Next Article in Journal
Perioperative Mass Transfusion Affects In-Hospital but Not Follow-Up Survival in Patients with Acute Type A Aortic Dissection
Next Article in Special Issue
Amelioration of Rheumatoid Arthritis by Fragaria nubicola (Wild Strawberry) via Attenuation of Inflammatory Mediators in Sprague Dawley Rats
Previous Article in Journal
Chromatographic Analyses of Spirulina (Arthrospira platensis) and Mechanism of Its Protective Effects against Experimental Obesity and Hepatic Steatosis in Rats
Previous Article in Special Issue
Insights into Microbiota in Sjögren’s Syndrome
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 59
Issue 10
10.3390/medicina59101824
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between PYTPN22 rs2476601, VEGF rs833070, TNFAIP3 rs6920220 Polymorphisms and Risk for Rheumatoid Arthritis in Early Undifferentiated Arthritis Patients: A Pilot Study

by
Regina Sakalyte
1,2,*,
Sigita Stropuviene
1,2,
Gabija Jasionyte
1,
Loreta Bagdonaite
3 and
Algirdas Venalis
1,2
1
The Clinic of Rheumatology, Traumatology Orthopaedics and Reconstructive Surgery, Institute of Clinical Medicine of the Faculty of Vilnius University, M. K. Čiurlionio Str. 21, 03101 Vilnius, Lithuania
2
State Research Institute Centre for Innovative Medicine, Santariškių g. 5, 08406 Vilnius, Lithuania
3
Department of Physiology, Biochemistry, Microbiology and Laboratory Medicine, Faculty of Medicine, Vilnius University, M. K. Čiurlionio Str. 21, 03101 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Medicina 2023, 59(10), 1824; https://doi.org/10.3390/medicina59101824
Submission received: 26 August 2023 / Revised: 5 October 2023 / Accepted: 10 October 2023 / Published: 13 October 2023
(This article belongs to the Special Issue Recent Advances in Autoimmune Rheumatic Diseases)
Review Reports Versions Notes

Abstract

:
Background and Objectives: About 40% of early undifferentiated arthritis (UA) progresses to rheumatoid (RA) or other chronic arthritis. Novel diagnostic tools predicting the risk for this progression are needed to identify the patients who would benefit from early aggressive treatment. Evidence on the role of single-nucleotide polymorphisms (SNPs) in the development of RA has emerged. The aim of our study was to investigate the association between rs2476601, rs833070, and rs6920220 SNPs and UA progression to RA. Materials and Methods: Ninety-two UA patients were observed for 12 months. At study entry, demographic and clinical characteristics were recorded, musculoskeletal ultrasonography was performed, and blood samples were drawn to investigate levels of inflammatory markers, rheumatoid factor (RF), anti-citrullinated protein antibodies (anti-CCP)detect SNPs. After 12 months, UA outcomes were assessed, and patients were divided into two (RA and non-RA) groups. The association between the risk of progression to chronic inflammatory arthritis and analyzed SNPs was measured by computing odds ratios (OR). Results: After a 12-month follow-up, 27 (29.3%) patients developed RA, and 65 (70.7%) patients were assigned to the non-RA group. The arthritis of 21 patients (22.8%) from the non-RA group resolved completely, while the other 44 (47.2%) patients were diagnosed with another rheumatic inflammatory disease. The patients who developed RA had a significantly greater number of tender and swollen joints (p = 0.010 and p = 0.021 respectively) and were more frequently RF or anti-CCP (p < 0.001), and both RF and anti-CCP positive (p < 0.001) at the baseline as compared with the patients in the non-RA group. No significant association between rs2476601 (OR = 0.99, p = 0.98), rs833070 (OR = 1.0, p = 0.97), and rs6920220 (OR = 0.48, p = 0.13) polymorphisms and the risk of developing RA were found. Conclusions: No association between analyzed SNPs and a greater risk to progress from UA to RA was confirmed, although patients with rs6920220 AA + AG genotypes had fewer tender joints at the disease onset.

1. Introduction

Early undifferentiated arthritis (UA) is an autoimmune inflammatory joint disease; it is a very common diagnosis among individuals assessed in rheumatology practice [1], and it displays signs and symptoms of inflammatory arthritis that do not meet the classification criteria for any specific rheumatic disease [2,3]. UA can be an early manifestation of defined arthritis such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), or ankylosing spondylitis (AS), or it can be a self-limited syndrome of an unknown cause that resolves on its own [4]. While up to 60% of UA patients can remit spontaneously, the remaining ones progress to RA or other chronic arthritis, often within one year [2,5]. There is strong evidence that early aggressive treatment of UA patients can postpone progression to RA, preventing further joint damage and thus preserving functional ability [6]; drug-free remission can also be achieved [7]. Therefore, it is crucial to develop new diagnostic tools to predict which patients with UA are likely to develop chronic erosive arthritis such as rheumatoid arthritis so that early aggressive treatment can be specifically targeted at this patient group.
The current knowledge suggests the presence of specific autoantibodies (rheumatoid factor (RF)anti-citrullinated, anti-carbamylated, and anti-acetylated peptide antibodies) [8], elevated erythrocyte sedimentation rate (ESR), C- reactive protein values [9,10], and tenosynovitis [8] as possible predictors of progression to RA. Although genetic factors are thought to be responsible for up to 66% of predisposition to RA [11,12], and a large number of genetic variants have been associated with RA [13], there are only few studies that have examined genetic variants as predictors of progression from UA to RA [13,14]. A human genome-wide association analysis has identified a large number of SNPs that are associated with RA [15]. A meta-analysis of over 100,000 individuals of European and Asian descent (29,880 RA cases and 73,758 controls) evaluated ~10 million SNPs and identified 101 polymorphisms associated with RA risk. In addition, a few SNPs (rs2476601, rs833070, and rs6920220) were found to be associated with RA diagnostic markers [16,17,18].
rs2476601 is polymorphism in the non-receptor type 22 gene (PTPN22), which encodes lymphoid-specific phosphatase (OR 1.06) [19]. This protein is known to be important in the etiopathogenesis of RA and other autoimmune rheumatic diseases [20,21]. A meta-analysis confirmed that RA patients who had elevated anti-CCP and/or RF value carried T allele and CT + TT genotypes significantly more frequently than healthy controls did [16].
rs833070 is located in the VEGF gene, which is known to be involved in autoimmune disease etiopathogenesis [22]. The analysis showed that RA patients who had the AA genotype had higher VEGF levels and DAS 28 values as compared with those who had AG or GG genotypes. On the other hand, in patients who had AG or GG genotypes, a US analysis revealed higher synovitis and more active blood circulation as compared with the patients who had the AA genotype [23]. There are a few more studies that detected the association between this polymorphism and VEGF levels in sera of RA patients [18].
Polymorphism rs6920220 is in the tumor necrosis factor-alpha inducible protein 3 (TNFAIP3) gene [24]. Meta-analysis showed the association between this SNP and RA [24]; another study confirmed this association particularly with positive anti-CCP and RF profile [17].
The hypothesis of this undifferentiated arthritis cohort pilot study is as follows: rs2476601, rs833070, and rs6920220 SNPs can be prognostic markers for UA progression to RA.
The aim of this study was to investigate the rs2476601, rs833070, and rs6920220 SNPs’ relationship with clinical and laboratory parameters that are applied every day in early undifferentiated arthritis differential diagnoses and to determine if tested SNPs are associated with UA progression to RA.

