively correlated and 51 were negatively correlated between synovial fluid and serum, indicating the potential for the development of metabolomic biomarkers. Synovial fluid captured injury-induced differences in metabolomic profiles at both Days 1 and 8 after injury whereas serum did not. However, synovial fluid and serum were distinct at both time points after injury. In synovial fluid, pathways of interest mapped to amino acid synthesis and degradation, bupropion degradation, and transfer RNA (tRNA) charging. In serum, pathways were amino acid synthesis and degradation, the phospholipase pathway, and nicotine degradation. These results provide a rich picture of the injury response at early time points after joint injury. Furthermore, the correlations between synovial fluid and serum metabolites suggest the potential to gain insight into intra-articular pathophysiology through analysis of serum metabolites.

Metabolomics with severity of radiographic knee osteoarthritis and early phase synovitis in middle-aged women from the Iwaki Health Promotion Project: a cross-sectional study

Background

Osteoarthritis (OA) is one of the costliest and most disabling forms of arthritis, and it poses a major public health burden; however, its detailed etiology, pathophysiology, and metabolism remain unclear. Therefore, the purpose of this study was to investigate the key plasma metabolites and metabolic pathways, especially focusing on radiographic OA severity and synovitis, from a large sample cohort study.

Methods

We recruited 596 female volunteers who participated in the Iwaki Health Promotion Project in 2017. Standing anterior-posterior radiographs of the knee were classified by the Kellgren-Lawrence (KL) grade. Radiographic OA was defined as a KL grade of ≥ 2. Individual effusion-synovitis was scored according to the Whole-Organ Magnetic Resonance Imaging Scoring System. Blood samples were collected, and metabolites were extracted from the plasma. Metabolome analysis was performed using capillary electrophoresis time-of-flight mass spectrometry. To investigate the relationships among metabolites, the KL grade, and effusion-synovitis scores, partial least squares with rank order of groups (PLS-ROG) analyses were performed.

Results

Among the 82 metabolites examined in this assay, PLS-ROG analysis identified 42 metabolites that correlated with OA severity. A subsequent metabolite set enrichment analysis using the significant metabolites showed the urea cycle and tricarboxylic acid cycle as key metabolic pathways. Moreover, further PLS-ROG analysis identified cystine (p = 0.009), uric acid (p = 0.024), and tyrosine (p = 0.048) as common metabolites associated with both OA severity and effusion-synovitis. Receiver operating characteristic analyses showed that cystine levels were moderately associated with radiographic OA (p < 0.001, area under the curve 0.714, odds ratio 3.7).

Conclusion

Large sample metabolome analyses revealed that cystine, an amino acid associated with antioxidant activity and glutamate homeostasis, might be a potential metabolic biomarker for radiographic osteoarthritis and early phase synovitis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02830-w.

Serum Metabolomic Signatures for Knee Cartilage Volume Loss over 10 Years in Community-Dwelling Older Adults

Osteoarthritis (OA) is the most prevalent joint disorder characterized by joint structural pathological changes with the loss of articular cartilage as its hallmark. Tools that can predict cartilage loss would help identify people at high risk, thus preventing OA development. The recent advance of the metabolomics provides a new avenue to systematically investigate metabolic alterations in disease and identify biomarkers for early diagnosis. Using a metabolomics approach, the current study aimed to identify serum metabolomic signatures for predicting knee cartilage volume loss over 10 years in the Tasmania Older Adult Cohort (TASOAC). Cartilage volume was measured in the medial, lateral, and patellar compartments of the knee by MRI at baseline and follow-up. Changes in cartilage volume over 10 years were calculated as percentage change per year. Fasting serum samples collected at 2.6-year follow-up were metabolomically profiled using the TMIC Prime Metabolomics Profiling Assay and pairwise metabolite ratios as the proxies of enzymatic reaction were calculated. Linear regression was used to identify metabolite ratio(s) associated with change in cartilage volume in each of the knee compartments with adjustment for age, sex, and BMI. The significance level was defined at α = 3.0 × 10−6 to control multiple testing. A total of 344 participants (51% females) were included in the study. The mean age was 62.83 ± 6.13 years and the mean BMI was 27.48 ± 4.41 kg/m2 at baseline. The average follow-up time was 10.84 ± 0.66 years. Cartilage volume was reduced by 1.34 ± 0.72%, 1.06 ± 0.58%, and 0.98 ± 0.46% per year in the medial, lateral, and patellar compartments, respectively. Our data showed that the increased ratios of hexadecenoylcarnitine (C16:1) to tetradecanoylcarnitine (C14) and C16:1 to dodecanoylcarnitine (C12) were associated with 0.12 ± 0.02% reduction per year in patellar cartilage volume (both p < 3.03 × 10−6). In conclusion, our data suggested that alteration of long chain fatty acid β-oxidation was involved in patellar cartilage loss. While confirmation is needed, the ratios of C16:1 to C14 and C12 might be used to predict long-term cartilage loss.

Fundamentals of OA. An initiative of Osteoarthritis and Cartilage. Obesity and metabolic factors in OA

OBJECTIVE

Obesity was once considered a risk factor for knee osteoarthritis (OA) primarily for biomechanical reasons. Here we provide an additional perspective by discussing how obesity also increases OA risk by altering metabolism and inflammation.

DESIGN

This narrative review is presented in four sections: 1) metabolic syndrome and OA, 2) metabolic biomarkers of OA, 3) evidence for dysregulated chondrocyte metabolism in OA, and 4) metabolic inflammation: joint tissue mediators and mechanisms.

RESULTS

Metabolic syndrome and its components are strongly associated with OA. However, evidence for a causal relationship is context dependent, varying by joint, gender, diagnostic criteria, and demographics, with additional environmental and genetic interactions yet to be fully defined. Importantly, some aspects of the etiology of obesity-induced OA appear to be distinct between men and women, especially regarding the role of adipose tissue. Metabolomic analyses of serum and synovial fluid have identified potential diagnostic biomarkers of knee OA and prognostic biomarkers of disease progression. Connecting these biomarkers to cellular pathophysiology will require future in vivo studies of joint tissue metabolism. Such studies will help reveal when a metabolic process or a metabolite itself is a causal factor in disease progression. Current evidence points towards impaired chondrocyte metabolic homeostasis and metabolic-immune dysregulation as likely factors connecting obesity to the increased risk of OA.

CONCLUSIONS

A deeper understanding of how obesity alters metabolic and inflammatory pathways in synovial joint tissues is expected to provide new therapeutic targets and an improved definition of "metabolic" and "obesity" OA phenotypes.

Effects of long-term exercise and a high-fat diet on synovial fluid metabolomics and joint structural phenotypes in mice: an integrated network analysis

OBJECTIVE

To explore how systemic factors that modify knee osteoarthritis risk are connected to 'whole-joint' structural changes by evaluating the effects of high-fat diet and wheel running exercise on synovial fluid (SF) metabolomics.

METHODS

Male mice were fed a defined control or high-fat (60% kcal fat) diet from 6 to 52 weeks of age, and half the animals were housed with running wheels from 26 to 52 weeks of age (n = 9-13 per group). Joint tissue structure and osteoarthritis pathology were evaluated by histology and micro-computed tomography. Systemic metabolic and inflammatory changes were evaluated by body composition, glucose tolerance testing, and serum biomarkers. SF metabolites were analyzed by high performance-liquid chromatography mass spectrometry. We built correlation-based network models to evaluate the connectivity between systemic and local metabolic biomarkers and osteoarthritis structural pathology within each experimental group.

RESULTS

High-fat diet caused moderate osteoarthritis, including cartilage pathology, synovitis and increased subchondral bone density. In contrast, voluntary exercise had a negligible effect on these joint structure components. 1,412 SF metabolite features were detected, with high-fat sedentary mice being the most distinct. Diet and activity uniquely altered SF metabolites attributed to amino acids, lipids, and steroids. Notably, high-fat diet increased network connections to systemic biomarkers such as interleukin-1β and glucose intolerance. In contrast, exercise increased local joint-level network connections, especially among subchondral bone features and SF metabolites.

CONCLUSION

Network mapping showed that obesity strengthened SF metabolite links to blood glucose and inflammation, whereas exercise strengthened SF metabolite links to subchondral bone structure.

Endotypes of primary osteoarthritis identified by plasma metabolomics analysis

OBJECTIVE

To identify endotypes of osteoarthritis (OA) by a metabolomics analysis.

METHODS

Study participants included hip/knee OA patients and controls. Fasting plasma samples were metabolomically profiled. Common factor analysis and K-means clustering were applied to the metabolomics data to identify the endotypes of OA patients. Logistic regression was utilized to identify the most significant metabolites contributing to the endotypes. Clinical and epidemiological factors were examined in relation to the identified OA endotypes.

