Correlation analysis between risk factors, BMD and serum osteocalcin, CatheK, PINP, β-crosslaps, TRAP, lipid metabolism and BMI in 128 patients with postmenopausal osteoporotic fractures

L. Jia, M. Cheng

Jinniu District People’s Hospital of Chengdu, Chengdu, China. 17084599@qq.com

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• Endocrinology
• Orthopedics

OBJECTIVE: Our aim is to investigate the correlation between risk factors of postmenopausal osteoporotic fracture, BMD and Bone turnover markers, lipid metabolism and BMI.

SUBJECTS AND METHODS: The Cox proportional hazard model was used to conduct univariate and multivariate analysis to screen the risk factors related to postmenopausal osteoporotic fractures. Blood samples were collected to detect biochemical markers of bone turnover, blood lipids content, and then measure the BMI of the survey subjects. BMD was measured and its correlation with biochemical markers of bone turnover, lipid metabolism and BMI was analyzed.

RESULTS: Cox univariate analysis indicated that average age, menopause, years since menopause, number of deliveries, and limb spasm are associated covariates of postmenopausal osteoporotic fractures. Where, BMD severity, history of hysterectomy or ovariectomy, and years since menopause are significant covariates for the incidence of postmenopausal osteoporotic fractures. The correlation study with lipid metabolism found that the smaller the BMI value, the greater the BMD loss; the smaller the TG value, the greater the BMD loss, exhibiting a downward trend. No difference was observed between HDL-C and LDL-C content, and the difference was not statistically significant (p>0.05). Femoral neck BMD was negatively correlated with CatheK, serum osteocalcin, PINP, β-crosslaps and TRAP, and lumbar spine BMD was also negatively correlated with CatheK, serum osteocalcin, PINP, β-crosslaps and TRAP.

CONCLUSIONS: Biochemical markers of bone turnover are highly expressed in postmenopausal women and increase with the decrease of bone density, which can be used as markers for disease prediction. Combined with BMI, triglyceride and other related indicators, and closely related factors such as the patient’s age, the number of deliveries, it is possible to predict the incidence of PMOP fractures early.

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