The ratio adipsin/MCP-1 is strongly associated with structural changes and CRP/MCP-1 with symptoms in obese knee osteoarthritis subjects: data from the Osteoarthritis Initiative

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.

Pro-Inflammatory Biomarkers Combined with Body Composition Display a Strong Association with Knee Osteoarthritis in a Community-Based Study

Knee osteoarthritis (KOA) is one of the most common progressive, age-dependent chronic degenerative joint diseases. KOA often develops as a result of a gradual articular cartilage loss caused by its wear and tear. Numerous studies suggest that the degradation of the knee joint involves inflammatory components. This process is also associated with body composition, particularly being overweight and muscle mass loss. The present study aimed to search for novel circulating KOA inflammatory biomarkers, taking into account body composition characteristics. To this aim, we recruited 98 patients diagnosed and radiologically confirmed with KOA and 519 healthy controls from the Arab community in Israel. A panel of soluble molecules, related to inflammatory, metabolic, and musculoskeletal disorders, was measured by ELISA in plasma samples, while several body composition parameters were assessed with bioimpedance analysis. Statistical analysis, including multivariable logistic regression, revealed a number of the factors significantly associated with KOA, independently of age and sex. The most significant independent associations [OR (95% CI)] were fat body mass/body weight index-1.56 (1.20-2.02), systemic immune-inflammation index-4.03 (2.23-7.27), circulating vaspin levels-1.39 (1.15-1.68), follistatin/FSTL1 ratio-1.32 (1.02-1.70), and activin A/FSTL1 ratio-1.33 (1.01-1.75). Further clinical studies are warranted to confirm the relevance of these KOA-associated biological factors. Hereafter, they could serve as reliable biomarkers for KOA in the general human population.

Adipokines as targets in musculoskeletal immune and inflammatory diseases

Adipokines are the principal mediators in adipose signaling. Nevertheless, besides their role in energy storage, these molecules can be produced by other cells, such as immune cells or chondrocytes. Given their pleiotropic effects, research over the past few years has also focused on musculoskeletal diseases, showing that these adipokines might have relevant roles in worsening the disease or improving the treatment response. In this review, we summarize recent advances in our understanding of adipokines and their role in the most prevalent musculoskeletal immune and inflammatory disorders.

Biological Targets of Multimolecular Therapies in Middle-Age Osteoarthritis

Knee osteoarthritis (OA) is a common condition, prevalent in middle-agedness, associated with chronic pain and impaired quality of life. Two interrelated biological processes fuel early OA progression: inflammation and structural tissues catabolism. Procatabolic and proinflammatory mediators are interconnected and form part of a self-perpetuating loop. They leverage OA research complexity because of the impossibility to discern certain spatiotemporal tissues' changes from others. Both are shared targets of versatile regenerative multimolecular therapies. In particular, platelet-rich plasma can interfere with inflammation and inflammatory pain. The therapeutic approach is to alter the vicious inflammatory loop by modifying the molecular composition of the synovial fluid, thereby paracrine cellular cross talk. Intra-articular injections of platelet-rich plasma can provide key factors balancing proinflammatory and anti-inflammatory factors, targeting macrophage dysfunction and modulating immune mechanisms within the knee.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

Background

Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.

Methods

Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.

Results

In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).

Conclusion

Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02801-1.

Associations of Body Mass Index With Pain and the Mediating Role of Inflammatory Biomarkers in People With Hand Osteoarthritis

OBJECTIVE

To examine the association of body mass index (BMI) with pain in people with hand osteoarthritis (OA), and explore whether this association, if causal, is mediated by systemic inflammatory biomarkers.

METHODS

In 281 Nor-Hand study participants, we estimated associations between BMI and hand pain, as measured by the Australian/Canadian Osteoarthritis Hand Index (AUSCAN; range 0-20) and Numerical Rating Scale (NRS; range 0-10); foot pain, as measured by NRS (range 0-10); knee/hip pain, as measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC; range 0-20); painful total body joint count; and pain sensitization. We fit natural-effects models to estimate natural direct and natural indirect effects of BMI on pain through inflammatory biomarkers.

RESULTS

Each 5-unit increase in BMI was associated with more severe hand pain (on average increased AUSCAN by 0.64 [95% confidence interval (95% CI) 0.23, 1.08]), foot pain (on average increased NRS by 0.65 [95% CI 0.36, 0.92]), knee/hip pain (on average increased WOMAC by 1.31 [95% CI 0.87, 1.73]), generalized pain, and pain sensitization. Mediation analyses suggested that the effects of BMI on hand pain and painful total body joint count were partially mediated by leptin and high-sensitivity C-reactive protein (hsCRP), respectively. Effect sizes for mediation by leptin were larger for the hands than for the lower extremities, and were statistically significant for the hands only.

