{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mico V"],"funding":["European Research Council"],"pagination":["936956"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9487178"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13"],"pubmed_abstract":["Metabolic syndrome (MetS) is one of the most important medical problems around the world. Identification of patient´s singular characteristic could help to reduce the clinical impact and facilitate individualized management. This study aimed to categorize MetS patients using phenotypical and clinical variables habitually collected during health check-ups of individuals considered to have high cardiovascular risk. The selected markers to categorize MetS participants included anthropometric variables as well as clinical data, biochemical parameters and prescribed pharmacological treatment. An exploratory factor analysis was carried out with a subsequent hierarchical cluster analysis using the z-scores from factor analysis. The first step identified three different factors. The first was dete"],"journal":["Frontiers in endocrinology"],"pubmed_title":["Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis."],"pmcid":["PMC9487178"],"funding_grant_id":["340918"],"pubmed_authors":["Babio N","Garcia-Gavilan J","Macias-Gonzalez M","Vioque J","Toledo E","Mico V","Schroder H","Martinez-Urbistondo D","Lopez-Miranda J","Pinto X","Tinahones FJ","Lapetra J","Martinez JA","Alonso-Gomez AM","Garcia-Rios A","Garcia-Arellano A","Estruch R","Munoz Bravo C","Delgado-Rodriguez M","Martin R","Warnberg J","Chaplin A","Serra-Majem JL","Gonzalez JI","Romaguera D","Pertusa-Martinez S","Sorli JV","Corella D","Salas-Salvado J","Portillo MP","Perez-Diaz-Del-Campo N","Ruiz-Canela M","Vazquez C","Bullon V","San-Cristobal R","Vidal J","Martin Sanchez V","Matia-Martin P","Fito M","Tur JA","Martinez-Gonzalez MA","Daimiel L","Bueno-Cavanillas A"],"additional_accession":[]},"is_claimable":false,"name":"Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis.","description":"Metabolic syndrome (MetS) is one of the most important medical problems around the world. Identification of patient´s singular characteristic could help to reduce the clinical impact and facilitate individualized management. This study aimed to categorize MetS patients using phenotypical and clinical variables habitually collected during health check-ups of individuals considered to have high cardiovascular risk. The selected markers to categorize MetS participants included anthropometric variables as well as clinical data, biochemical parameters and prescribed pharmacological treatment. An exploratory factor analysis was carried out with a subsequent hierarchical cluster analysis using the z-scores from factor analysis. The first step identified three different factors. The first was dete","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-18T15:48:04.741Z","creation":"2025-02-19T01:37:45.945Z"},"accession":"S-EPMC9487178","cross_references":{"pubmed":["36147576"],"doi":["10.3389/fendo.2022.936956"]}}