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Copyright of Chinese Journal of Osteoporosis is the property of Chinese Journal of Osteoporosis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Chinese Journal of Osteoporosis is the property of Chinese Journal of Osteoporosis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Osteoporosis can easily lead to a decline in the self-care ability of middle-aged and elderly people, and an increase in mortality and related medical expenses. The relationship among the main body components of the human body (fat, muscle, and bone) has not been clarified. Therefore, it is necessary to study their relationship and identify potential predictors of osteoporosis in patients. OBJECTIVE: To investigate the correlation between body composition and bone mineral density of the middle-aged and elderly people. METHODS: A total of 109 middle-aged and elderly people were selected as subjects, and their heights, body mass and other information were recorded. The dual-energy X-ray bone densitometer was used to detect the bone mineral density and body composition. According to the results of body mineral density, the subjects were divided into an osteoporosis group and a non-osteoporosis group. The distribution and relationship of basic data, bone mineral density, bone mineral content, lean mass and fat mass of each part were analyzed between the two groups. Chi-square test was used to detect the ratio of osteoporosis in different sexes, and Pearson correlation analysis was used to obtain the correlation degree of each factor and bone mineral density. RESULTS AND CONCLUSION: Women were more likely to suffer from osteoporosis. The age of the osteoporosis group was significantly higher than that of the non-osteoporosis group. Body mass, body mass index, bone mineral density, bone mineral content (overall, head, trunk, lower limbs), lean mass (overall, head, trunk, lower limbs), and fat mass (overall, head, upper limbs, lower limbs) in the osteoporosis group were significantly lower than those in the non-osteoporosis group. Pearson correlation analysis indicated that the bone mineral density of middle-aged and elderly people was significantly positively correlated with height, body mass, bone mineral content (overall, head, trunk, upper limbs, lower limbs), lean mass (overall, head, trunk, lower limbs) and fat mass (head), and negatively correlated with age. Therefore, osteoporosis is more common in women, and the middle-aged and elderly people with osteoporosis are older. Their bone mineral content, lean mass and fat mass of each part are lower than those without osteoporosis. The bone mineral density of the middle-aged and elderly people is highly correlated with the bone mineral content and lean mass of each part. Therefore, body composition can be used as an indirect reference index to evaluate the bone health status. Bone mineral content, lean mass and fat mass of each part have certain influence on the bone mineral density. [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]