Phenylalanine Is a Novel Marker for Radiographic Knee Osteoarthritis Progression: The MOST Study

Early microRNA and metabolite changes after anterior cruciate ligament reconstruction surgery

OBJECTIVES

Anterior cruciate ligament (ACL) reconstruction after injury does not prevent post-traumatic osteoarthritis (PTOA). Circulating microRNA (miRNA) and metabolite changes emerging shortly after ACL injury and reconstruction remain insufficiently defined, potentially harbouring early cues contributing to PTOA evolution. Moreover, their differential expression between females and males also may influence PTOA's natural trajectory. This study aims to determine alterations in plasma miRNA and metabolite levels in the early stages following ACL reconstruction and between females and males.

METHODS

A cohort of 43 ACL reconstruction patients was examined. Plasma was obtained at baseline, 2 weeks, and 6 weeks post-surgery (129 biospecimens in total). High-throughput miRNA sequencing and metabolomics were conducted. Differentially expressed miRNAs and metabolites were identified using negative binomial and linear regression models, respectively. Associations between miRNAs and metabolites were explored using time and sex as co-variants, (pre- versus 2- and 6-week post-surgery). Using computational biology, miRNA-metabolite-gene interaction and pathway analyses were performed.

RESULTS

Levels of 46 miRNAs were increased at 2 weeks post-surgery compared to pre-surgery (baseline) using miRNA sequencing. Levels of 13 metabolites were significantly increased while levels of 6 metabolites were significantly decreased at 2 weeks compared to baseline using metabolomics. Hsa-miR-145-5p levels were increased in female subjects at both 2 weeks (log2-fold-change 0.71, 95%CI 0.22,1.20) and 6 weeks (log2-fold-change 0.75, 95%CI 0.07,1.43) post-surgery compared to males. In addition, hsa-miR-497-5p showed increased levels in females at 2 weeks (log2-fold-change 0.77, 95%CI 0.06,1.48) and hsa-miR-143-5p at 6 weeks (log2-fold-change 0.83, 95%CI 0.07,1.59). Five metabolites were decreased at 2 weeks post-surgery in females compared to males: L-leucine (-1.44, 95%CI -1.75,-1.13), g-guanidinobutyrate (-1.27, 95%CI 1.54,-0.99), creatinine (-1.17, 95%CI -1.44,-0.90), 2-methylbutyrylcarnitine (-1.76, 95%CI -2.17,-1.35), and leu-pro (-1.13, 95%CI -1.44,-0.83). MiRNA-metabolite-gene interaction analysis revealed key signalling pathways based on post-surgical time-point and in females versus males.

CONCLUSION

MiRNA and metabolite profiles were modified by time and by sex early after ACL reconstruction surgery, which could influence surgical response and ultimately risk of developing PTOA.

Specific discrimination of pathogenic bacteria causing septic arthritis using Raman spectroscopy: In-vitro study

In this study we performed preliminary experiments using Raman spectroscopy as an evolving technology in biofluid and microbial characterization, to explore its potential for rapid diagnosis of pathogenic bacteria in an in-vitro synovial fluid infection model. Normal human synovial fluids samples were collected from patients undergoing knee surgery and the three most common pathogenic bacteria introduced in-vitro into the samples. The bacterial growth was systematically monitored using a Raman spectroscopy. Multivariate regression analysis of acquired spectra showed bacterial characteristic Raman bands related to bacterial cell membranes and DNA structures to increase continuously as the incubation period was increased. Spectra signature recorded from cultured synovial fluid samples showed a significant loss in synovial quality and protein morphology over time compared to control samples. In this study, Raman spectroscopy shows promise for rapid pathogenic bacteria identification in synovial fluid. Marker peaks distinguished inoculated bacteria, while chemical changes reveal infection dynamics.

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.

Unraveling sex-specific risks of knee osteoarthritis before menopause: Do sex differences start early in life?

OBJECTIVE

Sufficient evidence within the past two decades have shown that osteoarthritis (OA) has a sex-specific component. However, efforts to reveal the biological causes of this disparity have emerged more gradually. In this narrative review, we discuss anatomical differences within the knee, incidence of injuries in youth sports, and metabolic factors that present early in life (childhood and early adulthood) that can contribute to a higher risk of OA in females.

DESIGN

We compiled clinical data from multiple tissues within the knee joint-since OA is a whole joint disorder-aiming to reveal relevant factors behind the sex differences from different perspectives.

RESULTS

The data gathered in this review indicate that sex differences in articular cartilage, meniscus, and anterior cruciate ligament are detected as early as childhood and are not only explained by sex hormones. Aiming to unveil the biological causes of the uneven sex-specific risks for knee OA, we review the current knowledge of sex differences mostly in young, but also including old populations, from the perspective of (i) human anatomy in both healthy and pathological conditions, (ii) physical activity and response to injury, and (iii) metabolic signatures.

CONCLUSIONS

We propose that to close the gap in health disparities, and specifically regarding OA, we should address sex-specific anatomic, biologic, and metabolic factors at early stages in life, as a way to prevent the higher severity and incidence of OA in women later in life.

