BACKGROUND: Carotid intima-media thickness (cIMT) is associated with CV events in adults. Thicker cIMT is found in youth with CV risk factors including obesity. Which risk factors have the most effect upon cIMT in youth and whether obesity has direct or indirect effects is not known. We used structural equation modeling to elucidate direct and indirect pathways through which obesity and other risk factors were associated with cIMT. METHODS: We collected demographics, anthropometrics and laboratory data on 784 subjects age 10-24 years (mean 18.0 ± 3.3 years). Common, bulb and internal carotid cIMT were measured by ultrasound. Multivariable regression analysis was performed to assess independent determinants of cIMT. Analyses were repeated with structural equation modeling to determine direct and indirect effects. RESULTS: Multivariable regression models explained 11%-22% of variation of cIMT. Age, sex and systolic blood pressure (BP) z-score were significant determinants of all cIMT segments. Body mass index (BMI) z-score, race, presence of type 2 diabetes mellitus (T2DM), hemoglobin A1c (HbA1c) and non-HDL were significant for some segments (all p = 0.05). The largest direct effect on cIMT was age (0.312) followed by BP (0.228), Blood glucose control (0.108) and non-HDL (0.134). BMI only had a significant indirect effect through blood glucose control, BP & non-HDL. High sensitivity C-reactive protein (CRP) had a small indirect effect through blood glucose control (all p = 0.05). CONCLUSIONS: Age and BP are the major factors with direct effect on cIMT. Glucose and non-HDL were also important in this cohort with a high prevalence of T2DM. BMI only has indirect effects, through other risk factors. Traditional CV risk factors have important direct effects on cIMT in the young, but adiposity exerts its influence only through other CV risk factors.
BACKGROUND:Carotid intima-media thickness (cIMT) is associated with CV events in adults. Thicker cIMT is found in youth with CV risk factors including obesity. Which risk factors have the most effect upon cIMT in youth and whether obesity has direct or indirect effects is not known. We used structural equation modeling to elucidate direct and indirect pathways through which obesity and other risk factors were associated with cIMT. METHODS: We collected demographics, anthropometrics and laboratory data on 784 subjects age 10-24 years (mean 18.0 ± 3.3 years). Common, bulb and internal carotid cIMT were measured by ultrasound. Multivariable regression analysis was performed to assess independent determinants of cIMT. Analyses were repeated with structural equation modeling to determine direct and indirect effects. RESULTS: Multivariable regression models explained 11%-22% of variation of cIMT. Age, sex and systolic blood pressure (BP) z-score were significant determinants of all cIMT segments. Body mass index (BMI) z-score, race, presence of type 2 diabetes mellitus (T2DM), hemoglobin A1c (HbA1c) and non-HDL were significant for some segments (all p = 0.05). The largest direct effect on cIMT was age (0.312) followed by BP (0.228), Blood glucose control (0.108) and non-HDL (0.134). BMI only had a significant indirect effect through blood glucose control, BP & non-HDL. High sensitivity C-reactive protein (CRP) had a small indirect effect through blood glucose control (all p = 0.05). CONCLUSIONS: Age and BP are the major factors with direct effect on cIMT. Glucose and non-HDL were also important in this cohort with a high prevalence of T2DM. BMI only has indirect effects, through other risk factors. Traditional CV risk factors have important direct effects on cIMT in the young, but adiposity exerts its influence only through other CV risk factors.
Authors: Caroline C Geerts; Annemieke M V Evelein; Michiel L Bots; Cornelis K van der Ent; Diederick E Grobbee; Cuno S P M Uiterwaal Journal: Ann Med Date: 2011-03-01 Impact factor: 4.709
Authors: A Zanchetti; G Crepaldi; M G Bond; G V Gallus; F Veglia; A Ventura; G Mancia; G Baggio; L Sampieri; P Rubba; S Collatina; E Serrotti Journal: J Hypertens Date: 2001-01 Impact factor: 4.844
Authors: Juri Park; Seong H Kim; Goo-Yeong Cho; Inkyung Baik; Nan H Kim; Hong E Lim; Eung J Kim; Chang G Park; Sang Y Lim; Yong H Kim; Hyun Kim; Seung K Lee; Chol Shin Journal: J Hypertens Date: 2011-09 Impact factor: 4.844
Authors: Amy S Shah; Elaine M Urbina; Philip R Khoury; Thomas R Kimball; Lawrence M Dolan Journal: J Clin Lipidol Date: 2013-05-18 Impact factor: 4.766
Authors: Emily R Perito; Andrew Phelps; Tabitha Vase; Vickie A Feldstein; Robert H Lustig; Philip Rosenthal Journal: J Pediatr Date: 2017-12-07 Impact factor: 4.406
Authors: Joseph M Kindler; Andrea Kelly; Philip R Khoury; Lorraine E Levitt Katz; Elaine M Urbina; Babette S Zemel Journal: Diabetes Care Date: 2020-08-10 Impact factor: 19.112
Authors: Victoria Garcia-Espinosa; Daniel Bia; Juan Castro; Agustina Zinoveev; Mariana Marin; Gustavo Giachetto; Pedro Chiesa; Yanina Zócalo Journal: High Blood Press Cardiovasc Prev Date: 2018-07-02