Front Pharmacol. 2024 Dec 10;15:1507860. doi: 10.3389/fphar.2024.1507860. eCollection 2024.
ABSTRACT
Age-related macular degeneration (AMD) is a leading cause of blindness among the elderly worldwide. Anti-vascular endothelial growth factor (anti-VEGF) injections remain the first-line therapy for AMD. However, their high cost and the need for frequent administration pose challenges to long-term adherence, highlighting the need for accessible and cost-effective preventive strategies. Emerging evidence suggests that traditional antidiabetic drugs, such as metformin, sulfonylureas, and thiazolidinediones, may offer neuroprotective benefits, opening new avenues for AMD prevention. Among these, metformin has emerged as the most promising candidate, demonstrating significant potential in reducing AMD risk, even at low cumulative doses, primarily through AMP-activated protein kinase (AMPK) activation. Sulfonylureas, although effective in stimulating insulin secretion, carry risks such as hypoglycemia, hyperinsulinemia, and a possible association with increased cancer risk. Similarly, thiazolidinediones, while improving insulin sensitivity, are associated with adverse effects, including cardiovascular risks and macular edema, limiting their broader application in AMD prevention. This paper explores the preventive potential and underlying mechanisms of these antidiabetic drugs in AMD and discusses the role of artificial intelligence in optimizing individualized prevention strategies. By advancing precision medicine, these approaches may improve public health outcomes and reduce the burden of aging-related vision loss.
PMID:39720591 | PMC:PMC11666363 | DOI:10.3389/fphar.2024.1507860