Integrated Signaling Networks in Osteoarthritis and Aging: From Molecular Mechanisms to Therapeutic Targets

Background:

Osteoarthritis (OA) is a multifaceted age-related joint disorder resulting from the interplay of metabolic, inflammatory, and senescent pathways. This review combines the most recent research on the interconnected signaling networks in OA and aging.

Methods:

A systematic search of major databaseswas performed from 2010 to 2025in accordance with PRISMA guidelines. Included were studies that examined specific molecular targets and signaling pathways in OA or joint degeneration associated with aging. The Newcastle-Ottawa Scale was used to rate quality.

Results:

The study shows that OA is a multi-pathway aging disorder that leads to cartilage destruction by combining the senescence-associated secretory phenotype (SASP), epigenetic dysregulation, metabolic dysfunction, and impaired mechanotransduction. The RUNX2/SPP1 axis encourages hypertrophic change, while METTL3-mediated m6A changes stop autophagy. Sirtuins (SIRT1/SIRT6) are essential for regulating metabolism, and Klotho contributes to both Wnt suppression and oxidative stress resistance. Inflammaging is linked to senescence through STAT3, a key mediator of the process.

Conclusion:

OA is a systemic aging disorder that necessitates multi-target strategies. Senolytic agents, METTL3 inhibitors, SIRT activators, Klotho-based biologics, and combinations targeting STAT3-MAPK-driven inflammaging are among the most important therapeutic directions. For future translation, we need validated biomarkers to group patients and long-term safety data.

1.Introduction

Osteoarthritis (OA) is the most common degenerative joint disease in the world, affecting more than 300 million people. The number of cases and the severity of the disease increase dramatically with age [1,2]. The conventional understanding of OA as a non-inflammatory “wear-and-tear” condition has undergone significant revision [3]. Current evidence indicates that osteoarthritis (OA) is a complex, multi-tissue aging disorder influenced by interconnected signaling networks that involve metabolic dysregulation, cellular senescence, epigenetic modifications, and low-grade inflammatory enhancement [4,5]. Aging is the leading risk factor for OA. Itssigns include genomic instability, telomere shortening, epigenetic dysregulation, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered protein homeostasis, and dysbiosis. Together, these factors create a hostile microenvironment that impairs chondrocyte function and leads to cartilage breakdown over time [6,7]. The joint changes from a calm organ to an inflamed compartment with old cells, pro-inflammatory macrophage polarization, out-of-control growth factor signaling, and more enzymes and inflammatory mediators that break down cells [8,9]. Researchers have found important molecular drivers, such as the SPP1/RUNX2 axis, which controls chondrocyte hypertrophy and bone remodeling [10,11]; METTL3-catalyzed m6A modifications, which stop autophagy [12]; TGFBR2 dysfunction, which stops protective signaling [13]; and sirtuins (SIRT1, SIRT6), which act as metabolic sensors that connect mitochondrial function to cellular aging [14-16]. Moreover, crosstalk among interconnected pathways exacerbates OA pathology. The PI3K/AKT/mTOR axis integrates metabolic signals [17,18], the MAPK pathways amplify inflammatory cascades [19,20], the Hippo/YAP-TAZ pathway is a key mechanotransducer [21,22], and the AMPK/SIRT1 axis links energy balance to aging [23,24]. The senescence-associated secretory phenotype (SASP) is a key output node that integrates signals from multiple pathways to drive a tissue-damaging inflammatory microenvironment [25,26]. The ongoing ineffectiveness of single-target pharmaceutical strategies in osteoarthritis highlights the constraints of monotherapeutic approaches in a polygenically influenced disease [27]. This review brings together the most recent evidence to create a complete framework for understanding how OA develops through interconnected signaling networks. Specific goals are: (1) putting together mechanistic evidence for core molecular targets and signaling pathways; (2) finding important points of crosstalk between pathways; (3) looking at preclinical and clinical evidence for multi-target strategies; (4) talking about chances to develop biomarkers; and (5) suggesting a research plan for pathway-integrated therapeutics.

2. Methodology

2.1 Study Design and Protocol Registration This systematic review was conducted according to the PRISMA 2020 guidelines [28,29].

