Abstract
Background. Rheumatoid arthritis (RA) is a systemic chronic autoimmune disease characterized by erosive and destructive joint lesions leading to their ankylosis, as well as damage to other organs and systems. The main types of RA treatment are reducing the severity of pain, slowing the rate of disease progression and improving the quality of life of patients. Despite advances in the treatment of RA, few significant biomarkers have been identified to date for the diagnosis and effectiveness of drug therapy control in RA. The results of scientific studies have shown that the pathogenesis of RA is based on complex biochemical phenomena involving metabolites arising from a variety of metabolic pathways. The overall metabolism of RA has not been fully studied, and the reliability of metabolic markers for the effectiveness of treatment at the cellular and molecular level is currently lacking. Metabolomics is a science that studies intermediate and conical products of metabolism depending on the quantitative assessment of the levels and dynamics of various metabolites in biological samples (blood, mocha and synovial fluid). metabolism is still not fully understood.
Aim. To identify changes in the metabolomic profile in patients with RA during biologic disease-modifying anti-rheumatic drugs.
Material and methods. The study participants were divided into 3 groups: the group of patients with RA not receiving antirheumatic therapy "RA de novo" included 14 people, the group of patients undergoing genetic engineering biological therapy "RA-bDMARDS" – 16 people and the control group "healthy volunteers" – 15 people. The study of intestinal metabolites of blood was carried out using ultra-efficient liquid chromatography in conjunction with a triple quadrupole analyzer. A correlation analysis of significant metabolites was performed in three groups of patients with active disease according to DAS28, CRP and ESR levels, the presence of RF and ACPA.
Results. When examining the metabolites of patients in all 3 groups, statistically significant levels of the following were identified: leucine/isoleucine (p=0.010), lysine (p<0.001), tryptophan metabolites (kyneurines (p<0.001), ornithine (p<0.001)), phenylalanine (p<0.001), valine (p=0.022), long-chain acylcarnitine’s (C14, C14-OH, C16-1, C18) (<0.001), proline (p<0.001), glutamine (p<0.001), tyrosine (p<0.001), aspartate (p<0.001). We identified a statistical comparison in the "RA de novo" and "RA-bDMARDS" groups between metabolites: phenylalanine (p=0.018), valine (p=0.026), tryptophan metabolites – kyneurines (p=0.047), leucine/isoleucine (p=0.047), at this level of metabolites of the "RA-bDMARDS" group (tyrosine and tryptophan, ornithine, proline) was close to the group of healthy volunteers.
Conclusion. Metabolomics allows us to identify the metabolites most associated with a disease, particularly RA, which opens up new opportunities for improving diagnostic accuracy and personalizing treatment.
Keywords: rheumatoid arthritis, metabolomic profiling, metabolites, biomarkers.
Aim. To identify changes in the metabolomic profile in patients with RA during biologic disease-modifying anti-rheumatic drugs.
Material and methods. The study participants were divided into 3 groups: the group of patients with RA not receiving antirheumatic therapy "RA de novo" included 14 people, the group of patients undergoing genetic engineering biological therapy "RA-bDMARDS" – 16 people and the control group "healthy volunteers" – 15 people. The study of intestinal metabolites of blood was carried out using ultra-efficient liquid chromatography in conjunction with a triple quadrupole analyzer. A correlation analysis of significant metabolites was performed in three groups of patients with active disease according to DAS28, CRP and ESR levels, the presence of RF and ACPA.
Results. When examining the metabolites of patients in all 3 groups, statistically significant levels of the following were identified: leucine/isoleucine (p=0.010), lysine (p<0.001), tryptophan metabolites (kyneurines (p<0.001), ornithine (p<0.001)), phenylalanine (p<0.001), valine (p=0.022), long-chain acylcarnitine’s (C14, C14-OH, C16-1, C18) (<0.001), proline (p<0.001), glutamine (p<0.001), tyrosine (p<0.001), aspartate (p<0.001). We identified a statistical comparison in the "RA de novo" and "RA-bDMARDS" groups between metabolites: phenylalanine (p=0.018), valine (p=0.026), tryptophan metabolites – kyneurines (p=0.047), leucine/isoleucine (p=0.047), at this level of metabolites of the "RA-bDMARDS" group (tyrosine and tryptophan, ornithine, proline) was close to the group of healthy volunteers.
Conclusion. Metabolomics allows us to identify the metabolites most associated with a disease, particularly RA, which opens up new opportunities for improving diagnostic accuracy and personalizing treatment.
Keywords: rheumatoid arthritis, metabolomic profiling, metabolites, biomarkers.
For citation:Musaeva L.M., Menshikova I.V., Appolonova S.A., Shestakova K.M. Metabolomic changes in rheumatoid arthritis: focus on biological disease-modifying antirheumatic drugs. Clinical review for general practice. 2024; 5 (11): 62–69 (In Russ.). DOI: 10.47407/kr2024.5.11.00518
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