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Terminologies and definitions used to classify patients with osteoarthritis: a scoping review

BMC Rheumatology volume 9, Article number: 32 (2025) Cite this article

Abstract

Objectives

Osteoarthritis (OA), a prevalent and disabling condition, significantly burdens individuals and healthcare systems worldwide. It is characterized by joint pain, stiffness, and structural changes in cartilage, bone, and synovium. The clinical manifestations of OA vary widely, reflecting complex interactions among genetic, metabolic, biomechanical, and environmental factors. Despite progress in identifying OA clinical phenotypes, inconsistent terminology, including “phenotypes,” “subtypes,” and “subgroups,” hinders effective communication and research translation. This review aims to synthesize existing literature on clinical OA phenotypes, terminology, and definitions and propose a research agenda.

Method

This scoping review followed PRISMA-ScR guidelines, focusing on publications from 2010 to 2023 investigating clinical phenotypes in adult OA patients. Searches were conducted in MEDLINE, SCOPUS, and EBSCOhost using combinations of terms related to clinical phenotypes in OA. Studies were screened, duplicates removed, and relevant data were charted and analyzed by two independent reviewers.

Results

From 196 identified studies, 50 were included in the final analysis. Eight clinical phenotypes were categorized, including inflammatory, biomechanical, metabolic, and pain-sensitization. minimal joint disease, psychologically driven, menopause, severe radiographic. Most studies focused on knee OA, with limited exploration of hand, midfoot, and hip OA. Phenotype-based management strategies demonstrated potential for improving treatment outcomes and guiding research.

Conclusion

Standardizing terminology and leveraging phenotype-based frameworks hold promise for advancing personalized OA care and research. Future efforts should focus on validating criteria, developing accessible diagnostic tools, and addressing understudied OA phenotypes. This work highlights the value of tailoring interventions to specific OA phenotypes for improved patient outcomes.

Clinical trial number

Not applicable

Peer Review reports

Background

Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people worldwide. It is a growing global burden related to the direct costs of treatment with limited effectiveness and the long-term societal impact. The disease is characterized by biochemical and morphological changes in articular cartilage, including fissuration, progressive cartilage loss, and focal matrix mineralization, bone changes such as osteophytes, subchondral cysts, subchondral sclerosis, and several changes in the synovial membrane, including secondary inflammatory processes [1, 2]. Although OA can affect any joint, the disease most commonly affects joints in the hands, feet, knees, hips, and cervical and lumbar spine. The disease is typically characterized by joint pain and stiffness, while clinical manifestations can vary significantly among individuals, reflecting a complex interaction between genetic, metabolic, biomechanical, and environmental factors [3].

The word phenotype commonly refers to the observable expression of an individual’s genotype. While the individual’s unique genetic composition characterizes genotypes, phenotypes are most readily observed as appearance, signs, and symptoms related to a particular disease [2]. A phenotype results from the interaction between their genotype and their environment. However, the connection between them is not clear-cut, and proteins, cells, and activated biological pathways differ between individuals. Clinical phenotypes in OA are identified and defined based on symptoms, physical examination findings, and imaging features [4]. These clinical phenotypes are essential because they help health professionals and researchers to better categorize patients into more homogeneous groups, which can influence treatment decisions and prognostic assessments. For example, phenotypes such as the inflammatory phenotype, characterized by signs of inflammation including swelling, heat, redness, and pain of the affected joints, and the metabolic phenotype, associated with obesity and metabolic disorders, have been identified [5]. However, these clinical phenotypes are not precisely defined, and there may be overlapping phenotypes and heterogeneity between patients with the same phenotype. Recognizing and understanding the clinical phenotypes of OA is crucial for several reasons. First, it would facilitate a more personalized approach to treatment. Given the heterogeneity of OA, a one-size-fits-all treatment approach is often ineffective while tailoring treatments to the individual’s disease characteristics and mechanisms by identifying specific clinical phenotypes, potentially improving outcomes. Second, understanding clinical phenotypes can facilitate the development of new therapeutic targets. By distinguishing the pathways and mechanisms that drive different OA phenotypes, researchers can identify novel targets for drug development [6].

