The causal effect of metabolic syndrome and its components on benign prostatic hyperplasia: A univariable and multivariable Mendelian randomization study.

BACKGROUND: Previous observational studies have indicated that metabolic abnormalities are associated with benign prostatic hyperplasia (BPH). The limitations of the research methodology of observational studies do not allow causal inference to be drawn; however, Mendelian randomization (MR) can clarify this. METHODS: Using summary-level data from genome-wide association studies, we conducted a two-sample MR study to examine the causality of the metabolic syndrome (MetS) and its components on BPH (26,358 BPH cases and 110,070 controls). The random-effects inverse-variance weighted was employed as the primary method for MR analyses. RESULTS: We observed that genetically predicted waist circumference (WC) (odds ratio [OR] = 1.236, 95% confidence interval [CI]: 1.034-1.478, p = 0.020) and diastolic blood pressure (DBP) (OR = 1.011, 95% CI: 1.002-1.020, p = 0.020) were significantly positively associated with BPH risk. We did not identify a causal effect of MetS (OR = 0.975, 95% CI: 0.922-1.031, p = 0.375), systolic blood pressure (OR = 1.004, 95% CI: 0.999-1.008, p = 0.115), triglycerides (OR = 1.016, 95% CI: 0.932-1.109, p = 0.712), high-density lipoprotein (OR = 1.005, 95% CI: 0.930-1.086, p = 0.907), and fasting blood glucose (OR = 1.037, 95% CI: 0.874-1.322, p = 0.678) on BPH. In the multivariable MR analysis, we observed that the risk effect of DBP (OR = 1.013, 95% CI: 1.000-1.026, p = 0.047) on BPH persisted after conditioning with WC (OR = 1.132, 95% CI: 0.946-1.356, p = 0.177). CONCLUSIONS: Our study provides genetic evidence supporting the causal effect of DBP on BPH, although the effect of WC needs to be further validated.

Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer.

Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group and a control group, comprising non-ovarian malignant gynecological tumor (NOMGT), benign gynecological disease (BGD), and healthy control subgroups. Cases were randomly assigned to training and internal validation sets. Two linear (logistic regression (LR) and Fisher's linear discriminant (FLD)) and three nonlinear models (support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) were constructed using 22 conventional laboratory indicators and three demographic characteristics. Model performance was compared. In an independent prospectively recruited validation set, the order of diagnostic efficiency was RF, SVM, ANN, FLD, LR, and carbohydrate antigen 125 (CA125)-only (AUC, accuracy: 0.989, 95.6%; 0.985, 94.4%; 0.974, 93.4%; 0.915, 82.1%; 0.859, 80.1%; and 0.732, 73.0%, respectively). RF maintained satisfactory classification performance for identifying different ovarian cancer stages and for discriminating it from NOMGT-, BGD-, or CA125-positive control. Nonlinear models outperformed linear models, indicating that nonlinear machine learning models can efficiently use conventional laboratory indicators for ovarian cancer diagnosis.

Interleukin-6 and Hypoxia Synergistically Promote EMT-Mediated Invasion in Epithelial Ovarian Cancer via the IL-6/STAT3/HIF-1α Feedback Loop.

Extensive peritoneal spread and capacity for distant metastasis account for the majority of mortality from epithelial ovarian cancer (EOC). Accumulating evidence shows that interleukin-6 (IL-6) promotes tumor invasion and migration in EOC, although the molecular mechanisms remain to be fully elucidated. Meanwhile, the hypoxic microenvironment has been recognized to cause metastasis by triggering epithelial-mesenchymal transition (EMT) in several types of cancers. Here, we studied the synergy between IL-6 and hypoxia in inducing EMT in two EOC cell lines, A2780 cells and SKOV3 cells. Exogenous recombination of IL-6 and autocrine production of IL-6 regulated by plasmids both induced EMT phenotype in EOC cells characterized by downregulated E-cadherin as well as upregulated expression of vimentin and EMT-related transcription factors. The combined effects of IL-6 and hypoxia were more significant than those of either one treatment on EMT. Suppression of hypoxia-inducible factor-1α (HIF-1α) before IL-6 treatment inhibited the EMT phenotype and invasion ability of EOC cells, indicating that HIF-1α occupies a key position in the regulatory pathway of EMT associated with IL-6. EMT score was found positively correlated with mRNA levels of IL-6, signal transducer and activator of transcription 3 (STAT3), and HIF-1α, respectively, in 489 ovarian samples from The Cancer Genome Atlas dataset. Next, blockade of the abovementioned molecules by chemical inhibitors reversed the alteration in the protein levels of EMT markers induced by either exogenous or endogenous IL-6. These findings indicate a positive feedback loop between IL-6 and HIF-1α, and induce and maintain EMT phenotype through STAT3 signaling, which might provide a novel rationale for prognostic prediction and therapeutic targets in EOC.

