Cardiorespiratory fitness predicts clustered cardiometabolic risk in 10–11.9-year-olds

The aim of this study was to investigate levels of clustered cardiometabolic risk and the odds of being ‘at risk’ according to cardiorespiratory fitness status in children. Data from 88 10–11.9-year-old children (mean age 11.05 ± 0.51 years), who participated in either the REACH Year 6 or the Benefi...

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Bibliographic Details
Published in:European journal of pediatrics 2013-07, Vol.172 (7), p.913-918
Main Authors: Houston, Emma L., Baker, Julien S., Buchan, Duncan S., Stratton, Gareth, Fairclough, Stuart J., Foweather, Lawrence, Gobbi, Rebecca, Graves, Lee E. F., Hopkins, Nicola, Boddy, Lynne M.
Format: Article
Language:English
Subjects:
Adiponectin - blood
At risk youth
Blood Glucose - analysis
Blood Pressure
Body Mass Index
C-Reactive Protein - analysis
Cardiovascular disease
Cardiovascular Diseases - diagnosis
Child
Cluster Analysis
Exercise
Female
Humans
Intervention
Investigations
Laboratories
Lipoproteins - blood
Logistic Models
Male
Medical screening
Medicine
Medicine & Public Health
Metabolic Diseases - diagnosis
Morbidity
Mortality
Original Article
Oxygen Consumption - physiology
Pediatrics
Physical fitness
Physical Fitness - physiology
Risk Assessment
Risk factors
ROC Curve
United Kingdom
Waist Circumference
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Summary:The aim of this study was to investigate levels of clustered cardiometabolic risk and the odds of being ‘at risk’ according to cardiorespiratory fitness status in children. Data from 88 10–11.9-year-old children (mean age 11.05 ± 0.51 years), who participated in either the REACH Year 6 or the Benefits of Fitness Circuits for Primary School Populations studies were combined. Waist circumference, systolic blood pressure, diastolic blood pressure, glucose, triglycerides, high-density lipoprotein cholesterol, adiponectin and C-reactive protein were assessed and used to estimate clustered cardiometabolic risk. Participants were classified as ‘fit’ or ‘unfit’ using recently published definitions (46.6 and 41.9 mL/kg/min for boys and girls, respectively), and continuous clustered risk scores between fitness groups were assessed. Participants were subsequently assigned to a ‘normal’ or ‘high’ clustered cardiometabolic risk group based on risk scores, and logistic regression analysis assessed the odds of belonging to the increased cardiometabolic risk group according to fitness. The unfit group exhibited significantly higher clustered cardiometabolic risk scores ( p  
ISSN:0340-6199
1432-1076
DOI:10.1007/s00431-013-1973-z