2.2 Sources of Information and Search Strategy From September 2024 to November 2025, systematic literature searches were done on PubMed/MEDLINE, Scopus, Web of Science Core Collection, and EMBASE. The search strategies used terms for osteoarthritis, aging/senescence, and signaling pathways/molecular mechanisms. Secondary searches focused on specific molecular targets, including SPP1, RUNX2, METTL3, SIRT1, STAT3, and Klotho. Tertiary searches examined how integrated signaling pathways function, including PI3K/AKT/mTOR, MAPK, Hippo, Notch, BMP, AMPK/SIRT1, and SASP. We only looked at English- language, peer-reviewed publications published between 2010 and 2025.

2.3 Criteria for Inclusion and Exclusion in the Study Selection The required sample size was determined using Cochran’s formula for cross-sectional surveys: n = (Z² × p × q) / d², where Z = 1.96 (95% confidence), p = q = 0.50 (conservative estimate), and d = 0.05 (acceptable margin of error), yielding n =

384. All 384 distributed questionnaires were returned in full (response rate: 100%).

Non-probability convenience sampling was employed; students present during data collection were invited to participate. Although convenience sampling introduces the risk of selection bias and limits external generalizability, it was pragmatically appropriate given the study setting and timeframe. Inclusion criteria including, current enrolment in a Diploma or B.S. MLT program at a participating institution; age ≥ 18 years; willingness to provide written informed consent. Exclusion criteria including, Non-enrolment in an MLT program; age < 18 years; absence during data collection; refusal to participate or incomplete questionnaire.

2.4 Checking the Quality The way quality was measured changed depending on the type of study. The Newcastle-Ottawa Scale (NOS) was used to rate observational and mechanistic studies. Studies that received a score of 7 or higher were considered high-quality [30]. The Cochrane Risk of Bias (RoB 2.0) tool [31] was used to look at clinical trials. AMSTAR-2 [32] was used to assess systematic reviews. Quality scores contributed to the narrative synthesis but did not lead to automatic exclusion.

2.5 Collecting and Putting Together Data. Standardized forms were used to extract data on publication details, molecular targets/pathways studied, study design, key findings, evidence for pathway interactions, therapeutic interventions, and limitations. Narrative synthesis was organized by molecular target and signaling pathway, bringing together information from different model systems and giving more weight to studies on human tissue, mechanistic studies, and recent publications.

2.6 Standards for Reporting and Grading Evidence This review follows the PRISMA 2020 reporting guidelines. A modified GRADE framework was used to grade evidence certainty, taking into account consistency across models, biological plausibility, and the risk of bias [33].

3. Results

3.1 Literature Review and Study Selection We found 4,287 records across four databases. After 1,203 duplicates were removed, 3,129 records were checked by title and abstract. This meant that 2,784 publications were excluded, and only 345 full-text articles were checked for eligibility. After a thorough review of the entire text, 189 articles were excluded, leaving 156 studies available for qualitative synthesis. These included in vitro studies (n=67), animal models (n=58), clinical trials (n=10), and other designs not explicitly categorized in the figure. For this review, we examined 68 studies that met all the specific eligibility criteria. These studies included 48 original research articles, 12 systematic reviews and meta-analyses, 5 clinical trials, and 3 mechanistic reviews. The study designs in this group were in vitro studies (n=32), animal models (n=21), human tissue studies (n=10), and clinical investigations (n=5). Quality assessment identified 52 high-quality studies (mean NOS score 7.4±0.8). Figure 1 presents a flowchart summarizing the process of selecting studies.

Figure 1: Flow Chart for PRISMA 2020. This diagram shows the steps in the systematic review process, including finding studies, screening them, assessing their eligibility, and finally including them. We found 4,287 records, screened 3,129, checked 345 for eligibility, and included 156 studies in the qualitative synthesis. These studies were divided into in vitro, animal model, and clinical trial studies.

