Hiccups can significantly reduce the quality of life of patients and can occur as a drug side effect. Previous reports have revealed sex-specific differences in the incidence of drug-induced hiccups. However, the pathogenesis of drug-induced hiccups remains unknown, and there is limited evidence on its treatment or prevention. This study examined molecular initiating events (MIEs), which are the starting point of adverse events, to investigate the drug-induced pathways of hiccups. We extracted drugs suspected to cause hiccups using the FDA Adverse Event Reporting System, a large database on adverse drug reactions. Information on drugs suspected to be associated with hiccups was extracted from the overall population and sex-specific subgroups were divided. In each data table, the predicted activity values of nuclear receptors and stress response pathways for each drug were calculated using the Toxicity Predictor, a machine-learning model. Transforming growth factor-beta and antioxidant response elements were considered an independent factor for hiccups in the male and female subgroups, respectively. This report first examined one of the mechanisms of drug-induced hiccups and identified MIEs associated with drug-induced hiccups. The use of an adverse event database and the machine-learning model, Toxicity Predictor, may be useful for generating hypotheses for other adverse effects with unknown mechanisms.

Hiccups can significantly reduce the quality of life of patients and can occur as a drug side effect. Previous report using the Drug Adverse Event Database revealed sex-specific differences in the incidence of drug-induced hiccups. However, the pathogenesis of drug-induced hiccups remains unknown, and there is limited evidence on its treatment or prevention. This study examined molecular initiating events (MIEs), which are the starting point of adverse events, to investigate the drug-induced pathways of hiccups. We extracted drugs suspected to cause hiccups using FDA Adverse Event Reporting System, a large database on adverse drug reactions. Information on drugs suspected to be associated with hiccups were extracted from the overall population and sex-specific subgroups divided. In each data table, the predicted activity values of nuclear receptors (NRs) and stress response pathways (SPs) for each drug were calculated using the toxicity predictor, a machine-learning model. Univariate analysis extracted NRs and SPs associated with hiccups, and a multivariate analysis was performed to evaluate independent factors. Transforming growth factor-beta and antioxidant response element were considered an independent factor for hiccups in the male and female subgroups, respectively. This report first examined one of the mechanisms of drug-induced hiccups and identified MIEs associated with drug-induced hiccups.

Frailty is a geriatric syndrome associated with adverse outcomes, particularly in super-aged societies. Hospitalization-related inactivity may contribute to frailty progression and worsen prognosis and post-discharge quality of life; however, frailty identification at admission is challenging. This study used diagnosis procedure combination (DPC) data to examine associations between patient and medication factors at admission and hospitalization-induced frailty. The cohort comprised patients aged ≥65 years hospitalized at Shonantobu General Hospital with records of oral medication prescribed or brought in at admission. Four PRISMA-7 frailty risk index items were evaluated. Patients meeting at least three criteria were defined as the frailty risk group. Bivariate and multivariate analyses assessed the influence of patient background and medication categories on frailty risk. The frailty risk group included 888/1948 cases. Risk and nonrisk groups differed significantly in sex, age, and body mass index (BMI). Logistic regression analysis showed that low BMI, antithrombotics, mineral supplements, and antiparkinsonian drugs were associated with frailty risk, although the findings for male sex and advanced age should be interpreted cautiously because these variables were included as components of the modified frailty-risk definition. Patient background and medication information from DPC data at admission may contribute to future frailty risk estimation. Future prospective studies using detailed clinical data and machine learning models are needed to clarify the relationship between medication use and frailty.