RESULTS

Six hundred and fifteen primary OA patients and 237 controls were included. Among the 186 metabolites measured, 162 passed the quality control analysis. The 615 OA patients were classified in three clusters (A, 66; B, 200; and C, 349). Patients in cluster A had a significantly higher concentration of butyrylcarnitine (C4) than other clusters and controls (all P < 0.0002). Elevated C4 is thought to be related to muscle weakness and wasting. Patients in cluster B had a significantly lower arginine concentration than other clusters and controls (all P < 7.98 × 10-11). Cluster C patients had a significantly lower concentration of lysophosphatidylcholine (with palmitic acid), which is a pro-inflammatory bioactive compound, than other clusters and controls (P < 3.79 × 10-6). Further, cluster A had a higher BMI and prevalence of diabetes than other clusters (all P ≤ 0.0009), and also a higher prevalence of coronary heart disease than cluster C (P = 0.04). Cluster B had a higher prevalence of coronary heart disease than cluster C (P = 0.003) whereas cluster C had a higher prevalence of osteoporosis (P = 0.009).

CONCLUSION

Our data suggest three possible clinically actionable endotypes in primary OA: muscle weakness, arginine deficit and low inflammatory OA.

Intersections Between Mitochondrial Metabolism and Redox Biology Mediate Posttraumatic Osteoarthritis

PURPOSE OF REVIEW

This review will cover foundational studies and recent findings that established key concepts for understanding the importance of redox biology to chondrocyte mitochondrial function and osteoarthritis pathophysiology after injury.

RECENT FINDINGS

Articular chondrocyte mitochondria can be protected with a wide variety of antioxidants that will be discussed within a framework suggested by classic studies. These agents not only underscore the importance of thiol metabolism and associated redox function for chondrocyte mitochondria but also suggest complex interactions with signal transduction pathways and other molecular features of osteoarthritis that require more thorough investigation. Emerging evidence also indicates that reductive stress could occur alongside oxidative stress. Recent studies have shed new light on historic paradoxes in chondrocyte redox and mitochondrial physiology, leading to the development of promising disease-modifying therapies for posttraumatic osteoarthritis.

Metabolic networks of plasma and joint fluid base on differential correlation

Whether osteoarthritis (OA) is a systemic metabolic disorder remains controversial. The aim of this study was to investigate the metabolic characteristics between plasma and knee joint fluid (JF) of patients with advanced OA using a differential correlation metabolic (DCM) networks approach. Plasma and JF were collected during the joint replacement surgery of patients with knee OA. The biological samples were pretreated with standard procedures for metabolite analysis. The metabolic profiling was conducted by means of liquid mass spectrometry coupled with a AbsoluteIDQ kit. A DCM network approach was adopted for analyzing the metabolomics data between the plasma and JF. The variation in the correlation of the pairwise metabolites was quantified across the plasma and JF samples, and networks analysis was used to characterize the difference in the correlations of the metabolites from the two sample types. Core metabolites that played an important role in the DCM networks were identified via topological analysis. One hundred advanced OA patients (50 men and 50 women) were included in this study, with an average age of 65.0 ± 7.6 years (65.6 ± 7.1 years for females and 64.4 ± 8.1 years for males) and a mean BMI of 32.6 ± 5.8 kg/m² (33.4 ± 6.3 kg/m² for females and 31.7 ± 5.3 kg/m² for males). Age and BMI matched between the male and female groups. One hundred and forty-five nodes, 567 edges, and 131 nodes, 407 edges were found in the DCM networks (p < 0.05) of the female and male groups, respectively. Six metabolites in the female group and 5 metabolites in the male group were identified as key nodes in the network. There was a significant difference in the differential correlation metabolism networks of plasma and JF that may be related to local joint metabolism. Focusing on these key metabolites may help uncover the pathogenesis of knee OA. In addition, the differential metabolic correlation between plasma and JF mostly overlapped, indicating that these common correlations of pairwise metabolites may be a reflection of systemic characteristics of JF and that most significant correlation variations were just a result of "housekeeping” biological reactions.

Association of Visceral Adiposity With Pain but Not Structural Osteoarthritis

OBJECTIVE

Osteoarthritis (OA) and pain are both made more severe by low-grade inflammation. This study was undertaken to examine whether visceral fat, a major source of inflammatory cytokines and adipokines, is associated with an increased risk of knee OA or musculoskeletal pain.

METHODS

Subjects in the Multicenter Osteoarthritis Study cohort, who were age 50-79 years and had or were at high risk of knee OA, underwent whole-body dual x-ray absorptiometry (DXA) at baseline. At baseline, 30 months, and 60 months radiographs and magnetic resonance images (MRIs) of the knees were obtained, and patients were asked to score the severity of their knee pain and to identify sites of joint pain using a body homunculus. Baseline DXA scans were used to measure total body fat and visceral and subcutaneous fat in the torso. The association of fat depot size with structural outcomes (incident radiographic OA and cartilage loss and synovitis on MRI) and with pain outcomes (worsening knee pain, number of painful joints, and widespread pain) was assessed. Regression analyses were adjusted for age, sex, race, education level, smoking status, physical activity, body mass index (BMI), and depressive symptoms.

