Advancing Early Detection of Osteoarthritis Through Biomarker Profiling and Predictive Modelling: A Review

Abstract

Osteoarthritis (OA) is a multifactorial chronic musculoskeletal disorder characterised by cartilage degradation, synovial inflammation, and subchondral bone remodelling. Conventional diagnostic modalities, including radiographic imaging and symptom-based assessments, primarily detect disease in its later stages, limiting the potential for timely intervention. Inflammatory biomarkers, particularly Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO), have emerged as biologically relevant indicators of disease activity, with potential applications as companion diagnostics in precision medicine. This review examines the diagnostic and prognostic relevance of IL-6, TNF-α, and MPO in OA, focusing on their mechanistic roles in inflammation and joint degeneration, particularly through the activity of fibroblast-like synoviocytes (FLSs). The influence of sample type (serum, plasma, synovial fluid) and analytical performance, including enzyme-linked immunosorbent assay (ELISA), is discussed in the context of biomarker detectability. Advanced statistical and computational methodologies, including rank-based analysis of covariance (ANCOVA), discriminant function analysis (DFA), and Cox proportional hazards modelling, are explored for their capacity to validate biomarker associations, adjust for demographic variability, and stratify patient risk. Further, the utility of synthetic data generation, hierarchical clustering, and dimensionality reduction techniques (e.g., t-distributed stochastic neighbour embedding) in addressing inter-individual variability and enhancing model generalisability is also examined. Collectively, this synthesis supports the integration of biomarker profiling with advanced analytical modelling to improve early OA detection, enable patient-specific classification, and inform the development of targeted therapeutic strategies.

1. Introduction

1.1. Background on Osteoarthritis

Osteoarthritis (OA) represents the most common chronic musculoskeletal disorder, primarily described as a degenerative joint disease [1,2]. This definition has been refined to emphasise the complex pathological mechanisms underlying OA, moving beyond the earlier concept of a simple “wear and tear” [3,4]. On a global scale, OA affects approximately 11% of the hips and 24% of the knees of the population, with prevalence particularly high among individuals over 60 years of age [4,5]. Around 10% of men and 18% of women are diagnosed with OA; the percentage of people over the age of 65 who are at risk is projected to increase by 14% by 2040 [6]. Age-standardised prevalence is also projected to increase from 6.28% to 7.03% in men and from 10.82% to 12.18% in women between 2025 and 2040 [7]. OA affects more than 43 million people in the United States [8]. In 2020, 595 million people were affected, having increased by 132.2% since 1990. By 2050, the incidence of knee, hand and hip OA is projected to rise by 74.9%, 48.6% and 78.6%, respectively, highlighting the vital need for improved early diagnostic strategies to prevent disease progression and mitigate long-term disability [4,9,10].

OA predominantly impacts synovial joints, leading to degradation of articular cartilage, joint space narrowing (JSN), sclerosis of subchondral bone, and formation of osteophytes [1,11,12]. As the disease advances, these structural alterations contribute to joint pain, stiffness, swelling, decreased range of motion, and ultimately, disability [13,14]. OA significantly compromises quality of life and represents a major burden on individuals and healthcare systems worldwide [15].

Despite its high prevalence, early detection of OA remains a challenge due to the gradual and non-specific nature of early symptoms. Conventional diagnostic methods such as radiography are limited to detecting structural changes in later stages [16], whereas advances in magnetic resonance imaging (MRI) now allow earlier assessment of soft-tissue alterations, including the infrapatellar fat pad, cartilage, and synovial membrane [17]. Nevertheless, imaging alone remains insufficient for capturing the molecular complexity of disease onset, highlighting the need for reliable prognostic biomarkers to enhance patient outcomes by improving risk stratification and guiding early therapeutic decisions [18].

OA is recognised as a multifactorial disease driven by a combination of pathological processes and the activity of various cell populations, including immune cells, chondrocytes and synoviocytes, that together enhance disease variability and progression of the condition [4,19,20]. The durability of cartilage may be influenced by genetic predisposition, while degradation is promoted by biochemical signals, including inflammatory cytokines [21]. The complexity of OA underscores the importance of developing robust biomarkers to support earlier diagnosis and more effective disease monitoring. The diverse pathophysiology of OA complicates biomarker analysis, necessitating the use of advanced statistical methods to ensure analytical reliability [4,22,23].

Several risk factors contribute to the development of OA: joint injuries, age, ethnicity, gender, hereditary factors, body mass index (BMI), co-morbidity count and baseline severity [18,24]. Secondary causes include structural abnormalities, instability, fractures, developmental problems, inflammation and endocrine issues [6,25]. Studies revealed that young adults that suffer from knee injuries are more likely to develop OA later in life [16]. By the age of 85, approximately 25% of adults develop symptomatic hip OA and around 10% require total hip replacement due to progression to end-stage OA [26].

Individuals with joint injuries are susceptible to developing OA. Meniscal damage, a common form of knee injury, has been shown to increase OA risk by 2–7-fold [27]. More broadly, traumatic knee injuries, including ligamentous damage such as anterior cruciate ligament tears, are associated with up to a 4-fold increased risk of OA [28]. In addition, genetic studies have found eleven loci linked to OA [6]. There have been many risk factors identified, including genetics, age, alignment of limbs and biomechanical loading [29]. However, the underlying cause is unknown [11].

Obesity is a known risk factor of OA, including in non-weight bearing joints, suggesting systemic mechanisms beyond mechanical loading. Adipokines such as leptin have been investigated for their role in OA pathogenesis [30], particularly in hand OA, where they are believed to mediate this effect [6,31]. While early studies reported no clear association, recent evidence implicates leptin and complement factor D in fat-cartilage signalling and OA development [32]. Additionally, central obesity and elevated low-density lipoprotein cholesterol have been linked to radiographic hand OA, supporting a metabolic contribution to disease progression [33,34]. Collectively, these diverse risk factors underscore the substantial inter-patient variability in OA development and progression. This variability complicates the diagnosis and treatment of OA, reinforcing the importance of biomarker-based approaches and patient stratification to enable earlier detection and more personalised interventions.

OA remains one of the most common arthropathies leading to deformities and disabilities [25]. Radiographic changes and symptoms may not directly correlate, which complicates the diagnosis and treatment options. Patient education is vital to improve management and slow disease progression [35]. Lifestyle modifications, especially weight loss, can significantly reduce symptoms in overweight patients [36,37]. Statistics show that 46% are at risk of lifetime symptomatic knee OA [38]. The Arthritis Foundation recommends range of motion, strength, and cardiovascular workouts, and a programme for those with OA [39].

Current OA management strategies are largely directed at alleviating symptoms, involving non-pharmacological approaches such as exercise, weight control, and physiotherapy alongside pharmacological options including intra-articular injections, non-steroidal anti-inflammatory drugs (NSAIDs), and opioids [4,19,40,41]. While NSAIDs and corticosteroid injections provide pain relief, their benefits are temporary and often accompanied by side effects or placebo-associated effects [38,42,43,44,45]. However, these therapeutic options show only limited effectiveness in slowing or reversing disease progression, underscoring the importance of developing personalised treatment strategies and more advanced diagnostic methods [4,11,45,46].

Treatments tested in clinical trials have different effects depending on the patient’s lifestyle factors, gender and cell type [45]. Emerging therapies such as hyaluronic acid (HA) injections and monoclonal antibodies (e.g., TNF-α inhibitors) have shown mixed outcomes, with efficacy depending on OA subtype and patient characteristics [44,47,48]. Monoclonal antibodies, Tanezumab and Fulranumab have shown reduced pain and improved functions. However, in some patients, OA progressed rapidly, which was of significant concern [49]. Doxycycline, an antibiotic used for the treatment of OA, is a matrix metalloproteinase (MMP) inhibitor which revealed small benefits in JSN, but there was a lack of symptomatic improvements [50,51].

Disease modification OA drugs (DMOADs) aim to delay OA development; however human studies are required to confirm the benefit [42,52]. Trials targeting macrophage-produced cytokines have shown mixed results, with some symptomatic improvements yet overall limited success [48,53]. A major challenge is recruiting patients with early inflammatory OA, before bone and cartilage loss is evident [53]. Regenerative strategies including cartilage repair and transplantation require validation in large-scale trials over more extended periods [6,11]. More recent advances include hydrogels, gene therapy, and stem cell-based approaches show promise, particularly mesenchymal stromal cells for their regenerative and anti-inflammatory properties, though delivery, safety, and scalability challenges persist [54,55].

Surgery may be recommended if conservative measures do not alleviate OA symptoms. Total joint replacement of the knee or hip is recommended for end-stage OA. While the surgery is effective for alleviating pain and restoring mobility, there are risks associated with surgery, including infection, prosthesis failure, activity limitations, surface wear for younger active patients, and revision surgery complications [38]. Furthermore, surgery is costly and may not be suitable for all patients due to comorbidities, age and personal preferences [56,57].

