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

2021-02-15Journal article Open access
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dc.contributor.authorCorreia, Marta
dc.contributor.authorKagenaar, Eva
dc.contributor.authorvan Schalkwijk, Daniël Bernardus
dc.contributor.authorBourbon, Mafalda
dc.contributor.authorGama-Carvalho, Margarida
dc.date.accessioned2022-02-01T15:49:48Z
dc.date.available2022-02-01T15:49:48Z
dc.date.issued2021-02-15
dc.description.abstractFamilial 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.pt_PT
dc.description.sponsorshipThis work was supported by UIDB/04046/2020 Research Unit grant from FCT, Portugal (to BioISI). MC is recipient of a fellowship from the BioSys Ph.D. programme PD65-2012 (Ref PD/BD/114387/2016) from FCT (Portugal).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSci Rep. 2021 Feb 15;11(1):3801. doi: 10.1038/s41598-021-83392-wpt_PT
dc.identifier.doi10.1038/s41598-021-83392-wpt_PT
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10400.18/7904
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Researchpt_PT
dc.relationBiosystems and Integrative Sciences Institute
dc.relationLiPID - Lipid profile ID . Identification of novel biomarkers to distinguish polygenic and monogenic dyslipidemia by a system biology approach
dc.relation.publisherversionhttps://www.nature.com/articles/s41598-021-83392-wpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectFamilial Hypercholesterolaemiapt_PT
dc.subjectCardiovascular Diseasept_PT
dc.subjectCardiovascular Disease Riskpt_PT
dc.subjectDoenças Cardio e Cérebro-vascularespt_PT
dc.titleMachine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemiapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleBiosystems and Integrative Sciences Institute
oaire.awardTitleLiPID - Lipid profile ID . Identification of novel biomarkers to distinguish polygenic and monogenic dyslipidemia by a system biology approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04046%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/PD%2FBD%2F114387%2F2016/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage3801pt_PT
oaire.citation.titleScientific Reportspt_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamOE
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.embargofctAcesso de acordo com política editorial da revista.pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isProjectOfPublicationdc433369-36fd-4935-bd52-c56aa49c72e1
relation.isProjectOfPublicationde9c898c-74fd-42b3-b57c-31cfe3611d1e
relation.isProjectOfPublication.latestForDiscoverydc433369-36fd-4935-bd52-c56aa49c72e1

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