Third, the identification of clinical phenotypes has important implications for research. It could enable more precise patient selection for clinical trials, reducing variability in study outcomes and increasing the likelihood of detecting the effects of interventions. This precision can accelerate the pace of OA research and the development of new effective therapies [7].

Recognizing clinical phenotypes in OA represents a considerable advancement in understanding and managing OA. It underscores the importance of a personalized medicine approach, considering the unique characteristics of each patient’s disease. As research continues to unravel the complexities of OA and its phenotypes, the prospects for more targeted and effective interventions improve, offering hope for individuals suffering from this debilitating condition.

Finally, various terminology is used in the literature to define a subset of patients, such as phenotype, subtype, or subgroups. This heterogeneity in terminology makes communication between researchers and healthcare professionals difficult. We need to standardize terminology to facilitate communication between stakeholders. For these reasons, a scoping review was conducted to map the research done in this area and to identify any existing gaps in knowledge [8].

The primary objective was to present a synthesis of the literature about clinical phenotypes based on commonly used stratification terminology. Secondary objectives were to describe the existing phenotype of patients with OA and define the research agenda to address the prioritized objectives.

Method

Protocol

The scoping review followed the regulations “Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews " (PRISMA-ScR) and the recommendations of the Cochrane Collaboration [9].

Eligibility criteria

The criteria for being included in the current scoping review were papers published between 2010 and 2023, written in English, involving human participants over 18 years of age diagnosed with primary or secondary OA in any part of the body. They can include any system or method used to classify OA, such as radiographic classifications or clinical classification systems, but not molecular-based methods. The molecular-based patient classification was addressed in another paper drafted by WG3 COST action NetwOArk consortium. No geographic limitation was introduced. Quantitative, qualitative, and mixed-method studies were included to consider different aspects of measuring treatment burden. Publications were excluded if using a term without defining or classifying it, sub-classifications of molecular phenotypes or endotypes, or analyses performed together with other musculoskeletal problems (i.e., rheumatoid arthritis, fibromyalgia, etc.). Including other musculoskeletal conditions could compromise the precision of the classification, as the features of those different conditions may overlap or mask the features specific to OA. This makes it difficult to isolate the characteristics specific to OA.

Information sources

To identify potentially relevant documents, the following bibliographic databases were searched from 2010 to 2023: The databases selected were MEDLINE, SCOPUS, and EBSCOhost, as they provide comprehensive coverage of peer-reviewed publications in the fields of rheumatology, musculoskeletal disorders, and clinical classification research. A specialist in a literature review (GGN) drafted the search strategies, which were further refined through expert consultation team discussions (European Cooperation in Science & Technology (COST - CA21110 - Building an open European Network on OsteoArthritis research (NetwOArk) working group dedicated to clinical phenotype). The final search strategy for MEDLINE, SCOPUS, and EBSCOhost can be found in supplementary file 1. The results were downloaded and imported into the Rayyan QCRI tool to help eliminate duplicates.

Search

The search words included Osteoarthritis OR OA and combination with the classification term clinical + (phenotype, subtype, subgroup), and the following keyword combinations. The keywords used in the search strategy were carefully selected based on terminology commonly employed in osteoarthritis literature, including variations related to clinical phenotypes, classification systems, and disease progression:

  • “clinical phenotypes” AND (osteoarthritis OR OA).

  • “clinical subtypes” AND (osteoarthritis[tiab] OR OA[tiab].

  • “clinical subgroups” AND (osteoarthritis[tiab] OR OA[tiab])

This search did not include restrictions regarding the date or language. A secondary search analyzing the references in the articles obtained was also performed. Unpublished studies were not included. The last search was performed in November 2023.

Selection of sources of evidence

The selection of the studies was done by two researchers, both blinded, and any disagreements were discussed to come to a consensus. After selecting the publications from the databases, the duplicates were eliminated. After the elimination, titles, and abstracts of the selected papers were screened based on the inclusion and exclusion criteria by two reviewers working in pairs. The selected studies were then analyzed to assess compliance with the eligibility criteria. The information on the phases of the selection process was described through a PRISMA flow diagram (Fig. 1).

Data charting process and data items

A table was created that contained the data extracted from the included studies. The leading researcher (RK) extracted all relevant data from the studies, together with the rest of the authors such as: characteristics of the publication (author, title, country, year, study design), OA type and stage, number and sex of participants, diagnosis, terminology used [clinical phenotype, subtype, subgroup, other], and patients. Another researcher (GGN) then controlled to ensure the data was correct and independently assessed all articles, achieving a Cohen’s kappa statistic of 0.85, indicating excellent inter-rater reliability and objective study selection. Any disagreements were resolved through discussion between the two reviewers or further adjudication by a third reviewer (AB). The other authors were involved in the design of the scoping review, data analysis, paper drafting, or reviewing and consensus. To better visualize the trends in terminology usage and the relationships between key concepts, two graphical representations were generated. A cumulative frequency graph was created to illustrate the temporal evolution of terminology mentions across the included studies. Additionally, a word cloud was constructed to highlight the most frequently used terms related to OA classification. These visualizations provide a clearer understanding of terminology trends and associations, complementing the quantitative data extracted from the studies.

Synthesis of results

The studies were grouped by the types of topics they investigated. They summarized the kind of settings, populations (OA and clinical phenotype), and study designs for each group, along with the measures used and main findings. The studies were grouped by their primary topic of investigation, defined by the localization of OA. Thereafter, subgroups of patients were formed based on OA patient characteristics and classified into eight categories according to symptoms, course, comorbidities, risk factors, imaging features, and pathophysiological mechanism: For the knee, there is minimal joint disease, biomechanical, pain sensitivity and psychological factors, inflammation, metabolic influences, and menopause-driven and severe disease. The temporomandibular joint (TMJ), hand, hip, and midfoot OA were treated separately. This grouping facilitated the synthesis of results into distinct phenotype categories, ensuring a clear progression from the included studies to the phenotypes described. When a scoping review was identified, the number of studies included in the review that potentially met our inclusion criteria was counted, and how many studies had been missed by our search was noted (Fig. 1).

The data synthesis in this scoping review was conducted using a narrative synthesis approach. This method allowed us to integrate and summarize findings from diverse study designs and methodologies, focusing on patterns, relationships, and themes across the included literature.

Fig. 1
figure 1

Flow diagram PRISMA 2020. *Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers). **If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.n71 For more information, visit: http://www.prisma-statement.org/

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Risk of bias in individual studies

To assess the risk of bias in individual studies, we employed the Newcastle-Ottawa Scale (NOS), a widely used tool for evaluating the methodological quality of observational studies. The NOS assesses three key domains: selection of study participants (max. 4 points), comparability of groups (max. 2 points), and outcome assessment (max. 3 points), with a total score of 9 indicating the highest methodological rigor. This tool was chosen for its robustness in appraising study design quality, risk of bias, and the reliability of reported outcomes, ensuring a systematic and objective evaluation of the included studies [10].

Results

Overview

The initial database search identified 196 studies, of which 50 were included in the final analysis after applying the inclusion and exclusion criteria. The studies encompassed various geographical locations, with the majority of them conducted in the USA and UK (Fig. 2).

Fig. 2
figure 2

Percentage papers by countries

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Study characteristics

The study designs included in the review were primarily cohort studies (34.7%) and cross-sectional studies (28.6%), with the remainder comprising various other methodologies. The studies analyzed a wide range of clinical phenotypes and subtypes of OA, mainly focusing on knee OA and hand and midfoot OA, among others (Table 1).

The graph illustrates the evolution of research on osteoarthritis (OA) locations from 2011 to 2024. Knee OA consistently dominates the publications, peaking in 2015, 2021, and 2022. In contrast, studies on hip and hand OA remain limited, with a slight increase observed in recent years, while general OA (General OA that affects multiple joints in the body) research shows sporadic activity (Fig. 3).

Fig. 3
figure 3

Cumulative numbers of papers on osteoarthritis by location from the years 2011 to 2024

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Table 1 Frequency and distribution of clinical osteoarthritis locations
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Fig. 4
figure 4

Distribution of frequency and percentage of osteoarthritis by location

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The frequency distribution of OA by location shows knee OA being the most prevalent (78.4%). Other locations, such as hand and hip, account for significantly lower percentages (5.9% each), emphasizing the dominance of knee OA in clinical studies (Fig. 4).