Serum Metabolomic Alteration in Rats with Osteoarthritis Treated with Palm Tocotrienol-Rich Fraction Alone or in Combination with Glucosamine Sulphate

Osteoarthritis (OA) is a degenerative joint condition with limited disease-modifying treatments currently. Palm tocotrienol-rich fraction (TRF) has been previously shown to be effective against OA, but its mechanism of action remains elusive. This study aims to compare serum metabolomic alteration in Sprague-Dawley rats with monosodium iodoacetate (MIA)-induced OA which were treated with palm TRF, glucosamine sulphate, or a combination of both. This study was performed on thirty adult male rats, which were divided into normal control (n = 6) and OA groups (n = 24). The OA group received intra-articular injections of MIA and daily oral treatments of refined olive oil (vehicle, n = 6), palm TRF (100 mg/kg, n = 6), glucosamine sulphate (250 mg/kg, n = 6), or a combination of TRF and glucosamine (n = 6) for four weeks. Serum was collected at the study's conclusion for metabolomic analysis. The findings revealed that MIA-induced OA influences amino acid metabolism, leading to changes in metabolites associated with the biosynthesis of phenylalanine, tyrosine and tryptophan as well as alterations in the metabolism of phenylalanine, tryptophan, arginine and proline. Supplementation with glucosamine sulphate, TRF, or both effectively reversed these metabolic changes induced by OA. The amelioration of metabolic effects induced by OA is linked to the therapeutic effects of TRF and glucosamine. However, it remains unclear whether these effects are direct or indirect in nature.

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.

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.

Restricting Branched-Chain Amino Acids within a High-Fat Diet Prevents Obesity

Obesity is a global pandemic, but there is yet no effective measure to control it. Recent metabolomics studies have identified a signature of altered amino acid profiles to be associated with obesity, but it is unclear whether these findings have actionable clinical potential. The aims of this study were to reveal the metabolic alterations of obesity and to explore potential strategies to mitigate obesity. We performed targeted metabolomic profiling of the plasma/serum samples collected from six independent cohorts and conducted an individual data meta-analysis of metabolomics for body mass index (BMI) and obesity. Based on the findings, we hypothesized that restriction of branched-chain amino acids (BCAAs), phenylalanine, or tryptophan may prevent obesity and tested our hypothesis in a dietary restriction trial with eight groups of 4-week-old male C57BL/6J mice (n = 5/group) on eight different types of diets, respectively, for 16 weeks. A total of 3397 individuals were included in the meta-analysis. The mean BMI was 30.7 ± 6.1 kg/m2, and 49% of participants were obese. Fifty-eight metabolites were associated with BMI and obesity (all p ≤ 2.58 × 10-4), linked to alterations of the BCAA, phenylalanine, tryptophan, and phospholipid metabolic pathways. The restriction of BCAAs within a high-fat diet (HFD) maintained the mice's weight, fat and lean volume, subcutaneous and visceral adipose tissue weight, and serum glucose and insulin at levels similar to those in the standard chow group, and prevented obesity, adipocyte hypertrophy, adipose inflammation, and insulin resistance induced by HFD. Our data suggest that four metabolic pathways, BCAA, phenylalanine, tryptophan, and phospholipid metabolic pathways, are altered in obesity and restriction of BCAAs within a HFD can prevent the development of obesity and insulin resistance in mice, providing a promising strategy to potentially mitigate diet-induced obesity.

A Review of Sensors and Biosensors Modified with Conducting Polymers and Molecularly Imprinted Polymers Used in Electrochemical Detection of Amino Acids: Phenylalanine, Tyrosine, and Tryptophan

Recently, the studies on developing sensors and biosensors-with an obvious interdisciplinary character-have drawn the attention of many researchers specializing in various fundamental, but also complex domains such as chemistry, biochemistry, physics, biophysics, biology, bio-pharma-medicine, and bioengineering. Along these lines, the present paper is structured into three parts, and is aimed at synthesizing the most relevant studies on the construction and functioning of versatile devices, of electrochemical sensors and biosensors, respectively. The first part presents examples of the most representative scientific research focusing on the role and the importance of the phenylalanine, tyrosine, and tryptophan amino acids, selected depending on their chemical structure and their impact on the central nervous system. The second part is dedicated to presenting and exemplifying conductor polymers and molecularly imprinted polymers used as sensitive materials in achieving electrochemical sensors and biosensors. The last part of the review analyzes the sensors and biosensors developed so far to detect amino acids with the aid of conductor polymers and molecularly imprinted polymers from the point of view of the performances obtained, with emphasis on the detection methods, on the electrochemical reactions that take place upon detection, and on the electroanalytical performances. The present study was carried out with a view to highlighting, for the benefit of specialists in medicine and pharmacy, the possibility of achieving and purchasing efficient devices that might be used in the quality control of medicines, as well as in studying and monitoring diseases associated with these amino acids.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM)

PURPOSE

Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint.

MATERIALS AND METHODS

A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics.

RESULTS

A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set.

CONCLUSIONS

The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future.

KEY POINTS

• Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.