Articles producció científica> Bioquímica i Biotecnologia

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

  • Identification data

    Identifier: imarina:9282215
    Authors:
    Micó VSan-Cristobal RMartín RMartínez-González MÁSalas-Salvadó JCorella DFitó MAlonso-Gómez ÁMWärnberg JVioque JRomaguera DLópez-Miranda JEstruch RTinahones FJLapetra JSerra-Majem JLBueno-Cavanillas ATur JAMartín Sánchez VPintó XDelgado-Rodríguez MMatía-Martín PVidal JVázquez CGarcía-Arellano APertusa-Martinez SChaplin AGarcia-Rios AMuñoz Bravo CSchröder HBabio NSorli JVGonzalez JIMartinez-Urbistondo DToledo EBullón VRuiz-Canela MPortillo MPMacías-González MPerez-Diaz-del-Campo NGarcía-Gavilán JDaimiel LMartínez JA
    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.
  • Others:

    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: jordi.salas@urv.cat nancy.babio@urv.cat
    Author identifier: 0000-0003-2700-7459 0000-0003-3527-5277
    Record's date: 2024-09-07
    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: https://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
  • Keywords:

    Endocrinology & Metabolism,Endocrinology, Diabetes and Metabolism
    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
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Interdisciplinar
    Farmacia
    Endocrinology, diabetes and metabolism
    Endocrinology & metabolism
    Ciências biológicas ii
    Ciências biológicas i
  • Documents:

  • Cerca a google

    Search to google scholar