Many interventions are only provided when the disease is in the later stages, when significant joint damage has occurred. Therefore, there is a requirement for developing methods for earlier diagnosis and intervention, before radiographic changes become apparent, allowing for the implementation of strategies that may delay or prevent progression to more severe stages of the disease.

The identification and validation of robust biomarkers are essential for improving early diagnosis and personalised management of OA [58]. Among the candidates under investigation, IL-6, TNF-α, and MPO represent biologically plausible markers reflecting inflammatory, catabolic and oxidative stress pathways [59,60,61]. These molecules are expressed in synovial tissues, particularly FLSs, linking them directly to joint pathology [62,63]. However, OA is recognised as a heterogenous, degenerative condition that may not always present with marked inflammation [64]. Multiple processes, including cartilage degradation, subchondral bone remodelling, synovial membrane activation, meniscal degeneration, and infrapatellar fat pad changes, contribute to disease progression [17,20]. These involve diverse cell types such as chondrocytes, synoviocytes, meniscal cells, adipose derived stromal cells, and infiltrating immune cells, each of which may influence biomarker expression patterns [58,65]. For instance, meniscal degeneration and infrapatellar fat pad activation have been linked to elevated IL-6 production [17], while oxidative stress in subchondral bone and cartilage degradation may drive MPO release [66]. In contrast, mechanically driven phenotypes with low synovial inflammation may show minimal biomarker elevation [64,67]. Understanding these multifactorial mechanisms is essential for selecting biomarkers that are relevant across different OA subtypes, including those with a low-grade or predominantly non-inflammatory pathology [64,67,68].

1.2. Importance of Early Detection

Although multiple demographic and clinical risk factors are associated with OA progression (see Section 1.1), there is a lack of agreement between studies about what constitutes disease progression, making prognosis challenging and limiting comparisons across research [37].

Early detection of OA would potentially enable preventative treatment to slow disease progression [5]. The primary change is the loss of articular cartilage; however, the disease also involves complex cellular and mechanical alterations that affect the subchondral bone, osteophyte formation, bone marrow lesions, synovium, joint capsule, ligaments, meniscal tears, infrapatellar fat pad and nerves [17,69]. A comprehensive understanding of these joint structures is essential in understanding OA pathogenesis. In particular, the infrapatellar fat pad is increasingly recognised as an active participant in OA, releasing adipokines and pro-inflammatory mediators such as IL-6 that contribute to cartilage degradation and synovial inflammation [17,70].

It was hypothesised that stratifying OA into distinct phenotypes could improve therapeutic outcomes by enabling treatments to be tailored more precisely to disease subtypes. For instance, inflammatory phenotypes, which are characterised by features such as effusion-synovitis detectable through MRI or clinical examination, may respond more effectively to anti-cytokine therapies [71]. In contrast, mechanical phenotypes, often linked to joint instability or malalignment, are thought to benefit more from physical interventions such as targeted exercise, aligning with phenotype-driven treatment frameworks proposed in the literature [72]. This underscores the value of developing diagnostic tools that capture such variation early in the disease course.

Although there is currently no cure for OA, there are a number of effective therapies available to manage symptoms. However, it reduces the quality of life and causes varying degrees of pain depending on the type or stage of the disease. Studies are ongoing, and over time, knowledge is improving on the pathogenesis of the disease. This increased understanding enhances diagnostic accuracy, the ability to predict disease progression, and the development of interventions to preserve joint structure, offering more insight into the disease pathway [73]. Research attempting to translate novel biological discoveries into relevant applications could improve patient outcomes and long-term joint health [74,75].

1.3. Limitations of Current Diagnostic Approaches

Although certain risk factors, including joint injury and genetic predisposition, are known to increase OA susceptibility, their presence alone is not predictive. Many individuals with these risk factors never develop OA, illustrating the disease’s complex and multifactorial nature [76].

The diagnosis of OA currently relies on clinical evaluation supported by imaging techniques, with X-rays and MRI remaining the standard diagnostic tools. However, these modalities are largely confined to detecting OA at moderate to advanced stages. Biomarkers in blood, synovial fluid, and urine have been investigated as tools for earlier diagnosis, but they are not yet fully validated for routine clinical use and their usefulness remains uncertain [66].

Research to date has looked at diagnosing the disease through blood tests, radiographs, MRIs and ultrasonography [16]. Radiographs highlight JSN as cartilage is degrading and, in the later stages, the formation of osteophytes and bony sclerosis [53]. If the condition was detected in the early stages, development of the disease could possibly be delayed, joint structure preserved, and the need for invasive treatment could be avoided [26]. Due to the slow and asymptomatic development of OA, diagnosis of the disease usually occurs when the disease is in the later stages.

Advances in biochemical markers and imaging techniques are enhancing diagnostic precision and supporting more effective treatment planning [6,72,77]. High-resolution imaging modalities, such as MRI, electron microscopy, and laser-scanning confocal arthroscopy, have advanced the ability to characterise both structural and cellular changes in OA [4,78,79]. Integrating these technologies with biochemical biomarkers offers a complementary approach, linking inflammatory signals with tissue-level alterations to improve diagnostic and prognostic accuracy. Such multimodal strategies may facilitate earlier disease detection, allowing precise classification of OA subtypes, and support more effective monitoring of therapeutic interventions [4]. Evidence highlights the potential of combining fluid-based biomarkers with MRI to improve early OA detection, especially in active individuals and athletes [80].

The number of targets aimed at preventing the development of OA has increased as knowledge of the aetiology and pathogenesis of OA has grown. Imaging tools such as X-rays are accessible and affordable; however, they are limited in detecting early-stage OA [6]. Structural changes in cartilage and bone, such as JSN, subchondral sclerosis and osteophyte formation, are typically only visible in moderate to severe OA, corresponding to late-stage disease [27,81]. Radiographic grading systems such as the Kellgren and Lawrence (KL) scale were originally developed and validated for the assessment of knee osteoarthritis [82]. Since then, the KL scale has been widely applied to other joint sites such as the distal interphalangeal joint (DIP), metacarpophalangeal joint (MCP), carpometacarpal joint, wrist, cervical spine, lumbar spine, and hip; however these applications may require further adaptation and validation, particularly with respect to inter-observer reliability [81,82,83].

While several radiographic classification systems exist for grading knee OA, the Kellgren-Lawrence (KL) scale is one of the most widely used and most extensively studied [83]. It demonstrates a correlation coefficient ranging from 0.51 to 0.89 with clinical outcomes [82]. The KL grading criteria outline that Grades 3 and 4 typically represent moderate to severe OA, marked by substantial structural damage. The KL classification illustrates increasing radiographic severity from Grade 1 through Grade 4. However, despite its regular use, the KL scale has been subject to variation in interpretation, particularly Grade 2, which is often used as a diagnostic threshold. These inconsistencies have been reported across studies and highlight the need for ongoing re-evaluations and validation of patients’ status [81].

The Osteoarthritis Research Society International (OARSI) recommended MRI as the preferred tool for evaluating disease progression in clinical trials [84]. MRI offers high sensitivity, three-dimensional resolution, and reproducibility [60]. However, its use in routine clinical settings is constrained due to cost, accessibility and the lack of standardised scoring systems for early OA [85,86]. MRI excels at visualising soft tissue structures such as cartilage and subchondral bone, but a significant limitation remains in the often-weak correlation between imaging-detected structural changes and patient reported symptoms [87,88]. In addition to x-rays and MRI, computed tomography (CT), ultrasonography, bone scans, joint aspiration and arthroscopy are performed, though have varying levels of sensitivity and specificity, with CT offering limited utility in OA diagnosis [89]. Recent advancements in imaging, including high-resolution MRI and artificial intelligence-assisted analysis, have improved the sensitivity of early OA detection by visualising cartilage and subchondral bone changes before they are evident radiographically [74].

Beyond these conventional modalities, several high-resolution imaging techniques have been explored for their ability to capture microstructural cartilage changes at earlier disease stages. Confocal laser scanning arthroscopy enables real-time, in situ visualisation of chondrocyte morphology, collagen organisation, and surface fibrillation, with strong correlation to histological grading using the modified Mankin score [90]. Similarly, second harmonic generation (SHG) microscopy provides label-free imaging of collagen fibril architecture and orientation, revealing early extracellular matrix alterations not detectable with standard imaging [91]. This approach has been applied to study age-dependent remodelling of the infrapatellar fat pad, a tissue actively implicated in OA pathogenesis, where SHG enabled detailed assessment of collagen content and orientation [92]. While these approaches offer unparalleled detail, they remain largely research-focused due to their invasiveness, cost and limited availability. Moreover, the analysis of such high-resolution images is often reliant on manual annotation, which is time consuming and prone to observer variability [93]. These limitations reinforce the value of integrative strategies, combining advanced imaging, biomarker profiling, and computational modelling, to improve the objectivity, reproducibility, and scalability of early OA detection.