2. Patients and Methods

2.1. Patients

Ninety-two UA patients were recruited to participate in the prospective UA cohort study at Vilnius University Hospital, Santaros Klinikos, Rheumatology Center. The Vilnius Regional Biomedical Research Ethics Committee has approved this study (permit No. 158200-16-859-368). All patients signed informed consent before they were enrolled in the study. Patients were enrolled if they were at least 18 years of age, had no established inflammatory arthritis diagnosis at study entry [25,26,27,28,29], and had at least one swollen joint and the patient-reported duration of joint swelling was shorter than 12 months. Patients whose joint swelling was due to infectious arthritis, arthritis due to trauma, microcrystal, or paraneoplastic arthritis or osteoarthritis were excluded from the study. Participants were observed prospectively for 12 months in order to measure the outcomes of their UA. The disease outcome was the following: either the patient was diagnosed with inflammatory rheumatic disease based on rheumatic diseases classification criteria (American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) 2010 rheumatoid arthritis classification criteria for RA [25], classification criteria for psoriatic arthritis (ClASsification for Psoriatic Arthritis (CASPAR)) [26], Assessment of Spondylarthritis International Society (ASAS) classification criteria for axial and peripheral spondylarthritis [27,28], 2012 Systemic Lupus International Collaborating Clinics criteria (SLICC’12) for systemic lupus erythematosus (SLE) [29], or the patient’s arthritis resolved (remission observed after 6-month follow-up (no swollen joints) without any need for steroids or DMARDs). Patients whose arthritis resolved completely had very low probability of progressing into chronic arthritis in the future. The final diagnosis of patients who did not attend a follow-up visit after 12 months was verified by reviewing medical electronic records. For statistical analysis based on the established diagnosis, first, all patients were divided into two groups: patients who developed RA (RA group) and those who did not (non-RA group). Second, patients in the non-RA group were subdivided into groups—patients whose arthritis resolved completely during the follow-up period and patients who developed other inflammatory rheumatic disease (e.g., psoriatic arthritis (PsA), ankylosing spondylitis (AS), SLE, etc.).

2.2. Data and Sample Collection

At study entry, the patient‘s sex, age, height, weight, ethnicity, level of education, and smoking history were recorded. Body mass index (BMI) was calculated. The following clinical characteristics of the disease were recorded: comorbidities, patient-reported duration arthralgia and joint swelling in months, the duration of morning stiffness in minutes. A rheumatologist assessed 68 tender and 66 swollen joint counts (68 TJC/66 SJC) [30]; collected data on the patient’s and physician’s global assessment of disease activity and the patient’s pain assessment on 100 mm visual analogue scales (patient’s global VAS, physician’s global VAS, patient’s pain VAS); calculated the disease activity score 28 (DAS 28) based on the assessment of 28 tender and swollen joints; and recorded the patient’s global assessment, VAS, and erythrocyte sedimentation rate (ESR). The patients’ movement function or functional disability was evaluated via completion of the Health Assessment Questionnaire (HAQ) [31]. Ultrasound assessment of tender and swollen joints was also carried out. Synovitis, erosions, and power Doppler (PD) findings were scored using a scale of 0–3, and scores from each joint were summed up to calculate synovitis, power Doppler and erosion scores [32,33]. Blood samples drawn at baseline were used to determine ESR (Westergren method; reference value ≤ 20 mm/h) and C reactive protein (CRP) (turbidimetric method; reference value ≤ 5 mg/L) levels, as in routine rheumatologist clinical practice, to determine arthritis activity, as well as to determine vascular endothelial growth factor (VEGF) (enzyme-linked immunosorbent assay (ELISA) method, IBL International, Germany; reference value ≤ 43 pg/mL) levels and to extract DNA (for SNP detection: rs2476601, rs833070, and rs6920220). Anti-citrullinated protein antibodies (anti-CCP) (ELISA method, Phadia AB, Uppsala, Sweden; reference value < 5 U/mL), the rheumatoid factor (RF) (turbidimetric method; reference value < 30 kU/L), human leukocyte antigen B27 (HLA-B27) (flow cytometry method), anti-nuclear antibodies (ANA) (indirect immunofluorescence method; reference value < 1:40 titer) were all performed at the discretion of the treating rheumatologist as clinically indicated.

2.3. Sample Preparation and SNP Genotyping

Blood samples were collected in EDTA tubes and frozen. Genomic DNA was extracted from frozen whole blood using the Gentra Puregene Blood Kit (Qiagen, Hilden, Germany). Each patient sample was genotyped on the Illumina BeadLab1000 platform using the ImmunoChip V2 and the Infinium HD assay. Genotypes were identified using a score threshold of 0.15 in the Illumina BeadStudio software (https://www.illumina.com/Documents/products/datasheets/datasheet_beadstudio.pdf, accessed on 9 October 2023). Data cleaning was performed using the PLINK software program.

2.4. Statistical Analysis

Mean and standard deviation were used to describe the quantitative characteristics of research. Frequencies (n) and percentages (%) were used for qualitative characteristics. Data distribution normality was assessed using the Shapiro–Wilk normality test. Depending on applicable assumptions, Student’s t-test for independent samples was used to compare means of a particular qualitative characteristic of different samples. Differences in the qualitative characteristics of experimental groups were assessed using the Chi-square test. To compare nonparametric data sample means the Mann–Whitney U and the Kruskal–Wallis tests were performed. Correlations between all cohort data were calculated using Spearman’s correlation test. The association strength between the risk for UA to progress to chronic inflammatory arthritis and analyzed SNPs (rs2476601, rs833070, and rs6920220) were assessed by computing odds ratio (OR) with 95% confidence intervals (95%CI). All SNPs were tested for Hardy–Weinberg equilibrium, and all had minor allele frequencies (MAF) ≥10%. Statistical analysis and visualization were performed using Microsoft Office (Microsoft Corporation, Redmond, W.A., USA), SPSS (Version 26.0 IBM Corp, Armonk, NY, USA), and PLINK (Version 1.9). The selected level of significance was p < 0.05.

