Lipidomic identification of plasma lipids associated with pain behaviour and pathology in a mouse model of osteoarthritis

The role of obesity and adipose tissue dysfunction in osteoarthritis pain

Obesity has a pivotal and multifaceted role in pain associated with osteoarthritis (OA), extending beyond the mechanistic influence of BMI. It exerts its effects both directly and indirectly through various modifiable risk factors associated with OA-related pain. Adipose tissue dysfunction is highly involved in OA-related pain through local and systemic inflammation, immune dysfunction, and the production of pro-inflammatory cytokines and adipokines. Adipose tissue dysfunction is intricately connected with metabolic syndrome, which independently exerts specific effects on OA-related pain, distinct from its association with BMI. The interplay among obesity, adipose tissue dysfunction and metabolic syndrome influences OA-related pain through diverse pain mechanisms, including nociceptive pain, peripheral sensitization and central sensitization. These complex interactions contribute to the heightened pain experience observed in individuals with OA and obesity. In addition, pain management strategies are less efficient in individuals with obesity. Importantly, therapeutic interventions targeting obesity and metabolic syndrome hold promise in managing OA-related pain. A deeper understanding of the intricate relationship between obesity, metabolic syndrome and OA-related pain is crucial and could have important implications for improving pain management and developing innovative therapeutic options in OA.

Proteomic and lipidomic landscape of the infrapatellar fat pad and its clinical significance in knee osteoarthritis

Osteoarthritis (OA) is a prevalent joint disease that can be exacerbated by lipid metabolism disorders. The intra-articular fat pad (IFP) has emerged as an active participant in the pathological changes of knee OA (KOA). However, the proteomic and lipidomic differences between IFP tissues from KOA and control individuals remain unclear. Samples of IFP were collected from individuals with and without OA (n = 6, n = 6). Subsequently, these samples underwent liquid chromatography/mass spectrometry-based label-free quantitative proteomic and lipidomic analysis to identify differentially expressed proteins (DEPs) and lipid metabolites (DELMs). The DEPs were further subjected to enrichment analysis, and hub DEPs were identified using multiple algorithms. Additionally, an OA diagnostic model was constructed based on the identified hub DEPs or DELMs. Furthermore, CIBERSORT was utilized to investigate the correlation between hub protein expression and immune-related modules in IFP of OA. Our results revealed the presence of 315 DEPs and eight DELMs in IFP of OA patients compared to the control group. Enrichment analysis of DEPs highlighted potential alterations in pathways related to coagulation, complement, fatty acid metabolism, and adipogenesis. The diagnostic model incorporating four hub DEPs (AUC = 0.861) or eight DELMs (AUC = 0.917) exhibited excellent clinical validity for diagnosing OA. Furthermore, the hub DEPs were found to be associated with immune dysfunction in IFP of OA. This study presents a distinct proteomic and lipidomic landscape of IFP between individuals with OA and those without. These findings provide valuable insights into the molecular changes associated with potential mechanisms underlying OA.

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.

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.

Metabolic Responses to an Acute Glucose Challenge: The Differential Effects of Eight Weeks of Almond vs. Cracker Consumption in Young Adults

