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Repisalud author Micó, Víctor author San-Cristobal, Rodrigo author Martín, Roberto author Martínez-González, Miguel Ángel author Salas-Salvadó, Jordi author Corella, Dolores author Fitó, Montserrat author Alonso-Gómez, Ángel M author Wärnberg, Julia author Vioque, Jesús author Romaguera, Dora author López-Miranda, José author Estruch, Ramon author Tinahones, Francisco J author Lapetra, José author Serra-Majem, J Luís author Bueno-Cavanillas, Aurora author Tur, Josep A author Martín Sánchez, Vicente author Pintó, Xavier author Delgado-Rodríguez, Miguel author Matía-Martín, Pilar author Vidal, Josep author Vázquez, Clotilde author García-Arellano, Ana author Pertusa-Martinez, Salvador author Chaplin, Alice author Garcia-Rios, Antonio author Muñoz Bravo, Carlos author Schröder, Helmut author Babio, Nancy author Sorli, Jose V author Gonzalez, Jose I author Martinez-Urbistondo, Diego author Toledo, Estefania author Bullón, Vanessa author Ruiz-Canela, Miguel author Puy-Portillo, María author Macías-González, Manuel author Perez-Diaz-Del-Campo, Nuria author García-Gavilán, Jesús author Daimiel, Lidia author Martínez, J Alfredo 2024-02-27T15:23:52Z 2024-02-27T15:23:52Z 2022-09-06 Micó V, San-Cristobal R, Martín R, Martínez-González MÁ, Salas-Salvadó J, Corella D, et al. Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis. Front Endocrinol (Lausanne). 2022 Sep 6;13. 1664-2392 http://hdl.handle.net/10668/20567http://hdl.handle.net/20.500.13003/18644 http://hdl.handle.net/20.500.12105/18810 36147576 10.3389/fendo.2022.936956 Frontiers in endocrinology 2-s2.0-85138414223 884324100001 L2019158083 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. eng Biomarkers Cluster Dyslipidemia Glucose disorders Hepatic enzymes Metabolic syndrome Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis research article