BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

Objective Mendelian randomization was used to analyze the causal association between 20 serum metabolites and the risk of ovarian cancer.Methods The gene variations of 20 serum metabolites were obtained from the MRC IEU database as tool variables reflecting exposure levels,while gene variations of ovarian cancer patients were used as instrumental variables reflecting outcome levels.The ovarian cancer dataset ieu-a-1120 included 66,450 European women samples(of which 25,509 were ovarian cancer),and the dataset ieu-a-1228 included 54,990 European women samples(of which 14,049 were ovarian cancer).Two-sample two-way Mendelian randomization analysis was performed on both datasets.This study used inverse variance-weighted(IVW),weigh-ted median method,MR-Egger regression,and combined various analysis methods such as simple mode,and weighted mode.The caus-al effects of 20 metabolites and the risk of ovarian cancer were analyzed.Cochran's Q test was used to perform sensitivity analysis and to verify the reliability of the results.MR Egger intercept test was used to assess the horizontal pleiotropy of tool variables,and use the leave-one-out method to assess whether there were single nucleotide polymorphisms(SNPs)in the results that might have a potential impact on the incidence of ovarian cancer.Finally,the effects of uridine on ovarian cancer cells were verified through cell proliferation and apoptosis experiments.Results The results showed a negative correlation between uridine and the occurrence of ovarian cancer,with statistically significance(ieu-a-1228:P=0.025;ieu-a-1120:P=0.017).MR-Egger regression analysis confirmed the sensitiv-ity and robustness of the analysis results.The CCK8 assay confirmed that uridine inhibited the proliferation of ovarian cancer cells in a concentration-dependent manner,and uridine at a concentration of 10 mM significantly promoted apoptosis in SKOV3 cells(P<0.001)and A2780 cells(P<0.001).Flow cytometry analysis showed that after treatment for 24 hours,uridine at a concentration of 10 mM had the strongest pro-apoptotic effect on ovarian cancer cells,which was significant in SKOV3 and A2780 cells(P<0.05 and P<0.001,respectively).Conclusion Uridine is negatively correlated with the risk of ovarian cancer,which lays a theoretical founda-tion for further understanding the pathogenesis of ovarian cancer and optimizing clinical treatment strategies.

Objective Mendelian randomization was used to analyze the causal association between 20 serum metabolites and the risk of ovarian cancer.Methods The gene variations of 20 serum metabolites were obtained from the MRC IEU database as tool variables reflecting exposure levels,while gene variations of ovarian cancer patients were used as instrumental variables reflecting outcome levels.The ovarian cancer dataset ieu-a-1120 included 66,450 European women samples(of which 25,509 were ovarian cancer),and the dataset ieu-a-1228 included 54,990 European women samples(of which 14,049 were ovarian cancer).Two-sample two-way Mendelian randomization analysis was performed on both datasets.This study used inverse variance-weighted(IVW),weigh-ted median method,MR-Egger regression,and combined various analysis methods such as simple mode,and weighted mode.The caus-al effects of 20 metabolites and the risk of ovarian cancer were analyzed.Cochran's Q test was used to perform sensitivity analysis and to verify the reliability of the results.MR Egger intercept test was used to assess the horizontal pleiotropy of tool variables,and use the leave-one-out method to assess whether there were single nucleotide polymorphisms(SNPs)in the results that might have a potential impact on the incidence of ovarian cancer.Finally,the effects of uridine on ovarian cancer cells were verified through cell proliferation and apoptosis experiments.Results The results showed a negative correlation between uridine and the occurrence of ovarian cancer,with statistically significance(ieu-a-1228:P=0.025;ieu-a-1120:P=0.017).MR-Egger regression analysis confirmed the sensitiv-ity and robustness of the analysis results.The CCK8 assay confirmed that uridine inhibited the proliferation of ovarian cancer cells in a concentration-dependent manner,and uridine at a concentration of 10 mM significantly promoted apoptosis in SKOV3 cells(P<0.001)and A2780 cells(P<0.001).Flow cytometry analysis showed that after treatment for 24 hours,uridine at a concentration of 10 mM had the strongest pro-apoptotic effect on ovarian cancer cells,which was significant in SKOV3 and A2780 cells(P<0.05 and P<0.001,respectively).Conclusion Uridine is negatively correlated with the risk of ovarian cancer,which lays a theoretical founda-tion for further understanding the pathogenesis of ovarian cancer and optimizing clinical treatment strategies.

Pregnancy ; Female ; Humans ; Cesarean Section ; Retrospective Studies ; East Asian People ; Maternal Age ; Policy

Objective The study aimed to construct a composite score method based on multidimensional quality indi-cators and conduct simulation trials to validate the method. Besides,the quality of breast cancer care for both hospitals and sur-geons was evaluated by the method. Methods The two-parameter logistic latent variable model was constructed as measure-ment model;the latent variables in the measurement model were further incorporated into multilevel structural model as depend-ent variables and one pseudo level was designed for representing multiple latent variables. MCMC method was used to estimate model parameters. Three level and two-dimensional latent variable model was used to analyze the actual data. Results The sim-ulation study showed that the number of quality indicators and surgeons should not be less than 20 to obtain efficient estimate of quality of care;the multilevel and multidimensional latent variable model was applied to analyze the data;surgeons and hospitals who provided superior quality of breast cancer diagnosis and operative procedure were obtained. Conclusion The newly con-structed multilevel and multidimensional latent variable model could effectively address the hieratical structure in quality of care data as well as the multidimensional nature of quality of care,thus,the model can be used to comprehensively and rationally as-sess the quality of care;comprehensive evaluation of quality of care provided ground for linking the ranking of hospitals and per-formance appraisal of surgeons to the quality of care.

Objective The study aimed to construct a composite score method based on multidimensional quality indi-cators and conduct simulation trials to validate the method. Besides,the quality of breast cancer care for both hospitals and sur-geons was evaluated by the method. Methods The two-parameter logistic latent variable model was constructed as measure-ment model;the latent variables in the measurement model were further incorporated into multilevel structural model as depend-ent variables and one pseudo level was designed for representing multiple latent variables. MCMC method was used to estimate model parameters. Three level and two-dimensional latent variable model was used to analyze the actual data. Results The sim-ulation study showed that the number of quality indicators and surgeons should not be less than 20 to obtain efficient estimate of quality of care;the multilevel and multidimensional latent variable model was applied to analyze the data;surgeons and hospitals who provided superior quality of breast cancer diagnosis and operative procedure were obtained. Conclusion The newly con-structed multilevel and multidimensional latent variable model could effectively address the hieratical structure in quality of care data as well as the multidimensional nature of quality of care,thus,the model can be used to comprehensively and rationally as-sess the quality of care;comprehensive evaluation of quality of care provided ground for linking the ranking of hospitals and per-formance appraisal of surgeons to the quality of care.