This study investigated the dynamic responses to an acute glucose challenge following chronic almond versus cracker consumption for 8 weeks (clinicaltrials.gov ID: NCT03084003). Seventy-three young adults (age: 18-19 years, BMI: 18-41 kg/m2) participated in an 8-week randomized, controlled, parallel-arm intervention and were randomly assigned to consume either almonds (2 oz/d, n=38) or an isocaloric control snack of graham crackers (325 kcal/d, n=35) daily for 8 weeks. Twenty participants from each group underwent a 2-hour oral glucose tolerance test (oGTT) at the end of the 8-week intervention. Metabolite abundances in the oGTT serum samples were quantified using untargeted metabolomics, and targeted analyses for free PUFAs, total fatty acids, oxylipins, and endocannabinoids. Multivariate, univariate, and chemical enrichment analyses were conducted to identify significant metabolic shifts. Findings exhibit a biphasic lipid response distinguished by higher levels of unsaturated triglycerides in the earlier periods of the oGTT followed by lower levels in the latter period in the almond versus cracker group (p-value<0.05, chemical enrichment analyses). Almond (vs. cracker) consumption was also associated with higher AUC120 min of aminomalonate, and oxylipins (p-value<0.05), but lower AUC120 min of L-cystine, N-acetylmannosamine, and isoheptadecanoic acid (p-value<0.05). Additionally, the Matsuda Index in the almond group correlated with AUC120 min of CE 22:6 (r=-0.46; p-value<0.05) and 12,13 DiHOME (r=0.45; p-value<0.05). Almond consumption for 8 weeks leads to dynamic, differential shifts in response to an acute glucose challenge, marked by alterations in lipid and amino acid mediators involved in metabolic and physiological pathways.

HR/MS-based lipidome analysis of rat brain modulated by tolcapone

Lipids play key roles in the body, influencing cellular regulation, function, and signalling. Tolcapone, a potent catechol-O-methyltransferase (COMT) inhibitor described to enhance cognitive performance in healthy subjects, was previously shown to impact fatty acid β-oxidation and oxidative phosphorylation. However, its impact on the brain lipidome remains unexplored. Hence, this study aimed to assess how tolcapone affects the lipidome of the rat pre-frontal cortex (PFC), a region of the brain highly relevant to tolcapone therapeutic effect, while evaluating its influence on operant behaviour. Tolcapone at 20 mg/kg was chronically administered to Wistar rats during a behavioural task and an untargeted liquid chromatography high-resolution mass spectrometry (LC-HR/MS) approach was employed to profile lipid species. The untargeted analysis identified 7227 features, of which only 33% underwent statistical analysis following data pre-processing. The results revealed an improved cognitive performance and a lipidome remodelling promoted by tolcapone. The lipidomic analysis showed 32 differentially expressed lipid species in tolcapone-treated animals (FC ≥ 1.2, p-value ≤ 0.1), and among these several triacylglycerols, cardiolipins and N-acylethanolamine (NAE 16:2) were found upregulated whereas fatty acids, hexosylceramides, and several phospholipids including phosphatidylcholines and phosphatidylethanolamines were downregulated. These preliminary findings shed light on tolcapone impact on lipid pathways within the brain. Although tolcapone improved cognitive performance and literature suggests the significance of lipids in cognition, this study did not conclusively establish that lipids directly drove or contributed to this outcome. Nevertheless, it underscores the importance of lipid modulation and encourages further exploration of tolcapone-associated mechanisms in the central nervous system (CNS).

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Risk of metabolic abnormalities in osteoarthritis: a new perspective to understand its pathological mechanisms

Although aging has traditionally been viewed as the most important risk factor for osteoarthritis (OA), an increasing amount of epidemiological evidence has highlighted the association between metabolic abnormalities and OA, particularly in younger individuals. Metabolic abnormalities, such as obesity and type II diabetes, are strongly linked to OA, and they affect both weight-bearing and non-weight-bearing joints, thus suggesting that the pathogenesis of OA is more complicated than the mechanical stress induced by overweight. This review aims to explore the recent advances in research on the relationship between metabolic abnormalities and OA risk, including the impact of abnormal glucose and lipid metabolism, the potential pathogenesis and targeted therapeutic strategies.

Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study

BACKGROUND

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and is the leading cause of pain and disability in the aged population. However, the underlying biological mechanism has not been fully understood. This study aims to reveal the causal effect of circulation metabolites on OA susceptibility.

METHODS

A two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of GDMs on OA. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas 8 different OA phenotypes, including any-site OA (All OA), knee and/or hip OA (knee/hip OA), knee OA, hip OA, spine OA, finger and/or thumb OA (hand OA), finger OA, thumb OA, were set the outcomes. Inverse-variance weighted (IVW) was used for calculating causal estimates. Methods including weight mode, weight median, MR-egger, and MR-PRESSO were used for the sensitive analysis. Furthermore, metabolic pathway analysis was performed via the web-based Metaconflict 4.0. All statistical analyses were performed in R software.

RESULTS

In this MR analysis, a total of 235 causative associations between metabolites and different OA phenotypes were observed. After false discovery rate (FDR) correction and sensitive analysis, 9 robust causative associations between 7 metabolites (e.g., arginine, kynurenine, and isovalerylcarnitine) and 5 OA phenotypes were finally identified. Additionally, eleven significant metabolic pathways in 4 OA phenotypes were identified by metabolic pathway analysis.

CONCLUSION

The finding of our study suggested that identified metabolites and metabolic pathways can be considered useful circulating metabolic biomarkers for OA screening and prevention in clinical practice, and can also serve as candidate molecules for future mechanism exploration and drug target selection.

Dietary Cholesterol Differentially Regulates the Muscle Lipidomics of Farmed Turbot and Tiger Puffer

Exogenous cholesterol has been supplemented into aqua-feeds due to the reduced proportions of fishmeal and fish oil. This study aimed to investigate the effects of dietary cholesterol supplementation on the muscle lipidomics of two marine fish species, turbot and tiger puffer. A 70-day feeding trial was conducted, where two low-fishmeal diets supplemented with 0 or 1% cholesterol were used. The lipidomic analysis with targeted tandem mass spectrometry showed that, in turbot, a total of 49 individual lipids exhibited significant differences in their abundance in response to dietary cholesterol, whereas the number was 30 for tiger puffer. Dietary cholesterol up-regulated the abundance of cholesterol and cholesterol ester in both species. In turbot, the dietary cholesterol also increased the abundance of triacylglycerol and acylcarnitine, whereas in tiger puffer, it primarily regulated the abundance of phospholipids and BMP. This was the first time the responses of marine fish muscle lipidomics to dietary cholesterol supplementation have been investigated.

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.

Optical photothermal infrared spectroscopy can differentiate equine osteoarthritic plasma extracellular vesicles from healthy controls

Equine osteoarthritis is a chronic degenerative disease of the articular joint, characterised by cartilage degradation resulting in pain and reduced mobility and thus is a prominent equine welfare concern. Diagnosis is usually at a late stage through clinical examination and radiographic imaging, whilst treatment is symptomatic not curative. Extracellular vesicles are nanoparticles that are involved in intercellular communication. The objective of this study was to investigate the feasibility of Raman and Optical Photothermal Infrared Spectroscopies to detect osteoarthritis using plasma-derived extracellular vesicles, specifically differentiating extracellular vesicles in diseased and healthy controls within the parameters of the techniques used. Plasma samples were derived from thoroughbred racehorses. A total of 14 samples were selected (control; n = 6 and diseased; n = 8). Extracellular vesicles were isolated using differential ultracentrifugation and characterised using nanoparticle tracking analysis, transmission electron microscopy, and human tetraspanin chips. Samples were then analysed using combined Raman and Optical Photothermal Infrared Spectroscopies. Infrared spectra were collected between 950-1800 cm^-1. Raman spectra had bands between the wavelengths of 900-1800 cm^-1 analysed. Spectral data for both Raman and Optical Photothermal Infrared Spectroscopy were used to generate clustering via principal components analysis and classification models were generated using partial least squared discriminant analysis in order to characterize the techniques' ability to distinguish diseased samples. Optical Photothermal Infrared Spectroscopy could differentiate osteoarthritic extracellular vesicles from healthy with good classification (93.4% correct classification rate) whereas Raman displayed poor classification (correct classification rate = -64.3%). Inspection of the infrared spectra indicated that plasma-derived extracellular vesicles from osteoarthritic horses contained increased signal for proteins, lipids and nucleic acids. For the first time we demonstrated the ability to use optical photothermal infrared spectroscopy combined with Raman spectroscopy to interrogate extracellular vesicles and osteoarthritis-related samples. Optical Photothermal Infrared Spectroscopy was superior to Raman in this study, and could distinguish osteoarthritis samples, suggestive of its potential use diagnostically to identify osteoarthritis in equine patients. This study demonstrates the potential of Raman and Optical Photothermal Infrared Spectroscopy to be used as a future diagnostic tool in clinical practice, with the capacity to detect changes in extracellular vesicles from clinically derived samples.

