Practical Significance of Biomarkers in Axial Spondyloarthritis: Updates on Diagnosis, Disease Activity, and Prognosis.
Alexandra-Diana DiaconuAlexandr CeasovschihVictorița ȘorodocCristina PomîrleanuCătălina LionteLaurențiu ȘorodocCodrina Mihaela AncutaPublished in: International journal of molecular sciences (2022)
Axial spondyloarthritis (axSpA) is a chronic inflammatory disease that can lead to ankylosis by secondary ossification of inflammatory lesions, with progressive disability and a significant impact on quality of life. It is also a risk factor for the occurrence of comorbidities, especially cardiovascular diseases (CVDs), mood disorders, osteoporosis, and malignancies. Early diagnosis and treatment are needed to prevent or decrease functional decline and to improve the patient's prognosis. In respect of axSpA, there is an unmet need for biomarkers that can help to diagnose the disease, define disease activity and prognosis, and establish personalized treatment approaches. The aim of this review was to summarize the available information regarding the most promising biomarkers for axSpA. We classified and identified six core categories of biomarkers: (i) systemic markers of inflammation; (ii) molecules involved in bone homeostasis; (iii) HLA-B27 and newer genetic biomarkers; (iv) antibody-based biomarkers; (v) microbiome biomarkers; and (vi) miscellaneous biomarkers. Unfortunately, despite efforts to validate new biomarkers, few of them are used in clinical practice; however, we believe that these studies provide useful data that could aid in better disease management.
Keyphrases
- disease activity
- systemic lupus erythematosus
- rheumatoid arthritis
- ankylosing spondylitis
- oxidative stress
- rheumatoid arthritis patients
- cardiovascular disease
- multiple sclerosis
- healthcare
- type diabetes
- juvenile idiopathic arthritis
- gene expression
- bone mineral density
- postmenopausal women
- case report
- depressive symptoms
- coronary artery disease
- bipolar disorder
- machine learning
- metabolic syndrome
- artificial intelligence
- dna methylation
- electronic health record
- copy number
- drug induced