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.

Background/Objectives: Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used but can cause clinically significant gastrointestinal adverse events (GI-AEs). However, genetic susceptibility to NSAID-related GI toxicity remains insufficiently characterized in East Asian populations. This exploratory study aimed to identify genetic loci associated with grade ≥ 2 NSAID-related GI-AEs in Korean patients treated with commonly used NSAIDs. Methods: We conducted an exploratory genome-wide association study in a multicenter Korean cohort of NSAID-treated patients recruited from four Korean hospitals. A total of 339 patients were included, comprising 28 cases with grade ≥ 2 GI-AEs and 311 controls without documented GI-AEs. Genotyping was performed using the Korea Biobank Array, followed by imputation and quality control. Genome-wide association analysis was conducted under an additive logistic regression model adjusted for age, sex, and the first two principal components, with additional sensitivity analyses accounting for the specific NSAID type, cumulative DDD, and concomitant medications. Polygenic risk score analysis was performed using summary statistics from an arthritis-treated subset of the KoGES Ansan/Ansung cohort, and the selected variant were further evaluated using multivariable Firth penalized logistic regression. The discriminatory performance of clinical, laboratory, and genetic models was assessed using bootstrap internal validation. Results: The exploratory GWAS identified 20 suggestive variants associated with NSAID-related GI-AEs at p < 1 × 10⁻⁵, although no variant reached genome-wide significance. The KoGES-derived polygenic risk score was significantly associated with GI-AE status in the NSAID cohort, and 10 suggestive variants overlapped with SNPs included in the best-fit PRS model. rs1925245 maintained a consistent association in multivariable Firth penalized logistic regression. Conclusions: These findings suggest that NSAID-related GI-AEs in Korean patients may reflect a multifactorial susceptibility pattern involving both clinical context and polygenic risk. Although exploratory, this study provides candidate genetic signals for future replication and supports the need for larger ancestry-matched pharmacogenomic studies of NSAID-related GI toxicity.

Kyung Hyun Min, Jun Hyeob Kim, Jin Yeon Gil, Jun Hyuk Park, Kyung Eun Lee. DTT 2025;4:121-9. https://doi.org/10.58502/DTT.25.0003

Jin Yeon Gil, Kyung Hyun Min, Woorim Kim, Jun Hyeob Kim, Ji Min Han, Kyung Eun Lee. DTT 2023;2:56-61. https://doi.org/10.58502/DTT.23.0004

Jin Yeon Gil, Jun Hyeob Kim, Sunny Park, Ji Min Han, Kyung Eun Lee. DTT 2026;5:182-91. https://doi.org/10.58502/DTT.26.0019