Metabolomics of Synovial Fluid and Infrapatellar Fat Pad in Patients with Osteoarthritis or Rheumatoid Arthritis

Metabolomic profiles of cartilage and bone reflect tissue type, radiography-confirmed osteoarthritis, and spatial location within the joint

Osteoarthritis is the most common chronic joint disease, characterized by the abnormal remodeling of joint tissues including articular cartilage and subchondral bone. However, there are currently no therapeutic drug targets to slow the progression of disease because disease pathogenesis is largely unknown. Thus, the goals of this study were to identify metabolic differences between articular cartilage and subchondral bone, compare the metabolic shifts in osteoarthritic grade III and IV tissues, and spatially map metabolic shifts across regions of osteoarthritic hip joints. Articular cartilage and subchondral bone from 9 human femoral heads were obtained after total joint arthroplasty, homogenized and metabolites were extracted for liquid chromatography-mass spectrometry analysis. Metabolomic profiling revealed that distinct metabolic endotypes exist between osteoarthritic tissues, late-stage grades, and regions of the diseased joint. The pathways that contributed the most to these differences between tissues were associated with lipid and amino acid metabolism. Differences between grades were associated with nucleotide, lipid, and sugar metabolism. Specific metabolic pathways such as glycosaminoglycan degradation and amino acid metabolism, were spatially constrained to more superior regions of the femoral head. These results suggest that radiography-confirmed grades III and IV osteoarthritis are associated with distinct global metabolic and that metabolic shifts are not uniform across the joint. The results of this study enhance our understanding of osteoarthritis pathogenesis and may lead to potential drug targets to slow, halt, or reverse tissue damage in late stages of osteoarthritis.

Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning

OBJECTIVES

To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis.

MATERIALS AND METHODS

This retrospective study included 376 consecutive patients with pathologically confirmed knee synovitis (rheumatoid arthritis, gouty arthritis, and pigmented villonodular synovitis) from two institutions. A semantic segmentation model was trained on manually annotated sagittal proton density-weighted images. The segmentation results of the regions of interest and patients' sex and age were used to classify knee synovitis after feature processing. Classification by the DL method was compared to the classification performed by radiologists.

RESULTS

Data of the 376 patients (mean age, 42 ± 15 years; 216 men) were separated into a training set (n = 233), an internal test set (n = 93), and an external test set (n = 50). The automated segmentation model showed good performance (mean accuracy: 0.99 and 0.99 in the internal and external test sets). On the internal test set, the DL model performed better than the senior radiologist (accuracy: 0.86 vs. 0.79; area under the curve [AUC]: 0.83 vs. 0.79). On the external test set, the DL diagnostic model based on automatic segmentation performed as well or better than senior and junior radiologists (accuracy: 0.79 vs. 0.79 vs. 0.73; AUC: 0.76 vs. 0.77 vs. 0.70).

CONCLUSION

DL models for segmentation of SC and IPFD can accurately classify knee synovitis and aid radiologic diagnosis.

Association Between Human Metabolomics and Rheumatoid Arthritis: A Systematic Review and Meta-analysis

OBJECTIVE

The underdiagnosis and inadequate treatment of rheumatoid arthritis (RA) can be attributed to the various clinical manifestations presented by patients. To address this concern, we conducted an extensive review and meta-analysis, focusing on RA-related metabolites.

METHODS

A comprehensive literature search was conducted in PubMed, the Cochrane Library, Web of Science, and Embase to identify relevant studies published up to October 5, 2022. The quality of the included articles was evaluated and, subsequently, a meta-analysis was conducted using Review Manager software to analyze the association between metabolites and RA.

RESULTS

Forty nine studies met the inclusion criteria for the systematic review, and six of these studies were meta-analyzed to evaluate the association between 28 reproducible metabolites and RA. The results indicated that, compared to controls, the levels of histidine (RoM = 0.83, 95% CI = 0.79-0.88, I2 = 0%), asparagine (RoM = 0.83, 95% CI = 0.75-0.91, I2 = 0%), methionine (RoM = 0.82, 95% CI = 0.69-0.98, I2 = 85%), and glycine (RoM = 0.81, 95% CI = 0.67-0.97, I2 = 68%) were significantly lower in RA patients, while hypoxanthine levels (RoM = 1.14, 95% CI = 1.09-1.19, I2 = 0%) were significantly higher.

CONCLUSION

This study identified histidine, methionine, asparagine, hypoxanthine, and glycine as significantly correlated with RA, thus offering the potential for the advancement of biomarker discovery and the elucidation of disease mechanisms in RA.

