Construction of cynomolgus monkey type 2 diabetes models by combining genetic prediction model with high-energy diet

Type 2 diabetes mellitus (T2D) is a significant health concern. Research using non-human primates, which develop T2D with similar symptoms and pancreatic changes as humans, is crucial but limited by long timelines and low success rates.

We targeted capture sequenced 61 normal and 81 T2D cynomolgus monkeys using a primer panel that captured 269 potential regulatory regions potentially associated with T2D in the cynomolgus monkey genome. 80 variants were identified to be associated with T2D and were used to construct a genetic prediction model. Among 8 machine learning algorithms tested, we found that the best prediction performance was achieve when the model using support vector machine with polynomial kernel as the machine learning algorithm (AUC = 0.933). Including age and sex in this model did not significantly improve the prediction performance. Using the genetic prediction model, we further screened 22 monkeys and found 13 were high risk while 9 were low risk. After feeding the 22 monkeys with high-energy food for 32 weeks, we found all the 9 low risk monkeys did not develop T2D while 4 out of 13 high risk monkeys (31 %) develop T2D.

This method greatly increased the success rate of establishing T2D monkey models while decreased the time needed compared to traditional methods. Therefore, we developed a new high-efficiency method to establish T2D monkey models by combining the genetic prediction model and high-energy diet, which will greatly contribute to the research on the clinical characteristics, pathogenesis, complications and potential new treatments.