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<p>Diabetes mellitus is a major global health challenge, affecting 11.1% of adults worldwide, with nearly half of the cases remaining undiagnosed. Despite extensive progress, its multifactorial pathogenesis requires further investigation to advance therapeutic development. Experimental models are indispensable for understanding disease mechanisms and evaluating interventions, although no single model fully recapitulates human diabetes. Chemical agents such as streptozotocin and alloxan simulate β-cell loss, whereas dietary and obesity-induced models reflect insulin resistance and metabolic disturbances. Genetic models, including <i>ob/ob</i>, <i>db/db</i>, and non-obese diabetic mice, provide insights into obesity-associated and autoimmune pathways, whereas in vitro systems enable controlled mechanistic studies and drug screening. This review integrates evidence from diverse experimental platforms, highlighting their comparative strengths, limitations, and translational applicability to support rational model selection and enhance the efficiency of diabetes research and therapeutic innovation.</p>

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Experimental models in diabetes research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Lalit P. Dewalkar

摘要

Diabetes mellitus is a major global health challenge, affecting 11.1% of adults worldwide, with nearly half of the cases remaining undiagnosed. Despite extensive progress, its multifactorial pathogenesis requires further investigation to advance therapeutic development. Experimental models are indispensable for understanding disease mechanisms and evaluating interventions, although no single model fully recapitulates human diabetes. Chemical agents such as streptozotocin and alloxan simulate β-cell loss, whereas dietary and obesity-induced models reflect insulin resistance and metabolic disturbances. Genetic models, including ob/ob, db/db, and non-obese diabetic mice, provide insights into obesity-associated and autoimmune pathways, whereas in vitro systems enable controlled mechanistic studies and drug screening. This review integrates evidence from diverse experimental platforms, highlighting their comparative strengths, limitations, and translational applicability to support rational model selection and enhance the efficiency of diabetes research and therapeutic innovation.