Fig. 5
figure 5

Word could have related terms

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Fig. 6
figure 6

Cumulative evolution of terminology metions

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The word cloud in Fig. 5 provides an overview of the most frequently co-occurring terms in the included studies. The prominence of terms such as phenotype, group, cluster, pain, OA, muscle strength, and knee highlights the key concepts associated with OA classification. The presence of terminology related to anatomical structures, symptomatology, and risk factors suggests that studies have primarily focused on clinical and biomechanical characteristics when defining OA phenotypes. This visualization helps to identify the central themes in the literature and their relative importance.

In addition, the cumulative frequency graph in Fig. 6 illustrates the evolution of terminology usage over time. The data reveal a steady increase in the use of the term phenotype, which has become the predominant classification term in recent years. In contrast, subtype has shown a moderate increase, whereas endotype and other terminology categories have remained relatively infrequent. These trends suggest a shift in the preferred nomenclature for classifying osteoarthritis (OA) subgroups in research, aligning with the broader focus observed in the word cloud.

Together, these visualizations provide a comprehensive perspective on terminology trends in OA research, complementing the quantitative findings extracted from the studies.

Clinical phenotypes described in knee OA (Fig. 7)

Minimal joint disease phenotype [11, 28, 47, 48, 50]

Description: This phenotype is characterized by minimal clinical symptoms, including low levels of pain (Pain intensity < 3 on a VAS scale) and minimal radiographic evidence of knee OA (Kellgren & Lawrence grade I or II at 24 months).

Implications: Patients with this phenotype may not require invasive interventions but should be monitored for disease progression. Lifestyle modifications and physical therapy are sufficient to manage their condition initially [28].

Pain sensitization and psychologically driven phenotype [13, 18, 22,23,24, 26, 28, 32, 36, 38, 40, 43, 46]

Description: This group experiences heightened pain sensitivity and psychological distress, which significantly affect their quality of life. Defined by chronic pain with a Center for Epidemiologic Studies Depression Scale-Revised (CESD-R) score ≥ 16 or the presence of tender points above and below the waist on both sides of the body. And associated with high pain sensitivity, psychological distress, depressive symptoms, radiographic severity, BMI, loss of muscle strength, inflammation, and comorbidities [13, 26].

Implications: Management strategies should include comprehensive pain management approaches, including pharmacological pain treatments, physical therapy, and psychological support to address both the physical and emotional aspects of the disease. A multidisciplinary approach is recommended to improve patient outcomes [26].

Biomechanical OA phenotype [8, 16, 17, 27, 33, 34, 39, 41, 46, 55]

Description: this phenotype emphasizes the role of mechanical stress factors, such as varus or valgus alignment (inward or outward angulation of the leg to the thigh), knee flexion dynamic varus or valgus during gait, overweight, professional or sports knee trauma, meniscus lesion professional mechanical factors, and loss of muscle performance in the development and progression of OA [16, 17].

Implications: Interventions may include weight management, physical therapy, physical activity, including exercise, orthotic supports, and, in some cases, surgical options to correct alignment and reduce mechanical stress on the knee joints [27].

Inflammatory phenotype [8, 20, 26, 28, 29, 31, 34]

Description: significant inflammation is characterized by a MOAKS score of synovitis/effusion = 3 [28], pain at rest, and pain intensity flare.

Clinical Implications: treatment may require anti-inflammatory medications and close monitoring of inflammatory markers. Interventions to reduce inflammation can help manage symptoms and slow disease progression [28].

Metabolic sensitization phenotype [14, 19, 20, 26, 31, 35, 51, 54, 57]

Description: associated with the presence of diabetes and a BMI > 30, or either condition combined with controlled blood pressure (systolic pressure < 140 mmHg or diastolic pressure < 100 mmHg).

Clinical Implications: Management strategies should focus on controlling metabolic conditions such as diabetes and hypertension. Weight management and lifestyle modifications are crucial for these patients [20].

Menopause-driven phenotype OA [12, 31, 59]

Description: It is noted for its prevalence in women around menopause and is associated with estrogen receptors [12].

Clinical Implications: Hormonal treatments may be considered in managing symptoms alongside standard OA treatments such as pain management and physical therapy [12].

