Lysophosphatidylcholines to phosphatidylcholines ratio predicts advanced knee osteoarthritis

Alteration of Metabolic Pathways in Osteoarthritis

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

Human C-reactive protein aggravates osteoarthritis development in mice on a high-fat diet

OBJECTIVE

C-reactive protein (CRP) levels can be elevated in osteoarthritis (OA) patients. In addition to indicating systemic inflammation, it is suggested that CRP itself can play a role in OA development. Obesity and metabolic syndrome are important risk factors for OA and also induce elevated CRP levels. Here we evaluated in a human CRP (hCRP)-transgenic mouse model whether CRP itself contributes to the development of 'metabolic' OA.

DESIGN

Metabolic OA was induced by feeding 12-week-old hCRP-transgenic males (hCRP-tg, n = 30) and wild-type littermates (n = 15) a 45 kcal% high-fat diet (HFD) for 38 weeks. Cartilage degradation, osteophytes and synovitis were graded on Safranin O-stained histological knee joint sections. Inflammatory status was assessed by plasma lipid profiling, flow cytometric analyses of blood immune cell populations and immunohistochemical staining of synovial macrophage subsets.

RESULTS

Male hCRP-tg mice showed aggravated OA severity and increased osteophytosis compared with their wild-type littermates. Both classical and non-classical monocytes showed increased expression of CCR2 and CD86 in hCRP-tg males. HFD-induced effects were evident for nearly all lipids measured and indicated a similar low-grade systemic inflammation for both genotypes. Synovitis scores and synovial macrophage subsets were similar in the two groups.

CONCLUSIONS

Human CRP expression in a background of HFD-induced metabolic dysfunction resulted in the aggravation of OA through increased cartilage degeneration and osteophytosis. Increased recruitment of classical and non-classical monocytes might be a mechanism of action through which CRP is involved in aggravating this process. These findings suggest interventions selectively directed against CRP activity could ameliorate metabolic OA development.

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.

Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females.

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

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

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.

What did we learn from 'omics' studies in osteoarthritis

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Osteoarthritis biomarkers: year in review

OBJECTIVE

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

DESIGN

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

RESULTS

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

CONCLUSIONS

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

Metabolomics as a promising tool for early osteoarthritis diagnosis

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

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.

Osteoarthritis year in review 2016: genetics, genomics and epigenetics

The purpose of this narrative review is to provide an overview of last year's publications in the field of genetics, genomics and epigenetics in the osteoarthritis (OA) field. Major themes arising from a Pubmed search on (epi)genetics in OA were identified. In addition, general developments in the fast evolving field of (epi)genetics are reviewed and relevance for the OA field is summarized. In the last 5 years, a number of genome-wide association studies have identified a modest number of genetic loci associated to OA. Continued functional research into these DNA variants is showing putative biological mechanisms underlying these associations. Over the last year, no additional large genome-wide association studies were published, but there clearly remains much to be discovered in the OA genetic field. A lot of research has been done into the epigenetics of OA over the last year. Several genome-wide screens examining the methylome of osteoarthritic cartilage were done. Pathway analysis confirmed deregulation of developmental and extracellular pathways in OA cartilage. Over the last year many microRNAs (miRNAs) have been identified that potentially play important roles in cartilage homeostasis and/or OA process. Continued research will learn whether these identified miRNAs are truly causal and can be used in clinical applications. Many of the epigenetic findings need further confirmation, but they highlight potential novel pathways involved in cartilage biology and OA.

MALDI mass spectrometry imaging in rheumatic diseases

Mass spectrometry imaging (MSI) is a technique used to visualize the spatial distribution of biomolecules such as peptides, proteins, lipids or other organic compounds by their molecular masses. Among the different MSI strategies, MALDI-MSI provides a sensitive and label-free approach for imaging of a wide variety of protein or peptide biomarkers from the surface of tissue sections, being currently used in an increasing number of biomedical applications such as biomarker discovery and tissue classification. In the field of rheumatology, MALDI-MSI has been applied to date for the analysis of joint tissues such as synovial membrane or cartilage. This review summarizes the studies and key achievements obtained using MALDI-MSI to increase understanding on rheumatic pathologies and to describe potential diagnostic or prognostic biomarkers of these diseases. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.