RESULTS

Of the 2,961 participants at baseline, 60.7% were women. The mean age was 62.5 years and mean BMI was 30.5 kg/m² . After adjustment for covariates, no fat measures were associated with any structural outcomes. However, total and visceral, but not subcutaneous, fat were positively associated with worsening knee pain (P = 0.0005 for total fat and P = 0.007 for visceral fat) and widespread pain (P = 0.001 for total fat and P = 0.02 for visceral fat), and the amount of visceral fat was associated with the number of painful joints (P = 0.07).

CONCLUSION

Our findings indicate that visceral fat is associated with an increased risk of musculoskeletal and widespread pain.

Can joint fluid metabolic profiling (or "metabonomics") reveal biomarkers for osteoarthritis and inflammatory joint disease?: A systematic review

Aims

Metabolic profiling is a top-down method of analysis looking at metabolites, which are the intermediate or end products of various cellular pathways. Our primary objective was to perform a systematic review of the published literature to identify metabolites in human synovial fluid (HSF), which have been categorized by metabolic profiling techniques. A secondary objective was to identify any metabolites that may represent potential biomarkers of orthopaedic disease processes.

Methods

A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines using the MEDLINE, Embase, PubMed, and Cochrane databases. Studies included were case series, case control series, and cohort studies looking specifically at HSF.

Results

The primary analysis, which pooled the results from 17 published studies and four meeting abstracts, identified over 200 metabolites. Seven of these studies (six published studies, one meeting abstract) had asymptomatic control groups and collectively suggested 26 putative biomarkers in osteoarthritis, inflammatory arthropathies, and trauma. These can broadly be categorized into amino acids plus related metabolites, fatty acids, ketones, and sugars.

Conclusion

The role of metabolic profiling in orthopaedics is fast evolving with many metabolites already identified in a variety of pathologies. However, these results need to be interpreted with caution due to the presence of multiple confounding factors in many of the studies. Future research should include largescale epidemiological metabolic profiling studies incorporating various confounding factors with appropriate statistical analysis to account for multiple testing of the data.

Cite this article:Bone Joint Res. 2020;9(3):108–119.

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

Although total joint replacement (TJR) surgery is considered as the most effective treatment for advanced osteoarthritis (OA) patients, up to one-third of patients reported unfavorable long-term post-operative pain outcomes. We aimed to identify metabolic biomarkers to predict non-responders to TJR using a metabolomics approach. TJR patients were recruited and followed-up at least 1-year post-surgery; TJR outcomes were assessed by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function subscales. Targeted metabolomic profiling was performed on plasma samples collected pre-surgery and pairwise metabolite ratios, as proxies for enzymatic reactions, were calculated. Association tests were performed between each metabolite ratio and non-responders. The metabolome-wide significance was defined as p < 2 × 10^-5 . A total of 461 TJR patients due to primary OA were included in the analysis. Fifteen percent of patients were classified as pain non-responders; 16% were classified as function non-responders. Lower baseline WOMAC pain and function scores were significantly associated with pain and function non-responders, respectively (both p < 0.03). Two metabolite ratios were significantly associated with pain non-responders; acetylcarnitine (C2) to phosphatidylcholine acyl-alkyl C40:1 (PC ae C40:1) was five times higher in pain non-responders whereas phosphatidylcholine diacyl C36:4 (PC aa C36:4) to isoleucine was twenty one times lower in pain non-responders than responders (all p ≤ 1.93 × 10^-5 ). One metabolite ratio, glutamine to isoleucine, was significantly lower in function non-responders than responders (eight times lower; p = 1.08 × 10^-5 ). Three metabolite ratios (C2 to PC ae C40:1, PC aa C36:4, and glutamine to isoleucine) related to inflammation and muscle breakdown could be considered as novel plasma markers for predicting non-responders to TJR and warrant further investigation. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:793-802, 2020.

