{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:30:41Z","timestamp":1770456641151,"version":"3.49.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T00:00:00Z","timestamp":1580774400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Eur J Prev Cardiolog"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:sec><jats:title>Aims<\/jats:title><jats:p> Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores \u2013 for example, the Dutch Lipid Score \u2013 are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. The aim of this study is to obtain a more reliable approach to FH diagnosis by a \u201cvirtual\u201d genetic test using machine-learning approaches. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and results<\/jats:title><jats:p> We used three machine-learning algorithms (a classification tree (CT), a gradient boosting machine (GBM), a neural network (NN)) to predict the presence of FH-causative genetic mutations in two independent FH cohorts: the FH Gothenburg cohort (split into training data ( N\u2009=\u2009174) and internal test ( N\u2009=\u200974)) and the FH-CEGP Milan cohort (external test, N\u2009=\u2009364). By evaluating their area under the receiver operating characteristic (AUROC) curves, we found that the three machine-learning algorithms performed better (AUROC 0.79 (CT), 0.83 (GBM), and 0.83 (NN) on the Gothenburg cohort, and 0.70 (CT), 0.78 (GBM), and 0.76 (NN) on the Milan cohort) than the clinical Dutch Lipid Score (AUROC 0.68 and 0.64 on the Gothenburg and Milan cohorts, respectively) in predicting carriers of FH-causative mutations. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p> In the diagnosis of FH-causative genetic mutations, all three machine-learning approaches we have tested outperform the Dutch Lipid Score, which is the clinical standard. We expect these machine-learning algorithms to provide the tools to implement a virtual genetic test of FH. These tools might prove particularly important for lipid clinics without access to genetic testing. <\/jats:p><\/jats:sec>","DOI":"10.1177\/2047487319898951","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T05:50:00Z","timestamp":1580881800000},"page":"1639-1646","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":44,"title":["Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning"],"prefix":"10.1093","volume":"27","author":[{"given":"Ana","family":"Pina","sequence":"first","affiliation":[{"name":"CEDOC \u2013 Centro de Estudos de Doen\u00e7as Cr\u00f3nicas, NOVA Medical School\/Faculdade de Ci\u00eancias M\u00e9dicas, Universidade Nova de Lisboa, Portugal"},{"name":"Portuguese Diabetes Association, Education and Research Center (APDP-ERC), Portugal"},{"name":"Department of Medical Sciences, University of Aveiro, Portugal"}]},{"given":"Saga","family":"Helgadottir","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Gothenburg, Sweden"}]},{"given":"Rosellina Margherita","family":"Mancina","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Sweden"}]},{"given":"Chiara","family":"Pavanello","sequence":"additional","affiliation":[{"name":"Centro E. Grossi Paoletti, Dipartimento di Scienze Farmacologiche e Biomolecolari, Universit\u00e0 degli Studi di Milano, Italy"}]},{"given":"Carlo","family":"Pirazzi","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Sahlgrenska University Hospital, Sweden"}]},{"given":"Tiziana","family":"Montalcini","sequence":"additional","affiliation":[{"name":"Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, Italy"}]},{"given":"Roberto","family":"Henriques","sequence":"additional","affiliation":[{"name":"NOVA Information Management School, Campus de Campolide, Portugal"}]},{"given":"Laura","family":"Calabresi","sequence":"additional","affiliation":[{"name":"Centro E. Grossi Paoletti, Dipartimento di Scienze Farmacologiche e Biomolecolari, Universit\u00e0 degli Studi di Milano, Italy"}]},{"given":"Olov","family":"Wiklund","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Sweden"}]},{"given":"M Paula","family":"Macedo","sequence":"additional","affiliation":[{"name":"CEDOC \u2013 Centro de Estudos de Doen\u00e7as Cr\u00f3nicas, NOVA Medical School\/Faculdade de Ci\u00eancias M\u00e9dicas, Universidade Nova de Lisboa, Portugal"},{"name":"Portuguese Diabetes Association, Education and Research Center (APDP-ERC), Portugal"},{"name":"Department of Medical Sciences, University of Aveiro, Portugal"}]},{"given":"Luca","family":"Valenti","sequence":"additional","affiliation":[{"name":"Translational Medicine, Department of Transfusion Medicine and Hematology, Fondazione IRCCS Ca\u2019 Granda Ospedale Maggiore Policlinico and Department of Pathophysiology and Transplantation, Universit\u00e0 degli Studi di Milano, Italy"}]},{"given":"Giovanni","family":"Volpe","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Gothenburg, Sweden"}]},{"given":"Stefano","family":"Romeo","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Wallenberg Laboratory, University of Gothenburg, Sweden"},{"name":"Department of Cardiology, Sahlgrenska University Hospital, Sweden"},{"name":"Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, 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