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Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis

The identification of the cardiovascular risk factor (CVRF) profile of individual patients is key to the prevention of cardiovascular disease (CVD), and the development of personalized preventive approaches. Using data from annual medical examinations in a cohort of workers, the aim of the study was...

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Autores principales: Castel-Feced, Sara, Maldonado, Lina, Aguilar-Palacio, Isabel, Malo, Sara, Moreno-Franco, Belén, Mur-Vispe, Eusebio, Alcalá-Nalvaiz, José-Tomás, Rabanaque-Hernández, María José
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197309/
https://www.ncbi.nlm.nih.gov/pubmed/34074004
http://dx.doi.org/10.3390/ijerph18115610
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author Castel-Feced, Sara
Maldonado, Lina
Aguilar-Palacio, Isabel
Malo, Sara
Moreno-Franco, Belén
Mur-Vispe, Eusebio
Alcalá-Nalvaiz, José-Tomás
Rabanaque-Hernández, María José
author_facet Castel-Feced, Sara
Maldonado, Lina
Aguilar-Palacio, Isabel
Malo, Sara
Moreno-Franco, Belén
Mur-Vispe, Eusebio
Alcalá-Nalvaiz, José-Tomás
Rabanaque-Hernández, María José
author_sort Castel-Feced, Sara
collection PubMed
description The identification of the cardiovascular risk factor (CVRF) profile of individual patients is key to the prevention of cardiovascular disease (CVD), and the development of personalized preventive approaches. Using data from annual medical examinations in a cohort of workers, the aim of the study was to characterize the evolution of CVRFs and the CVD risk score (SCORE) over three time points between 2009 and 2017. For descriptive analyses, mean, standard deviation, and quartile values were used for quantitative variables, and percentages for categorical ones. Cluster analysis was performed using the Kml3D package in R software. This algorithm, which creates distinct groups based on similarities in the evolution of variables of interest measured at different time points, divided the cohort into 2 clusters. Cluster 1 comprised younger workers with lower mean body mass index, waist circumference, blood glucose values, and SCORE, and higher mean HDL cholesterol values. Cluster 2 had the opposite characteristics. In conclusion, it was found that, over time, subjects in cluster 1 showed a higher improvement in CVRF control and a lower increase in their SCORE, compared with cluster 2. The identification of subjects included in these profiles could facilitate the development of better personalized medical approaches to CVD preventive measures.
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spelling pubmed-81973092021-06-13 Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis Castel-Feced, Sara Maldonado, Lina Aguilar-Palacio, Isabel Malo, Sara Moreno-Franco, Belén Mur-Vispe, Eusebio Alcalá-Nalvaiz, José-Tomás Rabanaque-Hernández, María José Int J Environ Res Public Health Article The identification of the cardiovascular risk factor (CVRF) profile of individual patients is key to the prevention of cardiovascular disease (CVD), and the development of personalized preventive approaches. Using data from annual medical examinations in a cohort of workers, the aim of the study was to characterize the evolution of CVRFs and the CVD risk score (SCORE) over three time points between 2009 and 2017. For descriptive analyses, mean, standard deviation, and quartile values were used for quantitative variables, and percentages for categorical ones. Cluster analysis was performed using the Kml3D package in R software. This algorithm, which creates distinct groups based on similarities in the evolution of variables of interest measured at different time points, divided the cohort into 2 clusters. Cluster 1 comprised younger workers with lower mean body mass index, waist circumference, blood glucose values, and SCORE, and higher mean HDL cholesterol values. Cluster 2 had the opposite characteristics. In conclusion, it was found that, over time, subjects in cluster 1 showed a higher improvement in CVRF control and a lower increase in their SCORE, compared with cluster 2. The identification of subjects included in these profiles could facilitate the development of better personalized medical approaches to CVD preventive measures. MDPI 2021-05-24 /pmc/articles/PMC8197309/ /pubmed/34074004 http://dx.doi.org/10.3390/ijerph18115610 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Castel-Feced, Sara
Maldonado, Lina
Aguilar-Palacio, Isabel
Malo, Sara
Moreno-Franco, Belén
Mur-Vispe, Eusebio
Alcalá-Nalvaiz, José-Tomás
Rabanaque-Hernández, María José
Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis
title Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis
title_full Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis
title_fullStr Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis
title_full_unstemmed Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis
title_short Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis
title_sort evolution of cardiovascular risk factors in a worker cohort: a cluster analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197309/
https://www.ncbi.nlm.nih.gov/pubmed/34074004
http://dx.doi.org/10.3390/ijerph18115610
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