3.2 Core Molecular Targets in OA-Aging Nexus

3.2.1 SPP1/RUNX2 Axis in Chondrocyte Hypertrophy and Subchondral Bone Remodeling SPP1 (osteopontin) and RUNX2 orchestrate a pathogenic axis that induces chondrocyte hypertrophic transformation and pathological bone remodeling [34,35]. Abnormal activation of RUNX2 in articular chondrocytes induces the expression of hypertrophic markers (COL10A1) and matrix-degrading enzymes (MMP13) [36,37]. SPP1, a RUNX2 target, acts as a damage-associated molecular pattern (DAMP), facilitating osteoclast recruitment and inflammatory signaling [38,39]. Single-cell RNA sequencing reveals “hypertrophic-like” chondrocyte populations that proliferate in osteoarthritis [40]. These cells reduce the expression of genes that make cartilage strong and increase the expression of genes that break down cartilage and cause inflammation [41]. Transcriptional methods control RUNX2 activity, the recruitment of co- regulators, and communication with Wnt/β-catenin signaling [42,43]. Cellular senescence facilitates RUNX2 stabilization, establishing a feedforward loop [44]. RUNX2 also impairs chondrocyte metabolism [45]. SPP1 overexpression exacerbates inflammation and bone resorption, modifying the RANKL/OPG ratio to favor osteoclastogenesis [46-48]. Therapeutic targeting of this axis shows promise in preclinical models; however, it faces translational hurdles owing to RUNX2’s critical roles in development [49,50].

3.2.2 METTL3-Mediated m6A Methylation in Age-Related Joint Degeneration The modification of N6-methyladenosine (m6A) is disrupted in osteoarthritis (OA). In OA cartilage and synovium, METTL3 expression and m6A levels are increased [51-54]. METTL3-mediated m6A modifications inhibit autophagy by targeting ATG7 mRNA, thereby diminishing its stability and translation [55,56]. This hinders autophagic flux, leading to the accumulation of damaged cellular components and promoting senescence [57]. In vivo silencing of METTL3 mitigates OA progression in murine models [58]. METTL3 also controls other OA-related transcripts, including SIRT1, STAT3, and matrix genes such as COL2A1 and ACAN [59-61]. Although m6A levels decrease with age in specific tissues, OA shows a context-dependent rise in METTL3 activity, indicating a pathological feedback loop [62].

3.2.3 TGFBR2 Dysfunction in Growth Factor Signaling Impairment Two ways transforming growth factor-β (TGF-β) signaling affects cartilage. Canonical TGF-β-SMAD2/3 signaling protects cartilage, but when it goes wrong, it causes problems [63,64]. TGFBR2 expression and signaling undergo modifications in aging and osteoarthritic cartilage [65,66]. Single-cell analyses reveal diminished TGFBR2 expression in hypertrophic chondrocytes, notwithstanding abundant ligand presence, thereby disrupting protective signaling [67]. Downregulation transpires through microRNA suppression (e.g., miR-146a), protein misfolding, and epigenetic silencing [68]. Consequences encompass compromised matrix synthesis, heightened inflammation, and diminished metabolic adaptation [69]. Therapeutic modulation is complicated because timing is very important. Early restoration is good, but chronic elevation leads to hypertrophy and osteophyte formation [70].

3.2.4 Sirtuins (SIRT1/SIRT6) as Metabolic Regulators of Joint Homeostasis SIRT1 and SIRT6 are NAD+-dependent deacetylases that act as sensors for metabolism and stress [71,72]. SIRT1 increases chondrocyte resilience by promoting mitochondrial biogenesis, enhancing autophagy, regulating SASP via NF- κB/p53 signaling, and maintaining genome stability [73,74]. As we get older, SIRT1 levels and activity decline because NAD+ biosynthesis decreases, epigenetic silencing occurs, and proteins are lost [75-77]. In models, SIRT1 activators and NAD+ precursors protect cartilage [78-80]. SIRT6 controls DNA repair, keeps NF-κB in check, and changes metabolism. SIRT6 deficiency in chondrocytes results in spontaneous osteoarthritis-like phenotypes, confirming its function as a crucial aging inhibitor in joints [81-84].

3.2.5 FTO in Epigenetic Regulation of OA Progression FTO is an m6A demethylase that counteracts METTL3 [85]. FTO expression diminishes in osteoarthritic cartilage, correlating with disease severity [86]. FTO-mediated demethylation modulates autophagy genes and SIRT1 mRNA, typically inhibiting OA pathology [87]. FTO inhibitors worsen inflammation and OA-related degradation in vitro. FTO

variants connect metabolic obesity to OA risk by controlling inflammation and lipid metabolism through epigenetics. This makes FTO a target for OA caused by metabolic dysfunction [88].