Emerging molecular biomarkers in osteoarthritis pathology

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

Rapid skin biomarker discovery using hydrogel-phase sampling followed by semi-automated liquid-phase re-extraction high-resolution mass spectrometry

A facile and rapid skin metabolomics protocol is proposed. The liquid microjunction-surface sampling probe system has been partly automated, and used in conjunction with hydrogel probes for skin metabolite analysis. A control device was built to precisely control the segmented solvent flow and analyte re-extraction into the liquid microjunction. This mode provides rapid online re-extraction of the analytes from hydrogel probes. Humectant was added to the hydrogel, and moist heat treatment was used to make the hydrogel probes rugged for sampling in the clinical setting. The developed method was validated for the analysis of choline - a putative biomarker of psoriasis. A linear relationship over six calibration levels from 3.18 × 10^-5 to 3.18 × 10^-4 mol m^-2 has been obtained. The limit of detection was 6.6 × 10^-6 mol m^-2, while the recoveries range from 92 to 109%. The within-run and between-run precision were evaluated and the coefficients of variation range from 1.84 to 7.13%. Furthermore, the developed method has been used to screen patients (n = 10) and healthy participants (control group; n = 10) for psoriasis-related skin metabolites. Metabolomic profiling of the skin excretion-related signals identified potential biomarkers of psoriasis: choline, pipecolic acid, ornithine, urocanic acid, and methionine.

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.

The bridge of the gut–joint axis: Gut microbial metabolites in rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by joint destruction, synovitis, and pannus formation. Gut microbiota dysbiosis may exert direct pathogenic effects on gut homeostasis. It may trigger the host’s innate immune system and activate the “gut–joint axis”, which exacerbates the RA. However, although the importance of the gut microbiota in the development and progression of RA is widely recognized, the mechanisms regulating the interactions between the gut microbiota and the host immune system remain incompletely defined. In this review, we discuss the role of gut microbiota-derived biological mediators, such as short-chain fatty acids, bile acids, and tryptophan metabolites, in maintaining intestinal barrier integrity, immune balance and bone destruction in RA patients as the bridge of the gut–joint axis.

Metabolomics in rheumatoid arthritis: Advances and review

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.

Synovial Fluid Regulates the Gene Expression of a Pattern of microRNA via the NF-κB Pathway: An In Vitro Study on Human Osteoarthritic Chondrocytes

Synovial fluid (SF) represents the primary source of nutrients of articular cartilage and is implicated in maintaining cartilage metabolism. We investigated the effects of SF, from patients with osteoarthritis (OA), rheumatoid arthritis (RA), and controls, on a pattern of microRNA (miRNA) in human OA chondrocytes. Cells were stimulated with 50% or 100% SF for 24 h and 48 h. Apoptosis and superoxide anion production were detected by cytometry; miRNA (34a, 146a, 155, 181a), cytokines, metalloproteinases (MMPs), type II collagen (Col2a1), antioxidant enzymes, B-cell lymphoma (BCL)2, and nuclear factor (NF)-κB by real-time PCR. The implication of the NF-κB pathway was assessed by the use of NF-κB inhibitor (BAY-11-7082). RA and OA SF up-regulated miR-34a, -146a, -155, -181a, interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, MMP-1, MMP-13, and ADAMTs-5 gene expression, while it down-regulated Col2a1. Pathological SF also induced apoptosis, reduced viability, and decreased BCL2 mRNA, whereas it increased superoxide anions, the expression of antioxidant enzymes, p65 and p50 NF-κB. Opposite and positive results were obtained with 100% control SF. Pre-incubation with BAY-11-7082 counteracted SF effects on miRNA. We highlight the role of the SF microenvironment in regulating some miRNA involved in inflammation and cartilage degradation during OA and RA, via the NF-κB pathway.

Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity

Immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis (Ps), represent autoinflammatory and autoimmune disorders, as well as conditions that have an overlap of both categories. Understanding the underlying pathogeneses, making diagnoses, and choosing individualized treatments remain challenging due to heterogeneous disease phenotypes and the lack of reliable biomarkers that drive the treatment choice. In this review, we provide an overview of the low-molecular-weight metabolites that might be employed as biomarkers for various applications, e.g., early diagnosis, disease activity monitoring, and treatment-response prediction, in RA, PsA, and Ps. The literature was evaluated, and putative biomarkers in different matrices were identified, categorized, and summarized. While some of these candidate biomarkers appeared to be disease-specific, others were shared across multiple IMIDs, indicating common underlying disease mechanisms. However, there is still a long way to go for their application in a routine clinical setting. We propose that studies integrating omics analyses of large patient cohorts from different IMIDs should be performed to further elucidate their pathomechanisms and treatment options. This could lead to the identification and validation of biomarkers that might be applied in the context of precision medicine to improve the clinical outcomes of these IMID patients.