期刊
JOURNAL OF STROKE & CEREBROVASCULAR DISEASES
卷 28, 期 5, 页码 1346-1352出版社
ELSEVIER
DOI: 10.1016/j.jstrokecerebrovasdis.2019.01.035
关键词
Cardiovascular disease; atherosclerosis; Nigeria; stroke; risk factors; risk prediction
Background: Risk factors for carotid intima-media thickness (cIMT) and carotid plaque (CP) differ by ethnicity; however, this is not well understood in some ethnic populations. This work examines the risk factors for cIMT and CP in an indigenous Nigerian population. Methods: We assessed cIMT and CP in 122 participants and then performed biochemical analysis: fasting blood glucose (FBG), hemoglobin A1c, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and total cholesterol (TC). The clinical history and anthropometric characteristics of participants were recorded. Linear models were used to assess the factors associated with cIMT and CP, and stepwise multivariate regression analyses were conducted to assess the predictors of cIMT and CP. Results: The cIMT thickness varied from .5 mm to 1.3 mm. Family history of heart disease (FHHDx), physical activity, FBG, HDL-C, TG, TC, body mass index (BMI), systolic pressure, and waist circumference were significantly associated with cIMT (P <= .01). High systolic (beta = .008) and diastolic (beta = .17) pressure, FHHDx (beta = .24), age (beta = .004), physical activity (beta = -.09), and waist circumference (beta = -.017) significantly predicted 85% of the variation in cIMT (P < .001 for all). Family history of hypertension (FHH), LDL-C, and high blood pressure were significantly associated with CP (P <= .05). The significant predictors of CP were FHH (beta = .145, P = .03), smoking (beta = .167, P = .01), HDL-C (beta = .283, P < .001), weight (beta = .150, P = .04), and BMI (beta = .183, P = .01), explaining most of the 43.2% variation in CP. Conclusions: Some of the risk factors differ from those of other ethnicities, suggesting a need for population-specific approach to risk assessment and early detection of subclinical disease.
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