Biochemical markers, such as cytokines, enzymes, precursors or degradation products of collagen and proteoglycans have potential as diagnostic tools. Although the Food and Drug Administration (FDA) accept that disease-modifying OA drugs are effective to treat joint space width (JSW); they are not sensitive, lack specificity and are unable to detect damage to the cartilage [6].

The destruction of the cartilage in OA occurs due to mechanical loading and biological factors. Stimulation of intracellular signals, cytokines, chemokines and inflammatory mediators from synovial cells and chondrocytes cause MMP and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS) families to become upregulated. Adipose tissues, particularly the infrapatellar fat pad, also release adipokines and cytokines that contribute to inflammatory and catabolic activity in OA [17,70]. Osteophytes can be formed due to the build-up of bone morphogenetic proteins (BMPs) and transforming growth factor-beta (TGF-β). Chondrocyte proliferation, hypertrophy, cartilage calcification, microfractures and matrix loss can be observed in samples from late stage OA patients [94].

Standardisation of imaging protocols and biomarker thresholds are required to improve early diagnosis [95]. Combining structural imaging with molecular profiling may improve diagnostic accuracy [96]. Recent advances in biomarker research and statistical modelling offer promising avenues for detecting early changes at the molecular level before they manifest structurally [75,97]. Despite these advances, several challenges persist in OA prognostic research. Many studies fail to report key statistical metrics, with variability in study methodologies, inconsistent reporting of hazard ratios, and limited sample sizes [98,99]. Furthermore, inconsistencies in cut-off levels for biomarker thresholds make it difficult to establish standardised diagnostic criteria [100,101]. Improvements in biomarker research, imaging standardisation, and computational analysis are essential to address these limitations and advance the development of effective early interventions.

2. Pathophysiology of Osteoarthritis

2.1. Joint Structure and Degenerative Change

OA exhibits three main phenotypes in OA, cartilage-driven, inflammation-driven and bone-driven, interacting with hormonal, genetic and metabolic factors [45]. These phenotypes may overlap; for example, bone-driven phenotypes can also exhibit inflammatory features. Accurate phenotyping may enhance therapeutic targeting [6].

Cartilage, bone and synovium are central to OA pathogenesis and are closely linked to inflammation [50]. Cartilage protects the bones within the joint by acting as a shock absorber and a lubricant [102]. Ligaments and tendons stabilise the joint, with tendons connecting muscles to bones [103]. The synovial membrane contains synovial fluid, which facilitates the movement of the joints [104]. In the knee, the synovial membrane and infrapatellar fat pad (IFP) function as an anatomo-functional unit, with the IFP acting as both a mechanical cushion and an active secretory tissue that produces cytokines and adipokines contributing to joint inflammation [17,70].

Subchondral bone undergoes morphological changes during OA progression, and these changes are accompanied by increased levels of pro-inflammatory cytokines in the subchondral region [105]. The subchondral region is situated between cartilage and trabecular bone, including the cortical plate and trabecular bone, both affected by OA [5]. Bone remodelling leads to increased volume, sclerosis and formation of osteophytes at joint margins, often preceding any articular cartilage changes that are detected on radiographs. This could suggest that bone tissue could initiate the loss of cartilage. Osteophytes in OA have been shown to express genes associated with neutrophil degranulation, suggesting a strong link between osteophyte formation and neutrophil activity [106].

Many diseases can lead to the degeneration of articular cartilage, including OA, RA and other inflammatory or post-traumatic joint conditions. These diseases differ in aetiology but share common features of cartilage breakdown mediated by mechanical stress, inflammatory cytokines, or immune cell infiltration [6,53,107,108,109]. In severe cases, total hip replacement (THR) may be required, involving the replacement of damaged cartilage and bone with prosthetic components. The hip, one of the body’s largest weight-bearing ball and socket joints, connects the femur to the acetabulum, and is lined with articular cartilage, which acts as a cushion allowing smooth movements of the joint. Hip OA, which leads to progressive cartilage loss, is a major cause of chronic pain and disability [110].

The knee joint, comprising the femur, tibia and patella, is cushioned by the meniscus: a shock absorber. Total knee replacement (TKR) replaces damaged surfaces with artificial components, particularly in severe OA. A study comparing prostheses reported 89.84% patient satisfaction post-surgery, with no significant differences between prosthesis types [111,112]. In a healthy knee, articular cartilage allows for smooth movement of the joint, whereas in OA, the cartilage is thin or disappears, bones become thicker and form bony spurs, and inflammation may persist despite conservative treatments.

Cartilage is a protein matrix that reduces friction and cushions joints. Cartilage comprises water, type II collagen, proteoglycans, chondrocytes and other matrix proteins, with OA marked by increased proteoglycan degradation [38]. Type II collagen, the main protein in cartilage, provides tensile strength but has no capacity for regeneration [6]. It is hypothesised that OA may develop from impaired chondrocyte-mediated cartilage repair. Chondrocytes respond to mechanical injury and joint instability, influenced by genetic factors and stimuli such as cytokines, leading to structural changes in the cartilage matrix [12]. Their functions are affected by mechanical influences, but responses to molecular signals would vary by region [94]. Recent research further supports that early-stage dysregulation of key signalling pathways, such as Nuclear factor kappa B (NF-κB), contributes to the onset of OA and highlights the importance of targeting these processes for early intervention [113].

Proteoglycans contribute to the compressive strength of cartilage by retaining water. Chondrocytes adapt to environmental stimuli, and both chondrocytes and osteoblasts secrete cytokines and degradative enzymes in response to mechanical stress [114]. Cytokines such as interleukin-1 Beta (IL-1β), IL-6, TNF-α and MMPs are inflammatory response proteins that contribute to cartilage remodelling [60]. These processes influence both cartilage and bone integrity, with bone remodelling playing a significant role in OA-related pain.

Immune-mediated damage reduces cartilage integrity, potentially leading to eburnation: bone-on-bone contact. This increases pain, limits mobility, and may exacerbate musculoskeletal dysfunction [38]. When chondrocytes fail to maintain homeostasis, cartilage degradation accelerates. Microfractures and trauma-induced inflammation increase enzyme activity. While macrophages clear some debris, excessive wear promotes inflammation. Chondrocytes then release enzymes and cytokines that degrade the matrix by promoting metalloproteinase activity, inhibiting collagen synthesis, and eventually exposing the subchondral bone [5].

OA progression is driven by imbalanced catabolic and anabolic processes, with inflammation playing a critical role [53]. Histological analysis revealed Leonurine Hydrochloride (LH) reduced expression of MMPs: MMP-1, MMP-3, MMP-13, IL-6 and ADAMTS-5 through the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signalling pathway [115]. Increased aggrecan and MMP activity, often mediated by mitogen-activated protein kinase (MAPK) activation, contributes to cartilage breakdown [116].

Inhibiting NF-κB and MAPKs are key therapeutic targets in OA. Studies on chondrocytes have elucidated several critical signalling pathways and transcription factors involved in cartilage damage. Understanding these cellular mechanisms is essential for developing protective therapies that interrupt early joint degradation. Intra-articular injection of LH significantly reduced cartilage degradation in a rat model of OA, indicating its potential as a disease-modifying agent via NF-κB inhibition [115]. Recent reviews further highlight the complex interplay between pathways such as NF-κB, AMP-activated protein kinase (AMPK), mechanistic target of rapamycin (mTOR), and fibroblast growth factor (FGF), suggesting that early intervention targeting chondrocyte-mediated signalling may offer the most effective therapeutic strategy [113].

2.2. Role of Inflammation in OA Progression

Although OA has been historically classified as a non-inflammatory arthropathy, accumulating evidence indicates that inflammation contributes substantially to disease progression, especially in moderate to severe stages [117]. Although not all patients present with overt inflammation, synovial inflammation is increasingly recognised as a key contributor to structural joint damage. This is supported by correlations between inflammatory biomarkers and radiographic progression [38,53].

The innate immune system is activated by damage-associated molecular patterns released from injured joint tissues. A series of factors, such as lipid mediators, chemokines and pro-inflammatory cytokines, cause and activate signal transduction pathways, particularly NF-κB, which controls the fate of inflammation [118]. Although inflammation is a critical host-defence mechanism that enables the repair of tissues, in chronic conditions such as OA, prolonged immune activation leads to tissue destruction and fibrosis [119].

Multiple immune cells, lymphocytes, dendritic cells, macrophages, leukocytes, neutrophils and megakaryocytes, participate in the inflammatory response [67,120,121,122,123,124]. Lymphocytes, monocytes and macrophages produce pro-inflammatory cytokines such as IL-1 and TNF-α, and IL-6, which are detectable even in early OA [60,70]. These cytokines activate connective tissues to produce MMPs, which degrade collagen, proteoglycans and other extracellular matrix components.

Simultaneously, anti-inflammatory cytokines and protease attempt to counteract tissue destruction. However, sustained inflammation promotes the activation of chondrocytes, osteoclasts, osteoblasts and the release of prostaglandin E2, which contributes to the degradation of cartilage and bone mineralisation. Synovial hypertrophy, hyperplasia, and infiltration by macrophages and T and B lymphocytes further exacerbate joint pathology [96].

The synovium, connective tissue lining the joint cavity, produces synovial fluid, which sustains the cartilage and lubricates the joint. The synovial membrane’s luminal surface has a distinct cellular lining in the joint capsule, lined by synoviocytes, which become activated during inflammation. Synovial inflammation causes swelling, joint redness, and synovial effusion. Chronic inflammation stretches the joint capsule leading to laxity and instability which contributes to progressive joint deformity [125].

Synoviocytes are divided into two major types: macrophage cells (type A) and fibroblast-like cells (type B). Type A synoviocytes are derived from circulating mononuclear cells and function as resident macrophages capable of antigen presentation and phagocytosis. Type B cells produce essential components of the synovial matrix, including hyaluronan, collagens, and fibronectin [126].

Synovitis is a common feature in OA, with varying severity across patients. It is characterised by increased infiltration of immune cells, synovial hypertrophy, increased vascularity and proliferation of synoviocytes. Synovitis is associated with the production of pro-inflammatory cytokines and degradative enzymes, which cause the synovial tissue to drive progressive joint degeneration such as cartilage degradation and joint destruction [127]. During synovitis, blood-derived immune cells infiltrate the joint via the vascular endothelium, contributing to ongoing inflammation [118]. While commonly observed in both early and late OA, synovitis may be challenging to detect consistently due to variability in patient presentation and limitations in diagnostic imaging.

3. Fibroblast-like Synoviocytes (FLSs) in Osteoarthritis

3.1. Introduction to FLSs and Their Role in OA Pathogenesis

Building on the biomarker-focused discussion in Section 3, it is important to highlight the cellular origins of these signals within the joint. Fibroblast-like synoviocytes (FLSs) represent a major source of IL-6 and TNF-α and contribute to oxidative stress pathways associated with MPO, thereby linking local joint pathology with systemic biomarker changes [63,68].

FLSs play a critical role in the pathogenesis of OA, acting not merely as structural elements but as mediators of inflammation and joint destruction [128]. FLSs are located within the synovial membrane and contribute to joint homeostasis under physiological conditions. However, under pathological stimuli, these cells become activated and secrete matrix-degrading enzymes and pro-inflammatory mediators, accelerating cartilage damage [108,129,130].

The pro-inflammatory capacity of FLSs has been demonstrated across multiple investigations. José Alcaraz et al. investigated how FLSs respond to different inflammatory stimuli [129]. Additional research examining the molecular mechanisms regulating FLS activity in OA has identified key pathways that may serve as therapeutic targets [130,131]. Further studies have also demonstrated that the surrounding cytokine environment plays an important role in shaping FLS behaviour [128].

Activated FLSs produces a range of pro-inflammatory mediators, including nitric oxide (NO), prostaglandin E2 (PGE2), and cytokines such as IL-6 and TNF-α, all of which show a strong correlation with clinical manifestations of OA, such as joint pain, swelling, and disease advancement [108]. Inflammatory stimuli modulate FLS phenotype and function, which in turn influence disease progression.

Research has examined FLSs in both OA and rheumatoid arthritis (RA) to distinguish disease specific characteristics. In a 2019 study, FLSs were isolated from the synovial tissue samples of 22 patients (11 OA, 11 RA), and both cohorts displayed comparable fibroblast morphology and overlapping surface antigen profiles [131]. The study evaluated their response to TNF-α and methotrexate (MTX), reinforcing the therapeutic relevance of FLSs in inflammatory joint diseases [132].

Hyaluronic acid (HA) has recently been implicated in joint inflammation through its interaction with FLSs in OA. The study demonstrated that rheumatoid arthritis (RA)-derived FLSs, when co-cultured with activated 4-positive T lymphocytes (CD4 T cells), produced an HA-enriched extracellular matrix (ECM). This matrix promoted monocyte adhesion, a pivotal process in the initiation of inflammatory responses [128,131]. Co-cultures of OA and RA FLSs with activated CD4 T cells were associated with elevated cytokine expression, indicating the presence of an HA-enriched ECM contributes to the development of a pathogenic microenvironment. Collectively, this evidence suggests that interactions between FLSs and HA play an important role in OA pathogenesis.

3.2. Cytokine and Inflammatory Responses in FLSs

Cell culture techniques provide a vital tool for studying physiology and inflammatory signalling of FLSs in OA. These techniques enable in vitro exploration of drug responses, cytokine stimulation and gene expression in a controlled environment [133]. Primary cultures of FLSs are typically derived from enzymatically digested synovial tissue, and their maintenance in a physicochemically regulated medium with essential nutrients, growth factors and hormones [134,135].

Primary cultures, once established, reflect the in vivo characteristics of FLSs but can only be maintained for a limited time. Subculturing allows for continued growth and experimental manipulation. Most primary cultures eventually become fibroblast-dominant, especially after multiple passages [133].

Lipopolysaccharide (LPS), a component of Gram-negative bacterial outer membranes, is frequently used to model inflammation in vitro [125]. LPS stimulates FLSs and chondrocytes to express cytokines (e.g., IL-1β, TNF-α) and MMPs to activate toll-like receptors (TLRs), thereby mimicking the inflammatory conditions observed in OA [136,137]. Additionally, LPS has been found to reduce cartilage synthesis and is considered a valid stimulant in OA research despite its original use in septic arthritis models [138,139].

Emerging evidence connected gut-derived LPS to OA. A study carried out on mice in 2021 found that LPS levels were elevated in animals on a high-fat diet but decreased following moderate exercise. This correlated with reduced inflammation and joint degradation, suggesting that gut microbiota and systemic LPS may influence OA pathogenesis [140]. FLSs exposed to LPS secrete increased levels of pro-inflammatory cytokines, including IL-1β, TNF-α and MMP-3, reinforcing their central role in OA-related inflammation and tissue degradation [114].

4. Biomarkers in Osteoarthritis

4.1. Overview of Biomarkers: IL-6, TNF-α and MPO

Determining biomarkers that can predict OA progression is important to help clinical trial success rates and understanding disease progression. A system developed by the FDA classifies biomarkers as B (burden of disease), I (investigative biomarkers still to be defined), P (prognostic), E (efficacy of intervention), D (Diagnostic) and S (Safety) it is called the BIPEDS [100]. The development of these criteria evolved due to numerous failed drug developments for OA, with the aim of developing personalised medicine roadmaps. The approach involves the use of predefined statistical analyses to test specific hypotheses [45]. A combination of biomarkers is required in achieving prediction and single biomarkers would generally be insufficient in predicting outcomes [141].

Currently, diagnosis depends on several factors: duration of symptoms, the extent of inflammation and tissue damage, and the types of tissues involved [142]. Biomarker studies have expanded to proteomics which has found biomarkers that are joint-specific [6]. More studies are required to find biomarkers that allow OA analysis with disease phenotyping to choose appropriate treatment. Combining markers and imaging provides higher values diagnostic values [100].

The use of predictive biomarkers for therapy outcomes would allow for personalised therapy [141]. Among the biomarkers being explored, cytokines have emerged as key candidates. Cytokines are small proteins released by cells that regulate immune responses and inflammation. They are classified as pro-inflammatory (e.g., IL-1β, IL-6, and TNF-α) or anti-inflammatory (e.g., IL-4, IL-10, IL-11, and IL-13). Pro-inflammatory cytokines are primarily produced by activated macrophages and are involved in the upregulation of inflammatory reactions, contributing to pathological pain, nerve injuries, inflammation-induced central sensitisation [143]. They are involved in the development of contralateral hyperalgesia/allodynia [144]. The anti-inflammatory cytokines control the pro-inflammatory cytokine response, with specific cytokine inhibitors and soluble receptors to regulate the human immune response [145]. Cytokines may undergo autocrine, paracrine or endocrine action.

Prognostic markers, including IL-6, TNF-α and MPO, have been identified as potential indicators of disease progression due to their roles in systemic inflammation and oxidative stress [97]. These three biomarkers have been consistently linked to key pathological mechanisms in OA, IL-6 as a mediator of synovial inflammation, TNF-α as a central driver of chondrocyte catabolism and cartilage degradation [60,146,147], and MPO as marker of oxidative stress and neutrophil activity [148,149]. Their established links to joint structural changes and inflammation make them relevant candidates for detailed discussion as potential prognostic and therapeutic biomarkers. However, variability in study methodologies, inconsistent reporting of hazard ratios and small sample sizes have limited their clinical applicability [98].

