Bibliographic Details
| Title: |
Assessing the cardioprotective effects of exercise in APOE mouse models using deep learning and photon-counting micro-CT. |
| Authors: |
Allphin, Alex J.1 (AUTHOR), Nadkarni, Rohan1 (AUTHOR), Han, Zay Y.1 (AUTHOR), Clark, Darin P.1 (AUTHOR), Ghaghada, Ketan B.2,3 (AUTHOR), Badea, Alexandra1 (AUTHOR), Badea, Cristian T.1 (AUTHOR) cristian.badea@duke.edu |
| Source: |
PLoS ONE. 4/10/2025, Vol. 20 Issue 4, p1-19. 19p. |
| Subject Terms: |
*EXERCISE physiology, *LIPID metabolism, *APOLIPOPROTEIN E4, *COMPUTED tomography, *X-ray computed microtomography, *DEEP learning |
| Abstract: |
Background: The allelic variations of the apolipoprotein E (APOE) gene play a critical role in regulating lipid metabolism and significantly impact cardiovascular disease risk (CVD). This study aimed to evaluate the impact of exercise on cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT) and deep learning-based segmentation. Methods: A total of 140 mice were grouped based on APOE genotype (APOE2, APOE3, APOE4), sex, and exercise regimen. All mice were maintained on a controlled diet to isolate the effects of exercise. Low dose cardiac photon counting micro-CT imaging with intrinsic gating was performed using a custom-built micro-PCCT system and data was reconstructed with an iterative algorithm incorporating both temporal and spectral dimensions. A liposomal-iodine nanoparticle contrast agent was intravenously administered to uniformly opacify cardiovascular structures. Cardiac structures were segmented using a 3D U-Net deep learning model that was trained and validated on manually labeled data. Statistical analyses, including ANOVA, post-hoc analysis, and stratified group comparisons, were used to assess the effects of genotype, sex, and exercise on key cardiac metrics, including ejection fraction and cardiac index. Results: The PCCT imaging pipeline provided high-resolution images with enhanced contrast between blood compartment and myocardium allowing for precise segmentation of cardiac features. Deep learning-based segmentation achieved high accuracy with an average Dice coefficient of 0.85. Exercise significantly improved cardiac performance, with ejection fraction increasing by up to 18% and cardiac index by 46% in exercised males, who generally benefited more from exercise. Females, particularly those with the APOE4 genotype, also showed improvements, with a 31% higher ejection fraction in exercised versus non-exercised mice. Stratified analyses confirmed that both sexes benefited from exercise, with males showing larger effect sizes. APOE3 and APOE4 genotypes derived the greatest benefit, while APOE2 mice showed no significant improvement. Conclusions: This study demonstrates the utility of PCCT combined with deep learning segmentation in assessing the cardioprotective effects of exercise in APOE mouse models. These findings highlight the importance of genotype-specific approaches in understanding and potentially mitigating the impact of CVD through lifestyle interventions such as exercise. [ABSTRACT FROM AUTHOR] |
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