Lipidomics of Cell Secretome Combined with the Study of Selected Bioactive Lipids in an In Vitro Model of Osteoarthritis

Analytical advancements in lipidomics have enabled large-scale investigations of lipid biology. Herein, we focused on four bioactive lipid families, namely polyunsaturated fatty acids, eicosanoids, endocannabinoids, and N-acylethanolamines, and their involvement in the mesenchymal stem cells (MSC)-related inflammatory scenario. Since MSC secretome may represent a valid therapeutic alternative, here, the complete secretome and its vesicular component from adipose- and bone marrow-derived MSC and dermal fibroblasts were characterized by targeted mass spectrometry lipidomics. The 2-arachidonoylglycerol (2AG) and the palmitoylethanolamide (PEA), previously quantified in the MSC's secretome, were further investigated by assessing hypothetical effects in an in vitro model of osteoarthritis (OA) based on human primary articular chondrocytes (CH) stimulated with tumor necrosis factor alpha (TNFα). TNFα enhances the release of the inflammatory lipid prostaglandin E2 (PGE2), and an additional increment was observed when CH were treated with both TNFα and 2AG. In contrast, PEA downmodulates the PGE2 release to the levels of unstimulated CH suggesting a protective effect. TNFα also increases the expression of cyclooxygenase 2 (COX2), in particular when combined with 2AG, while PEA partly blunts TNFα-induced COX2 expression. In addition, TNFα-stimulated CH produce significantly higher levels of the inflammatory mediator nitric oxide (NO) both in the presence and in the absence of 2AG, and PEA was able to partially reduce NO release. Our results show a first partial lipidomic profile of MSC and DF secretome and suggest a possible implication of bioactive lipids in the OA scenario and in the future use of these cell-free products as innovative therapeutics.

Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain

Lipid mediators have been suggested to have a role in pain sensitivity and response; however, longitudinal data on lipid metabolites and persistent multisite musculoskeletal pain (MSMP) are lacking. This study was to identify lipid metabolic markers for persistent MSMP. Lipidomic profiling of 807 lipid species was performed on serum samples of 536 participants from a cohort study. MSMP was measured by a questionnaire and defined as painful sites ≥4. Persistent MSMP was defined as having MSMP at every visit. Logistic regression was used with adjustment for potential confounders. The Benjamini–Hochberg method was used to control for multiple testing. A total of 530 samples with 807 lipid metabolites passed quality control. Mean age at baseline was 61.54 ± 6.57 years and 50% were females. In total, 112 (21%) of the participants had persistent MSMP. Persistent MSMP was significantly associated with lower levels of monohexosylceramide (HexCer)(d18:1/22:0 and d18:1/24:0), acylcarnitine (AC)(26:0) and lysophosphatidylcholine (LPC)(18:1 [sn1], 18:2 [sn1], 18:2 [sn2], and 15-MHDA[sn1] [104_sn1]) after controlling for multiple testing. After adjustment for age, sex, body mass index, comorbidities, and physical activity, HexCer(d18:1/22:0 and d18:1/24:0) and LPC(15-MHDA [sn1] [104_sn1]) were significantly associated with persistent MSMP [Odds Ratio (OR) ranging from 0.25–0.36]. Two lipid classes—HexCer and LPC—were negatively associated with persistent MSMP after adjustment for covariates (OR = 0.22 and 0.27, respectively). This study identified three novel lipid signatures of persistent MSMP, suggesting that lipid metabolism is involved in the pathogenesis of persistent pain.

