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

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  • Sara Castel-Feced

    (Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain
    Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain
    GRISSA Research Group, 50009 Zaragoza, Spain)

  • Lina Maldonado

    (Department of Economic Structure, Economic History and Public Economics, University of Zaragoza, 50005 Zaragoza, Spain)

  • Isabel Aguilar-Palacio

    (Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain
    Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain
    GRISSA Research Group, 50009 Zaragoza, Spain)

  • Sara Malo

    (Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain
    Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain
    GRISSA Research Group, 50009 Zaragoza, Spain)

  • Belén Moreno-Franco

    (Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain
    Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain)

  • Eusebio Mur-Vispe

    (Prevention Department, Stellantis Spain, 50639 Figueruelas, Spain)

  • José-Tomás Alcalá-Nalvaiz

    (Department of Statistical Methods, University of Zaragoza, 50005 Zaragoza, Spain
    Institute of Mathematics and Applications (IUMA), 50009 Zaragoza, Spain
    These authors contributed equally to this work and served as senior co-authors.)

  • María José Rabanaque-Hernández

    (Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain
    Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain
    GRISSA Research Group, 50009 Zaragoza, Spain
    These authors contributed equally to this work and served as senior co-authors.)

Abstract

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.

Suggested Citation

  • Sara Castel-Feced & Lina Maldonado & Isabel Aguilar-Palacio & Sara Malo & Belén Moreno-Franco & Eusebio Mur-Vispe & José-Tomás Alcalá-Nalvaiz & María José Rabanaque-Hernández, 2021. "Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5610-:d:561286
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    References listed on IDEAS

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    1. Christophe Genolini & Bruno Falissard, 2010. "KmL: k-means for longitudinal data," Computational Statistics, Springer, vol. 25(2), pages 317-328, June.
    2. Genolini, Christophe & Alacoque, Xavier & Sentenac, Mariane & Arnaud, Catherine, 2015. "kml and kml3d: R Packages to Cluster Longitudinal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i04).
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