AIMS: To investigate whether blood thyroid stimulating hormone (TSH), cortisol, insulin and glucose concentrations (plus glucose:insulin ratio; GIR) could improve the accuracy of resting metabolic rate (RMR) prediction in normal, overweight and obese persons. METHODS: Predictive equations were developed and compared against indirect calorimetry measures for RMR in 217 weight-control clinic participants (n = 128 males and n = 89 females: ∼24% normal weight, ∼39% overweight and ∼37% obese). RESULTS: Using the common accuracy criteria of the proportion of predicted RMR within ±10% of measured RMR, our equations (using age, height, weight and gender, plus the blood factors, both independently and in combination) were accurate ∼36-44% of the time, for the whole sample, and when separated by gender and weight class. Specifically, the addition of the blood hormone and glucose concentrations improved the accuracy of predicted RMR by only 1-8% (NS). CONCLUSIONS: Including blood TSH, cortisol, insulin, glucose and GIR into RMR prediction equations did not significantly improve estimation accuracy, which in any case only met a criterion of ±10% of the measured RMR ∼40% of the time. Further work to refine the prediction of RMR is still needed, and at present, direct measurements should be made wherever possible.
AIMS: To investigate whether blood thyroid stimulating hormone (TSH), cortisol, insulin and glucose concentrations (plus glucose:insulin ratio; GIR) could improve the accuracy of resting metabolic rate (RMR) prediction in normal, overweight and obesepersons. METHODS: Predictive equations were developed and compared against indirect calorimetry measures for RMR in 217 weight-control clinic participants (n = 128 males and n = 89 females: ∼24% normal weight, ∼39% overweight and ∼37% obese). RESULTS: Using the common accuracy criteria of the proportion of predicted RMR within ±10% of measured RMR, our equations (using age, height, weight and gender, plus the blood factors, both independently and in combination) were accurate ∼36-44% of the time, for the whole sample, and when separated by gender and weight class. Specifically, the addition of the blood hormone and glucose concentrations improved the accuracy of predicted RMR by only 1-8% (NS). CONCLUSIONS: Including blood TSH, cortisol, insulin, glucose and GIR into RMR prediction equations did not significantly improve estimation accuracy, which in any case only met a criterion of ±10% of the measured RMR ∼40% of the time. Further work to refine the prediction of RMR is still needed, and at present, direct measurements should be made wherever possible.