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

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

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

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

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

Mass Spectrometry-Based Metabolomic and Lipidomic Analysis of the Effect of High Fat/High Sugar Diet and GreenshellTM Mussel Feeding on Plasma of Ovariectomized Rats

This study aimed to examine the changes in lipid and metabolite profiles of ovariectomized (OVX) rats with diet-induced metabolic syndrome-associated osteoarthritis (MetOA) after supplementation with greenshell mussel (GSM) using an untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach. Ninety-six rats were fed with one of four diets: control, control supplemented with GSM + GSM, high fat/high sugar (HFHS), or high fat/high sugar enriched with GSM (HFHS + GSM). After 8 weeks on experimental diets, half of the rats in each group underwent OVX and the other half were sham operated. After being fed for an additional 28 weeks, blood samples were collected for the metabolomics analysis. Lipid and polar metabolites were extracted from plasma and analysed by LC-MS. We identified 29 lipid species from four lipid subclasses (phosphatidylcholine, lysophosphatidylcholine, diacylglycerol, and triacylglycerol) and a set of eight metabolites involved in amino acid metabolism (serine, threonine, lysine, valine, histidine, pipecolic acid, 3-methylcytidine, and cholic acid) as potential biomarkers for the effect of HFHS diet and GSM supplementation. GSM incorporation more specifically in the control diet generated significant alterations in the levels of several lipids and metabolites. Further studies are required to validate these findings that identify potential biomarkers to follow OA progression and to monitor the impact of GSM supplementation.

Alterations in the gut microbiota and metabolite profiles of patients with Kashin-Beck disease, an endemic osteoarthritis in China

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.

Association of dyslipidemia with chronic non-malignant pain in elderly patients with femoral neck fractures treated by primary total hip arthroplasty: a retrospective study

OBJECTIVE

The association of chronic non-malignant pain (CNP) with dyslipidemia is unclear. This retrospective study was performed to evaluate the association between CNP and dyslipidemia in elderly patients with femoral neck fractures (FNFs) treated by primary unilateral total hip arthroplasty (THA).

METHODS

We retrospectively identified 521 consecutive patients with FNFs (AO/OTA type 31B) who underwent primary unilateral THA from 2009 to 2021. The study population was divided into patients with and without CNP. Serum lipids were measured for each patient. The association between CNP and dyslipidemia was assessed using a multivariate binary logistic regression model.

RESULTS

In total, 436 patients (220 with CNP, 216 without CNP) were eligible for analysis. In the quantile regression, the adverse effect of CNP was significantly attenuated by resilience in patients with a high high-density lipoprotein (HDL) concentration and low low-density lipoprotein (LDL) concentration. The multivariate binary logistic regression model showed that the HDL and LDL concentrations were the only variables significantly associated with the development of CNP.

CONCLUSION

Both a low HDL and high LDL concentration may result in the occurrence of CNP in elderly patients with FNFs treated by primary unilateral THA.

Elevated Nesfatin-1 Level in Synovium and Synovial Fluid is Associated with Pro-Inflammatory Cytokines in Patients with Rheumatoid Arthritis

Background

Adipocytokines have been proven to be involved in the progression of autoimmune diseases, including rheumatoid arthritis (RA). Nesfatin-1, a newly discovered adipokine, has recently been reported to possess potent anti-inflammatory, antiapoptotic, and antioxidative properties. However, its role in RA has not yet been reported. Therefore, this study aimed to determine nesfatin-1 levels in the synovium and synovial fluid (SF) of patients with RA and examine their correlation with clinical manifestations and proinflammatory cytokine levels.

