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Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.

Journal article
Authors Ana Pina
Saga Helgadottir
Rosellina Margherita Mancina
Chiara Pavanello
Carlo Pirazzi
Tiziana Montalcini
Roberto Henriques
Laura Calabresi
Olov Wiklund
Ma Pula Macedo
Luca Valenti
Giovanni Volpe
Stefano Romeo
Published in European journal of preventive cardiology
Pages 2047487319898951
ISSN 2047-4881
Publication year 2020
Published at Wallenberg Laboratory
Department of Physics (GU)
Institute of Medicine, Department of Molecular and Clinical Medicine
Pages 2047487319898951
Language en
Links dx.doi.org/10.1177/2047487319898951
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Cardiovascular medicine

Abstract

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 - for example, the Dutch Lipid Score - 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 "virtual" genetic test using machine-learning approaches.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 = 174) and internal test (N = 74)) and the FH-CEGP Milan cohort (external test, N = 364). 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.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.

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