Severe radiographic OA phenotype [13, 16, 17, 25, 28, 53, 58]

Description

Characterized by severe radiographic changes in the knee joint, often accompanied by significant structural damage [17, 28].

Clinical implications

Advanced imaging techniques and more aggressive treatments, such as injections or surgery, may be necessary for these patients [28].

Other osteoarthritis phenotypes

Clinical phenotypes in hand OA [21, 44, 52]

Description

Characterized by a highly symptomatic cluster within a specific digital cohort, showcasing variability in symptom presentation in hand OA. Symptoms can range from minimal to severe, affecting daily activities and quality of life [20]. Erosive and non-erosive are the two main forms identified. The most Painful hand OA is associated with the erosive form.

Implications

Treatment may include physical therapy, orthoses, and targeted interventions to manage pain and maintain hand function. The variability in symptoms necessitates personalized treatment plans [21].

Clinical phenotypes in midfoot OA [15]

Description

This phenotype is more common among females, individuals over 75, and those with specific comorbidities. It indicates a distinct clinical presentation, possibly affecting mobility and balance [15, 60].

Implications

Management strategies should focus on footwear modifications, pain management, and interventions that reduce, preserve, or improve mobility and reduce the risk of falls [27].

Clinical phenotypes in hip OA [7, 30, 49, 50]

  • Description: Hip OA phenotypes often involve muscle imbalance and structural damage. Cluster analyses highlight phenotypes associated with hip muscle strength deficits, such as reduced hip flexion and internal rotation strength.

  • Implications: Patients with muscle weakness phenotypes exhibit a higher risk of disease progression. Rehabilitation strategies should emphasize strength restoration and joint stabilization [7, 49].

Clinical phenotypes in temporomandibular joint (TMJ) OA [56]

  • Description: TMJ OA presents distinct structural changes, including loss of bone volume and condylar thinning. Phenotypic groups exhibit varying degrees of morphological damage and functional limitations.

  • Implications: Early diagnosis through advanced imaging (CBCT) can facilitate targeted therapies to mitigate joint deterioration and functional impairment [56].

Fig. 7
figure 7

Graphical summary of clinical phenotypes in osteoarthritis and their therapeutic implications

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Table 2 outlines a prioritized research agenda highlighting the necessary steps to advance the definition, validation, and implementation of clinical osteoarthritis (OA) phenotypes. This framework is essential for guiding future research, improving patient stratification, and developing personalized treatment strategies based on clinical phenotype recognition.

Table 2 Research agenda
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Risk of bias

Following the application of the Newcastle-Ottawa Scale (NOS) to the reviewed articles, the majority of the studies demonstrated moderate to high methodological quality. Most studies received scores ranging from 7 to 9 out of a maximum of 9 points, indicating strong adherence to participant selection criteria, comparability, and outcome assessment.

  • Selection (max. 4 points): Most studies scored 3 or 4, reflecting well-defined inclusion criteria and appropriate study designs.

  • Comparability (max. 2 points): The majority achieved 2 points, indicating adequate control of confounding factors through statistical adjustments or study design.

  • Outcome (max. 3 points): The distribution was balanced between 2 and 3 points, demonstrating that most studies employed valid methods for outcome assessment and ensured appropriate follow-up.

Overall, studies that obtained 9 points can be considered high-quality, making them highly reliable for drawing robust conclusions regarding clinical phenotypes and osteoarthritis progression. Studies scoring 7 points present some methodological limitations but remain valuable in the analysis of the topic.(supplementary file).

This assessment suggests that the evidence base used is strong and appropriate to support the review on terminologies and definitions in osteoarthritis classification. However, it would be advisable to incorporate more rigorous prospective studies with extended longitudinal follow-up to further enhance the validity of conclusions.

Discussion

This scoping review represents a significant step forward in categorizing OA patient subgroups by summarizing the terminology used in the literature. The predominant term identified, “phenotype,” aligns with the definition as an observable characteristic resulting from gene expression [1, 2]. Our analysis of the current research area has allowed for the identification of seven proposed phenotypes in knee OA. These phenotypes include minimal joint disease, pain sensitization and psychologically driven, biomechanical, inflammatory, metabolic sensitization, menopause, severe radiographic [16, 21].

