Use of some bone-related cytokines as predictors for rheumatoid arthritis severity by neural network analysis

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Background. Rheumatoid arthritis (RA) is characterized by synovial membrane inflammation that results in joint damage. Many earlier studies have measured cytokines for a better diagnosis of RA. In the present study, three bone biomarkers [osteopontin, stromelysin-1 (MMP3), and vascular endothelial growth factor-A (VEGF)] are examined for their ability to estimate the severity of disease by using artificial neural network (NN) analysis and binary logistic regression analysis. Methods. The study enrolled 87 RA patients and 44 healthy control subjects. The biomarkers were measured by the enzyme-linked immunosorbent assay technique. Disease Activity Score (28 joints) and C-reactive protein (CRP) (DAS28-CRP) was calculated by using DAS28-CRP calculator. The patients with DAS28-CRP ≥ 5.1 are considered as having high disease activity (HDA). While patients’ group with DAS28-CRP < 5.1 are considered as moderate disease activity (MDA). The neural network (NN) analysis was used for the differentiation between groups. Results. Results showed that the most sensitive predictor for high disease activity (HDA) of RA is MMP3, followed by osteopontin and VEGF. These three biomarkers can differentiate significantly between HDA and MDA with a relatively high size effect (Partial η2 = 0.323, p < 0.001). The HDA group has a significantly higher MMP3, CRP, RF, and anti-citrullinated protein antibodies (ACPA) than the MDA group. MMP3 is strongly associated with two inflammatory indicators; CRP and ESR. Conclusion. There was a significant elevation in the serum level of MMP3 in RA patients with HDA compared to the MDA and control groups. High DAS28, RF, CRP, and ACPA were found in HDA patients compared with the MDA group. The use of the NN analysis indicated that the measured biomarkers help predict the HDA state in RA patients. MMP3 and osteopontin are diagnostic biomarkers for the severity of RA and are related to many disease-related characteristics with a sensitivity of 88.9% and specificity of 68.4%.

作者简介

R. Saleh

Al-Maarif University College

Email: sc.kfwi72@uoanbar.edu.iq

Medical Laboratory Techniques Department

伊拉克, Ramadi, Al Anbar Governorate

L. Mahmood

College of Medicine, University of Anbar

Email: sc.kfwi72@uoanbar.edu.iq

Assistent Professor

伊拉克, Ramadi, Al Anbar Governorate

M. Mohammed

General Directorate of Anbar Education, Ministry of Education

Email: sc.kfwi72@uoanbar.edu.iq
伊拉克, Al Anbar

K. Al-Rawi

College of Science, University of Anbar

编辑信件的主要联系方式.
Email: sc.kfwi72@uoanbar.edu.iq

PhD, Professor

俄罗斯联邦, Ramadi, Al Anbar Governorate

H. Al-Hakeim

College of Science, University of Kufa

Email: sc.kfwi72@uoanbar.edu.iq

Professor

伊拉克, Kufa, Najaf Governorate

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