Author, as appears in the article.: Micó V; San-Cristobal R; Martín R; Martínez-González MÁ; Salas-Salvadó J; Corella D; Fitó M; Alonso-Gómez ÁM; Wärnberg J; Vioque J; Romaguera D; López-Miranda J; Estruch R; Tinahones FJ; Lapetra J; Serra-Majem JL; Bueno-Cavanillas A; Tur JA; Martín Sánchez V; Pintó X; Delgado-Rodríguez M; Matía-Martín P; Vidal J; Vázquez C; García-Arellano A; Pertusa-Martinez S; Chaplin A; Garcia-Rios A; Muñoz Bravo C; Schröder H; Babio N; Sorli JV; Gonzalez JI; Martinez-Urbistondo D; Toledo E; Bullón V; Ruiz-Canela M; Portillo MP; Macías-González M; Perez-Diaz-del-Campo N; García-Gavilán J; Daimiel L; Martínez JA
Department: Bioquímica i Biotecnologia
URV's Author/s: Babio Sánchez, Nancy Elvira / Salas Salvadó, Jorge
Keywords: Metabolic syndrome Hepatic enzymes Glucose disorders Food-frequency questionnaire Dyslipidemia Cluster Biomarkers validity risk population outcomes obesity neutrophil-lymphocyte ratio mini-mental state metabolic syndrome inflammation glucose disorders dyslipidemia cluster biomarkers association
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.
Thematic Areas: Medicina veterinaria Medicina iii Medicina ii Medicina i Interdisciplinar Farmacia Endocrinology, diabetes and metabolism Endocrinology & metabolism Ciências biológicas ii Ciências biológicas i
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: nancy.babio@urv.cat jordi.salas@urv.cat
Author identifier: 0000-0003-3527-5277 0000-0003-2700-7459
Record's date: 2023-05-20
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://internal-journal.frontiersin.org/articles/10.3389/fendo.2022.936956/full
Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Frontiers In Endocrinology. 13
APA: Micó V; San-Cristobal R; Martín R; Martínez-González MÁ; Salas-Salvadó J; Corella D; Fitó M; Alonso-Gómez ÁM; Wärnberg J; Vioque J; Romaguera D; López (2022). Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis. Frontiers In Endocrinology, 13(), -. DOI: 10.3389/fendo.2022.936956
Article's DOI: 10.3389/fendo.2022.936956
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV