Adiposity has no direct effect on carotid intima-media thickness in adolescents and young adults: Use of structural equation modeling to elucidate indirect & direct pathways. (March 2016)

• Record Type: Journal Article
• Authors: Gao, Zhiqian; Khoury, Philip R.; McCoy, Connie E.; Shah, Amy S.; Kimball, Thomas R.; Dolan, Lawrence M.; Urbina, Elaine M.
• Is Part Of: Atherosclerosis. Volume 246(2016)
• Page Start: 29
• Page End: 35
• Publication Date: 2016-03
• Subjects: Carotid arteries -- Obesity -- Pediatrics -- Risk factors -- Statistics; BMI Body mass index -- BP blood pressure -- cIMT carotid intima-media thickness -- CRP high sensitivity C-reactive protein -- CV cardiovascular -- DBP diastolic blood pressure -- HbA1c hemoglobin A1c -- HDL high density lipoprotein cholesterol concentration -- LDL Low-density lipoprotein cholesterol concentration -- Non-HDL non-high density lipoprotein cholesterol concentration -- SEM Structural equation modeling -- SBP Systolic blood pressure -- T2DM type 2 diabetes mellitus; Arteriosclerosis -- Periodicals; Electronic journals; 616.136
• DOI: 10.1016/j.atherosclerosis.2015.11.033
• Languages: English
• ISSNs: 0021-9150

Abstract

Abstract: 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. Highlights: Structural equation modeling was used to explore direct and indirect pathways through which risk factors associate with cIMT. Age and BP are the major factors with direct effect on cIMT. Glucose and non-HDL were also import in this cohort with a high prevalence of T2DM. BMI only has indirect effects, through traditional CV risk factors.