Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis.
K LoM WongP KhalechelvamWilson Wai Sun Wai Sun TamPublished in: Obesity reviews : an official journal of the International Association for the Study of Obesity (2016)
Waist-to-height ratio (WHtR) is superior to body mass index and waist circumference for measuring adult cardio-metabolic risk factors. However, there is no meta-analysis to evaluate its discriminatory power in children and adolescents. A meta-analysis was conducted using multiple databases, including Embase and Medline. Studies were included that utilized receiver-operating characteristics curve analysis and published area under the receiver-operating characteristics curves (AUC) for adiposity indicators with hyperglycaemia, elevated blood pressure, dyslipidemia, metabolic syndrome and other cardio-metabolic outcomes. Thirty-four studies met the inclusion criteria. AUC values were extracted and pooled using a random-effects model and were weighted using the inverse variance method. The mean AUC values for each index were greater than 0.6 for most outcomes including hypertension. The values were the highest when screening for metabolic syndrome (AUC > 0.8). WHtR did not have significantly better screening power than other two indexes in most outcomes, except for elevated triglycerides when compared with body mass index and high metabolic risk score when compared with waist circumference. Although not being superior in discriminatory power, WHtR is convenient in terms of measurement and interpretation, which is advantageous in practice and allows for the quick identification of children with cardio-metabolic risk factors at an early age.
Keyphrases
- body mass index
- risk factors
- metabolic syndrome
- blood pressure
- weight gain
- body weight
- systematic review
- physical activity
- insulin resistance
- healthcare
- young adults
- case control
- magnetic resonance
- clinical trial
- uric acid
- randomized controlled trial
- computed tomography
- magnetic resonance imaging
- machine learning
- quality improvement
- cardiovascular risk factors