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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Published in | International journal of environmental research and public health Vol. 18; no. 11; p. 5610 |
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Main Authors | , , , , , , , |
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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. |
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AbstractList | 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. 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.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. |
Author | Mur-Vispe, Eusebio Castel-Feced, Sara Aguilar-Palacio, Isabel Alcalá-Nalvaiz, José-Tomás Maldonado, Lina Moreno-Franco, Belén Rabanaque-Hernández, María José Malo, Sara |
AuthorAffiliation | 1 Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain; iaguilar@unizar.es (I.A.-P.); smalo@unizar.es (S.M.); mbmoreno@unizar.es (B.M.-F.); rabanake@unizar.es (M.J.R.-H.) 7 Institute of Mathematics and Applications (IUMA), 50009 Zaragoza, Spain 6 Department of Statistical Methods, University of Zaragoza, 50005 Zaragoza, Spain; jtalcala@unizar.es 3 GRISSA Research Group, 50009 Zaragoza, Spain 5 Prevention Department, Stellantis Spain, 50639 Figueruelas, Spain; eusebio.mur@stellantis.com 2 Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain 4 Department of Economic Structure, Economic History and Public Economics, University of Zaragoza, 50005 Zaragoza, Spain; lmguaje@unizar.es |
AuthorAffiliation_xml | – name: 6 Department of Statistical Methods, University of Zaragoza, 50005 Zaragoza, Spain; jtalcala@unizar.es – name: 1 Department of Preventive Medicine and Public Health, University of Zaragoza, 50009 Zaragoza, Spain; iaguilar@unizar.es (I.A.-P.); smalo@unizar.es (S.M.); mbmoreno@unizar.es (B.M.-F.); rabanake@unizar.es (M.J.R.-H.) – name: 4 Department of Economic Structure, Economic History and Public Economics, University of Zaragoza, 50005 Zaragoza, Spain; lmguaje@unizar.es – name: 7 Institute of Mathematics and Applications (IUMA), 50009 Zaragoza, Spain – name: 2 Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain – name: 3 GRISSA Research Group, 50009 Zaragoza, Spain – name: 5 Prevention Department, Stellantis Spain, 50639 Figueruelas, Spain; eusebio.mur@stellantis.com |
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Cites_doi | 10.1016/j.phrs.2016.09.040 10.1007/s00180-009-0178-4 10.1186/s41043-019-0201-5 10.1038/s41598-019-53546-y 10.1093/eurheartj/ehw334 10.1109/EMBC.2019.8856507 10.18637/jss.v065.i04 10.1093/ajh/hpx028 10.1001/jama.2013.285122 10.1161/CIRCULATIONAHA.115.020090 10.1186/1471-2261-12-45 10.1016/S0140-6736(19)32008-2 10.5334/gh.764 10.1016/j.jacc.2020.11.010 10.1038/jhh.2017.20 10.1016/S0195-668X(03)00114-3 10.1093/aje/kwq171 10.1007/s40292-016-0139-2 |
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SubjectTerms | Alcohol use Blood pressure Body mass index Cardiovascular disease Cholesterol Cluster analysis Diabetes Factories Glucose Hypertension Longitudinal studies Population Prevention Risk factors Smoking Variables Workers |
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