Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis
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Hepatic enzymesMetabolic syndromeClusterBiomarkersGlucose disordersDyslipidemia
Micó V... [et al.]. (2022) Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis. Front. Endocrinol. 13:936956. doi: [10.3389/fendo.2022.936956]
SponsorshipEuropean Research Council (ERC) European Commission 340918; official Spanish institutions for funding scientific biomedical research; CIBER Fisiopatologia de la Obesidad y Nutricion (CIBEROBN); Instituto de Salud Carlos III (ISCIII) through the Fondo de Investigacio' n para la Salud (FIS) - European Regional Development Fund 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; The Instituto de Salud Carlos III (ISCIII) through the Fondo de Investigacio' n para la Salud (FIS) - European Regional Development Fund 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; Especial Action Project "Implementacion y evaluacion de una intervencion intensiva sobre la actividad fisica Cohorte PREDIMED-Plus"; La Caixa Foundation 2013ACUP00194; ICREA under the ICREA Academia program; SEMERGEN grant; Department of Health of the Government of Navarra 61/ 2015; FundacioLa Maratode TV 201630.10; AstraZeneca; Junta de Andalucia PI0458/2013 PS0358/2016 PI0137/2018; Center for Forestry Research & Experimentation (CIEF); European Commission PROMETEO/ 2017/017; Balearic Islands Government 35/2011; European Commission 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
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.