Strategies for inducing diabetes in laboratory animals: Advances from chemical, surgical, immunologic, dietary, and genetic approaches.

Diabetes mellitus is a metabolic disorder characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It involves complex metabolic, hormonal, and immunological mechanisms that cannot be fully reproduced in vitro, making in vivo animal models essential for mechanistic and translational research. This review provides a comprehensive overview of strategies used to induce diabetes in laboratory animals. The principal approaches are categorized as follows: (i) chemical induction (e.g., streptozotocin, alloxan, dithizone), (ii) dietary and hormonal interventions (e.g., high-fat diets, glucocorticoids), (iii) immune- and virus-mediated models (e.g., Coxsackievirus-triggered diabetes), (iv) surgical models (e.g., partial pancreatectomy), and (v) genetically engineered or spontaneously diabetic strains (e.g., NOD mice, GK rats, db/db mice). For each category, we describe the underlying mechanisms, typical protocols (including doses, duration, and onset of hyperglycemia), and their relevance for modeling type 1 or type 2 diabetes. Although chemical models are widely applied because of their technical simplicity, reproducibility, and relatively low cost, they may not fully replicate autoimmune or progressive metabolic features of human disease. Comparative tables summarize model characteristics, including mechanisms, strengths, limitations, disease onset, and research applications, to facilitate informed model selection. No single animal model fully reproduces the species-specific immune complexity, progressive metabolic deterioration, and long-term complications observed in human diabetes. Nevertheless, these models provide controlled and mechanistically tractable systems for investigating β-cell dysfunction, insulin resistance, inflammatory pathways, and therapeutic interventions. Careful alignment between model characteristics and research objectives-and, when appropriate, the complementary use of multiple models-is therefore essential to enhance translational relevance.