Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study
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2017Author(s)
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10.1371/journal.pone.0173393Metadata
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Wang X. Dalmeijer GW. Den Ruijter HM. Anderson TJ. Britton AR. Dekker J. Engström G. Evans GW. De Graaf J. Grobbee DE. Hedblad B. Holewijn S. Ikeda A. Kauhanen J. Kitagawa K. Kitamura A. Kurl S. Lonn EM. Lorenz MW. Mathiesen EB et al. (2017). Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study. PLOS ONE, 12 (3) , 1-10. 10.1371/journal.pone.0173393.Rights
Abstract
Background
The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation.
Methods
Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.
Results
Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.
Conclusion
Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.
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http://dx.doi.org/10.1371/journal.pone.0173393Collections
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