¹Belarusian State Medical University; Minsk, Belarus.
² Institute of Genetics and Cytology, Belarusian National Academy of Science; Minsk, Belarus.
*Corresponding author: Professor Nikolay F. Soroka, MD, PhD, Chief of the 2nd Department of Internal Diseases Belarusian State Medical University. 83, Dzerzhinsky Avenue, 220116 Minsk, Belarus. Tel: 375-17-2725793; Fax: 375-17-2725793 E-mail: email@example.com
Background: Secondary or acquired amyloidosis (AA) develops in patients with chronic infections and inflammatory diseases, including rheumatoid arthritis (RA). Risk factor searching is one of most important objectives, which will help to improve patient’s survival.
Methods and results. We observed 104 patients: 45 (1st group) with RA complicated by renal amyloidosis (histologically confirmed) and 59 RA patients without this complication (2nd group). All patients were Belarusian citizens. Medical history and data of previous check-up were analyzed. All patients had undergone analyses for detection of C. trachomatis infection. Urethra or cervical scrapes were analyzed by polymerase chain reaction (PCR) method and/or by cultural method; the presence of C. trachomatis G and M antibodies was detected by immune-enzyme assay. We also compared the influence of the SAA1 gene allele polymorphism in AA-positive RA patients with those in AA-negative RA. To detect possible risk factors of renal amyloidosis secondary to RA statistical analysis was conducted. An odds-ratio (OR) calculated for the SAA1 α/α genotype was 45.26, and the 95% confidence interval (95%CI) was 9.9-206.8. It was shown that the SAA1 α/α genotype dominated in both groups and consisted 95.6% (1st group) and 32.2% (2nd group), respectively. OR for C. trachomatis infection was 26.6; 95%CI (9.26-76.37). We also worked up a prognostic model for risk of renal amyloidosis development in patients with RA. Conclusions. Risk of secondary amyloidosis in RA patients significantly depends on SAA1 genotype and the presence of C. trachomatis infection and can be calculated using prognostic model.
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