Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects.
BACKGROUND
The most commonly used predictive equation for basal metabolic rate (BMR) is the Schofield equation, which only uses information on body weight, age and sex to derive the prediction. However, because body composition is a key influencing factor, there will be error in calculating an individual's basal requirements based on this prediction.


OBJECTIVE
To investigate whether adding additional anthropometric measures to the standard measures can enhance the predictability of BMR and to cross-validate this within a separate subgroup.


METHODS
Cross-sectional study of 150 Caucasian adults from Scotland, with a body mass index range of 16.7-49.3 kg/m(2). All subjects underwent measurement of BMR, body composition, and 148 also had basic skinfold and circumference measures taken. The resultant equation was tested in a subgroup of 39 obese males.


RESULTS
The average difference between the predicted (Schofield equation) and measured BMR was 502 kJ/day. There was a slight systematic bias in this error, with the Schofield equation underestimating the lowest values. The average discrepancy between predicted and actual BMR was reduced to 452 kJ/day, with the addition of fat mass, fat-free mass, an overall 10% improvement on the Schofield equation (P=0.054). Using an equation derived from principal components analysis of anthropometry measurements similarly decreased the difference to 458 kJ/day (P=0.039). Testing the equation in a separate group indicated a 33% improvement in predictability of BMR, compared to the Schofield equation.


CONCLUSIONS
In the absence of detailed information on body composition, utilizing anthropometric data provides a useful alternative methodology to improve the predictability of BMR beyond that achieved from the standard Schofield prediction equation. This should be confirmed in more individuals, both within the obese and normal weight category.
Johnstone
A M
AM
Aberdeen Centre for Energy Regulation and Obesity (ACERO), Division of Obesity and Metabolic Health, Rowett Research Institute, Aberdeen, UK. A.Johnstone@rowett.ac.uk
Rance
K A
KA
Murison
S D
SD
Duncan
J S
JS
Speakman
J R
JR
D000328:Adult; D000886:Anthropometry; D001481:Basal Metabolism / Q000502:physiology*; D001823:Body Composition / Q000502:physiology*; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008433:Mathematics; D008875:Middle Aged; D009751:Nutritional Requirements; D009765:Obesity / Q000175:diagnosis*; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity