Predicting Osteoporosis with Body Compositions in Postmenopausal Women: A Noninvasive Method

Abstract Background: The prevalence of osteoporosis is rising steadily as the aging population increases. Bone mineral density (BMD) assessment is a more effort procedure to analyze osteoporosis. However, the accessibility and radiation exposure limited its role in community screening. A more convenient approach for screening is suggested.Methods: A total of 363 postmenopausal women over 50 age were included in this study and assessed with the body composition [including fat-free mass (FFM), fat mass (FM), and basal metabolic rate (BMR)] and BMD. Normal distributions and correlation coefficients among variables were calculated using the Shapiro-Wilk test and Pearson’s correlation analysis, respectively. A receiver operating characteristic (ROC) curve was plotted and the area under ROC curves (AUC) was determined the optimal cut-off values of the body composition variables for osteoporosis prediction.Results: The correlation coefficient of FFM, FM, FM ratio and BMR with femur neck T-score were 0.373, 0.266, 0.165, and 0.369, respectively while with spine T-score were 0.350, 0.251, 0.166, and 0.352, respectively (p<0.01 for all). FFM, FM, and BMR showed an optimal cut-off value of 37.9 kg, 18.6 kg, and 1187.5 kcal for detecting osteoporosis.Conclusions: The present study provided a model to predict osteoporosis in postmenopausal women and the optimal cut-off value of FFM, FM, and BMR could be calculated in Asian population. Among these factors, BMR seemed a better predictor than others. The BMR could be a target for exercise intervention in postmenopausal women for maintaining or improving BMD.