Methods

Synovium and SF samples were collected from patients with RA and non-RA patients during joint surgery. Immunohistochemistry was used to measure nesfatin-1 protein expression in the synovium. Enzyme-linked immunosorbent assay was used to measure nesfatin-1, interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α) levels in the synovium and SF. Pearson correlation analysis was used to evaluate the correlations between nesfatin-1 levels, RA clinical features, and proinflammatory cytokines. The diagnostic value of synovium nesfatin-1 for RA was assessed using receiver operating characteristic (ROC) curve analysis.

Results

The results showed that nesfatin-1, IL-1β, and TNF-α levels in the synovium were significantly higher in patients with RA than in controls, with age and body mass index as covariates. Moreover, the results of Pearson correlation analysis showed that nesfatin-1 levels were positively correlated with IL-1β and TNF-α levels in the synovium of patients with RA. Furthermore, there was a positive relationship between synovium nesfatin-1 levels and rheumatoid factor in patients with RA. Additionally, the results of the ROC curve analysis revealed an area under the curve of 0.733 with 77.5% sensitivity and 60.0% specificity for synovium nesfatin-1 in discriminating patients with RA from controls.

Conclusion

These findings suggest that increased nesfatin-1 levels in the synovium may be associated with proinflammatory cytokines and RA severity.

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.

Abnormal Lipid Profile in Fast-Growing Broilers With Spontaneous Femoral Head Necrosis

This study investigated lipid metabolism in broilers with spontaneous femoral head necrosis (FHN) by determining the levels of markers of the blood biochemistry and bone metabolism. The birds were divided into a normal group and FHN group according to the femoral head scores of 3-, 4-, and 5-week-old chickens with FHN, and a comparative study was conducted. The study showed that spontaneous FHN broilers had a lipid metabolism disorder, hyperlipidemia, and an accumulation of lipid droplets in the femur. In addition, there were significant changes in the bone parameters and blood bone biochemistry markers, and the expression of genes related to lipid metabolism in the femoral head was also significantly increased. Therefore, FHN may result from dyslipidemia, which affects the bone growth and development of broilers.

Heterogeneity of Lipid and Protein Cartilage Profiles Associated with Human Osteoarthritis with or without Type 2 Diabetes Mellitus

Osteoarthritis (OA) is a multifactorial pathology and comprises a wide range of distinct phenotypes. In this context, the characterization of the different molecular profiles associated with each phenotype can improve the classification of OA. In particular, OA can coexist with type 2 diabetes mellitus (T2DM). This study investigates lipidomic and proteomic differences between human OA/T2DM- and OA/T2DM+ cartilage through a multimodal mass spectrometry approach. Human cartilage samples were obtained after total knee replacement from OA/T2DM- and OA/T2DM+ patients. Label-free proteomics was employed to study differences in protein abundance and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for spatially resolved-lipid analysis. Label-free proteomic analysis showed differences between OA/T2DM- and OA/T2DM+ phenotypes in several metabolic pathways such as lipid regulation. Interestingly, phospholipase A2 protein was found increased within the OA/T2DM+ cohort. In addition, MALDI-MSI experiments revealed that phosphatidylcholine and sphingomyelin species were characteristic of the OA/T2DM- group, whereas lysolipids were more characteristic of the OA/T2DM+ phenotype. The data also pointed out differences in phospholipid content between superficial and deep layers of the cartilage. Our study shows distinctively different lipid and protein profiles between OA/T2DM- and OA/T2DM+ human cartilage, demonstrating the importance of subclassification of the OA disease for better personalized treatments.

Identification of a distinct lipidomic profile in the osteoarthritic synovial membrane by mass spectrometry imaging

OBJECTIVE

Synovial inflammation is one of the most characteristic events in different types of arthritis, including Osteoarthritis (OA). Emerging evidence also suggests the involvement of lipids in the regulation of inflammatory processes. The aim of this study was to elucidate the heterogeneity and spatial distribution of lipids in the OA synovial membrane and explore their putative involvement in inflammation.

METHOD

The abundance and distribution of lipids were examined in human synovial membranes. To this end, histological cuts from this tissue were analysed by matrix-assisted laser desorption ionization - mass spectrometry imaging (MALDI-MSI). The lipidomic profile of OA synovium was characterized and compared with healthy and other forms of inflammatory arthropathies as Rheumatoid Arthritis (RA) and Psoriatic Arthritis (PsA) using principal component analysis and discriminant analysis methods. Lipid identification was undertaken by tandem MS analyses and database queries.

RESULTS

Our results reveal differential and characteristic lipidomic profiles between OA and control samples. Specifically, we unveiled that OA synovium presents elevated levels of phosphatidylcholines, fatty acids and lysophosphatidic acids and lower levels of lysophosphatidylcholines compared to control tissues. The spatial distribution of particular glycerophospholipids was also correlated with hypertrophic, inflamed or vascularized synovial areas. Compared with other inflammatory arthritis, the OA tissue showed lower amounts of phosphatidylethanolamine-based plasmalogens.

CONCLUSIONS

This study provides a novel insight into the lipid profiles of synovial membrane and differences in abundance between OA and control tissues. The lipidomic alterations improves understanding of the pathogenic mechanisms of OA and may be important for its diagnosis.

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.

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.

"Modeling-Prediction" Strategy for Deep Profiling of Lysophosphatidic Acids by Liquid Chromatography-Mass Spectrometry: Exploration Biomarkers of Breast Cancer

Lysophosphatidic acids (LPAs) are important bioactive phospholipids consisting of various species involved in a wide array of physiological and pathological processes. However, LPAs were rarely identified in untargeted lipidomics studies because of the incompatibility with analytical methods. Moreover, in targeted studies, the coverages of LPAs remained unsatisfactorily low due to the limitation of reference standards. Herein, a "modeling-prediction" workflow for deep profiling of LPAs by liquid chromatography-mass spectrometry was developed. Multiple linear regression models of qualitative and quantitative parameters were established according to features of fatty acyl tails of the commercial standards to predict the corresponding parameters for unknown LPAs. Then 72 multiple reaction monitoring (MRM) transitions were monitored simultaneously and species of LPA 14:0, LPA 16:1, LPA 18:3, LPA 20:3 and LPA 20:5 were firstly characterized and quantified in plasma. Finally, the workflow was applied to explore the changes of LPAs in plasma of breast cancer patients compared with healthy volunteers. Multi-LPAs indexes with strong diagnostic ability for breast cancer were identified successfully using Student's t- test, orthogona partial least-squares discrimination analysis (OPLS-DA) and logistic regression- receiver operating characteristic (ROC) curve analysis. The proposed workflow with high sensitivity, high accuracy, high coverage and reliable identification would be a powerful complement to untargeted lipidomics and shed a light on the analysis of other lipids.

Emerging Technologies and Platforms for the Immunodetection of Multiple Biochemical Markers in Osteoarthritis Research and Therapy