3. Results

3.1. Patient Characteristics

A total of 92 patients, mean age 44.25 ± 15.82 years, were enrolled in the present study; 61 (66.3%) were females; 30 (32.6%) reported a family history of rheumatic diseases. The mean UA duration was 5.27 ± 3.22 months. All of the enrolled patients (100%) were Caucasians. The mean numbers of swollen joints in 66/28 SJC were 3.17 ± 2.14 and 2.73 ± 1.60, respectively, and of tender joints in 68/28 TJC, 7.33 ± 5.77 and 5.37 ± 3.75, respectively. Median values of inflammation markers were as follows: ESR 32.55 [2–144] mm/h, CRP 17, 65 [0.16–144.30] mg/L; the median VEGF value was 516.16 [25.75–3438.25] pg/mL. Of 82 tested participants, 30 (36.1%) were positive for anti-CCP. At enrollment, 80 (86.96) of UA patients were DMARD-naïve. After a 12-month follow-up period had passed, as many as 27 (29.3%) patients had developed RA (RA group), and 65 (70.7%) patients were assigned to the non-RA group. Arthritis of 21 (22.8%) patients in the non-RA group resolved completely, and 44 (47.2%) of them were diagnosed with other rheumatic inflammatory diseases (PsA, AS, SLE, etc.) (Table 1).
The sociodemographic, clinical, and laboratory characteristics at the baseline and the comparison of data between UA patients who developed RA after 12 months and the non-RA group (whose arthritis resolved or who were diagnosed with other inflammatory rheumatic diseases) after a 12-month follow-up are presented in Table 1. Patients who developed RA within 12 months, as compared with those in the non-RA group, at the study baseline were present with a significantly greater number of tender (68/28 TJC) (p = 0.010/p = 0.021) and swollen (66/28 SJC) joints (p < 0.001/p < 0.001) and higher DAS 28 (ESR) scores (p < 0.005). The RA group vs. the non-RA one demonstrated significantly more frequent RF, or anti-CCP (p < 0.001), and or both RF and anti-CCP positive (p < 0.001). On the other hand, in the non-RA group, HLA-B27 was significantly more often expressed (p = 0.01). VEGF analysis revealed that patients who developed RA had higher VEGF levels as compared with those in the non-RA group, although it was not significant (p = 0.083) (Table 1).
UA patients whose arthritis resolved completely as compared with the patients in the RA group at the baseline visit were more often diagnosed with active infection (p = 0.014). The number of swollen joints (66/28 SJC), and RF and anti-CCP positivity rates were significantly higher in the RA group as compared with the group of patients whose arthritis resolved (p < 0.001) (Table 1).
Compared with the group of patients who were later diagnosed with other inflammatory rheumatic (PsA, AS, etc.) diseases, the RA group patients had significantly higher numbers of swollen joints (66/28 SJC) and higher DAS 28 scores (p < 0.001) at the study baseline. The analysis of laboratory tests revealed that patients who developed RA had higher CRP (p = 0.036), RF, and anti-CCP values; additionally, RF and anti-CCP positive values (p < 0.001) and HLA-B27 were less expressed (p = 0.010) (Table 1).
Patients who developed RA had a higher rate of history of rheumatic diseases in blood relatives (as compared with the patients in the non-RA group and patients whose arthritis resolved); however, it was not statistically significant (p = 0.119 and p = 0.435 respectively) (Table 1).
The ultrasound analysis of tender and swollen joints of UA patients revealed that rates of synovitis, measured via power Doppler, at the onset of disease were significantly higher in patients who later developed RA as compared with those in the non-RA group, patients whose arthritis resolved, and patients who were diagnosed with another inflammatory rheumatic disease within a 12-month follow-up period (p < 0.001). Results were similar in the grade of erosions seen on US: RA vs. non-RA (p < 0.001), RA vs. the group where arthritis resolved (p < 0.001), and RA vs. other inflammatory rheumatic diseases (p < 0.004). Patients who developed RA at the onset of UA were more often diagnosed with bone erosions seen on US as compared with the patients in the non-RA group (p = 0.004) and patients whose arthritis resolved (p < 0.001) (Table 2).

3.2. SNP Analysis Results

As regards SNP variants tested, no statistical significance was found between the patients who developed RA and those who developed other disease outcomes (Table 3).
To analyze the significance of SNPs to sociodemographic, clinical, laboratory, and instrumental variables, patients were divided by genotype into two groups: rs2476601 AA + AG and GG; and rs833070 GG + AG and AA; rs6920220 AA + AG and GG.
The SNP minor allele distribution (homozygous and heterozygous) analysis with sociodemographic, clinical, and laboratory findings revealed that patients whose rs6920220 were present with the minor allele (AA or AG) had a significantly greater number of tender joints (68/28 TJS) (Table 4).

4. Discussion

The association between RA and various genomic variants, i.e., single nucleotide polymorphisms, has been widely studied and described in scientific publications. By carrying out large-scale genomic studies, researchers are trying to identify SNP variants that are relevant to RA [34]. In the present pilot study, the distribution between investigated SNPs (rs2476601 on the PYTPN22 gene, rs833070 on the VEGF gene, and rs6920220 on the TNFAIP3 gene) in the cohort of patients with early undifferentiated arthritis was assessed, and SNPs’ minor alleles’ association with sociodemographic, clinical, laboratory, ultrasound data and UA outcomes was tested.
The meta-analysis demonstrated SNP rs2476601 association with the increased risk of developing RA [19]. This meta-analysis showed that CT + TT genotypes of the PTPN22 gene were statistically significantly associated with RA in the European population (OR = 1.683) [16]. In the current pilot study, we did not confirm an association between the risk of UA progressing to RA and PTPN22 gene polymorphism. Moreover, regarding the risk of developing RA associated with rs2476601 polymorphism, the RA group was compared with the non-RA group, which was highly heterogeneous and consisted of the patients whose arthritis resolved during the study period as well as the patients who were later diagnosed with SpA, PsA, SLE, etc. Published literature indicates that this polymorphism is associated with the risk of SLE [35] as well as other autoimmune diseases [20,21,36] and that PTPN22 rs2476601 genotypes have been found to be associated with exaggerated immune system response and the inflammatory process [37]. This allows us to suggest that the control group in the study was also prone to an exaggerated immune response and maintenance of inflammatory processes and that the results of the study may have been influenced by the comorbidities of the patients studied, as 16% of diabetic patients are known to have PTPN22 polymorphism, while only 6% of the healthy population have PTPN22 polymorphism [38,39]. A meta-analysis revealed the association between rs2476601 and RA markers (RF and anti-CCP) [16]. In the present study, the association between this polymorphism and RF and anti-CCP was not confirmed.
There are several published studies supporting an association between VEGF gene rs833070 polymorphism and the risk of developing RA [18,23,29]; our literature review identified two studies that analyzed the association between VEGF rs833070 polymorphism and RA. The sample sizes referenced in these two studies were relatively small too: 98 RA subjects and 100 controls. Both studies showed an association between VEGF polymorphisms and serum VEGF levels. These studies also confirm that the frequency of the A allele was higher in RA patients as compared to controls [23,29]. The association between AA genotype and serum VEGF levels was also confirmed, as was an association between disease activity (DAS 28) and joint abnormalities seen on US scan [23]. The study analysed the VEGF gene rs833070 polymorphism and its association with risk of developing RA, although no assotiation was confirmed. There was found no link between VEGF gene rs833070 polymorphism and RA diagnostic markers, such as greater anti-CCP, RF, ESR, CRP values, grade of synovitis seen on US, or serum VEGF levels.
The rs6920220 polymorphism in the TNFAIP3 gene is associated with the risk of many autoimmune diseases, including RA [40,41,42]. This polymorphism may regulate immune cells via the protein it encodes and therefore is associated with inflammatory processes and autoimmune diseases [43,44]. A meta-analysis supported the association between TNFAIP3 polymorphism and the risk of RA, and a statistically significant association was also found in RA patients who were anti-CCP positive [17]. The present study failed to establish the association between TNFAIP3 polymorphism and the risk of RA but confirmed a significant association between AA + AG genotypes and the number of painful joints (68 and 28). These results suggest that a larger cohort of patients could help to clarify the genetic origin of arthralgias. In the present pilot study, we did not confirm the association between the polymorphisms studied and the risk of RA in the population with early undifferentiated arthritis. This is most likely due to the small sample size. In addition, the fact that the control group of RA patients was composed of the individuals who also suffered from other inflammatory arthritis (PsA, SpA, SLE, etc.) might have influenced the results as well. As mentioned above, two of the analyzed polymorphisms (rs2476601, rs6920220) are associated with increased immune reactivity and susceptibility to the inflammatory process [37,43,44]. The fact that patients with the SNP rs6920220 GG and GA genotypes had a significantly greater number of tender joints at the study baseline could suggest that a more comprehensive prospective study on the patients suffering from arthralgia could provide knowledge of whether individuals with these genotypes will later develop inflammatory arthritis. To better understand the role the analyzed polymorphisms play in the etiopathogenesis of inflammatory diseases and the possible relevance to UA outcomes, a large sample of research subjects would be needed, and it would be very useful to include a control group of healthy subjects. The present study is distinctive in that it examined a population of individuals with early undifferentiated arthritis. The cohort studied was homogeneous, with only Caucasians; the cohort was distributed over a relatively small residential area; and there was a difference with the deletions found in the European population studied in the SNP database (the T allele was found in the European population [45], whereas the G recessive allele was identified in the population of this pilot study).

