Animal Models of Diabetic Neuropathic Pain
Abstract
Diabetic neuropathy is a common complication of diabetes. It occurs in approximately 10-20% of patients with diabetes, or roughly 40-50% patients with diabetic neuropathy. However, the pathogenesis of diabetic neuropathic pain is still largely unknown. Several animal models have been used to study the underlying mechanisms for this complication. Some commonly used animal models include streptozotocin-induced rat and mouse models, diet/nutrition-induced models, combination of chemically- and nutrition-induced model, Zucker diabetic fatty rat model, type 1 insulinopenic BB/Wor and type 2 hyperinsulinemic diabetic BBZDR/Wor rat models, and transgenic/knock-out models. Even though the manifestations of diabetic neuropathic pain vary from thermal or chemical hyperalgesia, thermal or chemical hypoalgeia, allodynia, to spontaneous pain, some pathogenesis factors are shared among these symptoms. Increased AR activity, oxidative-nitrosative stress, protein kinase C, PARP and ACE activations, C-peptide deficiency, impaired neurotrophism, and proinflammatory responses have been identified in the development of diabetic neuropathic pain. This review discusses selected animal models for diabetic neuropathic pain, as well as some commonly shared pathways in these models.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.