# Systematic Review: Exploring Inter-Species Variability in Diabetes Mellitus for Translational Medicine

## TL;DR

This systematic review compares how diabetes differs across species to improve the translation of research findings to human medicine.

## Contribution

The study provides a comprehensive comparison of diabetes features across species to guide translational research model selection.

## Key findings

- Spontaneous diabetes in dogs closely resembles human type 1 diabetes.
- Feline obesity-associated diabetes mirrors key features of human type 2 diabetes.
- Non-human primates show the highest physiological similarity to humans for diabetes research.

## Abstract

Interspecies variability in diabetes mellitus (DM) represents a critical challenge for translational medicine, as metabolic pathways, pancreatic architecture, and therapeutic responses differ substantially across animal models. This systematic review, conducted according to PRISMA 2020 guidelines, synthesized evidence from 86 eligible studies published between 2001 and 2025. Comparative data from rodents, dogs, cats, pigs, non-human primates, and humans were analyzed to identify species-specific patterns in insulin secretion, insulin resistance (IR), β-cell dysfunction, microbiota–metabolism interactions, and susceptibility to diabetic complications. Results indicate that spontaneous diabetes in dogs closely mirrors human type 1 diabetes (T1DM), whereas feline obesity-associated diabetes reflects key features of human type 2 diabetes (T2DM). Rodent models remain essential for mechanistic and genetic studies but show limited chronicity and lower predictive fidelity for long-term outcomes. Non-human primates exhibit the highest physiological similarity to humans, especially regarding β-cell structure and incretin response, supporting their role in advanced translational studies. Major limitations included methodological heterogeneity and inconsistent molecular reporting. Integrating spontaneous models with standardized protocols and multi-omics approaches enhances translational relevance and supports more accurate model selection in diabetes research.