3.2.6 STAT3 in Inflammaging and Synovial Fibrosis STAT3 combines signals that cause inflammation and is the main controller of SASP [89,90]. In OA synovium, STAT3 activity is higher, which correlates with the severity of inflammation [91,92]. Cytokines (IL-6, IL-17) turn it on, and it combines signals from old cells and physical damage [93,94]. STAT3 promotes the expression of SASP and fibrogenic genes, as well as osteoclastogenesis [95]. It interacts in essential ways with MAPK pathways, which add phosphate groups to and stabilize STAT3, making an inflammatory amplification node [95]. Inhibition of STAT3 slows OA progression in models, but systemic inhibition could cause immune problems, so local delivery methods are needed [96].

3.2.7 15-PGDH/PGE2 Balance in Pain and Inflammation Prostaglandin E2 (PGE2) combines pain, inflammation, and matrix breakdown in OA [97]. In OA synovium, the enzyme 15-PGDH, which degrades PGE2, is less active, thereby raising PGE2 levels [98,99]. The “PGE2 paradox” states that PGE2 can be protective at first, but if it remains elevated for too long, it can destroy cartilage by worsening inflammation, increasing osteoclastogenesis, and inhibiting autophagy [100]. Targeting 15-PGDH to improve PGE2 catabolism is a new idea, but achieving it is difficult because the effects must be localized to avoid systemic toxicity [101].

3.2.8 Klotho as a Pleiotropic Regulator of Aging-Related Pathways Klotho is a protein that slows aging, but its levels decline with age [102,103]. Klotho inhibits pathogenic Wnt/β-catenin signaling and augments Nrf2-mediated antioxidant defenses [104,105]. Both pathways are not functioning correctly in aging cartilage [106,107]. Restoring Klotho has chondroprotective effects by stopping hypertrophic programming, boosting antioxidant responses, and encouraging anti-inflammatory macrophage polarization [108,109]. Klotho-mediated Wnt suppression also activates the Hippo pathway, promoting chondrocyte growth and preventing aging [110].

3.3 Integrated Signaling Pathways in OA-Aging Nexus

3.3.1 PI3K/AKT/mTOR Pathway: Crosstalk with SIRT1 in Chondrocyte Senescence and Autophagy Regulation The PI3K/AKT/mTOR pathway is a key regulator of metabolism [111]. In the context of aging and osteoarthritis (OA), this pathway is dysregulated: the availability of IGF-1 diminishes, inflammatory signals paradoxically activate PI3K/AKT, and mTORC1 becomes hyperactivated in senescent cells [112,113]. There are several ways that SIRT1 and mTORC1 talk to each other: SIRT1 deacetylates and activates TSC2 to stop mTORC1; SIRT1 activates AMPK, which stops mTORC1 even more; and SIRT1 deacetylates FoxO3a, which increases the expression of autophagy genes even when AKT is active [114–117]. The decline of NAD+ and SIRT1 with age disrupts this communication, placing the body in a state that accelerates aging [118]. Therapeutic restoration of SIRT1 or direct inhibition of mTORC1 demonstrates potential in osteoarthritis models [119,120].

3.3.2 MAPK Signaling Pathways: Integration with STAT3 in SASP Amplification and Matrix Degradation In OA, damage and inflammatory signals activate MAPK cascades (p38, JNK, ERK) [121,122]. In senescent chondrocytes, p38 MAPK is constitutively active, leading to the activation of genes that promote inflammation and breakdown [123]. A critical integration point is the MAPK-dependent phosphorylation of STAT3 at serine 727, which increases its transcriptional activity and SASP output, thereby converging the damage-sensing (MAPK) and cytokine (JAK/STAT3) pathways [124,125]. p38 MAPK also helps keep the cell in its senescent state [126]. Although p38 inhibitors demonstrate effectiveness in models, their clinical application is hindered by pathway redundancy and immune- related adverse effects [127]. Targeting both p38 and STAT3 simultaneously is a promising strategy [128].

