Utilizing Global Burden of Disease Study (GBD 2021) data, this study aims to illustrate trends and spatiotemporal patterns of multidrug-resistant tuberculosis (MDR-TB) burden from 1990 to 2021, and explore their potential mechanisms. This research extracted core indicators including incidence, mortality, prevalence, and disability-adjusted life years (DALYs), with their age-standardized rate (ASR). Joinpoint regression, age-period-cohort analysis, inequality analysis, and frontier analysis were applied to describe the temporal and spatial trends of the disease burden. Decomposition analysis and risk factor analysis were performed to explore factors associated with MDR-TB burden fluctuation. Bayesian Age-Period-Cohort (BAPC) model was used to project the disease burden till 2050. Global MDR-TB cases and ASRs of all indicators rose from 1990 to 2021, with heavier burden in older populations and lower socioeconomic regions. Cross-country inequality widened over time. Frontier analysis identified countries including India and Russia with considerable potential for improvement in disease control. Decomposition analysis uncovered epidemiological changes as the main driver of the growing burden globally. Risk factors of MDR-TB in different regions and age groups were heterogeneous. The numbers and ASRs of all indicators are predicted to increase by 2050. This study revealed that the global disease burden of MDR-TB increased from 1990 to 2021 and is predicted to grow till 2050. Disparities among different social-demographic regions were remarkable and extended over time. Epidemiological changes contributed most to the escalated disease burden. Targeted public health strategies should be adopted for patients in specific regions and age groups.

We intended to establish the threshold for anti-Mullerian hormone (AMH) in the diagnosis of polycystic ovary syndrome (PCOS) in China. A total of 771 women (653 with PCOS and 118 healthy controls) were enrolled. The serum AMH, follicle-stimulating hormone (FSH), luteinizing hormone (LH), FSH/LH, prolactin, estradiol, testosterone (T), dehydroepiandrosterone sulfate (DHEA-S), sex hormone-binding globulin (SHBG), 17α-OH progesterone (17α-OHP), fasting insulin (INS), fasting glucose, free androgen index (FAI%) and homeostasis model assessment for insulin resistance (HOMA-IR) index were analyzed, and the diagnostic utility of AMH, LH/FSH, T and INS was established using receiver operator characteristic (ROC) curves. With AMH, LH/FSH, T and INS as independent variables, a logistic regression model was established, and the ROC curve for combined detection was fitted with the probability value of the model. The serum level of FSH, LH, LH/FSH, AMH, FAI%, 17α-OHP, fasting INS, T, SHBG, DHEA-S and HOMA-IR were altered in the PCOS patients. The best compromise between sensitivity and specificity was found at an AMH cut-off level of 8.16 ng/ml and 5.89 ng/ml for the age groups 20–29 and 30–39 years, with the corresponding area under the curve being 0.846 and 0.865 respectively. The area under the ROC curve for combined detection was 0.951, which was significantly greater than that of each index. Finally, the concentration of AMH was associated with FSH, LH, LH/FSH, T, and ovarian volume in PCOS patients. The optimal AMH diagnostic threshold for PCOS was 8.16 ng/ml (20–29 years) and 5.89 ng/ml (30–39 years) in the Chinese population of this study. Moreover, serum AMH, LH/FSH, T and INS could be used in combination to improve the diagnostic specificity and sensitivity for the detection of PCOS.

To investigate the effects of pre-pregnancy BMI and gestational weight gain on adverse pregnancy outcomes and complications of gestational diabetes mellitus. 3966 pregnant women were enrolled in this study. Multivariate logistic regression analysis was conducted to estimate the relative risk between pre-pregnancy BMI, gestational weight gain, and adverse pregnancy outcome. Pre-pregnancy BMI was found to be a risk factor for preeclampsia (OR = 1.159), gestational diabetes mellitus (OR = 1.191), gestational hypertension (OR = 1.221), and macrosomia (OR = 1.165). Gestational weight gain was a risk factor for preeclampsia (OR = 1.783), placental abruption (OR = 2.209), and macrosomia (OR = 1.506). Total weight gain during pregnancy cannot be used as a predictor of GDM. Pre-pregnancy BMI is a risk factor for gestational diabetes mellitus complicated with preeclampsia, preterm delivery, gestational hypertension, and macrosomia.

Previous evidence has indicated that insulin resistance may be an early pathological state of preeclampsia (PE). As a novel biomarker, the triglyceride glucose (TyG) index can reflect the level of insulin resistance in the body. The present study aimed to investigate the association between the TyG index and risk of PE. This study included 41,694 singleton pregnant women, comprising 2,308 PE patients and 39,386 healthy controls from three tertiary hospitals from January 2019 to June 2024. Datas were retrospectively collected via medical record review. The TyG index was measured before 20 weeks of gestation, and participants were grouped via the TyG index quartiles. The primary outcome was PE, and the secondary outcomes were preterm birth and low birth weight (LBW). Multivariable logistic regression was used to calculate the odds ratios (ORs) for the TyG index quartiles compared to the lowest quartile for the primary and secondary outcomes. Subgroup analyses were conducted to evaluate the effect of age, body mass index (BMI), parity and TyG test week on these associations. The predictive efficacy of the TyG index for PE was assessed using receiver operating characteristic (ROC) curve analysis. After adjusting for confounders, compared to TyG index Q1, a higher TyG index was positively associated with PE (TyG index Q3 OR = 1.23, 95% confidence interval (CI): 1.06–1.43, P = 0.0067; TyG index Q4 OR = 1.31, 95% CI: 1.11–1.53, P = 0.0011) and preterm birth (TyG index Q4 OR = 1.18, 95% CI: 1.01–1.37, P = 0.0376), negatively associated with LBW (TyG index Q3 OR = 0.84, 95% CI: 0.74–0.97, P = 0.0147). In Model I, a significant association was observed between higher TyG quartiles and preterm birth (P = 0.0472 for Q3 and P = 0.0000 for Q4), but this association was not significant in Model II after adjusting for confounders. Subgroup analyses revealed that age, pre-pregnancy BMI, parity and test week did not influence these associations (interaction P > 0.05). The area under the ROC curve (AUC) for the predictive model was 0.596 (95% CI: 0.584–0.608), with a sensitivity of 65.4% and a specificity of 49.6%. The present findings suggested that the TyG index associated with a high risk of PE. Clinical evaluation incorporating the TyG index during early pregnancy may help in screening for patients at high risk of PE.