Effectiveness and drug retention of biologic disease modifying antirheumatic drugs in Korean patients with late onset ankylosing spondylitis

Current Pharmacological Therapies for the Management of Spondyloarthritis: Special Considerations in Older Patients

Spondylarthritis (SpA) is generally observed in young male patients but can be diagnosed in older patients. These cases correspond to late-onset SpA (LoSpA) with two main clinical presentations, axial and peripheral SpA. Another increasingly common situation is that of older patients who have had SpA for many years. The therapeutic management of LoSpA is quite smilar to the management of patients with an early-onset disease, combining both non-pharmacological and pharmacological treatments. The treatments that can be used in LoSpA include non-steroidal anti-inflammatory drugs (NSAIDs) and biological agents targeting TNFα or IL-17A. Janus kinase inhibitors (JAKi) were recently introduced on the market for SpA. TNF inhibitors and IL-17inhibitors are very effective drugs in early-onset SpA. The effectiveness and safety of targeted therapies have not been specifically evaluated in LoSpA or older patients, and thus caution is required for these patients with comorbidities and/or polymedication. According to indirect data, biological agents seem to be less effective in LoSpA compared with early-onset disease. In parallel, a careful evaluation for the risk of infection, malignancy and cardiovascular events is recommended before initiating these drugs in this age category. JAKi may be used in LoSpA, but only in selected patients according to recent recommendations from the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). When considering that the prevalence of such situations is expected to increase as ageing progresses, it is certainly time to consider this patient category as a distinct subgroup within the spectrum of SpA. Specific studies evaluating targeted agents in this age category are thus desirable.

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.