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 12 weeks starting at the age of 8 weeks. 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 period, 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 between the CC lines, which have different genetic backgrounds and sex effects in different 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).
Type 2 diabetes mellitus (T2DM) is a severe chronic epidemic that results from the body’s improper usage of the hormone insulin. Globally, 700 million people are expected to have received a diabetes diagnosis by 2045, according to the International Diabetes Federation (IDF). Cancer and macro- and microvascular illnesses are only a few immediate and long-term issues it could lead to. T2DM accelerates the effect of organ weights by triggering a hyperinflammatory response in the body’s organs, inhibiting tissue repair and resolving inflammation. Understanding how genetic variation translates into different clinical presentations may highlight the mechanisms through which dietary elements may initiate or accelerate inflammatory disease processes and suggest potential disease-prevention techniques. To address the host genetic background effect on the organ weight by utilizing the newly developed mouse model, the Collaborative Cross mice (CC). The study was conducted on 207 genetically different CC mice from 8 CC lines of both sexes. The experiment started with 8-week-old mice for 12 weeks. During this period, one group maintained a standard chow diet (CHD), while the other group maintained a high-fat diet (HFD). In addition, body weight was recorded bi-weekly, and at the end of the study, a glucose tolerance test, as well as tissue collection (liver, spleen, heart), were conducted. Our study observed a strong effect of HFD on blood glucose clearance among different CC lines. The HFD decreased the blood glucose clearance displayed by the significant Area Under Curve (AUC) values in both populations. In addition, variation in body weight changes among the different CC lines in response to HFD. The female liver weight significantly increased compared to males in the overall population when exposed to HFD. Moreover, males showed higher heritability values than females on the same diet. Regardless of the dietary challenge, the liver weight in the overall male population correlated positively with the final body weight. The liver weight results revealed that three different CC lines perform well under classification models. The regression results also varied among organs. Accordingly, the differences among these lines correspond to the genetic variance, and we suspect that some genetic factors invoke different body responses to HFD. Further investigations, such as quantitative trait loci (QTL) analysis and genomic studies, could find these genetic elements. These findings would prove critical factors for developing personalized medicine, as they could indicate future body responses to numerous situations early, thus preventing the development of complex diseases.
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