Waiting-List and early posttransplant prognosis among ethnoracial groups: Data from the organ procurement and transplantation network.

Background: Racial/ethnic disparity in waiting-list mortality among candidates listed for kidney transplantation (KT) in the United States remains unclear. We aimed to assess racial/ethnic disparity in waiting-list prognosis among patients listed for KT in the United States in the current era. Methods: We compared waiting-list and early posttransplant in-hospital mortality or primary nonfunction (PNF) among adult (age ≥18 years) white, black, Hispanic, and Asian patients listed for only KT in the United States between July 1, 2004 and March 31, 2020. Results: Of the 516,451 participants, 45.6%, 29.8%, 17.5%, and 7.1% were white, black, Hispanic, and Asian, respectively. Mortality on the 3-year waiting list (including patients who were removed for deterioration) was 23.2%, 16.6%, 16.2%, and 13.8% in white, black, Hispanic, and Asian patients, respectively. The cumulative incidence of posttransplant in-hospital death or PNF after KT was 3.3%, 2.5%, 2.4%, and 2.2% in black, white, Hispanic, and Asian patients,respectively. White candidates had the highest mortality risk on the waiting list or of becoming too sick for a transplant, while black (adjusted hazard ratio, [95% confidence interval, CI], 0.67 [0.66-0.68]), Hispanic (0.59 [0.58-0.60]), and Asian (0.54 [0.52-0.55]) candidates had a lower risk. Black KT recipients (odds ratio, [95% CI] 1.29 [1.21-1.38]) had a higher risk of PNF or death before discharge than white patients. After controlling confounders, black recipients (0.99 [0.92-1.07]) had a similar higher risk of posttransplant in-hospital mortality or PNF as white patients than Hispanic and Asian counterparts. Conclusions: Despite having a better socioeconomic status and being allocated better kidneys, white patients had the worst prognosis during the waiting periods. Black recipients and white recipients have higher posttransplant in-hospital mortality or PNF.

Relationship between serum indirect bilirubin and prostate volume in patients with benign prostatic hyperplasia: a cross-sectional study.

Background: Due to its anti-oxidative effects, bilirubin may protect against a spectrum of diseases. However, the role of bilirubin in patients with benign prostatic hyperplasia (BPH) is poorly explored. This study aimed to investigate the cross-sectional associations between serum indirect bilirubin (IBIL) and prostate volume (PV) in patients with BPH. Methods: The medical records of 722 BPH patients were retrospectively analyzed. Body mass index (BMI) was calculated as body weight (kg)/height (m)2. PV was obtained as height (cm) × width (cm) × length (cm) × π/6. Other biochemical indexes were measured by the automatic biochemical analyzer. A univariable linear regression analysis was performed to detect confounders. The IBIL-PV relationship was examined using unadjusted and covariate-adjusted regression models. Furthermore, a segmented linear regression was conducted to analyze the linear trend of IBIL levels and PV. Finally, the sensitivity analysis was stratified by BMI and low-density lipoprotein cholesterol (LDL-C) cutoffs. Results: In this study, the mean age of the patients was 68 (range, 43-93) years. By univariable line regression test, we observed that PV was positively correlated with age, BMI, and LDL-C (ß=0.113, 0.096, and 0.135, respectively). IBIL was negatively associated with PV in full adjusted model in men age ≤75 years (ß=-1.01; 95% CI: -1.81, -0.22; P=0.01). A statistically significant inverse trend was observed between serum IBIL intervals and PV in patients aged ≤75 years (adjusted for age, BMI, and LDL-C, P for trend =0.015). In sensitivity analysis, significantly negative IBIL-PV relationship only existed in men with normal BMI (adjusted ß=-1.328; 95% CI: -2.467, -0.190; P=0.022), overweight men (adjusted ß=-1.296; 95% CI: -2.519, -0.074; P=0.038), and men with normal LDL-C level (adjusted ß=-1.017; 95% CI: -1.869, -0.164; P=0.019). Conclusions: IBIL is negatively associated with PV in the non-obese population ≤75 years with normal LDL-C. These results suggest that higher serum IBIL possibly provides a degree of protection to BPH by mitigating oxidative stress (OS) related to aging and lipid peroxidation. Nevertheless, these preliminary findings from a single-center, retrospective study have limitations and need to be confirmed by future studies.