Objective:To investigate the causal relationships between multiple obesity indices, including body mass index (BMI), body fat percentage, whole-body fat mass, trunk fat mass, leg fat percentage, arm fat percentage, waist circumference, and hip circumference, and preeclampsia (PE) using Mendelian randomization (MR), and to evaluate the mediating effect of triglycerides.Methods:Genome-wide association studies (GWAS) summary statistics from European populations were utilized. Independent genetic loci associated with obesity indices and PE served as instrumental variables of exposure and outcomes. Obesity data (approximately 191 000 female samples) came from UK Biobank; PE data ( n=242 852) from FinnGen Biobank. Causal effects were assessed primarily via inverse variance weighted (IVW), supplemented by MR-Egger, weighted median, MR-pleiotropy residual sum and outlier (MR-PRESSO), and Bayesian weighted MR. Bonferroni correction was applied. Cochran's Q test evaluated heterogeneity; MR-Egger intercept test assessed horizontal pleiotropy; leave-one-out, funnel, and scatter plots conducted sensitivity analyses. Odds ratio ( OR) measured effect sizes. Two-step MR explored triglyceride mediation. Results:Eighty-two to 112 single nucleotide polymorphisms were included as instrumental variables. After Bonferroni correction, significant positive causal associations with PE were observed for: BMI (IVW: OR=1.703, 95% CI: 1.469-1.974, P<0.001), body fat percentage (IVW: OR=1.595, 95% CI: 1.321-1.925, P<0.001), whole-body fat mass (IVW: OR=1.639, 95% CI: 1.389-1.934, P<0.001), right leg fat percentage (IVW: OR=1.610, 95% CI: 1.360-1.905, P<0.001), left leg fat percentage (IVW: OR=1.622, 95% CI: 1.363-1.930, P<0.001), right arm fat percentage (IVW: OR=1.591, 95% CI: 1.351-1.872, P<0.001), left arm fat percentage (IVW: OR=1.710, 95% CI: 1.444-2.024, P<0.001), and waist circumference (IVW: OR=1.815, 95% CI: 1.534-2.148, P<0.001). Sensitivity analyses confirmed robustness. Triglycerides mediated 4.6%-8.2% of these effects. Trunk fat mass and hip circumference showed potential positive associations (IVW: OR>1, 0.005≤ P<0.05). Conclusions:Higher BMI, body fat percentage, whole-body fat mass, leg/arm fat percentages, and waist circumference may increase PE risk, with waist circumference showing the strongest association. These effects may be partially mediated by triglycerides.

Objective To analyze serum prolactin(PRL)levels during pregnancy in healthy single pregnant women of childbearing age in Suzhou,and to establish and verify the reference interval of serum PRL.Methods From January to March,2022,the data of pregnant women with healthy single pregnancy at child-bearing age were collected and prospectively followed up until delivery.According to the gestational age,the subjects were divided into early pregnancy group(less than 14 weeks),middle pregnancy group(14-27+6 weeks)and late pregnancy group(≥28 weeks).PRL was determined by Soling LIAISON XL automatic chemi-luminescence immunoassay and LIAISON? prolactin.After eliminating outliers,the medical reference interval of serum PRL during pregnancy was established by percentile method(P2.5-P97.5).In addition,20 samples of healthy single pregnant women of childbearing age were randomly collected in early,middle and late pregnancy to verify the established reference interval.When no more than two PRL measurements in each group exceeded the established reference interval,they were considered validated.Results A total of 170 participants were included in the early pregnancy group,229 participants in the middle pregnancy group and 130 participants in the late pregnancy group.There were significant differences in serum PRL levels in pregnant women at different stages of pregnancy.With the increase of gestational age,serum PRL level increased.The reference intervals of serum PRL in early,middle and late pregnancy were 477-4270 mIU/L,1060-6574 mIU/L and 3497-18 274 mIU/L,respectively.All the established reference intervals were verified.Conclusion This study has established the reference interval of serum PRL during pregnancy of healthy single pregnant women of childbear-ing age in Suzhou area,to provide help for clinical rational application of this index,and further reference for the prevention of pregnancy-related diseases.

Objective:To investigate the causal relationships between multiple obesity indices, including body mass index (BMI), body fat percentage, whole-body fat mass, trunk fat mass, leg fat percentage, arm fat percentage, waist circumference, and hip circumference, and preeclampsia (PE) using Mendelian randomization (MR), and to evaluate the mediating effect of triglycerides.Methods:Genome-wide association studies (GWAS) summary statistics from European populations were utilized. Independent genetic loci associated with obesity indices and PE served as instrumental variables of exposure and outcomes. Obesity data (approximately 191 000 female samples) came from UK Biobank; PE data ( n=242 852) from FinnGen Biobank. Causal effects were assessed primarily via inverse variance weighted (IVW), supplemented by MR-Egger, weighted median, MR-pleiotropy residual sum and outlier (MR-PRESSO), and Bayesian weighted MR. Bonferroni correction was applied. Cochran's Q test evaluated heterogeneity; MR-Egger intercept test assessed horizontal pleiotropy; leave-one-out, funnel, and scatter plots conducted sensitivity analyses. Odds ratio ( OR) measured effect sizes. Two-step MR explored triglyceride mediation. Results:Eighty-two to 112 single nucleotide polymorphisms were included as instrumental variables. After Bonferroni correction, significant positive causal associations with PE were observed for: BMI (IVW: OR=1.703, 95% CI: 1.469-1.974, P<0.001), body fat percentage (IVW: OR=1.595, 95% CI: 1.321-1.925, P<0.001), whole-body fat mass (IVW: OR=1.639, 95% CI: 1.389-1.934, P<0.001), right leg fat percentage (IVW: OR=1.610, 95% CI: 1.360-1.905, P<0.001), left leg fat percentage (IVW: OR=1.622, 95% CI: 1.363-1.930, P<0.001), right arm fat percentage (IVW: OR=1.591, 95% CI: 1.351-1.872, P<0.001), left arm fat percentage (IVW: OR=1.710, 95% CI: 1.444-2.024, P<0.001), and waist circumference (IVW: OR=1.815, 95% CI: 1.534-2.148, P<0.001). Sensitivity analyses confirmed robustness. Triglycerides mediated 4.6%-8.2% of these effects. Trunk fat mass and hip circumference showed potential positive associations (IVW: OR>1, 0.005≤ P<0.05). Conclusions:Higher BMI, body fat percentage, whole-body fat mass, leg/arm fat percentages, and waist circumference may increase PE risk, with waist circumference showing the strongest association. These effects may be partially mediated by triglycerides.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.