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Measurement of obesity accuracy

Calculating a patient’s waist-to-height ratio is the most accurate and efficient way of identifying whether or not they are at risk of obesity, a new study shows.

The research, published in PLOS One, examined the whole-body fat percentage and visceral adipose tissue mass of a group of 81 men and women.

The British authors discovered that 36.5% more adults would be classified as obese using whole-body fat data (one in two participants) rather than BMI (around one in seven participants, or 13.5%).

To conduct their study, they gathered accurate whole-body and abdominal fat data using a total body dual energy X-ray absorptiometry (DXA) scanner — a highly accurate way of measuring body composition and fat content.

They then calculated five predictors of whole-body fat and visceral adipose tissue that could be easily replicated in a GP’s office, and compared the results with those of the DXA scan to determine which simple predictor of obesity was the most accurate.

The five predictors tested were: BMI, waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and waist-to-height ratio0.5 (WHtR0.5).

Lead researcher Dr Michelle Swainson, senior lecturer in exercise physiology at Leeds Beckett University, says although there are benefits to the conventional BMI method, there is concern that it is a misleading measurement.

“This is most definitely the case when people have a 'normal’ BMI but high abdominal fat that is often dismissed,” Dr Swainson says.

The results from the study show the best predictor of whole-body fat percentage and visceral adipose tissue in both men and women is WHtR.

This simple method of waist circumference divided by height measurement is not a new obesity classification but, despite evidence supporting its use, it is still not routinely measured in clinical settings, the authors note.

Cut-points for predicting whole body obesity were 0.53 in men and 0.54 in women. The cut-point for predicting abdominal obesity was 0.59 in both sexes



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