5. Conclusions

In conclusion, the present study highlighted various clinical, laboratory, and ultrasound differences among the patients with early undifferentiated arthritis based on disease outcomes at follow-up. While associations with the number of tender and swollen joints, RF, anti-CCP positivity, and ultrasound scores were observed, the tested SNP variants did not show any significant associations with disease outcomes. On the other hand, patients with minor alleles (AA or AG) of rs6920220 exhibited a significantly greater number of tender joints. Further research on larger cohorts is necessary to provide deeper insight into genetic and clinical factors influencing progression of early undifferentiated arthritis to rheumatoid arthritis and other inflammatory rheumatic diseases.

Author Contributions

Conceptualization, R.S., S.S., L.B. and A.V.; methodology, R.S., S.S., L.B. and A.V.; software, R.S. and S.S.; validation, R.S., S.S., L.B., G.J. and A.V.; formal analysis, R.S., S.S. and G.J.; investigation, R.S., S.S., L.B. and G.J.; resources, R.S., S.S., L.B., G.J. and A.V.; data curation, R.S., S.S., L.B., G.J. and A.V.; writing—original draft preparation, R.S., S.S., L.B. and G.J.; writing—review and editing, R.S., S.S., L.B. and G.J.; visualization, R.S., S.S. and G.J.; supervision, S.S., L.B. and A.V.; project administration, R.S., S.S., L.B., G.J. and A.V.; funding acquisition, R.S., S.S. and A.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by AbbVie “Investigator Initiated Study” grant.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by Vilnius Regional Biomedical Research Ethics Committee (Approval No. 158200-16-859-368, date of approval 10 July 2016).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original datasets are not publicly available due to data protection policies. The data presented in this study are available on scientific request from the corresponding author.

