Differences in the composition of hip and knee synovial fluid in osteoarthritis: a nuclear magnetic resonance (NMR) spectroscopy study of metabolic profiles

Intra-articular steroids for the treatment of coxarthrosis; a retrospective cohort study comparing three contrast techniques

INTRODUCTION

Intra-articular steroid injections (IAS) are a treatment for coxarthrosis. This study examines the efficacy of three fluoroscopy-guided IAS contrast techniques for coxarthrosis: contrast-assisted (Iohexol), air arthrogram-assisted and blind (contrast/air free) and stratifies efficacy based on multiple patient variables.

MATERIALS AND METHODS

A cohort of 307 hip IAS was retrospectively analysed over a four-year period. The primary outcome was efficacy of IAS between each technique group, defined by duration of symptomatic relief. The secondary outcome was efficacy based on multiple patient variables. Variables included age, BMI, gender, type of osteoarthritis, grade of osteoarthritis, smoking status, co-morbidity index and duration of pre-injection symptoms. Chi-squared, Pearson, One Way ANOVA and F-tests were used for statistical analysis.

RESULTS

Total failure (< 1 week symptomatic relief) was 20% (contrast 20%, air 14%, blind 26%). >3 months of symptomatic relief was experienced by 35%, with the air arthrogram technique containing the largest proportion of IAS achieving > 3months of relief within its own group (contrast 35%, air 38%, blind 28%). Non-smokers experienced a longer duration of symptomatic relief in the air arthrogram group (p = 0.04). Older patients had a longer duration of symptomatic relief with the blind technique (p = < 0.001). There were no significant differences between the three techniques based on the other patient variables.

CONCLUSION

Air arthrogram is an effective method of confirming injection placement in hip IAS for coxarthrosis and the use of a contrast agent (e.g., Iohexol) may not be required. Non-contrast techniques may produce longer duration of symptomatic relief in non-smokers and in older patients.

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Physical activity increases synovial fluid in ankle tendon sheaths: an adjustment of MR Criteria is needed

OBJECTIVES

To compare the amount of fluid in synovial sheaths of the ankle before and after running. Our hypothesis was that this amount would increase and that the threshold for what is normally acceptable should be adjusted after physical activity.

METHODS

Twenty-one healthy volunteers (n = 42 ankles) ran for 40 min on a treadmill. They underwent 3 T MRI before and immediately after running using a dedicated ankle coil. The images were stored and subsequently measured in a standardized way and independently read by two readers for fluid in the tendon sheaths in the retro and inframalleolar area. Statistics were performed for each tendon (Wilcoxon signed rank test), and also for the pooled data. Intraclass correlation coefficients were calculated.

RESULTS

For reader 1, for all tendons the values after running increased without reaching statistical significance. For reader 2 this was not the case for all tendons but for most. When all the data were pooled (n = 800 measurements), the statistical difference before and after running was significant (p < 0.001).

CONCLUSION

Data pre and post-running show a trend of increasing synovial fluid, however, not significant for each individual tendon. The pooled data for all tendons, (n = 800) show a statistically significant increase after running (p < 0.001). The clinical implication is that the threshold for normally acceptable fluid should be adjusted if the patient undergoes an MR study after recent physical activity.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

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

Objective

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

Design

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

Results

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

Conclusion

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

Nuclear magnetic resonance spectroscopy of biofluids for osteoarthritis

BACKGROUND

Osteoarthritis is a common degenerative musculoskeletal disease of synovial joints. It is characterized by a metabolic imbalance resulting in articular cartilage degradation, reduced elastoviscosity of synovial fluid and an altered chondrocyte phenotype. This is often associated with reduced mobility, pain and poor quality of life. Subsequently, with an ageing world population, osteoarthritis is of increasing concern to public health. Nuclear magnetic resonance (NMR) spectroscopy can be applied to characterize the metabolomes of biofluids, determining changes associated with osteoarthritis pathology, identifying potential biomarkers of disease and alterations to metabolic pathways.

SOURCES OF DATA

A comprehensive search of PubMed and Web of Science databases using combinations of the following keywords: 'NMR Spectroscopy', 'Blood', 'Plasma', 'Serum', 'Urine', 'Synovial Fluid' and 'Osteoarthritis' for articles published from 2000 to 2020.

AREAS OF AGREEMENT

The number of urine metabolomics studies using NMR spectroscopy to investigate osteoarthritis is low, whereas the use of synovial fluid is significantly higher. Several differential metabolites have previously been identified and mapped to metabolic pathways involved in osteoarthritis pathophysiology.

AREAS OF CONTROVERSY

Conclusions are sometimes conservative or overinflated, which may reflect the variation in reporting standards. NMR metabolic experimental design may require further consideration, as do the animal models used for such studies.

GROWING POINTS

There are various aspects which require improvement within the field. These include stricter adherence to the Metabolomics Standards Initiative, inclusive of the standardization of metabolite identifications; increased utilization of integrating NMR metabolomics with other 'omic' disciplines; and increased deposition of raw experimental files into open access online repositories, allowing greater transparency and enabling additional future analyses.

AREAS TIMELY FOR DEVELOPING RESEARCH

Overall, this research area could be improved by the inclusion of more heterogeneous cohorts, reflecting varying osteoarthritis phenotypes, and larger group sizes ensuring studies are not underpowered. To correlate local and systemic environments, the use of blood for diagnostic purposes, over the collection of synovial fluid, requires increased attention. This will ultimately enable biomarkers of disease to be determined that may provide an earlier diagnosis, or provide potential therapeutic targets for osteoarthritis, ultimately improving patient prognosis.

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.

Differences between infected and noninfected synovial fluid

AIMS

The diagnosis of joint infections is an inexact science using combinations of blood inflammatory markers and microscopy, culture, and sensitivity of synovial fluid (SF). There is potential for small molecule metabolites in infected SF to act as infection markers that could improve accuracy and speed of detection. The objective of this study was to use nuclear magnetic resonance (NMR) spectroscopy to identify small molecule differences between infected and noninfected human SF.

METHODS

In all, 16 SF samples (eight infected native and prosthetic joints plus eight noninfected joints requiring arthroplasty for end-stage osteoarthritis) were collected from patients. NMR spectroscopy was used to analyze the metabolites present in each sample. Principal component analysis and univariate statistical analysis were undertaken to investigate metabolic differences between the two groups.

RESULTS

A total of 16 metabolites were found in significantly different concentrations between the groups. Three were in higher relative concentrations (lipids, cholesterol, and N-acetylated molecules) and 13 in lower relative concentrations in the infected group (citrate, glycine, glycosaminoglycans, creatinine, histidine, lysine, formate, glucose, proline, valine, dimethylsulfone, mannose, and glutamine).

CONCLUSION

Metabolites found in significantly greater concentrations in the infected cohort are markers of inflammation and infection. They play a role in lipid metabolism and the inflammatory response. Those found in significantly reduced concentrations were involved in carbohydrate metabolism, nucleoside metabolism, the glutamate metabolic pathway, increased oxidative stress in the diseased state, and reduced articular cartilage breakdown. This is the first study to demonstrate differences in the metabolic profile of infected and noninfected human SF, using a noninfected matched cohort, and may represent putative biomarkers that form the basis of new diagnostic tests for infected SF. Cite this article: Bone Joint Res 2021;10(1):85-95.