Cardiometabolic Risk Variables in Preadolescent Children: A Factor Analysis

Abstract

BackgroundAtherosclerosis begins during preadolescence and is occurring at an accelerated rate. This acceleration has been linked to poor lifestyle behaviors and subsequent cardiometabolic complications. Although the clustering of cardiometabolic risk factors has been recognized for over 2 decades, previous studies in children have predominantly examined the relationships between atherosclerosis and individual cardiometabolic risk factors or have grouped together preadolescent and adolescent children. Further, no known studies have included glycated hemoglobin or central hemodynamic measures such as central systolic blood pressure and augmentation index.Methods and ResultsPrincipal component analysis was performed on a cross‐sectional sample of 392 children (aged 9.5 years, 50% girls) from 3 representative sample sites across New Zealand. Four factors explained 60% of the variance in the measured variables. In order of variance explained, the factors were: blood pressure (central systolic blood pressure and peripheral systolic and diastolic blood pressure), adiposity (waist circumference, body mass index, and glycated hemoglobin), lipids (total cholesterol, low‐density lipoprotein cholesterol, and high‐density lipoprotein cholesterol), and vascular (augmentation index, heart rate, and fasting blood glucose).ConclusionsIn accordance with previous findings in adults and adolescents, one common factor is unlikely to define cardiometabolic health in preadolescent children. Each of the factors, except vascular, which was predominantly explained by augmentation index, are in agreement with previous findings in adolescents. An additional novel finding was that glycated hemoglobin and fasting blood glucose loaded onto different factors, supporting previous work suggesting that fasting blood glucose indicates short‐term glycemic control, whereas glycated hemoglobin reflects chronic glycemic control.Clinical Trial Registration URL: www.anzctr.org.au/. ID: ACTRN12614000433606.

Asthma and high-intensity interval training have no effect on clustered cardiometabolic risk or arterial stiffness in adolescents

“…The current findings regarding a similar clustering of cardiometabolic risk factors irrespective of asthma status in normal weight youth are therefore of interest. Indeed, our findings are in accord with previous exploratory factor analysis studies in children that have consistently highlighted that a single factor cannot explain the cardiometabolic health of youth (Goodman et al 2009 ; Stoner et al 2017 ; Lambert et al 2004 ). However, in contrast to the two-four factors most commonly reported elsewhere (Goodman et al 2009 ; Stoner et al 2017 ; Lambert et al 2004 ), we identified seven factors that explained 88% of the variation in cardiometabolic health in the current adolescents.…”

“…However, a methodological limitation of this, and, indeed, many earlier studies, is their focus on the components of cardiometabolic risk as individual factors, despite considerable evidence of the clustering of cardiometabolic risk factors (Reaven 1988 ). Indeed, one factor is unlikely to define cardiometabolic health in youth (Stoner et al 2017 ) and thus influencing individual factors does not necessarily translate to beneficial changes in the overall risk score. Nonetheless, the specific composition of the clustered cardiometabolic risk score in youth continues to be a contentious issue, with recent evidence suggesting that central blood pressure and measures of arterial stiffness should be included (Stoner et al 2017 ).…”

“…Indeed, one factor is unlikely to define cardiometabolic health in youth (Stoner et al 2017 ) and thus influencing individual factors does not necessarily translate to beneficial changes in the overall risk score. Nonetheless, the specific composition of the clustered cardiometabolic risk score in youth continues to be a contentious issue, with recent evidence suggesting that central blood pressure and measures of arterial stiffness should be included (Stoner et al 2017 ).…”

Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior, a cross-sectional study

“…We then used orthogonal rotation (varimax rotation) to obtain factor loadings. We used an absolute loading value of >0.4 to interpret the factor pattern, which has been used by previous major factor analysis studies [30,31].…”

Automatic segmentation to characterize anthropometric parameters and cardiovascular indicators in children

“…Stoner et al (2017) used only principal component analysis (PCA) in 392 children in New Zealand between 8 and 10 years to verify that four factors explained 60% of the variance: blood pressure (BP), adiposity, lipids, and vascular factors (e.g., heart rate, augmentation index). But with only these data, it is not possible to classify children according to each anthropometric profile.…”