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Background: Microvascular dysfunction (MVD) is a recognized sign of disease in heart failure progression. Intact blood vessels exhibit abnormal vasoreactivity in early stage, subsequently deteriorating to rarefaction and reduced perfusion. In managing heart failure with preserved ejection fraction (HFpEF), earlier diagnosis is key to improving management. In this study, we applied a steady-state blood-pool magnetic resonance imaging (MRI) method to investigate if it can sensitively detect abnormal leg muscle vasoreactivity, a sign of MVD, posited to manifest before structural and functional cardiac changes emerge in a diabetes model of HFpEF.

Methods: Male and female Sprague-Dawley rats were maintained on either a high-fat, high-sugar diet or a control diet for 6 months after the induction of diabetes (n = 5 per group). Beginning at month 1 or 2 post-diabetes and every 2 months thereafter, rats underwent steady-state blood-pool MRI to assess vasoreactivity in the heart or skeletal muscle, respectively. A T1-reducing blood-pool agent was administered and the T1 relaxation time dynamically measured as animals breathed in elevated CO₂ levels to modulate vessels.

Results: In male rats, the normally unresponsive heart to 10% CO₂ revealed a pro-vasoconstriction response beginning at 5 months post-diabetes. Abnormal leg skeletal muscle vasoreactivity appeared even earlier, at 2 months: the usual vasodilatory response to 5% CO₂ was interrupted with periods of vasoconstriction in diseased rats. In female rats, differences were observed between healthy and diseased animals only in the first 2 months post-diabetes and not later. In the heart, vasodilation to 10% CO₂ seen in healthy females was abolished in diabetic females. In skeletal muscle, 5% CO₂ was suboptimal in inducing reproducible vasoreactivity, but young diabetic females responded by vasodilation only.

Conclusions: Abnormal vasoreactivity presented earlier than overt functional changes in both heart and skeletal muscle in diabetic cardiomyopathy, and steady-state blood-pool MRI offered early diagnosis of microvascular dysfunction.

Cardiovascular diseases are a major cause of mortality and morbidity. Fast detection of life-threatening emergency events and an earlier start of the therapy would save many lives and reduce successive disabilities. Understanding the specific risk factors associated with heart attack and the degree of association is crucial in the clinical diagnosis. Considering the potential benefits of intelligent models in healthcare, many researchers have developed a variety of machine learning (ML)-based models to identify patients at risk of a heart attack. However, the common problem of previous works that used ML concepts was the lack of transparency in black-box models, which makes it difficult to understand how the model made the prediction. In this study, an automated smart recommender system (Explainable Artificial Intelligence) for heart attack prediction and risk stratification was developed. For the purpose, the CatBoost classifier was applied as the initial step. Then, the SHAP (SHapley Additive exPlanation) explainable algorithm was employed to determine reasons behind high or low risk classification. The recommender system can provide insights into the reasoning behind the predictions, including group-based and patient-specific explanations. In the final step, we integrated a Large Language Model (LLM) called BioMistral for chatting functionally to talk to users based on the model output as a digital doctor for consultation. Our smart recommender system achieved high accuracy in predicting a patient risk level with an average AUC of 0.88 and can explain the results transparently. Moreover, a Django-based online application that uses patient data to update medical information about an individual's heart attack risk was created. The LLM chatbot component would answer user questions about heart attacks and serve as a virtual companion on the route to heart health, our system also can locate nearby hospitals by applying Google Maps API and alert the users. The recommender system could improve patient management and lower heart attack risk while timely therapy aids in avoiding subsequent disabilities.