摘要Background:Type 2 diabetes(T2D)is an adult-onset and obese form of diabetes caused by an interplay between genetic,epigenetic,and environmental components.Here,we have assessed a cohort of 11 genetically different collaborative cross(CC)mouse lines comprised of both sexes for T2D and obesity developments in response to oral infection and high-fat diet(HFD)challenges.Methods:Mice were fed with either the HFD or the standard chow diet(control group)for 12weeks starting at the age of 8weeks.At week 5 of the experiment,half of the mice of each diet group were infected with Porphyromonas gingivalis and Fusobacterium nucleatum bacteria strains.Throughout the 12-week experimental pe-riod,body weight(BW)was recorded biweekly,and intraperitoneal glucose tolerance tests were performed at weeks 6 and 12 of the experiment to evaluate the glucose tolerance status of mice.Results:Statistical analysis has shown the significance of phenotypic variations be-tween the CC lines,which have different genetic backgrounds and sex effects in dif-ferent experimental groups.The heritability of the studied phenotypes was estimated and ranged between 0.45 and 0.85.We applied machine learning methods to make an early call for T2D and its prognosis.The results showed that classification with random forest could reach the highest accuracy classification(ACC=0.91)when all the attributes were used.Conclusion:Using sex,diet,infection status,initial BW,and area under the curve(AUC)at week 6,we could classify the final phenotypes/outcomes at the end stage of the experiment(at 12 weeks).