title: Cardiorespiratory fitness predicts clustered cardiometabolic risk in 10-11.9-year-olds.
abstract: 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 < 0.001) than the fit group. A clear association between fitness group and being at increased cardiometabolic risk (B = 2.509, p = 0.001) was also identified, and participants classed as being unfit were found to have odds of being classified as 'at risk' of 12.30 (95 % CI = 2.64-57.33). Conclusion Assessing cardiorespiratory fitness is a valid method of identifying children most at risk of cardiometabolic pathologies. The ROC thresholds could be used to identify populations of children most at risk and may therefore be used to effectively target a cardiometabolic risk-reducing public health intervention.
authors: Royal Liverpool University Hospital, Prescot Street, Liverpool, L7 8XP, UK. ehouston@rlbuht.nhs.uk
authors: Emma L
authors: EL
authors: Houston
authors: Julien S
authors: JS
authors: Baker
authors: Duncan S
authors: DS
authors: Buchan
authors: Gareth
authors: G
authors: Stratton
authors: Stuart J
authors: SJ
authors: Fairclough
authors: Lawrence
authors: L
authors: Foweather
authors: Rebecca
authors: R
authors: Gobbi
authors: Lee E F
authors: LE
authors: Graves
authors: Nicola
authors: N
authors: Hopkins
authors: Lynne M
authors: LM
authors: Boddy
mesh_terms: D052242:Adiponectin / Q000097:blood; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D015992:Body Mass Index; D002097:C-Reactive Protein / Q000032:analysis; D002318:Cardiovascular Diseases / Q000175:diagnosis*; D002648:Child; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008074:Lipoproteins / Q000097:blood; D016015:Logistic Models; D008297:Male; D008659:Metabolic Diseases / Q000175:diagnosis*; D010101:Oxygen Consumption / Q000502:physiology*; D010809:Physical Fitness / Q000502:physiology*; D012372:ROC Curve; D018570:Risk Assessment; D006113:United Kingdom; D055105:Waist Circumference
chemical_list: D052242:Adiponectin; D001786:Blood Glucose; D008074:Lipoproteins; D002097:C-Reactive Protein