4.1.1. Interleukin-6 (IL-6)

IL-6 is a multifunctional cytokine produced by T cells, B cells, monocytes, fibroblasts, osteoblasts, osteophytes, adipocytes as well as joint-associated tissues such as the infrapatellar fat pad and meniscus [17,150]. IL-6 is involved in immune responses, inflammation, acute phase reactions and is regulated at both transcriptional and post-transcriptional levels [151,152]. IL-6 contributes to synovitis and initiates catabolic pathways in joint tissues, which drive cartilage degeneration, bone erosion and subsequent joint damage [144,153,154]. Synoviocyte proliferation is stimulated by IL-6 which activates osteoclasts, resulting in the formation of synovial pannus and the production of MMPs, leading to joint degradation [60].

The role of IL-6 has been disputed in many studies, some stating it is a pro-inflammatory cytokine and others an anti-inflammatory mediator [146,151,153]. IL-6 has been described as having a complex, context-dependent role in chronic diseases, noting its ability to both enhance and suppress inflammatory activity. This behaviour illustrates its involvement in multiple inflammatory pathways and possible role as a biomarker for disease development [155]. This lack of specificity restricts the diagnostic value when used in isolation.

Nevertheless, several clinical studies link IL-6 to OA-related changes. Serum levels of IL-6 are associated with JSN in the medial tibiofemoral compartment, as well as with tibial cartilage loss in older adults [60,156]. Elevated levels of IL-6 correlate with knee radiographic OA and cartilage loss and are associated with increased disease severity [113,147]. Patients with high levels of circulating IL-6 are more likely to have radiographic knee OA [27]. In addition, synovial IL-6 concentrations correlate with KL radiographic grade in patients with knee OA [157]. These findings suggest IL-6 may contribute to disease activity, but tis clinical utility likely lies in being combined with other biomarkers or integrated into a predictive modelling framework rather than serving as a standalone diagnostic marker.

Mechanistic reviews have identified it as a central mediator in OA pathogenesis and a potential therapeutic target. IL-6 is also hypothesised to play an important role in early cartilage loss [146]. It has also been proposed that IL-6 contributes to the initial stages of cartilage degradation [60]. The context-dependent actions of IL-6 emphasise the importance of further investigation to clarify its contribution to OA progression and its implications for disease management [60,158]. IL-6 remains a promising biomarker for OA risk and progression, with higher circulating levels prospectively associated with knee cartilage volume loss and a strong mechanistic rationale for therapeutic targeting in OA [60,146].

4.1.2. Tumour Necrosis Factor Alpha (TNF-α)

TNF-α is an important cytokine in inflammation, exerting cytotoxic, regulatory and pro-inflammatory effects on various cell types including fibroblasts, tumour and lymphoid cells [159]. TNF-α is a soluble 17.5 kDa protein and a 26 kDa membrane-associated form, and promotes chemotaxis, phagocytosis, fibroblast proliferation, cytolysis and cytostasis of tumour cell lines in vitro [160].

TNF-α is a key mediator of systemic inflammation, driving cartilage degradation, joint inflammation and bone resorption [47,60,161,162]. TNF-α influences both anabolic and catabolic processes in chondrocytes, ultimately accelerating cartilage damage [105,163]. Lowering TNF-α activity has been associated with clinical improvement in patients with OA [2,109,164].

TNF-α induces IL-6, MMPs, and prostaglandins, while suppressing the production of type II collagen, contributing to cartilage matrix degradation. Elevated TNF-α messenger ribonucleic acid (mRNA) levels have been detected in OA cartilage and are linked to disease progression, particularly in later stages characterised by radiographic JSN [60]. Recent findings show that TNF-α promotes a catabolic shift in chondrocytes by up-regulating MMP-1, MMP-3, and MMP-13 and downregulating collagen type II and aggrecan via NF-κB activation. These effects were attenuated by the anti-TNF-α agent Golimumab, supporting its potential as a therapeutic strategy to slow cartilage degradation [165].

Alongside IL-6, TNF-α promotes joint inflammation and synovial degradation. It induces expression of cell adhesion molecules on endothelium, mediating immune cell recruitment [105,166]. Elevated TNF-α has been reported in OA cartilage and synovial fluid, correlating with radiographic progression and joint inflammation, particularly following meniscal injury [27]. However, TNF-α is not specific to OA, as systemic elevations are also linked to conditions such as advanced prostate cancer [167] and age-related mortality risk, particularly in men [168]. This lack of specificity limits its diagnostic utility as a standalone biomarker but retains value in OA research when considered within a broader biomarker panel. Recent work emphasises that validated biochemical markers, including IL-6 and TNF-α, may support the identification of molecular endotypes of OA. Such approaches could enable dynamic monitoring, improve patient stratification and guide the development of targeted therapeutic strategies by distinguishing disease subgroups with distinct pathobiological drivers [101].

4.1.3. Myeloperoxidase (MPO)

MPO is a heme-containing peroxidase enzyme located in the azurophilic granules of neutrophils, promoting oxidative stress and tissue damage, which represents a central driver of the inflammatory mechanisms underlying OA pathology [148,169,170]. MPO catalyses the conversion of hydrogen peroxide (H₂O₂) into hypochlorous acid (HOCl) that kills pathogens during infections [170].

In sterile inflammation, MPO and MPO-derived oxidants are thought to be pathogenic and promote inflammation and tissue damage [171]. Clinical data indicated that a deficiency in MPO does not necessarily rule out an individual to increased life-threatening infections, indicating redundancy in host defence mechanisms. However, it supports that MPO could be a very beneficial therapeutic target for modulating inflammation [172]. Supporting this, MPO-deficient animal models often show exaggeration of the inflammatory responses [169]. The biochemical cascade involving MPO and nicotinamide adenine dinucleotide phosphate, reduced form (NADPH) oxidase within the synovial membrane-associated inflammatory cells, leads to the formation of chlorinated peptides and degradation of articular cartilage through MMP activity [173].

Increased MPO activity and protein levels have been observed in many inflammatory conditions, including early-stage OA [66,171]. Recent studies have identified MPO expression in osteophytes, linking neutrophil-driven degranulation and oxidative stress directly to structural joint changes characteristic of OA [106].

In osteoarthritis, MPO levels vary with disease stage. In early OA, MPO levels in synovial fluid and serum are significantly elevated, correlating with inflammation and cartilage presence, while levels decrease in late OA despite persistent inflammation [66,173]. Control patients with healthy cartilage and no inflammation exhibit normal MPO levels [174].

MPO levels in plasma and urine MPO are significantly higher in early knee OA (Grade 1–Grade 2) and decrease in later stages (Grade 3–4), suggesting its utility as a non-invasive biomarker for early diagnosis and disease monitoring [173]. Elevated serum MPO levels were also noted in erosive hand OA and correlated with disease duration and inflammation [175]. Studies found increased serum MPO levels in patients with advanced knee and hip OA, which decreased following joint replacement surgery, confirming ongoing neutrophil activation and oxidative stress even in late-stage disease [176].

MPO has also shown relevance in psoriatic arthritis, where higher serum and synovial MPO levels were linked to disease activity [177]. Despite its potential, there are still significant gaps in understanding MPO’s extracellular mechanisms in inflamed tissues. Further investigation is required to explore its role and safety as a therapeutic target. Although MPO inhibition has shown benefits in experimental models, the long-term effects of reducing MPO would have a negative effect on innate immune responses due to its broad role in host defence [170].

Together, IL-6, TNF-α and MPO represent promising yet complex biomarkers for OA, with potential diagnostic and prognostic applications that may improve as study methods and cohort sizes become more standardised. Building on this mechanistic rationale, the following section evaluates their clinical relevance and diagnostic utility and considers how these biomarkers may also be contextualised within emerging therapeutic strategies.

4.2. Relevance and Diagnostic Utility

Mechanical wear and tear due to ageing and physical activity has long been hypothesised as a contributor to OA development [13,14]. However, inflammatory and oxidative stress biomarkers such as IL-6, TNF-α and MPO offer deeper insights into the molecular underpinnings of OA. These biomarkers are play integral roles in OA pathophysiology, with MPO serving as an indicator of oxidative stress, while IL-6 and TNF-α are key mediators of inflammatory pathways [4,95,107,142,178].

