Association between Obesity and Bone Mineral Density by Gender and Menopausal Status.

BACKGROUND: We investigated whether there were gender differences in the effect of obesity on bone mineral density (BMD) based on menopausal status. METHODS: We assessed 5,892 consecutive patients 20 to 91 years old who were referred for dual-energy X-ray absorptiometry (DXA) scans. All subjects underwent a standard BMD scan of the hip (total hip and femoral neck) and lumbar spine (L1 to L4) using a DXA scan and body size assessment. Body mass index was used to categorize the subjects as normal weight, overweight, and obese. RESULTS: BMD was higher in obese and overweight versus normal weight men, premenopausal women, and postmenopausal women. Compared to men ≥50 years and postmenopausal women with normal weight, the age-adjusted odds ratio of osteopenia was 0.19 (95% confidence interval [CI], 0.07 to 0.56) and 0.38 (95% CI, 0.29 to 0.51) for obese men ≥50 years and postmenopausal women. Corresponding summaries for osteoporosis were 0.26 (95% CI, 0.11 to 0.64) and 0.15 (95% CI, 0.11 to 0.20), respectively. Compared to men <50 years and premenopausal women with normal weight, the age-adjusted odds ratio of low bone mass was 0.22 (95% CI, 0.11 to 0.45) and 0.16 (95% CI, 0.10 to 0.26) for obese men <50 years and premenopausal women, respectively. CONCLUSION: Obesity is associated with BMD of the hip and lumbar spine and overweight and obese individuals have similar degrees of osteoporosis. This result was not significantly different based on gender and menopausal status, which could be an important issue for further investigation.

Anthropometric predictive equations for estimating body composition.

BACKGROUND: Precise and accurate measurements of body composition are useful in achieving a greater understanding of human energy metabolism in physiology and in different clinical conditions, such as, cardiovascular disease and overall mortality. Dual-energy x-ray absorptiometry (DXA) can be used to measure body composition, but the easiest method to assess body composition is the use of anthropometric indices. This study has been designed to evaluate the accuracy and precision of body composition prediction equations by various anthropometric measures instead of a whole body DXA scan. MATERIALS AND METHODS: We identified 143 adult patients underwent DXA evaluation of the whole body. The anthropometric indices were also measured. Datasets were split randomly into two parts. Multiple regression analysis with a backward stepwise elimination procedure was used as the derivation set and then the estimates were compared with the actual measurements from the whole-body scans for a validation set. The SPSS version 20 for Windows software was used in multiple regression and data analysis. RESULTS: Using multiple linear regression analyses, the best equation for predicting the whole-body fat mass (R(2) = 0.808) included the body mass index (BMI) and gender; the best equation for predicting whole-body lean mass (R(2) = 0.780) included BMI, WC, gender, and age; and the best equation for predicting trunk fat mass (R(2) = 0.759) included BMI, WC, and gender. CONCLUSIONS: Combinations of anthropometric measurements predict whole-body lean mass and trunk fat mass better than any of these single anthropometric indices. Therefore, the findings of the present study may be used to verify the results in patients with various diseases or diets.

Use of conventional regional DXA scans for estimating whole body composition.

BACKGROUND: Using soft-tissue composition in conventional regional dual-energy X-ray absorptiometry (DXA) scans of the spine and hip to predict whole body composition (whole-body fat mass, whole-body lean mass and trunk-fat mass) instead of a whole body DXA scan. METHODS: We identified 143 adult patients who underwent DXA evaluation of the whole body. Anthropometric indices were also measured. Datasets were split randomly into two parts; the derivation set including a sample of 100 subjects, and the validation set including a sample of 43 subjects. Multiple regression analysis with the backward stepwise elimination procedure was used for the derivation set and the estimates were then compared with the actual measurements from the whole-body scans for the validation set. The Ra2 (adjusted coefficient of multiple determination) and SSE (error sum of squares) criteria were applied to compare regression models. RESULTS: Using multiple linear regression analyses, the best equation for predicting whole-body fat mass (Ra2= 0. 945) included gender, height, weight, waist circumference (WC), spine fat fraction and hip fat fraction; the best equation for predicting whole-body lean mass (Ra2 = 0. 970) included gender, weight, WC, spine fat fraction and hip fat fraction; and the best equation for predicting trunk-fat mass (Ra2 = 0. 944) included gender, weight, spine fat fraction and hip fat fraction. CONCLUSION: The results of this study show that regional DXA scans of the spine and hip can be used to accurately predict body composition.

Relationship between Weight, Body Mass Index, and Bone Mineral Density in Men Referred for Dual-Energy X-Ray Absorptiometry Scan in Isfahan, Iran.

Objective. Although several studies have investigated the association between body mass index (BMI) and bone mineral density (BMD), the results are inconsistent. The aim of this study was to further investigate the relation between BMI, weight and BMD in an Iranian men population. Methods. A total of 230 men 50-79 years old were examined. All men underwent a standard BMD scans of hip (total hip, femoral neck, trochanter, and femoral shaft) and lumbar vertebrae (L2-L4) using a Dual-Energy X-ray Absorptiometry (DXA) scan and examination of body size. Participants were categorised in two BMI group: normal weight <25.0 kg/m(2) and overweight and obese, BMI ≥ 25 kg/m(2). Results. Compared to men with BMI ≥ 25, the age-adjusted odds ratio of osteopenia was 2.2 (95% CI 0.85, 5.93) and for osteoporosis was 4.4 (1.51, 12.87) for men with BMI < 25. It was noted that BMI and weight was associated with a high BMD, compatible with a diagnosis of osteoporosis. Conclusions. These data indicate that both BMI and weight are associated with BMD of hip and vertebrae and overweight and obesity decreased the risk for osteoporosis. The results of this study highlight the need for osteoporosis prevention strategies in elderly men as well as postmenopausal women.