Delineating phenotypes has profound clinical implications, underscoring the necessity for phenotype-based management approaches. For instance, while both are influenced by weight, metabolic sensitization and biomechanical phenotypes require distinct therapeutic strategies. Weight loss is universally beneficial for overweight individuals, but additional interventions, such as physical therapy or surgical correction of malalignment, may be crucial for biomechanical phenotypes [17, 27]. In contrast, the metabolic sensitization phenotype requires weight loss, decreased fat tissue, increased lean mass, and control of diabetes and hypertension. Such differentiation in treatment highlights the limitations of a one-size-fits-all approach and emphasizes the value of tailoring interventions to the unique characteristics of each phenotype.

Another critical observation is the utility of these phenotypes in enhancing patient stratification for research and clinical trials. Homogeneous grouping of patients based on phenotype can reduce variability, leading to more robust outcomes and accelerating the development of targeted therapies [28]. Additionally, the potential role of artificial intelligence (AI) in clustering phenotypes by analyzing clinical, diagnostic, and imaging markers is promising. Incorporating AI-driven models could improve the precision of phenotype identification and facilitate their integration into routine clinical practice [31].

The identification of phenotypes also has implications for early diagnosis and preventive strategies. For example, the inflammatory phenotype can benefit from targeted anti-inflammatory treatments, potentially delaying disease progression and preserving joint function [8, 20]. Similarly, the metabolic phenotype emphasizes the importance of managing comorbid conditions, such as diabetes and hypertension, to mitigate their impact on OA symptoms and progression [21]. Early identification of the minimal joint disease phenotype, when symptoms are not a limiting factor for physical activity and rehabilitation, is a key challenge of OA prevention. These examples highlight the relevance of an early interdisciplinary approach to managing OA involving endocrinology, psychology, and rehabilitation specialists.

Rare phenotypes of osteoarthritis, such as those associated with Mendelian disorders (e.g., camptodactyly-arthropathy-coxa vara-pericarditis syndrome) and chondrodysplasias, highlight the influence of genetic factors in the development and progression of the disease. These conditions demonstrate that specific genetic mutations can disrupt cartilage and joint homeostasis, leading to early-onset osteoarthritis. Consequently, the inclusion of a ‘genetically driven OA’ phenotype is essential to capture the spectrum of osteoarthritis presentations influenced by hereditary factors. Recognizing these phenotypes not only enhances the classification framework but also underscores the need for targeted therapeutic strategies tailored to these unique genetic drivers.

Despite these advancements, challenges remain in refining the criteria used to define phenotypes. Future research should prioritize the identification of specific and sensitive clinical markers that differentiate phenotypes effectively. Furthermore, accessible and cost-effective diagnostic tools are critical for implementing phenotype-based management in clinical settings. These tools should focus on easily identifiable markers, such as clinical examination findings, biochemical markers and/or patient-reported outcomes, that can be directly linked to specific therapeutic protocols [26]. Cohort studies and longitudinal analyses will be indispensable in validating these markers.

The variability observed in hand and midfoot OA phenotypes suggests additional research is necessary to understand their unique characteristics and treatment requirements [15, 26, 60]. Unlike knee OA, these phenotypes are less frequently studied, representing an area of potential exploration for improving patient outcomes.

Meniscal injuries alter knee biomechanics by disrupting load distribution, increasing joint instability, and accelerating cartilage degeneration. This leads to abnormal tibiofemoral contact forces and contributes to osteoarthritis (OA) progression. Understanding these mechanical changes is essential for accurately defining OA phenotypes and their clinical implications.

This scoping review adopted a rigorous methodology guided by the PRISMA-ScR checklist, ensuring transparency and reproducibility in synthesizing the existing literature. It is the first scoping review to systematically examine clinical phenotypes in OA, thereby addressing a critical gap in the field. Including studies spanning 2010 to 2023 and focusing on diverse phenotypes across various OA subtypes provides a comprehensive overview. Nevertheless, some limitations must be acknowledged. First, excluding unpublished and ongoing research may have led to omitting relevant findings, particularly emerging data in this rapidly evolving field. Second, while our search strategy was robust, the reliance on English-language publications may have introduced a language bias. Lastly, the heterogeneity in defining and categorizing OA phenotypes among included studies highlights the need for standardization, which could influence the reproducibility of our findings. Despite these limitations, the review provides a valuable foundation for advancing phenotype-based approaches in OA research and clinical practice.