Activation of The Phosphatidylcholine to Lysophosphatidylcholine Pathway Is Associated with Osteoarthritis Knee Cartilage Volume Loss Over Time

To identify serum biomarker(s) for predicting knee cartilage volume loss over time, we studied 139 knee osteoarthritis (OA) patients from a previous 24-month clinical trial cohort. Targeted metabolomic profiling was performed on serum collected at baseline. The pairwise metabolite ratios as proxies for enzymatic reaction were calculated and used in the analysis. Cartilage volume loss between baseline and 24 months was assessed quantitatively by magnetic resonance imaging (MRI). Data revealed an association between the serum ratio of lysophosphatidylcholine 18:2 (lysoPC 18:2) to phosphatidylcholine 44:3 (PC44:3) and the cartilage volume loss in the lateral compartment (β = -0.21 ± 0.04, p = 8.53*10^-7) and with joint degradation markers, COMP (r = 0.32, p = 0.0002) and MMP1 (r = 0.26, p = 0.002). The significance remained after adjustment for age, sex, BMI, diabetes, hypertension, dyslipidemia, and the treatment taken in the original study. As the ratio indicated the over activation of the conversion pathway of PC to lysoPC catalyzed by phospholipase A₂ (PLA₂), we assessed and found that a specific PLA₂, PLA₂G5, was significantly increased in human OA cartilage and synovial membrane (85% and 19% respectively, both p < 0.04) compared to controls, and its overexpression correlated with IL-6 (r = 0.63, p = 0.0008). Our data suggest that the serum lysoPC 18:2 to PC44:3 ratio is highly associated with a greater risk of cartilage volume loss of the knee and warrants further investigation in an independent cohort.

Synovial fluid biomarkers associated with osteoarthritis severity reflect macrophage and neutrophil related inflammation

BACKGROUND

To identify a synovial fluid (SF) biomarker profile characteristic of individuals with an inflammatory osteoarthritis (OA) endotype.

METHODS

A total of 48 knees (of 25 participants) were characterized for an extensive array of SF biomarkers quantified by Rules Based Medicine using the high-sensitivity multiplex immunoassay, Myriad Human InflammationMAP® 1.0, which included 47 different cytokines, chemokines, and growth factors related to inflammation. Multivariable regression with generalized estimating equations (GEE) and false discovery rate (FDR) correction was used to assess associations of SF RBM biomarkers with etarfolatide imaging scores reflecting synovial inflammation; radiographic knee OA severity (based on Kellgren-Lawrence (KL) grade, joint space narrowing, and osteophyte scores); knee joint symptoms; and SF biomarkers associated with activated macrophages and knee OA progression including CD14 and CD163 (shed by activated macrophages) and elastase (shed by activated neutrophils).

RESULTS

Significant associations of SF biomarkers meeting FDR < 0.05 included soluble (s)VCAM-1 and MMP-3 with synovial inflammation (FDR-adjusted p = 0.025 and 1.06 × 10^-7); sVCAM-1, sICAM-1, TIMP-1, and VEGF with radiographic OA severity (p = 1.85 × 10^-5 to 3.97 × 10^-4); and VEGF, MMP-3, TIMP-1, sICAM-1, sVCAM-1, and MCP-1 with OA symptoms (p = 2.72 × 10^-5 to 0.050). All these SF biomarkers were highly correlated with macrophage markers CD163 and CD14 in SF (r = 0.43 to 0.90, FDR < 0.05); all but MCP-1 were also highly correlated with neutrophil elastase in SF (r = 0.62 to 0.89, FDR < 0.05).

CONCLUSIONS

A subset of six SF biomarkers was related to synovial inflammation in OA, as well as radiographic and symptom severity. These six OA-related SF biomarkers were specifically linked to indicators of activated macrophages and neutrophils. These results attest to an inflammatory OA endotype that may serve as the basis for therapeutic targeting of a subset of individuals at high risk for knee OA progression.

TRIAL REGISTRATION

Written informed consent was received from participants prior to inclusion in the study; the study was registered at ClinicalTrials.gov ( NCT01237405 ) on November 9, 2010, prior to enrollment of the first participant.

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.

Amino Acid Profile of Synovial Fluid Following Intra-articular Ankle Fracture

BACKGROUND

Post-traumatic osteoarthritis (PTOA) is a frequent complication in patients with a previous traumatic joint injury, and the pathophysiology is not well understood. The goal of this study was to characterize the biochemical signature of amino acids, peptides, and amino acid metabolites in ankle synovial fluid following intra-articular fracture.

METHODS

Synovial fluid from both the injured and contralateral ankles of 19 patients with an intra-articular ankle fracture was obtained and analyzed via metabolic profiling. Follow-up analysis was performed after 6 months in 7 of these patients.

