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

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. Ho...

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Autores principales:	Marta Correia, Eva Kagenaar, Daniël Bernardus van Schalkwijk, Mafalda Bourbon, Margarida Gama-Carvalho
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ab9736d59e0404a97e8cefb7a0a19cd
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spelling	oai:doaj.org-article:2ab9736d59e0404a97e8cefb7a0a19cd2021-12-02T10:54:15ZMachine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia10.1038/s41598-021-83392-w2045-2322https://doaj.org/article/2ab9736d59e0404a97e8cefb7a0a19cd2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83392-whttps://doaj.org/toc/2045-2322Abstract 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.Marta CorreiaEva KagenaarDaniël Bernardus van SchalkwijkMafalda BourbonMargarida Gama-CarvalhoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marta Correia Eva Kagenaar Daniël Bernardus van Schalkwijk Mafalda Bourbon Margarida Gama-Carvalho Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
description	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.
format	article
author	Marta Correia Eva Kagenaar Daniël Bernardus van Schalkwijk Mafalda Bourbon Margarida Gama-Carvalho
author_facet	Marta Correia Eva Kagenaar Daniël Bernardus van Schalkwijk Mafalda Bourbon Margarida Gama-Carvalho
author_sort	Marta Correia
title	Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
title_short	Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
title_full	Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
title_fullStr	Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
title_full_unstemmed	Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
title_sort	machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ab9736d59e0404a97e8cefb7a0a19cd
work_keys_str_mv	AT martacorreia machinelearningmodellingofbloodlipidbiomarkersinfamilialhypercholesterolaemiaversuspolygenicenvironmentaldyslipidaemia AT evakagenaar machinelearningmodellingofbloodlipidbiomarkersinfamilialhypercholesterolaemiaversuspolygenicenvironmentaldyslipidaemia AT danielbernardusvanschalkwijk machinelearningmodellingofbloodlipidbiomarkersinfamilialhypercholesterolaemiaversuspolygenicenvironmentaldyslipidaemia AT mafaldabourbon machinelearningmodellingofbloodlipidbiomarkersinfamilialhypercholesterolaemiaversuspolygenicenvironmentaldyslipidaemia AT margaridagamacarvalho machinelearningmodellingofbloodlipidbiomarkersinfamilialhypercholesterolaemiaversuspolygenicenvironmentaldyslipidaemia
_version_	1718396459288625152

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

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