Background: In March 2020, the US Food and Drug Administration decided that the dangers related to neuropsychiatric events (NPEs) of montelukast, one of the leukotriene modifying agents (LTMAs), should be communicated through ‘boxed warning’. In case of NPEs, the prevalence has been the highest in elderly people. Because the characteristics of the elderly such as old age itself can act as risk factors. Therefore, an investigation on safety of LTMAs related to NPEs in elderly using LTMAs is needed. Method: A nested case-control study using an elderly sample cohort from the Korean National Health Insurance Service database was used. The asthma cohort included asthma patients newly diagnosed between 2003 and 2013. Within the asthma cohort, the case group was defined as patients who were diagnosed with NPEs. Among patients who had never been diagnosed with NPEs, the control group was selected by matching 1:1 by propensity score. Patients who were prescribed LTMAs for 1 year prior to index date were defined as the exposure group. The logistic regression model was used to measure the effect of LTMAs on NPEs. Results: We identified 141,165 patients with newly diagnosed asthma, and selected 31,992 patients per each case and control group. Exposure to LTMAs significantly increased the risk of overall NPEs about in comparison with the absence of exposure (crude odds ratio [OR] 1.58, 95% CI 1.50–1.68). After adjusting for confounding factors, the overall NPEs risk increased (adjusted OR, 1.67, 95% CI 1.58–1.78). Conclusion: This study suggests that elderly asthma patients prescribed LTMAs had a higher risk of NPEs than patients who were not treated with LTMAs. Therefore, clinicians should be aware of the potential risks of LTMAs.

Adrenal insufficiency is a rare, yet life-threatening immune-related adverse event of immune checkpoint inhibitors (ICIs). This study aimed to establish a risk scoring system for adrenal insufficiency in patients receiving anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. Moreover, several machine learning methods were utilized to predict such complications. This study included 209 ICI-treated patients from July 2015 to February 2021, excluding those with prior adrenal insufficiency, previous steroid therapy, or incomplete data to ensure data integrity. Patients were continuously followed up at Gyeongsang National University Hospital, with morning blood samples taken for basal cortisol level measurements, facilitating a comprehensive analysis of their adrenal insufficiency risk. Using a chi-squared test and logistic regression model, we derived the odds ratio and adjusted odds ratio (AOR) through univariate and multivariable analyses. This study utilized machine learning algorithms, such as decision trees, random forests, support vector machines (SVM), and logistic regression to predict adrenal insufficiency in patients treated with ICIs. The performance of each algorithm was evaluated using metrics like accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve (AUROC), ensuring rigorous assessment and reproducibility. A risk scoring system was developed from the multivariable and machine learning analyses. In a multivariable analysis, proton pump inhibitors (PPIs) (AOR 4.5), and α-blockers (AOR 6.0) were significant risk factors for adrenal insufficiency after adjusting for confounders. Among the machine learning models, logistic regression and elastic net showed good predictions, with AUROC values of 0.75 (0.61–0.90) and 0.76 (0.64–0.89), respectively. Based on multivariable and machine learning analyses, females (1 point), age ≥ 65 (1 point), PPIs (1 point), α-blockers (2 points), and antipsychotics (3 points) were integrated into the risk scoring system. From the logistic regression curve, patients with 0, 1, 2, 4, 5, and 6 points showed approximately 1.1%, 2.8%, 7.3%, 17.6%, 36.8%, 61.3%, and 81.2% risk for adrenal insufficiency, respectively. The application of our scoring system could prove beneficial in patient assessment and clinical decision-making while administering PD-1/PD-L1 inhibitors.

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

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.

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