As the global population ages, the prevalence of comorbidities and health-deteriorating diseases increases, impacting the overall well-being and quality of life. In individuals aging normally, without severe diseases or comorbidities, chronological and biological age are often equivalent. Chronological age signifies the time a person has lived, while biological age encompasses the accumulation of time, genetics, environment, lifestyle, and other variables influencing aging, making it more closely tied to the functional state of the body/tissues. Accelerated cardiac aging indicates a faster decline in heart function than the average, thereby associating with the risk of premature death.
Our hypothesis is that the use of advanced descriptors based on non-invasive electrocardiographic imaging, coupled with artificial intelligence, can enhance the definition of the cardiac age paradigm and establish a model for cardiac aging, creating a new descriptive biomarker for the general population.
To test this hypothesis, we will employ non-invasive electrocardiographic imaging (ECGI) alongside artificial intelligence strategies. Our primary objectives include:
(1) Recruitment and Analysis of a New Database: Gather and analyze a new database of healthy subjects spanning various ages and genders, utilizing ECGI technology.
(2) Development of a Cardiac Aging Model: Employ deep learning techniques to create a cardiac aging model, quantifying each cardiac region described through ECGI. This model will generate an estimate of biological cardiac age upon data acquisition.
(3) Comparative Analysis in Patients with Cardiac Conditions: Compare chronological and biological cardiac age in a second group of patients with cardiac conditions against the aging model generated from healthy subjects. This aims to obtain estimators of cardiac health and assess the progression of deterioration caused by these diseases.
Through these objectives, we aim to shed light on the intricate dynamics of cardiac aging, paving the way for a comprehensive understanding of cardiovascular health in the general population.
CIGE/2022/2 was funded by Generalitat Valenciana