Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/191808
Title: Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis
Author: Micó, Víctor
San Cristobal, Rodrigo
Martín, Roberto
Martínez-González, Miguel Ángel, 1957-
Salas Salvadó, Jordi
Corella Piquer, Dolores
Fitó Colomer, Montserrat
Alonso Gómez, Ángel M.
Wärnberg, Julia
Vioque, Jesús
Romaguera, Dora
López Miranda, José
Estruch Riba, Ramon
Tinahones, Francisco J.
Lapetra, José
Serra Majem, Lluís
Bueno Cavanillas, Aurora
Tur, Josep A.
Martín Sánchez, Vicente
Pintó Sala, Xavier
Delgado Rodríguez, Miguel
Matía Martín, Pilar
Vidal i Cortada, Josep
Vázquez, Clotilde
García Arellano, Ana
Pertusa Martinez, Salvador
Chaplin, Alice
García Ríos, Antonio
Muñoz Bravo, Carlos
Schröder, Helmut, 1958-
Babio, Nancy
Sorlí, José V.
Gonzalez, Jose I.
Martinez Urbistondo, Diego
Toledo Atucha, Estefanía
Bullón, Vanessa
Ruiz Canela, Miguel
Portillo, María Puy
Macías González, Manuel
Perez Diaz del Campo, Nuria
García Gavilán, Jesús
Daimiel, Lidia
Martínez, J. Alfredo, 1957-
Keywords: Marcadors bioquímics
Síndrome metabòlica
Anàlisi de conglomerats
Biochemical markers
Metabolic syndrome
Cluster analysis
Issue Date: 6-Sep-2022
Publisher: Frontiers Media SA
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 determined by hypercholesterolemia and associated treatments, the second factor exhibited glycemic disorders and accompanying treatments and the third factor was characterized by hepatic enzymes. Subsequently four clusters of patients were identified, where cluster 1 was characterized by glucose disorders and treatments, cluster 2 presented mild MetS, cluster 3 presented exacerbated levels of hepatic enzymes and cluster 4 highlighted cholesterol and its associated treatments Interestingly, the liver status related cluster was characterized by higher protein consumption and cluster 4 with low polyunsaturated fatty acid intake. This research emphasized the potential clinical relevance of hepatic impairments in addition to MetS traditional characterization for precision and personalized management of MetS patients.
Note: Reproducció del document publicat a: https://doi.org/10.3389/fendo.2022.936956
It is part of: Frontiers in Endocrinology, 2022, vol. 13
URI: http://hdl.handle.net/2445/191808
Related resource: https://doi.org/10.3389/fendo.2022.936956
ISSN: 1664-2392
Appears in Collections:Publicacions de projectes de recerca finançats per la UE
Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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