Here we propose and agree that Asian populations need to be evaluated by their own cut-off values in terms of BMI BF% and associated health risks.
This indicated a false positive result for 28% subjects who may be left unnoticed for detection of disorders if BF% was not measured simultaneously.
This shows that BF% is also a better predictor of underweight who were misclassified by BMI alone.
The true obese declared by both BMI and BF% were 72%.
Another interesting finding in our study was that greater proportion of subjects 29% were declared overweight by BF% compared to 18% by BMI (p=less than 0.
The study emphasises the need to measure BF% together with BMI and catalogue misclassified persons especially for categorisation.
GBMI-BF) significant difference in the mean BF% values between the DEXA and the BMI-based BF% equations across the entire cohort of female athletes.
Athletes have been shown to have significantly lower levels of BF% compared to non-athletes of the same BMI (19,24).
The results of the current study suggests considerable variation in actual BF% at any given BMI-predicted BF%.
More research is warranted to compare BMI-based BF% regression equations to multi-compartment BF% models in female athletes, as well as in other populations.
The current investigation sought to determine if BMI-based BF% equations were accurate means for determining BF% in female athletes.
BMI-based BF% regression equations that were cross-validated within the study.