Acknowledgments

Authors thank Vilnius University Hospital Santaros Klinikos for collaboration and opportunity to conduct the study as well as Damini Jawaheer from Nothwestern University, IL, USA for the help to perform SNP analysis.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Pazmino, S.; Westhovens, R.; Doumen, M.; Bertrand, D.; Verschueren, P. Undifferentiated arthritis, not just another early manifestation of rheumatoid arthritis. Rheumatology 2022, 61, 3101–3103. [Google Scholar] [CrossRef] [PubMed]
  2. van Aken, J.; Heimans, L.; Gillet-van Dongen, H.; Visser, K.; Ronday, H.K.; Speyer, I.; Peeters, A.J.; Huizinga, T.W.; Allaart, C.F. Five-year outcomes of probable rheumatoid arthritis treated with methotrexate or placebo during the first year (the PROMPT study). Ann. Rheum. Dis. 2014, 73, 396–400. [Google Scholar] [CrossRef]
  3. Harrison, B.J.; Hutchinson, C.E.; Adams, J.; Bruce, I.N.; Herrick, A.L. Assessing periarticular bone mineral density in patients with early psoriatic arthritis or rheumatoid arthritis. Ann. Rheum. Dis. 2002, 61, 1007–1011. [Google Scholar] [CrossRef] [PubMed]
  4. Zeidler, H. Drug-free sustained remission or spontaneous remission by natural history in rheumatoid arthritis? An unsolved question: Comment on the article of Ajeganova et al. Ann. Rheum. Dis. 2017, 76, e16. [Google Scholar] [CrossRef] [PubMed]
  5. Nissila, M.; Lehtinen, K.; Leirisalo-Repo, M.; Luukkainen, R.; Mutru, O.; Yli-Kerttula, U. Sulfasalazine in the treatment of ankylosing spondylitis. A twenty-six-week, placebo-controlled clinical trial. Arthritis Rheum. 1988, 31, 1111–1116. [Google Scholar] [CrossRef]
  6. Aletaha, D.; Maa, J.F.; Chen, S.; Park, S.H.; Nicholls, D.; Florentinus, S.; Furtner, D.; Smolen, J.S. Effect of disease duration and prior disease-modifying antirheumatic drug use on treatment outcomes in patients with rheumatoid arthritis. Ann. Rheum. Dis. 2019, 78, 1609–1615. [Google Scholar] [CrossRef]
  7. Akdemir, G.; Heimans, L.; Bergstra, S.A.; Goekoop, R.J.; van Oosterhout, M.; van Groenendael, J.; Peeters, A.J.; Steup-Beekman, G.M.; Lard, L.R.; de Sonnaville, P.B.J.; et al. Clinical and radiological outcomes of 5-year drug-free remission-steered treatment in patients with early arthritis: IMPROVED study. Ann. Rheum. Dis. 2018, 77, 111–118. [Google Scholar] [CrossRef]
  8. Nieuwenhuis, W.P.; Krabben, A.; Stomp, W.; Huizinga, T.W.; van der Heijde, D.; Bloem, J.L.; van der Helm-van Mil, A.H.; Reijnierse, M. Evaluation of magnetic resonance imaging-detected tenosynovitis in the hand and wrist in early arthritis. Arthritis Rheumatol. 2015, 67, 869–876. [Google Scholar] [CrossRef]
  9. Combe, B.; Landewe, R.; Daien, C.I.; Hua, C.; Aletaha, D.; Alvaro-Gracia, J.M.; Bakkers, M.; Brodin, N.; Burmester, G.R.; Codreanu, C.; et al. 2016 update of the EULAR recommendations for the management of early arthritis. Ann. Rheum. Dis. 2017, 76, 948–959. [Google Scholar] [CrossRef]
  10. Hua, C.; Daien, C.I.; Combe, B.; Landewe, R. Diagnosis, prognosis and classification of early arthritis: Results of a systematic review informing the 2016 update of the EULAR recommendations for the management of early arthritis. RMD Open 2017, 3, e000406. [Google Scholar] [CrossRef]
  11. Scherer, H.U.; Haupl, T.; Burmester, G.R. The etiology of rheumatoid arthritis. J. Autoimmun. 2020, 110, 102400. [Google Scholar] [CrossRef] [PubMed]
  12. van der Woude, D.; Houwing-Duistermaat, J.J.; Toes, R.E.; Huizinga, T.W.; Thomson, W.; Worthington, J.; van der Helm-van Mil, A.H.; de Vries, R.R. Quantitative heritability of anti-citrullinated protein antibody-positive and anti-citrullinated protein antibody-negative rheumatoid arthritis. Arthritis Rheum. 2009, 60, 916–923. [Google Scholar] [CrossRef] [PubMed]
  13. Scott, I.C.; Rijsdijk, F.; Walker, J.; Quist, J.; Spain, S.L.; Tan, R.; Steer, S.; Okada, Y.; Raychaudhuri, S.; Cope, A.P.; et al. Do Genetic Susceptibility Variants Associate with Disease Severity in Early Active Rheumatoid Arthritis? J. Rheumatol. 2015, 42, 1131–1140. [Google Scholar] [CrossRef] [PubMed]
  14. Ursum, J.; van der Weijden, M.A.; van Schaardenburg, D.; Prins, A.P.; Dijkmans, B.A.; Twisk, J.W.; Crusius, J.B.; van der Horst-Bruinsma, I.E. IL10 GGC haplotype is positively and HLA-DQA1*05-DQB1*02 is negatively associated with radiographic progression in undifferentiated arthritis. J. Rheumatol. 2010, 37, 1431–1438. [Google Scholar] [CrossRef]
  15. Negi, S.; Juyal, G.; Senapati, S.; Prasad, P.; Gupta, A.; Singh, S.; Kashyap, S.; Kumar, A.; Kumar, U.; Gupta, R.; et al. A genome-wide association study reveals ARL15, a novel non-HLA susceptibility gene for rheumatoid arthritis in North Indians. Arthritis Rheum. 2013, 65, 3026–3035. [Google Scholar] [CrossRef]
  16. Elshazli, R.; Settin, A. Association of PTPN22 rs2476601 and STAT4 rs7574865 polymorphisms with rheumatoid arthritis: A meta-analysis update. Immunobiology 2015, 220, 1012–1024. [Google Scholar] [CrossRef]
  17. Patsopoulos, N.A.; Ioannidis, J.P. Susceptibility variants for rheumatoid arthritis in the TRAF1-C5 and 6q23 loci: A meta-analysis. Ann. Rheum. Dis. 2010, 69, 561–566. [Google Scholar] [CrossRef]
  18. Zhang, Y.; Qiu, H.; Zhang, H.; Wang, L.; Zhuang, C.; Liu, R. Vascular endothelial growth factor A (VEGFA) polymorphisms in Chinese patients with rheumatoid arthritis. Scand. J. Rheumatol. 2013, 42, 344–348. [Google Scholar] [CrossRef]
  19. Okada, Y.; Wu, D.; Trynka, G.; Raj, T.; Terao, C.; Ikari, K.; Kochi, Y.; Ohmura, K.; Suzuki, A.; Yoshida, S.; et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 2014, 506, 376–381. [Google Scholar] [CrossRef]
  20. Brownlie, R.J.; Zamoyska, R.; Salmond, R.J. Regulation of autoimmune and anti-tumour T-cell responses by PTPN22. Immunology 2018, 154, 377–382. [Google Scholar] [CrossRef]
  21. Klareskog, L.; Catrina, A.I.; Paget, S. Rheumatoid arthritis. Lancet 2009, 373, 659–672. [Google Scholar] [CrossRef] [PubMed]
  22. Lee, Y.H.; Bae, S.C. Correlation between circulating VEGF levels and disease activity in rheumatoid arthritis: A meta-analysis. Z. Rheumatol. 2018, 77, 240–248. [Google Scholar] [CrossRef] [PubMed]
