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Optimizing diabetic kidney disease animal models: Insights from a meta-analytic approach
Fanghong Li1, Zhi Ma2, Yajie Cai1
1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Abstract:
Diabetic kidney disease (DKD) is a prevalent complication of diabetes, often leading to end-stage renal disease. Animal models have been widely used to study the pathogenesis of DKD and evaluate potential therapies. However, current animal models often fail to fully capture the pathological characteristics of renal injury observed in clinical patients with DKD. Additionally, modeling DKD is often a time-consuming, costly, and labor-intensive process. The current review aims to summarize modeling strategies in the establishment of DKD animal models by utilizing meta-analysis related methods and to aid in the optimization of these models for future research. A total of 1215 articles were retrieved with the keywords of "diabetic kidney disease" and "animal experiment" in the past 10 years. Following screening, 84 articles were selected for inclusion in the meta-analysis. Review manager 5.4.1 was employed to analyze the changes in blood glucose, glycosylated hemoglobin, total cholesterol, triglyceride, serum creatinine, blood urea nitrogen, and urinary albumin excretion rate in each model. Renal lesions shown in different models that were not suitable to be included in the meta-analysis were also extensively discussed. The above analysis suggested that combining various stimuli or introducing additional renal injuries to current models would be a promising avenue to overcome existing challenges and limitations. In conclusion, our review article provides an in-depth analysis of the limitations in current DKD animal models and proposes strategies for improving the accuracy and reliability of these models that will inspire future research efforts in the DKD research field.
Insights
Current diabetic kidney disease (DKD) animal models inadequately replicate human pathology. Combining stimuli or adding renal injuries can improve DKD model accuracy for future research.
Area of Science:
- Nephrology
- Endocrinology
- Translational Medicine
Background:
- Diabetic kidney disease (DKD) is a major diabetes complication, frequently progressing to end-stage renal disease.
- Animal models are crucial for DKD research but often lack clinical pathological relevance and are resource-intensive.
- Existing models present limitations in accurately reflecting human renal injury seen in DKD patients.
Approach:
- A comprehensive review and meta-analysis of 1215 articles published in the last decade using keywords 'diabetic kidney disease' and 'animal experiment'.
- Inclusion of 84 selected articles for meta-analysis, utilizing Review Manager 5.4.1 to assess key biomarkers: blood glucose, HbA1c, cholesterol, triglycerides, creatinine, BUN, and albuminuria.
- Qualitative discussion of renal lesions in models not suitable for meta-analysis to provide a holistic view.
Key Points:
- Meta-analysis revealed significant variations in biomarker levels and renal pathology across different DKD animal models.
- Current models often fail to recapitulate the complex multifactorial nature of human DKD.
- The review identified specific limitations in established DKD models regarding disease progression and endpoint manifestation.
Conclusions:
- Optimizing DKD animal models requires strategies that better mimic human disease complexity.
- Combining multiple insults or introducing secondary renal injuries shows promise for enhancing model fidelity.
- Improved animal models are essential to accelerate the development of effective DKD therapies and reduce research costs.
