Objective To explore the potential targets and mechanism of Gufang Granules in treating osteoporosis through network pharmacology,molecular dynamics simulations,and in vitro experiment validation.Methods The active components of Gufang Granules were obtained from the TCMSP database and literature,and their related targets were predicted using SwissTargetPrediction database.Core drug targets were selected through protein-protein interaction(PPI)network analysis and machine learning models,and the predictive performance of the models was assessed by drawing receiver operating characteristic(ROC)curves on independent validation datasets.Gene Set Enrichment Analysis(GSEA)was used to analyze the expression and pathways of core targets.Molecular dynamics(MD)simulations were applied to evaluate the structural stability and interactions of the compound-target complexes.Non-cytotoxic concentrations of Gufang Granules containing serum were determined by the CCK-8 assay.RAW264.7 cells were treated with low,medium,and high concentrations of drug containing serum,respectively.The number of osteoclasts was quantified using TRAP staining.The expression levels of relevant genes and proteins were analyzed through qRT-PCR and Western blot methods.Results A total of 251 potential active components and 1 078 related targets of Gufang Granules were identified.The high expressions of core targets SRC and TNF were mainly associated with osteoclast differentiation,MAPK signaling pathway and PI3K/Akt signaling pathway.MD simulations showed that the core active component Glabridin exhibited strong stability and interaction with the SRC and TNF target proteins.The number of TRAP positive cells in all concentration groups of Gufang Granules was significantly reduced compared to the RANKL group(P<0.01,P<0.001).The serum containing Gufang Granules significantly reduced the mRNA expression of NFATc1,CTSK,SRC and TNF-α,and also downregulated the protein expression of NFATc1,CTSK,p-SRC and TNF-α(P<0.05,P<0.01,P<0.001).Conclusion Gufang Granules may inhibit osteoclast differentiation by downregulating the expression of NFATc1,CTSK,p-SRC and TNF-α,thereby slowing the pathological progression of osteoporosis.

BACKGROUND:The controlling nutritional status score and systemic immune-inflammation index,as tools to assess individual nutritional and inflammatory states,have been proven to be related to the risk and prognosis of various chronic diseases.However,their value in predicting postmenopausal osteoporosis has not yet been fully explored.OBJECTIVE:To explore the applicative value of the controlling nutritional status score and systemic immune-inflammation index in predicting postmenopausal osteoporosis.METHODS:A retrospective analysis was conducted on the clinical data of 420 postmenopausal patients treated from January 2022 to April 2024 at the Second Affiliated Hospital of Liaoning University of Traditional Chinese Medicine and its Kangping branch.There were 205 cases in osteoporosis group and 215 in normal bone mass group.Age,years of menopause,body mass index,history of fracture,smoking history and alcohol consumption were selected as covariates.Patients were matched at a 1∶1 ratio using the nearest neighbor method of propensity score matching to balance covariates between the two groups.Therefore,there were 142 patients in each of the two groups after matching.Serum levels of type Ⅰ precollagen amino-terminal prepeptide,β-collagen degradation products,parathyroid hormone,and 25-hydroxyvitamin D were measured in both groups.The controlling nutritional status score and systemic immune-inflammation index were assessed by serum albumin,total cholesterol levels,neutrophil counts,lymphocyte counts,and platelet counts.The receiver operating characteristic curve was employed to analyze the optimal cutoff values and predictive effectiveness of the controlling nutritional status score and systemic immune-inflammation index.Pearson or Spearman correlations were used to analyze the relationships among the controlling nutritional status score,systemic immune-inflammation index,and bone mineral density.A multivariable logistic regression model was utilized to identify factors influencing postmenopausal osteoporosis.RESULTS AND CONCLUSION:(1)After matching,compared with the normal bone mass group,the osteoporosis group had higher serum pre-collagen type I amino-terminal prepeptide,β-collagen degradation products,and parathyroid hormone levels(P＜0.001),lower 25-hydroxyvitamin D levels(P＜0.001),and higher malnutrition rates and immunoinflammatory indices(P＜0.001).(2)Correlation analysis showed a positive correlation between the controlling nutritional status score and systemic immune-inflammation index(r=0.462,P＜0.001),and both were negatively correlated with femoral neck bone density and lumbar spine L1-L4 bone mineral density(r=-0.322,P＜0.001;r=-0.362,P＜0.001;r=-0.322,P＜0.001;r=-0.340,P＜0.001).(3)Multivariable logistic regression analyses,before and after propensity score matching,indicated that both the controlling nutritional status score and systemic immune-inflammation index were risk factors for osteoporosis in postmenopausal patients.(4)The receiver operating characteristic curves post-matching showed that the areas under the curve for the controlling nutritional status score and systemic immune-inflammation index were 0.758 and 0.754,respectively,and the two best cutoff values were 2.50 and 694.62,respectively,suggesting that both tools perform well in predicting postmenopausal osteoporosis.To conclude,the controlling nutritional status score and systemic immune-inflammation index are effective tools for predicting postmenopausal osteoporosis,suitable for clinical use in prevention and early identification of high-risk individuals.These findings also suggest that nutritional status and inflammatory markers may be part of the pathogenesis of postmenopausal osteoporosis.

Objective To explore the potential targets and mechanism of Gufang Granules in treating osteoporosis through network pharmacology,molecular dynamics simulations,and in vitro experiment validation.Methods The active components of Gufang Granules were obtained from the TCMSP database and literature,and their related targets were predicted using SwissTargetPrediction database.Core drug targets were selected through protein-protein interaction(PPI)network analysis and machine learning models,and the predictive performance of the models was assessed by drawing receiver operating characteristic(ROC)curves on independent validation datasets.Gene Set Enrichment Analysis(GSEA)was used to analyze the expression and pathways of core targets.Molecular dynamics(MD)simulations were applied to evaluate the structural stability and interactions of the compound-target complexes.Non-cytotoxic concentrations of Gufang Granules containing serum were determined by the CCK-8 assay.RAW264.7 cells were treated with low,medium,and high concentrations of drug containing serum,respectively.The number of osteoclasts was quantified using TRAP staining.The expression levels of relevant genes and proteins were analyzed through qRT-PCR and Western blot methods.Results A total of 251 potential active components and 1 078 related targets of Gufang Granules were identified.The high expressions of core targets SRC and TNF were mainly associated with osteoclast differentiation,MAPK signaling pathway and PI3K/Akt signaling pathway.MD simulations showed that the core active component Glabridin exhibited strong stability and interaction with the SRC and TNF target proteins.The number of TRAP positive cells in all concentration groups of Gufang Granules was significantly reduced compared to the RANKL group(P<0.01,P<0.001).The serum containing Gufang Granules significantly reduced the mRNA expression of NFATc1,CTSK,SRC and TNF-α,and also downregulated the protein expression of NFATc1,CTSK,p-SRC and TNF-α(P<0.05,P<0.01,P<0.001).Conclusion Gufang Granules may inhibit osteoclast differentiation by downregulating the expression of NFATc1,CTSK,p-SRC and TNF-α,thereby slowing the pathological progression of osteoporosis.

BACKGROUND:The controlling nutritional status score and systemic immune-inflammation index,as tools to assess individual nutritional and inflammatory states,have been proven to be related to the risk and prognosis of various chronic diseases.However,their value in predicting postmenopausal osteoporosis has not yet been fully explored.OBJECTIVE:To explore the applicative value of the controlling nutritional status score and systemic immune-inflammation index in predicting postmenopausal osteoporosis.METHODS:A retrospective analysis was conducted on the clinical data of 420 postmenopausal patients treated from January 2022 to April 2024 at the Second Affiliated Hospital of Liaoning University of Traditional Chinese Medicine and its Kangping branch.There were 205 cases in osteoporosis group and 215 in normal bone mass group.Age,years of menopause,body mass index,history of fracture,smoking history and alcohol consumption were selected as covariates.Patients were matched at a 1∶1 ratio using the nearest neighbor method of propensity score matching to balance covariates between the two groups.Therefore,there were 142 patients in each of the two groups after matching.Serum levels of type Ⅰ precollagen amino-terminal prepeptide,β-collagen degradation products,parathyroid hormone,and 25-hydroxyvitamin D were measured in both groups.The controlling nutritional status score and systemic immune-inflammation index were assessed by serum albumin,total cholesterol levels,neutrophil counts,lymphocyte counts,and platelet counts.The receiver operating characteristic curve was employed to analyze the optimal cutoff values and predictive effectiveness of the controlling nutritional status score and systemic immune-inflammation index.Pearson or Spearman correlations were used to analyze the relationships among the controlling nutritional status score,systemic immune-inflammation index,and bone mineral density.A multivariable logistic regression model was utilized to identify factors influencing postmenopausal osteoporosis.RESULTS AND CONCLUSION:(1)After matching,compared with the normal bone mass group,the osteoporosis group had higher serum pre-collagen type I amino-terminal prepeptide,β-collagen degradation products,and parathyroid hormone levels(P＜0.001),lower 25-hydroxyvitamin D levels(P＜0.001),and higher malnutrition rates and immunoinflammatory indices(P＜0.001).(2)Correlation analysis showed a positive correlation between the controlling nutritional status score and systemic immune-inflammation index(r=0.462,P＜0.001),and both were negatively correlated with femoral neck bone density and lumbar spine L1-L4 bone mineral density(r=-0.322,P＜0.001;r=-0.362,P＜0.001;r=-0.322,P＜0.001;r=-0.340,P＜0.001).(3)Multivariable logistic regression analyses,before and after propensity score matching,indicated that both the controlling nutritional status score and systemic immune-inflammation index were risk factors for osteoporosis in postmenopausal patients.(4)The receiver operating characteristic curves post-matching showed that the areas under the curve for the controlling nutritional status score and systemic immune-inflammation index were 0.758 and 0.754,respectively,and the two best cutoff values were 2.50 and 694.62,respectively,suggesting that both tools perform well in predicting postmenopausal osteoporosis.To conclude,the controlling nutritional status score and systemic immune-inflammation index are effective tools for predicting postmenopausal osteoporosis,suitable for clinical use in prevention and early identification of high-risk individuals.These findings also suggest that nutritional status and inflammatory markers may be part of the pathogenesis of postmenopausal osteoporosis.