  23. Yi, J.P.; Wu, Y.Z.; Yu, N.; Yu, Z.W.; Xie, F.Y.; Yuan, Q. VEGF Gene Polymorphisms Affect Serum Protein Levels and Alter Disease Activity and Synovial Lesions in Rheumatoid Arthritis. Med. Sci. Monit. 2016, 22, 316–324. [Google Scholar] [CrossRef]
  24. Ciccacci, C.; Latini, A.; Perricone, C.; Conigliaro, P.; Colafrancesco, S.; Ceccarelli, F.; Priori, R.; Conti, F.; Perricone, R.; Novelli, G.; et al. TNFAIP3 Gene Polymorphisms in Three Common Autoimmune Diseases: Systemic Lupus Erythematosus, Rheumatoid Arthritis, and Primary Sjogren Syndrome-Association with Disease Susceptibility and Clinical Phenotypes in Italian Patients. J. Immunol. Res. 2019, 2019, 6728694. [Google Scholar] [CrossRef] [PubMed]
  25. Krabben, A.; Abhishek, A.; Britsemmer, K.; Filer, A.; Huizinga, T.W.; Raza, K.; van Schaardenburg, D.J.; van der Helm-van Mil, A.H. Risk of rheumatoid arthritis development in patients with unclassified arthritis according to the 2010 ACR/EULAR criteria for rheumatoid arthritis. Rheumatology 2013, 52, 1265–1270. [Google Scholar] [CrossRef]
  26. Taylor, W.; Gladman, D.; Helliwell, P.; Marchesoni, A.; Mease, P.; Mielants, H.; Group, C.S. Classification criteria for psoriatic arthritis: Development of new criteria from a large international study. Arthritis Rheum. 2006, 54, 2665–2673. [Google Scholar] [CrossRef] [PubMed]
  27. Rudwaleit, M.; van der Heijde, D.; Landewe, R.; Listing, J.; Akkoc, N.; Brandt, J.; Braun, J.; Chou, C.T.; Collantes-Estevez, E.; Dougados, M.; et al. The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): Validation and final selection. Ann. Rheum. Dis. 2009, 68, 777–783. [Google Scholar] [CrossRef]
  28. Rudwaleit, M.; van der Heijde, D.; Landewe, R.; Akkoc, N.; Brandt, J.; Chou, C.T.; Dougados, M.; Huang, F.; Gu, J.; Kirazli, Y.; et al. The Assessment of SpondyloArthritis International Society classification criteria for peripheral spondyloarthritis and for spondyloarthritis in general. Ann. Rheum. Dis. 2011, 70, 25–31. [Google Scholar] [CrossRef]
  29. Petri, M.; Orbai, A.M.; Alarcon, G.S.; Gordon, C.; Merrill, J.T.; Fortin, P.R.; Bruce, I.N.; Isenberg, D.; Wallace, D.J.; Nived, O.; et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012, 64, 2677–2686. [Google Scholar] [CrossRef]
  30. Duarte-Garcia, A.; Leung, Y.Y.; Coates, L.C.; Beaton, D.; Christensen, R.; Craig, E.T.; de Wit, M.; Eder, L.; Fallon, L.; FitzGerald, O.; et al. Endorsement of the 66/68 Joint Count for the Measurement of Musculoskeletal Disease Activity: OMERACT 2018 Psoriatic Arthritis Workshop Report. J. Rheumatol. 2019, 46, 996–1005. [Google Scholar] [CrossRef]
  31. Pincus, T.; Summey, J.A.; Soraci, S.A., Jr.; Wallston, K.A.; Hummon, N.P. Assessment of patient satisfaction in activities of daily living using a modified Stanford Health Assessment Questionnaire. Arthritis Rheum. 1983, 26, 1346–1353. [Google Scholar] [CrossRef] [PubMed]
  32. Szkudlarek, M.; Court-Payen, M.; Jacobsen, S.; Klarlund, M.; Thomsen, H.S.; Ostergaard, M. Interobserver agreement in ultrasonography of the finger and toe joints in rheumatoid arthritis. Arthritis Rheum. 2003, 48, 955–962. [Google Scholar] [CrossRef]
  33. Wakefield, R.J.; Balint, P.V.; Szkudlarek, M.; Filippucci, E.; Backhaus, M.; D’Agostino, M.A.; Sanchez, E.N.; Iagnocco, A.; Schmidt, W.A.; Bruyn, G.A.; et al. Musculoskeletal ultrasound including definitions for ultrasonographic pathology. J. Rheumatol. 2005, 32, 2485–2487. [Google Scholar]
  34. Dedmon, L.E. The genetics of rheumatoid arthritis. Rheumatology 2020, 59, 2661–2670. [Google Scholar] [CrossRef] [PubMed]
  35. Hu, L.Y.; Cheng, Z.; Zhang, B.; Yin, Q.; Zhu, X.W.; Zhao, P.P.; Han, M.Y.; Wang, X.B.; Zheng, H.F. Associations between PTPN22 and TLR9 polymorphisms and systemic lupus erythematosus: A comprehensive meta-analysis. Arch. Dermatol. Res. 2017, 309, 461–477. [Google Scholar] [CrossRef] [PubMed]
  36. Bottini, N.; Vang, T.; Cucca, F.; Mustelin, T. Role of PTPN22 in type 1 diabetes and other autoimmune diseases. Semin. Immunol. 2006, 18, 207–213. [Google Scholar] [CrossRef]
  37. Shaw, A.M.; Qasem, A.; Naser, S.A. Modulation of PTPN2/22 Function by Spermidine in CRISPR-Cas9-Edited T-Cells Associated with Crohn’s Disease and Rheumatoid Arthritis. Int. J. Mol. Sci. 2021, 22, 8883. [Google Scholar] [CrossRef] [PubMed]
  38. Sharp, R.C.; Beg, S.A.; Naser, S.A. Polymorphisms in Protein Tyrosine Phosphatase Non-receptor Type 2 and 22 (PTPN2/22) Are Linked to Hyper-Proliferative T-Cells and Susceptibility to Mycobacteria in Rheumatoid Arthritis. Front. Cell Infect. Microbiol. 2018, 8, 11. [Google Scholar] [CrossRef] [PubMed]
  39. Sharp, R.C.; Beg, S.A.; Naser, S.A. Role of PTPN2/22 polymorphisms in pathophysiology of Crohn’s disease. World J. Gastroenterol. 2018, 24, 657–670. [Google Scholar] [CrossRef]
  40. Ramos, P.S.; Criswell, L.A.; Moser, K.L.; Comeau, M.E.; Williams, A.H.; Pajewski, N.M.; Chung, S.A.; Graham, R.R.; Zidovetzki, R.; Kelly, J.A.; et al. A comprehensive analysis of shared loci between systemic lupus erythematosus (SLE) and sixteen autoimmune diseases reveals limited genetic overlap. PLoS Genet. 2011, 7, e1002406. [Google Scholar] [CrossRef]
  41. Shimane, K.; Kochi, Y.; Horita, T.; Ikari, K.; Amano, H.; Hirakata, M.; Okamoto, A.; Yamada, R.; Myouzen, K.; Suzuki, A.; et al. The association of a nonsynonymous single-nucleotide polymorphism in TNFAIP3 with systemic lupus erythematosus and rheumatoid arthritis in the Japanese population. Arthritis Rheum. 2010, 62, 574–579. [Google Scholar] [CrossRef] [PubMed]
  42. Zhou, X.J.; Lu, X.L.; Nath, S.K.; Lv, J.C.; Zhu, S.N.; Yang, H.Z.; Qin, L.X.; Zhao, M.H.; Su, Y.; Shen, N.; et al. Gene-gene interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in systemic lupus erythematosus. Arthritis Rheum. 2012, 64, 222–231. [Google Scholar] [CrossRef] [PubMed]
  43. Catrysse, L.; Vereecke, L.; Beyaert, R.; van Loo, G. A20 in inflammation and autoimmunity. Trends Immunol. 2014, 35, 22–31. [Google Scholar] [CrossRef] [PubMed]
  44. Zhou, Q.; Wang, H.; Schwartz, D.M.; Stoffels, M.; Park, Y.H.; Zhang, Y.; Yang, D.; Demirkaya, E.; Takeuchi, M.; Tsai, W.L.; et al. Loss-of-function mutations in TNFAIP3 leading to A20 haploinsufficiency cause an early-onset autoinflammatory disease. Nat. Genet. 2016, 48, 67–73. [Google Scholar] [CrossRef]
  45. Available online: https://www.ncbi.nlm.nih.gov/snp/ (accessed on 20 September 2023).
Table 1. Sociodemographic, clinical, and laboratory data from early undifferentiated arthritis patient cohort; comparison of sociodemographic, clinical, and laboratory data between rheumatoid arthritis and other patients outcomes groups.
Table 1. Sociodemographic, clinical, and laboratory data from early undifferentiated arthritis patient cohort; comparison of sociodemographic, clinical, and laboratory data between rheumatoid arthritis and other patients outcomes groups.
VariableAll UA Patients (n = 92)RA Group (n = 27)Non-RA Group (n = 65)p Value *Arthritis Resolved (n = 21)p Value **Other Rheumatic Inflammatory Disease (n = 44)p Value ***
Sociodemographics
    Age, years44.25 ± 15.8247.78 ± 16.3642.79 ± 15.480.17843.10 ± 15.670.26642.34 ± 15.560.220
    Females, n (%)61 (66.3)20 (74.1)41 (63.1)0.31014 (66.7)0.57527 (61.4)0.272
    BMI, kg/m224.14 ± 3.4323.71 ± 3.4024.32 ± 3.180.31823.74 ± 3.070.77124.60 ± 3.220.225
    Daily smokers, n (%)14 (15.2)4 (14.8)10 (15.4)0.9455 (23.8)0.4285 (11.4)0.671
    Education, years13.40 ± 2.1913.59 ± 1.9513.32 ± 2.290.75113.67 ± 2.40.86513.16 ± 2.240.592
    Presence of rheumatic disease in family, n (%) 30 (32.6)12 (44.4)18 (27.7)0.1197 (33.3)0.43511 (25.0)0.671
Clinical data
    Presence of active infection 1, n (%)23 (31.5)4 (14.8)25 (38.5)0.08411 (52.4)0.01414 (31.8)0.257
    Presence of comorbidities, n (%)62 (67)21 (77.8)41 (63.1)0.17113 (61.9)0.23028 (63.6)0.211
    Duration of joint pain, months6.74 ± 4.966.26 ± 4.686.94 ± 5.090.3966.52 ± 3.530.4977.14 ± 5.10.432
    Duration of joint swelling, months5.27 ± 3.224.85 ± 3.215.45 ± 3.230.4255.76 ± 2.910.2335.92 ± 3.390.672
    Duration of morning stiffness, minutes 68.04 [0–300]92.78 [0–300]57.77 [0–300]0.11752.86 [0–120]0.28130 [0–300]0.122
    Patient’s joint pain VAS, mm 45.42 ± 16.9943.89 ± 16.1846.06 ± 17.390.43145.29 ± 16.160.68446.43 ± 18.120.391
    Patient’s global VAS, mm47.98 ± 16.4049.70 ± 18.0447.26 ± 15.770.64646.86 ± 17.130.44647.46 ± 15.280.840
    Physician’s global VAS, mm45.80 ± 13.8946.07 ± 13.6845.69 ± 14.080.93144.67 ± 13.790.94246.18 ± 14.350.938
    66 SJC3.17 ± 2.14 [1–14]4.63 ± 2.80 [2–14]2.57 ± 1.44 [1–8]<0.0012.19 ± 0.87<0.0012.75 ± 1.61 [1–8]<0.001
    28 SJC2.73 ± 1.6 [0–8]3.81 ± 1.71 [2–8]2.28 ± 1.24 [0–7]<0.0012.19 ± 1.21<0.0012.32 ± 1.41 [0–7]<0.001
    68 TJC7.33 ± 5.77 [0–28]9.59 ± 6.03 [1–28]6.39 ± 5.43 [0–28]0.0104.95 ± 4.230.0037.07 ± 5.84 [0–28]0.060
    28 TJC5.37 ± 3.75 [0–18]6.70 ± 3.75 [1–14]4.82 ± 3.63 [0–18]0.0214.00 ± 3.220.0155.20 ± 3.78 [0–18]0.071
    HAQ 0.63 ± 0.410.742 ± 0.470.58 ± 0.380.1060.51 ± 0.320.0520.61 ± 0.400.258
    DAS 284.50 ± 1.095.01 ± 0.864.29 ± 1.110.0054.67 ± 1.180.3134.11 ± 1.05<0.001
Laboratory findings
    ESR, mm/h32.55 [2–144]39.67 ± 34.5129.45 ± 23.850.23932.89 ±25.750.64727.93 ± 23.110.178
    CRP, mg/L17.65 [0.16–144.30]22.42 [0.39–111.50]15.67 [0.16–144.30]0.06518.99 [0.30–84.90]0.4304.6 [0.16–144.30]0.036
    RF positive, n (%)31 (33.7)24 (88.9) (n = 27)7 (10.8) (n = 65)<0.0012 (9.5) (n = 21)<0.00055 (11.4) (n = 44)<0.001
    Anti-CCP 2 positive, n (%)30 (36.1)24 (89.9) (n = 27)6 (10.7) (n = 56)<0.0011 (5.0) (n = 19)<0.00055 (13.5) (n = 37)<0.001
    RF and anti-CCP positive, n (%)27 (31.0)22 (81.5)5 (8.9)<0.0011 (5.0)<0.00054 (10.8)<0.001
    HLA B27 3 expressed, n (%)20 (28.2)1 (6.3) (n = 16)19 (34.5) (n = 55)0.0103 (17.6) (n = 17)0.32116 (42.1) (n = 38)0.010
    ANA 4 positive, n (%)19 (45.2)5 (38.5) (n = 13)14 (48.3) (n = 29)0.2381 (14.3) (n = 7)0.26013 (59.1) (n = 22)0.555
    VEGF 5, pg/mL516.16 [25.75–3438.25]697.60 [25.75–3438.25] (n = 23)437.42 [53.97–1854.91] (n = 53)0.086449.50 [53.97–1437.35] (n = 20)0.242283.42 [58.47–1854.91] (n = 33)0.083
Continuous data are presented in median [minimum and maximum] values or mean ± standard deviation, counted as numbers and valid percentages. BMI, body mass index; VAS, visual analogue scale; SJC, swollen joint count; TJC, tender joint count; HAQ, Health Assessment Questionnaire; DAS 28, Disease Activity Score 28 using erythrocyte sedimentation rate; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; RF, rheumatoid factor; anti-CCP, anti-citrullinated protein antibodies; HLA B27, human leukocyte antigen B27; ANA, antinuclear antibodies, VEGF, vascular endothelial growth factor; UA, early undifferentiated arthritis; RA, rheumatoid arthritis; non-RA group, patients who at a 12-month follow-up had not developed rheumatoid arthritis; other rheumatic inflammatory diseases, patients who at a 12-month follow-up were diagnosed with psoriatic arthritis, ankylosing spondylitis, systemic lupus erythematosus, etc. p significant if <0.05. Total tested: 1—N = 62, 2—N = 82, 3—N = 72, 4—N = 42, 5—N = 76. * p value between RA and non-RA groups; ** p value between RA group and patients whose arthritis resolved; *** p value between RA group and patients’ group who developed other inflammatory rheumatic diseases, statistically significant data is bolded.
Table 2. Ultrasound findings in early undifferentiated arthritis patients cohort at study baseline; comparison of ultrasound findings between rheumatoid arthritis and other patients outcomes groups.
Table 2. Ultrasound findings in early undifferentiated arthritis patients cohort at study baseline; comparison of ultrasound findings between rheumatoid arthritis and other patients outcomes groups.
VariableAll UA Patients (n = 92)RA Group (n = 27)Non-RA Group (n = 65)p Value *Arthritis Resolved (n = 21)p Value **Other Rheumatic Inflammatory Disease (n = 44)p Value ***
Synovitis score6.35 ± 4.589.78 ± 5.204.92 ± 3.44<0.0013.81 ± 2.21<0.0015.46 ± 3.81<0.001
Power Doppler score2.0 [0–14]6.0 [0–14]1.0 [0–10]<0.0011.0 [0–4]<0.0012.0 [0–10]<0.001
