Pre-AdolescentCardio-MetabolicAssociations andCorrelates: PACMAC methodology and study protocol

Castro, Nicholas; Faulkner, J.; Skidmore, Paula; Williams, Michelle A.; Lambrick, Danielle; Signal, Leigh; Thunders, Michelle; Muller, Diane; Lark, Sally; Hamlin, Michael J.; Lane, Andrew M.; Kingi, Te Kani; Stoner, Lee

doi:10.1136/bmjopen-2014-005815

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…The data and analyses described in this article were part of a larger cross‐sectional study of the associations between measurements of cardiac and metabolic variables and measurements related to physical performance, the PACMAC (Pre‐Adolescent Cardio‐Metabolic Associations and Correlates), and details of the larger study have been previously published . All measurements described in this article were assessed in the child participants’ schools between 9 am and 12 pm and children were asked to have been fasting for at least 3 hours and to have refrained from exercise for 24 hours before assessment.…”

Section: Methodsmentioning

confidence: 99%

Cardiometabolic Risk Variables in Preadolescent Children: A Factor Analysis

Stoner

Weatherall

Skidmore

et al. 2017

JAHA

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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.

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Section: Methodsmentioning

confidence: 99%

Cardiometabolic Risk Variables in Preadolescent Children: A Factor Analysis

Stoner

Weatherall

Skidmore

et al. 2017

JAHA

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“…This observational study was carried out in accordance with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [ 17 ]. The methodology was prospectively detailed in Castro et al [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Adiposity in preadolescent children: Associations with cardiorespiratory fitness

Castro

Bates²,

Zieff³

et al. 2022

PLoS ONE

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Lifestyle factors contribute to childhood obesity risk, however it is unclear which lifestyle factors are most strongly associated with childhood obesity. The purpose of this cross-sectional study was to simultaneously investigate the associations among dietary patterns, activity behaviors, and physical fitness with adiposity (body fat %, fat mass, body mass index [BMI], and waist to hip ratio) in preadolescent children. Preadolescent children (N = 392, 50% female, age: 9.5 ± 1.1year, BMI: 17.9 ± 3.3 kg/m2) were recruited. Body fat (%) and fat mass (kg) were measured with bioelectrical impedance analysis. Cardiorespiratory fitness (VO2 max), muscular strength (hand-grip strength), activity, sleep, and dietary pattern was assessed. Multivariable analysis revealed that cardiorespiratory fitness associated most strongly with all four indicators of adiposity (body fat (%) (β = -0.2; p < .001), fat mass (β = -0.2; p < .001), BMI (β = -0.1; p < .001) and waist to hip ratio (β = -0.2; p < .001). Additionally, fruit and vegetable consumption patterns were associated with body fat percentage, but the association was negligible (β = 0.1; p = 0.015). Therefore, future interventions should aim to promote the use of cardiorespiratory fitness as a means of reducing the obesity epidemic in children.

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“…Body fat (%) and fat mass (kg) were measured using multi-frequency bio-impedance analysis (BodyStat Quadscan 4000, Isle of Man, UK), which has previously been validated against deuterium oxide dilution in children [26]. Following at least 5 min supine rest, participants were studied in the supine position on a non-conductive surface, with arms and legs abducted at a 30–45° angle from the trunk to avoid medial body contact by upper and lower extremities.…”

Section: Methodsmentioning

confidence: 99%

Food Consumption Patterns and Body Composition in Children: Moderating Effects of Prop Taster Status

et al. 2019

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This cross-sectional study determined whether 6-n-propylthiouracil (PROP) taster status moderates the relationship between food consumption patterns and body composition in children. Children were recruited (n = 342, 50% female, 8-10 y) from across New Zealand. Using a food frequency questionnaire, these food consumption patterns were derived: Processed Foods, Fruit and Vegetables, and Breakfast Foods. Body composition variables included: body fat (%), fat mass (kg), fat mass index (FMI, kg/m 2 ), body mass index (kg/m 2 ) and waist to height ratio (W:Ht). Following adjustment for confounders, Processed Foods were positively associated with %fat (p = 0.015), fat mass (p = 0.004) and FMI (p = 0.016). Taste test strips determined PROP status. For Breakfast Foods, there were small negative associations with all body composition variables (p ≤ 0.001 to 0.037). The population sample was also stratified by PROP taster status. For the non-tasters, there were small to moderate negative associations between Breakfast Foods and each body composition variable (p = 0.003-0.045) except W:Ht (p = 0.112), and these relationships were stronger for girls compared to boys. For the tasters, there were small to moderate positive associations between Processed Foods with %fat (p = 0.030), fat mass (p ≤ 0.001) and FMI (p = 0.014). In conclusion, sensitivity to bitterness may moderate the relationship between food consumption patterns and body composition in children.

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Pre-AdolescentCardio-MetabolicAssociations andCorrelates: PACMAC methodology and study protocol

Cited by 12 publications

References 35 publications

Cardiometabolic Risk Variables in Preadolescent Children: A Factor Analysis

Cardiometabolic Risk Variables in Preadolescent Children: A Factor Analysis

Adiposity in preadolescent children: Associations with cardiorespiratory fitness

Food Consumption Patterns and Body Composition in Children: Moderating Effects of Prop Taster Status

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