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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.)

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.)

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]

Objective To explore the influential factors of muscle mass in postmenopausal women in Guangzhou. Methods The clinical data of 120 postmenopausal women who volunteered to participate in this study in Guangzhou were collected from June 2019 to December 2020. Among them, 90 patients met the inclusion criteria. They were 47~88 years old, with an average age of 62.4±7.46 years old. Age, menopausal age, menopausal years, height, and weight of all patients were recorded. BMI was calculated. Bone mineral density (BMD) and muscle mass were measured and body composition was analyzed. The patients were divided into sarcopenia group and non-sarcopenia group according to ASMI. The differences of age, menopausal age, menopausal years, BMI, BMD, and muscle mass between the two groups were analyzed. The ratio of osteoporosis between the two groups was compared. Pearson correlation analysis was used to study the correlation between various factors and muscle mass. Multiple linear regression analysis was used to analyze the correlation between each index and muscle mass, and the regression equation was obtained. Results BMI and ASMI in the sarcopenia group were significantly lower than those in the non-sarcopenia group (P<0.05). The proportion of osteoporosis in the sarcopenia group was higher than that in the non-sarcopenia group (P>0.05). Pearson correlation analysis showed that menopausal age (r=0.262, P=0.012) and BMI (r=0.771, P<0.001) were positively correlated with ASMI. Multiple linear regression analysis showed that the main factors affecting ASMI of postmenopausal women were menopausal age (P=0.037, B=0.034) and BMI (P<0.001, B=0.177). The regression equation was ASMI=0.685+0.034xmenopausal age+0.177xBMI. Conclusion Patients with sarcopenia have a lower BMI than those without. Menopausal age and BMI are positively correlated with muscle mass in postmenopausal women. [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.)