Abstract
BackgroundAnkylosing spondylitis (AS) is an autoimmune rheumatic disease mostly affecting the axial skeleton. Currently, anti-tumour necrosis factor α (anti-TNF-α) represents an effective treatment for AS that may delay the progression of the disease and alleviate the symptoms if the diagnosis can be made early. Unfortunately, effective diagnostic biomarkers for AS are still lacking; therefore, most patients with AS do not receive timely and effective treatment. The intent of this study was to determine several key metabolites as potential biomarkers of AS using metabolomic methods to facilitate the early diagnosis of AS.MethodsFirst, we collected samples of plasma, urine, and ligament tissue around the hip joint from AS and control groups. The samples were examined by nuclear magnetic resonance spectrometry, and multivariate data analysis was performed to find metabolites that differed between the groups. Subsequently, according to the correlation coefficients, variable importance for the projection (VIP) and P values of the metabolites obtained in the multivariate data analysis, the most crucial metabolites were selected as potential biomarkers of AS. Finally, metabolic pathways involving the potential biomarkers were determined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the metabolic pathway map was drawn.ResultsForty-four patients with AS agreed to provide plasma and urine samples, and 30 provided ligament tissue samples. An equal number of volunteers were recruited for the control group. Multidimensional statistical analysis suggested significant differences between the patients with AS and control subjects, and the models exhibited good discrimination and predictive ability. A total of 20 different metabolites ultimately met the requirements for potential biomarkers. According to KEGG analysis, these marker metabolites were primarily related to fat metabolism, intestinal microbial metabolism, glucose metabolism and choline metabolism pathways, and they were also probably associated with immune regulation.ConclusionsOur work demonstrates that the potential biomarkers that were identified appeared to have diagnostic value for AS and deserve to be further investigated. In addition, this work also suggests that the metabolomic profiling approach is a promising screening tool for the diagnosis of patients with AS.
Highlights
Ankylosing spondylitis (AS) is an autoimmune rheumatic disease mostly affecting the axial skeleton
Volunteers were recruited from among patients admitted to the emergency department of our hospital for the surgical treatment of femoral neck fracture (FNF) as control subjects for ligament tissue sample analysis
Clinical population Upon our invitation, 44 patients with AS and 44 healthy individuals as the control group consented to participate in the study of plasma and urine samples, and another 30 patients with AS and 30 patients with FNF consented to participate in the study of tissue samples
Summary
Ankylosing spondylitis (AS) is an autoimmune rheumatic disease mostly affecting the axial skeleton. Anti-tumour necrosis factor α (anti-TNF-α) represents an effective treatment for AS that may delay the progression of the disease and alleviate the symptoms if the diagnosis can be made early. Ankylosing spondylitis (AS) is a form of chronic inflammatory arthritis that predominantly affects the axial skeleton causing patients to experience severe stiffness and pain [1]. The structural changes on x-rays are the result of the inflammatory process but do not make the inflammatory process itself known [4] It takes several years before the changes are visible on xrays. Diagnostic biomarkers such as rheumatoid factor or autoantibodies against citrullinated proteins are used for the early diagnosis of rheumatoid arthritis (RA) with high specificity and selectivity [5, 6]. The high association of human leucocyte antigen B27 with AS, combined with a relatively high population prevalence, does not make it a good candidate for use as a diagnostic marker alone, but it may be useful in a combined model [4]
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