Development of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients.

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.

Oral diabetes medication and risk of dementia in elderly patients with type 2 diabetes.

AIM: To examine the effect of oral diabetes medication on the risk of dementia in an elderly cohort with type 2 diabetes. METHODS: This was a population-based cohort study using the Korean National Health Insurance claims data from 2002 to 2013. Elderly subjects (60 years of age or older) with and without type 2 diabetes were included; patients with new-onset type 2 diabetes were further divided into the oral diabetes medication group and no-medication group. RESULTS: Among 278,290 patients with type 2 diabetes, 56,587 developed dementia (20.3%) over 11 years of follow-up. Type 2 diabetes was associated with a 1.69-fold increased risk of dementia (95% CI 1.66-1.72). Among patients with newly diagnosed type 2 diabetes, the risk of dementia was lower in the oral diabetes medication group than in the no-medication group (adjusted hazard ratio [aHR], 0.79; 95% CI 0.77-0.81). Lower risk of dementia was particularly noticeable in all of the combination therapy groups and especially lower in the combination therapy group treated with dipeptidyl peptidase 4 inhibitor (aHR 0.48, 95% CI 0.45-0.51). CONCLUSION: Overall, the use of oral diabetes medication in type 2 diabetes patients significantly decreased the risk of dementia.