Increased concentrations of IL-6 and TNF-α are frequently reported in OA patients and have been associated with radiographic progression and cartilage loss in older populations [60,105,145]. Although targeting these cytokines may offer a potential avenue for intervention, biologics directed against IL-6 and TNF-α have not demonstrated consistent or significant efficacy in the treatment of OA [38,151,161]. Including biomarkers related to the activation of immune cells and remodelling of the extracellular matrix, may improve the precision of OA diagnosis [2]. MPO, in particular, is under investigation as a diagnostic marker due to its association with oxidative stress and inflammatory damage [169]. Its evaluation could support early diagnosis and disease monitoring, and the development of targeted interventions to mitigate disease damage [179].

In osteophytes, increased expression of genes, including MMP-13 and mast cell markers (including Chymase 1 (CMA1), Carboxypeptidase A3 (CPA3), Membrane spanning 4-domains A2 (MS4A2), and Tryptase Alpha/Beta 1 (TPSAB1)) illustrates the complexity of OA pathology and supports the rationale for a multi-biomarker strategy to improve predictive modelling. The clinical utility of these biomarkers must account for demographic variability and dataset heterogeneity. Therefore, rigorous statistical validation is essential [22,23]. Rank-based ANCOVA and regression analyses have been applied to non-age-matched datasets, offering a robust framework for the validation of biomarkers [4,180,181].

Although biological agents directly targeting IL-6 and TNF-α have not demonstrated consistent or significant efficacy in the treatment of OA [151,161], these cytokines remain central to disease pathophysiology. Their persistent association with synovial inflammation, cartilage degradation, and systemic inflammatory burden supports their utility as biomarkers rather than standalone therapeutic targets. Importantly, their measurement can provide insights into disease activity and may serve as surrogate markers for monitoring the effects of disease modifying interventions [182].

Agents such as bone morphogenetic protein-7 (BMP-7) and fibroblast growth factor-18 (FGF-18) target cartilage regeneration and matrix synthesis, processes that may be indirectly monitored through changes in inflammatory and oxidative stress markers such as IL-6, TNF-α, and MPO [45,183,184,185]. Inhibitors of IL-1 signalling [186,187], β-nerve growth factor (β-NGF) antibodies, and inverse agonists of retinoic acid-related orphan receptor alpha (RORα) [182,188,189] act on pathways with established crosstalk to IL-6 and TNF-α signalling cascades, suggesting potential utility of these biomarkers for tracking treatment response. Similarly, interventions involving matrix extracellular phosphoglycoprotein (MEPE) or human serum albumin (HSA) formulations could influence oxidative stress and catabolic activity, where MPO may serve as a relevant marker [182,190]. Aligning biomarker profiling with these targeted therapeutic strategies could enable a more comprehensive, mechanism-based approach to both monitoring and personalising OA treatment.

These biomarkers map directly onto inflammatory and oxidative stress pathways central to OA pathogenesis. IL-6 via the janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway [188,191], TNF-α through the NF-κB and MAPK cascades [192], and MPO through oxidative stress and neutrophil activation [66]. Notably, these are the same signalling networks targeted, directly or indirectly, by emerging DMOADs, including IL-1 inhibitors, RORα antagonists, and cartilage-anabolic agents such as FGF-18 and BMP-7 [182,188]. Thus, IL-6, TNF-α and MPO provide mechanistic anchors for monitoring pharmacodynamic effects and stratifying patients according to pathway activity.

4.3. Importance of Sample Type

Biomarkers can be evaluated using laboratory assays and advanced imaging techniques [80]. While a variety of biological matrices are available, methods that are accessible and minimally invasive, such as blood, saliva or urine sampling, are preferred for early-stage detection [141]. In contrast, synovial fluid and joint tissue samples, though more invasive, may better reflect disease status in the later stages. These samples allow for the assessment of inter-patient variability, supporting the development of personalised treatment strategies. Prognostic markers, in particular, could be utilised to facilitate earlier interventions, guide inform therapeutic decision-making and improve outcomes while reducing healthcare costs [141].

The identification of reliable biomarkers is crucial for distinguishing fast-progressing OA patients and for stratifying individuals based on disease morphology. This enables selective enrolment in clinical trials and the development of tailored treatments. Personalised healthcare strategies rely on biomarker-informed decisions derived from individual genetic, proteomic, or molecular profiles, making precise prognostic prediction essential. Biomarkers may also be monitored over time to distinguish between a normal biological activity, a pathophysiologic condition, and therapeutic response [160].

For a biomarker to be clinically useful, it must exhibit both high sensitivity (ability to correctly identify affected individuals) and high specificity (ability to correctly exclude those without disease). Ideally, a biomarker would achieve 100% in both parameters. However, this is rarely possible, especially when dealing with early detection, where sensitivity is often compromised due to heterogeneity in symptom onset. For example, sensitivity in newly symptomatic patients may be as low as 30–50%, even if the specificity is closer to 95% [193].

Levene’s test has been employed to assess variance homogeneity across groups, where significant outcomes indicate unequal variances [4,194]. Sample type selection is important when analysing biomarkers. Serum and plasma can yield distinct biomarker profiles; these differences can significantly affect data interpretation and clinical decision-making [194,195,196,197]. Notably, TNF-α and MPO have exhibited substantial variability between serum and plasma, reinforcing their diagnostic potential in OA [198].

4.4. Enzyme Linked Immunosorbent Assay (ELISA)

Detection of cytokines can be carried out using ELISA or multiplexing technologies. There have been issues with multiplex solid-phase assays with sensitivity and interference from autoantibodies [199]. ELISAs, by contrast, have excellent precision, good reproducibility and specificity [141], and have been widely used in OA research to quantify cytokines [156]. Potential variations can occur depending on the blood sample used (serum or plasma), time to process the sample, storage temperature, freeze–thaw cycles, assay type, interferences, standardisation, and quality control [141]. To minimise such variability, sample collection in the morning reduces potential confounding effects of diurnal variation. This approach aligns with recommendations to improve pre-analytical standardisation and enhance the reliability of biomarker measurements [117,200].

5. Statistical and Modelling Approaches in OA Research

Robust statistical and machine learning approaches have been increasingly employed in OA research to address challenges such as small sample sizes, data skewness, and heterogenous patient populations [201,202]. This section reviews commonly used analytical techniques in OA biomarker research, supported by recent literature, and highlights their relevance for improving diagnostic accuracy, risk stratification and patient subgroup identification.

5.1. Basic Statistical Techniques

Although these methods are often regarded as routine, they are the critical first steps in ensuring data quality and robustness. Establishing appropriate handing of non-normal distributions, group variability, and covariate effects provides the statistical foundation upon which advanced predictive modelling approaches such as discriminant analysis, cox regression, and clustering, can be reliably built.

5.1.1. Normality Testing

Assessment of data distribution is fundamental in selecting appropriate statistical tests. Levene’s test is widely used to assess the assumption of homogeneity of variances across groups, with significant results indicating unequal variances, which may affect the suitability of parametric tests [194]. The Shapiro–Wilk test is frequently applied to assess the assumption of normality, particularly in smaller datasets [203]. Graphical approaches, including quantile-quantile (Q-Q) plots provide an additional means of evaluating distribution patterns. The Kolmogorov–Smirnov test is also commonly employed to compare empirical data with theoretical distributions and is widely applicable in biomedical research [131,204,205]. When population parameters (means and variance) are estimated from the sample rather than known priori, the Kolmogorov–Smirnov test is adjusted using Lilliefors significance correction. This correction provides a more accurate lower-bound p-value, accounting for parameter estimation and enhancing test validity [206]. Recent applications in biomedical research continue to support the relevance of this correction, assessing the distribution of clinical and laboratory data using the Lilliefors adjusted Kolmogorov–Smirnov test [207].

When data deviates from normality, the Kruskal–Wallis test, is often employed to compare group medians [208]. Furthermore, when comparing two independent non-parametric groups, the Mann–Whitney U (MWU) test is used, as it evaluates differences in central tendency without assuming equal variances [195]. The Dwass–Steel–Critchlow–Fligner (DSCF) is a pairwise comparison method used to detect group differences, such as under different inflammatory or treatment conditions, and offers a robust post hoc approach for multiple comparisons in non-parametric datasets [209].

5.1.2. Data Imputation and Pre-Processing

Missing data are frequently encountered in clinical research and can introduce bias into statistical estimates [2]. Linear interpolation (LINT) is a simple yet effective method used to handle missing data points based on surrounding data points, improving the reliability by minimising bias, compared to complete-case analysis or mean imputation [210,211]. Z-score normalisation is often applied to standardise data, allowing for meaningful comparisons across variables with different units or scales [212].

5.1.3. Data Transformation

Data transformation techniques, particularly logarithm base 10 (log10) transformation, are frequently employed in OA biomarker research to address skewed data distributions and stabilise variance [213,214]. Transformations enhance the ability of parametric statistical models to capture overall trends by improving their fit to the data [215].

