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PLoS One. 2016 Sep 6;11(9):e0162386. doi: 10.1371/journal.pone.0162386. eCollection 2016.

A Global View of the Relationships between the Main Behavioural and Clinical Cardiovascular Risk Factors in the GAZEL Prospective Cohort.

Author information

1
INSERM U1142 LIMICS, UMR_S 1142 Sorbonne Université, UPMC Université Paris 06, Université Paris 13, Paris, France.
2
Centre Psychiatrie et Neurosciences, INSERM U894, Université Paris Descartes, AP-HP Hôpitaux Universitaires Paris Ouest, Paris, France.
3
INSERM UVSQ UMS 011 and UMR-S 1168 VIMA, Villejuif, France.
4
Department of Biostatistics, Department of Mathematics and Statistics, Boston University, Boston, MA, United States of America.
5
Framingham Heart Study, Department of Medicine, Boston University, Boston, MA, United States of America.
6
INSERM/AP-HP CIC1418, Université Paris Descartes, AP-HP Hôpitaux Universitaires Paris Ouest, Paris, France.

Abstract

Although it has been recognized for a long time that the predisposition to cardiovascular diseases (CVD) is determined by many risk factors and despite the common use of algorithms incorporating several of these factors to predict the overall risk, there has yet been no global description of the complex way in which CVD risk factors interact with each other. This is the aim of the present study which investigated all existing relationships between the main CVD risk factors in a well-characterized occupational cohort. Prospective associations between 12 behavioural and clinical risk factors (gender, age, parental history of CVD, non-moderate alcohol consumption, smoking, physical inactivity, obesity, hypertension, dyslipidemia, diabetes, sleep disorder, depression) were systematically tested using Cox regression in 10,736 middle-aged individuals free of CVD at baseline and followed over 20 years. In addition to independently predicting CVD risk (HRs from 1.18 to 1.97 in multivariable models), these factors form a vast network of associations where each factor predicts, and/or is predicted by, several other factors (n = 47 with p<0.05, n = 37 with p<0.01, n = 28 with p<0.001, n = 22 with p<0.0001). Both the number of factors associated with a given factor (1 to 9) and the strength of the associations (HRs from 1.10 to 6.12 in multivariable models) are very variable, suggesting that all the factors do not have the same influence within this network. These results show that there is a remarkably extensive network of relationships between the main CVD risk factors which may have not been sufficiently taken into account, notably in preventive strategies aiming to lower CVD risk.

PMID:
27598908
PMCID:
PMC5012694
DOI:
10.1371/journal.pone.0162386
[Indexed for MEDLINE]
Free PMC Article

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