Abstract
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.
Highlights
Familial hypercholesterolaemia increases circulating low-density lipoprotein cholesterol (LDL-C) levels and leads to premature cardiovascular disease when undiagnosed or untreated
Previous work using data from this study revealed that the approximately 60% of children that complied with the Simon Broome (SB) clinical criteria for Familial hypercholesterolaemia (FH) were negative for mutations in the hallmark genes, most likely corresponding to cases of polygenic hypercholesterolaemia[12]
Given that the available information on lipid parameters varied between individuals and considering the three lipid profiles defined for this study—‘Basic’, ‘Advanced’, and ‘Lipoprint’, we began by establishing distinct data subsets regarding all the possible combinations of these profiles (Fig. 1)
Summary
Familial hypercholesterolaemia increases circulating LDL-C levels and leads to premature cardiovascular disease when undiagnosed or untreated. 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. 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. FH increases circulating LDL-C mainly by affecting LDL receptor function, with undiagnosed and untreated subjects being at extremely high risk of premature cardiovascular disease (CVD)[3,6] These dyslipidaemic subjects present the most severe phenotype and prompt and accurate diagnosis is essential for CVD prevention, allowing earlier and/or more aggressive therapeutic measures, which have been shown to be effective at reducing cardiovascular morbidity and mortality in both adults and children[6,7,8]. The yield of FH genetic screening programs is relatively low, assuming significant costs for patients and/or national health systems
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