RESULTS

Statistical comparisons between injured and contralateral ankles revealed that 19 of the 66 measured amino acids, peptides, and amino acid metabolites were significantly elevated at time of fracture. Metabolites associated with glutathione metabolism exhibited the most elevated mean-fold changes, indicating a possible role for oxidative stress in fractured ankles. None of the metabolites elevated at baseline were significantly elevated after 6 months, but 6 metabolites had mean-fold changes greater than 2.1 at this time point. Multiple metabolites also exhibited significant correlations ( r > 0.575) with matrix metalloproteinase-1 and -9.

CONCLUSION

These results indicate the presence of amino acid metabolic products in the setting of ankle fracture and suggest that these changes in amino acid metabolism may be chronic and indicate a role for inflammation and collagen degradation in disease progression.

CLINICAL RELEVANCE

Changes in amino acid metabolism following intra-articular fracture may contribute to the progression to PTOA. This knowledge may allow for the identification and early treatment of patients at risk of developing PTOA.

LEVEL OF EVIDENCE

Level III, comparative series.

A classification modeling approach for determining metabolite signatures in osteoarthritis

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

Local and systemic inflammatory lipid profiling in a rat model of osteoarthritis with metabolic dysregulation

Objective

Bioactive oxidised lipids (oxylipins) are important signalling mediators, capable of modulating the inflammatory state of the joint and anticipated to be of importance in joint homeostasis and status of osteoarthritis. The aim of this study was to quantify oxylipin levels in plasma and synovial fluid from rats with experimentally induced osteoarthritis to investigate the potential role of oxylipins as a marker in the disease process of early osteoarthritis.

Design

Forty rats were randomly allocated to a standard or high-fat diet group. After 12 weeks, local cartilage damage was induced in one knee joint in 14 rats of each diet group. The remaining 6 rats per group served as controls. At week 24, samples were collected. Oxylipin levels were quantified by liquid chromatography–mass spectrometry.

Results

Overall, 31 lipid-derived inflammatory mediators were detected in fasted plasma and synovial fluid. Principal component analysis identified four distinct clusters associated with histopathological changes. Diet induced differences were evident for 13 individual plasma oxylipins, as well as 5,6-EET in synovial fluid. Surgical-model induced differences were evident for three oxylipins in synovial fluid (15-HETE, 8,9-DHET and 17R-ResolvinD1) with a different response in lipid concentrations for synovial fluid and plasma.

Conclusions

We demonstrate the quantification of oxidised lipids in rat plasma and synovial fluid in a model of early experimental osteoarthritis. Oxylipins in the synovial fluid that were altered as consequence of the surgically induced osteoarthritis were not represented in the plasma. Our findings suggest differential roles of the oxylipins in the local versus peripheral compartment.

An evolutionary learning and network approach to identifying key metabolites for osteoarthritis

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.

Biomedical research has entered a new era where a large number of molecules and different components in biological systems can be quantitatively examined to investigate the causes of common human diseases. However, given the complexity of biological systems, those causes may not contribute to diseases individually but through interactions. The identification of those interactions, or the synergy of multiple factors, is a very challenging task due to the computational limitation, as well as the lack of effective methodologies for investigating multiple factors simultaneously. In this study, we proposed to model such an interaction effect through a self-learning algorithm using mechanisms inspired by natural evolution. Moreover, by constructing a synergy network using those evolved models, we were able to identify a set of interacting factors associated with a particular disease.

High-fat diet-induced acceleration of osteoarthritis is associated with a distinct and sustained plasma metabolite signature

Metabolic changes induced by high fat diet (HFD) that contribute to osteoarthritis (OA) are poorly understood. We investigated longitudinal changes to metabolites and their contribution to OA pathogenesis in response to HFD. HFD-fed mice exhibited acceleration of spontaneous age-related and surgically-induced OA compared to lean diet (LD)-fed mice. Using metabolomics, we identified that HFD-fed mice exhibited a distinct and sustained plasma metabolite signature rich in phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPCs), even after resumption of normal chow diet. Using receiver operator curve analysis and prediction modelling, we showed that the concentration of these identified metabolites could efficiently predict the type of diet and OA risk with an accuracy of 93%. Further, longitudinal evaluation of knee joints of HFD- compared to LD- fed mice showed a greater percentage of leptin-positive chondrocytes. Mechanistic data showed that leptin-treated human OA chondrocytes exhibited enhanced production of lysoPCs and expression of autotaxin and catabolic MMP-13. Leptin-induced increased MMP13 expression was reversed by autotaxin inhibition. Together, this study is the first to describe a distinct and sustained HFD-induced metabolite signature. This study suggests that in addition to increased weight, identified metabolites and local leptin-signaling may also contribute in part, towards the accelerated OA-phenotype observed in HFD mice.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

