SGLT1/2 as the potential biomarkers of renal damage under Apoe(−/−) and chronic stress via the BP neural network model and support vector machine

BACKGROUND: Chronic stress (CS) could produce negative emotions. The molecular mechanism of SGLT1 and SGLT2 in kidney injury caused by chronic stress combined with atherosclerosis remains unclear. METHODS: In total, 60 C57BL/6J mice were randomly divided into four groups, namely, control (CON, n = 15), control diet + chronic stress (CON+CS, n = 15), high-fat diet + Apoe(−/−) (HF + Apoe(−/−), n = 15), and high-fat diet + Apoe(−/−) + chronic stress (HF+Apoe(−/−) + CS, n = 15) groups. The elevated plus maze and open field tests were performed to examine the effect of chronic stress. The expression of SGLT1 and SGLT2 in the kidney was detected. The support vector machine (SVM) and back propagation (BP) neural network model were constructed to explore the predictive value of the expression of SGLT1/2 on the renal pathological changes. The receiver operating characteristic (ROC) curve analysis was used. RESULTS: A chronic stress model and atherosclerosis model were constructed successfully. Edema, broken reticular fiber, and increased glycogen in the kidney would be obvious in the HF + Apoe(−/−) + CS group. Compared with the CON group, the expression of SGLT1/2 in the kidney was upregulated in the HF + Apoe(−/−) + CS group (P < 0.05). There existed positive correlations among edema, glycogen, reticular fiber, expression of SGLT1/2 in the kidney. There were higher sensitivity and specificity of diagnosis of SGLT1/2 for edema, reticular fiber, and glycogen in the kidney. The result of the SVM and BP neural network model showed better predictive values of SGLT1 and SGLT2 for edema and glycogen in the kidney. CONCLUSION: In conclusion, SGLT1/2 might be potential biomarkers of renal damage under Apoe(−/−) and chronic stress, which provided a potential research direction for future related explorations into this mechanism.