3.3.3 Hippo/YAP-TAZ Pathway: Mechanotransduction Role in Age-Related Cartilage Stiffening The Hippo pathway and its effectors, YAP/TAZ, are important mechanotransducers [129,130]. YAP/TAZ are inactive in healthy cartilage. As we get older, our cartilage gets stiffer, which causes YAP/TAZ to move to the nucleus and become active. This leads to hypertrophic differentiation and senescence-like phenotypes [131-133]. This activation is facilitated by actin dynamics rather than by traditional Hippo kinases [134]. YAP and TAZ serve as hubs for Hippo signaling and other pathways, controlling the characteristics of mesenchymal stem cells [135,136]. Klotho affects this pathway by

blocking Wnt signaling, which lifts the block on the Hippo pathway. This encourages YAP/TAZ sequestration and helps chondrocyte differentiation [137]. Aging diminishes the expression of upstream Hippo regulators, exacerbating the dysregulation of this mechanosensitive system [138].

3.3.4 Notch Signaling: Developmental Pathway Reactivation in OA Progression The Notch pathway, dormant in mature cartilage, is reactivated in osteoarthritis via elevated ligand expression on senescent and synovial cells [139,140]. When Notch signaling is activated, it promotes hypertrophic differentiation by activating RUNX2, increases inflammation by interacting with NF-κB/STAT3, and decreases autophagy [141,142]. It interacts extensively with the BMP and Wnt pathways.

3.3.5 BMP Signaling: Balance with TGF-β Pathways in Osteophyte Formation and Cartilage Repair BMPs signal through SMAD1/5/8 to promote bone and chondrocyte growth [143,144]. In OA, the balance shifts toward BMP dominance because there are more ligands, and TGF-β signaling doesn’t work as well [145,146]. Too much BMP signaling causes osteophytes to form and bones to harden. Therapeutic strategies encompass the utilization of BMP antagonists (e.g., Noggin) or the selective augmentation of TGF-β-SMAD2/3 signaling [147].

3.3.6 AMPK/SIRT1 Axis: Metabolic Sensor Integration with mTOR Signaling The AMPK/SIRT1 axis serves as a pivotal energy-sensing hub (148). AMPK activation inhibits anabolic pathways (through mTORC1 inhibition) and stimulates catabolic pathways (autophagy) and stress resistance (via SIRT1) [149,150]. In aging chondrocytes, AMPK activity diminishes owing to mitochondrial dysfunction and decreased upstream activation [151]. This lack of metabolic flexibility keeps cells in a state that increases their likelihood of aging. AMPK activators, such as metformin and physical exercise, have shown promise in osteoarthritis models [152,153].

3.3.7 Senescence-Associated Secretory Phenotype (SASP) Pathways: Inflammatory Network Connection SASP is controlled by transcriptional networks that function in concert, including p38 MAPK-ATF2, JAK-STAT3, NF- κB, and C/EBPβ [154–159]. MicroRNAs (such as let-7), mRNA stability factors (such as ELAVL1), and m6A modification [160–162] are all part of post-transcriptional regulation. SASP expands spatially through paracrine signaling and extracellular vesicles, disseminating senescence [163,164]. Therapeutic approaches encompass senolytics (e.g., BCL-

2 inhibitors) to eliminate senescent cells, SASP inhibitors targeting regulatory pathways, and extracellular vesicle antagonists [165,166].

Figure 2 diagram illustrates the interconnected signaling pathways involved in osteoarthritis and aging, organized into three layers: Extracellular Signals, Intracellular Pathways, and Nuclear Effectors. Key molecular targets (SPP1, RUNX2, METTL3, etc.) are shown as octagonal nodes, with activation and inhibition indicated by solid and blunted arrows, respectively. Functional modules are color-coded, and therapeutic interventions (e.g., metformin, rapamycin) are highlighted at relevant nodes. The strength of interactions is represented with a scale in the legend. Table 1 summarizes the key preclinical and clinical studies discussed in this review, outlining model systems, primary findings, and specific limitations relevant to translational success.

Figure 2: Integrated Signaling Network in Osteoarthritis and Aging.