5.2. Advanced Statistical Techniques

5.2.1. Rank-Based ANCOVA

Rank-based ANCOVA effectively addresses non-parametric data, adjusting covariates in datasets with non-normal distributions [216]. Compared to inverse normal transformations (INTs), rank-based methods demonstrate better control over Type I error, the probability of falsely rejecting a true null hypothesis (i.e., detecting a statistically significant effect when none exists) and maintain statistical power, particularly in small or heterogenous clinical datasets [23,217].

By reducing dependence on p-values in small datasets, these analyses help ensure more reliable demographic adjustments and enable clearer interpretation of complex data [180,181,218]. To strengthen statistical validity, advanced approaches such as rank-based ANCOVA and regression models are applied, offering robust inferences and improved covariate adjustment [4,181,218].

5.2.2. Regression Analysis

Regression analysis is a statistical method used to examine the relationship between one or more independent variables and a continuous outcome. In biomedical research, it is frequently applied to explore associations between clinical or demographic factors (e.g., age and gender) and biomarker concentrations. When data meet parametric assumptions, linear and logistic regression models are appropriate; however, these assumptions are often violated in clinical datasets [219]. Rank-based regression is a robust alternative to traditional linear regression, which is particularly useful when data violates assumptions of normality or contains influential outliers, as it reduces sensitivity to extreme values while still enabling hypothesis testing [220,221].

To ensure the reliability of regression models, extreme value testing or sensitivity analyses are often conducted. Comprehensive strategies for regression modelling, including covariate selection, assumption checking, residual diagnostics, and validation, have been detailed in applied biostatistics literature and are critical to ensuring valid and interpretable results [181]. Such analyses enhance confidence in the generalisability of results, particularly when working with small or heterogeneous samples [222,223].

5.2.3. Discriminant Function Analysis (DFA)

Discriminant function analysis (DFA) is a statistical method that assigns individuals to predefined groups based on measured characteristics [2]. Independent variables (IV), such as biomarker concentrations, are used to predict group membership, for example, distinguishing OA patients from volunteers [224,225].

A classification accuracy as high as 95% was obtained in a blood droplet analysis demonstrating DFA’s utility in identifying physiological conditions [226]. Multiple sclerosis patients were differentiated from healthy controls using electroglottograph (EGG) variables [227]. EGG data was used in DFA and Classification and Regression Tree (CART) analysis to differentiate age groups, reporting higher predictive performance compared to binary logistic regression [228].

Kulkarni et al. (2022) showed that integrating transcriptomic and proteomic biomarkers can improve predictive accuracy and yield a broader understanding of OA pathology, illustrating the value of multi-omics approaches in OA research [106].

Several forms of DFA are available, selected according to the underlying data structure. Quadratic Discriminant Analysis (QDA) accommodates non-linear relationships and provides greater flexibility, although its requirement for separate covariance matrices in each group increases the likelihood of overfitting in studies with small sample sizes [229]. By contrast, Linear Discriminant Functions (LDF), also referred to as Fisher’s Linear Discriminant Analysis (LDA), is well suited to situations where predictor numbers exceed sample size. LDA produces linear boundaries that maximise separation between groups by reference to their group centroid [2,230].

Robust LDA methods that incorporate the Modified One-Step M-Estimator with Qn scale (MOM-Qn) have been applied in financial research to classify banks into ‘distress’ and ‘non-distress’ groups, thereby overcoming problems associated with outliers and non-normal data distributions [231]. DFA has proven valuable for identifying meaningful linking between predictor variables and group membership, highlighting its versatility as a classification technique [232]. Approaches to address deviations form normality include iterative classification procedures, while Box’s M test is commonly applied to detect violations of multivariate normality, which can then be corrected through refinement [2,214].

To evaluate model performance, Wilks’ lambda is frequently employed, smaller values indicate greater group discrimination, and statistical significance (p < 0.05) confirms the reliability of classification [233,234]. Studies on cardiovascular disease classification and financial risk modelling have applied Wilks’ Lambda [235]. Compared with support vector machines (SVM), random forests, and logistic regression, DFA is particularly advantageous when working with limited datasets or those that do not follow normal distribution [2]. Although SVMs and random forests are able to capture complex interactions, their optimal performance depends on access to larger sample sizes [236].

Logistic regression, although straightforward, may not effectively manage non-linear or interacting features. Recent studies exploring these models in health screening contexts have highlighted the importance of model selection based on data characteristics [237]. DFA remains a valuable and interpretable classification tool in clinical research, particularly when sample size and distributional assumptions limit the use of more data-intensive methods [238].

5.2.4. Survival Analysis (Cox Regression)

Survival analysis, particularly the Cox proportional hazards model, is a well-established statistical approach used to predict time-to-event outcomes, for example, disease onset or progression [239]. Unlike standard progression techniques, it accommodates censored data making it highly applicable for chronic disease research. The Cox model allows for multivariable analysis, accounting for both continuous and categorical predictor variables [240].

In OA research, Cox regression has been applied to explore various risk factors. Xu et al. (2020) used the model to evaluate the impact of dietary factors to knee OA progression, accounting for demographic, socioeconomic, and clinical covariates [241]. Bruunsgaard et al. (2003) investigated the association between cytokines and mortality, reporting that TNF-α was linked to increased mortality in men, while elevated IL-6 and TNF-α levels in older adults were associated with age-related pathologies and increased mortality risk [168].

Riley (2007) emphasised the advantages of individual patient data in survival models, which enables greater flexibility in defining marker combinations, adjusting cut-offs, and improving risk stratification accuracy [99]. Risk mitigation matrices provide an additional layer of analysis, incorporating both clinical and serological markers to estimate disease progression risk and informing treatment decisions [242]. These matrices, while established in fields such as cardiovascular disease and infectious disease modelling [243,244], have limited application in OA to date.

Risk mitigation matrix evaluates the probability of disease development by ranking risks from high to low based on probability and impact using elements referred to as Heuristic Ideation Technique (HIT), a structured decision-making tool that harnesses expert judgement to systematically identify, categorise, and prioritise risks based on their likelihood and potential severity [243,245,246]. These tools support the development of models that track patient responses overtime and refine risk trajectories [242,247].

Survival models also contribute to threshold identification in OA risk stratification, with higher thresholds indicating more advanced disease risk [75,248]. Interpretation of hazard ratios, derived from the exponentiation of the B coefficient (Exp(B)) helps quantify the effect size of predictors. Values below one suggests a protective factor, values above one indicate increased risk, and a value of one implies no effect. Confidence intervals (CI) are used to assess statistical significance, with the null hypothesis assuming a population hazard ratio of 1 [249,250].

Recent studies have emphasised the value of Cox regression in evaluating modifiable lifestyle factors. The impact of dietary factors on OA progression [251,252] and the effectiveness of exercise interventions [253]. These findings underscore the importance of assessing biomarker stability and predictive value. Integration of survival analysis with machine learning and imaging aims to enhance diagnostic precision and support personalised OA treatment strategies [254].

5.3. Machine Learning and Clustering Methods

5.3.1. Synthetic Data Generation

When datasets are limited or incomplete, synthetic data generation is increasingly used, particularly in biomedical research to simulate patient variability and support model development [255,256]. By maintaining the statistical characteristics of real patient datasets and simultaneously enlarging the sample size, this approach strengthens analytical reliability. To verify their accuracy, synthetic datasets are typically addressed using methods such as k-nearest neighbour (kNN) classification and receiver operating characteristic (ROC) analysis. Clustering approaches have also been shown to be valuable for understanding immune variability, and studies suggest that applying clustering-based validation to synthetic data can provide enhanced insight into the heterogeneity of inflammatory diseases [131,202].

5.3.2. Hierarchical Clustering Analysis (HCA)

Hierarchical clustering analysis (HCA) is a powerful tool for identifying disease subtypes and uncovering patterns within biomarker and morphological data. A dendrogram, a tree-like structure, is generated by grouping data points based on calculated distances, with each branch representing clusters of similar characteristics [257].

Methods such as Ward.D2, nearest neighbour, and centroid linkage are commonly used, with Ward.D2 method minimising intra-cluster variance, and cluster validity is assessed using the cophenetic correlation coefficient [258]. This technique has been widely applied in biomedical research. In a study by Abdulrahim et al. (2021), HCA was used in conjunction with Principal Component Analysis (PCA) to examine morphological features of the hip and pelvis [259]. PCA reduced the dimensionality of 14 shape variables, highlighting key patterns of variation to support clustering. The study identified the vertical sourcil angle as strongly associated with hip OA, with combined morphological variants contributing more to OA risk compared to other risk factors combined [259]. Similarly, Sørlie et al. (2003) used HCA to analyse gene expression profiles in breast tumours, successfully differentiating cancer subtypes [260].

Gene expressions and morphological datasets have been analysed using HCA, enabling the identification of clinically relevant subgroups in OA and other diseases. To support these analyses, the gap statistic is commonly applied, assessing intra-cluster variability across k values to determine the number of clusters that provide meaningful separation [259,260].

5.3.3. k-Nearest Neighbour (kNN) Analysis

k-nearest neighbour (kNN) is a non-parametric classification method that classifies data points based on their nearest neighbours in feature space. It has been effectively used in OA and RA research to distinguish between disease states by analysing gene expression and biomarker profiles, demonstrating high predictive accuracy (0.96, 0.96, 0.96 and 0.91) across several models [261,262]. Multiple classification algorithms, including kNN, support vector machines, and naïve Bayes, have been applied to identify a novel 16-gene signature that was able to differentiate between RA and OA with a high accuracy of over 90% [262]. The robustness of kNN lies in its simplicity and effectiveness for classifying disease states in biomedical datasets.

5.3.4. Receiver Operating Characteristic (ROC) Curves

The diagnostic ability of classification models can be evaluated using receiver operating characteristic (ROC) curves, which plot the true positive rate against false positive rate across different threshold values. Model performance is quantified by the area under the curve (AUC), with larger values indicating higher accuracy [131,263]. ROC analysis has also been used to assess the reliability of kNN classifications when distinguishing inflammatory response categories. In a study exploring the incidence and progression of knee pain, ROC curves were used to evaluate central and peripheral factors, demonstrating that peripheral factors were more predictive of incident knee pain, whereas central mechanisms were more strongly associated with symptom progression [264].

5.3.5. t-Distributed Stochastic Neighbour Embedding (t-SNE)

t-distributed stochastic neighbour embedding (t-SNE) is a non-linear dimensionality reduction method that facilitates the visualisation of complex, high-dimensional datasets by maintaining local relationships and underlying cluster patterns [131]. Unlike linear methods such as PCA, t-SNE maps data into a lower-dimensional space by minimising the Kullback–Leibler divergence, a measure of entropy, between probability distributions in the high-dimensional and low-dimensional spaces. This allows it to preserve local structure and uncover non-linear relationships within complex, multidimensional datasets [265,266]. This approach has been used to map data to resolve tumour heterogeneity using mass spectrometry imaging, revealing clinically relevant subpopulations associated statistically with patient survival in gastric cancer and metastasis status of tumours in breast cancers [260]. Its capacity to map complex, high-dimensional biomarker data makes it a promising technique for future investigations supporting a clear understanding of patient-specific inflammatory responses.

6. Challenges and Limitations in OA Biomarker Research

6.1. Individual Variability and Lifestyle Factors

Inflammatory responses in OA vary widely between patients, influenced by factors such as genetics, comorbidities, and clinical history. This heterogeneity is complicated the establishment of universal biomarkers or uniform treatment strategies, emphasising the need for personalised therapeutic approaches [267,268].

Chronic comorbidities, including diabetes and hypertension, have been linked to altered immune function. Individuals with these conditions often display reduced MPO responses, reflecting changes in inflammatory thresholds [269]. Such findings align with earlier reports that associate comorbidities with immune modulation in OA and highlight the importance of stratified therapeutic strategies. Severe OA is often characterised by increased levels of TNF-α, a central pro-inflammatory cytokine, and MPO, an oxidative stress marker. These elevations may indicate systemic inflammation, further intensified by obesity and cardiovascular disease. These accompanying conditions can alter biomarker profiles and complicate their interpretation in relation to OA-specific pathology [4,270].

To address variability and improve analytical robustness, synthetic datasets are increasingly validated through advanced modelling techniques such as HCA, t-SNE, kNN, and ROC curve analysis [134,271,272]. By expanding the dataset size while maintaining statistical properties, synthetic data facilitates a more reliable investigation of dose–response patterns and inter-patient variability, thereby supporting precision biomarker profiling.

Additional analyses also demonstrate that demographic variables, particularly age, can influence TNF-α and MPO levels [195]. Lifestyle factors, including physical activity, further shape biomarker profiles [273]. As such, future research should incorporate detailed demographic and lifestyle information, such as BMI, comorbidities, and activity levels into study designs [156].

Complementary statistical frameworks, including rank-based ANCOVA and regression models, provide a robust strategies for evaluating non-parametric data, particularly when heterogeneity, non-normality, or small sample sizes are present [1,22,23,220,274]. The observed variability in IL-6 and MPO underscores the potential of patient-specific inflammatory profiles for stratifying individuals and guiding therapies based on individual thresholds [131]. Integrating such finding with molecular methods, including single-cell sequencing, may help clarify the mechanisms of FLS variability and accelerate the development of stratified OA treatments [275].

Due to the complex nature of OA, single biomarkers alone lack adequate diagnostic sensitivity, underscoring the importance of composite biomarker panels or multi-modal approaches to improve diagnostic accuracy [1,73,276]

6.2. Sample Size Limitations and Statistical Power

Biomarker investigations are often limited by challenges such as small cohort sizes, demographic variability, and non-normal data distributions, all of which hinder the identification of diagnostic markers with strong statistical reliability [23,220,274]. Whole restricted small sample size remains a limitation, early exploratory studies in smaller cohorts can still generate important insights [197,273,276].

To address these challenges, machine learning approaches, particularly clustering algorithms, can be used to detect patterns within complex datasets. Their performance, however, is dependent on a sufficient sample size, addressed using validated synthetic datasets [277]. In recent years, the use of synthetic data has expanded within biomedical research, enabling the expansion of sample sizes, improving statistical power and enhancing interpretability of results [214,255,278].

Accurate sample size determination continues to be essential for biomarker validation. Established references, such as Jacob Cohen’s: “Statistical Power Analysis for the Behavioural Sciences” and Chow et al.: “Sample Size Calculations in Clinical Research”, provide methodological guidance for achieving an 80% statistical power at a 0.05 significance threshold, consistent with best practices in the field [279].

6.3. Use of Synthetic Data to Overcome Research Limitations

Synthetic data can be integrated with machine learning for classification and interpretation purposes. The integration of synthetic data alongside experimental approaches enables more comprehensive analyses by accounting for inter-patient variability and supporting the development of personalised therapeutic strategies. Moreover, synthetic datasets can be used to validate observed trends under controlled conditions, helping to overcome sample size limitations and strengthen model reliability [73,181].

Synthetic data has been beneficial in resource-constrained research environments, presenting frameworks for optimising weight maps to minimise required sample sizes in OA imaging studies, demonstrating reductions of up to 58% in cartilage thickness assessment [201,278]. Using synthetic data in a pathology-independent classifier for spinal posture analysis, demonstrating its utility alongside explainable AI techniques, such work underscores the broader potential of synthetic data in heterogeneous populations [201].

The use of generative adversarial networks (GANs), and other artificial intelligence-based methods expanded the possibilities for creating high-fidelity synthetic datasets, though rigorous validation remains essential to minimise bias [255,256]. Advanced clustering approaches, including HCA and t-SNE are effective for detecting subgroups within heterogeneous datasets [201]. Validation techniques such as kNN classification and ROC analysis further establish the alignment of synthetic, with experimental observations, ensuring biological relevance [261,263].

In OA studies, synthetic data supported the identification of inflammatory phenotypes, enhanced understanding of inter-patient variability, and enabled robust dose–response modelling [134,266]. This integrative strategy was designed to overcome challenges associated with small datasets and patient-specific variability, offering potential to advance personalised medicine through improved stratification and tailored therapeutic interventions.

7. Conclusions

The identification and validation of robust biomarkers remain essential to improving early diagnosis and personalised management of osteoarthritis. Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO) are key mediators of inflammation and oxidative stress and represent biologically plausible candidates for diagnostic and prognostic applications. Their expression in synovial tissues, particularly by fibroblast-like synoviocytes, underscores their relevance to joint pathology.

However, clinical translation of these biomarkers requires careful consideration of sample type, assay standardisation, and population variability. Importantly, OA is a heterogeneous disease, and biomarker expression may vary across inflammatory and mechanically driven subtypes [64,67]. This highlights the need for biomarker panels, where IL-6 and TNF-α may better capture synovial driven disease, while MPO and matrix turnover markers reflect cartilage and bone-driven pathology [101,121,131].

Integration of advanced statistical techniques, such as rank-based ANCOVA, discriminant function analysis, and Cox regression, can mitigate issues arising from non-normal data distributions and demographic heterogeneity. The use of synthetic data and clustering methods further enables the characterisation of patient subgroups and enhances model robustness. Future efforts should focus on the harmonisation of biomarker thresholds, expansion of longitudinal cohort studies, and the incorporation of multi-modal data, including imaging and genomics, to refine risk stratification. By aligning biological insight with rigorous modelling, these approaches hold promise for the development of precision diagnostics and the identification of high-risk OA phenotypes suitable for targeted therapeutic intervention.