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Objective To investigate the changes of age, body mass index (BMI) and body composition in postmenopausal women and their relationship with osteoporosis. Methods The body composition and bone mineral density (BMD) data of 98 postmenopausal women over 45 years collected from the outpatient department of the Third Affiliated Hospital of Guangzhou University of Chinese Medicine from December 2019 to January 2021 were analyzed. According to the age and BMI of the subjects, the body composition of different age and BMI groups were compared.Spearman's rank correlation was used to analyze the effects of age, BMI and body composition on osteoporosis, and multiple linear regression was used to study the influencing factors of BMD and overall bone mineral content ( BMC). Results Both BMD(P < 0. 001) and BMC (P < 0. 05) in each age group decreased with age, while BMC (P < 0. 05 ), lean mass ( P <0. 001 ) and fat mass (P < 0. 001) in each BMI group increased with the BMI Spearman's rank correlation analysis showed that the severity of osteoporosis increased with age (P = 0. 005, r = 0. 281). The severity of osteoporosis was decreased with the increase of BMI ( P = 0. 019, r = -0. 237), BMC (P < 0. 001, r = -0. 719), lean mass (P = 0. 014, r = -0. 249) and fat mass ( P = 0. 013, r = - 0. 249). The result of multiple linear regression analysis showed that age was negatively correlated with BMD ( P = 0. 002, B = -0. 004) and BMC (P = 0. 000, B = -0. 013), while lean mass ( P = 0. 018, B = 0. 022) and fat mass ( P = 0. 037, B = 0. 027) were positively correlated with BMC in postmenopausal women Conclusion Age is a risk factor for osteoporosis in postmenopausal women, while BMI, lean mass and fat mass are protective factors, which can improve the BMD and BMC. [ABSTRACT FROM AUTHOR]

To identify the differences of biomarkers between high and low peak bone mass population, and their value in the diagnosis and treatment of osteoporosis. Methods The gene expression dataset ( GSE7158) of high and low peak bone mass population was obtained from GEO database. The differentially expressed genes were analyzed with limma package in R. Then GO functional annotation and KEGG pathway enrichment analysis were performed. All differential genes were input into the STRING database to obtain the protein-protein interaction (PPI) network. Cytohubba plug-in in Cytoscape and R was used to identify key genes and their interaction network was further mapped by Cytoscape. Finally, further verification of the expression and diagnostic value of key genes in osteoporosis was performed. Results A total of 182 differentially expressed genes were screened in high and low peak bone mass population, among which 73 were down-regulated, and 109 were up-regulated. KEGG pathway analysis showed that osteoclast differentiation pathway, PI3K-Akt signaling pathway, and AGE-RAGE signaling pathway in diabetic complications and ferroptosis were worthy of attention. PPI network analysis revealed 11 key genes : MCM7, Bub3, RBBP7, GNG2, FSHR, PCNA, CCR5, CDK16, SRSF7, NPMl, and CPSF6. Further validation analysis found that CCR5, CDK16, RBBP7, and SRSF7 were closely related to osteoporosis. Conclusion CCR5, CDK16, RBBP7, and SRSF7 may be closely related to the incidence of osteoporosis, and may become biomarkers for early screening of people at high risk of osteoporosis. This provides an effective basis for further experimental research and clinical treatment. [ABSTRACT FROM AUTHOR]

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