D-P Kuo1,2, Y-W Chiu3, P-T Chen4, Y-J Tsai5, C-H Hou6,7, Y-L Chen2, C-M Chu8,9,10,11,12,13,14. 1. Department of Medical Imaging, Taipei Medical University Hospital, No. 252, Wu-Hsing St, Taipei City, 11031, Taiwan. 2. Department of Radiology, Taoyuan Armed Forces General Hospital, No. 168, Zhongxing Rd., Longtan Dist., Taoyuan County, 32551, Taiwan. 3. Department of Nuclear Medicine, Saint Paul's Hospital, No. 123, Jianxin Street, Taoyuan Dist., Taoyuan County, 33069, Taiwan. 4. Department of Radiology, Kang-Ning General Hospital, No. 26, Lane 420, Section 5, Chenggong Rd., Neihu Dist., Taipei City, 11490, Taiwan. 5. Department of Radiology, Keelung Hospital of the Ministry of Health and Welfare, No. 268, Xin 2nd Rd., Xinyi Dist., Keelung City, 20148, Taiwan. 6. Division of Nuclear Medicine, Taoyuan Armed Forces General Hospital, No. 168, Zhongxing Rd., Longtan Dist., Taoyuan County, 32551, Taiwan. 7. Department of Nuclear Medicine, Tri-Service General Hospital, National Defense Medical Center, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan. 8. Graduate Institute of Life Sciences, National Defense Medical Center, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan. chuchiming@web.de. 9. Graduate Institute of Medical Sciences, National Defense Medical Center, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan. chuchiming@web.de. 10. School of Public Health, National Defense Medical Center, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan. chuchiming@web.de. 11. Big Data Research Center, Fu-Jen Catholic University, No. 510, Zhongzheng Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan. chuchiming@web.de. 12. Department of Healthcare Administration and Medical Informatics, College of Health Sciences, Kaohsiung Medical University, No. 100, Shiquan 1st Rd., Sanmin Dist., Kaohsiung City, 80708, Taiwan. chuchiming@web.de. 13. Department of Medical Research, Kaohsiung Medical University Hospital, No. 100, Shiquan 1st Rd., Sanmin Dist., Kaohsiung City, 80708, Taiwan. chuchiming@web.de. 14. School of Public Health, China Medical University, No. 91, Hsueh-Shih Road, Taichung, 40402, Taiwan. chuchiming@web.de.
Abstract
INTRODUCTION: Osteoporosis is a metabolic bone disease with low bone mineral density (BMD) and high incidence of vertebral fractures (VFs). Postmenopausal women with osteoporosis have decreased total fat and lean mass. This study aimed to investigate the associations between body composition and VF risk and explore the potential predictor of VF risk in postmenopausal women. METHODS: Enrolled 731 postmenopausal women were referred by various departments and outpatient clinics to assess vertebral status between October 2016 and November 2017. The main measures were total body lean mass, fat mass, and BMD. Patients were divided into osteopenia, osteoporosis, and normal groups based on T-scores. Logistic regression analyses were performed to evaluate associations between body composition parameters and VF. RESULTS: VF was significantly associated with increased age, lower height, and lighter weight in all participants, and higher BMI was observed in VF participants. Participants in the osteoporosis group were older and had lower height, weight, and BMD than those in normal and osteopenia groups. Femoral and total hip T-scores as well as T-scores for lumbar spine were significantly lower in participants with VF than in non-VF participants. Percentage of bone mass was also significantly lower in VF participants compared to that of non-VF participants. Women with increased BMD and lower bone mass had reduced odds for VF occurrence. Bone mass was significantly able to identify VF occurrence. CONCLUSIONS: Body composition analysis discerns differences in the bone status of postmenopausal women with and without VF. The cutoff value of the bone mass might be used effectively as an indicator of risk for VF occurrence.
INTRODUCTION: Osteoporosis is a metabolic bone disease with low bone mineral density (BMD) and high incidence of vertebral fractures (VFs). Postmenopausal women with osteoporosis have decreased total fat and lean mass. This study aimed to investigate the associations between body composition and VF risk and explore the potential predictor of VF risk in postmenopausal women. METHODS: Enrolled 731 postmenopausal women were referred by various departments and outpatient clinics to assess vertebral status between October 2016 and November 2017. The main measures were total body lean mass, fat mass, and BMD. Patients were divided into osteopenia, osteoporosis, and normal groups based on T-scores. Logistic regression analyses were performed to evaluate associations between body composition parameters and VF. RESULTS: VF was significantly associated with increased age, lower height, and lighter weight in all participants, and higher BMI was observed in VF participants. Participants in the osteoporosis group were older and had lower height, weight, and BMD than those in normal and osteopenia groups. Femoral and total hip T-scores as well as T-scores for lumbar spine were significantly lower in participants with VF than in non-VF participants. Percentage of bone mass was also significantly lower in VF participants compared to that of non-VF participants. Women with increased BMD and lower bone mass had reduced odds for VF occurrence. Bone mass was significantly able to identify VF occurrence. CONCLUSIONS: Body composition analysis discerns differences in the bone status of postmenopausal women with and without VF. The cutoff value of the bone mass might be used effectively as an indicator of risk for VF occurrence.