@misc{10481/78406,
year = {2022},
month = {9},
url = {https://hdl.handle.net/10481/78406},
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.},
organization = {European Research Council (ERC)
European Commission	340918},
organization = {official Spanish institutions for funding scientific biomedical research},
organization = {CIBER Fisiopatologia de la Obesidad y Nutricion (CIBEROBN)},
organization = {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},
organization = {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},
organization = {Especial Action Project "Implementacion y evaluacion de una intervencion intensiva sobre la actividad fisica Cohorte PREDIMED-Plus"},
organization = {La Caixa Foundation	2013ACUP00194},
organization = {ICREA under the ICREA Academia program},
organization = {SEMERGEN grant},
organization = {Department of Health of the Government of Navarra	61/ 2015},
organization = {FundacioLa Maratode TV	201630.10},
organization = {AstraZeneca},
organization = {Junta de Andalucia	PI0458/2013	
PS0358/2016	
PI0137/2018},
organization = {Center for Forestry Research & Experimentation (CIEF)},
organization = {European Commission	PROMETEO/ 2017/017},
organization = {Balearic Islands Government	35/2011},
organization = {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},
publisher = {Frontiers},
keywords = {Hepatic enzymes},
keywords = {Metabolic syndrome},
keywords = {Cluster},
keywords = {Biomarkers},
keywords = {Glucose disorders},
keywords = {Dyslipidemia},
title = {Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis},
doi = {10.3389/fendo.2022.936956},
author = {Micó, Víctor and Bueno Cavanillas, Aurora},
}