Introduction: The cardiovascular renin–angiotensin system (RAS) could be affected by gender and dietary regime. We hypothesized that male rats will be more susceptible to activation of RAS in the heart and aorta, as a response to a fructose-rich diet (FRD). Materials and methods: Both male and female Wistar rats were given a 10% (w/v) fructose solution for 9 weeks. We measured the biochemical parameters, blood pressure (BP) and heart rate. We used Western blot and real-time polymerase chain reaction (PCR) to quantify protein and gene expression. Results: In the male rats, the FRD elevated BP and expression of cardiac angiotensin-converting enzyme (ACE), while the expression of angiotensin-converting enzyme 2 (ACE2) and angiotensin II Type 2 receptor (AT₂R) were significantly decreased. In female rats, there were no changes in cardiac RAS expression due to FRD. Furthermore, the ACE/AT₁R axis was overexpressed in the FRD male rats’ aortae, while only AT₁R was upregulated in the FRD female rats’ aortae. ACE2 expression remained unchanged in the aortae of both genders receiving the FRD. Conclusions: The FRD induced gender-specific changes in the expression of the RAS in the heart and aortae of male rats. Further investigations are required in order to get a comprehensive understanding of the underlying mechanisms of gender-specific fructose-induced cardiovascular pathologies.

Introduction: Abdominal aortic aneurysms (AAAs) and peripheral artery disease (PAD) are two vascular diseases with a significant risk of major adverse cardiovascular events and mortality. A challenge in current disease management is the unpredictable disease progression in individual patients. The VASCUL-AID-RETRO study aims to develop trustworthy multimodal predictive artificial intelligence (AI) models for multiple tasks including risk stratification of disease progression and cardiovascular events in patients with AAA and PAD. Methods: The VASCUL-AID-RETRO study will collect data from 5000 AAA and 6000 PAD patients across multiple European centers of the VASCUL-AID consortium using electronic health records from 2015 to 2024. This retrospectively-collected data will be enriched with additional data from existing biobanks and registries. Multimodal data, including clinical records, radiological imaging, proteomics, and genomics, will be collected to develop AI models predicting disease progression and cardiovascular risks. This will be done while integrating the international ethics guidelines and legal standards for trustworthy AI, to ensure a socially-responsible data integration and analysis. Proposed Analyses: A consensus-based variable list of clinical parameters and core outcome set for both diseases will be developed through meetings with key opinion leaders. Blood, plasma, and tissue samples from existing biobanks will be analyzed for proteomic and genomic variations. AI models will be trained on segmented AAA and PAD artery geometries for estimation of hemodynamic parameters to quantify disease progression. Initially, risk prediction models will be developed for each modality separately, and subsequently, all data will be combined to be used as input to multimodal prediction models. During all processes, data security, data quality, and ethical guidelines and legal standards will be carefully considered. As a next step, the developed models will be further adjusted with prospective data and internally validated in a prospective cohort (VASCUL-AID-PRO study). Conclusion: The VASCUL-AID-RETRO study will utilize advanced AI techniques and integrate clinical, imaging, and multi-omics data to predict AAA and PAD progression and cardiovascular events. Clinical Trial Registration: The VASCUL-AID-RETRO study is registered at www.clinicaltrials.gov under the identification number NCT06206369. Clinical Impact: The VASCUL-AID-RETRO study aims to improve clinical practice of vascular surgery by developing artificial intelligence-driven multimodal predictive models for patients with abdominal aortic aneurysms or peripheral artery disease, enhancing personalized medicine. By integrating comprehensive data sets including clinical, imaging, and multi-omics data, these models have the potential to provide accurate risk stratification for disease progression and cardiovascular events. An innovation lies in the extensive European data set in combination with multimodal analyses approaches, which enables the development of advanced models to facilitate better understanding of disease mechanisms and progression. For clinicians, this means that more precise, individualized treatment plans can be established, ultimately aiming to improve patient outcomes.