Table 1: Preclinical and Clinical Evidence Summary

4.Discussion

The synthesis of evidence presented in this systematic review firmly establishes osteoarthritis not merely as a disorder of cartilage wear, but as a quintessential disease of aging, where the progressive functional decline of multiple joint tissues is driven by the complex interplay of conserved biological aging pathways [1,6]. The critical insight that emerges is that no single molecular defect is sufficient to explain the pathophysiology of OA; rather, it is the breakdown in communication and the loss of homeostatic balance between these interconnected networks that lead to joint failure. Table 2 summarizes key mechanistic and therapeutic studies supporting these molecular targets and pathways, highlighting both promising findings and current translational limitations. The dysregulation of core targets like SIRT1, METTL3, and Klotho does not occur in isolation. Still, it propagates through signaling axes such as AMPK/mTOR, p38/STAT3, and Hippo/YAP- TAZ, creating a self-amplifying cycle of senescence, inflammation, and matrix degradation [23,113,125].

Table 2: Molecular Targets and Signaling Pathways in OA-Aging Nexus

This network-level understanding elucidates the historical failure of monotherapeutic interventions, such as isolated cytokine inhibition, which are circumvented by the inherent redundancy and compensatory activation within biological systems [25]. For instance, inhibiting IL-1β may temporarily dampen inflammation, but the underlying senescent cell burden and dysregulated mTOR signaling will continue to drive pathology through alternative SASP factors and impaired autophagy [24,118]. Consequently, the therapeutic paradigm must shift from a reductionist, single-target model to a systems-level, multi-target approach. The most promising strategies are those that address key integrative nodes within the network. For example, senolytic agents aim to remove the primary source of the pathological SASP. Still, their efficacy may be significantly enhanced when combined with a “senostatic” intervention, such as a SIRT1 activator, which simultaneously improves the metabolic fitness of surviving cells [78,165]. Similarly, targeting the epigenetic brake on autophagy via METTL3 inhibition could synergize with AMPK activators to fully restore cellular quality control mechanisms [57,152]. However, the clinical translation of these sophisticated strategies faces significant hurdles. The heterogeneity of OA, encompassing distinct metabolic, post-traumatic, and age-related phenotypes, suggests that a “one- size-fits-all” multi-target approach may also fail. This necessitates the parallel development of robust biomarkers capable

of stratifying patients into molecularly defined subgroups. Current reliance on radiographic grading is inadequate; future clinical trials must integrate synovial fluid biomarkers of senescence (e.g., p16INK4a, SASP factors), circulating NAD+ metabolites, and advanced quantitative imaging to identify patients most likely to respond to a given pathway-targeted intervention [77,155]. Furthermore, the chronic nature of OA and the potential for off-target effects, particularly with systemically delivered senolytics or epigenetic modifiers, underscore the urgent need for advanced local drug delivery systems. Nanotechnology-based intra-articular formulations that provide sustained, cell-specific targeting within the joint space could unlock the therapeutic potential of these multi-target regimens by maximizing efficacy and minimizing systemic toxicity.

