BACKGROUND:Sarcopenia is an age-related systemic skeletal muscle disease,which is associated with a variety of adverse outcomes such as falls,functional decline,frailty,and death.Postmenopausal women are one of the high-risk groups for sarcopenia.OBJECTIVE:To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.METHODS:Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study(CHARLS),and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019(AWGS2019)recommended metrics.The study cohort was randomized into a training set(70％)and a validation set(30％).Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator,ten-fold cross-validation,and logistic regression.Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors,and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS AND CONCLUSION:The prevalence of sarcopenia in this study was 23.50％and age,place of residence,sleep quality,cognitive function,depression,and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women.The nomogram model showed good discrimination between the training and validation sets,with an AUC value of 0.751(95％confidence interval=0.724-0.778,P＜0.001),a specificity of 72.2％,and a sensitivity of 63.2％in the training set,and an AUC value of 0.763(95％confidence interval=0.721-0.805,P＜0.001),with a specificity of 69.6％and a sensitivity of 70.8％.The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations,and the decision curve analysis demonstrated broad and good clinical utility.To conclude,the nomogram to assess the risk of sarcopenia constructed based on age,place of residence,sleep quality,cognitive function,depression,and number of chronic diseases,provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women,and helps to reduce the incidence of sarcopenia.

BACKGROUND:Sarcopenia is an age-related systemic skeletal muscle disease,which is associated with a variety of adverse outcomes such as falls,functional decline,frailty,and death.Postmenopausal women are one of the high-risk groups for sarcopenia.OBJECTIVE:To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.METHODS:Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study(CHARLS),and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019(AWGS2019)recommended metrics.The study cohort was randomized into a training set(70％)and a validation set(30％).Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator,ten-fold cross-validation,and logistic regression.Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors,and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS AND CONCLUSION:The prevalence of sarcopenia in this study was 23.50％and age,place of residence,sleep quality,cognitive function,depression,and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women.The nomogram model showed good discrimination between the training and validation sets,with an AUC value of 0.751(95％confidence interval=0.724-0.778,P＜0.001),a specificity of 72.2％,and a sensitivity of 63.2％in the training set,and an AUC value of 0.763(95％confidence interval=0.721-0.805,P＜0.001),with a specificity of 69.6％and a sensitivity of 70.8％.The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations,and the decision curve analysis demonstrated broad and good clinical utility.To conclude,the nomogram to assess the risk of sarcopenia constructed based on age,place of residence,sleep quality,cognitive function,depression,and number of chronic diseases,provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women,and helps to reduce the incidence of sarcopenia.

BACKGROUND:With the continuous exploration of the pathogenesis of osteonecrosis,more and more research evidence shows that neuroimaging change is closely related to the onset of osteonecrosis.However,the specific causal relationship between neuroimaging change and osteonecrosis is still unclear.OBJECTIVE:To evaluate the causal relationship between neuroimaging indices and osteonecrosis using Mendelian Randomization analysis.METHODS:Neuroimaging data were obtained from the UK Biobank database in the UK,which included a total of 36 778 individuals.Osteonecrosis data were obtained from the FinnGen database in Finland,including 1 543 cases and 391 037 controls.Instrumental variables were extracted and screened from outcome factors,and two-sample Mendelian randomization analysis was performed.The data were analyzed by inverse variance weighted method,MR-Egger,weighted median method,simple model method,and weighted model method.The inverse variance weighted method was used as the main analysis method,and the other four methods were used as supplements.To verify the feasibility and stability of the data,sensitivity analysis of the results was performed.Based on the complexity of causal inference,a reverse Mendelian randomization analysis was further performed to evaluate the potential reverse causal relationship.RESULTS AND CONCLUSION:(1)The results of inverse variance weighted analysis showed that 97 neuroimaging data were positively correlated with osteonecrosis(P＜0.05,OR＞1);2 data were heterogeneous and 6 data had horizontal pleiotropy.95 neuroimaging phenotypes were negatively correlated with osteonecrosis(P＜0.05,OR＜1);5 data were heterogeneous,and 9 data had horizontal pleiotropy;2 groups of data had reverse causal relationships.(2)The two-sample Mendelian randomization analysis established the causal relationship between neuroimaging indicators and osteonecrosis in the academic community.These large sample numbers from the UK and Finland provide a new theoretical basis for the pathophysiology of osteonecrosis,and also provide ideas and methods for the prediction,screening,early diagnosis and prognosis of osteonecrosis in China,which is conducive to improving the accuracy of clinical diagnosis and the effectiveness of treatment of osteonecrosis.

BACKGROUND:With the continuous exploration of the pathogenesis of osteonecrosis,more and more research evidence shows that neuroimaging change is closely related to the onset of osteonecrosis.However,the specific causal relationship between neuroimaging change and osteonecrosis is still unclear.OBJECTIVE:To evaluate the causal relationship between neuroimaging indices and osteonecrosis using Mendelian Randomization analysis.METHODS:Neuroimaging data were obtained from the UK Biobank database in the UK,which included a total of 36 778 individuals.Osteonecrosis data were obtained from the FinnGen database in Finland,including 1 543 cases and 391 037 controls.Instrumental variables were extracted and screened from outcome factors,and two-sample Mendelian randomization analysis was performed.The data were analyzed by inverse variance weighted method,MR-Egger,weighted median method,simple model method,and weighted model method.The inverse variance weighted method was used as the main analysis method,and the other four methods were used as supplements.To verify the feasibility and stability of the data,sensitivity analysis of the results was performed.Based on the complexity of causal inference,a reverse Mendelian randomization analysis was further performed to evaluate the potential reverse causal relationship.RESULTS AND CONCLUSION:(1)The results of inverse variance weighted analysis showed that 97 neuroimaging data were positively correlated with osteonecrosis(P＜0.05,OR＞1);2 data were heterogeneous and 6 data had horizontal pleiotropy.95 neuroimaging phenotypes were negatively correlated with osteonecrosis(P＜0.05,OR＜1);5 data were heterogeneous,and 9 data had horizontal pleiotropy;2 groups of data had reverse causal relationships.(2)The two-sample Mendelian randomization analysis established the causal relationship between neuroimaging indicators and osteonecrosis in the academic community.These large sample numbers from the UK and Finland provide a new theoretical basis for the pathophysiology of osteonecrosis,and also provide ideas and methods for the prediction,screening,early diagnosis and prognosis of osteonecrosis in China,which is conducive to improving the accuracy of clinical diagnosis and the effectiveness of treatment of osteonecrosis.