OBJECTIVE: Cardiovascular risk assessment is an accepted practice in adults and correlates with early changes in carotid structure and function. Its clinical use in pediatrics is less common. We sought to determine whether a simple method of clustering cardiovascular risks could detect early atherosclerotic changes in youth. In addition, we compared risk clustering with the accepted Patholobiological Determinants of Atherosclerosis in Youth score to assess its utility for predicting early vascular disease. PATIENTS AND METHODS: We collected demographic, anthropometric, laboratory, and vascular measures in a cross-sectional study. The study population (n = 474; mean age: 18 years) was divided into low-risk (0-1) or high-risk (≥ 2) groups on the basis of the number of cardiovascular risk factors present at evaluation. Group differences and vascular outcomes were compared. General linear models were used to compare clustering cardiovascular risks with the Patholobiological Determinants of Atherosclerosis in Youth score. RESULTS: The high-risk group had higher vascular thickness and stiffness compared with the low-risk group (P < .05). Regression models found that clustering cardiovascular risks is associated with abnormal vascular structure and function after adjustment for age, race, and gender. The Patholobiological Determinants of Atherosclerosis in Youth score also is associated with abnormal vascular structure and function but with lower R(2) values (P < .05). CONCLUSIONS: Cardiovascular risk clustering is a reliable tool for assessing abnormal vascular function. Its simplicity, compared with the Patholobiological Determinants of Atherosclerosis in Youth score, provides an advantageous tool for the practicing clinician to identify those youth who are at higher risk for early cardiovascular disease.
OBJECTIVE: Cardiovascular risk assessment is an accepted practice in adults and correlates with early changes in carotid structure and function. Its clinical use in pediatrics is less common. We sought to determine whether a simple method of clustering cardiovascular risks could detect early atherosclerotic changes in youth. In addition, we compared risk clustering with the accepted Patholobiological Determinants of Atherosclerosis in Youth score to assess its utility for predicting early vascular disease. PATIENTS AND METHODS: We collected demographic, anthropometric, laboratory, and vascular measures in a cross-sectional study. The study population (n = 474; mean age: 18 years) was divided into low-risk (0-1) or high-risk (≥ 2) groups on the basis of the number of cardiovascular risk factors present at evaluation. Group differences and vascular outcomes were compared. General linear models were used to compare clustering cardiovascular risks with the Patholobiological Determinants of Atherosclerosis in Youth score. RESULTS: The high-risk group had higher vascular thickness and stiffness compared with the low-risk group (P < .05). Regression models found that clustering cardiovascular risks is associated with abnormal vascular structure and function after adjustment for age, race, and gender. The Patholobiological Determinants of Atherosclerosis in Youth score also is associated with abnormal vascular structure and function but with lower R(2) values (P < .05). CONCLUSIONS: Cardiovascular risk clustering is a reliable tool for assessing abnormal vascular function. Its simplicity, compared with the Patholobiological Determinants of Atherosclerosis in Youth score, provides an advantageous tool for the practicing clinician to identify those youth who are at higher risk for early cardiovascular disease.
Authors: W P Newman; D S Freedman; A W Voors; P D Gard; S R Srinivasan; J L Cresanta; G D Williamson; L S Webber; G S Berenson Journal: N Engl J Med Date: 1986-01-16 Impact factor: 91.245
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Authors: Kunjal Patel; Jiajia Wang; Denise L Jacobson; Steven E Lipshultz; David C Landy; Mitchell E Geffner; Linda A Dimeglio; George R Seage; Paige L Williams; Russell B Van Dyke; George K Siberry; William T Shearer; Luciana Young; Gwendolyn B Scott; James D Wilkinson; Stacy D Fisher; Thomas J Starc; Tracie L Miller Journal: Circulation Date: 2013-12-23 Impact factor: 29.690
Authors: Raymond R Townsend; Ian B Wilkinson; Ernesto L Schiffrin; Alberto P Avolio; Julio A Chirinos; John R Cockcroft; Kevin S Heffernan; Edward G Lakatta; Carmel M McEniery; Gary F Mitchell; Samer S Najjar; Wilmer W Nichols; Elaine M Urbina; Thomas Weber Journal: Hypertension Date: 2015-07-09 Impact factor: 10.190