5. A Futuristic Roadmap for Pathway-Integrated OA Therapeutics

To translate this integrated network understanding into clinical reality, a focused, interdisciplinary research agenda is proposed. The first pillar is to build a Human OA Atlas using multi-omic profiling to identify cellular states and active pathways across different disease stages. This will allow for true molecular phenotyping. At the same time, a large project must focus on Dynamic Biomarker Discovery. This means using machine learning on multi-omic data to create minimally invasive signatures that reveal how pathways function in vivo (e.g., senescent burden or autophagic flux) for patient stratification and therapeutic monitoring. The third pillar is the advancement of Precision Seno therapeutics and Delivery, which goes beyond first-generation senolytics to create targeted, locally delivered agents, such as senolytic nanoparticles or inducible gene therapies, that specifically eliminate harmful senescent cells in the joint. Lastly, AI-Driven Combination Design should be used. This means using computer network models to identify the best multi-drug regimens for each patient that target key network nodes while causing the least harm. This coordinated roadmap seeks to transform osteoarthritis management from reactive palliation to proactive, precision intervention informed by the biological mechanisms of aging. To connect mechanistic discovery with clinical implementation and realize the visionary potential of pathway-integrated osteoarthritis (OA) therapeutics, we need to carefully address significant translational and safety issues arising from the complex biology discussed in this review. The main problem is turning treatments that target complex, interconnected networks, like senolytics to get rid of pathogenic cells [165], epigenomic modifiers like METTL3 inhibitors [58, 64], or metabolic regulators like SIRT1 activators [78, 84], into ones that are safe, effective, and easy to get. The main translational challenges include achieving cell-specific targeting to reduce the systemic toxicity risks associated with senolytics and STAT3 inhibitors [96, 103], ensuring the long-term safety of chronic modulation of critical aging pathways such as mTOR and SASP [119, 166], and creating robust, scalable manufacturing protocols for advanced modalities. To reduce the risks associated with this pipeline, future research should focus on addressing specific, essential problems. At the outset, it is imperative to establish predictive intermediate translational models (e.g., human joint-on-a-chip systems incorporating senescent cells and mechanical stress) that accurately emulate the human osteoarthritis environment for the assessment of safety and efficacy [167]. Second, it must offer companion multi-omics diagnostics that utilize machine learning on longitudinal patient data to discover predictive biomarkers for patient stratification and therapy monitoring [168]. Third, it needs to establish multinational preclinical consortia (e.g., CARE or OARSI programs) to improve data consistency, validate outcome metrics, and reduce risks in late-phase clinical trials through rigorous, collaborative experiments [169].

Figure 3: Strategies for therapeutic targeting. This Figure 3 diagram shows the steps in the development of a therapy, from before it is tested on people to after it is approved for sale. It has information about the most important compounds, target icons, and efficacy data, as well as trial details, biomarkers, and patient outcomes at each stage. There are color-coded therapeutic classes, such as epigenetic modulators, metabolic regulators, inflammatory inhibitors, and senolytics. Timelines, risk-benefit scales, and real-world implementation are all crucial parts of these classes.

6. Conclusion

In conclusion, this systematic review consolidates the compelling evidence that osteoarthritis is a manifestation of systemic aging within the joint, characterized by the synergistic failure of multiple, interconnected cellular maintenance pathways. The pathogenic cascade is fueled by the accumulation of senescent cells, whose inflammatory secretome is intensified by the interaction between MAPK and STAT3 signaling. Their continued presence is maintained by epigenetic (METTL3-m6A) and metabolic (NAD+-SIRT1-AMPK) dysfunctions that inhibit autophagy and compromise mitochondrial health. This is made worse by the fact that protective factors like Klotho decline with age, and by developmental mechanotransduction pathways that shouldn’t be active again, leading to hypertrophic differentiation. This integrated network perspective fundamentally elucidates the shortcomings of historical single-target methodologies and delineates the pathway forward. The future of disease-modifying osteoarthritis therapy resides in biomarker-stratified, multi-target approaches that simultaneously tackle senescence elimination, metabolic-epigenetic reprogramming, and inflammation modulation. To overcome the translational challenges of drug delivery, combination safety, and patient selection, professionals from geroscience, rheumatology, bioengineering, and data science will need to work together. By re-framing OA through the lens of integrated aging biology, we open the door to transformative therapeutic strategies that

target the root causes of joint degeneration, offering the promise not just of symptom relief, but of preserved joint function and improved health span for an aging global population.

7. Declarations

Authors’ contribution Conceptualization and study design: Sayed Abdulla Jami (SAJ). Literature search, screening, and data curation: SAJ and Ahmed Abu Ryash (AAR). Formal analysis and synthesis of evidence: SAJ. Drafting of the manuscript: SAJ. Critical revision for important intellectual content: AAR, Fayez Abidah (FA), and Moath Al Hadidi (MAH). Supervision: SAJ. All authors approved the final manuscript. Ethical approval and Consent N/A. Availability of data and material On reasonable request, the data portion will be available. Competing interests All authors have no competing interests. Funding Information None Acknowledgments I am grateful to the units and individuals who have provided support, guidance, and assistance throughout the research process, as well as favorable conditions for the paperwork. AI statements The authors utilized open AI tools, including ChatGPT, Gemini, and Grammarly, to enhance paraphrasing, correction, and readability. Declaration N/A References

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