Familial hypercholesterolemia (FH) is a common hereditary disease of low-density lipoprotein-cholesterol (LDL-C) metabolism. It is associated with a higher risk of early coronary heart disease. About 60% of patients with a clinical diagnosis of FH do not have a detectable mutation in the genes that cause FH and are more likely to have a polygenic cause for their increase in LDL-C. We evaluated the degree of preclinical atherosclerosis in patients treated with monogenic FH compared to polygenic hypercholesterolaemia.
60 patients (20 diagnosed with monogenic FH and 40 with clinical diagnosis of FH and polygenic profile) will be enrolled. FH mutation tests and genotypes of six single nucleotide polymorphisms (SNPs) associated with LDL-C will be determined using routine methods. In particular, those with a detected mutation (monogenic) and patients with a negative mutation with SNL-C SNP score in the two upper quartiles (polygenic) will be recruited. All patients will undergo Coronary CT Angiography (CCTA). CCTA images will be analyzed using the CA-RADS method. The groups will be compared in order to evaluate if there is a discrepancy in the amount of coronary atherosclerosis between FH patients with monogenic mutation and polygenic profile, and to test the capability of traditional risk stratification model based on clinical and laboratory data to predict events. A more sophisticated analysis of CCTA images -supported by artificial intelligence (AI) system will be applied to provide a more accurate quantitative assessment of coronary atherosclerotic burden and to train neural network architecture in predicting cardiac events starting from CCTA image dataset. AI-aided platform will be also tested in distinguishing FH patients compared to 100 controls by CCTA image phenotype.
The innovation of the research can be summarized in four points:
1- Familial hypercholesterolaemia is a genetic condition that causes an acceleration of coronary atherosclerosis, with an increase in the number of cardiovascular events that occurs earlier than in the general population. FH patients with monogenic and polygenic disease have different degrees of atherosclerosis and different complication rates. The CCTA could demonstrate differences in CAD severity, extent and pattern between the two population or it could identify subgroups with higher or lower atherosclerotic disease
2- The study could reveal the limitations of traditional risk stratification schemes in predicting the presence of coronary stenosis and suggest those clinical or lab parameters that better correct prognostic prediction in patients with FH.
3- CCTA can play a role not only for the identification of hemodynamically significant stenoses but also for staging of the degree of atherosclerosis, improving the ability to predict cardiovascular events in FH subjects, with possible implications in identifying patients who are better respond to therapy or have a condition of accelerated atherosclerotic.
4- Gender-differences among FH patients (mono- and poligenic) are not well codified and poor investigated; CCTA may contribute to deeply understood the potential accelerative or retardant effect of the specific gender.
5- The application of neural network systems and deep learning algoritms to the analysis of CCTA images could open new scenarios in predicting events based on imaging and could bring out innovative associations between clinical factors and the development of CAD, as well as ameliorating the stratification strategy for cv events.