Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

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• dc.contributor.author Micó, Víctor
• dc.contributor.author Fitó Colomer, Montserrat
• dc.contributor.author Schröder, Helmut, 1958-
• dc.contributor.author Martínez, José Alfredo
• dc.date.accessioned 2023-02-09T08:05:21Z
• dc.date.available 2023-02-09T08:05:21Z
• dc.date.issued 2022
• dc.description.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.
• dc.description.sponsorship The PREDIMED-Plus trial was supported by the European Research Council (Advanced Research grant 2014–2019; agreement #340918; granted to MM-G); the official Spanish institutions for funding scientific biomedical research, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN) and Instituto de Salud Carlos III (ISCIII) through the Fondo de Investigación para la Salud (FIS) which is co-funded by the European Regional Development Fund (coordinated FIS projects led by JS-S and JV, including the following projects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576, PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, PI20/01158), and the Especial Action Project “Implementación y evaluación de una intervención intensiva sobre la actividad física Cohorte PREDIMED-Plus” (JS-S); the Recercaixa (grant number 2013ACUP00194) (JS-S). Moreover, JS-S gratefully acknowledges the financial support by ICREA under the ICREA Academia program; the SEMERGEN grant; Department of Health of the Government of Navarra (61/2015), the Fundació La Marató de TV (Ref. 201630.10); the AstraZeneca Young Investigators Award in Category of Obesity and T2D 2017 (DR); grants from the Consejería de Salud de la Junta de Andalucía (PI0458/2013; PS0358/2016; PI0137/2018), the PROMETEO/2017/017 grant from the Generalitat Valenciana, the SEMERGEN grant; grant of support to research groups 35/2011 (Balearic Islands Government; FEDER funds) (JT).
• dc.format.mimetype application/pdf
• dc.identifier.citation 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: 936956. DOI: 10.3389/fendo.2022.936956
• dc.identifier.doi http://dx.doi.org/10.3389/fendo.2022.936956
• dc.identifier.issn 1664-2392
• dc.identifier.uri http://hdl.handle.net/10230/55680
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/ 340918
• dc.rights Copyright © 2022 Micó, San-Cristobal, Martín, Martínez-González, Salas-Salvadó, Corella, Fitó, Alonso-Gómez, Wärnberg, Vioque, Romaguera, López-Miranda, Estruch, Tinahones, Lapetra, Serra-Majem, Bueno-Cavanillas, Tur, Martín Sánchez, Pintó, Delgado-Rodríguez, Matía, Vidal, Vázquez, García-Arellano, Pertusa-Martinez, Chaplin, Garcia-Rios, Muñoz Bravo, Schröder, Babio, Sorli, Gonzalez, Martinez-Urbistondo, Toledo, Bullón, Ruiz-Canela, Portillo, Macías-González, Perez-Diaz-del-Campo, García-Gavilán, Daimiel and Martínez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) http://creativecommons.org/licenses/by/4.0/. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply w
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Biomarkers
• dc.subject.keyword Cluster
• dc.subject.keyword Dyslipidemia
• dc.subject.keyword Glucose disorders
• dc.subject.keyword Hepatic enzymes
• dc.subject.keyword Metabolic syndrome
• dc.title Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion