| アブストラクト | Background: drug-induced lipid metabolism disorders (DILMD) have emerged as a significant public health concern, particularly with the approval of certain medications in the past decade that may substantially increase the risk of lipid metabolism abnormalities. Despite increasing evidence, these adverse events are often underdiagnosed or misattributed to underlying conditions, receiving insufficient attention. Furthermore, comprehensive studies utilizing large-scale databases are scarce, leaving a significant gap in identifying risk signals and understanding the mechanisms of DILMD. Objective: this study aims to identify potential drug signals associated with DILMD using the U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) database, thereby addressing the lack of systematic large-scale analysis in this field. Methods: a disproportionality analysis (DPA) was conducted using the FAERS database (Q1 2004 to Q3 2024). The analysis employed MedDRA 26.1 terminology and algorithms such as reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), and empirical Bayes geometric mean (EBGM) to evaluate the strength of associations between drugs and lipid metabolism-related adverse events. Newly identified signals were cross-verified with FDA drug labels to confirm novelty. Results: a total of 140,110 reports of lipid metabolism disorder-related adverse events were analyzed. Significant signals were identified for drugs such as risperidone, adalimumab, and rofecoxib. Among the top 50 drugs, 29 lacked explicit mention of lipid metabolism risks in their labeling. Additionally, novel signals were detected for Carthamus Tinctorius, Rofecoxib, Alendronic Acid, Finasteride, and Eicosapentaenoic Acid5, indicating previously unrecognized risks of lipid metabolism abnormalities. The study also highlighted older adults and individuals with higher body weight as the most severely affected populations, aligning with prior knowledge of metabolic vulnerability. Conclusion: this study reveals the increasing prevalence of drug-induced lipid metabolism disorders and underscores the need for enhanced monitoring and timely updates to drug labels for high-risk medications. By leveraging real-world data and advanced signal detection methods, this research provides a robust framework for identifying emerging risks, contributing uniquely to public health and regulatory science. |
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