Arthritis Disease Activity Score
Introduction
Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation of the joints, leading to pain, swelling, stiffness, and progressive joint damage. This condition can significantly impair physical function and overall quality of life. The genetic predisposition to RA is well-established, with studies demonstrating a clear familial component. [1] Understanding and accurately quantifying the activity of arthritis is crucial for effective management and for advancing research into its causes and treatments.
Biological Basis
The development and progression of rheumatoid arthritis are driven by complex biological mechanisms involving immune system dysregulation and inflammatory pathways. Genetic studies have identified several loci associated with RA susceptibility. For instance, strong associations have been found with variants in the PTPN22 and HLA-DRB1 genes, particularly in individuals who are seropositive for autoantibodies against cyclic citrullinated peptide (anti-CCP). [2] Other genes like STAT4, CTLA4, and PADI4 have also shown associations, albeit with more modest statistical evidence. [1]
A significant risk locus for RA has been identified at TRAF1-C5, where the single nucleotide polymorphism (SNP) rs3761847 shows a higher frequency of its minor G allele in individuals with RA compared to controls, with an allelic odds ratio of 1.36. [1] Another region of interest is 6q23, which harbors two independent alleles associated with an increased risk of RA. Key SNPs in this region include rs10499194 and rs6920220, which have demonstrated strong associations. [1] The gene TNFAIP3, located near 6q23, encodes the A20 protein, which is induced by tumor necrosis factor alpha (TNF-alpha). A20 plays a critical role in regulating NF-kappaB signaling and cell death responses, pathways central to inflammation and immunity. [1] These genetic insights contribute to a deeper understanding of the molecular underpinnings of RA pathogenesis.
Clinical Relevance
The accurate assessment of arthritis disease activity is paramount in clinical practice. It guides treatment decisions, allows for monitoring of therapeutic efficacy, and helps in evaluating disease progression. Tools such as the Disease Activity Score (DAS28) are widely used to quantify disease activity by integrating clinical markers like tender and swollen joint counts, patient global assessment, and inflammatory markers. [3] Genetic markers identified through genomewide association studies can help identify individuals at higher risk for developing rheumatoid arthritis, especially those with the more severe anti-CCP-positive form. [1] Such genetic information, alongside clinical assessments, has the potential to facilitate earlier diagnosis, personalize treatment strategies, and predict response to specific therapies, ultimately leading to improved patient outcomes.
Social Importance
Rheumatoid arthritis carries a substantial social burden, impacting individuals, families, and healthcare systems. The chronic pain and functional limitations associated with the condition can lead to reduced productivity, disability, and decreased quality of life. From a societal perspective, a comprehensive understanding of RA, including its genetic risk factors and reliable methods for assessing disease activity, is essential. Accurate disease activity assessment enables clinicians to optimize treatment, potentially preventing irreversible joint damage and preserving patients' ability to participate in daily activities and work. [3] Research into genetic associations not only sheds light on disease mechanisms but also opens avenues for the development of novel diagnostic tools and targeted therapies, which can alleviate the burden of RA on individuals and contribute to a healthier, more productive society.
Limitations
Understanding the genetic underpinnings of arthritis disease activity is subject to several limitations inherent in large-scale genetic association studies. These constraints arise from study design, statistical considerations, population demographics, and the complex nature of the disease itself, all of which can impact the interpretation and generalizability of findings.
Methodological and Statistical Power Limitations
Many genetic studies exploring the basis of rheumatoid arthritis (RA) risk, which indirectly informs arthritis disease activity, face methodological constraints related to statistical power and the potential for spurious associations. Initial genome-wide association studies (GWAS) sometimes utilized control cohorts that were not epidemiologically matched, requiring careful statistical adjustments to address biases from population differences or technical artifacts. [1] While techniques like genomic control and principal component analysis are employed to manage population stratification, observed over-dispersion in test statistics suggests that some residual confounding factors may persist beyond basic population structure, posing challenges to robust quality control. [1]
Furthermore, identifying common genetic variants that confer only modest effects on disease risk or activity—such as those with odds ratios below 1.5—demands considerable statistical power, which is difficult to achieve consistently across all study stages. [1] For example, some replication cohorts have shown limited power (e.g., approximately 70% at P = 0.05), resulting in non-significant trends for otherwise important genetic markers. [1] The observation that even large consortia may not initially highlight regions found in other studies, or that findings require extensive replication, underscores the ongoing need for international collaboration and even larger cohorts to reliably validate all relevant genetic signals and prevent both false positives and missed associations. [1]
Population Specificity and Phenotypic Heterogeneity
The generalizability of genetic findings for rheumatoid arthritis, and by extension, for an arthritis disease activity score, is often restricted by the specific ancestry of the studied populations and inconsistencies in disease phenotype definition. Many significant genetic associations for RA have been discovered predominantly in individuals of European ancestry, where the implicated alleles are common. [1] While essential for initial discovery, this focus limits the direct applicability of these findings to other ethnically diverse global populations, who may possess distinct genetic backgrounds and disease prevalence patterns. Although methods using ancestry informative markers help control for substructure within European populations, a broader representation of global ancestries is crucial for a comprehensive understanding of genetic influences. [1]
Variations in how rheumatoid arthritis is defined or measured also impact the comparability and interpretation of genetic results. Studies may focus on specific subsets of RA, such as anti-CCP-positive cases, using standardized diagnostic criteria. [1] However, when replication efforts incorporate cohorts where such specific information (e.g., anti-CCP status) is not uniformly available, it can complicate the precise validation of genetic associations, as the genetic architecture may vary between different RA subtypes. [1] Furthermore, the inherent complexity in quantifying disease activity, as indicated by the existence of various indices like DAS28 for severity, means that the precise phenotypic characterization of "arthritis disease activity" itself can introduce heterogeneity across studies, affecting how genetic influences on such scores are reliably interpreted. [3]
Unaccounted Genetic and Environmental Influences
Despite significant progress in identifying genetic risk factors for rheumatoid arthritis, a substantial portion of the disease's heritability remains unexplained, highlighting the role of complex interactions and unmeasured influences. The genetic variants identified thus far, while statistically significant, likely represent only a fraction of the total genetic contribution to disease risk and, consequently, to variations in disease activity. [1] This phenomenon, often referred to as "missing heritability," suggests that numerous other common variants with even smaller effects, rare variants, or structural variations may yet be undiscovered, necessitating even larger and more comprehensive genetic investigations.
Beyond genetics, environmental factors play a critical, though often unquantified, role in both disease susceptibility and progression. For example, studies have demonstrated a clear influence of cigarette smoking on rheumatoid arthritis risk, which can act as a significant confounder or modifier of genetic effects. [4] The intricate interplay between genetic predispositions and environmental exposures (gene-environment interactions) is not fully elucidated in current genetic models, implying that observed genetic associations for arthritis disease activity may be influenced by, or dependent on, these external factors. Fully understanding the clinical significance of emerging genetic information, and its application to disease activity scores, therefore necessitates continued research into these complex gene-environment dynamics within large, collaborative cohorts. [1]
Variants
The genetic landscape of rheumatoid arthritis (RA) is complex, involving numerous single nucleotide polymorphisms (SNPs) that can influence disease susceptibility and activity by affecting gene function and immune pathways. Among these, *rs11762062* is a variant associated with the _FAM180A_ gene, also known as _MTPN_ (Myotrophin). _FAM180A_ encodes a protein implicated in cellular growth and differentiation, particularly in muscle tissue, and is involved in protein degradation via the ubiquitin-proteasome system. A variant like *rs11762062* could potentially alter the expression or function of the _FAM180A_ protein, thereby influencing cellular processes that might indirectly contribute to inflammatory responses or tissue remodeling seen in arthritis. The impact of such genetic variations on disease activity scores in RA is an important area of study, similar to how other loci, such as the _TRAF1_-_C5_ region, have been strongly linked to RA risk, with the *rs3761847* SNP showing a significant association with an odds ratio of 1.32 per risk allele. [5] Understanding these genetic contributions helps to elucidate the intricate mechanisms underlying RA and its progression, including how genetic factors may modulate systemic inflammation and affect biomarkers like C-reactive protein. [6]
Another variant, *rs11599217*, is associated with the _C10orf90_ gene, which is located in proximity to _DOCK1_ (Dedicator of Cytokinesis 1). _DOCK1_ is a crucial regulator of immune cell function, acting as a guanine nucleotide exchange factor for Rac GTPases, which are key components in processes like cell migration, phagocytosis, and the organization of the actin cytoskeleton. These functions are vital for the proper activity of immune cells such as macrophages and lymphocytes, which play central roles in the pathogenesis of inflammatory diseases like rheumatoid arthritis. Alterations caused by *rs11599217* could therefore modify the efficiency of immune cell responses, potentially leading to dysregulated inflammation or impaired tissue repair, affecting arthritis disease activity. The genetic predisposition to RA is well-documented, with established risk loci like _PTPN22_ and _HLA-DRB1_ demonstrating significant effects on disease susceptibility, highlighting the importance of immune-related genes. [2] Furthermore, studies have identified other immune-related genes such as _CD40_ and _CTLA-4_ as potential contributors to RA, underscoring the broad genetic involvement in immune system regulation and autoimmune pathology. [5]
Key Variants
| RS ID | Gene | Related Traits |
|---|---|---|
| rs11762062 | FAM180A - MTPN | arthritis disease activity score measurement |
| rs11599217 | C10orf90 - DOCK1 | response to TNF antagonist, joint damage measurement arthritis disease activity score measurement |
Classification and Definitional Frameworks of Rheumatoid Arthritis
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease primarily affecting the joints. Its precise definition and classification are crucial for diagnosis, research, and patient management. The widely recognized framework for categorizing RA cases has been the 1987 American College of Rheumatology (ACR) criteria, which serve as foundational diagnostic guidelines. [7] These criteria are essential for standardizing patient populations in clinical trials and epidemiological studies, although they have been noted to be modified for specific genetic studies to refine cohort selection. [8] Such modifications highlight the evolving understanding of the disease and the need for adaptable classification systems in research.
Further classification within RA acknowledges specific subtypes, most notably the distinction of anti-cyclic citrullinated peptide (anti-CCP) positive rheumatoid arthritis. [1] This subgroup is characterized by the presence of autoantibodies against cyclic citrullinated peptide, and genetic variants have been shown to elevate the risk primarily for this more severe form of RA. [1] The recognition of such serological markers contributes to a more nuanced nosological system, moving beyond purely clinical criteria to incorporate biological indicators. This categorical approach allows for the study of distinct disease mechanisms and responses to therapy, influencing both research designs and personalized treatment strategies.
Terminology and Operational Definitions of Disease Activity
In the context of rheumatoid arthritis, various terms are employed to describe the status and progression of the disease, with "disease activity" being a central concept. An "arthritis disease activity score" serves as an operational definition, providing a quantitative measure to assess the inflammatory burden and impact of the disease. Key terminology includes specific indices developed to capture this activity, such as the DAS28 (Disease Activity Score in 28 joints). [3] This score functions as a rheumatoid arthritis medical records-based index of severity, providing a standardized method for clinicians and researchers to gauge the current state of a patient's disease.
The development and use of such scores reflect a shift towards more standardized, measurable assessments of disease impact, moving beyond subjective clinical impressions. These indices aim to provide a comprehensive picture of disease activity, encompassing multiple facets that contribute to a patient's overall condition. The consistent application of these operational definitions allows for comparable evaluations across different clinical settings and research cohorts, which is vital for monitoring disease progression, evaluating treatment efficacy, and informing therapeutic decisions.
Measurement Approaches and Clinical Significance
The measurement of arthritis disease activity is critical for effective clinical management and robust research. Approaches like the DAS28 represent a systematic method for quantifying disease severity and activity, thereby offering a more objective basis for clinical criteria and research criteria. [3] The clinical significance of these scores lies in their ability to track changes over time, allowing healthcare providers to adjust treatments in response to the patient's disease trajectory. For instance, a decrease in an activity score post-intervention indicates therapeutic success, while a sustained high score may signal the need for more aggressive treatment.
In research, the validity of these measurement approaches is frequently assessed to ensure their reliability and accuracy. Studies comparing the performance of different methods, such as evaluating the validity of a medical records-based index against the DAS28, underscore the importance of robust measurement criteria. [3] This ongoing validation process refines the tools used to characterize disease, ensuring that clinical and research findings are based on precise and reproducible data. The utility of such scores extends to identifying patient subgroups with varying disease severities or responses, which is crucial for genetic studies seeking to link specific genetic variants to particular disease phenotypes.
Clinical Evaluation and Classification Criteria
The diagnosis of arthritis disease activity, particularly rheumatoid arthritis (RA), relies significantly on comprehensive clinical evaluation and established classification criteria. Patients are typically assessed based on the 1987 American College of Rheumatology (ACR) revised criteria for RA, which have been adapted for genetic studies. [1] This involves a detailed physical examination to identify characteristic findings, such as joint swelling, tenderness, and morning stiffness. Trained nurses may conduct extensive phenotyping to ensure accurate and consistent data collection for research purposes. [2] While the Disease Activity Score 28 (DAS28) is a recognized index for assessing disease severity and activity, its primary utility in the provided context is for comparison with other severity indices rather than initial diagnosis. [3]
Serological and Inflammatory Biomarkers
Laboratory testing for serological and inflammatory biomarkers plays a crucial role in diagnosing and characterizing arthritis disease activity. The presence of autoantibodies, particularly anti-cyclic citrullinated peptide (anti-CCP) antibodies, is a hallmark of a specific and often more severe subgroup of rheumatoid arthritis. [1] Beyond autoantibodies, a range of inflammatory markers can be assessed using commercially available ELISA kits, including intercellular adhesion molecule-1, interleukin-6, monocyte chemoattractant-1 (MCP1), P-selectin, tumor necrosis factor receptor 2, and high-sensitivity tumor necrosis factor-alpha. [6] C-reactive protein (CRP), a general marker of inflammation, is measured using nephelometry or less sensitive assays, providing insight into systemic inflammatory burden. [6] Other biochemical assays, such as liver function tests including gamma-glutamyl aminotransferase, and markers like CD40 ligand, myeloperoxidase, osteoprotegerin, natriuretic peptides, and vitamin D plasma levels, contribute to a broader understanding of systemic involvement and disease activity. [6]
Genetic Risk Assessment
Genetic testing and molecular markers are increasingly integral to understanding susceptibility and informing the diagnosis of rheumatoid arthritis, particularly in identifying individuals at higher risk. Genome-wide association studies (GWAS) have identified several common genetic variants associated with RA risk, including strong associations with PTPN22 and HLA-DRB1. [1] These genetic variants primarily elevate risk for the anti-CCP-positive subgroup of RA, highlighting their utility in refining diagnosis and prognosis. [1] Other promising candidate genes, such as STAT4 on chromosome 2q, CTLA4, and PADI4, also show associations, albeit with more modest statistical evidence. [1] Furthermore, specific single-nucleotide polymorphisms (SNPs) like rs3761847 in the TRAF1-C5 region and rs10499194 and rs6920220 at 6q23 have been identified as risk loci, with an odds ratio of 1.32 for rs3761847 and significant associations for the 6q23 alleles in case-control studies. [1] These genetic insights contribute to a more personalized approach to diagnosis and risk stratification.
Genetic Architecture of Rheumatoid Arthritis Susceptibility
Rheumatoid arthritis (RA) is a complex autoimmune disease with a significant genetic component, as indicated by studies of twins, families, and genome-wide linkage scans. [5] Several genetic loci have been strongly associated with an elevated risk of developing RA. Among these, variants in the PTPN22 and HLA-DRB1 genes are particularly notable, elevating risk primarily for a subgroup of severe rheumatoid arthritis characterized by the presence of autoantibodies against cyclic citrullinated peptide (anti-CCP-positive). [5]
Further genetic studies have identified additional risk loci, including a significant association at STAT4 on chromosome 2q and more modest evidence for genes such as CTLA4 and PADI4. [5] A prominent risk locus has also been discovered at TRAF1-C5, where a specific single-nucleotide polymorphism, rs3761847, shows a highly significant association with RA risk, with homozygotes for the susceptibility allele having a substantially increased odds ratio compared to those with the protective allele. [5] Additionally, independent alleles at 6q23, including the A20 gene, have been associated with RA risk. [1] These common genetic variants, often with modest effect sizes, collectively contribute to the polygenic nature of rheumatoid arthritis.
Inflammatory Signaling and Cellular Regulation
The intricate balance of inflammatory signaling pathways is critical in the pathogenesis of rheumatoid arthritis. Tumor necrosis factor alpha (TNF-alpha) is a pivotal cytokine in these pathways, initiating a cascade of cellular responses. [1] The protein A20, a zinc finger protein, is induced by TNF-alpha and serves as a crucial negative regulator of the TNF-induced NF-kappaB signaling pathway and cell death. [1]
The regulatory function of A20 is essential for maintaining cellular homeostasis; a failure to regulate TNF-induced NF-kappaB and cell death responses occurs in A20-deficient mice, highlighting its role in preventing uncontrolled inflammation and tissue damage. [9] The importance of TNF-alpha in RA pathophysiology is further underscored by the success of therapeutic interventions, such as chimeric monoclonal antibodies to TNF-alpha (cA2), in treating the disease. [10] This demonstrates how specific biomolecules and their regulatory networks are central to the disease process and therapeutic strategies.
Autoimmune Mechanisms and Disease Progression
Rheumatoid arthritis is fundamentally an autoimmune disease where the immune system mistakenly targets the body's own tissues, leading to chronic inflammation. A key characteristic and diagnostic marker for a subgroup of severe RA is the presence of autoantibodies against cyclic citrullinated peptide (anti-CCP). [5] These autoantibodies are indicative of a specific autoimmune process that contributes to the pathology of the disease.
The development of anti-CCP positivity is often linked to specific genetic predispositions, such as variants in PTPN22 and HLA-DRB1, suggesting that certain genetic backgrounds predispose individuals to this particular autoimmune phenotype. [5] This interplay between genetic susceptibility and the development of specific autoantibodies drives the pathophysiological processes that define rheumatoid arthritis, influencing disease severity and progression. The American College of Rheumatology has established criteria for classifying rheumatoid arthritis, which encompass these clinical and immunological features. [7]
Immune Cell Interactions and Tissue Damage
At the tissue and organ level, rheumatoid arthritis manifests as persistent inflammation, primarily affecting the joints, leading to pain, swelling, and eventually structural damage. This inflammation is orchestrated by a complex interplay of immune cells and their molecular mediators. Critical biomolecules like CD40, a receptor involved in immune cell activation and communication, and members of the 17q chemokine gene cluster, such as CCL1, CCL8, and CCL13, play roles in recruiting and activating immune cells at sites of inflammation. [5]
The dysregulation of these molecular and cellular interactions contributes to the sustained inflammatory environment characteristic of RA, ultimately leading to homeostatic disruptions within the joint tissues. Over time, this chronic inflammation can result in the erosion of cartilage and bone, reflecting the systemic consequences of an unchecked autoimmune response. Understanding these tissue-level interactions and the biomolecules that govern them is crucial for comprehending the progression of arthritis disease activity.
Assessment of Disease Severity and Monitoring
The 'arthritis disease activity score', specifically exemplified by the DAS28, serves as a crucial tool for evaluating the current state and severity of rheumatoid arthritis. Research has affirmed its validity by comparing it against medical records-based indices of severity, demonstrating its utility in quantitatively assessing disease burden. [3] This validation process is essential for establishing reliable clinical metrics that can inform standardized monitoring strategies and guide therapeutic adjustments in patient care. The consistent application of such scores allows for objective tracking of disease progression or improvement over time, contributing to evidence-based management of arthritis.
Prognostic Implications and Patient Stratification
The accurate assessment of disease severity through an 'arthritis disease activity score' inherently contributes to understanding prognostic implications for individuals with rheumatoid arthritis. By providing a validated measure of disease burden, these scores can assist clinicians in stratifying patients based on their current disease state. [3] This stratification is vital for identifying individuals who might be at higher risk for adverse outcomes, such as joint damage or functional decline, enabling more personalized medicine approaches and potentially more aggressive or targeted prevention strategies for those in high-risk categories.
Large-Scale Cohort Studies and Longitudinal Insights
Population studies investigating arthritis, particularly rheumatoid arthritis (RA), have leveraged extensive cohort designs to identify genetic and epidemiological factors. The North American Rheumatoid Arthritis Consortium (NARAC) comprises a significant collection, including multiplex families with erosive disease, sporadic cases with long-standing disease, and new-onset cases, providing a broad spectrum of disease presentations for analysis. Similarly, the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) is a population-based study focused on new-onset RA cases, offering insights into early disease characteristics and incidence patterns within a well-defined population. [1] These cohorts, alongside the Brigham Rheumatoid Arthritis Sequential Study (BRASS) and the diverse recruitment efforts of the Wellcome Trust Case Control Consortium (WTCCC) in the UK, have been instrumental in large-scale genetic association studies, such as the discovery of risk loci like TRAF1-C5 and 6q23. [1] The use of population-based control groups, such as the New York Cancer Project (NYCP) and participants from the Framingham Heart Study (FHS), further enhances the generalizability of findings by providing representative comparisons for genetic and epidemiological analyses. [1]
These large-scale studies often incorporate longitudinal elements by collecting data on new-onset cases or individuals with long-standing disease, implicitly allowing for the investigation of temporal patterns in disease development and progression. For instance, the EIRA study, with its high participation rate of 96% for cases drawn from a population-based early surveillance system, provides a robust foundation for understanding the incidence and early course of RA. [1] While specific 'arthritis disease activity score' measurements are not explicitly detailed in the provided context, the comprehensive phenotyping of cases, including classification by the American College of Rheumatology criteria and anti-CCP positivity, allows for the study of disease characteristics relevant to severity and potential activity. [1] Such extensive cohorts are vital for identifying common genetic variants with modest effect sizes that contribute to complex diseases like RA. [1]
Cross-Population and Ancestry Comparisons
Population studies in arthritis have also highlighted the importance of cross-population comparisons and the consideration of ancestry differences. The NARAC study primarily recruited individuals of self-reported white ancestry across North America, while the EIRA study focused on a Swedish population, and the WTCCC included Caucasian subjects from the UK. [1] To mitigate biases introduced by population stratification, researchers employed rigorous methodologies, such as matching case subjects with control subjects based on self-reported ethnic background, age, sex, and geographic location. [1]
Advanced statistical techniques like EIGENSTRAT and the use of European ancestry informative markers (AIMs) were applied to correct for underlying population substructure, ensuring that observed associations were genuinely related to disease risk rather than ancestral differences. [2] This careful approach to handling population diversity is crucial for understanding whether genetic risk factors, such as those at rs10499194 or within the TRAF1-C5 locus, exhibit consistent effects across different populations or if population-specific effects contribute to variations in arthritis prevalence and characteristics. [1] The focus on predominantly European ancestry populations in these studies also underscores the need for broader investigations into diverse ethnic groups to fully characterize the global genetic architecture of arthritis.
Methodological Approaches and Epidemiological Considerations
The rigorous methodologies employed in population studies of arthritis are critical for establishing reliable epidemiological associations and identifying genetic risk factors. Genome-wide association studies (GWAS) typically involve a discovery phase followed by replication in independent cohorts to validate findings, as seen with the NARAC and EIRA studies. [1] Sample sizes are substantial, with thousands of cases and controls, reflecting the need for high statistical power to detect common alleles with modest effect sizes. [1]
Methodological challenges include ensuring representativeness, especially when combining clinic-based case collections with population-based controls, and mitigating potential biases from shared controls or technical artifacts during genotyping. [1] Strict quality-control filtering, including thresholds for missing genotypes and Hardy-Weinberg equilibrium (HWE) P values, is applied to maintain data integrity. [1] While these studies primarily focus on identifying genetic susceptibility, the recruitment of new-onset cases from population-based registries, such as the Norfolk Arthritis Register, provides valuable data for understanding incidence rates and demographic risk factors, contributing to a comprehensive epidemiological understanding of arthritis. [2]
References
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[2] Wellcome Trust Case Control Consortium. "Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls." Nature, 2007, PMID: 17554300.
[3] Sato, M. et al. "The validity of a rheumatoid arthritis medical records-based index of severity compared with the DAS28." Arthritis Research & Therapy, vol. 8 (2006): R57.
[4] Stolt, P., et al. "Quantification of the influence of cigarette smoking on rheumatoid arthritis: results from a population based case-control study, using incident cases." Annals of the Rheumatic Diseases, vol. 62, no. 9, 2003, pp. 835-41.
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[8] MacGregor, A. J., Bamber, S., Silman, A. J. "A comparison of the performance of different methods of disease classification for rheumatoid arthritis. Results of an analysis from a nationwide twin study." J. Rheumatol., vol. 21, 1994, pp. 1420–1426.
[9] Lee EG, et al. "Failure to regulate TNF-induced NF-kappaB and cell death responses in A20-deficient mice." Science, 2000, pp. 2350–2354.
[10] Elliott MJ, et al. "Randomised double-blind comparison of chimeric monoclonal antibody to tumour necrosis factor alpha (cA2) versus placebo in rheumatoid arthritis." Lancet, 1994, pp. 1105–1110.