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Background: Rheumatoid arthritis (RA) is a chronic inflammatory disorder primarily targeting joints, significantly impacting patients' quality of life. The introduction of tumor necrosis factor-alpha (TNF-α) inhibitors has markedly improved RA management by reducing inflammation. However, these medications are associated with adverse skin reactions, which can vary greatly among patients due to genetic differences. Objectives: This study aimed to identify risk factors associated with skin adverse events by TNF-α in RA patients. Methods: A cohort study was conducted, encompassing patients with RA who were prescribed TNF-α inhibitors. This study utilized machine learning algorithms to analyze genetic data and identify markers associated with skin-related adverse events. Various machine learning algorithms were employed to predict skin and subcutaneous tissue-related outcomes, leading to the development of a risk-scoring system. Multivariable logistic regression analysis identified independent risk factors for skin and subcutaneous tissue-related complications. Results: After adjusting for covariates, individuals with the TT genotype of rs12551103, A allele carriers of rs13265933, and C allele carriers of rs73210737 exhibited approximately 20-, 14-, and 10-fold higher incidences of skin adverse events, respectively, compared to those with the C allele, GG genotype, and TT genotype. The machine learning algorithms used for risk prediction showed excellent performance. The risk of skin adverse events among patients receiving TNF-α inhibitors varied based on the risk score: 0 points, 0.6%; 2 points, 3.6%; 3 points, 8.5%; 4 points, 18.9%; 5 points, 36.7%; 6 points, 59.2%; 8 points, 90.0%; 9 points, 95.7%; and 10 points, 98.2%. Conclusions: These findings, emerging from this preliminary study, lay the groundwork for personalized intervention strategies to prevent TNF-α inhibitor-associated skin adverse events. This approach has the potential to improve patient outcomes by minimizing the risk of adverse effects while optimizing therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Aim: Cardiovascular (CV) risk and mortality associated with spondyloarthritis (SpA) remain controversial. Herein, we performed a meta‐analysis of the latest large‐scale population‐based studies to demonstrate the elevated risk of CV disease and mortality in patients with SpA than in the general population. Methods: MEDLINE and EMBASE databases were searched systematically for population‐based studies published between January 1997 and September 2019. Additional manual literature searches were also performed. All searches and data collection were performed independently by 2 reviewers. We calculated the risks of myocardial infarction (MI), stroke, and all‐cause mortality in a meta‐analysis and determined the risk ratios (RR) using the Mantel–Haenszel method. Results: Among the 641 identified articles, 16 articles involving 18 cases met the inclusion criteria for our meta‐analysis; these included 12 cases of ankylosing spondylitis, five cases of psoriatic arthritis, and 1 case of undifferentiated SpA. Our meta‐analysis revealed a significantly high risk of MI (RR: 1.52; 95% CI: 1.29‐1.80) and stroke (RR: 1.21; 95% CI: 1.0‐1.47) in patients with SpA than in the general population. However, this increased risk was not significant in terms of all‐cause mortality (RR: 1.23; 95% CI: 0.96‐1.57). Conclusions: Our meta‐analysis demonstrated that patients with SpA have a significantly increased risks of MI and stroke, but without a significant increase in the all‐cause mortality, than that in the general population. The higher risk of CV in patients with SpA than that in the general population indicates the need for strict risk factor correction and disease management. [ABSTRACT FROM AUTHOR]

Background/aims: The Gout Impact Scale (GIS), a part of the Gout Assessment Questionnaire 2.0, is used to measure gout-specific health-related quality of life (HRQOL). Although several studies have been conducted on the factors affecting the HRQOL of patients with gout, few have focused on lifestyle factors. This study aimed to investigate the correlation between lifestyle habits and HRQOL using the GIS in patients with gout., Methods: We used data from the Urate-Lowering TheRApy in Gout (ULTRA) registry, a prospective cohort of Korean patients with gout treated at multiple centers nationwide. The patients were aged ≥18 years and met the 2015 American College of Rheumatology/European League Against Rheumatism gout classification criteria. They were asked to complete a GIS and questions regarding their lifestyle habits at enrollment., Results: The study included 232 patients. 'Gout concern overall' scores in the GIS were significantly lower in patients who exercised more frequently and consumed soft drinks and meat less, and 'well-being during attack' scores were significantly lower in patients who consumed vegetables and exercised more frequently. The frequency of vegetable consumption had a negative linear relationship with the 'well-being during attack' and 'gout concern during attack' scores (p = 0.01, p = 0.001, respectively). The frequency of exercise had a negative linear relationship with the 'gout concern overall' and 'gout concern during attack' scores (p = 0.04 and p = 0.002, respectively)., Conclusion: Patients with gout who frequently consumed vegetables and exercised regularly experienced less impact of gout, exhibiting a better GIS that represented HRQOL.