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The study computes phenotype-specific genetic risk scores for MS-related biochemical markers and assesses clinical usefulness with machine learning models. Using longitudinal PUBMEP cohort data from 138 children across prepuberty to puberty, PRSice-2-derived scores and prepubertal clinical variables predict pubertal MS status, showing strong biomarker associations and improved prediction when genetics and environmental factors are integrated.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leveraging-machine-learning-and-genetic-risk-scores-for-the-prediction-of-metabolic-syndrome-in-children-with-obesity-abstract/124802/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/leveraging-machine-learning-and-genetic-risk-scores-for-the-prediction-of-metabolic-syndrome-in-children-with-obesity-abstract/124802.png","ImageObject",300,407,{"name":92,"@type":93},"Aurora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-27","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address?","Question",{"text":112,"@type":113},"The study targets early identification of children with obesity who are at high risk of developing metabolic syndrome during puberty.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were genetic risk scores created and used?",{"text":117,"@type":113},"Phenotype-specific genetic risk scores were generated from GWAS data using PRSice-2, then combined with clinical and environmental prepubertal data in predictive machine learning models.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main findings on prediction performance?",{"text":121,"@type":113},"Genetic scores showed strong associations with corresponding phenotypic biomarkers, and models incorporating prepubertal genetics, HDL levels, and sedentary lifestyle achieved reasonable performance for predicting pubertal obesity, while isolated risk scores gave limited results for MS.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},124802,1785894739,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","proceedings  \nAbstract  \nLeveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity †  \nConcepción M. Aguilera 1,2,3, *, Mireia Bustos-Aibar 1,2,4, Augusto Anguita-Ruiz 2,5, Álvaro Torres-Martos 1,2,3, Gloria Bueno 2,4,6, Rosaura Leis 7,8 and Jesús Alcalá-Fernández 9  \nCitation: Aguilera, C.M.;  \nBustos-Aibar, M.; Anguita-Ruiz, A.; Torres-Martos, Á.; Bueno, G.; Leis, R.; Alcalá-Fernández, J. Leveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity. Proceedings 2023, 91, 377. [https://](https://)[ ](https://)[doi.org/10.3390/proceedings2023091377](doi.org/10.3390/proceedings2023091377)  \nAcademic Editors: Sladjana Sobajicand Philip Calder  \nPublished: 27 February 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology“José Mataix”, Center of Biomedical Research, University of Granada, 18071 Granada, Spain; [mbustos@iisaragon.es](mbustos@iisaragon.es) (M.B.-A.); alvarotorres@ugr.es (Á .T.-M.)  \n2 The Center for Biomedical Research Network Physiopathology of Obesity and Nutrition (CIBEROBN), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain; [augusto.anguita@isglobal.org](augusto.anguita@isglobal.org) (A.A.-R.); [mgbuenol@unizar.es](mgbuenol@unizar.es) (G.B.)  \n3 Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain  \n4 GENUD (Growth, Exercise, NUtrition and Development) Research Group, Agri-Food Institute of  \nAragon (IA2), Aragon Health Research Institute (IIS Aragón), University of Zaragoza, 50018 Zaragoza, Spain  \n5 Institute for Global Health (ISGlobal), 08003 Barcelona, Spain  \n6 Unit of Pediatric Endocrinology, University Clinical Hospital Lozano Blesa, 50009 Zaragoza, Spain  \n7 Unit of Pediatric Gastroenterology, Hepatology and Nutrition, Pediatric Service, Hospital Clínico Universitario de Santiago, 15706 Santiago de Compostela, Spain; [mariarosaura.leis@usc.es](mariarosaura.leis@usc.es)  \n[8](8 Unit of Investigation in Nutrition)[ Unit of Investigation in Nutrition](8 Unit of Investigation in Nutrition), [Growth and Human Development of Galicia-USC](Growth and Human Development of Galicia-USC), [Pediatric Nutrition](Pediatric Nutrition)[ ](Pediatric Nutrition)Research Group-Health Research Institute of Santiago de Compostela (IDIS),  \n15706 Santiago de Compostela, Spain  \n9 Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain; [jalcala@decsai.ugr.es](jalcala@decsai.ugr.es)  \n* Correspondence: caguiler@ugr.es  \n† Presented at the 14th European Nutrition Conference FENS 2023, Belgrade, Serbia, 14–17 November 2023 .  \nAbstract: Background and objectives: Obesity is a growing global epidemic, associated with increased cardiometabolic disorders. Metabolic syndrome (MS) is defined by altered insulin, blood pressure, glucose, and lipid levels. Pubertal children with obesity are highly susceptible to developing MS, necessitating its early identification. This study aims to compute phenotype-specific genetic risk scores for MS-related biochemical markers and evaluate their clinical utility using machine learningbased models. Methods: Longitudinal data from the PUBMEP Spanish cohort were analyzed, including 138 children (71 girls and 67 boys) at two time points, spanning from prepuberty to puberty. Clinical, endogenous, environmental, and omics variables were measured. Genetic risk scores were generated using GWAS data and PRSice-2 software. Thes","cbCaihwN7WlJChj1","https://ap.wps.com/l/cbCaihwN7WlJChj1","pdf",174666,"English","# Abstract\n## Background and objectives\n## Methods\n## Results\n## Discussion","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets early identification of children with obesity who are at high risk of developing metabolic syndrome during puberty.\"},{\"question\":\"How were genetic risk scores created and used?\",\"answer\":\"Phenotype-specific genetic risk scores were generated from GWAS data using PRSice-2, then combined with clinical and environmental prepubertal data in predictive machine learning models.\"},{\"question\":\"What were the main findings on prediction performance?\",\"answer\":\"Genetic scores showed strong associations with corresponding phenotypic biomarkers, and models incorporating prepubertal genetics, HDL levels, and sedentary lifestyle achieved reasonable performance for predicting pubertal obesity, while isolated risk scores gave limited results for MS.\"}]","Leveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity - Abstract | PDF"]