Determining independence and associations among various cardiovascular disease risk factors in 9-12 years old school-children: a cross sectional study

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Department of Cognitive and Learning Sciences; Health Research Institute


BACKGROUND: Cardiovascular disease (CVD) risk assessment of children typically includes evaluating multiple CVD risk factors some of which tend to correlate each other. However, in older children and young adolescents, there are little data on the level of independence of CVD risk factors. The purpose of this study was to examine the relationships among various CVD risk factors to determine the level of independence of each risk factor in a sample of 5-grade public school students. METHOD: A cross-sectional analysis of 1525 children (856 girls and 669 boys; age: 9-12 years) who participated in baseline CVD risk assessment for the (S)Partners for Heart Health program from 2010 - 2018. Thirteen CVD risk factor variables were used in the analysis and included blood lipids [low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), and triglycerides], resting systolic and diastolic blood pressure (BP); anthropometrics [height, weight, body mass index (BMI), % body fat, waist circumference (WC)]. Additionally, acanthosis nigricans (a marker insulin resistance and diabetes), and cardiorespiratory fitness (VO2 ml/kg) was estimated using the PACER. Descriptive statistics, bivariate Pearson correlations, and principal component analysis were used to determine the relationships among these variables and the independence. RESULTS: Parallel analysis indicated two components should be extracted. Among the two components extracted, WC, % body fat, and BMI loaded highest on component 1, which explained 34% of the total variance. Systolic BP and diastolic BP loaded predominantly on component 2 and accounted for 17% of the variance. Cardiorespiratory fitness, acanthosis nigricans, HDL, and triglycerides loaded highest on the first component (loadings between 0.42 and 0.57) but still suggest some non-shared variance with this component. Low-density lipoprotein had low loadings on each component. Factor loadings were stable across sex. CONCLUSION: Among the various CVD risk indicators, measures of adiposity loaded highest on the component that explained the largest proportion of variability in the data reinforcing the importance of assessing adiposity in CVD risk assessment. In addition, blood pressure loaded highest on the second component, suggesting their relative independence when assessing CVD risk. The data also provide support and rationale for determining what CVD risk factors to include- based on resource needs. For example, researchers or public health programs may choose to assess WC instead of lipid profile for cardiovascular related problems if ease of assessment and cost are considerations.

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BMC public health