Conclusion

In conclusion, this scoping review overviews clinical classifications identified in the literature based on the most commonly used terminology. It describes the seven clinical knee phenotypes and a list of the associated signs and symptoms. We also suggest a list of future research questions that should allow us to better characterize the phenotypes in OA by developing robust clinical and biochemical diagnostic tools using cohort studies and longitudinal analyses.

Data availability

No datasets were generated or analysed during the current study.

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Funding

This article/publication is based upon work from COST Action CA21110 NetwOArk, supported by COST (European Cooperation in Science and Technology). https://www.cost.eu/

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Authors and Affiliations

  1. Department Nursing and Podiatry, University of Malaga, Malaga, Spain

    Gabriel Gijon-Nogueron

  2. Musculoskeletal Radiology Group, Semmelweis University, Budapest, Hungary

    Peter Balint

  3. Faculty of Medicine, Clinic of Rheumatology, Medical University of Plovdiv, University Hospital “Kaspela”, Plovdiv, Bulgaria

    Anastas Batalov & Rositsa Karalilova

  4. Institute of Rheumatology, School of Medicine, University of Belgrade, Belgrade, Serbia

    Predrag Ostojic

  5. Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany

    Nico Sollmann

  6. Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Nico Sollmann

  7. TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Nico Sollmann

  8. Department of General Practice, Erasmus MC Medical University Center Rotterdam, Rotterdam, The Netherlands

    Marienke van Middelkoop

  9. Erasmus Medical Center, Rotterdam, The Netherlands

    Rintje Agricola

  10. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Josefine E. Naili

  11. Motion Analysis Lab, Karolinska University Hospital, Stockholm, Sweden

    Josefine E. Naili

  12. Faculty of Medicine, Clinic for Orthopaedic Surgery and Traumatology, University of Belgrade, University Clinical Centre of Serbia, Belgrade, 11000, Serbia

    Darko Milovanovic

  13. Medical University of Plovdiv, Plovdiv, Bulgaria

    Stanislava Popova

  14. Department of Medical Biology, Research Institute, Medical University-Plovdiv, Plovdiv, Bulgaria

    Maria Kazakova

  15. Department of Trauma Surgery, General Hospital, Medical University of Vienna, Vienna, Austria

    Sylvia Nuernberger

  16. Austrian Cluster for Tissue Regeneration, Vienna, Austria

    Sylvia Nuernberger

  17. Department of Medicine Solna, Division of Rheumatology, Centre for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

    Cecilia Aulin

  18. Musculoskeletal Innovative Research Lab (mskil), University of Liège, Liège, Belgium

    Yves Henrotin

Authors
  1. Gabriel Gijon-Nogueron
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  2. Peter Balint
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Contributions

Conceptualization: All authors; Methodology: Gabriel Gijon-Nogueron (GGN), Yves Henrotin, and Rositsa Karalilova (RK); Formal analysis: Gabriel Gijon-Nogueron (GGN) and Yves Henrotin Data curation: Peter Balint(PB), Predrag Ostojic (PO), Marienke van Middelkoop (MvM), Rintje Agricola (RA), Josefine E. Naili (JN); Rositsa Karalilova (RK), Gabriel Gijon-Nogueron (GGN), Darko Milovanovic(DM), Stanislava Popova(SP) and Maria Kazakova (MK); Writing—original draft preparation: Gabriel Gijon-Nogueron (GGN), Yves Henrotin, Rositsa Karalilova (RK), Sylvia Nuernberger(SN), Cecilia Aulin(CA), Josefine E Naili(JN), Peter Balint(PB); Writing—review and editing: all authors.All authors have read and agreed to the published version of the manuscript.

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Correspondence to Gabriel Gijon-Nogueron.

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Gijon-Nogueron, G., Balint, P., Batalov, A. et al. Terminologies and definitions used to classify patients with osteoarthritis: a scoping review. BMC Rheumatol 9, 32 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41927-025-00482-2

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BMC Rheumatology

ISSN: 2520-1026

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