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A cross-sectional study based on the community was conducted to explore the association between sleep status and LUTS among middle and old-aged men. Male residents in Zhengzhou aged 40 or older were recruited. Participants received the Pittsburgh Sleep Quality Index questionnaire and the International Prostate Symptom Score questionnaire to evaluate sleep status and the severity of lower urinary tract symptoms (LUTS), respectively. Logistic regression analyses and linear regression analyses were performed to evaluate the relationship between sleep quality and sleep duration and LUTS. A total of 5785 participants were enrolled. Multivariable analyses showed a positive relationship between sleep quality and LUTS (β 0.716, 95% CI 0.647–0.784), and poor sleepers were significantly associated with moderate or severe LUTS (OR 2.486, 95% CI 2.095–2.950). U-shaped dose–response relationship revealed that sleeping less than 5.8 h/day and more than 7.9 h/day was related to moderate or severe LUTS and more than 7.9 h/day of sleep duration was associated with poor voiding and storage symptoms (P for nonlinearity < 0.001). Similar relationship was observed between sleep status and nocturia. It showed a significantly positive relationship between sleep quality and LUTS. U-shaped dose–response relationships between sleep duration and LUTS were observed. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954–5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092–7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden's index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden's index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans. [ABSTRACT FROM AUTHOR]