We report a chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) method generally applicable for tracking metabolomic changes from samples collected in an animal model for studying disease development and treatment. A rat model of surgically induced osteoarthritis (OA) was used as an example to illustrate the workflow and technical performance. Experimental duplicate analyses of 234 plasma samples were carried out using dansylation labeling LC-MS targeting the amine/phenol submetabolome. These samples composed of 39 groups (6 rats per group) were collected at multiple time points with sham operation, OA control group, and OA rats with treatment, separately, using glucosamine/Celecoxib and three traditional Chinese medicines (Epimedii folium, Chuanxiong Rhizoma and Bushen-Huoxue). In total, 3893 metabolites could be detected and 2923 of them were consistently detected in more than 50% of the runs. This high-coverage submetabolome dataset could be used to track OA progression and treatment. Many differentiating metabolites were found and 11 metabolites including 2-aminoadipic acid, saccharopine and GABA were selected as potential biomarkers of OA progression and OA treatment. This study illustrates that CIL LC-MS is a very useful technique for monitoring incremental metabolomic changes with high coverage and accuracy for studying disease progression and treatment in animal models.

Lysophosphatidylcholines to phosphatidylcholines ratio predicts advanced knee osteoarthritis

OBJECTIVE

To identify novel biomarker(s) for predicting advanced knee OA.

METHODS

Study participants were derived from the Newfoundland Osteoarthritis Study and the Tasmania Older Adult Cohort Study. All knee OA cases were patients who underwent total knee replacement (TKR) due to primary OA. Metabolic profiling was performed on fasting plasma. Four thousand and eighteen plasma metabolite ratios that were highly correlated with that in SF in our previous study were generated as surrogates for joint metabolism.

RESULTS

The discovery cohort included 64 TKR cases and 45 controls and the replication cohorts included a cross-sectional cohort of 72 TKR cases and 76 controls and a longitudinal cohort of 158 subjects, of whom 36 underwent TKR during the 10-year follow-up period. We confirmed the previously reported association of the branched chain amino acids to histidine ratio with advanced knee OA (P = 9.3 × 10(-7)) and identified a novel metabolic marker-the lysophosphatidylcholines (lysoPCs) to phosphatidylcholines (PCs) ratio-that was associated with advanced knee OA (P = 1.5 × 10(-7)) after adjustment for age, sex and BMI. When the subjects of the longitudinal cohort were categorized into two groups based on the optimal cut-off of the ratio of 0.09, we found the subjects with the ratio ⩾0.09 were 2.3 times more likely to undergo TKR than those with the ratio <0.09 during the 10-year follow-up (95% CI: 1.2, 4.3, P = 0.02).

CONCLUSION

We identified the ratio of lysoPCs to PCs as a novel metabolic marker for predicting advanced knee OA. Further studies are required to examine whether this ratio can predict early OA change.

Metabolomic analysis of human plasma reveals that arginine is depleted in knee osteoarthritis patients

OBJECTIVE

To identify novel biomarker(s) for knee osteoarthritis (OA) using a metabolomics approach.

METHOD

We utilized a two-stage case-control study design. Plasma samples were collected from knee OA patients and healthy controls after 8-h fasting and metabolically profiled using a targeted metabolomics assay kit. Linear regression was used to identify novel metabolic markers for OA. Receiver operating characteristic (ROC) analysis was used to examine diagnostic values. Gene expression analysis was performed on human cartilage to explore the potential mechanism for the novel OA marker(s).

RESULTS

Sixty-four knee OA patients and 45 controls were included in the discovery stage and 72 knee OA patients and 76 age and sex matched controls were included in the validation stage. We identified and confirmed six metabolites that were significantly associated with knee OA, of which arginine was the most significant metabolite (P < 3.5 × 10(-13)) with knee OA patients having on average 69 μM lower than that in controls. ROC analysis showed that arginine had the greatest diagnostic value with area under the curve (AUC) of 0.984. The optimal cutoff of arginine concentration was 57 μM with 98.3% sensitivity and 89% specificity. The depletion of arginine in OA patients was most likely due to the over activity of arginine to ornithine pathway, leading to imbalance between cartilage repair and degradation.