CONCLUSION

In people with hand OA, higher BMI is associated with greater pain severity in the hands, feet, and knees/hips. Systemic effects of obesity, measured by leptin, may play a larger mediating role for pain in the hands than in the lower extremities. Low-grade inflammation, measured by hsCRP, may contribute to generalized pain in overweight/obese individuals.

Relationship between Gut Microbiota and Bone Health

Gut microbiota (GM) are microorganisms that live in the host gastrointestinal tract, and their abundance varies throughout the host's life. With the development of sequencing technology, the role of GM in various diseases has been increasingly elucidated. Unlike earlier studies on orthopedic diseases, this review elucidates the correlation between GM health and bone health, and discusses the potential mechanism of GM effects on host metabolism, inflammation, and ability to induce or aggravate some common orthopedic diseases such as osteoarthritis, osteoporosis, rheumatoid arthritis, etc. Finally, the prospective methods of GM manipulation and evaluation of potential GM-targeting strategies in the diagnosis and treatment of orthopedic diseases are reviewed.

Soluble biological markers in osteoarthritis

In recent years, markers research has focused on the structural components of cartilage matrix. Specifically, a second generation of degradation markers has been developed against type II collagen neoepitopes generated by specific enzymes. A particular effort has been made to measure the degradation of minor collagens III and X of the cartilage matrix. However, because clinical data, including longitudinal controlled studies, are very scarce, it remains unclear whether they will be useful as an alternative to or in combination with current more established collagen biological markers to assess patients with osteoarthritis (OA). In addition, new approaches using high-throughput technologies allowed to detect new types of markers and improve the knowledge about the metabolic changes linked to OA. The relative advances coming from phenotype research are a first attempt to classify the heterogeneity of OA, and several markers could improve the phenotype characterization. These phenotypes could improve the selection of patients in clinical trials limiting the size of the studies by selecting patients with OA characteristics corresponding to the metabolic pathway targeted by the molecules evaluated. In addition, the inclusion of rapid progressors only in clinical trials would facilitate the demonstration of efficacy of the investigative drug to reduce joint degradation. The combination of selective biochemical markers appears as a promising and cost-effective approach to fulfill this unmet clinical need. Among the various potential roles of biomarkers in OA, their ability to monitor drug efficacy is probably one of the most important, in association with clinical and imaging parameters. Biochemical markers have the unique property to detect changes in joint tissue metabolism within a few weeks.

Association of serum levels of inflammatory markers and adipokines with joint symptoms and structures in participants with knee osteoarthritis

OBJECTIVE

To investigate the longitudinal associations of serum inflammatory markers and adipokines with joint symptoms and structures in participants with knee osteoarthritis (OA).

METHODS

Two hundred participants (46.5% female, mean age 63.1 years, mean BMI 29.5 kg/m2) from Tasmania part of the VIDEO (Vitamin D Effect on OA) study were randomly selected in the current study. Serum levels of 19 biomarkers, scores of Western Ontario and McMaster Universities Index (WOMAC), and magnetic resonance imaging-assessed knee structures were evaluated at baseline and month 24. The patterns of biomarkers were derived from principal component analysis and their association with knee symptoms and structures were examined using adjusted generalized estimating equations.

RESULTS

Five components explained 78% of the total variance. Interleukin (IL)-1β, -2, -4, -6, -8, -17A, -17F, -21, -22 and -23 loaded the highest on the first component, which was associated with increased bone marrow lesions (BMLs) and WOMAC dysfunction score. IL-10, -12 and granulocyte-macrophage colony-stimulating factor loaded on the second component, which was associated with increased cartilage volume, and decreased effusion-synovitis and WOMAC scores. Leptin, adipsin and C-reactive protein loaded on the third component, which was positively associated with WOMAC scores. Resistin loaded on the fourth component, which was associated with increased BMLs and cartilage defects. Apelin-36 and adiponectin loaded on the fifth component, which was associated with increased BMLs.

CONCLUSION

Various inflammatory and metabolic components were associated differently with joint symptoms and structural changes in knee OA, suggesting a complex inflammatory and metabolic inter-relationship in the pathogenesis of knee OA.

Predictors of Subjective Cognitive Decline Among People With Diabetes: Data From the Behavioral Risk Factor Surveillance System

PURPOSE

The purpose of this study was to examine the prevalence of subjective cognitive decline (SCD) and SCD-related functional limitations among people with diabetes and to identify socioeconomic and comorbidity risk factors associated with SCD.

METHODS

This study analyzed data from the 2017 Behavioral Risk Factor Surveillance System (BRFSS) with background variables (race, gender, education, and age), health-related factors (self-rated health, BMI, insurance, and comorbid conditions), and health behaviors (smoking, exercise, alcohol consumption) entered simultaneously to estimate logistic regression models of SCD.

RESULTS

Within the sample (n = 5263 adults with diabetes), 48% were age ≥65 years; 50% were male; 55% were non-Hispanic White; and of the 15% who reported having SCD, 57% had functional limitations. Increased odds of reporting SCD were observed among individuals who were Hispanic (odds ratio [OR] = 2.21, P < .001), male (OR = 1.47, P < .01), depressed (OR = 3.85, P < .001), or had arthritis (OR = 1.43, P < .03). Participants with better self-rated health had a reduced likelihood of SCD (OR = 0.51, P < .001).

CONCLUSIONS

Health care providers should assess high-risk patients for self-rated cognitive dysfunction and offer early interventions.

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening

Aim

In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time.

Methods

The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients.

Results

Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men.

Conclusion

This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors.

Plain language summary

Machine learning model for early knee osteoarthritis structural progression Knee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life - the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression.We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring.

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.

Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches

OBJECTIVE

The infrapatellar fat pad (IPFP) has been associated with knee osteoarthritis onset and progression. This study uses machine learning (ML) approaches to predict serum levels of some adipokines/related inflammatory factors and their ratios on knee IPFP volume of osteoarthritis patients.

METHODS

Serum and MRI were from the OAI at baseline. Variables comprised the 3 main osteoarthritis risk factors (age, gender, BMI), 6 adipokines, 3 inflammatory factors, and their 36 ratios. IPFP volume was assessed on MRI with a ML methodology. The best variables and models were identified in Total-cohort (n = 678), High-BMI (n = 341) and Low-BMI (n = 337), using a selection approach based on ML methods.

RESULTS

The best model for each group included three risk factors and adipsin/C-reactive protein combined for Total-cohort, adipsin/chemerin; High-BMI, chemerin/adiponectin HMW; and Low-BMI, interleukin-8. Gender separation improved the prediction (13-16%) compared to the BMI-based models. Reproducibility with osteoarthritis patients from a clinical trial was excellent (R: female 0.83, male 0.95). Pseudocodes based on gender were generated.

CONCLUSION

This study demonstrates for the first time that the combination of the serum levels of adipokines/inflammatory factors and the three main risk factors of osteoarthritis could predict IPFP volume with high reproducibility, with the superior performance of the model accounting for gender separation.

Association Between Monocyte Chemotactic Protein 1 Variants and Age-Related Macular Degeneration Onset Among Chinese People

Background

We assessed the potential association between monocyte chemotactic protein 1 (MCP-1) variants (rs1024611 and rs3760396) and age-related macular degeneration (AMD) susceptibility among Chinese Han people.

Material/Methods

Our research included 129 AMD patients and 131 healthy controls. Genotyping for MCP-1 variants was performed in the 2 groups, and genotype and allele distributions were checked between groups by χ² analysis. Odds ratio (OR) and 95% confidence interval (CI) reflected the potential association between MCP-1 variants and AMD risk. The linkage disequilibrium of polymorphisms was detected using Haploview.

Results

Significant differences in rs1024611 genotype distributions were detected between the 2 groups, and homozygous carriers with GG genotype had higher AMD incidence (P<0.05, OR=2.650, 95% CI=1.127–6.231). The rs1024611 G allele frequency was significantly higher in AMD patients, suggesting that the G allele promotes AMD onset (P<0.05, OR=1.447, 95% CI=1.013–2.068). Strong linkage disequilibrium was found between rs1024611 and rs3760396, and haplotype A_rs1024611–C_rs3760396 was significantly associated with decreased risk of AMD (P=0.001, OR=0.502, 95% CI=0.335–0.752).

Conclusions

MCP-1 rs1024611 variant appears to contribute to risk of AMD in the Chinese Han population, and the interaction of MCP-1 polymorphisms may also influence individual susceptibility to AMD.

In vivo protective effect of adipsin-deficiency on spontaneous knee osteoarthritis in aging mice

The adipokine adipsin is an emerging mediator of human osteoarthritis (OA) progression. Here, we investigated its in vivo role in the development of spontaneous OA in aging mice. We compared articular knee joint morphology, histology in knee cartilage, synovial membrane, subchondral bone, meniscus, and anterior cruciate ligament (ACL); and chondrogenesis in the ACL from adipsin-deficient (Df^-/-) and wild-type (Df^+/+) 20-week- and 20-month-old mice. Serum levels of a panel of adipokines, inflammatory factors, and metalloproteases known to be implicated in OA were investigated. Data first revealed that the early manifestation of OA appeared in the ACL of 20-week-old mice, progressing to severe alterations in the 20 month-old wild-type mice. Further results demonstrated that adipsin-deficiency protected the articular tissues from spontaneous OA progression and triggered significantly higher serum levels of the adipokines adiponectin and FGF-21 while lowering levels of the inflammatory factor interleukin 6 (IL-6) in both young and old mice. This work further underlines the clinical relevance of adipsin as a novel therapeutic approach of human OA. Moreover, this study shows the potential beneficial effect of the adipokine FGF-21 against OA, and provides support for this factor to be a new biomarker and/or target of primary OA therapeutic avenues.