Machine learning-based prediction of aortic microstructural changes using renal pathological indicators under chronic unpredictable mild stress

Abstract

Background

Chronic psychological stress is an increasingly recognized risk factor for both renal and cardiovascular diseases, yet the mechanisms linking stress exposure to vascular remodeling remain poorly defined. We aimed to investigate whether renal pathological markers can predict abdominal aortic microstructural changes under chronic unpredictable mild stress (CUMS) and to evaluate the predictive value of these markers using machine learning models in both normal and metabolically compromised murine models.

Methods

A total of 60 male mice, including 30 wild-type (WT) C57BL/6J and 30 ApoE-deficient mice (ApoE^–/–) were randomized into four groups (n =15 per group): WT+ normal diet (ND), WT + ND + CUMS, ApoE^–/– + western diet (WD), and ApoE^–/– + WD + CUMS. CUMS was administered for 12 weeks. Histopathological assessment of renal and aortic tissues was performed using hematoxylin and eosin (H&E), periodic acid-Schiff (PAS), reticular fiber, and immunohistochemical staining. Expression of sodium-glucose cotransporters (SGLT1 and SGLT2), edema, glycogen content, and abdominal aortic intima-media thickness (AA-IMT) were quantified. Correlation analyses were conducted between renal and vascular parameters. Two machine learning models—support vector machine (SVM) and backpropagation (BP) neural network—were used to evaluate the predictive relationship between renal biomarkers and aortic microstructural alterations.

Results

CUMS significantly increased renal SGLT1/SGLT2 expression, edema, and glycogen content in wild-type mice (all P < 0.01), along with increased AA-IMT and reduced aortic diameter (P < 0.05). ApoE^–/– mice exhibited pronounced aortic remodeling under CUMS, despite no further renal deterioration. Correlation analysis showed a strong association between renal and vascular indicators, particularly in the CUMS + ApoE^–/– group. Machine learning models demonstrated robust performance in predicting abdominal aorta diameter (AA-D) and AA-IMT based on SGLT1 and SGLT2 levels (SVM R² for AA-D: 0.79, BP network correlation for AA-IMT: 0.99), identifying these markers as early indicators of stress-induced vascular remodeling.

Conclusion

Renal injury biomarkers, particularly SGLT1 and SGLT2, can serve as reliable predictors of abdominal aortic microstructural changes under chronic stress. Integration of machine learning models enhances early risk stratification and provides a novel approach for mechanistic insight into cardiorenal stress responses.