Erosions (grade) median0.0 [0–10]2.0 [0–8]0.0 [0–10]<0.0010.0 [0,1]<0.0010.0 [0–10]0.004
Presence of erosions, n (%)34 (37.0)16 (59.3)18 (27.7)0.0042 (9.5)<0.00116 (36.4)0.060
Continuous data are presented in median [minimum and maximum] values or mean ± standard deviation counted as numbers and valid percentages. UA, early undifferentiated arthritis; RA, rheumatoid arthritis; non-RA group, patients who at a 12-month follow-up had not developed rheumatoid arthritis; other rheumatic inflammatory disease, patients who at a 12-month follow-up had been diagnosed with psoriatic arthritis, ankylosing spondylitis, systemic lupus erythematosus, etc. p significant if <0.05. * p value between RA and non-RA groups; ** p value between RA group and patients whose arthritis resolved; *** p value between RA group and patients’ group who developed other inflammatory rheumatic diseases, statistically significant data is bolded.
Table 3. Association between rs2476601, rs833070, and rs6920220 polymorphisms and risk of developing rheumatoid arthritis in early undifferentiated arthritis cohort.
Table 3. Association between rs2476601, rs833070, and rs6920220 polymorphisms and risk of developing rheumatoid arthritis in early undifferentiated arthritis cohort.
SNPChrPosition Base PairMinor AlleleMAF NOdds Ratio (OR)p Value
rs24766011113834946A0.1920.990.98
rs833070643774889G0.4921.00.97
rs69202206137685367A0.1920.480.13
SNP, single nucleotide polymorphism; Chr, chromosome; MAF, minor allele frequency; N, number of patients; OR, odds ratio. p significant if <0.05.
Table 4. Association between rs2476601, rs833070, and rs6920220 polymorphisms and sociodemographic, clinical, laboratory, and ultrasound data in the cohort of patient with early undifferentiated arthritis.
Table 4. Association between rs2476601, rs833070, and rs6920220 polymorphisms and sociodemographic, clinical, laboratory, and ultrasound data in the cohort of patient with early undifferentiated arthritis.
Single Nucleotide
Polymorphisms		rs2476601rs833070rs6920220
Variable p Value p Value p Value
Patients’ distribution based on genotypeAA + AG/GG
23/69		-GG + GA/AA
16/76		-AA + AG/GG
30/62		-
66 SJC2.96 ± 1.88; 2 [1–8] *0.6193.00 ± 1.90; 3 [1–8] *0.4853.1 ± 2.68; 2 [1–14] *0.171
3.27 ± 2.26; 3 [1–14] *3.25 ± 2.24; 2 [1–14] *3.21 ± 1.84; 3 [1–12] *
28 SJC2.46 ± 1.32; 2 [1–5] *0.7182.48 ± 1.34; 2 [1–5] *0.3852.53 ± 1.59; 2 [1–8] *0.262
2.84 ± 1.72; 2 [0–8] *2.83 ± 1.71; 2 [0–8] *2.82 ± 1.62; 2 [0–8] *
68 TJC6.61 ± 4.62; 6 [1–17] *0.3686.78 ± 4.62; 6 [1–17] *0.9015.87 ± 6.25; 4 [1–28] *0.019
7.64 ± 6.21; 6 [0–28] *7.55 ± 6.21; 6 [0–28] *8.03 ± 5.44; 7 [0–24] *
28 TJC4.96 ± 3.05; 4 [1–14] *0.6545.07 ± 3.05; 5 [1–14] *0.8364.37 ± 3.84; 3 [0–18] *0.025
5.54 ± 4.02; 4 [0–18] *5.49 ± 4.02; 4 [0–18] *5.85 ± 3.63; 5 [0–14] *
ESR, mm/h29 [2–144]0.75427.0 [2–144]0.59022.0 [2–110]0.853
17 [2–88]19.50 [2–88]27.0 [2–144]
CRP, mg/L5.03 [0.20–111.50]0.7326.47 [0.20–111.50]0.6464.3 [0.16–111.50]0.500
4.48 [0.16–144.30]4.35 [0.16–144.30]7.32 [0.20–144.30]
RF, kU/L20.0 [20.00–814.20]0.63720.0 [20.00–814.20]0.41720 [9.59–814.20]0.674
20.0 [9.59–306.00]20.0 [9.59–306.00]20 [20.00–240.30]
RF positive (>30 kU/L)11 (39.3)0.70211 (40.7)0.3579 (30.0)0.602
20 (31.3)20 (30.8)22 (35.5)
Anti-CCP, U/L2.0 [2–300]0.1362.0 [2–300]0.3872.0 [2–300]0.871
2.0 [2–300]2.0 [2–300]2.0 [2–300]
Anti-CCP positive (≥5 U/mL)11 (44.0)0.13410 (41.7)0.38410 (33.3)0.870
19 (30.6)20 (31.7)20 (35.1)
RF and anti-CCP positive10 (40.0)0.99110 (41.7)0.1869 (30.0)0.880
17 (27.4)17 (27.0)18 (31.6)
HLA B27 expressed6 (28.6)0.5166 (30.0)0.8305 (23.8)0.597
14 (28.0)14 (27.5)15 (30.0)
ANA positive7 (46.7)0.3276 (42.9)0.8264 (26.7)0.071
12 (44.4)13 (46.4)15 (55.6)
VEGF, pg/mL384.64 [25.75–3438.23]0.563374.37 [68.67–3438.23]0.773284.91 [115.88–3438.23]0.323
312.46 [68.67–1854.91]272.30 [25.75–1854.91]414.47 [25.75–1967.50]
Synovitis, scores6.29 ± 4.22; 5 [2–16] *0.8066.37 ± 4.28; 5 [2–16] *0.9385.87 ± 5.31; 4 [2–25] *0.215
6.37 ± 4.77; 5 [2–25] *6.34 ± 4.74; 4 [2–25] *6.5 ± 4.22; 5 [2–21] *
PD, scores2.79 ± 2.84; 2 [0–10] *0.6272.89 ± 2.85; 2 [0–10] *0.7982.70 ± 3.26; 2 [0–12] *0.164
3.17 ± 3.13; 3 [0–14] *3.12 ± 3.14; 2 [0–14] *3.23 ± 2.94; 3 [0–14] *
Erosions, scores1.18 ± 1.89; 0 [0–6] *0.7221.22 ± 1.91; 0 [0–6] *0.9881.03 ± 1.90; 0 [0–8] *0.468
1.31 ± 2.23; 0 [0–10] *1.29 ± 2.22; 0 [0–10] *1.39 ± 2.23; 0 [0–10] *
Erosions detected9 (32.1)0.8039 (33.3)0.6439 (30.0)0.336
25 (39.1)25 (38.5)25 (40.3)
Continuous data are presented in median [minimum and maximum] values or mean ±standard deviation counted as numbers and valid percentages. SJC, swollen joint count; TJC, tender joint count; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; RF, rheumatoid factor; anti-CCP, anti-citrullinated protein antibodies; HLA B27, human leukocyte antigen B27; ANA, antinuclear antibodies; VEGF, vascular endothelial growth factor; PD, Power Doppler * data is presented in both mean and median values., statistically significant data is bolded. p significant if <0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sakalyte, R.; Stropuviene, S.; Jasionyte, G.; Bagdonaite, L.; Venalis, A. Association between PYTPN22 rs2476601, VEGF rs833070, TNFAIP3 rs6920220 Polymorphisms and Risk for Rheumatoid Arthritis in Early Undifferentiated Arthritis Patients: A Pilot Study. Medicina 2023, 59, 1824. https://doi.org/10.3390/medicina59101824

AMA Style

Sakalyte R, Stropuviene S, Jasionyte G, Bagdonaite L, Venalis A. Association between PYTPN22 rs2476601, VEGF rs833070, TNFAIP3 rs6920220 Polymorphisms and Risk for Rheumatoid Arthritis in Early Undifferentiated Arthritis Patients: A Pilot Study. Medicina. 2023; 59(10):1824. https://doi.org/10.3390/medicina59101824

Chicago/Turabian Style

Sakalyte, Regina, Sigita Stropuviene, Gabija Jasionyte, Loreta Bagdonaite, and Algirdas Venalis. 2023. "Association between PYTPN22 rs2476601, VEGF rs833070, TNFAIP3 rs6920220 Polymorphisms and Risk for Rheumatoid Arthritis in Early Undifferentiated Arthritis Patients: A Pilot Study" Medicina 59, no. 10: 1824. https://doi.org/10.3390/medicina59101824

Article Metrics

Back to TopTop