Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia.

Familial hypercholesterolaemia increases circulating LDL-C levels and leads to premature cardiovascular disease when undiagnosed or untreated. Current guidelines support genetic testing in patients complying with clinical diagnostic criteria and cascade screening of their family members. However, most of hyperlipidaemic subjects do not present pathogenic variants in the known disease genes, and most likely suffer from polygenic hypercholesterolaemia, which translates into a relatively low yield of genetic screening programs. This study aims to identify new biomarkers and develop new approaches to improve the identification of individuals carrying monogenic causative variants. Using a machine-learning approach in a paediatric dataset of individuals, tested for disease causative genes and with an extended lipid profile, we developed new models able to classify familial hypercholesterolaemia patients with a much higher specificity than currently used methods. The best performing models incorporated parameters absent from the most common FH clinical criteria, namely apoB/apoA-I, TG/apoB and LDL1. These parameters were found to contribute to an improved identification of monogenic individuals. Furthermore, models using only TC and LDL-C levels presented a higher specificity of classification when compared to simple cut-offs. Our results can be applied towards the improvement of the yield of genetic screening programs and corresponding costs.