Background: The causal relationship between certain immune cells and erectile dysfunction (ED) is still uncertain. Aim: The study sought to investigate the causal effect of 731 types of immune cells on ED through Mendelian randomization (MR) using genome-wide association studies (GWAS). Methods: Genetic instruments for 731 immune cells were identified through GWAS, and ED data were obtained from the FinnGen database. Univariable and multivariable bidirectional MR studies were conducted to explore potential causal relationships between these immune cells and ED. The inverse-variance weighted method was primarily used, with Cochran’s Q test and MR-Egger intercept test assessing pleiotropy and heterogeneity. Bayesian weighted Mendelian randomization (BWMR) was also employed. Outcomes: Six immune cells were identified as related to ED. CD45 on Natural Killer (NK) cells, CD33dim HLA DR+ CD11b + Absolute Count, CD19 on IgD- CD38dim B cells, and CD3 on CD39+ resting CD4 regulatory T cells were identified as risk factors, whereas CD20 on IgD+ CD38dim B cells and Activated & resting CD4 regulatory T cell %CD4+ T cells were protective factors. Further multivariable MR analysis confirmed that 5 of these immune cells independently impacted ED, except for CD45 on NK cells. Reverse MR analysis indicated that ED occurrence decreases certain immune cell counts, but BWMR found no causal relationship for CD20 on IgD+ CD38dim B cells. Results: Our MR analysis confirmed a potential bidirectional causal relationship between immune cells and ED, providing new insights into potential mechanisms and therapeutic strategies. Clinical Translation: This study provides evidence for the impact of certain immune cells on the development of ED and suggests potential therapeutic targets. Strengths and Limitations: We performed both univariable and multivariable MR to strengthen the causal relationship between exposures and outcomes. However, the population in this study was limited to European ancestry. Conclusion: Our MR analysis confirmed a potential bidirectional causal relationship between immune cells and ED. This provides new insights into potential mechanisms of pathogenesis and subsequent therapeutic strategies.

Non-muscle invasive bladder cancer (NMIBC) has a high rate of postoperative recurrence and the efficacy of existing clinical prediction models is limited. This study aimed to combine multiparametric magnetic resonance imaging (mp-MRI) radiomic features with clinical characteristics to construct a machine learning model for accurately predicting the risk of recurrence within 2 years postoperatively in NMIBC patients. Retrospectively including 183 NMIBC patients (57 in the recurrence group, 126 in the non-recurrence group), radiomic features from mp-MRI imaging (T2W, ADC, and enhancement sequences) were extracted. Through LASSO selection, 4 key imaging features (MajorAxisLength, SZNN, S/V, Skewness) and 6 clinical features based on the EAU 2021 risk stratification were identified to constitute the clinical-imaging dataset. Through comparison with 10 machine learning models, Support Vector Machine (SVM) performed the best (training set AUC = 0.973, validation set AUC = 0.891), with external independent validation (108 cases) showing AUCs of 0.88 and 0.87, demonstrating good generalization ability. A bar chart integrating radiomics score (Rad-Score) with clinical features provides an intuitive prognostic tool. The study indicates that the clinical-imaging radiomics model based on SVM significantly enhances the efficacy of NMIBC recurrence prediction, addressing the shortcomings of traditional risk assessment and offering a reliable basis for personalized postoperative management. Study limitations include the retrospective design and the absence of molecular biomarkers, necessitating future multicenter prospective validation.

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An abstract is unavailable. This article is available as a PDF only.

This study explores the causal relationship between sedentary behavior and urological cancers, focusing on bladder cancer (BC), prostate cancer, and kidney cancer, using Bayesian Mendelian randomization and mediation analysis. A two-sample Mendelian randomization (MR) framework was employed, using genetic variants as instrumental variables. Bayesian and multivariate MR assessed causal effects of sedentary behaviors (TV watching, computer use, driving) on urological cancers. Sensitivity analyses (MR-Egger, MR-PRESSO, and Cochran Q) ensured robustness. Mediation analysis identified high-density lipoprotein (HDL) cholesterol ester levels as a primary mediator, validated through meta-analysis. Prolonged TV watching was significantly associated with increased BC risk (OR = 2.908; 95% CI = 1.221–6.930; P = .015). Mediation analysis showed small HDL cholesterol ester levels mediated 17.5% of this effect. No causal relationships were observed between computer use or driving and the cancers. Sensitivity analyses confirmed robust findings without heterogeneity or pleiotropy. Prolonged TV watching increases BC risk, mediated by small HDL cholesterol ester levels. Sedentary behavior is a modifiable risk factor, highlighting the importance of lifestyle interventions in prevention.