Comparison of predictive equations for resting energy expenditure among patients with schizophrenia in Japan

BACKGROUND: Recently, a relationship between obesity and schizophrenia has been reported. The prediction of resting energy expenditure (REE) is important to determine the energy expenditure of patients with schizophrenia. However, there is a lack of research concerning the most accurate REE predictive equations among Asian patients with schizophrenia. The purpose of the study reported here was to compare the validity of four REE equations for patients with schizophrenia taking antipsychotics. METHODS: For this cross-sectional study, we recruited patients (n=110) who had a Diagnostic and Statistical Manual of Mental Disorders, fourth edition, diagnosis of schizophrenia and were admitted to four psychiatric hospitals. The mean (± standard deviation) age of these patients was 45.9±13.2 years. Anthropometric measurements (of height, weight, body mass index) were taken at the beginning of the study. REE was measured using indirect calorimetry. Comparisons between the measured and estimated REEs from the four equations (Harris–Benedict, Mifflin–St Jeor, Food and Agriculture Organization/World Health Organization/United Nations University, and Schofield) were performed using simple linear regression analysis and Bland–Altman analysis. RESULTS: Significant trends were found between the measured and predicted REEs for all four equations (P<0.001), with the Harris–Benedict equation demonstrating the strongest correlation in both men and women (r=0.617, P<0.001). In all participants, Bland–Altman analysis revealed that the Harris–Benedict and Mifflin–St Jeor equations did not show a significant bias in the prediction of REE, however, a significant overestimation error was shown for the Food and Agriculture Organization/World Health Organization/United Nations University and Schofield equations. CONCLUSION: When estimating REE in patients with schizophrenia taking antipsychotics, the Harris–Benedict equation appears to be the most appropriate for clinical use.