Biomarkers, especially biochemical markers, are important in osteoarthritis (OA) research, clinical trials, and drug development and have potential for more extensive use in therapeutic monitoring. However, they have not yet had any significant impact on disease diagnosis and follow-up in a clinical context. Nevertheless, the development of immunoassays for the detection and measurement of biochemical markers in OA research and therapy is an active area of research and development. The evaluation of biochemical markers representing low-grade inflammation or extracellular matrix turnover may permit OA prognosis and expedite the development of personalized treatment tailored to fit particular disease severities. However, currently detection methods have failed to overcome specific hurdles such as low biochemical marker concentrations, patient-specific variation, and limited utility of single biochemical markers for definitive characterization of disease status. These challenges require new and innovative approaches for development of detection and quantification systems that incorporate clinically relevant biochemical marker panels. Emerging platforms and technologies that are already on the way to implementation in routine diagnostics and monitoring of other diseases could potentially serve as good technological and strategic examples for better assessment of OA. State-of-the-art technologies such as advanced multiplex assays, enhanced immunoassays, and biosensors ensure simultaneous screening of a range of biochemical marker targets, the expansion of detection limits, low costs, and rapid analysis. This paper explores the implementation of such technologies in OA research and therapy. Application of novel immunoassay-based technologies may shed light on poorly understood mechanisms in disease pathogenesis and lead to the development of clinically relevant biochemical marker panels. More sensitive and specific biochemical marker immunodetection will complement imaging biomarkers and ensure evidence-based comparisons of intervention efficacy. We discuss the challenges hindering the development, testing, and implementation of new OA biochemical marker assays utilizing emerging multiplexing technologies and biosensors.

Metabolomic Signature of Amino Acids, Biogenic Amines and Lipids in Blood Serum of Patients with Severe Osteoarthritis

Metabolomic analysis is an emerging new diagnostic tool, which holds great potential for improving the understanding of osteoarthritis (OA)-caused metabolomic shifts associated with systemic inflammation and oxidative stress. The main aim of the study was to map the changes of amino acid, biogenic amine and complex lipid profiles in severe OA, where the shifts should be more eminent compared with early stages. The fasting serum of 70 knee and hip OA patients and 82 controls was assessed via a targeted approach using the AbsoluteIDQ™ p180 kit. Changes in the serum levels of amino acids, sphingomyelins, phoshatidylcholines and lysophosphatidylcholines of the OA patients compared with controls suggest systemic inflammation in severe OA patients. Furthermore, the decreased spermine to spermidine ratio indicates excessive oxidative stress to be associated with OA. Serum arginine level was positively correlated with radiographic severity of OA, potentially linking inflammation through NO synthesis to OA. Further, the level of glycine was negatively associated with the severity of OA, which might refer to glycine deficiency in severe OA. The current study demonstrates significant changes in the amino acid, biogenic amine and low-molecular weight lipid profiles of severe OA and provides new insights into the complex interplay between chronic inflammation, oxidative stress and OA.

Large granular lymphocyte leukemia serum and corresponding hematological parameters reveal unique cytokine and sphingolipid biomarkers and associations with STAT3 mutations

Large granular lymphocyte (LGL) leukemia is a rare hematological disorder with expansion of the T-cell or natural killer (NK) cell lineage. Signal transducer and activator of transcription 3 (STAT3) exhibits somatic activating mutations in 30%-40% of LGL leukemia cases. Transcriptional targets of STAT3 include inflammatory cytokines, thus previous studies have measured cytokine levels of LGL leukemia patients compared to normal donors. Sphingolipid metabolism is a growing area of cancer research, with efforts focused on drug discovery. To date, no studies have examined serum sphingolipids in LGL leukemia patients, and only one study compared a subset of cytokines between the T-LGL and NK-LGL subtypes. Therefore, here, we included both LGL leukemia subtypes with the goals of (a) measuring serum sphingolipids for the first time, (b) measuring cytokines to find distinctions between the subtypes, and (c) establishing relationships with STAT3 mutations and clinical data. The serum analyses identified cytokines (EGF, IP-10, G-CSF) and sphingolipids (SMC22, SMC24, SMC20, LysoSM) significantly different in the LGL leukemia group compared to normal donors. In a mixed STAT3 mutation group, D661Y samples exhibited the highest mean corpuscular volume (MCV) values. We explored this further by expanding the cohort to include larger groups of single STAT3 mutations. Male D661Y STAT3 samples had lower Hgb and higher MCV compared to wild type (WT) or Y640F counterparts. This is the first report examining large groups of individual STAT3 mutations. Overall, our results revealed novel serum biomarkers and evidence that D661Y mutation may show different clinical manifestation compared to WT or Y640F STAT3.