CONCLUSION

Arginine is significantly depleted in refractory knee OA patients. Further studies within a longitudinal setting are required to examine whether arginine can predict early OA changes.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

Serum Metabolite Profiles Are Altered by Erlotinib Treatment and the Integrin α1-Null Genotype but Not by Post-Traumatic Osteoarthritis

The risk of developing post-traumatic osteoarthritis (PTOA) following joint injury is high. Furthering our understanding of the molecular mechanisms underlying PTOA and/or identifying novel biomarkers for early detection may help to improve treatment outcomes. Increased expression of integrin α1β1 and inhibition of epidermal growth factor receptor (EGFR) signaling protect the knee from spontaneous OA; however, the impact of the integrin α1β1/EGFR axis on PTOA is currently unknown. We sought to determine metabolic changes in serum samples collected from wild-type and integrin α1-null mice that underwent surgery to destabilize the medial meniscus and were treated with the EGFR inhibitor erlotinib. Following (1)H nuclear magnetic resonance spectroscopy, we generated multivariate statistical models that distinguished between the metabolic profiles of erlotinib- versus vehicle-treated mice and the integrin α1-null versus wild-type mouse genotype. Our results show the sex-dependent effects of erlotinib treatment and highlight glutamine as a metabolite that counteracts this treatment. Furthermore, we identified a set of metabolites associated with increased reactive oxygen species production, susceptibility to OA, and regulation of TRP channels in α1-null mice. Our study indicates that systemic pharmacological and genetic factors have a greater effect on serum metabolic profiles than site-specific factors such as surgery.

Osteoarthritis year in review 2015: soluble biomarkers and the BIPED criteria

OBJECTIVE

To review and summarize biomarker data published from April 2014 to May 2015 to provide insight to the ongoing work in the field of osteoarthritis (OA). Furthermore, to summarize the BIPED criteria and set it in context of the medical needs of 2015.

METHODS

PubMed was used as searching machine: Time period 2014/04/01-2015/05/01, MeSH term [Biomarker] AND [Osteoarthritis], Language; English, Full text available. Reviews were excluded. Only papers describing protein based biomarkers measured in human body fluids from OA patients were included.

RESULTS

Biomarkers of joint tissue turnover, cytokines, chemokines and peptide arrays were measured in different cohorts and studies. Amongst those were previously tested biomarkers such as osteocalcin, Carboxy-terminal cross-linked fragment of type II collagen (CTX-II) and cartilage oligomeric matrix protein (COMP). A majority of the biomarker were classified as I, B or B biomarkers according to the BIPED criteria. Work is continuing on testing biomarkers in OA. There is still a huge, unmet medical need to identify, test, validate and qualify novel and well-known biomarkers. A pre-requisite for this is better characterization and classification of biomarkers to their needs, which may not be reached before higher understanding of OA phenotypes has been gained. In addition, we provide some references to some recent guidelines from Food and Drug Administration (FDA) and European Medicines Agency (EMA) on qualification and usage of biomarkers for drug development and personalized medicine, which may provide value to the field.

Genome-wide DNA methylation study of hip and knee cartilage reveals embryonic organ and skeletal system morphogenesis as major pathways involved in osteoarthritis

Background

Evidence suggests that epigenetics plays a role in osteoarthrits (OA). The aim of the study was to describethe genome wide DNA methylation changes in hip and knee OA and identify novel genes and pathwaysinvolved in OA by comparing the DNA methylome of the hip and knee osteoarthritic cartilage tissues withthose of OA-free individuals.

Methods

Cartilage samples were collected from hip or knee joint replacement patients either due to primary OA or hip fractures as controls. DNA was extracted from the collected cartilage and assayed by Illumina Infinium HumanMethylation450 BeadChip array, which allows for the analysis of >480,000 CpG sites. Student T-test was conducted for each CpG site and those sites with at least 10 % methylation difference and a p value <0.0005 were defined as differentially methylated regions (DMRs) for OA. A sub-analysis was also done for hip and knee OA separately. DAVID v6.7 was used for the functional annotation clustering of the DMR genes. Clustering analysis was done using multiple dimensional scaling and hierarchical clustering methods.

Results

The study included 5 patients with hip OA, 6 patients with knee OA and 7 hip cartilage samples from OA-free individuals. The comparisons of hip, knee and combined hip/knee OA patients with controls resulted in 26, 72, and 103 DMRs, respectively. The comparison between hip and knee OA revealed 67 DMRs. The overall number of the sites after considering the overlaps was 239, among which 151 sites were annotated to 145 genes. One-fifth of these genes were reported in previous studies. The functional annotation clustering of the identified genes revealed clusters significantly enriched in skeletal system morphogenesis and development. The analysis revealed significant difference among OA and OA-free cartilage, but less different between hip OA and knee OA.

Conclusions

We found that a number of CpG sites and genes across the genome were differentially methylated in OA patients, a remarkable portion of which seem to be involved in potential etiologic mechanisms of OA. Genes involved in skeletal developmental pathways and embryonic organ morphogenesis may be a potential area for further OA studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12891-015-0745-5) contains supplementary material, which is available to authorized users.